






import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
		import torch

if is_vision_available():
		from PIL import Image

		from transformers import MobileViTImageProcessor



class   lowerCamelCase      (unittest.TestCase			):







		'''simple docstring'''



		def __init__(							self				,		_UpperCamelCase				,		_UpperCamelCase=7				,		_UpperCamelCase=3				,		_UpperCamelCase=1_8				,		_UpperCamelCase=3_0				,		_UpperCamelCase=4_0_0				,		_UpperCamelCase=True				,		_UpperCamelCase=None				,		_UpperCamelCase=True				,		_UpperCamelCase=None				,		_UpperCamelCase=True				,		)	->							Optional[int]:
							UpperCAmelCase_    :      Optional[int]            =						size if size is not None else {'shortest_edge': 2_0}
							UpperCAmelCase_    :      Tuple            =						crop_size if crop_size is not None else {'height': 1_8, 'width': 1_8}
							UpperCAmelCase_    :      Dict            =						parent
							UpperCAmelCase_    :      Optional[int]            =						batch_size
							UpperCAmelCase_    :      List[str]            =						num_channels
							UpperCAmelCase_    :      List[str]            =						image_size
							UpperCAmelCase_    :      str            =						min_resolution
							UpperCAmelCase_    :      Optional[int]            =						max_resolution
							UpperCAmelCase_    :      Union[str, Any]            =						do_resize
							UpperCAmelCase_    :      List[Any]            =						size
							UpperCAmelCase_    :      List[str]            =						do_center_crop
							UpperCAmelCase_    :      int            =						crop_size
							UpperCAmelCase_    :      Union[str, Any]            =						do_flip_channel_order



		def 					__UpperCAmelCase			(							self   )	->							Optional[Any]:
							return {
							    "do_resize": self.do_resize,
							    "size": self.size,
							    "do_center_crop": self.do_center_crop,
							    "crop_size": self.crop_size,
							    "do_flip_channel_order": self.do_flip_channel_order,
							}





@require_torch
@require_vision
class   lowerCamelCase      (_snake_case ,  unittest.TestCase			):







		'''simple docstring'''



		_snake_case    :    Tuple       						=  MobileViTImageProcessor if is_vision_available() else None



		def 					__UpperCAmelCase			(							self   )	->							int:
							UpperCAmelCase_    :      int            =						MobileViTImageProcessingTester(self   )



		@property
		def 					__UpperCAmelCase			(							self   )	->							Union[str, Any]:
							return self.image_processor_tester.prepare_image_processor_dict()



		def 					__UpperCAmelCase			(							self   )	->							str:
							UpperCAmelCase_    :      List[str]            =						self.image_processing_class(**self.image_processor_dict   )
							self.assertTrue(hasattr(_UpperCamelCase				,		'do_resize'   )   )
							self.assertTrue(hasattr(_UpperCamelCase				,		'size'   )   )
							self.assertTrue(hasattr(_UpperCamelCase				,		'do_center_crop'   )   )
							self.assertTrue(hasattr(_UpperCamelCase				,		'center_crop'   )   )
							self.assertTrue(hasattr(_UpperCamelCase				,		'do_flip_channel_order'   )   )



		def 					__UpperCAmelCase			(							self   )	->							List[str]:
							UpperCAmelCase_    :      Optional[Any]            =						self.image_processing_class.from_dict(self.image_processor_dict   )
							self.assertEqual(image_processor.size				,		{'shortest_edge': 2_0}   )
							self.assertEqual(image_processor.crop_size				,		{'height': 1_8, 'width': 1_8}   )

							UpperCAmelCase_    :      str            =						self.image_processing_class.from_dict(self.image_processor_dict				,		size=4_2				,		crop_size=8_4   )
							self.assertEqual(image_processor.size				,		{'shortest_edge': 4_2}   )
							self.assertEqual(image_processor.crop_size				,		{'height': 8_4, 'width': 8_4}   )



		def 					__UpperCAmelCase			(							self   )	->							List[str]:
							pass



		def 					__UpperCAmelCase			(							self   )	->							Tuple:
							# Initialize image_processing
							UpperCAmelCase_    :      Any            =						self.image_processing_class(**self.image_processor_dict   )
							# create random PIL images
							UpperCAmelCase_    :      Tuple            =						prepare_image_inputs(self.image_processor_tester				,		equal_resolution=_UpperCamelCase   )
							for image in image_inputs:
												self.assertIsInstance(_UpperCamelCase				,		Image.Image   )

