





"""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


_lowerCamelCase  					=     5e-5  # larger error tolerance than in our internal tests, to avoid flaky user-facing errors




def 		lowerCAmelCase_     (  lowercase_       :							Dict		):

 '''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       snake_case		(       _lowerCamelCase       ):

      @staticmethod
      def 						SCREAMING_SNAKE_CASE_      (      _lowerCamelCase       :int						):
       __SCREAMING_SNAKE_CASE			:      List[Any] 							=	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=__UpperCamelCase						,							required=__UpperCamelCase						,							help='''The model name, including owner/organization, as seen on the hub.'''						,							)
       train_parser.add_argument(
           '''--local-dir'''						,							type=__UpperCamelCase						,							default=''''''						,							help='''Optional local directory of the model repository. Defaults to /tmp/{model_name}'''						,							)
       train_parser.add_argument(
           '''--max-error'''						,							type=__UpperCamelCase						,							default=__UpperCamelCase						,							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=__UpperCamelCase						,							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=__UpperCamelCase						,							default=__UpperCamelCase						,							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=__UpperCamelCase						)

      @staticmethod
      def 						SCREAMING_SNAKE_CASE_      (      _lowerCamelCase       :Any						,							_lowerCamelCase       :Optional[int]						):
       __SCREAMING_SNAKE_CASE			:      Optional[Any] 							=	set(pt_outputs.keys()						)
       __SCREAMING_SNAKE_CASE			:      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(_lowerCamelCase       :Union[str, Any]						,							_lowerCamelCase       :int						,							_lowerCamelCase       :Any						,							_lowerCamelCase       :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(__UpperCamelCase						,							torch.Tensor						):
         __SCREAMING_SNAKE_CASE			:      Any 							=	np.max(np.abs(pt_out.numpy() - tf_out.numpy()						)						)
         __SCREAMING_SNAKE_CASE			:      Optional[Any] 							=	tensor_difference
        else:
         __SCREAMING_SNAKE_CASE			:      Union[str, Any] 							=	attr_name
         for i, pt_item in enumerate(__UpperCamelCase						):
          # If it is a named attribute, we keep the name. Otherwise, just its index.
          if isinstance(__UpperCamelCase						,							__UpperCamelCase						):
           __SCREAMING_SNAKE_CASE			:      List[Any] 							=	root_name + pt_item
           __SCREAMING_SNAKE_CASE			:      Union[str, Any] 							=	tf_out[pt_item]
           __SCREAMING_SNAKE_CASE			:      Union[str, Any] 							=	pt_out[pt_item]
          else:
           __SCREAMING_SNAKE_CASE			:      Optional[int] 							=	root_name + f'''[{i}]'''
           __SCREAMING_SNAKE_CASE			:      int 							=	tf_out[i]
          __SCREAMING_SNAKE_CASE			:      Any 							=	_find_pt_tf_differences(__UpperCamelCase						,							__UpperCamelCase						,							__UpperCamelCase						,							__UpperCamelCase						)

        return differences

       return _find_pt_tf_differences(__UpperCamelCase						,							__UpperCamelCase						,							{}						)

      def __init__(      self       :str						,							_lowerCamelCase       :int						,							_lowerCamelCase       :str						,							_lowerCamelCase       :Optional[Any]						,							_lowerCamelCase       :Union[str, Any]						,							_lowerCamelCase       :Dict						,							_lowerCamelCase       :Union[str, Any]						,							_lowerCamelCase       :List[str]						,							_lowerCamelCase       :int						,							*_lowerCamelCase       :List[str]						,							):
       __SCREAMING_SNAKE_CASE			:      Dict 							=	logging.get_logger('''transformers-cli/pt_to_tf'''						)
       __SCREAMING_SNAKE_CASE			:      Any 							=	model_name
       __SCREAMING_SNAKE_CASE			:      Tuple 							=	local_dir if local_dir else os.path.join('''/tmp'''						,							__UpperCamelCase						)
       __SCREAMING_SNAKE_CASE			:      Any 							=	max_error
       __SCREAMING_SNAKE_CASE			:      Tuple 							=	new_weights
       __SCREAMING_SNAKE_CASE			:      int 							=	no_pr
       __SCREAMING_SNAKE_CASE			:      Tuple 							=	push
       __SCREAMING_SNAKE_CASE			:      List[Any] 							=	extra_commit_description
       __SCREAMING_SNAKE_CASE			:      Tuple 							=	override_model_class

      def 						SCREAMING_SNAKE_CASE_      (      self       :List[Any]						,							_lowerCamelCase       :Union[str, Any]						,							_lowerCamelCase       :Optional[int]						,							_lowerCamelCase       :Tuple						):







