

import copy
import os
from typing import Union

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


a		=logging.get_logger(__name__)

a		={
    """google/pix2struct-textcaps-base""": (
        """https://huggingface.co/google/pix2struct-textcaps-base/resolve/main/config.json"""
    ),
}






class 	A_		(	SCREAMING_SNAKE_CASE    ):
							_UpperCAmelCase   :  Dict										=				'''pix2struct_text_model'''
							_UpperCAmelCase   :  Union[str, Any]										=				['''past_key_values''']
							_UpperCAmelCase   :  str										=				{
							    '''hidden_size''': '''hidden_size''',
							    '''num_attention_heads''': '''num_heads''',
							    '''num_hidden_layers''': '''num_layers''',
							}
							def __init__(		self		:      Tuple       ,SCREAMING_SNAKE_CASE__		:      Union[str, Any]=5_0_2_4_4       ,SCREAMING_SNAKE_CASE__		:      Optional[int]=7_6_8       ,SCREAMING_SNAKE_CASE__		:      str=6_4       ,SCREAMING_SNAKE_CASE__		:      Dict=2_0_4_8       ,SCREAMING_SNAKE_CASE__		:      List[str]=1_2       ,SCREAMING_SNAKE_CASE__		:      int=1_2       ,SCREAMING_SNAKE_CASE__		:      Optional[Any]=3_2       ,SCREAMING_SNAKE_CASE__		:      List[str]=1_2_8       ,SCREAMING_SNAKE_CASE__		:      List[Any]=0.1       ,SCREAMING_SNAKE_CASE__		:      Any=1E-6       ,SCREAMING_SNAKE_CASE__		:      str=1.0       ,SCREAMING_SNAKE_CASE__		:      Optional[Any]="gelu_new"       ,SCREAMING_SNAKE_CASE__		:      Tuple=0       ,SCREAMING_SNAKE_CASE__		:      List[str]=False       ,SCREAMING_SNAKE_CASE__		:      str=0       ,SCREAMING_SNAKE_CASE__		:      Union[str, Any]=1       ,SCREAMING_SNAKE_CASE__		:      int=False       ,SCREAMING_SNAKE_CASE__		:      Dict=True       ,**SCREAMING_SNAKE_CASE__		:      Tuple       ,):
								__lowerCamelCase    :    int 	=			vocab_size
								__lowerCamelCase    :    List[str] 	=			hidden_size
								__lowerCamelCase    :    int 	=			d_kv
								__lowerCamelCase    :    str 	=			d_ff
								__lowerCamelCase    :    Optional[int] 	=			num_layers
								__lowerCamelCase    :    Optional[int] 	=			num_heads
								__lowerCamelCase    :    Optional[Any] 	=			relative_attention_num_buckets
								__lowerCamelCase    :    Any 	=			relative_attention_max_distance
								__lowerCamelCase    :    int 	=			dropout_rate
								__lowerCamelCase    :    Union[str, Any] 	=			layer_norm_epsilon
								__lowerCamelCase    :    Optional[Any] 	=			initializer_factor
								__lowerCamelCase    :    Optional[Any] 	=			use_cache

								__lowerCamelCase    :    str 	=			eos_token_id
								__lowerCamelCase    :    List[str] 	=			decoder_start_token_id

								# for backwards compatibility
								__lowerCamelCase    :    int 	=			dense_act_fn

								super().__init__(
								    pad_token_id=SCREAMING_SNAKE_CASE__       ,eos_token_id=SCREAMING_SNAKE_CASE__       ,decoder_start_token_id=SCREAMING_SNAKE_CASE__       ,tie_word_embeddings=SCREAMING_SNAKE_CASE__       ,is_decoder=SCREAMING_SNAKE_CASE__       ,**SCREAMING_SNAKE_CASE__       ,)


							@classmethod
							def    lowerCAmelCase					(		cls		:      str       ,SCREAMING_SNAKE_CASE__		:      Union[str, os.PathLike]       ,**SCREAMING_SNAKE_CASE__		:      Dict):
								cls._set_token_in_kwargs(SCREAMING_SNAKE_CASE__)

