





"""simple docstring"""





import importlib
import inspect
import json
import os
from collections import OrderedDict

# Build the list of all feature extractors
from ...configuration_utils import PretrainedConfig
from ...dynamic_module_utils import get_class_from_dynamic_module, resolve_trust_remote_code
from ...feature_extraction_utils import FeatureExtractionMixin
from ...image_processing_utils import ImageProcessingMixin
from ...tokenization_utils import TOKENIZER_CONFIG_FILE
from ...utils import FEATURE_EXTRACTOR_NAME, get_file_from_repo, logging
from .auto_factory import _LazyAutoMapping
from .configuration_auto import (
    CONFIG_MAPPING_NAMES,
    AutoConfig,
    model_type_to_module_name,
    replace_list_option_in_docstrings,
)
from .feature_extraction_auto import AutoFeatureExtractor
from .image_processing_auto import AutoImageProcessor
from .tokenization_auto import AutoTokenizer


_lowerCamelCase       =	logging.get_logger(__name__)

_lowerCamelCase       =	OrderedDict(
    [
        ('''align''', '''AlignProcessor'''),
        ('''altclip''', '''AltCLIPProcessor'''),
        ('''bark''', '''BarkProcessor'''),
        ('''blip''', '''BlipProcessor'''),
        ('''blip-2''', '''Blip2Processor'''),
        ('''bridgetower''', '''BridgeTowerProcessor'''),
        ('''chinese_clip''', '''ChineseCLIPProcessor'''),
        ('''clap''', '''ClapProcessor'''),
        ('''clip''', '''CLIPProcessor'''),
        ('''clipseg''', '''CLIPSegProcessor'''),
        ('''flava''', '''FlavaProcessor'''),
        ('''git''', '''GitProcessor'''),
        ('''groupvit''', '''CLIPProcessor'''),
        ('''hubert''', '''Wav2Vec2Processor'''),
        ('''instructblip''', '''InstructBlipProcessor'''),
        ('''layoutlmv2''', '''LayoutLMv2Processor'''),
        ('''layoutlmv3''', '''LayoutLMv3Processor'''),
        ('''markuplm''', '''MarkupLMProcessor'''),
        ('''mctct''', '''MCTCTProcessor'''),
        ('''mgp-str''', '''MgpstrProcessor'''),
        ('''oneformer''', '''OneFormerProcessor'''),
        ('''owlvit''', '''OwlViTProcessor'''),
        ('''pix2struct''', '''Pix2StructProcessor'''),
        ('''sam''', '''SamProcessor'''),
        ('''sew''', '''Wav2Vec2Processor'''),
        ('''sew-d''', '''Wav2Vec2Processor'''),
        ('''speech_to_text''', '''Speech2TextProcessor'''),
        ('''speech_to_text_2''', '''Speech2Text2Processor'''),
        ('''speecht5''', '''SpeechT5Processor'''),
        ('''trocr''', '''TrOCRProcessor'''),
        ('''tvlt''', '''TvltProcessor'''),
        ('''unispeech''', '''Wav2Vec2Processor'''),
        ('''unispeech-sat''', '''Wav2Vec2Processor'''),
        ('''vilt''', '''ViltProcessor'''),
        ('''vision-text-dual-encoder''', '''VisionTextDualEncoderProcessor'''),
        ('''wav2vec2''', '''Wav2Vec2Processor'''),
        ('''wav2vec2-conformer''', '''Wav2Vec2Processor'''),
        ('''wavlm''', '''Wav2Vec2Processor'''),
        ('''whisper''', '''WhisperProcessor'''),
        ('''xclip''', '''XCLIPProcessor'''),
    ]
)

_lowerCamelCase       =	_LazyAutoMapping(CONFIG_MAPPING_NAMES, PROCESSOR_MAPPING_NAMES)




def 		lowerCAmelCase_	(  lowercase_   :							List[str]		):

 '''simple docstring'''






 for module_name, processors in PROCESSOR_MAPPING_NAMES.items():
  if class_name in processors:
   __SCREAMING_SNAKE_CASE :			Optional[int]												=       model_type_to_module_name(__UpperCamelCase		)

   __SCREAMING_SNAKE_CASE :			Optional[int]												=       importlib.import_module(F'''.{module_name}''' ,      '''transformers.models'''		)
   try:
    return getattr(__UpperCamelCase ,      __UpperCamelCase		)
   except AttributeError:
    continue

 for processor in PROCESSOR_MAPPING._extra_content.values():
  if getattr(__UpperCamelCase ,      '''__name__''' ,      __UpperCamelCase		) == class_name:
   return processor