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

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

       __SCREAMING_SNAKE_CASE			:      Dict 							=	set(inspect.signature(pt_model.forward						).parameters.keys()						)
       __SCREAMING_SNAKE_CASE			:      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:
        __SCREAMING_SNAKE_CASE			:      Dict 							=	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			:      List[str] 							=	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			:      Union[str, Any] 							=	feature_extractor_signature['padding'].default
         if default_strategy is not False and default_strategy is not None:
          __SCREAMING_SNAKE_CASE			:      Any 							=	default_strategy
         else:
          __SCREAMING_SNAKE_CASE			:      int 							=	True
        else:
         __SCREAMING_SNAKE_CASE			:      Union[str, 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}						)
       __SCREAMING_SNAKE_CASE			:      List[Any] 							=	processor(**__UpperCamelCase						,							return_tensors='''pt'''						)
       __SCREAMING_SNAKE_CASE			:      Optional[int] 							=	processor(**__UpperCamelCase						,							return_tensors='''tf'''						)

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

       return pt_input, tf_input



      def 						SCREAMING_SNAKE_CASE_      (      self       :Optional[Any]						):







       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			:      Tuple 							=	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			:      Optional[Any] 							=	AutoConfig.from_pretrained(self._local_dir						)
       __SCREAMING_SNAKE_CASE			:      Optional[Any] 							=	config.architectures
       if self._override_model_class is not None:
        if self._override_model_class.startswith('''TF'''						):
         __SCREAMING_SNAKE_CASE			:      str 							=	[self._override_model_class[2:]]
        else:
         __SCREAMING_SNAKE_CASE			:      Union[str, Any] 							=	[self._override_model_class]
        try:
         __SCREAMING_SNAKE_CASE			:      List[str] 							=	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			:      int 							=	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			:      List[str] 							=	getattr(import_module('''transformers'''						)						,							'''AutoModel'''						)
        __SCREAMING_SNAKE_CASE			:      List[str] 							=	getattr(import_module('''transformers'''						)						,							'''TFAutoModel'''						)
        self._logger.warning('''No detected architecture, using AutoModel/TFAutoModel'''						)
       else:  # Architecture defined -- use it
        if len(__UpperCamelCase						) > 1:
         raise ValueError(f'''More than one architecture was found, aborting. (architectures = {architectures})'''						)
        self._logger.warning(f'''Detected architecture: {architectures[0]}'''						)
        __SCREAMING_SNAKE_CASE			:      Dict 							=	getattr(import_module('''transformers'''						)						,							architectures[0]						)
        try:
         __SCREAMING_SNAKE_CASE			:      Union[str, 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
       __SCREAMING_SNAKE_CASE			:      Dict 							=	tf_class.from_config(__UpperCamelCase						)
       __SCREAMING_SNAKE_CASE			:      Any 							=	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			:      int 							=	pt_class.from_pretrained(self._local_dir						)
       pt_model.eval()

       __SCREAMING_SNAKE_CASE			:      List[str] 							=	self.get_inputs(__UpperCamelCase						,							__UpperCamelCase						,							__UpperCamelCase						)

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

       __SCREAMING_SNAKE_CASE			:      Dict 							=	tf_class.from_pretrained(self._local_dir						,							from_pt=__UpperCamelCase						)
       __SCREAMING_SNAKE_CASE			:      Tuple 							=	tf_from_pt_model(**__UpperCamelCase						,							output_hidden_states=__UpperCamelCase						,							training=__UpperCamelCase						)