							# Test not batched input
							UpperCAmelCase_    :      Optional[int]            =						image_processing(image_inputs[0]				,		return_tensors='pt'   ).pixel_values
							self.assertEqual(
							    encoded_images.shape				,		(
							        1,
							        self.image_processor_tester.num_channels,
							        self.image_processor_tester.crop_size['height'],
							        self.image_processor_tester.crop_size['width'],
							    )				,		)

							# Test batched
							UpperCAmelCase_    :      str            =						image_processing(_UpperCamelCase				,		return_tensors='pt'   ).pixel_values
							self.assertEqual(
							    encoded_images.shape				,		(
							        self.image_processor_tester.batch_size,
							        self.image_processor_tester.num_channels,
							        self.image_processor_tester.crop_size['height'],
							        self.image_processor_tester.crop_size['width'],
							    )				,		)



		def 					__UpperCAmelCase			(							self   )	->							str:
							# Initialize image_processing
							UpperCAmelCase_    :      Dict            =						self.image_processing_class(**self.image_processor_dict   )
							# create random numpy tensors
							UpperCAmelCase_    :      Dict            =						prepare_image_inputs(self.image_processor_tester				,		equal_resolution=_UpperCamelCase				,		numpify=_UpperCamelCase   )
							for image in image_inputs:
												self.assertIsInstance(_UpperCamelCase				,		np.ndarray   )

							# Test not batched input
							UpperCAmelCase_    :      Dict            =						image_processing(image_inputs[0]				,		return_tensors='pt'   ).pixel_values
							self.assertEqual(
							    encoded_images.shape				,		(
							        1,
							        self.image_processor_tester.num_channels,
							        self.image_processor_tester.crop_size['height'],
							        self.image_processor_tester.crop_size['width'],
							    )				,		)

							# Test batched
							UpperCAmelCase_    :      Optional[int]            =						image_processing(_UpperCamelCase				,		return_tensors='pt'   ).pixel_values
							self.assertEqual(
							    encoded_images.shape				,		(
							        self.image_processor_tester.batch_size,
							        self.image_processor_tester.num_channels,
							        self.image_processor_tester.crop_size['height'],
							        self.image_processor_tester.crop_size['width'],
							    )				,		)



		def 					__UpperCAmelCase			(							self   )	->							Tuple:
							# Initialize image_processing
							UpperCAmelCase_    :      Dict            =						self.image_processing_class(**self.image_processor_dict   )
							# create random PyTorch tensors
							UpperCAmelCase_    :      List[Any]            =						prepare_image_inputs(self.image_processor_tester				,		equal_resolution=_UpperCamelCase				,		torchify=_UpperCamelCase   )
							for image in image_inputs:
												self.assertIsInstance(_UpperCamelCase				,		torch.Tensor   )

							# Test not batched input
							UpperCAmelCase_    :      Union[str, Any]            =						image_processing(image_inputs[0]				,		return_tensors='pt'   ).pixel_values
							self.assertEqual(
							    encoded_images.shape				,		(
							        1,
							        self.image_processor_tester.num_channels,
							        self.image_processor_tester.crop_size['height'],
							        self.image_processor_tester.crop_size['width'],
							    )				,		)

							# Test batched
							UpperCAmelCase_    :      Union[str, Any]            =						image_processing(_UpperCamelCase				,		return_tensors='pt'   ).pixel_values
							self.assertEqual(
							    encoded_images.shape				,		(
							        self.image_processor_tester.batch_size,
							        self.image_processor_tester.num_channels,
							        self.image_processor_tester.crop_size['height'],
							        self.image_processor_tester.crop_size['width'],
							    )				,		)











from collections import UserDict
from typing import Union

import numpy as np
import requests

from ..utils import (
    add_end_docstrings,
    logging,
)
from .audio_classification import ffmpeg_read
from .base import PIPELINE_INIT_ARGS, Pipeline


a_   :List[Any]        =      logging.get_logger(__name__)




