
"""simple docstring"""




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__		:
      @staticmethod
      def      __magic_name__     (     *_a      ,       **_a							):
             pass

@is_pipeline_test
@require_vision
class 						a__		( unittest.TestCase		):
   @require_torch
   def      __magic_name__     (     self							):
          lowercase       :							Optional[int]									=				pipeline(
              model="hf-internal-testing/tiny-random-clip-zero-shot-image-classification"      ,       )
          lowercase       :							Union[str, Any]									=				Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"							)
          lowercase       :							Union[str, Any]									=				image_classifier(_a      ,       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(_a							)      ,       [
                  [{"score": 0.3_3_3, "label": "a"}, {"score": 0.3_3_3, "label": "b"}, {"score": 0.3_3_3, "label": "c"}],
                  [{"score": 0.3_3_3, "label": "a"}, {"score": 0.3_3_3, "label": "c"}, {"score": 0.3_3_3, "label": "b"}],
              ]      ,       )

          lowercase       :							str									=				image_classifier([image] * 5      ,       candidate_labels=["A", "B", "C"]      ,       batch_size=2							)
          self.assertEqual(
              nested_simplify(_a							)      ,       [
                  [
                      {"score": 0.3_3_3, "label": ANY(_a							)},
                      {"score": 0.3_3_3, "label": ANY(_a							)},
                      {"score": 0.3_3_3, "label": ANY(_a							)},
                  ],
                  [
                      {"score": 0.3_3_3, "label": ANY(_a							)},
                      {"score": 0.3_3_3, "label": ANY(_a							)},
                      {"score": 0.3_3_3, "label": ANY(_a							)},
                  ],
                  [
                      {"score": 0.3_3_3, "label": ANY(_a							)},
                      {"score": 0.3_3_3, "label": ANY(_a							)},
                      {"score": 0.3_3_3, "label": ANY(_a							)},
                  ],
                  [
                      {"score": 0.3_3_3, "label": ANY(_a							)},
                      {"score": 0.3_3_3, "label": ANY(_a							)},
                      {"score": 0.3_3_3, "label": ANY(_a							)},
                  ],
                  [
                      {"score": 0.3_3_3, "label": ANY(_a							)},
                      {"score": 0.3_3_3, "label": ANY(_a							)},
                      {"score": 0.3_3_3, "label": ANY(_a							)},
                  ],
              ]      ,       )
   @require_tf
   def      __magic_name__     (     self							):
          lowercase       :							Any									=				pipeline(
              model="hf-internal-testing/tiny-random-clip-zero-shot-image-classification"      ,       framework="tf"							)
          lowercase       :							Tuple									=				Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"							)
          lowercase       :							List[Any]									=				image_classifier(_a      ,       candidate_labels=["a", "b", "c"]							)

          self.assertEqual(
              nested_simplify(_a							)      ,       [{"score": 0.3_3_3, "label": "a"}, {"score": 0.3_3_3, "label": "b"}, {"score": 0.3_3_3, "label": "c"}]      ,       )

          lowercase       :							Tuple									=				image_classifier([image] * 5      ,       candidate_labels=["A", "B", "C"]      ,       batch_size=2							)
          self.assertEqual(
              nested_simplify(_a							)      ,       [
                  [
                      {"score": 0.3_3_3, "label": ANY(_a							)},
                      {"score": 0.3_3_3, "label": ANY(_a							)},
                      {"score": 0.3_3_3, "label": ANY(_a							)},
                  ],
                  [
                      {"score": 0.3_3_3, "label": ANY(_a							)},
                      {"score": 0.3_3_3, "label": ANY(_a							)},
                      {"score": 0.3_3_3, "label": ANY(_a							)},
                  ],
                  [
                      {"score": 0.3_3_3, "label": ANY(_a							)},
                      {"score": 0.3_3_3, "label": ANY(_a							)},
                      {"score": 0.3_3_3, "label": ANY(_a							)},
                  ],
                  [
                      {"score": 0.3_3_3, "label": ANY(_a							)},
                      {"score": 0.3_3_3, "label": ANY(_a							)},
                      {"score": 0.3_3_3, "label": ANY(_a							)},
                  ],
                  [
                      {"score": 0.3_3_3, "label": ANY(_a							)},
                      {"score": 0.3_3_3, "label": ANY(_a							)},
                      {"score": 0.3_3_3, "label": ANY(_a							)},
                  ],
              ]      ,       )
   @slow
   @require_torch
   def      __magic_name__     (     self							):
          lowercase       :							Union[str, 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
          lowercase       :							List[str]									=				Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"							)
          lowercase       :							Union[str, Any]									=				image_classifier(_a      ,       candidate_labels=["cat", "plane", "remote"]							)

          self.assertEqual(
              nested_simplify(_a							)      ,       [
                  {"score": 0.5_1_1, "label": "remote"},
                  {"score": 0.4_8_5, "label": "cat"},
                  {"score": 0.0_0_4, "label": "plane"},
              ]      ,       )

          lowercase       :							Union[str, Any]									=				image_classifier([image] * 5      ,       candidate_labels=["cat", "plane", "remote"]      ,       batch_size=2							)
          self.assertEqual(
              nested_simplify(_a							)      ,       [
                  [
                      {"score": 0.5_1_1, "label": "remote"},
                      {"score": 0.4_8_5, "label": "cat"},
                      {"score": 0.0_0_4, "label": "plane"},
                  ],
              ]
              * 5      ,       )