       # Confirms that cross loading PT weights into TF worked.
       __SCREAMING_SNAKE_CASE			:      int 							=	self.find_pt_tf_differences(__UpperCamelCase						,							__UpperCamelCase						)
       __SCREAMING_SNAKE_CASE			:      Union[str, Any] 							=	{k: v for k, v in crossload_differences.items() if 'hidden' not in k}
       __SCREAMING_SNAKE_CASE			:      Dict 							=	{k: v for k, v in crossload_differences.items() if 'hidden' in k}
       if len(__UpperCamelCase						) == 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			:      Optional[int] 							=	max(output_differences.values()						) if output_differences else 0.0
       __SCREAMING_SNAKE_CASE			:      int 							=	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			:      Any 							=	os.path.join(self._local_dir						,							__UpperCamelCase						)
       __SCREAMING_SNAKE_CASE			:      Optional[Any] 							=	os.path.join(self._local_dir						,							__UpperCamelCase						)
       if (not os.path.exists(__UpperCamelCase						) and not os.path.exists(__UpperCamelCase						)) 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			:      List[str] 							=	tf_class.from_pretrained(self._local_dir						)
       __SCREAMING_SNAKE_CASE			:      Union[str, Any] 							=	tf_model(**__UpperCamelCase						,							output_hidden_states=__UpperCamelCase						)

       __SCREAMING_SNAKE_CASE			:      Optional[Any] 							=	self.find_pt_tf_differences(__UpperCamelCase						,							__UpperCamelCase						)
       __SCREAMING_SNAKE_CASE			:      Any 							=	{k: v for k, v in conversion_differences.items() if 'hidden' not in k}
       __SCREAMING_SNAKE_CASE			:      Any 							=	{k: v for k, v in conversion_differences.items() if 'hidden' in k}
       if len(__UpperCamelCase						) == 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			:      int 							=	max(output_differences.values()						) if output_differences else 0.0
       __SCREAMING_SNAKE_CASE			:      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]						)						)

       __SCREAMING_SNAKE_CASE			:      Any 							=	'Update TF weights' if self._new_weights else 'Add TF weights'
       if self._push:
        repo.git_add(auto_lfs_track=__UpperCamelCase						)
        repo.git_commit(__UpperCamelCase						)
        repo.git_push(blocking=__UpperCamelCase						)  # 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			:      Optional[int] 							=	(
            '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(__UpperCamelCase						):
         __SCREAMING_SNAKE_CASE			:      str 							=	[
             CommitOperationAdd(path_in_repo=__UpperCamelCase						,							path_or_fileobj=__UpperCamelCase						)
         ]
         for shard_path in tf.io.gfile.glob(self._local_dir + '''/tf_model-*.h5'''						):
          operations += [
              CommitOperationAdd(path_in_repo=os.path.basename(__UpperCamelCase						)						,							path_or_fileobj=__UpperCamelCase						)
          ]
        else:
         __SCREAMING_SNAKE_CASE			:      List[Any] 							=	[CommitOperationAdd(path_in_repo=__UpperCamelCase						,							path_or_fileobj=__UpperCamelCase						)]

        __SCREAMING_SNAKE_CASE			:      Tuple 							=	create_commit(
            repo_id=self._model_name						,							operations=__UpperCamelCase						,							commit_message=__UpperCamelCase						,							commit_description=__UpperCamelCase						,							repo_type='''model'''						,							create_pr=__UpperCamelCase						,							).pr_url
        self._logger.warning(f'''PR open in {hub_pr_url}'''						)








"""simple docstring"""





import argparse
import json

import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm.models import create_model

from transformers import (
    BeitImageProcessor,
    DataaVecVisionConfig,
    DataaVecVisionForImageClassification,
    DataaVecVisionModel,
)




def 		lowerCAmelCase_     (  lowercase_       :							Tuple			,				lowercase_       :							Union[str, Any]=False			,				lowercase_       :							Optional[Any]=False			,				lowercase_       :							Dict="data2vec."		):

 '''simple docstring'''






