



'''simple docstring'''




import json
import os
from pathlib import Path

import pytest

from datasets.download.download_config import DownloadConfig
from datasets.download.download_manager import DownloadManager
from datasets.utils.file_utils import hash_url_to_filename


lowerCamelCase							:str				    =					'''http://www.mocksite.com/file1.txt'''
lowerCamelCase							:Optional[Any]				    =					'''"text": ["foo", "foo"]'''
lowerCamelCase							:List[Any]				    =					'''6d8ce9aa78a471c7477201efbeabd3bb01ac2e7d100a6dc024ba1608361f90a8'''







class 	_lowerCAmelCase							:
	__SCREAMING_SNAKE_CASE   :		Dict					=					200
	__SCREAMING_SNAKE_CASE   :		Tuple					=					{'Content-Length': '100'}
	__SCREAMING_SNAKE_CASE   :		Union[str, Any]					=					{}


	def     _a	(self       ,      **lowercase							):
				return [bytes(lowercase       ,      """utf-8"""							)]






def 	a			( *lowerCamelCase__						,      **lowerCamelCase__    ):
			'''simple docstring'''





			return MockResponse()



@pytest.mark.parametrize("""urls_type"""						,      [str, list, dict]    )
def 	a			( lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__    ):
			'''simple docstring'''





			import requests

			monkeypatch.setattr(lowerCamelCase__						,      """request"""						,      lowerCamelCase__    )

			A_	:						List[str]	=     URL
			if issubclass(lowerCamelCase__						,      lowerCamelCase__    ):
						A_	:						Optional[Any]	=     url
			elif issubclass(lowerCamelCase__						,      lowerCamelCase__    ):
						A_	:						Any	=     [url]
			elif issubclass(lowerCamelCase__						,      lowerCamelCase__    ):
						A_	:						Optional[int]	=     {"""train""": url}
			A_	:						Optional[Any]	=     """dummy"""
			A_	:						List[Any]	=     """downloads"""
			A_	:						int	=     tmp_path
			A_	:						Union[str, Any]	=     DownloadConfig(
			    cache_dir=os.path.join(lowerCamelCase__						,      lowerCamelCase__    )						,      use_etag=lowerCamelCase__						,      )
			A_	:						Optional[Any]	=     DownloadManager(dataset_name=lowerCamelCase__						,      download_config=lowerCamelCase__    )
			A_	:						Optional[int]	=     dl_manager.download(lowerCamelCase__    )
			A_	:						int	=     urls
			for downloaded_paths in [downloaded_paths]:
						if isinstance(lowerCamelCase__						,      lowerCamelCase__    ):
									A_	:						Optional[Any]	=     [downloaded_paths]
									A_	:						str	=     [urls]
						elif isinstance(lowerCamelCase__						,      lowerCamelCase__    ):
									assert "train" in downloaded_paths.keys()
									A_	:						Union[str, Any]	=     downloaded_paths.values()
									A_	:						Tuple	=     urls.values()
						assert downloaded_paths
						for downloaded_path, input_url in zip(lowerCamelCase__						,      lowerCamelCase__    ):
									assert downloaded_path == dl_manager.downloaded_paths[input_url]
									A_	:						Optional[Any]	=     Path(lowerCamelCase__    )
									A_	:						int	=     downloaded_path.parts
									assert parts[-1] == HASH
									assert parts[-2] == cache_subdir
									assert downloaded_path.exists()
									A_	:						List[str]	=     downloaded_path.read_text()
									assert content == CONTENT
									A_	:						Tuple	=     downloaded_path.with_suffix(""".json"""    )
									assert metadata_downloaded_path.exists()
									A_	:						Optional[int]	=     json.loads(metadata_downloaded_path.read_text()    )
									assert metadata_content == {"url": URL, "etag": None}



@pytest.mark.parametrize("""paths_type"""						,      [str, list, dict]    )
def 	a			( lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__    ):
			'''simple docstring'''





