



import copy
import os
from typing import Union

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


snake_case_			=     logging.get_logger(__name__)

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


class 	a__     (				_lowercase	):
	__magic_name__		:    int	    =     "pix2struct_text_model"
	__magic_name__		:    List[str]	    =     ["past_key_values"]
	__magic_name__		:    List[str]	    =     {
	    "hidden_size": "hidden_size",
	    "num_attention_heads": "num_heads",
	    "num_hidden_layers": "num_layers",
	}


	def __init__(self : List[Any],					__UpperCAmelCase : List[str]=50244,					__UpperCAmelCase : Optional[Any]=768,					__UpperCAmelCase : str=64,					__UpperCAmelCase : Optional[Any]=2048,					__UpperCAmelCase : str=12,					__UpperCAmelCase : List[Any]=12,					__UpperCAmelCase : Dict=32,					__UpperCAmelCase : List[str]=128,					__UpperCAmelCase : Dict=0.1,					__UpperCAmelCase : List[Any]=1e-6,					__UpperCAmelCase : int=1.0,					__UpperCAmelCase : Optional[Any]="gelu_new",					__UpperCAmelCase : Any=0,					__UpperCAmelCase : Dict=False,					__UpperCAmelCase : Optional[int]=0,					__UpperCAmelCase : Optional[Any]=1,					__UpperCAmelCase : Any=False,					__UpperCAmelCase : Any=True,					**__UpperCAmelCase : Optional[int],					)     ->			Union[str, Any]:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						List[Any]      					= vocab_size
					SCREAMING_SNAKE_CASE  :						str      					= hidden_size
					SCREAMING_SNAKE_CASE  :						Optional[int]      					= d_kv
					SCREAMING_SNAKE_CASE  :						List[Any]      					= d_ff
					SCREAMING_SNAKE_CASE  :						str      					= num_layers
					SCREAMING_SNAKE_CASE  :						List[Any]      					= num_heads
					SCREAMING_SNAKE_CASE  :						int      					= relative_attention_num_buckets
					SCREAMING_SNAKE_CASE  :						Any      					= relative_attention_max_distance
					SCREAMING_SNAKE_CASE  :						Any      					= dropout_rate
					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= layer_norm_epsilon
					SCREAMING_SNAKE_CASE  :						str      					= initializer_factor
					SCREAMING_SNAKE_CASE  :						Any      					= use_cache

					SCREAMING_SNAKE_CASE  :						Tuple      					= eos_token_id
					SCREAMING_SNAKE_CASE  :						List[str]      					= decoder_start_token_id

					# for backwards compatibility
					SCREAMING_SNAKE_CASE  :						List[str]      					= dense_act_fn

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






	@classmethod
	def 				lowercase__  (cls : str,					__UpperCAmelCase : Union[str, os.PathLike],					**__UpperCAmelCase : str      )     ->			"PretrainedConfig":

					"""simple docstring"""

					cls._set_token_in_kwargs(__UpperCAmelCase      )

					SCREAMING_SNAKE_CASE  :						Any      					= cls.get_config_dict(__UpperCAmelCase,					**__UpperCAmelCase      )

					# get the text config dict if we are loading from Pix2StructConfig
					if config_dict.get('''model_type'''      ) == "pix2struct":
									SCREAMING_SNAKE_CASE  :						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(__UpperCAmelCase,					**__UpperCAmelCase      )



class 	a__     (				_lowercase	):
	__magic_name__		:    Optional[Any]	    =     "pix2struct_vision_model"


	def __init__(self : Dict,					__UpperCAmelCase : Tuple=768,					__UpperCAmelCase : str=768,					__UpperCAmelCase : Optional[Any]=2048,					__UpperCAmelCase : int=64,					__UpperCAmelCase : str=12,					__UpperCAmelCase : Dict=12,					__UpperCAmelCase : Dict="gelu_new",					__UpperCAmelCase : Any=1e-6,					__UpperCAmelCase : str=0.0,					__UpperCAmelCase : int=0.0,					__UpperCAmelCase : List[str]=1e-10,					__UpperCAmelCase : Optional[Any]=1.0,					__UpperCAmelCase : Optional[int]=4096,					__UpperCAmelCase : List[str]=32,					__UpperCAmelCase : str=128,					**__UpperCAmelCase : str,					)     ->			int:

