

'''simple docstring'''





import os
from typing import List, Optional, Union

from ...image_processing_utils import BatchFeature
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
from ..auto import AutoTokenizer





class 		__SCREAMING_SNAKE_CASE       ( lowercase__			):
				lowerCamelCase_							=						['image_processor', 'tokenizer']
				lowerCamelCase_							=						'BlipImageProcessor'
				lowerCamelCase_							=						'AutoTokenizer'







				def __init__(					self    :		Dict		,				UpperCAmelCase__    :		List[str]		,				UpperCAmelCase__    :		Optional[int]		,				UpperCAmelCase__    :		List[Any]      ):
										'''simple docstring'''
										super().__init__(UpperCAmelCase__		,				UpperCAmelCase__      )

										# add QFormer tokenizer
										lowercase   :       Optional[int]								=qformer_tokenizer







				def __call__(					self    :		Union[str, Any]		,				UpperCAmelCase__    :		ImageInput = None		,				UpperCAmelCase__    :		Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None		,				UpperCAmelCase__    :		bool = True		,				UpperCAmelCase__    :		Union[bool, str, PaddingStrategy] = False		,				UpperCAmelCase__    :		Union[bool, str, TruncationStrategy] = None		,				UpperCAmelCase__    :		Optional[int] = None		,				UpperCAmelCase__    :		int = 0		,				UpperCAmelCase__    :		Optional[int] = None		,				UpperCAmelCase__    :		Optional[bool] = None		,				UpperCAmelCase__    :		bool = False		,				UpperCAmelCase__    :		bool = False		,				UpperCAmelCase__    :		bool = False		,				UpperCAmelCase__    :		bool = False		,				UpperCAmelCase__    :		bool = False		,				UpperCAmelCase__    :		bool = True		,				UpperCAmelCase__    :		Optional[Union[str, TensorType]] = None		,				**UpperCAmelCase__    :		Union[str, Any]		,				):
										'''simple docstring'''
										if images is None and text is None:
																raise ValueError('''You have to specify at least images or text.'''      )

										lowercase   :       List[str]								=BatchFeature()

										if text is not None:
																lowercase   :       str								=self.tokenizer(
																    text=UpperCAmelCase__		,				add_special_tokens=UpperCAmelCase__		,				padding=UpperCAmelCase__		,				truncation=UpperCAmelCase__		,				max_length=UpperCAmelCase__		,				stride=UpperCAmelCase__		,				pad_to_multiple_of=UpperCAmelCase__		,				return_attention_mask=UpperCAmelCase__		,				return_overflowing_tokens=UpperCAmelCase__		,				return_special_tokens_mask=UpperCAmelCase__		,				return_offsets_mapping=UpperCAmelCase__		,				return_token_type_ids=UpperCAmelCase__		,				return_length=UpperCAmelCase__		,				verbose=UpperCAmelCase__		,				return_tensors=UpperCAmelCase__		,				**UpperCAmelCase__		,				)
																encoding.update(UpperCAmelCase__      )
																lowercase   :       Optional[int]								=self.qformer_tokenizer(
																    text=UpperCAmelCase__		,				add_special_tokens=UpperCAmelCase__		,				padding=UpperCAmelCase__		,				truncation=UpperCAmelCase__		,				max_length=UpperCAmelCase__		,				stride=UpperCAmelCase__		,				pad_to_multiple_of=UpperCAmelCase__		,				return_attention_mask=UpperCAmelCase__		,				return_overflowing_tokens=UpperCAmelCase__		,				return_special_tokens_mask=UpperCAmelCase__		,				return_offsets_mapping=UpperCAmelCase__		,				return_token_type_ids=UpperCAmelCase__		,				return_length=UpperCAmelCase__		,				verbose=UpperCAmelCase__		,				return_tensors=UpperCAmelCase__		,				**UpperCAmelCase__		,				)
																lowercase   :       Any								=qformer_text_encoding.pop('''input_ids'''      )
																lowercase   :       Optional[Any]								=qformer_text_encoding.pop('''attention_mask'''      )

										if images is not None:
																lowercase   :       Union[str, Any]								=self.image_processor(UpperCAmelCase__		,				return_tensors=UpperCAmelCase__      )
																encoding.update(UpperCAmelCase__      )

										return encoding







				def    lowerCamelCase_  (					self    :		Dict		,				*UpperCAmelCase__    :		Optional[int]		,				**UpperCAmelCase__    :		Optional[Any]      ):
										'''simple docstring'''
										return self.tokenizer.batch_decode(*UpperCAmelCase__		,				**UpperCAmelCase__      )







				def    lowerCamelCase_  (					self    :		str		,				*UpperCAmelCase__    :		Dict		,				**UpperCAmelCase__    :		Optional[Any]      ):
										'''simple docstring'''
										return self.tokenizer.decode(*UpperCAmelCase__		,				**UpperCAmelCase__      )