    # We did not fine the class, but maybe it's because a dep is missing. In that case, the class will be in the main
    # init and we return the proper dummy to get an appropriate error message.
 __SCREAMING_SNAKE_CASE :			Optional[Any]												=       importlib.import_module('''transformers'''		)
 if hasattr(__UpperCamelCase ,      __UpperCamelCase		):
  return getattr(__UpperCamelCase ,      __UpperCamelCase		)

 return None






class 			snake_case		:

      def __init__(			self   :List[Any]						):
       raise EnvironmentError(
           '''AutoProcessor is designed to be instantiated '''
           '''using the `AutoProcessor.from_pretrained(pretrained_model_name_or_path)` method.'''						)

      @classmethod
      @replace_list_option_in_docstrings(__UpperCAmelCase						)
      def 						SCREAMING_SNAKE_CASE_			(			cls   :Tuple						,							_lowerCamelCase   :Dict						,							**_lowerCamelCase   :List[Any]						):
       __SCREAMING_SNAKE_CASE :			str												=       kwargs.pop('''config'''						,							__UpperCAmelCase						)
       __SCREAMING_SNAKE_CASE :			Tuple												=       kwargs.pop('''trust_remote_code'''						,							__UpperCAmelCase						)
       __SCREAMING_SNAKE_CASE :			Optional[int]												=       True

       __SCREAMING_SNAKE_CASE :			str												=       None
       __SCREAMING_SNAKE_CASE :			Any												=       None

       # First, let's see if we have a preprocessor config.
       # Filter the kwargs for `get_file_from_repo`.
       __SCREAMING_SNAKE_CASE :			Tuple												=       {
           key: kwargs[key] for key in inspect.signature(__UpperCAmelCase						).parameters.keys() if key in kwargs
       }
       # Let's start by checking whether the processor class is saved in an image processor
       __SCREAMING_SNAKE_CASE :			int												=       get_file_from_repo(
           __UpperCAmelCase						,							__UpperCAmelCase						,							**__UpperCAmelCase						)
       if preprocessor_config_file is not None:
        __SCREAMING_SNAKE_CASE							,      __SCREAMING_SNAKE_CASE :			Union[str, Any]												=       ImageProcessingMixin.get_image_processor_dict(__UpperCAmelCase						,							**__UpperCAmelCase						)
        __SCREAMING_SNAKE_CASE :			Optional[int]												=       config_dict.get('''processor_class'''						,							__UpperCAmelCase						)
        if "AutoProcessor" in config_dict.get('''auto_map'''						,							{}						):
         __SCREAMING_SNAKE_CASE :			List[str]												=       config_dict['''auto_map''']['''AutoProcessor''']

        # If not found, let's check whether the processor class is saved in a feature extractor config
       if preprocessor_config_file is not None and processor_class is None:
        __SCREAMING_SNAKE_CASE							,      __SCREAMING_SNAKE_CASE :			Tuple												=       FeatureExtractionMixin.get_feature_extractor_dict(__UpperCAmelCase						,							**__UpperCAmelCase						)
        __SCREAMING_SNAKE_CASE :			Union[str, Any]												=       config_dict.get('''processor_class'''						,							__UpperCAmelCase						)
        if "AutoProcessor" in config_dict.get('''auto_map'''						,							{}						):
         __SCREAMING_SNAKE_CASE :			str												=       config_dict['''auto_map''']['''AutoProcessor''']

       if processor_class is None:
        # Next, let's check whether the processor class is saved in a tokenizer
        __SCREAMING_SNAKE_CASE :			Optional[int]												=       get_file_from_repo(
            __UpperCAmelCase						,							__UpperCAmelCase						,							**__UpperCAmelCase						)
        if tokenizer_config_file is not None:
         with open(__UpperCAmelCase						,							encoding='''utf-8'''						) as reader:
          __SCREAMING_SNAKE_CASE :			int												=       json.load(__UpperCAmelCase						)

         __SCREAMING_SNAKE_CASE :			Dict												=       config_dict.get('''processor_class'''						,							__UpperCAmelCase						)
         if "AutoProcessor" in config_dict.get('''auto_map'''						,							{}						):
          __SCREAMING_SNAKE_CASE :			Union[str, Any]												=       config_dict['''auto_map''']['''AutoProcessor''']

       if processor_class is None:
        # Otherwise, load config, if it can be loaded.
        if not isinstance(__UpperCAmelCase						,							__UpperCAmelCase						):
         __SCREAMING_SNAKE_CASE :			List[str]												=       AutoConfig.from_pretrained(
             __UpperCAmelCase						,							trust_remote_code=__UpperCAmelCase						,							**__UpperCAmelCase						)

