from typing import List, Optional, Union

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





class 					lowercase__						(   UpperCamelCase_):
						UpperCamelCase_     =		["""image_processor""", """tokenizer"""]
						UpperCamelCase_     =		"""Pix2StructImageProcessor"""
						UpperCamelCase_     =		("""T5Tokenizer""", """T5TokenizerFast""")


						def __init__(       self	:				Dict    ,		UpperCamelCase__	:				Dict    ,		UpperCamelCase__	:				List[str]						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    List[str]							=							False
												super().__init__(UpperCamelCase__    ,		UpperCamelCase__						)


						def __call__(       self	:				Any    ,		UpperCamelCase__	:				Dict=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__	:				Optional[int] = 2048    ,		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 either images or text.'''						)

												# Get only text
												if images is None and not self.image_processor.is_vqa:
																		SCREAMING_SNAKE_CASE							:    int							=							self.tokenizer
																		SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							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__    ,		)
																		return text_encoding

												if not self.image_processor.is_vqa:
																		# add pixel_values
																		SCREAMING_SNAKE_CASE							:    int							=							self.image_processor(
																		    UpperCamelCase__    ,		return_tensors=UpperCamelCase__    ,		max_patches=UpperCamelCase__    ,		**UpperCamelCase__						)
												else:
																		# add pixel_values and bbox
																		SCREAMING_SNAKE_CASE							:    Optional[Any]							=							self.image_processor(
																		    UpperCamelCase__    ,		return_tensors=UpperCamelCase__    ,		max_patches=UpperCamelCase__    ,		header_text=UpperCamelCase__    ,		**UpperCamelCase__						)

												if text is not None and not self.image_processor.is_vqa:
																		SCREAMING_SNAKE_CASE							:    Optional[int]							=							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__    ,		)

																		if "attention_mask" in text_encoding:
																								SCREAMING_SNAKE_CASE							:    str							=							text_encoding.pop('''attention_mask'''						)
																		if "input_ids" in text_encoding:
																								SCREAMING_SNAKE_CASE							:    List[str]							=							text_encoding.pop('''input_ids'''						)
												else:
																		SCREAMING_SNAKE_CASE							:    List[Any]							=							None

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

												return encoding_image_processor


						def        __A       (       self	:				int    ,		*UpperCamelCase__	:				Optional[Any]    ,		**UpperCamelCase__	:				int						):





												'''simple docstring'''





												return self.tokenizer.batch_decode(*UpperCamelCase__    ,		**UpperCamelCase__						)


						def        __A       (       self	:				Dict    ,		*UpperCamelCase__	:				Union[str, Any]    ,		**UpperCamelCase__	:				Dict						):





												'''simple docstring'''





												return self.tokenizer.decode(*UpperCamelCase__    ,		**UpperCamelCase__						)



						@property
						def        __A       (       self	:				Optional[int]						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    Dict							=							self.tokenizer.model_input_names
												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							self.image_processor.model_input_names
												return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names						)						)






