



'''simple docstring'''
import argparse
import pathlib

import fairseq
import torch
from fairseq.models.roberta import RobertaModel as FairseqRobertaModel
from fairseq.modules import TransformerSentenceEncoderLayer
from packaging import version

from transformers import RobertaConfig, RobertaForMaskedLM, RobertaForSequenceClassification
from transformers.models.bert.modeling_bert import (
    BertIntermediate,
    BertLayer,
    BertOutput,
    BertSelfAttention,
    BertSelfOutput,
)
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()
_UpperCamelCase									=      logging.get_logger(__name__)

_UpperCamelCase									=      '''Hello world! cécé herlolip'''


def 	lowercase_						( lowerCAmelCase__						:	str      ,				lowerCAmelCase__						:	str      ,				lowerCAmelCase__						:	bool			):

			"""simple docstring"""





			__UpperCAmelCase :   List[str]    					=  FairseqRobertaModel.from_pretrained(lowerCAmelCase__			)
			roberta.eval()  # disable dropout
			__UpperCAmelCase :   Tuple    					=  roberta.model.encoder.sentence_encoder
			__UpperCAmelCase :   Dict    					=  RobertaConfig(
			    vocab_size=roberta_sent_encoder.embed_tokens.num_embeddings      ,				hidden_size=roberta.args.encoder_embed_dim      ,				num_hidden_layers=roberta.args.encoder_layers      ,				num_attention_heads=roberta.args.encoder_attention_heads      ,				intermediate_size=roberta.args.encoder_ffn_embed_dim      ,				max_position_embeddings=514      ,				type_vocab_size=1      ,				layer_norm_eps=1E-5      ,				)
			if classification_head:
						__UpperCAmelCase :   Optional[int]    					=  roberta.model.classification_heads["""mnli"""].out_proj.weight.shape[0]
			print("""Our BERT config:"""      ,				lowerCAmelCase__			)

			__UpperCAmelCase :   Any    					=  RobertaForSequenceClassification(lowerCAmelCase__			) if classification_head else RobertaForMaskedLM(lowerCAmelCase__			)
			model.eval()

			# Now let's copy all the weights.
			# Embeddings
			__UpperCAmelCase :   Tuple    					=  roberta_sent_encoder.embed_tokens.weight
			__UpperCAmelCase :   int    					=  roberta_sent_encoder.embed_positions.weight
			__UpperCAmelCase :   str    					=  torch.zeros_like(
			    model.roberta.embeddings.token_type_embeddings.weight			)  # just zero them out b/c RoBERTa doesn't use them.
			__UpperCAmelCase :   List[str]    					=  roberta_sent_encoder.emb_layer_norm.weight
			__UpperCAmelCase :   List[str]    					=  roberta_sent_encoder.emb_layer_norm.bias

			for i in range(config.num_hidden_layers			):
						# Encoder: start of layer
						__UpperCAmelCase :   BertLayer    					=  model.roberta.encoder.layer[i]
						__UpperCAmelCase :   TransformerSentenceEncoderLayer    					=  roberta_sent_encoder.layers[i]

						# self attention
						__UpperCAmelCase :   BertSelfAttention    					=  layer.attention.self
						assert (
						    roberta_layer.self_attn.k_proj.weight.data.shape
						    == roberta_layer.self_attn.q_proj.weight.data.shape
						    == roberta_layer.self_attn.v_proj.weight.data.shape
						    == torch.Size((config.hidden_size, config.hidden_size)			)
						)

						__UpperCAmelCase :   Dict    					=  roberta_layer.self_attn.q_proj.weight
						__UpperCAmelCase :   int    					=  roberta_layer.self_attn.q_proj.bias
						__UpperCAmelCase :   Union[str, Any]    					=  roberta_layer.self_attn.k_proj.weight
						__UpperCAmelCase :   List[Any]    					=  roberta_layer.self_attn.k_proj.bias
						__UpperCAmelCase :   Union[str, Any]    					=  roberta_layer.self_attn.v_proj.weight
						__UpperCAmelCase :   Optional[Any]    					=  roberta_layer.self_attn.v_proj.bias