        # And check if the config contains the processor class.
        __SCREAMING_SNAKE_CASE :			Optional[int]												=       getattr(__UpperCAmelCase						,							'''processor_class'''						,							__UpperCAmelCase						)
        if hasattr(__UpperCAmelCase						,							'''auto_map'''						) and "AutoProcessor" in config.auto_map:
         __SCREAMING_SNAKE_CASE :			Dict												=       config.auto_map['''AutoProcessor''']

       if processor_class is not None:
        __SCREAMING_SNAKE_CASE :			Union[str, Any]												=       processor_class_from_name(__UpperCAmelCase						)

       __SCREAMING_SNAKE_CASE :			Union[str, Any]												=       processor_auto_map is not None
       __SCREAMING_SNAKE_CASE :			int												=       processor_class is not None or type(__UpperCAmelCase						) in PROCESSOR_MAPPING
       __SCREAMING_SNAKE_CASE :			int												=       resolve_trust_remote_code(
           __UpperCAmelCase						,							__UpperCAmelCase						,							__UpperCAmelCase						,							__UpperCAmelCase						)

       if has_remote_code and trust_remote_code:
        __SCREAMING_SNAKE_CASE :			Optional[Any]												=       get_class_from_dynamic_module(
            __UpperCAmelCase						,							__UpperCAmelCase						,							**__UpperCAmelCase						)
        __SCREAMING_SNAKE_CASE :			Optional[Any]												=       kwargs.pop('''code_revision'''						,							__UpperCAmelCase						)
        if os.path.isdir(__UpperCAmelCase						):
         processor_class.register_for_auto_class()
        return processor_class.from_pretrained(
            __UpperCAmelCase						,							trust_remote_code=__UpperCAmelCase						,							**__UpperCAmelCase						)
       elif processor_class is not None:
        return processor_class.from_pretrained(
            __UpperCAmelCase						,							trust_remote_code=__UpperCAmelCase						,							**__UpperCAmelCase						)
       # Last try: we use the PROCESSOR_MAPPING.
       elif type(__UpperCAmelCase						) in PROCESSOR_MAPPING:
        return PROCESSOR_MAPPING[type(__UpperCAmelCase						)].from_pretrained(__UpperCAmelCase						,							**__UpperCAmelCase						)

       # At this stage, there doesn't seem to be a `Processor` class available for this model, so let's try a
       # tokenizer.
       try:
        return AutoTokenizer.from_pretrained(
            __UpperCAmelCase						,							trust_remote_code=__UpperCAmelCase						,							**__UpperCAmelCase						)
       except Exception:
        try:
         return AutoImageProcessor.from_pretrained(
             __UpperCAmelCase						,							trust_remote_code=__UpperCAmelCase						,							**__UpperCAmelCase						)
        except Exception:
         pass

        try:
         return AutoFeatureExtractor.from_pretrained(
             __UpperCAmelCase						,							trust_remote_code=__UpperCAmelCase						,							**__UpperCAmelCase						)
        except Exception:
         pass

       raise ValueError(
           f'''Unrecognized processing class in {pretrained_model_name_or_path}. Can\'t instantiate a processor, a '''
           '''tokenizer, an image processor or a feature extractor for this model. Make sure the repository contains'''
           '''the files of at least one of those processing classes.'''						)



      @staticmethod
      def 						SCREAMING_SNAKE_CASE_			(			_lowerCamelCase   :Dict						,							_lowerCamelCase   :Optional[int]						):
       PROCESSOR_MAPPING.register(__UpperCAmelCase						,							__UpperCAmelCase						)



"""simple docstring"""


import argparse
import os
import pathlib

import fairseq
import torch
from fairseq.modules import TransformerSentenceEncoderLayer
from packaging import version

from transformers import (
    DataaVecTextConfig,
    DataaVecTextForMaskedLM,
    DataaVecTextForSequenceClassification,
    DataaVecTextModel,
)
from transformers.models.bert.modeling_bert import (
    BertIntermediate,
    BertLayer,
    BertOutput,
    BertSelfAttention,
    BertSelfOutput,
)

