

"""simple docstring"""




import inspect
import os
from argparse import ArgumentParser, Namespace
from importlib import import_module

import huggingface_hub
import numpy as np
from packaging import version

from .. import (
    FEATURE_EXTRACTOR_MAPPING,
    IMAGE_PROCESSOR_MAPPING,
    PROCESSOR_MAPPING,
    TOKENIZER_MAPPING,
    AutoConfig,
    AutoFeatureExtractor,
    AutoImageProcessor,
    AutoProcessor,
    AutoTokenizer,
    is_datasets_available,
    is_tf_available,
    is_torch_available,
)
from ..utils import TF2_WEIGHTS_INDEX_NAME, TF2_WEIGHTS_NAME, logging
from . import BaseTransformersCLICommand


if is_tf_available():
      import tensorflow as tf

      tf.config.experimental.enable_tensor_float_aa_execution(False)

if is_torch_available():
      import torch

if is_datasets_available():
      from datasets import load_dataset


__UpperCAmelCase	=			5E-5  # larger error tolerance than in our internal tests, to avoid flaky user-facing errors
def 					lowerCAmelCase  (   __UpperCamelCase ):
 '''simple docstring'''





 return PTtoTFCommand(
     args.model_name							,      args.local_dir							,      args.max_error							,      args.new_weights							,      args.no_pr							,      args.push							,      args.extra_commit_description							,      args.override_model_class							,      )







class        __lowercase     (      __lowerCamelCase   ):

  @staticmethod
  def 				__lowercase    (					A						:     ArgumentParser      ):






   '''simple docstring'''
   UpperCAmelCase__   :					int   				=   parser.add_parser(
       """pt-to-tf"""   ,help=(
           """CLI tool to run convert a transformers model from a PyTorch checkpoint to a TensorFlow checkpoint."""
           """ Can also be used to validate existing weights without opening PRs, with --no-pr."""
       )   ,)
   train_parser.add_argument(
       """--model-name"""   ,type=A   ,required=A   ,help="""The model name, including owner/organization, as seen on the hub."""   ,)
   train_parser.add_argument(
       """--local-dir"""   ,type=A   ,default=""""""   ,help="""Optional local directory of the model repository. Defaults to /tmp/{model_name}"""   ,)
   train_parser.add_argument(
       """--max-error"""   ,type=A   ,default=A   ,help=(
           f"Maximum error tolerance. Defaults to {MAX_ERROR}. This flag should be avoided, use at your own risk."
       )   ,)
   train_parser.add_argument(
       """--new-weights"""   ,action="""store_true"""   ,help="""Optional flag to create new TensorFlow weights, even if they already exist."""   ,)
   train_parser.add_argument(
       """--no-pr"""   ,action="""store_true"""   ,help="""Optional flag to NOT open a PR with converted weights."""      )
   train_parser.add_argument(
       """--push"""   ,action="""store_true"""   ,help="""Optional flag to push the weights directly to `main` (requires permissions)"""   ,)
   train_parser.add_argument(
       """--extra-commit-description"""   ,type=A   ,default=""""""   ,help="""Optional additional commit description to use when opening a PR (e.g. to tag the owner)."""   ,)
   train_parser.add_argument(
       """--override-model-class"""   ,type=A   ,default=A   ,help="""If you think you know better than the auto-detector, you can specify the model class here. """
       """Can be either an AutoModel class or a specific model class like BertForSequenceClassification."""   ,)
   train_parser.set_defaults(func=A      )

  @staticmethod
  def 				__lowercase    (					A						:     Union[str, Any]   ,A						:     str      ):






   '''simple docstring'''
   UpperCAmelCase__   :					Dict   				=   set(pt_outputs.keys()      )
   UpperCAmelCase__   :					int   				=   set(tf_outputs.keys()      )
   if pt_out_attrs != tf_out_attrs:
    raise ValueError(
        f"The model outputs have different attributes, aborting. (Pytorch: {pt_out_attrs}, TensorFlow:"
        f" {tf_out_attrs})"      )

   # 2. For each output attribute, computes the difference
   def _find_pt_tf_differences(A						:     List[Any]   ,A						:     str   ,A						:     Optional[Any]   ,A						:     List[str]=""      ):
    # If the current attribute is a tensor, it is a leaf and we make the comparison. Otherwise, we will dig in
    # recursivelly, keeping the name of the attribute.
    if isinstance(A   ,torch.Tensor      ):
     UpperCAmelCase__   :					Optional[Any]   				=   np.max(np.abs(pt_out.numpy() - tf_out.numpy()      )      )
     UpperCAmelCase__   :					Union[str, Any]   				=   tensor_difference
    else:
     UpperCAmelCase__   :					int   				=   attr_name
     for i, pt_item in enumerate(A      ):
      # If it is a named attribute, we keep the name. Otherwise, just its index.
      if isinstance(A   ,A      ):
       UpperCAmelCase__   :					Optional[int]   				=   root_name + pt_item
       UpperCAmelCase__   :					Optional[Any]   				=   tf_out[pt_item]
       UpperCAmelCase__   :					Tuple   				=   pt_out[pt_item]
      else:
       UpperCAmelCase__   :					Tuple   				=   root_name + f"[{i}]"
       UpperCAmelCase__   :					Optional[Any]   				=   tf_out[i]
      UpperCAmelCase__   :					int   				=   _find_pt_tf_differences(A   ,A   ,A   ,A      )

    return differences

   return _find_pt_tf_differences(A   ,A   ,{}      )

  def __init__(					self						:     Optional[int]   ,A						:     str   ,A						:     str   ,A						:     float   ,A						:     bool   ,A						:     bool   ,A						:     bool   ,A						:     str   ,A						:     str   ,*A						:     Tuple   ,):






