



'''simple docstring'''
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available


if is_vision_available():
					from PIL import Image

					from transformers import AutoProcessor, BertTokenizer, BlipImageProcessor, BlipProcessor, PreTrainedTokenizerFast



@require_vision
class _A     (			unittest.TestCase			):





		def 							__A				(   self )				->   Optional[int]:

					'''simple docstring'''
					__UpperCAmelCase      :					List[str]   =   tempfile.mkdtemp()

					__UpperCAmelCase      :					Tuple   =   BlipImageProcessor()
					__UpperCAmelCase      :					Any   =   BertTokenizer.from_pretrained("""hf-internal-testing/tiny-random-BertModel""" )

					__UpperCAmelCase      :					Optional[int]   =   BlipProcessor(UpperCAmelCase__       ,				UpperCAmelCase__ )

					processor.save_pretrained(self.tmpdirname )





		def 							__A				(   self       ,				**__UpperCAmelCase )				->   Union[str, Any]:

					'''simple docstring'''
					return AutoProcessor.from_pretrained(self.tmpdirname       ,				**UpperCAmelCase__ ).tokenizer





		def 							__A				(   self       ,				**__UpperCAmelCase )				->   Any:

					'''simple docstring'''
					return AutoProcessor.from_pretrained(self.tmpdirname       ,				**UpperCAmelCase__ ).image_processor





		def 							__A				(   self )				->   Any:

					'''simple docstring'''
					shutil.rmtree(self.tmpdirname )





		def 							__A				(   self )				->   List[str]:

					'''simple docstring'''
					__UpperCAmelCase      :					Dict   =   [np.random.randint(255       ,				size=(3, 30, 400)       ,				dtype=np.uinta )]

					__UpperCAmelCase      :					Dict   =   [Image.fromarray(np.moveaxis(UpperCAmelCase__       ,				0       ,				-1 ) ) for x in image_inputs]

					return image_inputs





		def 							__A				(   self )				->   List[str]:

					'''simple docstring'''
					__UpperCAmelCase      :					int   =   BlipProcessor(tokenizer=self.get_tokenizer()       ,				image_processor=self.get_image_processor() )
					processor.save_pretrained(self.tmpdirname )

					__UpperCAmelCase      :					Union[str, Any]   =   self.get_tokenizer(bos_token="""(BOS)"""       ,				eos_token="""(EOS)""" )
					__UpperCAmelCase      :					Dict   =   self.get_image_processor(do_normalize=UpperCAmelCase__       ,				padding_value=1.0 )

					__UpperCAmelCase      :					Union[str, Any]   =   BlipProcessor.from_pretrained(
					    self.tmpdirname       ,				bos_token="""(BOS)"""       ,				eos_token="""(EOS)"""       ,				do_normalize=UpperCAmelCase__       ,				padding_value=1.0 )

					self.assertEqual(processor.tokenizer.get_vocab()       ,				tokenizer_add_kwargs.get_vocab() )
					self.assertIsInstance(processor.tokenizer       ,				UpperCAmelCase__ )

					self.assertEqual(processor.image_processor.to_json_string()       ,				image_processor_add_kwargs.to_json_string() )
					self.assertIsInstance(processor.image_processor       ,				UpperCAmelCase__ )





		def 							__A				(   self )				->   str:

					'''simple docstring'''
					__UpperCAmelCase      :					Tuple   =   self.get_image_processor()
					__UpperCAmelCase      :					List[Any]   =   self.get_tokenizer()

					__UpperCAmelCase      :					Union[str, Any]   =   BlipProcessor(tokenizer=UpperCAmelCase__       ,				image_processor=UpperCAmelCase__ )

					__UpperCAmelCase      :					Optional[int]   =   self.prepare_image_inputs()

					__UpperCAmelCase      :					Dict   =   image_processor(UpperCAmelCase__       ,				return_tensors="""np""" )
					__UpperCAmelCase      :					Any   =   processor(images=UpperCAmelCase__       ,				return_tensors="""np""" )

					for key in input_feat_extract.keys():
								self.assertAlmostEqual(input_feat_extract[key].sum()       ,				input_processor[key].sum()       ,				delta=1E-2 )





		def 							__A				(   self )				->   Any:

					'''simple docstring'''
					__UpperCAmelCase      :					int   =   self.get_image_processor()
					__UpperCAmelCase      :					Dict   =   self.get_tokenizer()

					__UpperCAmelCase      :					str   =   BlipProcessor(tokenizer=UpperCAmelCase__       ,				image_processor=UpperCAmelCase__ )

