


import unittest

from transformers import is_vision_available
from transformers.pipelines import pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_vision_available():
    from PIL import Image
else:



    class A__     :



      """simple docstring"""



      @staticmethod
      def    _UpperCamelCase( *lowerCamelCase__			:  Dict				,				**lowerCamelCase__			:  str       ):
            pass



@is_pipeline_test
@require_vision
class A__     ( unittest.TestCase  ):



  """simple docstring"""



  @require_torch
  def    _UpperCamelCase( self			:  Optional[int]       ):
        a__	:  int     =   pipeline(
            model="hf-internal-testing/tiny-random-clip-zero-shot-image-classification"				,				)
        a__	:  List[Any]     =   Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"       )
        a__	:  Optional[int]     =   image_classifier(lowerCamelCase__				,				candidate_labels=["a", "b", "c"]       )

        # The floating scores are so close, we enter floating error approximation and the order is not guaranteed across
        # python and torch versions.
        self.assertIn(
            nested_simplify(lowerCamelCase__       )				,				[
                [{"score": 0.333, "label": "a"}, {"score": 0.333, "label": "b"}, {"score": 0.333, "label": "c"}],
                [{"score": 0.333, "label": "a"}, {"score": 0.333, "label": "c"}, {"score": 0.333, "label": "b"}],
            ]				,				)

        a__	:  Tuple     =   image_classifier([image] * 5				,				candidate_labels=["A", "B", "C"]				,				batch_size=2       )
        self.assertEqual(
            nested_simplify(lowerCamelCase__       )				,				[
                [
                    {"score": 0.333, "label": ANY(lowerCamelCase__       )},
                    {"score": 0.333, "label": ANY(lowerCamelCase__       )},
                    {"score": 0.333, "label": ANY(lowerCamelCase__       )},
                ],
                [
                    {"score": 0.333, "label": ANY(lowerCamelCase__       )},
                    {"score": 0.333, "label": ANY(lowerCamelCase__       )},
                    {"score": 0.333, "label": ANY(lowerCamelCase__       )},
                ],
                [
                    {"score": 0.333, "label": ANY(lowerCamelCase__       )},
                    {"score": 0.333, "label": ANY(lowerCamelCase__       )},
                    {"score": 0.333, "label": ANY(lowerCamelCase__       )},
                ],
                [
                    {"score": 0.333, "label": ANY(lowerCamelCase__       )},
                    {"score": 0.333, "label": ANY(lowerCamelCase__       )},
                    {"score": 0.333, "label": ANY(lowerCamelCase__       )},
                ],
                [
                    {"score": 0.333, "label": ANY(lowerCamelCase__       )},
                    {"score": 0.333, "label": ANY(lowerCamelCase__       )},
                    {"score": 0.333, "label": ANY(lowerCamelCase__       )},
                ],
            ]				,				)

  @require_tf
  def    _UpperCamelCase( self			:  Optional[int]       ):
        a__	:  List[str]     =   pipeline(
            model="hf-internal-testing/tiny-random-clip-zero-shot-image-classification"				,				framework="tf"       )
        a__	:  int     =   Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"       )
        a__	:  str     =   image_classifier(lowerCamelCase__				,				candidate_labels=["a", "b", "c"]       )

        self.assertEqual(
            nested_simplify(lowerCamelCase__       )				,				[{"score": 0.333, "label": "a"}, {"score": 0.333, "label": "b"}, {"score": 0.333, "label": "c"}]				,				)

        a__	:  str     =   image_classifier([image] * 5				,				candidate_labels=["A", "B", "C"]				,				batch_size=2       )
        self.assertEqual(
            nested_simplify(lowerCamelCase__       )				,				[
                [
                    {"score": 0.333, "label": ANY(lowerCamelCase__       )},
                    {"score": 0.333, "label": ANY(lowerCamelCase__       )},
                    {"score": 0.333, "label": ANY(lowerCamelCase__       )},
                ],
                [
                    {"score": 0.333, "label": ANY(lowerCamelCase__       )},
                    {"score": 0.333, "label": ANY(lowerCamelCase__       )},
                    {"score": 0.333, "label": ANY(lowerCamelCase__       )},
                ],
                [
                    {"score": 0.333, "label": ANY(lowerCamelCase__       )},
                    {"score": 0.333, "label": ANY(lowerCamelCase__       )},
                    {"score": 0.333, "label": ANY(lowerCamelCase__       )},
                ],
                [
                    {"score": 0.333, "label": ANY(lowerCamelCase__       )},
                    {"score": 0.333, "label": ANY(lowerCamelCase__       )},
                    {"score": 0.333, "label": ANY(lowerCamelCase__       )},
                ],
                [
                    {"score": 0.333, "label": ANY(lowerCamelCase__       )},
                    {"score": 0.333, "label": ANY(lowerCamelCase__       )},
                    {"score": 0.333, "label": ANY(lowerCamelCase__       )},
                ],
            ]				,				)