								__lowerCamelCase    :    Any 	=			cls.get_config_dict(SCREAMING_SNAKE_CASE__       ,**SCREAMING_SNAKE_CASE__)

								# get the text config dict if we are loading from Pix2StructConfig
								if config_dict.get('model_type') == "pix2struct":
									__lowerCamelCase    :    Dict 	=			config_dict['text_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(SCREAMING_SNAKE_CASE__       ,**SCREAMING_SNAKE_CASE__)






class 	A_		(	SCREAMING_SNAKE_CASE    ):
							_UpperCAmelCase   :  Optional[Any]										=				'''pix2struct_vision_model'''
							def __init__(		self		:      Any       ,SCREAMING_SNAKE_CASE__		:      Dict=7_6_8       ,SCREAMING_SNAKE_CASE__		:      List[Any]=7_6_8       ,SCREAMING_SNAKE_CASE__		:      List[Any]=2_0_4_8       ,SCREAMING_SNAKE_CASE__		:      Dict=6_4       ,SCREAMING_SNAKE_CASE__		:      Any=1_2       ,SCREAMING_SNAKE_CASE__		:      str=1_2       ,SCREAMING_SNAKE_CASE__		:      Union[str, Any]="gelu_new"       ,SCREAMING_SNAKE_CASE__		:      Optional[int]=1E-6       ,SCREAMING_SNAKE_CASE__		:      str=0.0       ,SCREAMING_SNAKE_CASE__		:      str=0.0       ,SCREAMING_SNAKE_CASE__		:      int=1E-10       ,SCREAMING_SNAKE_CASE__		:      Optional[Any]=1.0       ,SCREAMING_SNAKE_CASE__		:      str=4_0_9_6       ,SCREAMING_SNAKE_CASE__		:      List[Any]=3_2       ,SCREAMING_SNAKE_CASE__		:      int=1_2_8       ,**SCREAMING_SNAKE_CASE__		:      List[str]       ,):
								super().__init__(**SCREAMING_SNAKE_CASE__)

								__lowerCamelCase    :    Any 	=			hidden_size
								__lowerCamelCase    :    Any 	=			patch_embed_hidden_size
								__lowerCamelCase    :    Any 	=			d_ff
								__lowerCamelCase    :    Optional[Any] 	=			dropout_rate
								__lowerCamelCase    :    List[str] 	=			num_hidden_layers
								__lowerCamelCase    :    Optional[int] 	=			num_attention_heads
								__lowerCamelCase    :    Dict 	=			initializer_range
								__lowerCamelCase    :    List[str] 	=			initializer_factor
								__lowerCamelCase    :    Union[str, Any] 	=			attention_dropout
								__lowerCamelCase    :    Any 	=			layer_norm_eps
								__lowerCamelCase    :    Dict 	=			dense_act_fn
								__lowerCamelCase    :    Tuple 	=			seq_len
								__lowerCamelCase    :    Union[str, Any] 	=			relative_attention_num_buckets
								__lowerCamelCase    :    Optional[Any] 	=			relative_attention_max_distance
								__lowerCamelCase    :    Tuple 	=			d_kv


							@classmethod
							def    lowerCAmelCase					(		cls		:      Optional[int]       ,SCREAMING_SNAKE_CASE__		:      Union[str, os.PathLike]       ,**SCREAMING_SNAKE_CASE__		:      Optional[int]):
								cls._set_token_in_kwargs(SCREAMING_SNAKE_CASE__)

								__lowerCamelCase    :    List[Any] 	=			cls.get_config_dict(SCREAMING_SNAKE_CASE__       ,**SCREAMING_SNAKE_CASE__)

								# get the vision config dict if we are loading from Pix2StructConfig
								if config_dict.get('model_type') == "pix2struct":
									__lowerCamelCase    :    Tuple 	=			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(SCREAMING_SNAKE_CASE__       ,**SCREAMING_SNAKE_CASE__)






