






import os

from apache_beam.io.filesystems import FileSystems
from apache_beam.pipeline import Pipeline

from .logging import get_logger


snake_case_   :	Any							      =2 << 20  # 2mb
snake_case_   :	Union[str, Any]							      =get_logger(__name__)





class        a__      (	lowerCAmelCase__		):






		def 					_lowerCamelCase					(      self						)				->				Tuple:
				__A    =							self._options.get_all_options().get("runner"						)
				return runner in [None, "DirectRunner", "PortableRunner"]



def      UpperCAmelCase						( lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__=False	):


		'''simple docstring'''

		__A    =							FileSystems
		if fs.exists(lowerCAmelCase__	):
				if force_upload:
						logger.info(F"""Remote path already exist: {remote_file_path}. Overwriting it as force_upload=True."""	)
				else:
						logger.info(F"""Remote path already exist: {remote_file_path}. Skipping it as force_upload=False."""	)
						return
		with fs.create(lowerCAmelCase__	) as remote_file:
				with open(lowerCAmelCase__					,     "rb"	) as local_file:
						__A    =							local_file.read(lowerCAmelCase__	)
						while chunk:
								remote_file.write(lowerCAmelCase__	)
								__A    =							local_file.read(lowerCAmelCase__	)





def      UpperCAmelCase						( lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__=False	):


		'''simple docstring'''

		__A    =							FileSystems
		if os.path.exists(lowerCAmelCase__	):
				if force_download:
						logger.info(F"""Local path already exist: {remote_file_path}. Overwriting it as force_upload=True."""	)
				else:
						logger.info(F"""Local path already exist: {remote_file_path}. Skipping it as force_upload=False."""	)
						return
		with fs.open(lowerCAmelCase__	) as remote_file:
				with open(lowerCAmelCase__					,     "wb"	) as local_file:
						__A    =							remote_file.read(lowerCAmelCase__	)
						while chunk:
								local_file.write(lowerCAmelCase__	)
								__A    =							remote_file.read(lowerCAmelCase__	)







'''simple docstring'''





import unittest

from transformers import (
    MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
    TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
    PreTrainedTokenizer,
    is_vision_available,
)
from transformers.pipelines import ImageClassificationPipeline, pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_torch,
    require_torch_or_tf,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_vision_available():
       from PIL import Image
else:






       class     lowercase  :






             @staticmethod
             def  A__(*a__			,	**a__       )							-> Union[str, Any]:
                pass






@is_pipeline_test
@require_torch_or_tf
@require_vision
class     lowercase  ( unittest.TestCase):
      __lowercase					=	MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
      __lowercase					=	TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING






      def  A__(self			,	a__			,	a__			,	a__       )							-> Dict:
         SCREAMING_SNAKE_CASE__      :					Optional[int]							    =			ImageClassificationPipeline(model=a__			,	image_processor=a__			,	top_k=2       )
         SCREAMING_SNAKE_CASE__      :					List[Any]							    =			[
             Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''       ),
             '''http://images.cocodataset.org/val2017/000000039769.jpg''',
         ]
         return image_classifier, examples






      def  A__(self			,	a__			,	a__       )							-> List[Any]:
         SCREAMING_SNAKE_CASE__      :					int							    =			image_classifier('''./tests/fixtures/tests_samples/COCO/000000039769.png'''       )

         self.assertEqual(
             a__			,	[
                 {'''score''': ANY(a__       ), '''label''': ANY(a__       )},
                 {'''score''': ANY(a__       ), '''label''': ANY(a__       )},
             ]			,	)

         import datasets

         SCREAMING_SNAKE_CASE__      :					Optional[Any]							    =			datasets.load_dataset('''hf-internal-testing/fixtures_image_utils'''			,	'''image'''			,	split='''test'''       )

         # Accepts URL + PIL.Image + lists
         SCREAMING_SNAKE_CASE__      :					Tuple							    =			image_classifier(
             [
                 Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''       ),
                 '''http://images.cocodataset.org/val2017/000000039769.jpg''',
                 # RGBA
                 dataset[0]['''file'''],
                 # LA
                 dataset[1]['''file'''],
                 # L
                 dataset[2]['''file'''],
             ]       )
         self.assertEqual(
             a__			,	[
                 [
                     {'''score''': ANY(a__       ), '''label''': ANY(a__       )},
                     {'''score''': ANY(a__       ), '''label''': ANY(a__       )},
                 ],
                 [
                     {'''score''': ANY(a__       ), '''label''': ANY(a__       )},
                     {'''score''': ANY(a__       ), '''label''': ANY(a__       )},
                 ],
                 [
                     {'''score''': ANY(a__       ), '''label''': ANY(a__       )},
                     {'''score''': ANY(a__       ), '''label''': ANY(a__       )},
                 ],
                 [
                     {'''score''': ANY(a__       ), '''label''': ANY(a__       )},
                     {'''score''': ANY(a__       ), '''label''': ANY(a__       )},
                 ],
                 [
                     {'''score''': ANY(a__       ), '''label''': ANY(a__       )},
                     {'''score''': ANY(a__       ), '''label''': ANY(a__       )},
                 ],
             ]			,	)






      @require_torch
      def  A__(self       )							-> List[str]:
         SCREAMING_SNAKE_CASE__      :					List[str]							    =			'''hf-internal-testing/tiny-random-vit'''
         SCREAMING_SNAKE_CASE__      :					Any							    =			pipeline('''image-classification'''			,	model=a__       )

         SCREAMING_SNAKE_CASE__      :					Any							    =			image_classifier('''http://images.cocodataset.org/val2017/000000039769.jpg'''       )
         self.assertEqual(
             nested_simplify(a__			,	decimals=4       )			,	[{'''label''': '''LABEL_1''', '''score''': 0.5_74}, {'''label''': '''LABEL_0''', '''score''': 0.4_26}]			,	)