import argparse

import torch

from transformers import YosoConfig, YosoForMaskedLM
def       A	(    _lowercase						):
						if "model" in orig_key:
												SCREAMING_SNAKE_CASE							:    int							=							orig_key.replace('''model.'''		,  ''''''						)
						if "norm1" in orig_key:
												SCREAMING_SNAKE_CASE							:    List[Any]							=							orig_key.replace('''norm1'''		,  '''attention.output.LayerNorm'''						)
						if "norm2" in orig_key:
												SCREAMING_SNAKE_CASE							:    str							=							orig_key.replace('''norm2'''		,  '''output.LayerNorm'''						)
						if "norm" in orig_key:
												SCREAMING_SNAKE_CASE							:    Tuple							=							orig_key.replace('''norm'''		,  '''LayerNorm'''						)
						if "transformer" in orig_key:
												SCREAMING_SNAKE_CASE							:    int							=							orig_key.split('''.'''						)[0].split('''_'''						)[-1]
												SCREAMING_SNAKE_CASE							:    List[str]							=							orig_key.replace(f"""transformer_{layer_num}"""		,  f"""encoder.layer.{layer_num}"""						)
						if "mha.attn" in orig_key:
												SCREAMING_SNAKE_CASE							:    Any							=							orig_key.replace('''mha.attn'''		,  '''attention.self'''						)
						if "mha" in orig_key:
												SCREAMING_SNAKE_CASE							:    Optional[int]							=							orig_key.replace('''mha'''		,  '''attention'''						)
						if "W_q" in orig_key:
												SCREAMING_SNAKE_CASE							:    List[Any]							=							orig_key.replace('''W_q'''		,  '''self.query'''						)
						if "W_k" in orig_key:
												SCREAMING_SNAKE_CASE							:    Optional[int]							=							orig_key.replace('''W_k'''		,  '''self.key'''						)
						if "W_v" in orig_key:
												SCREAMING_SNAKE_CASE							:    str							=							orig_key.replace('''W_v'''		,  '''self.value'''						)
						if "ff1" in orig_key:
												SCREAMING_SNAKE_CASE							:    Any							=							orig_key.replace('''ff1'''		,  '''intermediate.dense'''						)
						if "ff2" in orig_key:
												SCREAMING_SNAKE_CASE							:    List[Any]							=							orig_key.replace('''ff2'''		,  '''output.dense'''						)
						if "ff" in orig_key:
												SCREAMING_SNAKE_CASE							:    Dict							=							orig_key.replace('''ff'''		,  '''output.dense'''						)
						if "mlm_class" in orig_key:
												SCREAMING_SNAKE_CASE							:    Optional[int]							=							orig_key.replace('''mlm.mlm_class'''		,  '''cls.predictions.decoder'''						)
						if "mlm" in orig_key:
												SCREAMING_SNAKE_CASE							:    str							=							orig_key.replace('''mlm'''		,  '''cls.predictions.transform'''						)
						if "cls" not in orig_key:
												SCREAMING_SNAKE_CASE							:    List[str]							=							'''yoso.''' + orig_key

						return orig_key
def       A	(    _lowercase		,  _lowercase						):
						for key in orig_state_dict.copy().keys():
												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							orig_state_dict.pop(_lowercase						)

												if ("pooler" in key) or ("sen_class" in key):
																		continue
												else:
																		SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							val

						SCREAMING_SNAKE_CASE							:    List[str]							=							orig_state_dict['''cls.predictions.decoder.bias''']
						SCREAMING_SNAKE_CASE							:    Dict							=							torch.arange(_lowercase						).expand((1, -1)						) + 2

						return orig_state_dict
def       A	(    _lowercase		,  _lowercase		,  _lowercase						):
						SCREAMING_SNAKE_CASE							:    Tuple							=							torch.load(_lowercase		,  map_location='''cpu'''						)['''model_state_dict''']
						SCREAMING_SNAKE_CASE							:    List[Any]							=							YosoConfig.from_json_file(_lowercase						)
						SCREAMING_SNAKE_CASE							:    str							=							YosoForMaskedLM(_lowercase						)

						SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							convert_checkpoint_helper(config.max_position_embeddings		,  _lowercase						)

						print(model.load_state_dict(_lowercase						)						)
						model.eval()
						model.save_pretrained(_lowercase						)

						print(f"""Checkpoint successfuly converted. Model saved at {pytorch_dump_path}"""						)


if __name__ == "__main__":
					__UpperCamelCase		:       Tuple									=     argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    '--pytorch_model_path', default=None, type=str, required=True, help='Path to YOSO pytorch checkpoint.'
					)
					parser.add_argument(
					    '--config_file',
					    default=None,
					    type=str,
					    required=True,
					    help='The json file for YOSO model config.',
					)
					parser.add_argument(
					    '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
					)
					__UpperCamelCase		:       Optional[Any]									=     parser.parse_args()
					convert_yoso_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)





