'''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 __UpperCAmelCase							( *__lowerCAmelCase					:						List[Any] ,		**__lowerCAmelCase					:						str)   ->       Optional[int]:
                pass





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


   def __UpperCAmelCase							( self					:						Optional[int] ,		__lowerCAmelCase					:						int ,		__lowerCAmelCase					:						Dict ,		__lowerCAmelCase					:						Tuple)   ->       Optional[int]:
         lowercase_     			=   ImageClassificationPipeline(model=__A ,		image_processor=__A ,		top_k=2)
         lowercase_     			=   [
             Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"),
             "http://images.cocodataset.org/val2017/000000039769.jpg",
         ]
         return image_classifier, examples


   def __UpperCAmelCase							( self					:						List[str] ,		__lowerCAmelCase					:						Dict ,		__lowerCAmelCase					:						int)   ->       str:
         lowercase_     			=   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

         lowercase_     			=   datasets.load_dataset("hf-internal-testing/fixtures_image_utils" ,		"image" ,		split="test")

         # Accepts URL + PIL.Image + lists
         lowercase_     			=   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 __UpperCAmelCase							( self					:						Optional[int])   ->       List[Any]:
         lowercase_     			=   "hf-internal-testing/tiny-random-vit"
         lowercase_     			=   pipeline("image-classification" ,		model=__A)

         lowercase_     			=   image_classifier("http://images.cocodataset.org/val2017/000000039769.jpg")
         self.assertEqual(
             nested_simplify(__A ,		decimals=4) ,		[{"label": "LABEL_1", "score": 0.574}, {"label": "LABEL_0", "score": 0.426}] ,		)

         lowercase_     			=   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": 0.574}, {"label": "LABEL_0", "score": 0.426}],
                 [{"label": "LABEL_1", "score": 0.574}, {"label": "LABEL_0", "score": 0.426}],
             ] ,		)


   @require_tf
   def __UpperCAmelCase							( self					:						Tuple)   ->       Optional[int]:
         lowercase_     			=   "hf-internal-testing/tiny-random-vit"
         lowercase_     			=   pipeline("image-classification" ,		model=__A ,		framework="tf")

         lowercase_     			=   image_classifier("http://images.cocodataset.org/val2017/000000039769.jpg")
         self.assertEqual(
             nested_simplify(__A ,		decimals=4) ,		[{"label": "LABEL_1", "score": 0.574}, {"label": "LABEL_0", "score": 0.426}] ,		)

         lowercase_     			=   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": 0.574}, {"label": "LABEL_0", "score": 0.426}],
                 [{"label": "LABEL_1", "score": 0.574}, {"label": "LABEL_0", "score": 0.426}],
             ] ,		)


   def __UpperCAmelCase							( self					:						List[str])   ->       Any:
         lowercase_     			=   PreTrainedTokenizer()

         # Assert that the pipeline can be initialized with a feature extractor that is not in any mapping
         lowercase_     			=   pipeline(
             "image-classification" ,		model="hf-internal-testing/tiny-random-vit" ,		tokenizer=__A)

         self.assertIs(image_classifier.tokenizer ,		__A)


   @slow
   @require_torch
   def __UpperCAmelCase							( self					:						List[str])   ->       Optional[Any]:
         lowercase_     			=   pipeline("image-classification" ,		model="deepmind/vision-perceiver-conv")
         lowercase_     			=   image_classifier("http://images.cocodataset.org/val2017/000000039769.jpg")
         self.assertEqual(
             nested_simplify(__A ,		decimals=4) ,		[
                 {"score": 0.4385, "label": "tabby, tabby cat"},
                 {"score": 0.321, "label": "tiger cat"},
                 {"score": 0.0502, "label": "Egyptian cat"},
                 {"score": 0.0137, "label": "crib, cot"},
                 {"score": 0.007, "label": "radiator"},
             ] ,		)

         lowercase_     			=   pipeline("image-classification" ,		model="deepmind/vision-perceiver-fourier")
         lowercase_     			=   image_classifier("http://images.cocodataset.org/val2017/000000039769.jpg")
         self.assertEqual(
             nested_simplify(__A ,		decimals=4) ,		[
                 {"score": 0.5658, "label": "tabby, tabby cat"},
                 {"score": 0.1309, "label": "tiger cat"},
                 {"score": 0.0722, "label": "Egyptian cat"},
                 {"score": 0.0707, "label": "remote control, remote"},
                 {"score": 0.0082, "label": "computer keyboard, keypad"},
             ] ,		)

         lowercase_     			=   pipeline("image-classification" ,		model="deepmind/vision-perceiver-learned")
         lowercase_     			=   image_classifier("http://images.cocodataset.org/val2017/000000039769.jpg")
         self.assertEqual(
             nested_simplify(__A ,		decimals=4) ,		[
                 {"score": 0.3022, "label": "tabby, tabby cat"},
                 {"score": 0.2362, "label": "Egyptian cat"},
                 {"score": 0.1856, "label": "tiger cat"},
                 {"score": 0.0324, "label": "remote control, remote"},
                 {"score": 0.0096, "label": "quilt, comforter, comfort, puff"},
             ] ,		)










'''simple docstring'''




#
# Sort large text files in a minimum amount of memory
#
import argparse
import os





class _snake_case :
							"""simple docstring"""





							__a				       =  '''block_{0}.dat'''







							def __init__( self      ,					__A						)  ->int:



											"""simple docstring"""







											__lowerCamelCase		:   Dict           =       filename
											__lowerCamelCase		:   Union[str, Any]           =       []







							def 		__SCREAMING_SNAKE_CASE( self      ,					__A      ,					__A						)  ->int:



											"""simple docstring"""