# IMPORTANT: In order for this script to run, please make sure to download the dictionary: `dict.txt` from wget https://dl.fbaipublicfiles.com/fairseq/models/roberta.large.tar.gz
# File copied from https://github.com/pytorch/fairseq/blob/main/examples/data2vec/models/data2vec_text.py
from transformers.utils import logging


if version.parse(fairseq.__version__) < version.parse('0.9.0'):
			raise Exception('requires fairseq >= 0.9.0')


logging.set_verbosity_info()
__A       						=       logging.get_logger(__name__)

__A       						=       'Hello world! cécé herlolip'



def     _lowerCamelCase(__UpperCamelCase    , __UpperCamelCase    , __UpperCamelCase       )				-> Any:
	_lowerCAmelCase						,							_lowerCAmelCase								=os.path.split(__UpperCamelCase       )
	_lowerCAmelCase								=DataaVecTextModel.from_pretrained(
	    __UpperCamelCase    , checkpoint_file=__UpperCamelCase       )
	dataavec.eval()  # disable dropout
	_lowerCAmelCase								=dataavec.models[0]
	_lowerCAmelCase								=dataavec_model.encoder.sentence_encoder
	_lowerCAmelCase								=DataaVecTextConfig(
	    vocab_size=dataavec_sent_encoder.embed_tokens.num_embeddings    , hidden_size=dataavec_model.args.encoder_embed_dim    , num_hidden_layers=dataavec_model.args.encoder_layers    , num_attention_heads=dataavec_model.args.encoder_attention_heads    , intermediate_size=dataavec_model.args.encoder_ffn_embed_dim    , max_position_embeddings=514    , type_vocab_size=1    , layer_norm_eps=1E-5    , )
	if classification_head:
		_lowerCAmelCase								=dataavec.model.classification_heads["""mnli"""].out_proj.weight.shape[0]
	print("""Our BERT config:"""    , __UpperCamelCase       )

	_lowerCAmelCase								=DataaVecTextForSequenceClassification(__UpperCamelCase       ) if classification_head else DataaVecTextForMaskedLM(__UpperCamelCase       )
	model.eval()

	# Now let's copy all the weights.
	# Embeddings
	_lowerCAmelCase								=dataavec_sent_encoder.embed_tokens.weight
	_lowerCAmelCase								=dataavec_sent_encoder.embed_positions.weight
	_lowerCAmelCase								=torch.zeros_like(
	    model.dataavec_text.embeddings.token_type_embeddings.weight       )  # just zero them out b/c data2vec doesn't use them.
	_lowerCAmelCase								=dataavec_sent_encoder.layernorm_embedding.weight
	_lowerCAmelCase								=dataavec_sent_encoder.layernorm_embedding.bias

	for i in range(config.num_hidden_layers       ):
		# Encoder: start of layer
		_lowerCAmelCase								=model.dataavec_text.encoder.layer[i]
		_lowerCAmelCase								=dataavec_sent_encoder.layers[i]

		# self attention
		_lowerCAmelCase								=layer.attention.self
		assert dataavec_layer.self_attn.k_proj.weight.data.shape == torch.Size(
		    (config.hidden_size, config.hidden_size)       ), (
		    "Shape for data2vec_layer.self_attn.k_proj.weight.data should be"
		    F''' {torch.Size((config.hidden_size, config.hidden_size)       )}'''
		)
		assert dataavec_layer.self_attn.q_proj.weight.data.shape == torch.Size(
		    (config.hidden_size, config.hidden_size)       ), (
		    "Shape for data2vec_layer.self_attn.q_proj.weight.data should be"
		    F''' {torch.Size((config.hidden_size, config.hidden_size)       )}'''
		)
		assert dataavec_layer.self_attn.v_proj.weight.data.shape == torch.Size(
		    (config.hidden_size, config.hidden_size)       ), (
		    "Shape for data2vec_layer.self_attn.v_proj.weight.data should be"
		    F''' {torch.Size((config.hidden_size, config.hidden_size)       )}'''
		)

		_lowerCAmelCase								=dataavec_layer.self_attn.q_proj.weight
		_lowerCAmelCase								=dataavec_layer.self_attn.q_proj.bias
		_lowerCAmelCase								=dataavec_layer.self_attn.k_proj.weight
		_lowerCAmelCase								=dataavec_layer.self_attn.k_proj.bias
		_lowerCAmelCase								=dataavec_layer.self_attn.v_proj.weight
		_lowerCAmelCase								=dataavec_layer.self_attn.v_proj.bias