  @slow
  @require_torch
  def    _UpperCamelCase( self			:  str       ):
        a__	:  List[Any]     =   pipeline(
            task="zero-shot-image-classification"				,				model="openai/clip-vit-base-patch32"				,				)
        # This is an image of 2 cats with remotes and no planes
        a__	:  Tuple     =   Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"       )
        a__	:  Any     =   image_classifier(lowerCamelCase__				,				candidate_labels=["cat", "plane", "remote"]       )

        self.assertEqual(
            nested_simplify(lowerCamelCase__       )				,				[
                {"score": 0.511, "label": "remote"},
                {"score": 0.485, "label": "cat"},
                {"score": 0.004, "label": "plane"},
            ]				,				)

        a__	:  int     =   image_classifier([image] * 5				,				candidate_labels=["cat", "plane", "remote"]				,				batch_size=2       )
        self.assertEqual(
            nested_simplify(lowerCamelCase__       )				,				[
                [
                    {"score": 0.511, "label": "remote"},
                    {"score": 0.485, "label": "cat"},
                    {"score": 0.004, "label": "plane"},
                ],
            ]
            * 5				,				)

  @slow
  @require_tf
  def    _UpperCamelCase( self			:  Optional[int]       ):
        a__	:  Dict     =   pipeline(
            task="zero-shot-image-classification"				,				model="openai/clip-vit-base-patch32"				,				framework="tf"       )
        # This is an image of 2 cats with remotes and no planes
        a__	:  Optional[Any]     =   Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"       )
        a__	:  int     =   image_classifier(lowerCamelCase__				,				candidate_labels=["cat", "plane", "remote"]       )
        self.assertEqual(
            nested_simplify(lowerCamelCase__       )				,				[
                {"score": 0.511, "label": "remote"},
                {"score": 0.485, "label": "cat"},
                {"score": 0.004, "label": "plane"},
            ]				,				)

        a__	:  List[Any]     =   image_classifier([image] * 5				,				candidate_labels=["cat", "plane", "remote"]				,				batch_size=2       )
        self.assertEqual(
            nested_simplify(lowerCamelCase__       )				,				[
                [
                    {"score": 0.511, "label": "remote"},
                    {"score": 0.485, "label": "cat"},
                    {"score": 0.004, "label": "plane"},
                ],
            ]
            * 5				,				)







# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import argparse
import os

from accelerate.utils import ComputeEnvironment

from .cluster import get_cluster_input
from .config_args import cache_dir, default_config_file, default_yaml_config_file, load_config_from_file  # noqa: F401
from .config_utils import _ask_field, _ask_options, _convert_compute_environment  # noqa: F401
from .sagemaker import get_sagemaker_input


_lowerCamelCase      :						Optional[Any]							=  """Launches a series of prompts to create and save a `default_config.yaml` configuration file for your training system. Should always be ran first on your machine"""







def SCREAMING_SNAKE_CASE							(			)  ->			Dict:



       """simple docstring"""

       A__ 					=       _ask_options(
           '''In which compute environment are you running?'''					,							['''This machine''', '''AWS (Amazon SageMaker)''']					,							_convert_compute_environment					,							)
       if compute_environment == ComputeEnvironment.AMAZON_SAGEMAKER:
              A__ 					=       get_sagemaker_input()
       else:
              A__ 					=       get_cluster_input()
       return config







def SCREAMING_SNAKE_CASE							(			lowercase_=None		)  ->			List[Any]:



       """simple docstring"""

       if subparsers is not None:
              A__ 					=       subparsers.add_parser('''config'''					,							description=lowercase_		)
       else:
              A__ 					=       argparse.ArgumentParser('''Accelerate config command'''					,							description=lowercase_		)

       parser.add_argument(
           '''--config_file'''					,							default=lowercase_					,							help=(
               '''The path to use to store the config file. Will default to a file named default_config.yaml in the cache '''
               '''location, which is the content of the environment `HF_HOME` suffixed with \'accelerate\', or if you don\'t have '''
               '''such an environment variable, your cache directory (\'~/.cache\' or the content of `XDG_CACHE_HOME`) suffixed '''
               '''with \'huggingface\'.'''
           )					,							)

       if subparsers is not None:
              parser.set_defaults(func=lowercase_		)
       return parser







def SCREAMING_SNAKE_CASE							(			lowercase_		)  ->			Any:



       """simple docstring"""

       A__ 					=       get_user_input()
       if args.config_file is not None:
              A__ 					=       args.config_file
       else:
              if not os.path.isdir(lowercase_		):
                     os.makedirs(lowercase_		)
              A__ 					=       default_yaml_config_file

       if config_file.endswith('''.json'''		):
              config.to_json_file(lowercase_		)
       else:
              config.to_yaml_file(lowercase_		)
       print(f"""accelerate configuration saved at {config_file}"""		)







def SCREAMING_SNAKE_CASE							(			)  ->			Union[str, Any]:



       """simple docstring"""

       A__ 					=       config_command_parser()
       A__ 					=       parser.parse_args()
       config_command(lowercase_		)


if __name__ == "__main__":
  main()