   '''simple docstring'''
   UpperCAmelCase__   :					List[Any]   				=   logging.get_logger("""transformers-cli/pt_to_tf"""      )
   UpperCAmelCase__   :					Optional[int]   				=   model_name
   UpperCAmelCase__   :					str   				=   local_dir if local_dir else os.path.join("""/tmp"""   ,A      )
   UpperCAmelCase__   :					Optional[int]   				=   max_error
   UpperCAmelCase__   :					Tuple   				=   new_weights
   UpperCAmelCase__   :					int   				=   no_pr
   UpperCAmelCase__   :					Dict   				=   push
   UpperCAmelCase__   :					Dict   				=   extra_commit_description
   UpperCAmelCase__   :					List[Any]   				=   override_model_class

  def 				__lowercase    (					self						:     str   ,A						:     Tuple   ,A						:     Dict   ,A						:     Dict      ):






   '''simple docstring'''

   def _get_audio_input():
    UpperCAmelCase__   :					int   				=   load_dataset("""hf-internal-testing/librispeech_asr_dummy"""   ,"""clean"""   ,split="""validation"""      )
    UpperCAmelCase__   :					Optional[int]   				=   ds.sort("""id"""      ).select(range(2      )      )[:2]["""audio"""]
    UpperCAmelCase__   :					List[str]   				=   [x["""array"""] for x in speech_samples]
    return raw_samples

   UpperCAmelCase__   :					Union[str, Any]   				=   type(pt_model.config      )
   if model_config_class in PROCESSOR_MAPPING:
    UpperCAmelCase__   :					Any   				=   AutoProcessor.from_pretrained(self._local_dir      )
    if model_config_class in TOKENIZER_MAPPING and processor.tokenizer.pad_token is None:
     UpperCAmelCase__   :					Dict   				=   processor.tokenizer.eos_token
   elif model_config_class in IMAGE_PROCESSOR_MAPPING:
    UpperCAmelCase__   :					List[str]   				=   AutoImageProcessor.from_pretrained(self._local_dir      )
   elif model_config_class in FEATURE_EXTRACTOR_MAPPING:
    UpperCAmelCase__   :					Union[str, Any]   				=   AutoFeatureExtractor.from_pretrained(self._local_dir      )
   elif model_config_class in TOKENIZER_MAPPING:
    UpperCAmelCase__   :					int   				=   AutoTokenizer.from_pretrained(self._local_dir      )
    if processor.pad_token is None:
     UpperCAmelCase__   :					int   				=   processor.eos_token
   else:
    raise ValueError(f"Unknown data processing type (model config type: {model_config_class})"      )

   UpperCAmelCase__   :					List[str]   				=   set(inspect.signature(pt_model.forward      ).parameters.keys()      )
   UpperCAmelCase__   :					Tuple   				=   {}
   if "input_ids" in model_forward_signature:
    processor_inputs.update(
        {
            """text""": ["""Hi there!""", """I am a batch with more than one row and different input lengths."""],
            """padding""": True,
            """truncation""": True,
        }      )
   if "pixel_values" in model_forward_signature:
    UpperCAmelCase__   :					Dict   				=   load_dataset("""cifar10"""   ,"""plain_text"""   ,split="""test"""      )[:2]["""img"""]
    processor_inputs.update({"""images""": sample_images}      )
   if "input_features" in model_forward_signature:
    UpperCAmelCase__   :					Tuple   				=   inspect.signature(processor.feature_extractor      ).parameters
    # Pad to the largest input length by default but take feature extractor default
    # padding value if it exists e.g. "max_length" and is not False or None
    if "padding" in feature_extractor_signature:
     UpperCAmelCase__   :					Any   				=   feature_extractor_signature["""padding"""].default
     if default_strategy is not False and default_strategy is not None:
      UpperCAmelCase__   :					List[str]   				=   default_strategy
     else:
      UpperCAmelCase__   :					str   				=   True
    else:
     UpperCAmelCase__   :					Any   				=   True
    processor_inputs.update({"""audio""": _get_audio_input(), """padding""": padding_strategy}      )
   if "input_values" in model_forward_signature:  # Wav2Vec2 audio input
    processor_inputs.update({"""audio""": _get_audio_input(), """padding""": True}      )
   UpperCAmelCase__   :					Tuple   				=   processor(**A   ,return_tensors="""pt"""      )
   UpperCAmelCase__   :					Tuple   				=   processor(**A   ,return_tensors="""tf"""      )

   # Extra input requirements, in addition to the input modality
   if (
       config.is_encoder_decoder
       or (hasattr(A   ,"""encoder"""      ) and hasattr(A   ,"""decoder"""      ))
       or "decoder_input_ids" in tf_dummy_inputs
   ):
    UpperCAmelCase__   :					List[Any]   				=   np.asarray([[1], [1]]   ,dtype=A      ) * (pt_model.config.decoder_start_token_id or 0)
    pt_input.update({"""decoder_input_ids""": torch.tensor(A      )}      )
    tf_input.update({"""decoder_input_ids""": tf.convert_to_tensor(A      )}      )

   return pt_input, tf_input




  def 				__lowercase    (					self						:     Tuple      ):