						# self-attention output
						__UpperCAmelCase :   BertSelfOutput    					=  layer.attention.output
						assert self_output.dense.weight.shape == roberta_layer.self_attn.out_proj.weight.shape
						__UpperCAmelCase :   Tuple    					=  roberta_layer.self_attn.out_proj.weight
						__UpperCAmelCase :   str    					=  roberta_layer.self_attn.out_proj.bias
						__UpperCAmelCase :   List[str]    					=  roberta_layer.self_attn_layer_norm.weight
						__UpperCAmelCase :   List[Any]    					=  roberta_layer.self_attn_layer_norm.bias

						# intermediate
						__UpperCAmelCase :   BertIntermediate    					=  layer.intermediate
						assert intermediate.dense.weight.shape == roberta_layer.fca.weight.shape
						__UpperCAmelCase :   Optional[int]    					=  roberta_layer.fca.weight
						__UpperCAmelCase :   List[str]    					=  roberta_layer.fca.bias

						# output
						__UpperCAmelCase :   BertOutput    					=  layer.output
						assert bert_output.dense.weight.shape == roberta_layer.fca.weight.shape
						__UpperCAmelCase :   str    					=  roberta_layer.fca.weight
						__UpperCAmelCase :   Any    					=  roberta_layer.fca.bias
						__UpperCAmelCase :   str    					=  roberta_layer.final_layer_norm.weight
						__UpperCAmelCase :   List[Any]    					=  roberta_layer.final_layer_norm.bias
						# end of layer

			if classification_head:
						__UpperCAmelCase :   List[str]    					=  roberta.model.classification_heads["""mnli"""].dense.weight
						__UpperCAmelCase :   Optional[Any]    					=  roberta.model.classification_heads["""mnli"""].dense.bias
						__UpperCAmelCase :   Dict    					=  roberta.model.classification_heads["""mnli"""].out_proj.weight
						__UpperCAmelCase :   Tuple    					=  roberta.model.classification_heads["""mnli"""].out_proj.bias
			else:
						# LM Head
						__UpperCAmelCase :   int    					=  roberta.model.encoder.lm_head.dense.weight
						__UpperCAmelCase :   int    					=  roberta.model.encoder.lm_head.dense.bias
						__UpperCAmelCase :   Any    					=  roberta.model.encoder.lm_head.layer_norm.weight
						__UpperCAmelCase :   Optional[int]    					=  roberta.model.encoder.lm_head.layer_norm.bias
						__UpperCAmelCase :   List[str]    					=  roberta.model.encoder.lm_head.weight
						__UpperCAmelCase :   Union[str, Any]    					=  roberta.model.encoder.lm_head.bias

			# Let's check that we get the same results.
			__UpperCAmelCase :   torch.Tensor    					=  roberta.encode(lowerCAmelCase__			).unsqueeze(0			)  # batch of size 1

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

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


if __name__ == "__main__":
					_UpperCamelCase									=      argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    '''--roberta_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.'''
					)
					_UpperCamelCase									=      parser.parse_args()
					convert_roberta_checkpoint_to_pytorch(
					    args.roberta_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
					)









'''simple docstring'''
import os
import warnings
from pathlib import Path
from typing import Optional, Union

from .dynamic_module_utils import custom_object_save
from .tokenization_utils_base import PreTrainedTokenizerBase
from .utils import PushToHubMixin, copy_func, direct_transformers_import, logging


_UpperCamelCase									=      logging.get_logger(__name__)

# Dynamically import the Transformers module to grab the attribute classes of the processor form their names.
_UpperCamelCase									=      direct_transformers_import(Path(__file__).parent)


_UpperCamelCase									=      {
    '''AutoTokenizer''': '''PreTrainedTokenizerBase''',
    '''AutoFeatureExtractor''': '''FeatureExtractionMixin''',
    '''AutoImageProcessor''': '''ImageProcessingMixin''',
}



class _A	(     __SCREAMING_SNAKE_CASE     ):
		_SCREAMING_SNAKE_CASE       :     str       =      ["feature_extractor", "tokenizer"]
		# Names need to be attr_class for attr in attributes
		_SCREAMING_SNAKE_CASE       :     Optional[int]       =      None
		_SCREAMING_SNAKE_CASE       :     str       =      None
		_SCREAMING_SNAKE_CASE       :     List[str]       =      None





		def __init__(  self			,							*__UpperCAmelCase			,							**__UpperCAmelCase					)							->  Optional[int]:

