






"""simple docstring"""
from typing import List, Optional, Union

from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType


class _lowerCAmelCase						(    __UpperCAmelCase       ):
						SCREAMING_SNAKE_CASE_:					Any 					=				['image_processor', 'tokenizer']
						SCREAMING_SNAKE_CASE_:					str 					=				'BlipImageProcessor'
						SCREAMING_SNAKE_CASE_:					Dict 					=				'AutoTokenizer'







						def __init__(    self   ,				lowerCAmelCase_   ,				lowerCAmelCase_							)		->							Optional[int]:
										_SCREAMING_SNAKE_CASE   :		Dict       		=    False
										super().__init__(lowerCAmelCase_   ,				lowerCAmelCase_							)
										_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    self.image_processor







						def __call__(    self   ,				lowerCAmelCase_ = None   ,				lowerCAmelCase_ = None   ,				lowerCAmelCase_ = True   ,				lowerCAmelCase_ = False   ,				lowerCAmelCase_ = None   ,				lowerCAmelCase_ = None   ,				lowerCAmelCase_ = 0   ,				lowerCAmelCase_ = None   ,				lowerCAmelCase_ = None   ,				lowerCAmelCase_ = False   ,				lowerCAmelCase_ = False   ,				lowerCAmelCase_ = False   ,				lowerCAmelCase_ = False   ,				lowerCAmelCase_ = False   ,				lowerCAmelCase_ = True   ,				lowerCAmelCase_ = None   ,				**lowerCAmelCase_   ,				)		->							BatchEncoding:
										if images is None and text is None:
														raise ValueError('You have to specify either images or text.'							)

										# Get only text
										if images is None:
														_SCREAMING_SNAKE_CASE   :		int       		=    self.tokenizer
														_SCREAMING_SNAKE_CASE   :		Dict       		=    self.tokenizer(
														    text=lowerCAmelCase_   ,				add_special_tokens=lowerCAmelCase_   ,				padding=lowerCAmelCase_   ,				truncation=lowerCAmelCase_   ,				max_length=lowerCAmelCase_   ,				stride=lowerCAmelCase_   ,				pad_to_multiple_of=lowerCAmelCase_   ,				return_attention_mask=lowerCAmelCase_   ,				return_overflowing_tokens=lowerCAmelCase_   ,				return_special_tokens_mask=lowerCAmelCase_   ,				return_offsets_mapping=lowerCAmelCase_   ,				return_token_type_ids=lowerCAmelCase_   ,				return_length=lowerCAmelCase_   ,				verbose=lowerCAmelCase_   ,				return_tensors=lowerCAmelCase_   ,				**lowerCAmelCase_   ,				)
														return text_encoding

										# add pixel_values
										_SCREAMING_SNAKE_CASE   :		List[Any]       		=    self.image_processor(lowerCAmelCase_   ,				return_tensors=lowerCAmelCase_							)

										if text is not None:
														_SCREAMING_SNAKE_CASE   :		Tuple       		=    self.tokenizer(
														    text=lowerCAmelCase_   ,				add_special_tokens=lowerCAmelCase_   ,				padding=lowerCAmelCase_   ,				truncation=lowerCAmelCase_   ,				max_length=lowerCAmelCase_   ,				stride=lowerCAmelCase_   ,				pad_to_multiple_of=lowerCAmelCase_   ,				return_attention_mask=lowerCAmelCase_   ,				return_overflowing_tokens=lowerCAmelCase_   ,				return_special_tokens_mask=lowerCAmelCase_   ,				return_offsets_mapping=lowerCAmelCase_   ,				return_token_type_ids=lowerCAmelCase_   ,				return_length=lowerCAmelCase_   ,				verbose=lowerCAmelCase_   ,				return_tensors=lowerCAmelCase_   ,				**lowerCAmelCase_   ,				)
										else:
														_SCREAMING_SNAKE_CASE   :		Dict       		=    None

										if text_encoding is not None:
														encoding_image_processor.update(lowerCAmelCase_							)

										return encoding_image_processor







						def 			A      (    self   ,				*lowerCAmelCase_   ,				**lowerCAmelCase_							)		->							str:
										return self.tokenizer.batch_decode(*lowerCAmelCase_   ,				**lowerCAmelCase_							)







						def 			A      (    self   ,				*lowerCAmelCase_   ,				**lowerCAmelCase_							)		->							Tuple:
										return self.tokenizer.decode(*lowerCAmelCase_   ,				**lowerCAmelCase_							)







						@property
						# Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
						def 			A      (    self							)		->							Tuple:
										_SCREAMING_SNAKE_CASE   :		List[str]       		=    self.tokenizer.model_input_names
										_SCREAMING_SNAKE_CASE   :		Tuple       		=    self.image_processor.model_input_names
										return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names							)							)









"""simple docstring"""
import argparse

import torch

from transformers import GPTaLMHeadModel, RobertaForMaskedLM


if __name__ == "__main__":
			lowerCAmelCase__    						=			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')
			lowerCAmelCase__    						=			parser.parse_args()

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

			lowerCAmelCase__    						=			model.state_dict()
			lowerCAmelCase__    						=			{}

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

    # Transformer Blocks #
			lowerCAmelCase__    						=			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"]:
															lowerCAmelCase__    						=			state_dict[
															    F'{prefix}.h.{teacher_idx}.{layer}.{w}'
															]
									lowerCAmelCase__    						=			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"]:
															lowerCAmelCase__    						=			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"]:
									lowerCAmelCase__    						=			state_dict[F'{layer}']
						if args.vocab_transform:
									for w in ["weight", "bias"]:
												lowerCAmelCase__    						=			state_dict[F'lm_head.dense.{w}']
												lowerCAmelCase__    						=			state_dict[F'lm_head.layer_norm.{w}']
			elif args.model_type == "gpt2":
						for w in ["weight", "bias"]:
									lowerCAmelCase__    						=			state_dict[F'{prefix}.ln_f.{w}']
						lowerCAmelCase__    						=			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)