		# self-attention output
		_lowerCAmelCase								=layer.attention.output
		assert (
		    self_output.dense.weight.shape == dataavec_layer.self_attn.out_proj.weight.shape
		), F'''Shape for self_output.dense.weight should be {dataavec_layer.self_attn.out_proj.weight.shape}'''
		_lowerCAmelCase								=dataavec_layer.self_attn.out_proj.weight
		_lowerCAmelCase								=dataavec_layer.self_attn.out_proj.bias
		_lowerCAmelCase								=dataavec_layer.self_attn_layer_norm.weight
		_lowerCAmelCase								=dataavec_layer.self_attn_layer_norm.bias

		# intermediate
		_lowerCAmelCase								=layer.intermediate
		assert (
		    intermediate.dense.weight.shape == dataavec_layer.fca.weight.shape
		), F'''Shape for intermediate.dense.weight should be {dataavec_layer.fca.weight.shape}'''
		_lowerCAmelCase								=dataavec_layer.fca.weight
		_lowerCAmelCase								=dataavec_layer.fca.bias

		# output
		_lowerCAmelCase								=layer.output
		assert (
		    bert_output.dense.weight.shape == dataavec_layer.fca.weight.shape
		), F'''Shape for bert_output.dense.weight should be {dataavec_layer.fca.weight.shape}'''
		_lowerCAmelCase								=dataavec_layer.fca.weight
		_lowerCAmelCase								=dataavec_layer.fca.bias
		_lowerCAmelCase								=dataavec_layer.final_layer_norm.weight
		_lowerCAmelCase								=dataavec_layer.final_layer_norm.bias
		# end of layer

	if classification_head:
		_lowerCAmelCase								=dataavec.model.classification_heads["""mnli"""].dense.weight
		_lowerCAmelCase								=dataavec.model.classification_heads["""mnli"""].dense.bias
		_lowerCAmelCase								=dataavec.model.classification_heads["""mnli"""].out_proj.weight
		_lowerCAmelCase								=dataavec.model.classification_heads["""mnli"""].out_proj.bias
	else:
		# LM Head
		_lowerCAmelCase								=dataavec_model.encoder.lm_head.dense.weight
		_lowerCAmelCase								=dataavec_model.encoder.lm_head.dense.bias
		_lowerCAmelCase								=dataavec_model.encoder.lm_head.layer_norm.weight
		_lowerCAmelCase								=dataavec_model.encoder.lm_head.layer_norm.bias
		_lowerCAmelCase								=dataavec_model.encoder.lm_head.weight
		_lowerCAmelCase								=dataavec_model.encoder.lm_head.bias

	# Let's check that we get the same results.
	_lowerCAmelCase								=dataavec.encode(__UpperCamelCase       ).unsqueeze(0       )  # batch of size 1

	_lowerCAmelCase								=model(__UpperCamelCase       )[0]
	if classification_head:
		_lowerCAmelCase								=dataavec.model.classification_heads["""mnli"""](dataavec.extract_features(__UpperCamelCase       )       )
	else:
		_lowerCAmelCase								=dataavec_model(__UpperCamelCase       )[0]
	print(our_output.shape    , their_output.shape       )
	_lowerCAmelCase								=torch.max(torch.abs(our_output - their_output       )       ).item()
	print(F'''max_absolute_diff = {max_absolute_diff}'''       )  # ~ 1e-7
	_lowerCAmelCase								=torch.allclose(__UpperCamelCase    , __UpperCamelCase    , atol=1E-3       )
	print("""Do both models output the same tensors?"""    , """🔥""" if success else """💩"""       )
	if not success:
		raise Exception("""Something went wRoNg"""       )

	pathlib.Path(__UpperCamelCase       ).mkdir(parents=__UpperCamelCase    , exist_ok=__UpperCamelCase       )
	print(F'''Saving model to {pytorch_dump_folder_path}'''       )
	model.save_pretrained(__UpperCamelCase       )


if __name__ == "__main__":
			__A       						=       argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    '--checkpoint_path', default=None, type=str, required=True, help='Path the official PyTorch dump.'
			)
			parser.add_argument(
			    '--pytorch_dump_folder_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
			)
			parser.add_argument(
			    '--classification_head', action='store_true', help='Whether to convert a final classification head.'
			)
			__A       						=       parser.parse_args()
			convert_dataavec_checkpoint_to_pytorch(
			    args.checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
			)