   '''simple docstring'''
   # hub version 0.9.0 introduced the possibility of programmatically opening PRs with normal write tokens.
   if version.parse(huggingface_hub.__version__      ) < version.parse("""0.9.0"""      ):
    raise ImportError(
        """The huggingface_hub version must be >= 0.9.0 to use this command. Please update your huggingface_hub"""
        """ installation."""      )
   else:
    from huggingface_hub import Repository, create_commit
    from huggingface_hub._commit_api import CommitOperationAdd

   # Fetch remote data
   UpperCAmelCase__   :					List[Any]   				=   Repository(local_dir=self._local_dir   ,clone_from=self._model_name      )

   # Load config and get the appropriate architecture -- the latter is needed to convert the head's weights
   UpperCAmelCase__   :					str   				=   AutoConfig.from_pretrained(self._local_dir      )
   UpperCAmelCase__   :					List[str]   				=   config.architectures
   if self._override_model_class is not None:
    if self._override_model_class.startswith("""TF"""      ):
     UpperCAmelCase__   :					Tuple   				=   [self._override_model_class[2:]]
    else:
     UpperCAmelCase__   :					List[str]   				=   [self._override_model_class]
    try:
     UpperCAmelCase__   :					Tuple   				=   getattr(import_module("""transformers"""      )   ,architectures[0]      )
    except AttributeError:
     raise ValueError(f"Model class {self._override_model_class} not found in transformers."      )
    try:
     UpperCAmelCase__   :					List[Any]   				=   getattr(import_module("""transformers"""      )   ,"""TF""" + architectures[0]      )
    except AttributeError:
     raise ValueError(f"TF model class TF{self._override_model_class} not found in transformers."      )
   elif architectures is None:  # No architecture defined -- use auto classes
    UpperCAmelCase__   :					Union[str, Any]   				=   getattr(import_module("""transformers"""      )   ,"""AutoModel"""      )
    UpperCAmelCase__   :					Dict   				=   getattr(import_module("""transformers"""      )   ,"""TFAutoModel"""      )
    self._logger.warning("""No detected architecture, using AutoModel/TFAutoModel"""      )
   else:  # Architecture defined -- use it
    if len(A      ) > 1:
     raise ValueError(f"More than one architecture was found, aborting. (architectures = {architectures})"      )
    self._logger.warning(f"Detected architecture: {architectures[0]}"      )
    UpperCAmelCase__   :					Optional[int]   				=   getattr(import_module("""transformers"""      )   ,architectures[0]      )
    try:
     UpperCAmelCase__   :					Any   				=   getattr(import_module("""transformers"""      )   ,"""TF""" + architectures[0]      )
    except AttributeError:
     raise AttributeError(f"The TensorFlow equivalent of {architectures[0]} doesn't exist in transformers."      )

        # Check the TF dummy inputs to see what keys we need in the forward pass
   UpperCAmelCase__   :					List[Any]   				=   tf_class.from_config(A      )
   UpperCAmelCase__   :					Optional[int]   				=   tf_from_pt_model.dummy_inputs

   del tf_from_pt_model  # Try to keep only one model in memory at a time

   # Load the model and get some basic inputs
   UpperCAmelCase__   :					Dict   				=   pt_class.from_pretrained(self._local_dir      )
   pt_model.eval()

   UpperCAmelCase__				, UpperCAmelCase__   :					Union[str, Any]   				=   self.get_inputs(A   ,A   ,A      )

   with torch.no_grad():
    UpperCAmelCase__   :					Any   				=   pt_model(**A   ,output_hidden_states=A      )
   del pt_model  # will no longer be used, and may have a large memory footprint

   UpperCAmelCase__   :					Union[str, Any]   				=   tf_class.from_pretrained(self._local_dir   ,from_pt=A      )
   UpperCAmelCase__   :					Dict   				=   tf_from_pt_model(**A   ,output_hidden_states=A   ,training=A      )

   # Confirms that cross loading PT weights into TF worked.
   UpperCAmelCase__   :					Tuple   				=   self.find_pt_tf_differences(A   ,A      )
   UpperCAmelCase__   :					List[Any]   				=   {k: v for k, v in crossload_differences.items() if """hidden""" not in k}
   UpperCAmelCase__   :					Dict   				=   {k: v for k, v in crossload_differences.items() if """hidden""" in k}
   if len(A      ) == 0 and architectures is not None:
    raise ValueError(
        f"Something went wrong -- the config file has architectures ({architectures}), but no model head"
        """ output was found. All outputs start with 'hidden'"""      )
   UpperCAmelCase__   :					str   				=   max(output_differences.values()      ) if output_differences else 0.0
   UpperCAmelCase__   :					str   				=   max(hidden_differences.values()      )
   if max_crossload_output_diff > self._max_error or max_crossload_hidden_diff > self._max_error:
    raise ValueError(
        """The cross-loaded TensorFlow model has different outputs, something went wrong!\n"""
        + f"\nList of maximum output differences above the threshold ({self._max_error}):\n"
        + """\n""".join([f"{k}: {v:.3e}" for k, v in output_differences.items() if v > self._max_error]      )
        + f"\n\nList of maximum hidden layer differences above the threshold ({self._max_error}):\n"
        + """\n""".join([f"{k}: {v:.3e}" for k, v in hidden_differences.items() if v > self._max_error]      )      )

