


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


snake_case_  :     Optional[Any]							  =							5e-5  # larger error tolerance than in our internal tests, to avoid flaky user-facing errors







def   lowerCamelCase(				a__):
    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   A__							(						UpperCamelCase__					):




      @staticmethod
      def 							__UpperCamelCase			(    _a   : str					)							->       Tuple:


          """simple docstring"""





          _SCREAMING_SNAKE_CASE     					=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 							__UpperCamelCase			(    _a   : List[Any]			,      _a   : List[Any]					)							->       Any:


          """simple docstring"""





          _SCREAMING_SNAKE_CASE     					=set(pt_outputs.keys()					)
          _SCREAMING_SNAKE_CASE     					=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   : str			,      _a   : List[str]			,      _a   : Optional[int]			,      _a   : Tuple=""					):
              # 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					):
                  _SCREAMING_SNAKE_CASE     					=np.max(np.abs(pt_out.numpy() - tf_out.numpy()					)					)
                  _SCREAMING_SNAKE_CASE     					=tensor_difference
              else:
                  _SCREAMING_SNAKE_CASE     					=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					):
                          _SCREAMING_SNAKE_CASE     					=root_name + pt_item
                          _SCREAMING_SNAKE_CASE     					=tf_out[pt_item]
                          _SCREAMING_SNAKE_CASE     					=pt_out[pt_item]
                      else:
                          _SCREAMING_SNAKE_CASE     					=root_name + f"[{i}]"
                          _SCREAMING_SNAKE_CASE     					=tf_out[i]
                      _SCREAMING_SNAKE_CASE     					=_find_pt_tf_differences(_a			,      _a			,      _a			,      _a					)

              return differences

          return _find_pt_tf_differences(_a			,      _a			,      {}					)




      def __init__(    self   : List[str]			,      _a   : List[Any]			,      _a   : Optional[Any]			,      _a   : Dict			,      _a   : str			,      _a   : str			,      _a   : int			,      _a   : List[Any]			,      _a   : int			,      *_a   : Optional[int]			,      )							->       Union[str, Any]:


          """simple docstring"""





          _SCREAMING_SNAKE_CASE     					=logging.get_logger('''transformers-cli/pt_to_tf'''					)
          _SCREAMING_SNAKE_CASE     					=model_name
          _SCREAMING_SNAKE_CASE     					=local_dir if local_dir else os.path.join('''/tmp'''			,      _a					)
          _SCREAMING_SNAKE_CASE     					=max_error
          _SCREAMING_SNAKE_CASE     					=new_weights
          _SCREAMING_SNAKE_CASE     					=no_pr
          _SCREAMING_SNAKE_CASE     					=push
          _SCREAMING_SNAKE_CASE     					=extra_commit_description
          _SCREAMING_SNAKE_CASE     					=override_model_class




      def 							__UpperCamelCase			(    self   : Union[str, Any]			,      _a   : Dict			,      _a   : Tuple			,      _a   : List[Any]					)							->       List[str]:


          """simple docstring"""





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

          _SCREAMING_SNAKE_CASE     					=type(pt_model.config					)
          if model_config_class in PROCESSOR_MAPPING:
              _SCREAMING_SNAKE_CASE     					=AutoProcessor.from_pretrained(self._local_dir					)
              if model_config_class in TOKENIZER_MAPPING and processor.tokenizer.pad_token is None:
                  _SCREAMING_SNAKE_CASE     					=processor.tokenizer.eos_token
          elif model_config_class in IMAGE_PROCESSOR_MAPPING:
              _SCREAMING_SNAKE_CASE     					=AutoImageProcessor.from_pretrained(self._local_dir					)
          elif model_config_class in FEATURE_EXTRACTOR_MAPPING:
              _SCREAMING_SNAKE_CASE     					=AutoFeatureExtractor.from_pretrained(self._local_dir					)
          elif model_config_class in TOKENIZER_MAPPING:
              _SCREAMING_SNAKE_CASE     					=AutoTokenizer.from_pretrained(self._local_dir					)
              if processor.pad_token is None:
                  _SCREAMING_SNAKE_CASE     					=processor.eos_token
          else:
              raise ValueError(f"Unknown data processing type (model config type: {model_config_class})"					)

