


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    (__SCREAMING_SNAKE_CASE  ):





							"""simple docstring"""







							_UpperCAmelCase      =						["""image_processor""", """tokenizer"""]
							_UpperCAmelCase      =						"""Pix2StructImageProcessor"""
							_UpperCAmelCase      =						("""T5Tokenizer""", """T5TokenizerFast""")






							def __init__(      self			,						lowerCAmelCase__			,						lowerCAmelCase__       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     Dict					=					False
										super().__init__(lowerCAmelCase__			,						lowerCAmelCase__       )






							def __call__(      self			,						lowerCAmelCase__=None			,						lowerCAmelCase__ = None			,						lowerCAmelCase__ = True			,						lowerCAmelCase__ = False			,						lowerCAmelCase__ = None			,						lowerCAmelCase__ = None			,						lowerCAmelCase__ = 2_0_4_8			,						lowerCAmelCase__ = 0			,						lowerCAmelCase__ = None			,						lowerCAmelCase__ = None			,						lowerCAmelCase__ = False			,						lowerCAmelCase__ = False			,						lowerCAmelCase__ = False			,						lowerCAmelCase__ = False			,						lowerCAmelCase__ = False			,						lowerCAmelCase__ = True			,						lowerCAmelCase__ = None			,						**lowerCAmelCase__			,						):





										"""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_		:     Optional[Any]					=					self.tokenizer
													SCREAMING_SNAKE_CASE_		:     str					=					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

										if not self.image_processor.is_vqa:
													# add pixel_values
													SCREAMING_SNAKE_CASE_		:     List[Any]					=					self.image_processor(
													    lowerCAmelCase__			,						return_tensors=lowerCAmelCase__			,						max_patches=lowerCAmelCase__			,						**lowerCAmelCase__       )
										else:
													# add pixel_values and bbox
													SCREAMING_SNAKE_CASE_		:     Any					=					self.image_processor(
													    lowerCAmelCase__			,						return_tensors=lowerCAmelCase__			,						max_patches=lowerCAmelCase__			,						header_text=lowerCAmelCase__			,						**lowerCAmelCase__       )

										if text is not None and not self.image_processor.is_vqa:
													SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					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__			,						)

													if "attention_mask" in text_encoding:
																SCREAMING_SNAKE_CASE_		:     Any					=					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_		:     Union[str, Any]					=					None

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

										return encoding_image_processor






							def 					UpperCamelCase__     (      self			,						*lowerCAmelCase__			,						**lowerCAmelCase__       ):





										"""simple docstring"""




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






							def 					UpperCamelCase__     (      self			,						*lowerCAmelCase__			,						**lowerCAmelCase__       ):





										"""simple docstring"""




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






							@property
							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     List[str]					=					self.tokenizer.model_input_names
										SCREAMING_SNAKE_CASE_		:     Optional[int]					=					self.image_processor.model_input_names
										return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names       )       )









import copy
import os
from typing import Union

from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCAmelCase__			:      Tuple								=logging.get_logger(__name__)

lowerCAmelCase__			:      Optional[int]								={
    'microsoft/git-base': 'https://huggingface.co/microsoft/git-base/resolve/main/config.json',
}







class 	__lowercase    (__SCREAMING_SNAKE_CASE  ):





							"""simple docstring"""







							_UpperCAmelCase      =						"""git_vision_model"""






							def __init__(      self			,						lowerCAmelCase__=7_6_8			,						lowerCAmelCase__=3_0_7_2			,						lowerCAmelCase__=1_2			,						lowerCAmelCase__=1_2			,						lowerCAmelCase__=3			,						lowerCAmelCase__=2_2_4			,						lowerCAmelCase__=1_6			,						lowerCAmelCase__="quick_gelu"			,						lowerCAmelCase__=1E-5			,						lowerCAmelCase__=0.0			,						lowerCAmelCase__=0.02			,						**lowerCAmelCase__			,						):





										"""simple docstring"""




										super().__init__(**lowerCAmelCase__       )

										SCREAMING_SNAKE_CASE_		:     Optional[int]					=					hidden_size
										SCREAMING_SNAKE_CASE_		:     Optional[int]					=					intermediate_size
										SCREAMING_SNAKE_CASE_		:     Any					=					num_hidden_layers
										SCREAMING_SNAKE_CASE_		:     Dict					=					num_attention_heads
										SCREAMING_SNAKE_CASE_		:     Tuple					=					num_channels
										SCREAMING_SNAKE_CASE_		:     Tuple					=					patch_size
										SCREAMING_SNAKE_CASE_		:     Any					=					image_size
										SCREAMING_SNAKE_CASE_		:     Dict					=					initializer_range
										SCREAMING_SNAKE_CASE_		:     List[str]					=					attention_dropout
										SCREAMING_SNAKE_CASE_		:     Any					=					layer_norm_eps
										SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					hidden_act