   # Save the weights in a TF format (if needed) and confirms that the results are still good
   UpperCAmelCase__   :					int   				=   os.path.join(self._local_dir   ,A      )
   UpperCAmelCase__   :					str   				=   os.path.join(self._local_dir   ,A      )
   if (not os.path.exists(A      ) and not os.path.exists(A      )) or self._new_weights:
    tf_from_pt_model.save_pretrained(self._local_dir      )
   del tf_from_pt_model  # will no longer be used, and may have a large memory footprint

   UpperCAmelCase__   :					Dict   				=   tf_class.from_pretrained(self._local_dir      )
   UpperCAmelCase__   :					str   				=   tf_model(**A   ,output_hidden_states=A      )

   UpperCAmelCase__   :					int   				=   self.find_pt_tf_differences(A   ,A      )
   UpperCAmelCase__   :					int   				=   {k: v for k, v in conversion_differences.items() if """hidden""" not in k}
   UpperCAmelCase__   :					str   				=   {k: v for k, v in conversion_differences.items() if """hidden""" in k}
   if len(A      ) == 0 and architectures is not None:
    raise ValueError(
        f"Something went wrong -- the config file has architectures ({architectures}), but no model head"
        """ output was found. All outputs start with 'hidden'"""      )
   UpperCAmelCase__   :					Dict   				=   max(output_differences.values()      ) if output_differences else 0.0
   UpperCAmelCase__   :					List[Any]   				=   max(hidden_differences.values()      )
   if max_conversion_output_diff > self._max_error or max_conversion_hidden_diff > self._max_error:
    raise ValueError(
        """The converted TensorFlow model has different outputs, something went wrong!\n"""
        + f"\nList of maximum output differences above the threshold ({self._max_error}):\n"
        + """\n""".join([f"{k}: {v:.3e}" for k, v in output_differences.items() if v > self._max_error]      )
        + f"\n\nList of maximum hidden layer differences above the threshold ({self._max_error}):\n"
        + """\n""".join([f"{k}: {v:.3e}" for k, v in hidden_differences.items() if v > self._max_error]      )      )

   UpperCAmelCase__   :					Optional[int]   				=   """Update TF weights""" if self._new_weights else """Add TF weights"""
   if self._push:
    repo.git_add(auto_lfs_track=A      )
    repo.git_commit(A      )
    repo.git_push(blocking=A      )  # this prints a progress bar with the upload
    self._logger.warning(f"TF weights pushed into {self._model_name}"      )
   elif not self._no_pr:
    self._logger.warning("""Uploading the weights into a new PR..."""      )
    UpperCAmelCase__   :					List[Any]   				=   (
        """Model converted by the [`transformers`' `pt_to_tf`"""
        """ CLI](https://github.com/huggingface/transformers/blob/main/src/transformers/commands/pt_to_tf.py). """
        """All converted model outputs and hidden layers were validated against its PyTorch counterpart.\n\n"""
        f"Maximum crossload output difference={max_crossload_output_diff:.3e}; "
        f"Maximum crossload hidden layer difference={max_crossload_hidden_diff:.3e};\n"
        f"Maximum conversion output difference={max_conversion_output_diff:.3e}; "
        f"Maximum conversion hidden layer difference={max_conversion_hidden_diff:.3e};\n"
    )
    if self._max_error > MAX_ERROR:
     commit_descrition += (
         f"\n\nCAUTION: The maximum admissible error was manually increased to {self._max_error}!"
     )
    if self._extra_commit_description:
     commit_descrition += "\n\n" + self._extra_commit_description

    # sharded model -> adds all related files (index and .h5 shards)
    if os.path.exists(A      ):
     UpperCAmelCase__   :					Any   				=   [
         CommitOperationAdd(path_in_repo=A   ,path_or_fileobj=A      )
     ]
     for shard_path in tf.io.gfile.glob(self._local_dir + """/tf_model-*.h5"""      ):
      operations += [
          CommitOperationAdd(path_in_repo=os.path.basename(A      )   ,path_or_fileobj=A      )
      ]
    else:
     UpperCAmelCase__   :					int   				=   [CommitOperationAdd(path_in_repo=A   ,path_or_fileobj=A      )]

    UpperCAmelCase__   :					List[str]   				=   create_commit(
        repo_id=self._model_name   ,operations=A   ,commit_message=A   ,commit_description=A   ,repo_type="""model"""   ,create_pr=A   ,).pr_url
    self._logger.warning(f"PR open in {hub_pr_url}"      )








"""simple docstring"""




import json
import logging
import os
import random
import sys
from dataclasses import dataclass, field
from typing import Optional

import evaluate
import numpy as np
import torch
from datasets import load_dataset
from huggingface_hub import hf_hub_download
from PIL import Image
from torch import nn
from torchvision import transforms
from torchvision.transforms import functional

import transformers
from transformers import (
    AutoConfig,
    AutoImageProcessor,
    AutoModelForSemanticSegmentation,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    default_data_collator,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version
__UpperCAmelCase	=			logging.getLogger(__name__)

# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version('4.31.0')

require_version('datasets>=2.0.0', 'To fix: pip install -r examples/pytorch/semantic-segmentation/requirements.txt')
def 					lowerCAmelCase  (   __UpperCamelCase							,      __UpperCamelCase							,      __UpperCamelCase=0 ):
 '''simple docstring'''