 __SCREAMING_SNAKE_CASE			:      Optional[Any] 							=	'''backbone.''' if is_semantic else ''''''

 __SCREAMING_SNAKE_CASE			:      Any 							=	[]
 for i in range(config.num_hidden_layers		):
  # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
  rename_keys.append(
      (F'''{prefix}blocks.{i}.norm1.weight''', F'''{hf_prefix}encoder.layer.{i}.layernorm_before.weight''')		)
  rename_keys.append((F'''{prefix}blocks.{i}.norm1.bias''', F'''{hf_prefix}encoder.layer.{i}.layernorm_before.bias''')		)
  rename_keys.append(
      (F'''{prefix}blocks.{i}.attn.proj.weight''', F'''{hf_prefix}encoder.layer.{i}.attention.output.dense.weight''')		)
  rename_keys.append(
      (F'''{prefix}blocks.{i}.attn.proj.bias''', F'''{hf_prefix}encoder.layer.{i}.attention.output.dense.bias''')		)
  rename_keys.append(
      (F'''{prefix}blocks.{i}.norm2.weight''', F'''{hf_prefix}encoder.layer.{i}.layernorm_after.weight''')		)
  rename_keys.append((F'''{prefix}blocks.{i}.norm2.bias''', F'''{hf_prefix}encoder.layer.{i}.layernorm_after.bias''')		)
  rename_keys.append(
      (F'''{prefix}blocks.{i}.mlp.fc1.weight''', F'''{hf_prefix}encoder.layer.{i}.intermediate.dense.weight''')		)
  rename_keys.append(
      (F'''{prefix}blocks.{i}.mlp.fc1.bias''', F'''{hf_prefix}encoder.layer.{i}.intermediate.dense.bias''')		)
  rename_keys.append((F'''{prefix}blocks.{i}.mlp.fc2.weight''', F'''{hf_prefix}encoder.layer.{i}.output.dense.weight''')		)
  rename_keys.append((F'''{prefix}blocks.{i}.mlp.fc2.bias''', F'''{hf_prefix}encoder.layer.{i}.output.dense.bias''')		)

 # projection layer + position embeddings
 rename_keys.extend(
     [
         (F'''{prefix}cls_token''', F'''{hf_prefix}embeddings.cls_token'''),
         (F'''{prefix}patch_embed.proj.weight''', F'''{hf_prefix}embeddings.patch_embeddings.projection.weight'''),
         (F'''{prefix}patch_embed.proj.bias''', F'''{hf_prefix}embeddings.patch_embeddings.projection.bias'''),
     ]		)

 if has_lm_head:
  # mask token + shared relative position bias + layernorm
  rename_keys.extend(
      [
          ('''mask_token''', F'''{hf_prefix}embeddings.mask_token'''),
          (
              '''rel_pos_bias.relative_position_bias_table''',
              F'''{hf_prefix}encoder.relative_position_bias.relative_position_bias_table''',
          ),
          (
              '''rel_pos_bias.relative_position_index''',
              F'''{hf_prefix}encoder.relative_position_bias.relative_position_index''',
          ),
          ('''norm.weight''', '''layernorm.weight'''),
          ('''norm.bias''', '''layernorm.bias'''),
      ]		)
 elif is_semantic:
  # semantic segmentation classification heads
  rename_keys.extend(
      [
          ('''decode_head.conv_seg.weight''', '''decode_head.classifier.weight'''),
          ('''decode_head.conv_seg.bias''', '''decode_head.classifier.bias'''),
          ('''auxiliary_head.conv_seg.weight''', '''auxiliary_head.classifier.weight'''),
          ('''auxiliary_head.conv_seg.bias''', '''auxiliary_head.classifier.bias'''),
      ]		)
 else:
  # layernorm + classification head
  rename_keys.extend(
      [
          ('''fc_norm.weight''', F'''{hf_prefix}pooler.layernorm.weight'''),
          ('''fc_norm.bias''', F'''{hf_prefix}pooler.layernorm.bias'''),
          ('''head.weight''', '''classifier.weight'''),
          ('''head.bias''', '''classifier.bias'''),
      ]		)

 return rename_keys




def 		lowerCAmelCase_     (  lowercase_       :							Union[str, Any]			,				lowercase_       :							Union[str, Any]			,				lowercase_       :							Optional[Any]=False			,				lowercase_       :							Any=False			,				lowercase_       :							Dict="data2vec_vision."		):