class 	A_		(	SCREAMING_SNAKE_CASE    ):
							_UpperCAmelCase   :  Optional[int]										=				'''pix2struct'''
							_UpperCAmelCase   :  List[str]										=				True
							def __init__(		self		:      List[Any]       ,SCREAMING_SNAKE_CASE__		:      List[str]=None       ,SCREAMING_SNAKE_CASE__		:      Optional[int]=None       ,SCREAMING_SNAKE_CASE__		:      Union[str, Any]=1.0       ,SCREAMING_SNAKE_CASE__		:      str=0.02       ,SCREAMING_SNAKE_CASE__		:      Tuple=False       ,SCREAMING_SNAKE_CASE__		:      Optional[Any]=False       ,SCREAMING_SNAKE_CASE__		:      Union[str, Any]=True       ,**SCREAMING_SNAKE_CASE__		:      List[Any]       ,):
								super().__init__(tie_word_embeddings=SCREAMING_SNAKE_CASE__       ,is_encoder_decoder=SCREAMING_SNAKE_CASE__       ,**SCREAMING_SNAKE_CASE__)

								if text_config is None:
									__lowerCamelCase    :    Any 	=			{}
									logger.info('text_config is None. Initializing the Pix2StructTextConfig with default values.')

								if vision_config is None:
									__lowerCamelCase    :    Union[str, Any] 	=			{}
									logger.info('vision_config is None. Initializing the Pix2StructVisionConfig with default values.')

								__lowerCamelCase    :    Dict 	=			PixaStructTextConfig(**SCREAMING_SNAKE_CASE__)
								__lowerCamelCase    :    Any 	=			PixaStructVisionConfig(**SCREAMING_SNAKE_CASE__)

								__lowerCamelCase    :    int 	=			self.text_config.decoder_start_token_id
								__lowerCamelCase    :    Optional[int] 	=			self.text_config.pad_token_id
								__lowerCamelCase    :    Any 	=			self.text_config.eos_token_id

								__lowerCamelCase    :    Optional[Any] 	=			initializer_factor
								__lowerCamelCase    :    Optional[Any] 	=			initializer_range

								__lowerCamelCase    :    Optional[Any] 	=			self.initializer_range
								__lowerCamelCase    :    Tuple 	=			self.initializer_range

								__lowerCamelCase    :    Any 	=			is_vqa
							@classmethod
							def    lowerCAmelCase					(		cls		:      Optional[int]       ,SCREAMING_SNAKE_CASE__		:      PixaStructTextConfig       ,SCREAMING_SNAKE_CASE__		:      PixaStructVisionConfig       ,**SCREAMING_SNAKE_CASE__		:      Dict):
								return cls(text_config=text_config.to_dict()       ,vision_config=vision_config.to_dict()       ,**SCREAMING_SNAKE_CASE__)


							def    lowerCAmelCase					(		self		:      Union[str, Any]):
								__lowerCamelCase    :    List[Any] 	=			copy.deepcopy(self.__dict__)
								__lowerCamelCase    :    Union[str, Any] 	=			self.text_config.to_dict()
								__lowerCamelCase    :    Dict 	=			self.vision_config.to_dict()
								__lowerCamelCase    :    Dict 	=			self.__class__.model_type
								return output







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


a		={
    """configuration_pix2struct""": [
        """PIX2STRUCT_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """Pix2StructConfig""",
        """Pix2StructTextConfig""",
        """Pix2StructVisionConfig""",
    ],
    """processing_pix2struct""": ["""Pix2StructProcessor"""],
}

try:
					if not is_vision_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					a		=["""Pix2StructImageProcessor"""]


try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					a		=[
					    """PIX2STRUCT_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """Pix2StructPreTrainedModel""",
					    """Pix2StructForConditionalGeneration""",
					    """Pix2StructVisionModel""",
					    """Pix2StructTextModel""",
					]

if TYPE_CHECKING:
					from .configuration_pixastruct import (
					    PIX2STRUCT_PRETRAINED_CONFIG_ARCHIVE_MAP,
					    PixaStructConfig,
					    PixaStructTextConfig,
					    PixaStructVisionConfig,
					)
					from .processing_pixastruct import PixaStructProcessor

					try:
										if not is_vision_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .image_processing_pixastruct import PixaStructImageProcessor

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_pixastruct import (
										    PIX2STRUCT_PRETRAINED_MODEL_ARCHIVE_LIST,
										    PixaStructForConditionalGeneration,
										    PixaStructPreTrainedModel,
										    PixaStructTextModel,
										    PixaStructVisionModel,
										)

else:
					import sys

					a		=_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