 UpperCAmelCase__   :					Optional[Any]   				=   (size, size) if isinstance(__UpperCamelCase							,      __UpperCamelCase ) else size
 UpperCAmelCase__				, UpperCAmelCase__   :					Optional[int]   				=   img.size
 UpperCAmelCase__   :					Tuple   				=   size[1] - original_height if original_height < size[1] else 0
 UpperCAmelCase__   :					Dict   				=   size[0] - original_width if original_width < size[0] else 0
 UpperCAmelCase__   :					Dict   				=   functional.pad(__UpperCamelCase							,      (0, 0, pad_width, pad_height)							,      fill=__UpperCamelCase )
 return img







class        __lowercase     :

  def __init__(					self						:     Tuple   ,A						:     Optional[Any]      ):






   '''simple docstring'''
   UpperCAmelCase__   :					Tuple   				=   transforms




  def __call__(					self						:     Union[str, Any]   ,A						:     Dict   ,A						:     Optional[int]      ):






   '''simple docstring'''
   for t in self.transforms:
    UpperCAmelCase__				, UpperCAmelCase__   :					Tuple   				=   t(A   ,A      )
   return image, target







class        __lowercase     :

  def __init__(					self						:     List[str]      ):






   '''simple docstring'''
   pass




  def __call__(					self						:     List[str]   ,A						:     int   ,A						:     int      ):






   '''simple docstring'''
   return image, target







class        __lowercase     :

  def __init__(					self						:     Optional[int]   ,A						:     Optional[Any]      ):






   '''simple docstring'''
   UpperCAmelCase__   :					Optional[int]   				=   size




  def __call__(					self						:     Optional[Any]   ,A						:     Dict   ,A						:     Tuple      ):






   '''simple docstring'''
   UpperCAmelCase__   :					Any   				=   functional.resize(A   ,self.size      )
   UpperCAmelCase__   :					str   				=   functional.resize(A   ,self.size   ,interpolation=transforms.InterpolationMode.NEAREST      )
   return image, target







class        __lowercase     :

  def __init__(					self						:     List[Any]   ,A						:     Union[str, Any]   ,A						:     Any=None      ):






   '''simple docstring'''
   UpperCAmelCase__   :					Dict   				=   min_size
   if max_size is None:
    UpperCAmelCase__   :					List[Any]   				=   min_size
   UpperCAmelCase__   :					List[Any]   				=   max_size




  def __call__(					self						:     Dict   ,A						:     Optional[int]   ,A						:     Any      ):






   '''simple docstring'''
   UpperCAmelCase__   :					Dict   				=   random.randint(self.min_size   ,self.max_size      )
   UpperCAmelCase__   :					int   				=   functional.resize(A   ,A      )
   UpperCAmelCase__   :					Any   				=   functional.resize(A   ,A   ,interpolation=transforms.InterpolationMode.NEAREST      )
   return image, target







class        __lowercase     :

  def __init__(					self						:     Optional[Any]   ,A						:     str      ):






   '''simple docstring'''
   UpperCAmelCase__   :					Union[str, Any]   				=   size if isinstance(A   ,A      ) else (size, size)




  def __call__(					self						:     List[Any]   ,A						:     Any   ,A						:     Dict      ):






   '''simple docstring'''
   UpperCAmelCase__   :					Tuple   				=   pad_if_smaller(A   ,self.size      )
   UpperCAmelCase__   :					str   				=   pad_if_smaller(A   ,self.size   ,fill=255      )
   UpperCAmelCase__   :					List[str]   				=   transforms.RandomCrop.get_params(A   ,self.size      )
   UpperCAmelCase__   :					Any   				=   functional.crop(A   ,*A      )
   UpperCAmelCase__   :					str   				=   functional.crop(A   ,*A      )
   return image, target







class        __lowercase     :

  def __init__(					self						:     str   ,A						:     Union[str, Any]      ):






   '''simple docstring'''
   UpperCAmelCase__   :					Any   				=   flip_prob




  def __call__(					self						:     Dict   ,A						:     Tuple   ,A						:     List[Any]      ):






   '''simple docstring'''
   if random.random() < self.flip_prob:
    UpperCAmelCase__   :					Dict   				=   functional.hflip(A      )
    UpperCAmelCase__   :					List[Any]   				=   functional.hflip(A      )
   return image, target







class        __lowercase     :

  def __call__(					self						:     Dict   ,A						:     int   ,A						:     List[Any]      ):






   '''simple docstring'''
   UpperCAmelCase__   :					Tuple   				=   functional.pil_to_tensor(A      )
   UpperCAmelCase__   :					Any   				=   torch.as_tensor(np.array(A      )   ,dtype=torch.intaa      )
   return image, target







class        __lowercase     :

  def __init__(					self						:     Any   ,A						:     Dict      ):






   '''simple docstring'''
   UpperCAmelCase__   :					str   				=   dtype




  def __call__(					self						:     Dict   ,A						:     Dict   ,A						:     Dict      ):






   '''simple docstring'''
   UpperCAmelCase__   :					Dict   				=   functional.convert_image_dtype(A   ,self.dtype      )
   return image, target







class        __lowercase     :

  def __init__(					self						:     Any   ,A						:     Optional[int]   ,A						:     Union[str, Any]      ):






   '''simple docstring'''
   UpperCAmelCase__   :					Optional[int]   				=   mean
   UpperCAmelCase__   :					str   				=   std




  def __call__(					self						:     Optional[Any]   ,A						:     str   ,A						:     Any      ):






   '''simple docstring'''
   UpperCAmelCase__   :					Optional[int]   				=   functional.normalize(A   ,mean=self.mean   ,std=self.std      )
   return image, target







class        __lowercase     :

  def __call__(					self						:     Tuple   ,A						:     List[Any]   ,A						:     Dict      ):






