

from functools import partial
from typing import Any, Callable, Dict, List, Type, TypeVar, Union, overload

import torch
import torch.nn as nn
import torch.types




def        lowerCAmelCase(	SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )->		torch.Tensor:



    """simple docstring"""






    if not inplace:
        UpperCamelCase_          =		ma + ma
    else:
        ma += ma

    return ma




def        lowerCAmelCase(	SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )->		torch.Tensor:



    """simple docstring"""






    UpperCamelCase_          =		-1 * len(SCREAMING_SNAKE_CASE_       )
    UpperCamelCase_          =		list(range(len(tensor.shape[:zero_index]       )       )       )
    return tensor.permute(first_inds + [zero_index + i for i in inds]       )




def        lowerCAmelCase(	SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )->		torch.Tensor:



    """simple docstring"""






    return t.reshape(t.shape[:-no_dims] + (-1,)       )




def        lowerCAmelCase(	SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_ = 1E-4       )->		torch.Tensor:



    """simple docstring"""






    UpperCamelCase_          =		mask.expand(*value.shape       )
    return torch.sum(mask * value       ,		dim=SCREAMING_SNAKE_CASE_       ) / (eps + torch.sum(SCREAMING_SNAKE_CASE_       ,		dim=SCREAMING_SNAKE_CASE_       ))




def        lowerCAmelCase(	SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_ = 2.3125       ,		SCREAMING_SNAKE_CASE_ = 21.6875       ,		SCREAMING_SNAKE_CASE_ = 6_4       )->		torch.Tensor:



    """simple docstring"""






    UpperCamelCase_          =		torch.linspace(SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		no_bins - 1       ,		device=pts.device       )
    UpperCamelCase_          =		torch.sqrt(torch.sum((pts.unsqueeze(-2       ) - pts.unsqueeze(-3       )) ** 2       ,		dim=-1       )       )
    return torch.bucketize(SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )




def        lowerCAmelCase(	SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )->		dict:



    """simple docstring"""






    UpperCamelCase_          =		dicts[0]
    UpperCamelCase_          =		{}
    for k, v in first.items():
        UpperCamelCase_          =		[d[k] for d in dicts]
        if isinstance(SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ):
            UpperCamelCase_          =		dict_multimap(SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )
        else:
            UpperCamelCase_          =		fn(SCREAMING_SNAKE_CASE_       )

    return new_dict




def        lowerCAmelCase(	SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )->		torch.Tensor:



    """simple docstring"""






    UpperCamelCase_          =		v_bins.view(((1,) * len(x.shape       )) + (len(SCREAMING_SNAKE_CASE_       ),)       )
    UpperCamelCase_          =		x[..., None] - reshaped_bins
    UpperCamelCase_          =		torch.argmin(torch.abs(SCREAMING_SNAKE_CASE_       )       ,		dim=-1       )
    return nn.functional.one_hot(SCREAMING_SNAKE_CASE_       ,		num_classes=len(SCREAMING_SNAKE_CASE_       )       ).float()




def        lowerCAmelCase(	SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_ = 0       ,		SCREAMING_SNAKE_CASE_ = 0       )->		torch.Tensor:



    """simple docstring"""






    UpperCamelCase_          =		[]
    for i, s in enumerate(data.shape[:no_batch_dims]       ):
        UpperCamelCase_          =		torch.arange(SCREAMING_SNAKE_CASE_       )
        UpperCamelCase_          =		r.view(*(*((1,) * i), -1, *((1,) * (len(inds.shape       ) - i - 1)))       )
        ranges.append(SCREAMING_SNAKE_CASE_       )

    UpperCamelCase_          =		[slice(SCREAMING_SNAKE_CASE_       ) for _ in range(len(data.shape       ) - no_batch_dims       )]
    UpperCamelCase_          =		inds
    ranges.extend(SCREAMING_SNAKE_CASE_       )
    # Matt note: Editing this to get around the behaviour of using a list as an array index changing
    # in recent Numpy versions
    return data[tuple(SCREAMING_SNAKE_CASE_       )]


SCREAMING_SNAKE_CASE						:Dict      =			TypeVar("""T""")




def        lowerCAmelCase(	SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )->		Dict[Any, Union[dict, list, tuple, Any]]:



    """simple docstring"""






    UpperCamelCase_          =		{}
    for k, v in dic.items():
        if isinstance(SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ):
            UpperCamelCase_          =		dict_map(SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )
        else:
            UpperCamelCase_          =		tree_map(SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )

    return new_dict




@overload
def        lowerCAmelCase(	SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )->		Any:



    """simple docstring"""






    ...




@overload
def        lowerCAmelCase(	SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )->		dict:



    """simple docstring"""






    ...




@overload
def        lowerCAmelCase(	SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )->		list:



    """simple docstring"""






    ...




@overload
def        lowerCAmelCase(	SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )->		tuple:



    """simple docstring"""






    ...




def        lowerCAmelCase(	SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )->		Optional[int]:



    """simple docstring"""






    if isinstance(SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ):
        return dict_map(SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )
    elif isinstance(SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ):
        return [tree_map(SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ) for x in tree]
    elif isinstance(SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ):
        return tuple(tree_map(SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ) for x in tree       )
    elif isinstance(SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ):
        return fn(SCREAMING_SNAKE_CASE_       )
    else:
        print(type(SCREAMING_SNAKE_CASE_       )       )
        raise ValueError("Not supported"       )
SCREAMING_SNAKE_CASE						:Dict      =			partial(tree_map, leaf_type=torch.Tensor)



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 		__magic_name__ :






      @staticmethod
      def      UpperCAmelCase_							(							*_lowercase						,		**_lowercase     )->			Tuple:
          pass