			A_	:						Any	=     str(lowerCamelCase__    )
			if issubclass(lowerCamelCase__						,      lowerCamelCase__    ):
						A_	:						Tuple	=     filename
			elif issubclass(lowerCamelCase__						,      lowerCamelCase__    ):
						A_	:						str	=     [filename]
			elif issubclass(lowerCamelCase__						,      lowerCamelCase__    ):
						A_	:						int	=     {"""train""": filename}
			A_	:						List[str]	=     """dummy"""
			A_	:						Optional[Any]	=     xz_file.parent
			A_	:						int	=     """extracted"""
			A_	:						Union[str, Any]	=     DownloadConfig(
			    cache_dir=lowerCamelCase__						,      use_etag=lowerCamelCase__						,      )
			A_	:						Optional[int]	=     DownloadManager(dataset_name=lowerCamelCase__						,      download_config=lowerCamelCase__    )
			A_	:						Union[str, Any]	=     dl_manager.extract(lowerCamelCase__    )
			A_	:						Union[str, Any]	=     paths
			for extracted_paths in [extracted_paths]:
						if isinstance(lowerCamelCase__						,      lowerCamelCase__    ):
									A_	:						Dict	=     [extracted_paths]
									A_	:						Any	=     [paths]
						elif isinstance(lowerCamelCase__						,      lowerCamelCase__    ):
									assert "train" in extracted_paths.keys()
									A_	:						Union[str, Any]	=     extracted_paths.values()
									A_	:						Dict	=     paths.values()
						assert extracted_paths
						for extracted_path, input_path in zip(lowerCamelCase__						,      lowerCamelCase__    ):
									assert extracted_path == dl_manager.extracted_paths[input_path]
									A_	:						List[str]	=     Path(lowerCamelCase__    )
									A_	:						Optional[Any]	=     extracted_path.parts
									assert parts[-1] == hash_url_to_filename(lowerCamelCase__						,      etag=lowerCamelCase__    )
									assert parts[-2] == extracted_subdir
									assert extracted_path.exists()
									A_	:						Optional[Any]	=     extracted_path.read_text()
									A_	:						Any	=     text_file.read_text()
									assert extracted_file_content == expected_file_content



def 	a			( lowerCamelCase__						,      lowerCamelCase__    ):
			'''simple docstring'''





			assert path.endswith(""".jsonl"""    )
			for num_items, line in enumerate(lowerCamelCase__						,      start=1    ):
						A_	:						List[str]	=     json.loads(line.decode("""utf-8"""    )    )
						assert item.keys() == {"col_1", "col_2", "col_3"}
			assert num_items == 4



@pytest.mark.parametrize("""archive_jsonl"""						,      ["""tar_jsonl_path""", """zip_jsonl_path"""]    )
def 	a			( lowerCamelCase__						,      lowerCamelCase__    ):
			'''simple docstring'''





			A_	:						Union[str, Any]	=     request.getfixturevalue(lowerCamelCase__    )
			A_	:						List[Any]	=     DownloadManager()
			for num_jsonl, (path, file) in enumerate(dl_manager.iter_archive(lowerCamelCase__    )						,      start=1    ):
						_test_jsonl(lowerCamelCase__						,      lowerCamelCase__    )
			assert num_jsonl == 2



@pytest.mark.parametrize("""archive_nested_jsonl"""						,      ["""tar_nested_jsonl_path""", """zip_nested_jsonl_path"""]    )
def 	a			( lowerCamelCase__						,      lowerCamelCase__    ):
			'''simple docstring'''