   '''simple docstring'''
   if not isinstance(A   ,np.ndarray      ):
    UpperCAmelCase__   :					Union[str, Any]   				=   np.array(A      ).astype(np.uinta      )
   # avoid using underflow conversion
   UpperCAmelCase__   :					Tuple   				=   255
   UpperCAmelCase__   :					Optional[int]   				=   target - 1
   UpperCAmelCase__   :					Dict   				=   255

   UpperCAmelCase__   :					Tuple   				=   Image.fromarray(A      )
   return image, target







@dataclass
class        __lowercase     :
  snake_case_			    =    field(
      default="""segments/sidewalk-semantic"""		,     metadata={
          """help""": """Name of a dataset from the hub (could be your own, possibly private dataset hosted on the hub)."""
      }		,     )
  snake_case_			    =    field(
      default=__lowerCamelCase		,     metadata={"""help""": """The configuration name of the dataset to use (via the datasets library)."""}   )
  snake_case_			    =    field(
      default=0.15		,     metadata={"""help""": """Percent to split off of train for validation."""}   )
  snake_case_			    =    field(
      default=__lowerCamelCase		,     metadata={
          """help""": (
              """For debugging purposes or quicker training, truncate the number of training examples to this """
              """value if set."""
          )
      }		,     )
  snake_case_			    =    field(
      default=__lowerCamelCase		,     metadata={
          """help""": (
              """For debugging purposes or quicker training, truncate the number of evaluation examples to this """
              """value if set."""
          )
      }		,     )
  snake_case_			    =    field(
      default=__lowerCamelCase		,     metadata={"""help""": """Whether or not to reduce all labels by 1 and replace background by 255."""}		,     )

  def 				__lowercase    (					self						:     List[str]      ):






   '''simple docstring'''
   if self.dataset_name is None and (self.train_dir is None and self.validation_dir is None):
    raise ValueError(
        """You must specify either a dataset name from the hub or a train and/or validation directory."""      )







@dataclass
class        __lowercase     :
  snake_case_			    =    field(
      default="""nvidia/mit-b0"""		,     metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""}		,     )
  snake_case_			    =    field(
      default=__lowerCamelCase		,     metadata={"""help""": """Pretrained config name or path if not the same as model_name"""}   )
  snake_case_			    =    field(
      default=__lowerCamelCase		,     metadata={"""help""": """Where do you want to store the pretrained models downloaded from s3"""}   )
  snake_case_			    =    field(
      default="""main"""		,     metadata={"""help""": """The specific model version to use (can be a branch name, tag name or commit id)."""}		,     )
  snake_case_			    =    field(default=__lowerCamelCase		,     metadata={"""help""": """Name or path of preprocessor config."""}   )
  snake_case_			    =    field(
      default=__lowerCamelCase		,     metadata={
          """help""": (
              """Will use the token generated when running `huggingface-cli login` (necessary to use this script """
              """with private models)."""
          )
      }		,     )







def 					lowerCAmelCase  (   ):
 '''simple docstring'''





 UpperCAmelCase__   :					int   				=   HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
 if len(sys.argv ) == 2 and sys.argv[1].endswith(""".json""" ):
  # If we pass only one argument to the script and it's the path to a json file,
  # let's parse it to get our arguments.
  UpperCAmelCase__				, UpperCAmelCase__				, UpperCAmelCase__   :					List[str]   				=   parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
 else:
  UpperCAmelCase__				, UpperCAmelCase__				, UpperCAmelCase__   :					str   				=   parser.parse_args_into_dataclasses()

 # Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
 # information sent is the one passed as arguments along with your Python/PyTorch versions.
 send_example_telemetry("""run_semantic_segmentation"""							,      __UpperCamelCase							,      __UpperCamelCase )

 # Setup logging
 logging.basicConfig(
     format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s"""							,      datefmt="""%m/%d/%Y %H:%M:%S"""							,      handlers=[logging.StreamHandler(sys.stdout )]							,      )

 if training_args.should_log:
  # The default of training_args.log_level is passive, so we set log level at info here to have that default.
  transformers.utils.logging.set_verbosity_info()

 UpperCAmelCase__   :					List[str]   				=   training_args.get_process_log_level()
 logger.setLevel(__UpperCamelCase )
 transformers.utils.logging.set_verbosity(__UpperCamelCase )
 transformers.utils.logging.enable_default_handler()
 transformers.utils.logging.enable_explicit_format()

 # Log on each process the small summary:
 logger.warning(
     F"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"
     + F"distributed training: {bool(training_args.local_rank != -1 )}, 16-bits training: {training_args.fpaa}" )
 logger.info(F"Training/evaluation parameters {training_args}" )

 # Detecting last checkpoint.
 UpperCAmelCase__   :					Optional[int]   				=   None
 if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir:
  UpperCAmelCase__   :					int   				=   get_last_checkpoint(training_args.output_dir )
  if last_checkpoint is None and len(os.listdir(training_args.output_dir ) ) > 0:
   raise ValueError(
       F"Output directory ({training_args.output_dir}) already exists and is not empty. "
       """Use --overwrite_output_dir to overcome.""" )
  elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
   logger.info(
       F"Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change "
       """the `--output_dir` or add `--overwrite_output_dir` to train from scratch.""" )