   @slow
   @require_tf
   def      __magic_name__     (     self							):
          lowercase       :							str									=				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
          lowercase       :							Optional[int]									=				Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"							)
          lowercase       :							List[Any]									=				image_classifier(_a      ,       candidate_labels=["cat", "plane", "remote"]							)
          self.assertEqual(
              nested_simplify(_a							)      ,       [
                  {"score": 0.5_1_1, "label": "remote"},
                  {"score": 0.4_8_5, "label": "cat"},
                  {"score": 0.0_0_4, "label": "plane"},
              ]      ,       )

          lowercase       :							Optional[int]									=				image_classifier([image] * 5      ,       candidate_labels=["cat", "plane", "remote"]      ,       batch_size=2							)
          self.assertEqual(
              nested_simplify(_a							)      ,       [
                  [
                      {"score": 0.5_1_1, "label": "remote"},
                      {"score": 0.4_8_5, "label": "cat"},
                      {"score": 0.0_0_4, "label": "plane"},
                  ],
              ]
              * 5      ,       )




"""simple docstring"""




from argparse import ArgumentParser

from ..pipelines import Pipeline, PipelineDataFormat, get_supported_tasks, pipeline
from ..utils import logging
from . import BaseTransformersCLICommand


_A     :			Union[str, Any]  						=			logging.get_logger(__name__)  # pylint: disable=invalid-name


def     __magic_name__      ( __snake_case							:			str    )					->							List[str]:
       if not path:
              return "pipe"

       for ext in PipelineDataFormat.SUPPORTED_FORMATS:
              if path.endswith(__snake_case    ):
                     return ext

       raise Exception(
           f"""Unable to determine file format from file extension {path}. """
           f"""Please provide the format through --format {PipelineDataFormat.SUPPORTED_FORMATS}"""    )


def     __magic_name__      ( __snake_case							:			str    )					->							Optional[int]:
       lowercase       :							int									=				pipeline(
           task=args.task	,					model=args.model if args.model else None	,					config=args.config	,					tokenizer=args.tokenizer	,					device=args.device	,					)
       lowercase       :							Optional[Any]									=				try_infer_format_from_ext(args.input    ) if args.format == "infer" else args.format
       lowercase       :							Optional[int]									=				PipelineDataFormat.from_str(
           format=__snake_case	,					output_path=args.output	,					input_path=args.input	,					column=args.column if args.column else nlp.default_input_names	,					overwrite=args.overwrite	,					)
       return RunCommand(__snake_case	,					__snake_case    )

class 						a__		( a_		):
   def __init__(     self      ,       _a      ,       _a							):
          lowercase       :							List[str]									=				nlp
          lowercase       :							Optional[Any]									=				reader
   @staticmethod
   def      __magic_name__     (     _a							):
          lowercase       :							Any									=				parser.add_parser("run"      ,       help="Run a pipeline through the CLI"							)
          run_parser.add_argument("--task"      ,       choices=get_supported_tasks()      ,       help="Task to run"							)
          run_parser.add_argument("--input"      ,       type=_a      ,       help="Path to the file to use for inference"							)
          run_parser.add_argument("--output"      ,       type=_a      ,       help="Path to the file that will be used post to write results."							)
          run_parser.add_argument("--model"      ,       type=_a      ,       help="Name or path to the model to instantiate."							)
          run_parser.add_argument("--config"      ,       type=_a      ,       help="Name or path to the model's config to instantiate."							)
          run_parser.add_argument(
              "--tokenizer"      ,       type=_a      ,       help="Name of the tokenizer to use. (default: same as the model name)"							)
          run_parser.add_argument(
              "--column"      ,       type=_a      ,       help="Name of the column to use as input. (For multi columns input as QA use column1,columns2)"      ,       )
          run_parser.add_argument(
              "--format"      ,       type=_a      ,       default="infer"      ,       choices=PipelineDataFormat.SUPPORTED_FORMATS      ,       help="Input format to read from"      ,       )
          run_parser.add_argument(
              "--device"      ,       type=_a      ,       default=-1      ,       help="Indicate the device to run onto, -1 indicates CPU, >= 0 indicates GPU (default: -1)"      ,       )
          run_parser.add_argument("--overwrite"      ,       action="store_true"      ,       help="Allow overwriting the output file."							)
          run_parser.set_defaults(func=_a							)

   def      __magic_name__     (     self							):
          lowercase						,       lowercase       :							int									=				self._nlp, []

          for entry in self._reader:
                 lowercase       :							str									=				nlp(**_a							) if self._reader.is_multi_columns else nlp(_a							)
                 if isinstance(_a      ,       _a							):
                        outputs.append(_a							)
                 else:
                        outputs += output

        # Saving data
          if self._nlp.binary_output:
                 lowercase       :							Dict									=				self._reader.save_binary(_a							)
                 logger.warning(f"""Current pipeline requires output to be in binary format, saving at {binary_path}"""							)
          else:
                 self._reader.save(_a							)