@is_pipeline_test
@require_torch_or_tf
@require_vision
class 		__magic_name__ (				unittest.TestCase							):
     UpperCamelCase_     :Union[str, Any]									=	MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
     UpperCamelCase_     :Any									=	TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING






     def      UpperCAmelCase_							(							self						,		_lowercase						,		_lowercase						,		_lowercase     )->			List[Any]:
         UpperCamelCase_          =		ImageClassificationPipeline(model=_lowercase						,		image_processor=_lowercase						,		top_k=2     )
         UpperCamelCase_          =		[
             Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"     ),
             "http://images.cocodataset.org/val2017/000000039769.jpg",
         ]
         return image_classifier, examples






     def      UpperCAmelCase_							(							self						,		_lowercase						,		_lowercase     )->			List[str]:
         UpperCamelCase_          =		image_classifier("./tests/fixtures/tests_samples/COCO/000000039769.png"     )

         self.assertEqual(
             _lowercase						,		[
                 {"score": ANY(_lowercase     ), "label": ANY(_lowercase     )},
                 {"score": ANY(_lowercase     ), "label": ANY(_lowercase     )},
             ]						,		)

         import datasets

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

         # Accepts URL + PIL.Image + lists
         UpperCamelCase_          =		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(
             _lowercase						,		[
                 [
                     {"score": ANY(_lowercase     ), "label": ANY(_lowercase     )},
                     {"score": ANY(_lowercase     ), "label": ANY(_lowercase     )},
                 ],
                 [
                     {"score": ANY(_lowercase     ), "label": ANY(_lowercase     )},
                     {"score": ANY(_lowercase     ), "label": ANY(_lowercase     )},
                 ],
                 [
                     {"score": ANY(_lowercase     ), "label": ANY(_lowercase     )},
                     {"score": ANY(_lowercase     ), "label": ANY(_lowercase     )},
                 ],
                 [
                     {"score": ANY(_lowercase     ), "label": ANY(_lowercase     )},
                     {"score": ANY(_lowercase     ), "label": ANY(_lowercase     )},
                 ],
                 [
                     {"score": ANY(_lowercase     ), "label": ANY(_lowercase     )},
                     {"score": ANY(_lowercase     ), "label": ANY(_lowercase     )},
                 ],
             ]						,		)






     @require_torch
     def      UpperCAmelCase_							(							self     )->			str:
         UpperCamelCase_          =		"hf-internal-testing/tiny-random-vit"
         UpperCamelCase_          =		pipeline("image-classification"						,		model=_lowercase     )

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

         UpperCamelCase_          =		image_classifier(
             [
                 "http://images.cocodataset.org/val2017/000000039769.jpg",
                 "http://images.cocodataset.org/val2017/000000039769.jpg",
             ]						,		top_k=2						,		)
         self.assertEqual(
             nested_simplify(_lowercase						,		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:
         UpperCamelCase_          =		"hf-internal-testing/tiny-random-vit"
         UpperCamelCase_          =		pipeline("image-classification"						,		model=_lowercase						,		framework="tf"     )

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

         UpperCamelCase_          =		image_classifier(
             [
                 "http://images.cocodataset.org/val2017/000000039769.jpg",
                 "http://images.cocodataset.org/val2017/000000039769.jpg",
             ]						,		top_k=2						,		)
         self.assertEqual(
             nested_simplify(_lowercase						,		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     )->			Optional[Any]:
         UpperCamelCase_          =		PreTrainedTokenizer()

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

         self.assertIs(image_classifier.tokenizer						,		_lowercase     )






     @slow
     @require_torch
     def      UpperCAmelCase_							(							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
         UpperCamelCase_          =		pipeline("image-classification"						,		model="deepmind/vision-perceiver-conv"     )
         UpperCamelCase_          =		image_classifier("http://images.cocodataset.org/val2017/000000039769.jpg"     )
         self.assertEqual(
             nested_simplify(_lowercase						,		decimals=4     )						,		[
                 {"score": 0.4_385, "label": "tabby, tabby cat"},
                 {"score": 0.321, "label": "tiger cat"},
                 {"score": 0.0_502, "label": "Egyptian cat"},
                 {"score": 0.0_137, "label": "crib, cot"},
                 {"score": 0.007, "label": "radiator"},
             ]						,		)

         UpperCamelCase_          =		pipeline("image-classification"						,		model="deepmind/vision-perceiver-fourier"     )
         UpperCamelCase_          =		image_classifier("http://images.cocodataset.org/val2017/000000039769.jpg"     )
         self.assertEqual(
             nested_simplify(_lowercase						,		decimals=4     )						,		[
                 {"score": 0.5_658, "label": "tabby, tabby cat"},
                 {"score": 0.1_309, "label": "tiger cat"},
                 {"score": 0.0_722, "label": "Egyptian cat"},
                 {"score": 0.0_707, "label": "remote control, remote"},
                 {"score": 0.0_082, "label": "computer keyboard, keypad"},
             ]						,		)

         UpperCamelCase_          =		pipeline("image-classification"						,		model="deepmind/vision-perceiver-learned"     )
         UpperCamelCase_          =		image_classifier("http://images.cocodataset.org/val2017/000000039769.jpg"     )
         self.assertEqual(
             nested_simplify(_lowercase						,		decimals=4     )						,		[
                 {"score": 0.3_022, "label": "tabby, tabby cat"},
                 {"score": 0.2_362, "label": "Egyptian cat"},
                 {"score": 0.1_856, "label": "tiger cat"},
                 {"score": 0.0_324, "label": "remote control, remote"},
                 {"score": 0.0_096, "label": "quilt, comforter, comfort, puff"},
             ]						,		)