    # Load dataset
    # In distributed training, the load_dataset function guarantees that only one local process can concurrently
    # download the dataset.
    # TODO support datasets from local folders
 UpperCAmelCase__   :					str   				=   load_dataset(data_args.dataset_name							,      cache_dir=model_args.cache_dir )

 # Rename column names to standardized names (only "image" and "label" need to be present)
 if "pixel_values" in dataset["train"].column_names:
  UpperCAmelCase__   :					int   				=   dataset.rename_columns({"""pixel_values""": """image"""} )
 if "annotation" in dataset["train"].column_names:
  UpperCAmelCase__   :					List[Any]   				=   dataset.rename_columns({"""annotation""": """label"""} )

 # If we don't have a validation split, split off a percentage of train as validation.
 UpperCAmelCase__   :					Optional[int]   				=   None if """validation""" in dataset.keys() else data_args.train_val_split
 if isinstance(data_args.train_val_split							,      __UpperCamelCase ) and data_args.train_val_split > 0.0:
  UpperCAmelCase__   :					List[Any]   				=   dataset["""train"""].train_test_split(data_args.train_val_split )
  UpperCAmelCase__   :					Any   				=   split["""train"""]
  UpperCAmelCase__   :					Any   				=   split["""test"""]

 # Prepare label mappings.
 # We'll include these in the model's config to get human readable labels in the Inference API.
 if data_args.dataset_name == "scene_parse_150":
  UpperCAmelCase__   :					Union[str, Any]   				=   """huggingface/label-files"""
  UpperCAmelCase__   :					List[Any]   				=   """ade20k-id2label.json"""
 else:
  UpperCAmelCase__   :					Dict   				=   data_args.dataset_name
  UpperCAmelCase__   :					List[str]   				=   """id2label.json"""
 UpperCAmelCase__   :					Tuple   				=   json.load(open(hf_hub_download(__UpperCamelCase							,      __UpperCamelCase							,      repo_type="""dataset""" )							,      """r""" ) )
 UpperCAmelCase__   :					Dict   				=   {int(__UpperCamelCase ): v for k, v in idalabel.items()}
 UpperCAmelCase__   :					int   				=   {v: str(__UpperCamelCase ) for k, v in idalabel.items()}

 # Load the mean IoU metric from the datasets package
 UpperCAmelCase__   :					Tuple   				=   evaluate.load("""mean_iou""" )

 # Define our compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with a
 # predictions and label_ids field) and has to return a dictionary string to float.
 @torch.no_grad()
 def compute_metrics(__UpperCamelCase ):
  UpperCAmelCase__				, UpperCAmelCase__   :					List[str]   				=   eval_pred
  UpperCAmelCase__   :					Dict   				=   torch.from_numpy(__UpperCamelCase )
  # scale the logits to the size of the label
  UpperCAmelCase__   :					Dict   				=   nn.functional.interpolate(
      __UpperCamelCase							,      size=labels.shape[-2:]							,      mode="""bilinear"""							,      align_corners=__UpperCamelCase							,      ).argmax(dim=1 )

  UpperCAmelCase__   :					Optional[int]   				=   logits_tensor.detach().cpu().numpy()
  UpperCAmelCase__   :					Tuple   				=   metric.compute(
      predictions=__UpperCamelCase							,      references=__UpperCamelCase							,      num_labels=len(__UpperCamelCase )							,      ignore_index=0							,      reduce_labels=image_processor.do_reduce_labels							,      )
  # add per category metrics as individual key-value pairs
  UpperCAmelCase__   :					List[str]   				=   metrics.pop("""per_category_accuracy""" ).tolist()
  UpperCAmelCase__   :					Any   				=   metrics.pop("""per_category_iou""" ).tolist()

  metrics.update({F"accuracy_{idalabel[i]}": v for i, v in enumerate(__UpperCamelCase )} )
  metrics.update({F"iou_{idalabel[i]}": v for i, v in enumerate(__UpperCamelCase )} )

  return metrics

 UpperCAmelCase__   :					Optional[int]   				=   AutoConfig.from_pretrained(
     model_args.config_name or model_args.model_name_or_path							,      labelaid=__UpperCamelCase							,      idalabel=__UpperCamelCase							,      cache_dir=model_args.cache_dir							,      revision=model_args.model_revision							,      use_auth_token=True if model_args.use_auth_token else None							,      )
 UpperCAmelCase__   :					Union[str, Any]   				=   AutoModelForSemanticSegmentation.from_pretrained(
     model_args.model_name_or_path							,      from_tf=bool(""".ckpt""" in model_args.model_name_or_path )							,      config=__UpperCamelCase							,      cache_dir=model_args.cache_dir							,      revision=model_args.model_revision							,      use_auth_token=True if model_args.use_auth_token else None							,      )
 UpperCAmelCase__   :					Optional[Any]   				=   AutoImageProcessor.from_pretrained(
     model_args.image_processor_name or model_args.model_name_or_path							,      cache_dir=model_args.cache_dir							,      revision=model_args.model_revision							,      use_auth_token=True if model_args.use_auth_token else None							,      )