         SCREAMING_SNAKE_CASE__      :					Any							    =			image_classifier(
             [
                 '''http://images.cocodataset.org/val2017/000000039769.jpg''',
                 '''http://images.cocodataset.org/val2017/000000039769.jpg''',
             ]			,	top_k=2			,	)
         self.assertEqual(
             nested_simplify(a__			,	decimals=4       )			,	[
                 [{'''label''': '''LABEL_1''', '''score''': 0.5_74}, {'''label''': '''LABEL_0''', '''score''': 0.4_26}],
                 [{'''label''': '''LABEL_1''', '''score''': 0.5_74}, {'''label''': '''LABEL_0''', '''score''': 0.4_26}],
             ]			,	)






      @require_tf
      def  A__(self       )							-> Optional[int]:
         SCREAMING_SNAKE_CASE__      :					Optional[Any]							    =			'''hf-internal-testing/tiny-random-vit'''
         SCREAMING_SNAKE_CASE__      :					Optional[Any]							    =			pipeline('''image-classification'''			,	model=a__			,	framework='''tf'''       )

         SCREAMING_SNAKE_CASE__      :					List[str]							    =			image_classifier('''http://images.cocodataset.org/val2017/000000039769.jpg'''       )
         self.assertEqual(
             nested_simplify(a__			,	decimals=4       )			,	[{'''label''': '''LABEL_1''', '''score''': 0.5_74}, {'''label''': '''LABEL_0''', '''score''': 0.4_26}]			,	)

         SCREAMING_SNAKE_CASE__      :					Dict							    =			image_classifier(
             [
                 '''http://images.cocodataset.org/val2017/000000039769.jpg''',
                 '''http://images.cocodataset.org/val2017/000000039769.jpg''',
             ]			,	top_k=2			,	)
         self.assertEqual(
             nested_simplify(a__			,	decimals=4       )			,	[
                 [{'''label''': '''LABEL_1''', '''score''': 0.5_74}, {'''label''': '''LABEL_0''', '''score''': 0.4_26}],
                 [{'''label''': '''LABEL_1''', '''score''': 0.5_74}, {'''label''': '''LABEL_0''', '''score''': 0.4_26}],
             ]			,	)






      def  A__(self       )							-> Union[str, Any]:
         SCREAMING_SNAKE_CASE__      :					Optional[Any]							    =			PreTrainedTokenizer()

         # Assert that the pipeline can be initialized with a feature extractor that is not in any mapping
         SCREAMING_SNAKE_CASE__      :					Optional[int]							    =			pipeline(
             '''image-classification'''			,	model='''hf-internal-testing/tiny-random-vit'''			,	tokenizer=a__       )

         self.assertIs(image_classifier.tokenizer			,	a__       )






      @slow
      @require_torch
      def  A__(self       )							-> int:
         # Perceiver is not tested by `run_pipeline_test` properly.
         # That is because the type of feature_extractor and model preprocessor need to be kept
         # in sync, which is not the case in the current design
         SCREAMING_SNAKE_CASE__      :					Union[str, Any]							    =			pipeline('''image-classification'''			,	model='''deepmind/vision-perceiver-conv'''       )
         SCREAMING_SNAKE_CASE__      :					str							    =			image_classifier('''http://images.cocodataset.org/val2017/000000039769.jpg'''       )
         self.assertEqual(
             nested_simplify(a__			,	decimals=4       )			,	[
                 {'''score''': 0.43_85, '''label''': '''tabby, tabby cat'''},
                 {'''score''': 0.3_21, '''label''': '''tiger cat'''},
                 {'''score''': 0.05_02, '''label''': '''Egyptian cat'''},
                 {'''score''': 0.01_37, '''label''': '''crib, cot'''},
                 {'''score''': 0.0_07, '''label''': '''radiator'''},
             ]			,	)

         SCREAMING_SNAKE_CASE__      :					str							    =			pipeline('''image-classification'''			,	model='''deepmind/vision-perceiver-fourier'''       )
         SCREAMING_SNAKE_CASE__      :					Optional[Any]							    =			image_classifier('''http://images.cocodataset.org/val2017/000000039769.jpg'''       )
         self.assertEqual(
             nested_simplify(a__			,	decimals=4       )			,	[
                 {'''score''': 0.56_58, '''label''': '''tabby, tabby cat'''},
                 {'''score''': 0.13_09, '''label''': '''tiger cat'''},
                 {'''score''': 0.07_22, '''label''': '''Egyptian cat'''},
                 {'''score''': 0.07_07, '''label''': '''remote control, remote'''},
                 {'''score''': 0.00_82, '''label''': '''computer keyboard, keypad'''},
             ]			,	)

         SCREAMING_SNAKE_CASE__      :					Dict							    =			pipeline('''image-classification'''			,	model='''deepmind/vision-perceiver-learned'''       )
         SCREAMING_SNAKE_CASE__      :					Optional[int]							    =			image_classifier('''http://images.cocodataset.org/val2017/000000039769.jpg'''       )
         self.assertEqual(
             nested_simplify(a__			,	decimals=4       )			,	[
                 {'''score''': 0.30_22, '''label''': '''tabby, tabby cat'''},
                 {'''score''': 0.23_62, '''label''': '''Egyptian cat'''},
                 {'''score''': 0.18_56, '''label''': '''tiger cat'''},
                 {'''score''': 0.03_24, '''label''': '''remote control, remote'''},
                 {'''score''': 0.00_96, '''label''': '''quilt, comforter, comfort, puff'''},
             ]			,	)