 '''simple docstring'''






 for i in range(config.num_hidden_layers		):
  __SCREAMING_SNAKE_CASE			:      int 							=	'''backbone.''' if is_semantic else ''''''
  # queries, keys and values
  __SCREAMING_SNAKE_CASE			:      Union[str, Any] 							=	state_dict.pop(F'''{prefix}blocks.{i}.attn.qkv.weight'''		)
  __SCREAMING_SNAKE_CASE			:      List[Any] 							=	state_dict.pop(F'''{prefix}blocks.{i}.attn.q_bias'''		)
  __SCREAMING_SNAKE_CASE			:      List[Any] 							=	state_dict.pop(F'''{prefix}blocks.{i}.attn.v_bias'''		)

  __SCREAMING_SNAKE_CASE			:      Optional[Any] 							=	in_proj_weight[
      : config.hidden_size, :
  ]
  __SCREAMING_SNAKE_CASE			:      Union[str, Any] 							=	q_bias
  __SCREAMING_SNAKE_CASE			:      str 							=	in_proj_weight[
      config.hidden_size : config.hidden_size * 2, :
  ]
  __SCREAMING_SNAKE_CASE			:      Dict 							=	in_proj_weight[
      -config.hidden_size :, :
  ]
  __SCREAMING_SNAKE_CASE			:      Optional[Any] 							=	v_bias

  # gamma_1 and gamma_2
  # we call them lambda because otherwise they are renamed when using .from_pretrained
  __SCREAMING_SNAKE_CASE			:      List[Any] 							=	state_dict.pop(F'''{prefix}blocks.{i}.gamma_1'''		)
  __SCREAMING_SNAKE_CASE			:      int 							=	state_dict.pop(F'''{prefix}blocks.{i}.gamma_2'''		)

  __SCREAMING_SNAKE_CASE			:      Union[str, Any] 							=	gamma_a
  __SCREAMING_SNAKE_CASE			:      Optional[int] 							=	gamma_a

  # relative_position bias table + index
  if not has_lm_head:
   # each layer has its own relative position bias
   __SCREAMING_SNAKE_CASE			:      Dict 							=	state_dict.pop(F'''{prefix}blocks.{i}.attn.relative_position_bias_table'''		)
   __SCREAMING_SNAKE_CASE			:      int 							=	state_dict.pop(F'''{prefix}blocks.{i}.attn.relative_position_index'''		)

   __SCREAMING_SNAKE_CASE			:      List[str] 							=	table
   __SCREAMING_SNAKE_CASE			:      Any 							=	index




def 		lowerCAmelCase_     (  ):

 '''simple docstring'''






 __SCREAMING_SNAKE_CASE			:      List[str] 							=	argparse.ArgumentParser(
     '''Convert Data2VecVision to HF for image classification and pretraining'''			,				add_help=lowercase_		)
 parser.add_argument('''--hf_checkpoint_name'''			,				type=lowercase_		)
 parser.add_argument('''--input_size'''			,				default=224			,				type=lowercase_			,				help='''images input size'''		)
 parser.add_argument('''--beit_checkpoint'''			,				default=''''''			,				help='''beit checkpoint'''		)

 return parser.parse_args()




def 		lowerCAmelCase_     (  lowercase_       :							str			,				lowercase_       :							List[str]			,				lowercase_       :							Union[str, Any]		):

 '''simple docstring'''






 def load_state_dict(lowercase_       :							Optional[Any]			,				lowercase_       :							int			,				lowercase_       :							Union[str, Any]=""			,				lowercase_       :							Optional[int]="relative_position_index"		):
  __SCREAMING_SNAKE_CASE			:      Any 							=	[]
  __SCREAMING_SNAKE_CASE			:      List[Any] 							=	[]
  __SCREAMING_SNAKE_CASE			:      Dict 							=	[]
  # copy state_dict so _load_from_state_dict can modify it
  __SCREAMING_SNAKE_CASE			:      List[str] 							=	getattr(lowercase_			,				'''_metadata'''			,				lowercase_		)
  __SCREAMING_SNAKE_CASE			:      str 							=	state_dict.copy()
  if metadata is not None:
   __SCREAMING_SNAKE_CASE			:      int 							=	metadata

  def load(lowercase_       :							Any			,				lowercase_       :							Optional[Any]=""		):
   __SCREAMING_SNAKE_CASE			:      Dict 							=	{} if metadata is None else metadata.get(prefix[:-1]			,				{}		)
   module._load_from_state_dict(
       lowercase_			,				lowercase_			,				lowercase_			,				lowercase_			,				lowercase_			,				lowercase_			,				lowercase_		)
   for name, child in module._modules.items():
    if child is not None:
     load(lowercase_			,				prefix + name + '''.'''		)

  load(lowercase_			,				prefix=lowercase_		)