 # Define torchvision transforms to be applied to each image + target.
 # Not that straightforward in torchvision: https://github.com/pytorch/vision/issues/9
 # Currently based on official torchvision references: https://github.com/pytorch/vision/blob/main/references/segmentation/transforms.py
 if "shortest_edge" in image_processor.size:
  # We instead set the target size as (shortest_edge, shortest_edge) to here to ensure all images are batchable.
  UpperCAmelCase__   :					Any   				=   (image_processor.size["""shortest_edge"""], image_processor.size["""shortest_edge"""])
 else:
  UpperCAmelCase__   :					Optional[int]   				=   (image_processor.size["""height"""], image_processor.size["""width"""])
 UpperCAmelCase__   :					Any   				=   Compose(
     [
         ReduceLabels() if data_args.reduce_labels else Identity(),
         RandomCrop(size=__UpperCamelCase ),
         RandomHorizontalFlip(flip_prob=0.5 ),
         PILToTensor(),
         ConvertImageDtype(torch.float ),
         Normalize(mean=image_processor.image_mean							,      std=image_processor.image_std ),
     ] )
 # Define torchvision transform to be applied to each image.
 # jitter = ColorJitter(brightness=0.25, contrast=0.25, saturation=0.25, hue=0.1)
 UpperCAmelCase__   :					str   				=   Compose(
     [
         ReduceLabels() if data_args.reduce_labels else Identity(),
         Resize(size=__UpperCamelCase ),
         PILToTensor(),
         ConvertImageDtype(torch.float ),
         Normalize(mean=image_processor.image_mean							,      std=image_processor.image_std ),
     ] )

 def preprocess_train(__UpperCamelCase ):
  UpperCAmelCase__   :					List[Any]   				=   []
  UpperCAmelCase__   :					str   				=   []
  for image, target in zip(example_batch["""image"""]							,      example_batch["""label"""] ):
   UpperCAmelCase__				, UpperCAmelCase__   :					Dict   				=   train_transforms(image.convert("""RGB""" )							,      __UpperCamelCase )
   pixel_values.append(__UpperCamelCase )
   labels.append(__UpperCamelCase )

  UpperCAmelCase__   :					Any   				=   {}
  UpperCAmelCase__   :					Dict   				=   torch.stack(__UpperCamelCase )
  UpperCAmelCase__   :					Tuple   				=   torch.stack(__UpperCamelCase )

  return encoding

 def preprocess_val(__UpperCamelCase ):
  UpperCAmelCase__   :					List[str]   				=   []
  UpperCAmelCase__   :					Optional[Any]   				=   []
  for image, target in zip(example_batch["""image"""]							,      example_batch["""label"""] ):
   UpperCAmelCase__				, UpperCAmelCase__   :					Any   				=   val_transforms(image.convert("""RGB""" )							,      __UpperCamelCase )
   pixel_values.append(__UpperCamelCase )
   labels.append(__UpperCamelCase )

  UpperCAmelCase__   :					Dict   				=   {}
  UpperCAmelCase__   :					int   				=   torch.stack(__UpperCamelCase )
  UpperCAmelCase__   :					List[Any]   				=   torch.stack(__UpperCamelCase )

  return encoding

 if training_args.do_train:
  if "train" not in dataset:
   raise ValueError("""--do_train requires a train dataset""" )
  if data_args.max_train_samples is not None:
   UpperCAmelCase__   :					Optional[Any]   				=   (
       dataset["""train"""].shuffle(seed=training_args.seed ).select(range(data_args.max_train_samples ) )
   )
  # Set the training transforms
  dataset["train"].set_transform(__UpperCamelCase )

 if training_args.do_eval:
  if "validation" not in dataset:
   raise ValueError("""--do_eval requires a validation dataset""" )
  if data_args.max_eval_samples is not None:
   UpperCAmelCase__   :					List[str]   				=   (
       dataset["""validation"""].shuffle(seed=training_args.seed ).select(range(data_args.max_eval_samples ) )
   )
  # Set the validation transforms
  dataset["validation"].set_transform(__UpperCamelCase )

 # Initalize our trainer
 UpperCAmelCase__   :					Any   				=   Trainer(
     model=__UpperCamelCase							,      args=__UpperCamelCase							,      train_dataset=dataset["""train"""] if training_args.do_train else None							,      eval_dataset=dataset["""validation"""] if training_args.do_eval else None							,      compute_metrics=__UpperCamelCase							,      tokenizer=__UpperCamelCase							,      data_collator=__UpperCamelCase							,      )

 # Training
 if training_args.do_train:
  UpperCAmelCase__   :					str   				=   None
  if training_args.resume_from_checkpoint is not None:
   UpperCAmelCase__   :					Dict   				=   training_args.resume_from_checkpoint
  elif last_checkpoint is not None:
   UpperCAmelCase__   :					Optional[int]   				=   last_checkpoint
  UpperCAmelCase__   :					Dict   				=   trainer.train(resume_from_checkpoint=__UpperCamelCase )
  trainer.save_model()
  trainer.log_metrics("""train"""							,      train_result.metrics )
  trainer.save_metrics("""train"""							,      train_result.metrics )
  trainer.save_state()

 # Evaluation
 if training_args.do_eval:
  UpperCAmelCase__   :					Dict   				=   trainer.evaluate()
  trainer.log_metrics("""eval"""							,      __UpperCamelCase )
  trainer.save_metrics("""eval"""							,      __UpperCamelCase )

 # Write model card and (optionally) push to hub
 UpperCAmelCase__   :					int   				=   {
     """finetuned_from""": model_args.model_name_or_path,
     """dataset""": data_args.dataset_name,
     """tags""": ["""image-segmentation""", """vision"""],
 }
 if training_args.push_to_hub:
  trainer.push_to_hub(**__UpperCamelCase )
 else:
  trainer.create_model_card(**__UpperCamelCase )


if __name__ == "__main__":
      main()