					'''simple docstring'''
					# Sanitize args and kwargs
					for key in kwargs:
								if key not in self.attributes:
											raise TypeError(f'Unexpected keyword argument {key}.'					)
					for arg, attribute_name in zip(__UpperCAmelCase			,							self.attributes					):
								if attribute_name in kwargs:
											raise TypeError(f'Got multiple values for argument {attribute_name}.'					)
								else:
											__UpperCAmelCase :   int    					=  arg

					if len(__UpperCAmelCase					) != len(self.attributes					):
								raise ValueError(
								    f'This processor requires {len(self.attributes					)} arguments: {", ".join(self.attributes					)}. Got '
								    f'{len(__UpperCAmelCase					)} arguments instead.'					)

					# Check each arg is of the proper class (this will also catch a user initializing in the wrong order)
					for attribute_name, arg in kwargs.items():
								__UpperCAmelCase :   int    					=  getattr(self			,							f'{attribute_name}_class'					)
								# Nothing is ever going to be an instance of "AutoXxx", in that case we check the base class.
								__UpperCAmelCase :   str    					=  AUTO_TO_BASE_CLASS_MAPPING.get(__UpperCAmelCase			,							__UpperCAmelCase					)
								if isinstance(__UpperCAmelCase			,							__UpperCAmelCase					):
											__UpperCAmelCase :   str    					=  tuple(getattr(__UpperCAmelCase			,							__UpperCAmelCase					) for n in class_name if n is not None					)
								else:
											__UpperCAmelCase :   Optional[Any]    					=  getattr(__UpperCAmelCase			,							__UpperCAmelCase					)

								if not isinstance(__UpperCAmelCase			,							__UpperCAmelCase					):
											raise ValueError(
											    f'Received a {type(__UpperCAmelCase					).__name__} for argument {attribute_name}, but a {class_name} was expected.'					)

								setattr(self			,							__UpperCAmelCase			,							__UpperCAmelCase					)





		def __repr__(  self					)							->  List[str]:

					'''simple docstring'''
					__UpperCAmelCase :   Any    					=  [f'- {name}: {repr(getattr(self			,							__UpperCAmelCase					)					)}' for name in self.attributes]
					__UpperCAmelCase :   List[Any]    					=  """\n""".join(__UpperCAmelCase					)
					return f'{self.__class__.__name__}:\n{attributes_repr}'





		def 				__A							(  self			,							__UpperCAmelCase			,							__UpperCAmelCase = False			,							**__UpperCAmelCase					)							->  Any:

					'''simple docstring'''
					os.makedirs(__UpperCAmelCase			,							exist_ok=__UpperCAmelCase					)

					if push_to_hub:
								__UpperCAmelCase :   Optional[int]    					=  kwargs.pop("""commit_message"""			,							__UpperCAmelCase					)
								__UpperCAmelCase :   List[Any]    					=  kwargs.pop("""repo_id"""			,							save_directory.split(os.path.sep					)[-1]					)
								__UpperCAmelCase :   Any    					=  self._create_repo(__UpperCAmelCase			,							**__UpperCAmelCase					)
								__UpperCAmelCase :   List[str]    					=  self._get_files_timestamps(__UpperCAmelCase					)
					# If we have a custom config, we copy the file defining it in the folder and set the attributes so it can be
					# loaded from the Hub.
					if self._auto_class is not None:
								__UpperCAmelCase :   Union[str, Any]    					=  [getattr(self			,							__UpperCAmelCase					) for attribute_name in self.attributes]
								__UpperCAmelCase :   Dict    					=  [(a.init_kwargs if isinstance(__UpperCAmelCase			,							__UpperCAmelCase					) else a) for a in attrs]
								custom_object_save(self			,							__UpperCAmelCase			,							config=__UpperCAmelCase					)

					for attribute_name in self.attributes:
								__UpperCAmelCase :   Optional[int]    					=  getattr(self			,							__UpperCAmelCase					)
								# Include the processor class in the attribute config so this processor can then be reloaded with the
								# `AutoProcessor` API.
								if hasattr(__UpperCAmelCase			,							"""_set_processor_class"""					):
											attribute._set_processor_class(self.__class__.__name__					)
								attribute.save_pretrained(__UpperCAmelCase					)

					if self._auto_class is not None:
								# We added an attribute to the init_kwargs of the tokenizers, which needs to be cleaned up.
								for attribute_name in self.attributes:
											__UpperCAmelCase :   str    					=  getattr(self			,							__UpperCAmelCase					)
											if isinstance(__UpperCAmelCase			,							__UpperCAmelCase					):
														del attribute.init_kwargs["auto_map"]

					if push_to_hub:
								self._upload_modified_files(
								    __UpperCAmelCase			,							__UpperCAmelCase			,							__UpperCAmelCase			,							commit_message=__UpperCAmelCase			,							token=kwargs.get("""use_auth_token"""					)			,							)