					__UpperCAmelCase      :					Any   =   """lower newer"""

					__UpperCAmelCase      :					Optional[int]   =   processor(text=UpperCAmelCase__ )

					__UpperCAmelCase      :					Any   =   tokenizer(UpperCAmelCase__       ,				return_token_type_ids=UpperCAmelCase__ )

					for key in encoded_tok.keys():
								self.assertListEqual(encoded_tok[key]       ,				encoded_processor[key] )





		def 							__A				(   self )				->   List[Any]:

					'''simple docstring'''
					__UpperCAmelCase      :					Optional[int]   =   self.get_image_processor()
					__UpperCAmelCase      :					Any   =   self.get_tokenizer()

					__UpperCAmelCase      :					Any   =   BlipProcessor(tokenizer=UpperCAmelCase__       ,				image_processor=UpperCAmelCase__ )

					__UpperCAmelCase      :					Optional[int]   =   """lower newer"""
					__UpperCAmelCase      :					int   =   self.prepare_image_inputs()

					__UpperCAmelCase      :					List[str]   =   processor(text=UpperCAmelCase__       ,				images=UpperCAmelCase__ )

					self.assertListEqual(list(inputs.keys() )       ,				["""pixel_values""", """input_ids""", """attention_mask"""] )

					# test if it raises when no input is passed
					with pytest.raises(UpperCAmelCase__ ):
								processor()





		def 							__A				(   self )				->   Dict:

					'''simple docstring'''
					__UpperCAmelCase      :					str   =   self.get_image_processor()
					__UpperCAmelCase      :					List[Any]   =   self.get_tokenizer()

					__UpperCAmelCase      :					Any   =   BlipProcessor(tokenizer=UpperCAmelCase__       ,				image_processor=UpperCAmelCase__ )

					__UpperCAmelCase      :					int   =   [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

					__UpperCAmelCase      :					List[Any]   =   processor.batch_decode(UpperCAmelCase__ )
					__UpperCAmelCase      :					str   =   tokenizer.batch_decode(UpperCAmelCase__ )

					self.assertListEqual(UpperCAmelCase__       ,				UpperCAmelCase__ )





		def 							__A				(   self )				->   List[Any]:

					'''simple docstring'''
					__UpperCAmelCase      :					List[Any]   =   self.get_image_processor()
					__UpperCAmelCase      :					Union[str, Any]   =   self.get_tokenizer()

					__UpperCAmelCase      :					List[str]   =   BlipProcessor(tokenizer=UpperCAmelCase__       ,				image_processor=UpperCAmelCase__ )

					__UpperCAmelCase      :					Tuple   =   """lower newer"""
					__UpperCAmelCase      :					Optional[int]   =   self.prepare_image_inputs()

					__UpperCAmelCase      :					str   =   processor(text=UpperCAmelCase__       ,				images=UpperCAmelCase__ )

					# For now the processor supports only ['pixel_values', 'input_ids', 'attention_mask']
					self.assertListEqual(list(inputs.keys() )       ,				["""pixel_values""", """input_ids""", """attention_mask"""] )







"""simple docstring"""





import time
from contextlib import contextmanager
from pathlib import Path

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


_a       =							'__DUMMY_TRANSFORMERS_USER__'
_a       =							'Dummy User'
_a       =							'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'

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




@pytest.fixture
def   _A  (  UpperCamelCase_     :       List[Any])	->						Tuple:
   '''simple docstring'''


   monkeypatch.setattr(
       "huggingface_hub.file_download.HUGGINGFACE_CO_URL_TEMPLATE",			UpperCamelCase_)




@pytest.fixture
def   _A  (  UpperCamelCase_     :       int)	->						List[Any]:
   '''simple docstring'''


   monkeypatch.setattr("datasets.config.HF_ENDPOINT",			UpperCamelCase_)
   monkeypatch.setattr("datasets.config.HUB_DATASETS_URL",			UpperCamelCase_)




@pytest.fixture
def   _A  (  UpperCamelCase_     :       str)	->						Dict:
   '''simple docstring'''


   monkeypatch.setattr("huggingface_hub.hf_api.HfFolder.path_token",			UpperCamelCase_)




@pytest.fixture
def   _A  (  UpperCamelCase_     :       Optional[Any],			UpperCamelCase_     :       List[Any])	->						List[str]:
   '''simple docstring'''


   HfFolder.save_token(UpperCamelCase_)
   yield
   HfFolder.delete_token()




@pytest.fixture(scope="session")
def   _A  (  )	->						List[Any]:
   '''simple docstring'''


   return HfApi(endpoint=UpperCamelCase_)




@pytest.fixture(scope="session")
def   _A  (  UpperCamelCase_     :       HfApi)	->						List[Any]:
   '''simple docstring'''


