
"""simple docstring"""


from pathlib import PurePosixPath
from typing import Optional

import fsspec
from fsspec import AbstractFileSystem
from huggingface_hub.hf_api import DatasetInfo

from ..utils.file_utils import get_authentication_headers_for_url
from ..utils.hub import hf_hub_url







class  lowerCAmelCase_ (					_lowercase			):



						'''simple docstring'''


						_lowerCamelCase:			Any					=			''''''
						_lowerCamelCase:			int					=			'''hf-legacy'''  # "hf://"" is reserved for hffs


						def __init__(       self   :					List[str]     ,A_   :					Optional[DatasetInfo] = None     ,A_   :					Optional[str] = None     ,**A_   :					Union[str, Any]     ,)   ->      Dict:
							super().__init__(self     ,**A_					)
							A        =      repo_info
							A        =      token
							A        =      None


						def   _SCREAMING_SNAKE_CASE					(       self   :					int					)   ->      str:
							if self.dir_cache is None:
								A        =      {}
								for hf_file in self.repo_info.siblings:
									# TODO(QL): add sizes
									A        =      {
									    'name': hf_file.rfilename,
									    'size': None,
									    'type': 'file',
									}
									self.dir_cache.update(
									    {
									        str(A_					): {'name': str(A_					), 'size': None, 'type': 'directory'}
									        for d in list(PurePosixPath(hf_file.rfilename					).parents					)[:-1]
									    }					)


						def   _SCREAMING_SNAKE_CASE					(       self   :					List[str]     ,A_   :					str     ,A_   :					str = "rb"     ,**A_   :					Dict     ,)   ->      Union[str, Any]:
							if not isinstance(self.repo_info     ,A_					):
								raise NotImplementedError(F'Open is only implemented for dataset repositories, but got {self.repo_info}'					)
							A        =      hf_hub_url(self.repo_info.id     ,A_     ,revision=self.repo_info.sha					)
							return fsspec.open(
							    A_     ,mode=A_     ,headers=get_authentication_headers_for_url(A_     ,use_auth_token=self.token					)     ,client_kwargs={'trust_env': True}     ,).open()


						def   _SCREAMING_SNAKE_CASE					(       self   :					Any     ,A_   :					str     ,**A_   :					int					)   ->      Tuple:
							self._get_dirs()
							A        =      self._strip_protocol(A_					)
							if path in self.dir_cache:
								return self.dir_cache[path]
							else:
								raise FileNotFoundError(A_					)




						def   _SCREAMING_SNAKE_CASE					(       self   :					Union[str, Any]     ,A_   :					Union[str, Any]     ,A_   :					str=False     ,**A_   :					Tuple					)   ->      str:
							self._get_dirs()
							A        =      PurePosixPath(path.strip('/'					)					)
							A        =      {}
							for p, f in self.dir_cache.items():
								A        =      PurePosixPath(p.strip('/'					)					)
								A        =      p.parent
								if root == path:
									A        =      f
							A        =      list(paths.values()					)
							if detail:
								return out
							else:
								return sorted(f['name'] for f in out					)



import copy
import os
from typing import Union

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


UpperCamelCase								=	logging.get_logger(__name__)

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






class   _lowerCamelCase (					UpperCamelCase			):




       """simple docstring"""
       snake_case      =    "pix2struct_text_model"
       snake_case      =    ["past_key_values"]
       snake_case      =    {
           "hidden_size": "hidden_size",
           "num_attention_heads": "num_heads",
           "num_hidden_layers": "num_layers",
       }