  __SCREAMING_SNAKE_CASE			:      Optional[Any] 							=	[]
  __SCREAMING_SNAKE_CASE			:      Dict 							=	[]
  for key in missing_keys:
   __SCREAMING_SNAKE_CASE			:      Dict 							=	True
   for ignore_key in ignore_missing.split('''|'''		):
    if ignore_key in key:
     __SCREAMING_SNAKE_CASE			:      Dict 							=	False
     break
   if keep_flag:
    warn_missing_keys.append(lowercase_		)
   else:
    ignore_missing_keys.append(lowercase_		)

  __SCREAMING_SNAKE_CASE			:      Tuple 							=	warn_missing_keys

  if len(lowercase_		) > 0:
   print(
       '''Weights of {} not initialized from pretrained model: {}'''.format(
           model.__class__.__name__			,				lowercase_		)		)
  if len(lowercase_		) > 0:
   print('''Weights from pretrained model not used in {}: {}'''.format(model.__class__.__name__			,				lowercase_		)		)
  if len(lowercase_		) > 0:
   print(
       '''Ignored weights of {} not initialized from pretrained model: {}'''.format(
           model.__class__.__name__			,				lowercase_		)		)
  if len(lowercase_		) > 0:
   print('''\n'''.join(lowercase_		)		)

 __SCREAMING_SNAKE_CASE			:      Optional[int] 							=	{
     '''pretrained''': False,
     '''use_shared_rel_pos_bias''': True,
     '''use_abs_pos_emb''': False,
     '''init_values''': 0.1,
 }

 if is_finetuned:
  model_kwargs.update(
      {
          '''num_classes''': 1000,
          '''use_mean_pooling''': True,
          '''init_scale''': 0.001,
          '''use_rel_pos_bias''': True,
      }		)

 __SCREAMING_SNAKE_CASE			:      List[Any] 							=	create_model(
     '''beit_large_patch16_224''' if is_large else '''beit_base_patch16_224'''			,				**lowercase_			,				)
 __SCREAMING_SNAKE_CASE			:      List[Any] 							=	model.patch_embed.patch_size
 __SCREAMING_SNAKE_CASE			:      Union[str, Any] 							=	(args.input_size // patch_size[0], args.input_size // patch_size[1])
 __SCREAMING_SNAKE_CASE			:      Dict 							=	torch.load(args.beit_checkpoint			,				map_location='''cpu'''		)

 print(F'''Load ckpt from {args.beit_checkpoint}'''		)
 __SCREAMING_SNAKE_CASE			:      Any 							=	None
 for model_key in ("model", "module"):
  if model_key in checkpoint:
   __SCREAMING_SNAKE_CASE			:      Union[str, Any] 							=	checkpoint[model_key]
   print(F'''Load state_dict by model_key = {model_key}'''		)
   break

 __SCREAMING_SNAKE_CASE			:      Optional[int] 							=	list(checkpoint_model.keys()		)
 for key in all_keys:
  if "relative_position_index" in key:
   checkpoint_model.pop(lowercase_		)

  if "relative_position_bias_table" in key:
   __SCREAMING_SNAKE_CASE			:      str 							=	checkpoint_model[key]
   __SCREAMING_SNAKE_CASE							,				__SCREAMING_SNAKE_CASE			:      Optional[int] 							=	rel_pos_bias.size()
   __SCREAMING_SNAKE_CASE							,				__SCREAMING_SNAKE_CASE			:      Union[str, Any] 							=	model.state_dict()[key].size()
   __SCREAMING_SNAKE_CASE			:      Optional[Any] 							=	model.patch_embed.patch_shape
   if dst_patch_shape[0] != dst_patch_shape[1]:
    raise NotImplementedError()

 load_state_dict(lowercase_			,				lowercase_			,				prefix=''''''		)

 return model




def 		lowerCAmelCase_     (  ):