			A_	:						Dict	=     request.getfixturevalue(lowerCamelCase__    )
			A_	:						List[str]	=     DownloadManager()
			for num_tar, (path, file) in enumerate(dl_manager.iter_archive(lowerCamelCase__    )						,      start=1    ):
						for num_jsonl, (subpath, subfile) in enumerate(dl_manager.iter_archive(lowerCamelCase__    )						,      start=1    ):
									_test_jsonl(lowerCamelCase__						,      lowerCamelCase__    )
			assert num_tar == 1
			assert num_jsonl == 2



def 	a			( lowerCamelCase__    ):
			'''simple docstring'''





			A_	:						Union[str, Any]	=     DownloadManager()
			for num_file, file in enumerate(dl_manager.iter_files(lowerCamelCase__    )						,      start=1    ):
						assert os.path.basename(lowerCamelCase__    ) == ("test.txt" if num_file == 1 else "train.txt")
			assert num_file == 2




'''simple docstring'''




from typing import List, Optional, Union

from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType







class 	_lowerCAmelCase							(			__UpperCAmelCase ):
	__SCREAMING_SNAKE_CASE   :		Any					=					['image_processor', 'tokenizer']
	__SCREAMING_SNAKE_CASE   :		Union[str, Any]					=					'BlipImageProcessor'
	__SCREAMING_SNAKE_CASE   :		List[Any]					=					('BertTokenizer', 'BertTokenizerFast')


	def __init__(self       ,      lowercase       ,      lowercase							):
				A_	:						List[Any]	=     False
				super().__init__(lowercase       ,      lowercase							)
				A_	:						Tuple	=     self.image_processor


	def __call__(self       ,      lowercase = None       ,      lowercase = None       ,      lowercase = True       ,      lowercase = False       ,      lowercase = None       ,      lowercase = None       ,      lowercase = 0       ,      lowercase = None       ,      lowercase = None       ,      lowercase = False       ,      lowercase = False       ,      lowercase = False       ,      lowercase = False       ,      lowercase = False       ,      lowercase = True       ,      lowercase = None       ,      **lowercase       ,      ):
				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:
							A_	:						Optional[Any]	=     self.tokenizer
							A_	:						Tuple	=     self.tokenizer(
							    text=lowercase       ,      add_special_tokens=lowercase       ,      padding=lowercase       ,      truncation=lowercase       ,      max_length=lowercase       ,      stride=lowercase       ,      pad_to_multiple_of=lowercase       ,      return_attention_mask=lowercase       ,      return_overflowing_tokens=lowercase       ,      return_special_tokens_mask=lowercase       ,      return_offsets_mapping=lowercase       ,      return_token_type_ids=lowercase       ,      return_length=lowercase       ,      verbose=lowercase       ,      return_tensors=lowercase       ,      **lowercase       ,      )
							return text_encoding

				# add pixel_values
				A_	:						int	=     self.image_processor(lowercase       ,      return_tensors=lowercase							)

				if text is not None:
							A_	:						Optional[Any]	=     self.tokenizer(
							    text=lowercase       ,      add_special_tokens=lowercase       ,      padding=lowercase       ,      truncation=lowercase       ,      max_length=lowercase       ,      stride=lowercase       ,      pad_to_multiple_of=lowercase       ,      return_attention_mask=lowercase       ,      return_overflowing_tokens=lowercase       ,      return_special_tokens_mask=lowercase       ,      return_offsets_mapping=lowercase       ,      return_token_type_ids=lowercase       ,      return_length=lowercase       ,      verbose=lowercase       ,      return_tensors=lowercase       ,      **lowercase       ,      )
				else:
							A_	:						List[str]	=     None

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

				return encoding_image_processor


	def     _a	(self       ,      *lowercase       ,      **lowercase							):
				return self.tokenizer.batch_decode(*lowercase       ,      **lowercase							)


	def     _a	(self       ,      *lowercase       ,      **lowercase							):
				return self.tokenizer.decode(*lowercase       ,      **lowercase							)


	@property
	def     _a	(self							):
				A_	:						int	=     self.tokenizer.model_input_names
				A_	:						Any	=     self.image_processor.model_input_names
				return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names							)							)