


from collections import UserDict
from typing import List, Union

from ..utils import (
    add_end_docstrings,
    is_tf_available,
    is_torch_available,
    is_vision_available,
    logging,
    requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
 from PIL import Image

 from ..image_utils import load_image

if is_torch_available():
 from ..models.auto.modeling_auto import MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING

if is_tf_available():
 from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING
 from ..tf_utils import stable_softmax

SCREAMING_SNAKE_CASE :   Any							      =				logging.get_logger(__name__)






@add_end_docstrings(_a     )
class _lowerCamelCase(		_a     ):
       def __init__(      self,   **lowerCamelCase) ->     Tuple:




           """simple docstring"""

           super().__init__(**lowerCamelCase)

           requires_backends(self,   'vision')
           self.check_model_type(
               TF_MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING
               if self.framework == 'tf'
               else MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING)
       def __call__(      self,   lowerCamelCase,   **lowerCamelCase) ->     Optional[int]:




           """simple docstring"""

           return super().__call__(lowerCamelCase,   **lowerCamelCase)
       def     UpperCamelCase  (      self,   **lowerCamelCase) ->     str:




           """simple docstring"""

           _lowercase :							int   	=	{}
           if "candidate_labels" in kwargs:
               _lowercase :							int   	=	kwargs['candidate_labels']
           if "hypothesis_template" in kwargs:
               _lowercase :							Tuple   	=	kwargs['hypothesis_template']

           return preprocess_params, {}, {}
       def     UpperCamelCase  (      self,   lowerCamelCase,   lowerCamelCase=None,   lowerCamelCase="This is a photo of {}.") ->     Optional[Any]:




           """simple docstring"""

           _lowercase :							Optional[Any]   	=	load_image(lowerCamelCase)
           _lowercase :							Optional[Any]   	=	self.image_processor(images=[image],   return_tensors=self.framework)
           _lowercase :							List[Any]   	=	candidate_labels
           _lowercase :							str   	=	[hypothesis_template.format(lowerCamelCase) for x in candidate_labels]
           _lowercase :							Optional[int]   	=	self.tokenizer(lowerCamelCase,   return_tensors=self.framework,   padding=lowerCamelCase)
           _lowercase :							List[Any]   	=	[text_inputs]
           return inputs
       def     UpperCamelCase  (      self,   lowerCamelCase) ->     Union[str, Any]:




           """simple docstring"""

           _lowercase :							Optional[int]   	=	model_inputs.pop('candidate_labels')
           _lowercase :							int   	=	model_inputs.pop('text_inputs')
           if isinstance(text_inputs[0],   lowerCamelCase):
               _lowercase :							int   	=	text_inputs[0]
           else:
               # Batching case.
               _lowercase :							str   	=	text_inputs[0][0]

           _lowercase :							Union[str, Any]   	=	self.model(**lowerCamelCase,   **lowerCamelCase)

           _lowercase :							Union[str, Any]   	=	{
               'candidate_labels': candidate_labels,
               'logits': outputs.logits_per_image,
           }
           return model_outputs




       def     UpperCamelCase  (      self,   lowerCamelCase) ->     Dict:




           """simple docstring"""

           _lowercase :							List[Any]   	=	model_outputs.pop('candidate_labels')
           _lowercase :							Union[str, Any]   	=	model_outputs['logits'][0]
           if self.framework == "pt":
               _lowercase :							Union[str, Any]   	=	logits.softmax(dim=-1).squeeze(-1)
               _lowercase :							List[str]   	=	probs.tolist()
               if not isinstance(lowerCamelCase,   lowerCamelCase):
                   _lowercase :							str   	=	[scores]
           elif self.framework == "tf":
               _lowercase :							Optional[Any]   	=	stable_softmax(lowerCamelCase,   axis=-1)
               _lowercase :							int   	=	probs.numpy().tolist()
           else:
               raise ValueError(F'''Unsupported framework: {self.framework}''')

           _lowercase :							Optional[int]   	=	[
               {'score': score, 'label': candidate_label}
               for score, candidate_label in sorted(zip(lowerCamelCase,   lowerCamelCase),   key=lambda lowerCamelCase: -x[0])
           ]
           return result






import pytest


SCREAMING_SNAKE_CASE :   Optional[Any]							      =				"__dummy_dataset1__"

SCREAMING_SNAKE_CASE :   int							      =				"\nimport json\nimport os\n\nimport datasets\n\n\nREPO_URL = \"https://huggingface.co/datasets/albertvillanova/tests-raw-jsonl/resolve/main/\"\nURLS = {\"train\": REPO_URL + \"wikiann-bn-train.jsonl\", \"validation\": REPO_URL + \"wikiann-bn-validation.jsonl\"}\n\n\nclass __DummyDataset1__(datasets.GeneratorBasedBuilder):\n\n    def _info(self):\n        features = datasets.Features(\n            {\n                \"tokens\": datasets.Sequence(datasets.Value(\"string\")),\n                \"ner_tags\": datasets.Sequence(\n                    datasets.features.ClassLabel(\n                        names=[\n                            \"O\",\n                            \"B-PER\",\n                            \"I-PER\",\n                            \"B-ORG\",\n                            \"I-ORG\",\n                            \"B-LOC\",\n                            \"I-LOC\",\n                        ]\n                    )\n                ),\n                \"langs\": datasets.Sequence(datasets.Value(\"string\")),\n                \"spans\": datasets.Sequence(datasets.Value(\"string\")),\n            }\n        )\n        return datasets.DatasetInfo(features=features)\n\n    def _split_generators(self, dl_manager):\n        dl_path = dl_manager.download(URLS)\n        return [\n            datasets.SplitGenerator(datasets.Split.TRAIN, gen_kwargs={\"filepath\": dl_path[\"train\"]}),\n            datasets.SplitGenerator(datasets.Split.VALIDATION, gen_kwargs={\"filepath\": dl_path[\"validation\"]}),\n        ]\n\n    def _generate_examples(self, filepath):\n        with open(filepath, \"r\", encoding=\"utf-8\") as f:\n            for i, line in enumerate(f):\n                yield i, json.loads(line)\n"



@pytest.fixture
def        UpperCamelCase_(   )					->		Dict:
    return DATASET_LOADING_SCRIPT_NAME



@pytest.fixture
def        UpperCamelCase_(   )					->		List[Any]:
    return DATASET_LOADING_SCRIPT_CODE



@pytest.fixture
def        UpperCamelCase_(   lowerCamelCase_							,				lowerCamelCase_							,				lowerCamelCase_				)					->		int:
    _lowercase :							List[str]   	=	dataset_loading_script_name
    _lowercase :							Union[str, Any]   	=	tmp_path / 'datasets' / script_name
    script_dir.mkdir(parents=lowerCamelCase_				)
    _lowercase :							Optional[int]   	=	script_dir / F'''{script_name}.py'''
    with open(lowerCamelCase_							,				'w'				) as f:
        f.write(lowerCamelCase_				)
    return str(lowerCamelCase_				)


