






import time
from contextlib import contextmanager
from pathlib import Path

import pytest
import requests
from huggingface_hub.hf_api import HfApi, HfFolder


UpperCamelCase        =				"__DUMMY_TRANSFORMERS_USER__"
UpperCamelCase        =				"Dummy User"
UpperCamelCase        =				"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"

UpperCamelCase        =				"https://hub-ci.huggingface.co"
UpperCamelCase        =				CI_HUB_ENDPOINT + "/datasets/{repo_id}/resolve/{revision}/{path}"
UpperCamelCase        =				CI_HUB_ENDPOINT + "/{repo_id}/resolve/{revision}/{filename}"
UpperCamelCase        =				Path("~/.huggingface/hub_ci_token").expanduser()




@pytest.fixture
def 				A		(					lowercase__  :  List[Any]  )		-> Dict:
	monkeypatch.setattr(
	    """huggingface_hub.file_download.HUGGINGFACE_CO_URL_TEMPLATE""" ,		snake_case__  )




@pytest.fixture
def 				A		(					lowercase__  :  int  )		-> List[str]:
	monkeypatch.setattr("""datasets.config.HF_ENDPOINT""" ,		snake_case__  )
	monkeypatch.setattr("""datasets.config.HUB_DATASETS_URL""" ,		snake_case__  )




@pytest.fixture
def 				A		(					lowercase__  :  List[Any]  )		-> Tuple:
	monkeypatch.setattr("""huggingface_hub.hf_api.HfFolder.path_token""" ,		snake_case__  )




@pytest.fixture
def 				A		(					lowercase__  :  Tuple ,		lowercase__  :  Any  )		-> Union[str, Any]:
	HfFolder.save_token(snake_case__  )
	yield
	HfFolder.delete_token()




@pytest.fixture(scope="""session"""  )
def 				A		(					)		-> Union[str, Any]:
	return HfApi(endpoint=snake_case__  )




@pytest.fixture(scope="""session"""  )
def 				A		(					lowercase__  :  HfApi  )		-> Optional[Any]:
	UpperCamelCase__ :str						=      HfFolder.get_token()
	HfFolder.save_token(snake_case__  )
	yield CI_HUB_USER_TOKEN
	if previous_token is not None:
		HfFolder.save_token(snake_case__  )




@pytest.fixture
def 				A		(					lowercase__  :  Optional[int]  )		-> List[Any]:
	def _cleanup_repo(lowercase__  :  Dict  ):
		hf_api.delete_repo(snake_case__ ,		token=snake_case__ ,		repo_type="""dataset"""  )

	return _cleanup_repo




@pytest.fixture
def 				A		(					lowercase__  :  Optional[Any]  )		-> Dict:
	@contextmanager
	def _temporary_repo(lowercase__  :  Dict  ):
		try:
			yield repo_id
		finally:
			cleanup_repo(snake_case__  )

	return _temporary_repo




@pytest.fixture(scope="""session"""  )
def 				A		(					lowercase__  :  HfApi ,		lowercase__  :  Union[str, Any] ,		lowercase__  :  List[Any]  )		-> Tuple:
	UpperCamelCase__ :Dict						=      f"""repo_txt_data-{int(time.time() * 10E3  )}"""
	UpperCamelCase__ :int						=      f"""{CI_HUB_USER}/{repo_name}"""
	hf_api.create_repo(snake_case__ ,		token=snake_case__ ,		repo_type="""dataset""" ,		private=snake_case__  )
	hf_api.upload_file(
	    token=snake_case__ ,		path_or_fileobj=str(snake_case__  ) ,		path_in_repo="""data/text_data.txt""" ,		repo_id=snake_case__ ,		repo_type="""dataset""" ,		)
	yield repo_id
	try:
		hf_api.delete_repo(snake_case__ ,		token=snake_case__ ,		repo_type="""dataset"""  )
	except (requests.exceptions.HTTPError, ValueError):  # catch http error and token invalid error
		pass




@pytest.fixture()
def 				A		(					lowercase__  :  str ,		lowercase__  :  Dict ,		lowercase__  :  Dict  )		-> str:
	return hf_private_dataset_repo_txt_data_




@pytest.fixture(scope="""session"""  )
def 				A		(					lowercase__  :  HfApi ,		lowercase__  :  Any ,		lowercase__  :  str  )		-> Union[str, Any]:
	UpperCamelCase__ :Union[str, Any]						=      f"""repo_zipped_txt_data-{int(time.time() * 10E3  )}"""
	UpperCamelCase__ :List[str]						=      f"""{CI_HUB_USER}/{repo_name}"""
	hf_api.create_repo(snake_case__ ,		token=snake_case__ ,		repo_type="""dataset""" ,		private=snake_case__  )
	hf_api.upload_file(
	    token=snake_case__ ,		path_or_fileobj=str(snake_case__  ) ,		path_in_repo="""data.zip""" ,		repo_id=snake_case__ ,		repo_type="""dataset""" ,		)
	yield repo_id
	try:
		hf_api.delete_repo(snake_case__ ,		token=snake_case__ ,		repo_type="""dataset"""  )
	except (requests.exceptions.HTTPError, ValueError):  # catch http error and token invalid error
		pass