 '''simple docstring'''






 __SCREAMING_SNAKE_CASE			:      Union[str, Any] 							=	get_args()

 __SCREAMING_SNAKE_CASE			:      int 							=	'''ft1k''' in args.hf_checkpoint_name
 __SCREAMING_SNAKE_CASE			:      Tuple 							=	'''large''' in args.hf_checkpoint_name

 if is_finetuned:
  # To convert Beit's data2vec_vision to HF you need to copy
  # https://github.com/facebookresearch/data2vec_vision/blob/main/beit/modeling_finetune.py
  # into this folder.
  import modeling_finetune  # noqa: F401
 else:
  # To convert Beit's data2vec_vision to HF you need to copy
  # https://github.com/facebookresearch/data2vec_vision/blob/main/beit/modeling_cyclical.py
  # into this folder
  # IMPORTANT: Note that for now we've only converted the down-stream
  # model and not the full pretrained model. This means for the integration
  # test you need to add a `return x` after the following line:
  # https://github.com/facebookresearch/data2vec_vision/blob/af9a36349aaed59ae66e69b5dabeef2d62fdc5da/beit/modeling_cyclical.py#L197
  # to make the integration test pass.
  import modeling_cyclical  # noqa: F401

 # 1. Create model config
 __SCREAMING_SNAKE_CASE			:      Dict 							=	DataaVecVisionConfig()
 if is_finetuned:
  __SCREAMING_SNAKE_CASE			:      int 							=	True
  __SCREAMING_SNAKE_CASE			:      Optional[int] 							=	False
  __SCREAMING_SNAKE_CASE			:      Dict 							=	True
  __SCREAMING_SNAKE_CASE			:      int 							=	1000

  __SCREAMING_SNAKE_CASE			:      Any 							=	'''huggingface/label-files'''
  __SCREAMING_SNAKE_CASE			:      Dict 							=	'''imagenet-1k-id2label.json'''
  __SCREAMING_SNAKE_CASE			:      Dict 							=	json.load(open(hf_hub_download(lowercase_			,				lowercase_			,				repo_type='''dataset'''		)			,				'''r'''		)		)
  __SCREAMING_SNAKE_CASE			:      Dict 							=	{int(lowercase_		): v for k, v in idalabel.items()}
  __SCREAMING_SNAKE_CASE			:      List[str] 							=	idalabel
  __SCREAMING_SNAKE_CASE			:      Optional[Any] 							=	{v: k for k, v in idalabel.items()}
 else:
  __SCREAMING_SNAKE_CASE			:      Optional[Any] 							=	False
  __SCREAMING_SNAKE_CASE			:      Optional[Any] 							=	True
  __SCREAMING_SNAKE_CASE			:      Optional[int] 							=	False

 if is_large:
  __SCREAMING_SNAKE_CASE			:      str 							=	1024
  __SCREAMING_SNAKE_CASE			:      Any 							=	4096
  __SCREAMING_SNAKE_CASE			:      Optional[int] 							=	24
  __SCREAMING_SNAKE_CASE			:      Tuple 							=	16

 # 2. Load Beit model
 __SCREAMING_SNAKE_CASE			:      List[Any] 							=	load_beit_model(lowercase_			,				lowercase_			,				lowercase_		)
 orig_model.eval()

 # 3. Forward Beit model
 __SCREAMING_SNAKE_CASE			:      Optional[int] 							=	BeitImageProcessor(size=config.image_size			,				do_center_crop=lowercase_		)
 __SCREAMING_SNAKE_CASE			:      List[Any] 							=	Image.open('''../../../../tests/fixtures/tests_samples/COCO/000000039769.png'''		)
 __SCREAMING_SNAKE_CASE			:      Tuple 							=	image_processor(images=lowercase_			,				return_tensors='''pt'''		)
 __SCREAMING_SNAKE_CASE			:      Union[str, Any] 							=	encoding['''pixel_values''']