   __lowercase								=						HfFolder.get_token()
   HfFolder.save_token(UpperCamelCase_)
   yield CI_HUB_USER_TOKEN
   if previous_token is not None:
      HfFolder.save_token(UpperCamelCase_)




@pytest.fixture
def   _A  (  UpperCamelCase_     :       Dict)	->						int:
   '''simple docstring'''


   def _cleanup_repo(UpperCamelCase_     :       Optional[int]):
      hf_api.delete_repo(UpperCamelCase_,			token=UpperCamelCase_,			repo_type="dataset")

   return _cleanup_repo




@pytest.fixture
def   _A  (  UpperCamelCase_     :       str)	->						Any:
   '''simple docstring'''


   @contextmanager
   def _temporary_repo(UpperCamelCase_     :       Any):
      try:
         yield repo_id
      finally:
         cleanup_repo(UpperCamelCase_)

   return _temporary_repo




@pytest.fixture(scope="session")
def   _A  (  UpperCamelCase_     :       HfApi,			UpperCamelCase_     :       str,			UpperCamelCase_     :       Optional[int])	->						List[Any]:
   '''simple docstring'''


   __lowercase								=						F"""repo_txt_data-{int(time.time() * 10E3)}"""
   __lowercase								=						F"""{CI_HUB_USER}/{repo_name}"""
   hf_api.create_repo(UpperCamelCase_,			token=UpperCamelCase_,			repo_type="dataset",			private=UpperCamelCase_)
   hf_api.upload_file(
       token=UpperCamelCase_,			path_or_fileobj=str(UpperCamelCase_),			path_in_repo="data/text_data.txt",			repo_id=UpperCamelCase_,			repo_type="dataset",			)
   yield repo_id
   try:
      hf_api.delete_repo(UpperCamelCase_,			token=UpperCamelCase_,			repo_type="dataset")
   except (requests.exceptions.HTTPError, ValueError):  # catch http error and token invalid error
      pass




@pytest.fixture()
def   _A  (  UpperCamelCase_     :       Tuple,			UpperCamelCase_     :       Any,			UpperCamelCase_     :       Dict)	->						Optional[int]:
   '''simple docstring'''


   return hf_private_dataset_repo_txt_data_




@pytest.fixture(scope="session")
def   _A  (  UpperCamelCase_     :       HfApi,			UpperCamelCase_     :       int,			UpperCamelCase_     :       Optional[int])	->						int:
   '''simple docstring'''


   __lowercase								=						F"""repo_zipped_txt_data-{int(time.time() * 10E3)}"""
   __lowercase								=						F"""{CI_HUB_USER}/{repo_name}"""
   hf_api.create_repo(UpperCamelCase_,			token=UpperCamelCase_,			repo_type="dataset",			private=UpperCamelCase_)
   hf_api.upload_file(
       token=UpperCamelCase_,			path_or_fileobj=str(UpperCamelCase_),			path_in_repo="data.zip",			repo_id=UpperCamelCase_,			repo_type="dataset",			)
   yield repo_id
   try:
      hf_api.delete_repo(UpperCamelCase_,			token=UpperCamelCase_,			repo_type="dataset")
   except (requests.exceptions.HTTPError, ValueError):  # catch http error and token invalid error
      pass




@pytest.fixture()
def   _A  (  UpperCamelCase_     :       List[str],			UpperCamelCase_     :       Dict,			UpperCamelCase_     :       Any)	->						int:
   '''simple docstring'''


   return hf_private_dataset_repo_zipped_txt_data_




@pytest.fixture(scope="session")
def   _A  (  UpperCamelCase_     :       HfApi,			UpperCamelCase_     :       List[str],			UpperCamelCase_     :       List[str])	->						List[Any]:
   '''simple docstring'''


   __lowercase								=						F"""repo_zipped_img_data-{int(time.time() * 10E3)}"""
   __lowercase								=						F"""{CI_HUB_USER}/{repo_name}"""
   hf_api.create_repo(UpperCamelCase_,			token=UpperCamelCase_,			repo_type="dataset",			private=UpperCamelCase_)
   hf_api.upload_file(
       token=UpperCamelCase_,			path_or_fileobj=str(UpperCamelCase_),			path_in_repo="data.zip",			repo_id=UpperCamelCase_,			repo_type="dataset",			)
   yield repo_id
   try:
      hf_api.delete_repo(UpperCamelCase_,			token=UpperCamelCase_,			repo_type="dataset")
   except (requests.exceptions.HTTPError, ValueError):  # catch http error and token invalid error
      pass




@pytest.fixture()
def   _A  (  UpperCamelCase_     :       Union[str, Any],			UpperCamelCase_     :       List[str],			UpperCamelCase_     :       List[str])	->						str:
   '''simple docstring'''


   return hf_private_dataset_repo_zipped_img_data_