       def __init__(      self				,       _SCREAMING_SNAKE_CASE=5_0244				,       _SCREAMING_SNAKE_CASE=768				,       _SCREAMING_SNAKE_CASE=64				,       _SCREAMING_SNAKE_CASE=2048				,       _SCREAMING_SNAKE_CASE=12				,       _SCREAMING_SNAKE_CASE=12				,       _SCREAMING_SNAKE_CASE=32				,       _SCREAMING_SNAKE_CASE=128				,       _SCREAMING_SNAKE_CASE=0.1				,       _SCREAMING_SNAKE_CASE=1e-6				,       _SCREAMING_SNAKE_CASE=1.0				,       _SCREAMING_SNAKE_CASE="gelu_new"				,       _SCREAMING_SNAKE_CASE=0				,       _SCREAMING_SNAKE_CASE=False				,       _SCREAMING_SNAKE_CASE=0				,       _SCREAMING_SNAKE_CASE=1				,       _SCREAMING_SNAKE_CASE=False				,       _SCREAMING_SNAKE_CASE=True				,       **_SCREAMING_SNAKE_CASE				,       )->int:




         '''simple docstring'''





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

         A_      :				Optional[Any]		     =					eos_token_id
         A_      :				List[Any]		     =					decoder_start_token_id

         # for backwards compatibility
         A_      :				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        _snake_case       (      cls				,       _SCREAMING_SNAKE_CASE				,       **_SCREAMING_SNAKE_CASE		)->"PretrainedConfig":




         '''simple docstring'''





         cls._set_token_in_kwargs(_SCREAMING_SNAKE_CASE		)

         A_     ,       A_      :				Union[str, 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":
           A_      :				Any		     =					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   _lowerCamelCase (					UpperCamelCase			):




       """simple docstring"""
       snake_case      =    "pix2struct_vision_model"



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




         '''simple docstring'''





         super().__init__(**_SCREAMING_SNAKE_CASE		)

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







       @classmethod
       def        _snake_case       (      cls				,       _SCREAMING_SNAKE_CASE				,       **_SCREAMING_SNAKE_CASE		)->"PretrainedConfig":




         '''simple docstring'''





         cls._set_token_in_kwargs(_SCREAMING_SNAKE_CASE		)

         A_     ,       A_      :				str		     =					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":
           A_      :				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(_SCREAMING_SNAKE_CASE				,       **_SCREAMING_SNAKE_CASE		)






class   _lowerCamelCase (					UpperCamelCase			):




       """simple docstring"""
       snake_case      =    "pix2struct"
       snake_case      =    True



       def __init__(      self				,       _SCREAMING_SNAKE_CASE=None				,       _SCREAMING_SNAKE_CASE=None				,       _SCREAMING_SNAKE_CASE=1.0				,       _SCREAMING_SNAKE_CASE=0.0_2				,       _SCREAMING_SNAKE_CASE=False				,       _SCREAMING_SNAKE_CASE=False				,       _SCREAMING_SNAKE_CASE=True				,       **_SCREAMING_SNAKE_CASE				,       )->Any:




         '''simple docstring'''





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

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

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

         A_      :				int		     =					PixaStructTextConfig(**_SCREAMING_SNAKE_CASE		)
         A_      :				Dict		     =					PixaStructVisionConfig(**_SCREAMING_SNAKE_CASE		)

         A_      :				Optional[Any]		     =					self.text_config.decoder_start_token_id
         A_      :				Tuple		     =					self.text_config.pad_token_id
         A_      :				Union[str, Any]		     =					self.text_config.eos_token_id

         A_      :				str		     =					initializer_factor
         A_      :				Tuple		     =					initializer_range

         A_      :				List[str]		     =					self.initializer_range
         A_      :				int		     =					self.initializer_range

         A_      :				List[Any]		     =					is_vqa



       @classmethod
       def        _snake_case       (      cls				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       **_SCREAMING_SNAKE_CASE		)->Union[str, Any]:




         '''simple docstring'''





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







       def        _snake_case       (      self		)->Dict:




         '''simple docstring'''





         A_      :				List[Any]		     =					copy.deepcopy(self.__dict__		)
         A_      :				Dict		     =					self.text_config.to_dict()
         A_      :				int		     =					self.vision_config.to_dict()
         A_      :				List[str]		     =					self.__class__.model_type
         return output