 __SCREAMING_SNAKE_CASE			:      Tuple 							=	(pixel_values,) if is_finetuned else (pixel_values, None)
 with torch.no_grad():
  __SCREAMING_SNAKE_CASE			:      Tuple 							=	orig_model(*lowercase_		)

 # 4. Load HF Data2VecVision model
 if is_finetuned:
  __SCREAMING_SNAKE_CASE			:      Dict 							=	DataaVecVisionForImageClassification(lowercase_		)
  hf_model.eval()
  __SCREAMING_SNAKE_CASE			:      Any 							=	False
  __SCREAMING_SNAKE_CASE			:      Optional[Any] 							=	'''data2vec_vision.'''
 else:
  __SCREAMING_SNAKE_CASE			:      Dict 							=	DataaVecVisionModel(lowercase_		)
  hf_model.eval()
  __SCREAMING_SNAKE_CASE			:      str 							=	True
  __SCREAMING_SNAKE_CASE			:      Any 							=	''''''

 __SCREAMING_SNAKE_CASE			:      Dict 							=	create_rename_keys(lowercase_			,				hf_prefix=lowercase_			,				has_lm_head=lowercase_		)
 __SCREAMING_SNAKE_CASE			:      Dict 							=	orig_model.state_dict()
 for src, dest in rename_keys:
  __SCREAMING_SNAKE_CASE			:      Union[str, Any] 							=	state_dict.pop(lowercase_		)
  __SCREAMING_SNAKE_CASE			:      Optional[int] 							=	val

 read_in_q_k_v(lowercase_			,				lowercase_			,				hf_prefix=lowercase_			,				has_lm_head=lowercase_		)
 __SCREAMING_SNAKE_CASE							,				__SCREAMING_SNAKE_CASE			:      List[Any] 							=	hf_model.load_state_dict(lowercase_			,				strict=lowercase_		)
 print('''HF missing'''			,				lowercase_		)
 print('''HF unexpected_keys'''			,				lowercase_		)

 # 5. Forward HF Data2VecVision model
 with torch.no_grad():
  __SCREAMING_SNAKE_CASE			:      Union[str, Any] 							=	hf_model(lowercase_		)

 __SCREAMING_SNAKE_CASE			:      List[Any] 							=	hf_model_output.logits if is_finetuned else hf_model_output.last_hidden_state

 # 6. Compare
 __SCREAMING_SNAKE_CASE			:      Optional[int] 							=	torch.max(torch.abs(hf_output - orig_model_output		)		).item()

 print(F'''max_absolute_diff = {max_absolute_diff}'''		)
 __SCREAMING_SNAKE_CASE			:      str 							=	torch.allclose(lowercase_			,				lowercase_			,				atol=1E-3		)
 print('''Do both models output the same tensors?'''			,				'''🔥''' if success else '''💩'''		)
 if not success:
  raise Exception('''Something went wRoNg'''		)

 # 7. Save
 print(F'''Saving to {args.hf_checkpoint_name}'''		)
 hf_model.save_pretrained(args.hf_checkpoint_name		)
 image_processor.save_pretrained(args.hf_checkpoint_name		)


if __name__ == "__main__":
    main()
    # Run the following to convert checkpoints
    #  python ./convert_data2vec_vision_original_pytorch_checkpoint_to_pytorch.py \
    #          --beit_checkpoint ./pretrained_base.pt \
    #          --hf_checkpoint_name "./data2vec-vision-base"
    #  python ./convert_data2vec_vision_original_pytorch_checkpoint_to_pytorch.py \
    #          --beit_checkpoint ./finetuned_base.pt \
    #          --hf_checkpoint_name "./data2vec-vision-base-ft1k"
    #  python ./convert_data2vec_vision_original_pytorch_checkpoint_to_pytorch.py \
    #          --beit_checkpoint ./pretrained_large.pt \
    #          --hf_checkpoint_name "./data2vec-vision-large"
    #  python ./convert_data2vec_vision_original_pytorch_checkpoint_to_pytorch.py \
    #          --beit_checkpoint ./finetuned_large.pt \
    #          --hf_checkpoint_name "./data2vec-vision-large-ft1k"