				@property
				# Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
				def    lowerCamelCase_  (					self    :		List[str]      ):
										'''simple docstring'''
										lowercase   :       Optional[Any]								=self.tokenizer.model_input_names
										lowercase   :       Union[str, Any]								=self.image_processor.model_input_names
										return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names      )      )







				def    lowerCamelCase_  (					self    :		Dict		,				UpperCAmelCase__    :		int		,				**UpperCAmelCase__    :		str      ):
										'''simple docstring'''
										if os.path.isfile(UpperCAmelCase__      ):
																raise ValueError(F'''Provided path ({save_directory}) should be a directory, not a file'''      )
										os.makedirs(UpperCAmelCase__		,				exist_ok=UpperCAmelCase__      )
										lowercase   :       Tuple								=os.path.join(UpperCAmelCase__		,				'''qformer_tokenizer'''      )
										self.qformer_tokenizer.save_pretrained(UpperCAmelCase__      )
										return super().save_pretrained(UpperCAmelCase__		,				**UpperCAmelCase__      )







				@classmethod
				def    lowerCamelCase_  (					cls    :		Optional[int]		,				UpperCAmelCase__    :		Any		,				**UpperCAmelCase__    :		List[str]      ):
										'''simple docstring'''
										lowercase   :       Tuple								=AutoTokenizer.from_pretrained(UpperCAmelCase__		,				subfolder='''qformer_tokenizer'''      )
										lowercase   :       int								=cls._get_arguments_from_pretrained(UpperCAmelCase__		,				**UpperCAmelCase__      )
										args.append(UpperCAmelCase__      )
										return cls(*UpperCAmelCase__      )



'''simple docstring'''





import argparse

import torch

from transformers import GPTaLMHeadModel, RobertaForMaskedLM


if __name__ == "__main__":
							UpperCamelCase_							=					argparse.ArgumentParser(
							    description=(
							        """Extraction some layers of the full RobertaForMaskedLM or GPT2LMHeadModel for Transfer Learned"""
							        """ Distillation"""
							    )
							)
							parser.add_argument("""--model_type""", default="""roberta""", choices=["""roberta""", """gpt2"""])
							parser.add_argument("""--model_name""", default="""roberta-large""", type=str)
							parser.add_argument("""--dump_checkpoint""", default="""serialization_dir/tf_roberta_048131723.pth""", type=str)
							parser.add_argument("""--vocab_transform""", action="""store_true""")
							UpperCamelCase_							=					parser.parse_args()

							if args.model_type == "roberta":
														UpperCamelCase_							=					RobertaForMaskedLM.from_pretrained(args.model_name)
														UpperCamelCase_							=					"""roberta"""
							elif args.model_type == "gpt2":
														UpperCamelCase_							=					GPTaLMHeadModel.from_pretrained(args.model_name)
														UpperCamelCase_							=					"""transformer"""

							UpperCamelCase_							=					model.state_dict()
							UpperCamelCase_							=					{}

							# Embeddings #
							if args.model_type == "gpt2":
														for param_name in ["wte.weight", "wpe.weight"]:
																					UpperCamelCase_							=					state_dict[f'''{prefix}.{param_name}''']
							else:
														for w in ["word_embeddings", "position_embeddings", "token_type_embeddings"]:
																					UpperCamelCase_							=					f'''{prefix}.embeddings.{w}.weight'''
																					UpperCamelCase_							=					state_dict[param_name]
														for w in ["weight", "bias"]:
																					UpperCamelCase_							=					f'''{prefix}.embeddings.LayerNorm.{w}'''
																					UpperCamelCase_							=					state_dict[param_name]

    # Transformer Blocks #
							UpperCamelCase_							=					0
							for teacher_idx in [0, 2, 4, 7, 9, 11]:
														if args.model_type == "gpt2":
																					for layer in ["ln_1", "attn.c_attn", "attn.c_proj", "ln_2", "mlp.c_fc", "mlp.c_proj"]:
																												for w in ["weight", "bias"]:
																																			UpperCamelCase_							=					state_dict[
																																			    f'''{prefix}.h.{teacher_idx}.{layer}.{w}'''
																																			]
																					UpperCamelCase_							=					state_dict[f'''{prefix}.h.{teacher_idx}.attn.bias''']
														else:
																					for layer in [
																					    "attention.self.query",
																					    "attention.self.key",
																					    "attention.self.value",
																					    "attention.output.dense",
																					    "attention.output.LayerNorm",
																					    "intermediate.dense",
																					    "output.dense",
																					    "output.LayerNorm",
																					]:
																												for w in ["weight", "bias"]:
																																			UpperCamelCase_							=					state_dict[
																																			    f'''{prefix}.encoder.layer.{teacher_idx}.{layer}.{w}'''
																																			]
														std_idx += 1

							# Language Modeling Head ###s
							if args.model_type == "roberta":
														for layer in ["lm_head.decoder.weight", "lm_head.bias"]:
																					UpperCamelCase_							=					state_dict[f'''{layer}''']
														if args.vocab_transform:
																					for w in ["weight", "bias"]:
																												UpperCamelCase_							=					state_dict[f'''lm_head.dense.{w}''']
																												UpperCamelCase_							=					state_dict[f'''lm_head.layer_norm.{w}''']
							elif args.model_type == "gpt2":
														for w in ["weight", "bias"]:
																					UpperCamelCase_							=					state_dict[f'''{prefix}.ln_f.{w}''']
														UpperCamelCase_							=					state_dict["""lm_head.weight"""]

							print(f'''N layers selected for distillation: {std_idx}''')
							print(f'''Number of params transferred for distillation: {len(compressed_sd.keys())}''')

							print(f'''Save transferred checkpoint to {args.dump_checkpoint}.''')
							torch.save(compressed_sd, args.dump_checkpoint)