		@classmethod
		def 				__A							(  cls			,							__UpperCAmelCase			,							__UpperCAmelCase = None			,							__UpperCAmelCase = False			,							__UpperCAmelCase = False			,							__UpperCAmelCase = None			,							__UpperCAmelCase = "main"			,							**__UpperCAmelCase			,							)							->  Optional[int]:

					'''simple docstring'''
					__UpperCAmelCase :   List[str]    					=  cache_dir
					__UpperCAmelCase :   Tuple    					=  force_download
					__UpperCAmelCase :   Optional[int]    					=  local_files_only
					__UpperCAmelCase :   str    					=  revision

					__UpperCAmelCase :   int    					=  kwargs.pop("""use_auth_token"""			,							__UpperCAmelCase					)
					if use_auth_token is not None:
								warnings.warn(
								    """The `use_auth_token` argument is deprecated and will be removed in v5 of Transformers."""			,							__UpperCAmelCase					)
								if token is not None:
											raise ValueError(
											    """`token` and `use_auth_token` are both specified. Please set only the argument `token`."""					)
								__UpperCAmelCase :   Any    					=  use_auth_token

					if token is not None:
								# change to `token` in a follow-up PR
								__UpperCAmelCase :   Dict    					=  token

					__UpperCAmelCase :   Optional[int]    					=  cls._get_arguments_from_pretrained(__UpperCAmelCase			,							**__UpperCAmelCase					)
					return cls(*__UpperCAmelCase					)





		@classmethod
		def 				__A							(  cls			,							__UpperCAmelCase="AutoProcessor"					)							->  Dict:

					'''simple docstring'''
					if not isinstance(__UpperCAmelCase			,							__UpperCAmelCase					):
								__UpperCAmelCase :   List[Any]    					=  auto_class.__name__

					import transformers.models.auto as auto_module

					if not hasattr(__UpperCAmelCase			,							__UpperCAmelCase					):
								raise ValueError(f'{auto_class} is not a valid auto class.'					)

					__UpperCAmelCase :   Optional[Any]    					=  auto_class





		@classmethod
		def 				__A							(  cls			,							__UpperCAmelCase			,							**__UpperCAmelCase					)							->  List[str]:

					'''simple docstring'''
					__UpperCAmelCase :   Union[str, Any]    					=  []
					for attribute_name in cls.attributes:
								__UpperCAmelCase :   List[Any]    					=  getattr(cls			,							f'{attribute_name}_class'					)
								if isinstance(__UpperCAmelCase			,							__UpperCAmelCase					):
											__UpperCAmelCase :   str    					=  tuple(getattr(__UpperCAmelCase			,							__UpperCAmelCase					) if n is not None else None for n in class_name					)
											__UpperCAmelCase :   int    					=  kwargs.get("""use_fast"""			,							__UpperCAmelCase					)
											if use_fast and classes[1] is not None:
														__UpperCAmelCase :   int    					=  classes[1]
											else:
														__UpperCAmelCase :   Optional[Any]    					=  classes[0]
								else:
											__UpperCAmelCase :   str    					=  getattr(__UpperCAmelCase			,							__UpperCAmelCase					)

								args.append(attribute_class.from_pretrained(__UpperCAmelCase			,							**__UpperCAmelCase					)					)
					return args





		@property
		def 				__A							(  self					)							->  Tuple:

					'''simple docstring'''
					__UpperCAmelCase :   Optional[Any]    					=  getattr(self			,							self.attributes[0]					)
					return getattr(__UpperCAmelCase			,							"""model_input_names"""			,							__UpperCAmelCase					)


_UpperCamelCase									=      copy_func(ProcessorMixin.push_to_hub)
if ProcessorMixin.push_to_hub.__doc__ is not None:
					_UpperCamelCase									=      ProcessorMixin.push_to_hub.__doc__.format(
					    object='''processor''', object_class='''AutoProcessor''', object_files='''processor files'''
					)




