






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       _UpperCAmelCase					(       A__	):

				"""simple docstring"""




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




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

								'''simple docstring'''
								lowercase__    =			False
								super().__init__(lowerCamelCase,  lowerCamelCase  )




				def __call__(     self						:    Tuple,  lowerCamelCase						:    int=None,  lowerCamelCase						:    Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None,  lowerCamelCase						:    bool = True,  lowerCamelCase						:    Union[bool, str, PaddingStrategy] = False,  lowerCamelCase						:    Union[bool, str, TruncationStrategy] = None,  lowerCamelCase						:    Optional[int] = None,  lowerCamelCase						:    Optional[int] = 2_048,  lowerCamelCase						:    int = 0,  lowerCamelCase						:    Optional[int] = None,  lowerCamelCase						:    Optional[bool] = None,  lowerCamelCase						:    bool = False,  lowerCamelCase						:    bool = False,  lowerCamelCase						:    bool = False,  lowerCamelCase						:    bool = False,  lowerCamelCase						:    bool = False,  lowerCamelCase						:    bool = True,  lowerCamelCase						:    Optional[Union[str, TensorType]] = None,  **lowerCamelCase						:    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:
												lowercase__    =			self.tokenizer
												lowercase__    =			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
												lowercase__    =			self.image_processor(
												    lowerCamelCase,  return_tensors=lowerCamelCase,  max_patches=lowerCamelCase,  **lowerCamelCase  )
								else:
												# add pixel_values and bbox
												lowercase__    =			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:
												lowercase__    =			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:
																lowercase__    =			text_encoding.pop('''attention_mask'''  )
												if "input_ids" in text_encoding:
																lowercase__    =			text_encoding.pop('''input_ids'''  )
								else:
												lowercase__    =			None

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

								return encoding_image_processor




				def 				lowercase__   (     self						:    Any,  *lowerCamelCase						:    Any,  **lowerCamelCase						:    List[Any]  ):

								'''simple docstring'''
								return self.tokenizer.batch_decode(*lowerCamelCase,  **lowerCamelCase  )




				def 				lowercase__   (     self						:    Dict,  *lowerCamelCase						:    Optional[Any],  **lowerCamelCase						:    Optional[int]  ):

								'''simple docstring'''
								return self.tokenizer.decode(*lowerCamelCase,  **lowerCamelCase  )




				@property
				def 				lowercase__   (     self						:    str  ):

								'''simple docstring'''
								lowercase__    =			self.tokenizer.model_input_names
								lowercase__    =			self.image_processor.model_input_names
								return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names  )  )












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       _UpperCAmelCase					(       A__	):

				"""simple docstring"""




				lowercase__											=       """"""
				lowercase__											=       """hf-legacy"""  # "hf://"" is reserved for hffs




				def __init__(     self						:    List[str],  lowerCamelCase						:    Optional[DatasetInfo] = None,  lowerCamelCase						:    Optional[str] = None,  **lowerCamelCase						:    str,  ):

								'''simple docstring'''
								super().__init__(self,  **lowerCamelCase  )
								lowercase__    =			repo_info
								lowercase__    =			token
								lowercase__    =			None




				def 				lowercase__   (     self						:    Optional[Any]  ):

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




				def 				lowercase__   (     self						:    Any,  lowerCamelCase						:    str,  lowerCamelCase						:    str = "rb",  **lowerCamelCase						:    Any,  ):

								'''simple docstring'''
								if not isinstance(self.repo_info,  lowerCamelCase  ):
												raise NotImplementedError(F"""Open is only implemented for dataset repositories, but got {self.repo_info}"""  )
								lowercase__    =			hf_hub_url(self.repo_info.id,  lowerCamelCase,  revision=self.repo_info.sha  )
								return fsspec.open(
								    lowerCamelCase,  mode=lowerCamelCase,  headers=get_authentication_headers_for_url(lowerCamelCase,  use_auth_token=self.token  ),  client_kwargs={'''trust_env''': True},  ).open()




				def 				lowercase__   (     self						:    Dict,  lowerCamelCase						:    Any,  **lowerCamelCase						:    int  ):

								'''simple docstring'''
								self._get_dirs()
								lowercase__    =			self._strip_protocol(lowerCamelCase  )
								if path in self.dir_cache:
												return self.dir_cache[path]
								else:
												raise FileNotFoundError(lowerCamelCase  )




				def 				lowercase__   (     self						:    Optional[Any],  lowerCamelCase						:    List[str],  lowerCamelCase						:    Optional[int]=False,  **lowerCamelCase						:    str  ):

								'''simple docstring'''
								self._get_dirs()
								lowercase__    =			PurePosixPath(path.strip('''/'''  )  )
								lowercase__    =			{}
								for p, f in self.dir_cache.items():
												lowercase__    =			PurePosixPath(p.strip('''/'''  )  )
												lowercase__    =			p.parent
												if root == path:
																lowercase__    =			f
								lowercase__    =			list(paths.values()  )
								if detail:
												return out
								else:
												return sorted(f['''name'''] for f in out  )