          _SCREAMING_SNAKE_CASE     					=set(inspect.signature(pt_model.forward					).parameters.keys()					)
          _SCREAMING_SNAKE_CASE     					={}
          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:
              _SCREAMING_SNAKE_CASE     					=load_dataset('''cifar10'''			,      '''plain_text'''			,      split='''test'''					)[:2]['''img''']
              processor_inputs.update({'''images''': sample_images}					)
          if "input_features" in model_forward_signature:
              _SCREAMING_SNAKE_CASE     					=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:
                  _SCREAMING_SNAKE_CASE     					=feature_extractor_signature['''padding'''].default
                  if default_strategy is not False and default_strategy is not None:
                      _SCREAMING_SNAKE_CASE     					=default_strategy
                  else:
                      _SCREAMING_SNAKE_CASE     					=True
              else:
                  _SCREAMING_SNAKE_CASE     					=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}					)
          _SCREAMING_SNAKE_CASE     					=processor(**_a			,      return_tensors='''pt'''					)
          _SCREAMING_SNAKE_CASE     					=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
          ):
              _SCREAMING_SNAKE_CASE     					=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 							__UpperCamelCase			(    self   : str					)							->       int:


          """simple docstring"""





          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
          _SCREAMING_SNAKE_CASE     					=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
          _SCREAMING_SNAKE_CASE     					=AutoConfig.from_pretrained(self._local_dir					)
          _SCREAMING_SNAKE_CASE     					=config.architectures
          if self._override_model_class is not None:
              if self._override_model_class.startswith('''TF'''					):
                  _SCREAMING_SNAKE_CASE     					=[self._override_model_class[2:]]
              else:
                  _SCREAMING_SNAKE_CASE     					=[self._override_model_class]
              try:
                  _SCREAMING_SNAKE_CASE     					=getattr(import_module('''transformers'''					)			,      architectures[0]					)
              except AttributeError:
                  raise ValueError(f"Model class {self._override_model_class} not found in transformers."					)
              try:
                  _SCREAMING_SNAKE_CASE     					=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
              _SCREAMING_SNAKE_CASE     					=getattr(import_module('''transformers'''					)			,      '''AutoModel'''					)
              _SCREAMING_SNAKE_CASE     					=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]}"					)
              _SCREAMING_SNAKE_CASE     					=getattr(import_module('''transformers'''					)			,      architectures[0]					)
              try:
                  _SCREAMING_SNAKE_CASE     					=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
          _SCREAMING_SNAKE_CASE     					=tf_class.from_config(_a					)
          _SCREAMING_SNAKE_CASE     					=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
          _SCREAMING_SNAKE_CASE     					=pt_class.from_pretrained(self._local_dir					)
          pt_model.eval()

          _SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE     					=self.get_inputs(_a			,      _a			,      _a					)

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

          _SCREAMING_SNAKE_CASE     					=tf_class.from_pretrained(self._local_dir			,      from_pt=_a					)
          _SCREAMING_SNAKE_CASE     					=tf_from_pt_model(**_a			,      output_hidden_states=_a			,      training=_a					)

          # Confirms that cross loading PT weights into TF worked.
          _SCREAMING_SNAKE_CASE     					=self.find_pt_tf_differences(_a			,      _a					)
          _SCREAMING_SNAKE_CASE     					={k: v for k, v in crossload_differences.items() if '''hidden''' not in k}
          _SCREAMING_SNAKE_CASE     					={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\''''					)
          _SCREAMING_SNAKE_CASE     					=max(output_differences.values()					) if output_differences else 0.0
          _SCREAMING_SNAKE_CASE     					=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
          _SCREAMING_SNAKE_CASE     					=os.path.join(self._local_dir			,      _a					)
          _SCREAMING_SNAKE_CASE     					=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

          _SCREAMING_SNAKE_CASE     					=tf_class.from_pretrained(self._local_dir					)
          _SCREAMING_SNAKE_CASE     					=tf_model(**_a			,      output_hidden_states=_a					)

          _SCREAMING_SNAKE_CASE     					=self.find_pt_tf_differences(_a			,      _a					)
          _SCREAMING_SNAKE_CASE     					={k: v for k, v in conversion_differences.items() if '''hidden''' not in k}
          _SCREAMING_SNAKE_CASE     					={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\''''					)
          _SCREAMING_SNAKE_CASE     					=max(output_differences.values()					) if output_differences else 0.0
          _SCREAMING_SNAKE_CASE     					=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]					)					)

          _SCREAMING_SNAKE_CASE     					='''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...'''					)
              _SCREAMING_SNAKE_CASE     					=(
                  '''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					):
                  _SCREAMING_SNAKE_CASE     					=[
                      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:
                  _SCREAMING_SNAKE_CASE     					=[CommitOperationAdd(path_in_repo=_a			,      path_or_fileobj=_a					)]