							@classmethod
							def 					UpperCamelCase__     (      cls			,						lowerCAmelCase__			,						**lowerCAmelCase__       ):





										"""simple docstring"""




										cls._set_token_in_kwargs(lowerCAmelCase__       )

										SCREAMING_SNAKE_CASE_		,      SCREAMING_SNAKE_CASE_		:     List[str]					=					cls.get_config_dict(lowerCAmelCase__			,						**lowerCAmelCase__       )

										# get the vision config dict if we are loading from GITConfig
										if config_dict.get('model_type'       ) == "git":
													SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					config_dict['vision_config']

										if "model_type" in config_dict and hasattr(cls			,						'model_type'       ) and config_dict["model_type"] != cls.model_type:
													logger.warning(
													    F'''You are using a model of type {config_dict['model_type']} to instantiate a model of type '''
													    F'''{cls.model_type}. This is not supported for all configurations of models and can yield errors.'''       )

										return cls.from_dict(lowerCAmelCase__			,						**lowerCAmelCase__       )







class 	__lowercase    (__SCREAMING_SNAKE_CASE  ):





							"""simple docstring"""







							_UpperCAmelCase      =						"""git"""






							def __init__(      self			,						lowerCAmelCase__=None			,						lowerCAmelCase__=3_0_5_2_2			,						lowerCAmelCase__=7_6_8			,						lowerCAmelCase__=6			,						lowerCAmelCase__=1_2			,						lowerCAmelCase__=3_0_7_2			,						lowerCAmelCase__="gelu"			,						lowerCAmelCase__=0.1			,						lowerCAmelCase__=0.1			,						lowerCAmelCase__=1_0_2_4			,						lowerCAmelCase__=0.02			,						lowerCAmelCase__=1E-12			,						lowerCAmelCase__=0			,						lowerCAmelCase__="absolute"			,						lowerCAmelCase__=True			,						lowerCAmelCase__=False			,						lowerCAmelCase__=1_0_1			,						lowerCAmelCase__=1_0_2			,						lowerCAmelCase__=None			,						**lowerCAmelCase__			,						):





										"""simple docstring"""




										super().__init__(bos_token_id=lowerCAmelCase__			,						eos_token_id=lowerCAmelCase__			,						pad_token_id=lowerCAmelCase__			,						**lowerCAmelCase__       )

										if vision_config is None:
													SCREAMING_SNAKE_CASE_		:     Dict					=					{}
													logger.info('vision_config is None. initializing the GitVisionConfig with default values.'       )

										SCREAMING_SNAKE_CASE_		:     str					=					GitVisionConfig(**lowerCAmelCase__       )
										SCREAMING_SNAKE_CASE_		:     Dict					=					vocab_size
										SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					hidden_size
										SCREAMING_SNAKE_CASE_		:     Tuple					=					num_hidden_layers
										SCREAMING_SNAKE_CASE_		:     Any					=					num_attention_heads
										SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					hidden_act
										SCREAMING_SNAKE_CASE_		:     Any					=					intermediate_size
										SCREAMING_SNAKE_CASE_		:     Tuple					=					hidden_dropout_prob
										SCREAMING_SNAKE_CASE_		:     str					=					attention_probs_dropout_prob
										SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					max_position_embeddings
										SCREAMING_SNAKE_CASE_		:     int					=					initializer_range
										SCREAMING_SNAKE_CASE_		:     int					=					layer_norm_eps
										SCREAMING_SNAKE_CASE_		:     List[str]					=					position_embedding_type
										SCREAMING_SNAKE_CASE_		:     List[str]					=					use_cache
										SCREAMING_SNAKE_CASE_		:     Any					=					tie_word_embeddings
										SCREAMING_SNAKE_CASE_		:     Dict					=					num_image_with_embedding

										SCREAMING_SNAKE_CASE_		:     List[str]					=					bos_token_id
										SCREAMING_SNAKE_CASE_		:     str					=					eos_token_id






							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					copy.deepcopy(self.__dict__       )
										SCREAMING_SNAKE_CASE_		:     str					=					self.vision_config.to_dict()
										SCREAMING_SNAKE_CASE_		:     Optional[int]					=					self.__class__.model_type
										return output