											__lowerCamelCase		:   Optional[int]           =       self.BLOCK_FILENAME_FORMAT.format(__A						)
											with open(__A      ,					"w"						) as file:
															file.write(__A						)
											self.block_filenames.append(__A						)







							def 		__SCREAMING_SNAKE_CASE( self						)  ->int:



											"""simple docstring"""







											return self.block_filenames







							def 		__SCREAMING_SNAKE_CASE( self      ,					__A      ,					__A=None						)  ->Dict:



											"""simple docstring"""







											__lowerCamelCase		:   Optional[int]           =       0
											with open(self.filename						) as file:
															while True:
																			__lowerCamelCase		:   Union[str, Any]           =       file.readlines(__A						)

																			if lines == []:
																							break

																			if sort_key is None:
																							lines.sort()
																			else:
																							lines.sort(key=__A						)

																			self.write_block("".join(__A						)      ,					__A						)
																			i += 1







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



											"""simple docstring"""







											map(os.remove      ,					self.block_filenames						)





class _snake_case :
							"""simple docstring"""





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



											"""simple docstring"""







											__lowerCamelCase		:   Union[str, Any]           =       -1
											__lowerCamelCase		:   Optional[Any]           =       None

											for i in range(len(__A						)						):
															if min_str is None or choices[i] < min_str:
																			__lowerCamelCase		:   Tuple           =       i

											return min_index





class _snake_case :
							"""simple docstring"""





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



											"""simple docstring"""







											__lowerCamelCase		:   Tuple           =       files
											__lowerCamelCase		:   Tuple           =       set()
											__lowerCamelCase		:   Optional[Any]           =       len(__A						)
											__lowerCamelCase		:   List[str]           =       {i: None for i in range(self.num_buffers						)}







							def 		__SCREAMING_SNAKE_CASE( self						)  ->Any:



											"""simple docstring"""







											return {
											    i: self.buffers[i] for i in range(self.num_buffers						) if i not in self.empty
											}







							def 		__SCREAMING_SNAKE_CASE( self						)  ->Dict:



											"""simple docstring"""







											for i in range(self.num_buffers						):
															if self.buffers[i] is None and i not in self.empty:
																			__lowerCamelCase		:   Tuple           =       self.files[i].readline()

																			if self.buffers[i] == "":
																							self.empty.add(__A						)
																							self.files[i].close()

											if len(self.empty						) == self.num_buffers:
															return False

											return True







							def 		__SCREAMING_SNAKE_CASE( self      ,					__A						)  ->Any:



											"""simple docstring"""







											__lowerCamelCase		:   Tuple           =       self.buffers[index]
											__lowerCamelCase		:   List[Any]           =       None

											return value





class _snake_case :
							"""simple docstring"""





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



											"""simple docstring"""







											__lowerCamelCase		:   List[Any]           =       merge_strategy







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



											"""simple docstring"""







											__lowerCamelCase		:   List[str]           =       FilesArray(self.get_file_handles(__A      ,					__A						)						)
											with open(__A      ,					"w"      ,					__A						) as outfile:
															while buffers.refresh():
																			__lowerCamelCase		:   Optional[Any]           =       self.merge_strategy.select(buffers.get_dict()						)
																			outfile.write(buffers.unshift(__A						)						)







							def 		__SCREAMING_SNAKE_CASE( self      ,					__A      ,					__A						)  ->str:



											"""simple docstring"""







											__lowerCamelCase		:   Union[str, Any]           =       {}

											for i in range(len(__A						)						):
															__lowerCamelCase		:   Tuple           =       open(filenames[i]      ,					"r"      ,					__A						)  # noqa: UP015

											return files





class _snake_case :
							"""simple docstring"""





							def __init__( self      ,					__A						)  ->str:



											"""simple docstring"""







											__lowerCamelCase		:   Tuple           =       block_size







							def 		__SCREAMING_SNAKE_CASE( self      ,					__A      ,					__A=None						)  ->List[str]:



											"""simple docstring"""







											__lowerCamelCase		:   Optional[int]           =       self.get_number_blocks(__A      ,					self.block_size						)
											__lowerCamelCase		:   Optional[int]           =       FileSplitter(__A						)
											splitter.split(self.block_size      ,					__A						)

											__lowerCamelCase		:   List[str]           =       FileMerger(NWayMerge()						)
											__lowerCamelCase		:   str           =       self.block_size / (num_blocks + 1)
											merger.merge(splitter.get_block_filenames()      ,					filename + ".out"      ,					__A						)

											splitter.cleanup()







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



											"""simple docstring"""







											return (os.stat(__A						).st_size / block_size) + 1





def 				a_							(    lowercase     )      ->List[Any]:






				'''simple docstring'''

				if string[-1].lower() == "k":
								return int(string[:-1]     ) * 1024
				elif string[-1].lower() == "m":
								return int(string[:-1]     ) * 1024 * 1024
				elif string[-1].lower() == "g":
								return int(string[:-1]     ) * 1024 * 1024 * 1024
				else:
								return int(lowercase     )





def 				a_							(    )      ->Optional[Any]:






				'''simple docstring'''

				__lowerCamelCase		:   List[str]           =       argparse.ArgumentParser()
				parser.add_argument(
				    "-m"				,    "--mem"				,    help="amount of memory to use for sorting"				,    default="100M"     )
				parser.add_argument(
				    "filename"				,    metavar="<filename>"				,    nargs=1				,    help="name of file to sort"     )
				__lowerCamelCase		:   List[str]           =       parser.parse_args()

				__lowerCamelCase		:   Tuple           =       ExternalSort(parse_memory(args.mem     )     )
				sorter.sort(args.filename[0]     )


if __name__ == "__main__":
				main()