@pytest.fixture()
def 				A		(					lowercase__  :  Union[str, Any] ,		lowercase__  :  str ,		lowercase__  :  Dict  )		-> str:
	return hf_private_dataset_repo_zipped_txt_data_




@pytest.fixture(scope="""session"""  )
def 				A		(					lowercase__  :  HfApi ,		lowercase__  :  Union[str, Any] ,		lowercase__  :  Optional[int]  )		-> Optional[Any]:
	UpperCamelCase__ :List[str]						=      f"""repo_zipped_img_data-{int(time.time() * 10E3  )}"""
	UpperCamelCase__ :List[Any]						=      f"""{CI_HUB_USER}/{repo_name}"""
	hf_api.create_repo(snake_case__ ,		token=snake_case__ ,		repo_type="""dataset""" ,		private=snake_case__  )
	hf_api.upload_file(
	    token=snake_case__ ,		path_or_fileobj=str(snake_case__  ) ,		path_in_repo="""data.zip""" ,		repo_id=snake_case__ ,		repo_type="""dataset""" ,		)
	yield repo_id
	try:
		hf_api.delete_repo(snake_case__ ,		token=snake_case__ ,		repo_type="""dataset"""  )
	except (requests.exceptions.HTTPError, ValueError):  # catch http error and token invalid error
		pass




@pytest.fixture()
def 				A		(					lowercase__  :  Optional[int] ,		lowercase__  :  int ,		lowercase__  :  Union[str, Any]  )		-> List[str]:
	return hf_private_dataset_repo_zipped_img_data_

"""simple docstring"""


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 					lowerCAmelCase_					(   _lowercase     ):



						'''simple docstring'''


						_lowerCamelCase:     List[str]   =     ['''image_processor''', '''tokenizer''']
						_lowerCamelCase:     Optional[int]   =     '''Pix2StructImageProcessor'''
						_lowerCamelCase:     Dict   =     ('''T5Tokenizer''', '''T5TokenizerFast''')


						def __init__(			self  :   Optional[int]	,A_  :   List[str]	,A_  :   Optional[int]   )  -> int:
							A     = False
							super().__init__(A_	,A_   )


						def __call__(			self  :   Any	,A_  :   List[str]=None	,A_  :   Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None	,A_  :   bool = True	,A_  :   Union[bool, str, PaddingStrategy] = False	,A_  :   Union[bool, str, TruncationStrategy] = None	,A_  :   Optional[int] = None	,A_  :   Optional[int] = 2048	,A_  :   int = 0	,A_  :   Optional[int] = None	,A_  :   Optional[bool] = None	,A_  :   bool = False	,A_  :   bool = False	,A_  :   bool = False	,A_  :   bool = False	,A_  :   bool = False	,A_  :   bool = True	,A_  :   Optional[Union[str, TensorType]] = None	,**A_  :   Tuple	,)  -> BatchEncoding:
							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:
								A     = self.tokenizer
								A     = self.tokenizer(
								    text=A_	,add_special_tokens=A_	,padding=A_	,truncation=A_	,max_length=A_	,stride=A_	,pad_to_multiple_of=A_	,return_attention_mask=A_	,return_overflowing_tokens=A_	,return_special_tokens_mask=A_	,return_offsets_mapping=A_	,return_token_type_ids=A_	,return_length=A_	,verbose=A_	,return_tensors=A_	,**A_	,)
								return text_encoding

							if not self.image_processor.is_vqa:
								# add pixel_values
								A     = self.image_processor(
								    A_	,return_tensors=A_	,max_patches=A_	,**A_   )
							else:
								# add pixel_values and bbox
								A     = self.image_processor(
								    A_	,return_tensors=A_	,max_patches=A_	,header_text=A_	,**A_   )

							if text is not None and not self.image_processor.is_vqa:
								A     = self.tokenizer(
								    text=A_	,add_special_tokens=A_	,padding=A_	,truncation=A_	,max_length=A_	,stride=A_	,pad_to_multiple_of=A_	,return_attention_mask=A_	,return_overflowing_tokens=A_	,return_special_tokens_mask=A_	,return_offsets_mapping=A_	,return_token_type_ids=A_	,return_length=A_	,verbose=A_	,return_tensors=A_	,**A_	,)

								if "attention_mask" in text_encoding:
									A     = text_encoding.pop('attention_mask'   )
								if "input_ids" in text_encoding:
									A     = text_encoding.pop('input_ids'   )
							else:
								A     = None

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

							return encoding_image_processor


						def     _SCREAMING_SNAKE_CASE   (			self  :   Optional[int]	,*A_  :   Optional[Any]	,**A_  :   Dict   )  -> Union[str, Any]:
							return self.tokenizer.batch_decode(*A_	,**A_   )


						def     _SCREAMING_SNAKE_CASE   (			self  :   List[str]	,*A_  :   Tuple	,**A_  :   List[str]   )  -> Any:
							return self.tokenizer.decode(*A_	,**A_   )




						@property
						def     _SCREAMING_SNAKE_CASE   (			self  :   List[str]   )  -> Any:
							A     = self.tokenizer.model_input_names
							A     = self.image_processor.model_input_names
							return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names   )   )