					"""simple docstring"""

					super().__init__(**__UpperCAmelCase      )

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






	@classmethod
	def 				lowercase__  (cls : List[str],					__UpperCAmelCase : Union[str, os.PathLike],					**__UpperCAmelCase : Tuple      )     ->			"PretrainedConfig":

					"""simple docstring"""

					cls._set_token_in_kwargs(__UpperCAmelCase      )

					SCREAMING_SNAKE_CASE  :						str      					= cls.get_config_dict(__UpperCAmelCase,					**__UpperCAmelCase      )

					# get the vision config dict if we are loading from Pix2StructConfig
					if config_dict.get('''model_type'''      ) == "pix2struct":
									SCREAMING_SNAKE_CASE  :						Optional[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(__UpperCAmelCase,					**__UpperCAmelCase      )



class 	a__     (				_lowercase	):
	__magic_name__		:    Tuple	    =     "pix2struct"
	__magic_name__		:    List[Any]	    =     True


	def __init__(self : Dict,					__UpperCAmelCase : Optional[int]=None,					__UpperCAmelCase : Any=None,					__UpperCAmelCase : int=1.0,					__UpperCAmelCase : Optional[int]=0.02,					__UpperCAmelCase : Union[str, Any]=False,					__UpperCAmelCase : Union[str, Any]=False,					__UpperCAmelCase : Any=True,					**__UpperCAmelCase : Union[str, Any],					)     ->			Optional[Any]:

					"""simple docstring"""

					super().__init__(tie_word_embeddings=__UpperCAmelCase,					is_encoder_decoder=__UpperCAmelCase,					**__UpperCAmelCase      )

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

					if vision_config is None:
									SCREAMING_SNAKE_CASE  :						List[str]      					= {}
									logger.info('''vision_config is None. Initializing the Pix2StructVisionConfig with default values.'''      )

					SCREAMING_SNAKE_CASE  :						str      					= PixaStructTextConfig(**__UpperCAmelCase      )
					SCREAMING_SNAKE_CASE  :						List[Any]      					= PixaStructVisionConfig(**__UpperCAmelCase      )

					SCREAMING_SNAKE_CASE  :						Dict      					= self.text_config.decoder_start_token_id
					SCREAMING_SNAKE_CASE  :						Tuple      					= self.text_config.pad_token_id
					SCREAMING_SNAKE_CASE  :						List[str]      					= self.text_config.eos_token_id

					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= initializer_factor
					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= initializer_range

					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= self.initializer_range
					SCREAMING_SNAKE_CASE  :						List[str]      					= self.initializer_range

					SCREAMING_SNAKE_CASE  :						Optional[int]      					= is_vqa


	@classmethod
	def 				lowercase__  (cls : str,					__UpperCAmelCase : PixaStructTextConfig,					__UpperCAmelCase : PixaStructVisionConfig,					**__UpperCAmelCase : Optional[int]      )     ->			Optional[Any]:

					"""simple docstring"""

					return cls(text_config=text_config.to_dict(),					vision_config=vision_config.to_dict(),					**__UpperCAmelCase      )






	def 				lowercase__  (self : Any      )     ->			Dict:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Optional[int]      					= copy.deepcopy(self.__dict__      )
					SCREAMING_SNAKE_CASE  :						Any      					= self.text_config.to_dict()
					SCREAMING_SNAKE_CASE  :						Tuple      					= self.vision_config.to_dict()
					SCREAMING_SNAKE_CASE  :						Dict      					= self.__class__.model_type
					return output









'''simple docstring'''

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


snake_case_			=     {
    """configuration_timesformer""": ["""TIMESFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""", """TimesformerConfig"""],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	snake_case_			=     [
	    """TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """TimesformerModel""",
	    """TimesformerForVideoClassification""",
	    """TimesformerPreTrainedModel""",
	]

if TYPE_CHECKING:
	from .configuration_timesformer import TIMESFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, TimesformerConfig

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_timesformer import (
		    TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TimesformerForVideoClassification,
		    TimesformerModel,
		    TimesformerPreTrainedModel,
		)

else:
	import sys

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