              _SCREAMING_SNAKE_CASE     					=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}"					)



from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast
from ...utils import logging


snake_case_  :     Tuple							  =							logging.get_logger(__name__)

snake_case_  :     int							  =							{
    '''uclanlp/plbart-base''': '''https://huggingface.co/uclanlp/plbart-base/resolve/main/config.json''',
    # See all PLBART models at https://huggingface.co/models?filter=plbart
}

class   A__							(						UpperCamelCase__					):
						UpperCAmelCase     =  "plbart"
						UpperCAmelCase     =  ["past_key_values"]
						UpperCAmelCase     =  {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}




						def __init__(    self   : Union[str, Any]			,      _a   : List[str]=5_0005			,      _a   : List[str]=1024			,      _a   : int=6			,      _a   : Union[str, Any]=3072			,      _a   : List[str]=12			,      _a   : int=6			,      _a   : Optional[int]=3072			,      _a   : Any=12			,      _a   : Any=0.0			,      _a   : Optional[Any]=0.0			,      _a   : List[Any]=True			,      _a   : Any=True			,      _a   : Any="gelu"			,      _a   : int=768			,      _a   : int=0.1			,      _a   : int=0.1			,      _a   : List[str]=0.0			,      _a   : Optional[int]=0.02			,      _a   : Dict=0.0			,      _a   : List[str]=True			,      _a   : List[Any]=1			,      _a   : Tuple=0			,      _a   : Tuple=2			,      _a   : Optional[int]=2			,      **_a   : Optional[int]			,      )							->       Optional[int]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE     					=vocab_size
										_SCREAMING_SNAKE_CASE     					=max_position_embeddings
										_SCREAMING_SNAKE_CASE     					=d_model
										_SCREAMING_SNAKE_CASE     					=encoder_ffn_dim
										_SCREAMING_SNAKE_CASE     					=encoder_layers
										_SCREAMING_SNAKE_CASE     					=encoder_attention_heads
										_SCREAMING_SNAKE_CASE     					=decoder_ffn_dim
										_SCREAMING_SNAKE_CASE     					=decoder_layers
										_SCREAMING_SNAKE_CASE     					=decoder_attention_heads
										_SCREAMING_SNAKE_CASE     					=dropout
										_SCREAMING_SNAKE_CASE     					=attention_dropout
										_SCREAMING_SNAKE_CASE     					=activation_dropout
										_SCREAMING_SNAKE_CASE     					=activation_function
										_SCREAMING_SNAKE_CASE     					=init_std
										_SCREAMING_SNAKE_CASE     					=encoder_layerdrop
										_SCREAMING_SNAKE_CASE     					=decoder_layerdrop
										_SCREAMING_SNAKE_CASE     					=classifier_dropout
										_SCREAMING_SNAKE_CASE     					=use_cache
										_SCREAMING_SNAKE_CASE     					=encoder_layers
										_SCREAMING_SNAKE_CASE     					=scale_embedding  # scale factor will be sqrt(d_model) if True
										super().__init__(
										    pad_token_id=_a			,      bos_token_id=_a			,      eos_token_id=_a			,      is_encoder_decoder=_a			,      forced_eos_token_id=_a			,      **_a			,      )

class   A__							(						UpperCamelCase__					):




						@property
						def 							__UpperCamelCase			(    self   : int					)							->       Mapping[str, Mapping[int, str]]:


										"""simple docstring"""





										return OrderedDict(
										    [
										        ('''input_ids''', {0: '''batch''', 1: '''sequence'''}),
										        ('''attention_mask''', {0: '''batch''', 1: '''sequence'''}),
										    ]					)




						@property
						def 							__UpperCamelCase			(    self   : List[str]					)							->       Mapping[str, Mapping[int, str]]:


										"""simple docstring"""





										if self.use_past:
														return OrderedDict(
														    [
														        ('''last_hidden_state''', {0: '''batch''', 1: '''sequence'''}),
														        ('''past_keys''', {0: '''batch''', 2: '''sequence'''}),
														        ('''encoder_last_hidden_state''', {0: '''batch''', 1: '''sequence'''}),
														    ]					)
										else:
														return OrderedDict(
														    [
														        ('''last_hidden_state''', {0: '''batch''', 1: '''sequence'''}),
														        ('''encoder_last_hidden_state''', {0: '''batch''', 1: '''sequence'''}),
														    ]					)