@add_end_docstrings(lowerCAmelCase_     )
class 					snake_case__						(       lowerCAmelCase_     ):






     """simple docstring"""


     def __init__(       self  :  Optional[Any], **_snake_case  :  str					)   ->Dict:
            super().__init__(**_snake_case					)

            if self.framework != "pt":
                   raise ValueError(F'''The {self.__class__} is only available in PyTorch.'''					)
            # No specific FOR_XXX available yet






     def __call__(       self  :  Union[str, Any], _snake_case  :  Union[np.ndarray, bytes, str], **_snake_case  :  Tuple					)   ->Dict:
            return super().__call__(_snake_case, **_snake_case					)






     def    lowercase_   (       self  :  Tuple, **_snake_case  :  Any					)   ->Union[str, Any]:
            snake_case__							:	str   	=	{}
            if "candidate_labels" in kwargs:
                   snake_case__							:	str   	=	kwargs['candidate_labels']
            if "hypothesis_template" in kwargs:
                   snake_case__							:	str   	=	kwargs['hypothesis_template']

            return preprocess_params, {}, {}






     def    lowercase_   (       self  :  Dict, _snake_case  :  str, _snake_case  :  Optional[int]=None, _snake_case  :  List[str]="This is a sound of {}."					)   ->int:
            if isinstance(_snake_case, _snake_case					):
                   if audio.startswith('http://'					) or audio.startswith('https://'					):
                          # We need to actually check for a real protocol, otherwise it's impossible to use a local file
                          # like http_huggingface_co.png
                          snake_case__							:	List[Any]   	=	requests.get(_snake_case					).content
                   else:
                          with open(_snake_case, 'rb'					) as f:
                                 snake_case__							:	Union[str, Any]   	=	f.read()

            if isinstance(_snake_case, _snake_case					):
                   snake_case__							:	List[Any]   	=	ffmpeg_read(_snake_case, self.feature_extractor.sampling_rate					)

            if not isinstance(_snake_case, np.ndarray					):
                   raise ValueError('We expect a numpy ndarray as input'					)
            if len(audio.shape					) != 1:
                   raise ValueError('We expect a single channel audio input for ZeroShotAudioClassificationPipeline'					)

            snake_case__							:	Tuple   	=	self.feature_extractor(
                [audio], sampling_rate=self.feature_extractor.sampling_rate, return_tensors='pt'					)
            snake_case__							:	int   	=	candidate_labels
            snake_case__							:	int   	=	[hypothesis_template.format(_snake_case					) for x in candidate_labels]
            snake_case__							:	Optional[int]   	=	self.tokenizer(_snake_case, return_tensors=self.framework, padding=_snake_case					)
            snake_case__							:	List[Any]   	=	[text_inputs]
            return inputs






     def    lowercase_   (       self  :  Optional[int], _snake_case  :  Optional[Any]					)   ->int:
            snake_case__							:	Optional[int]   	=	model_inputs.pop('candidate_labels'					)
            snake_case__							:	str   	=	model_inputs.pop('text_inputs'					)
            if isinstance(text_inputs[0], _snake_case					):
                   snake_case__							:	Optional[Any]   	=	text_inputs[0]
            else:
                   # Batching case.
                   snake_case__							:	int   	=	text_inputs[0][0]

            snake_case__							:	Any   	=	self.model(**_snake_case, **_snake_case					)

            snake_case__							:	List[Any]   	=	{
                'candidate_labels': candidate_labels,
                'logits': outputs.logits_per_audio,
            }
            return model_outputs






     def    lowercase_   (       self  :  Union[str, Any], _snake_case  :  str					)   ->List[str]:
            snake_case__							:	int   	=	model_outputs.pop('candidate_labels'					)
            snake_case__							:	List[Any]   	=	model_outputs['logits'][0]

            if self.framework == "pt":
                   snake_case__							:	Tuple   	=	logits.softmax(dim=0					)
                   snake_case__							:	Union[str, Any]   	=	probs.tolist()
            else:
                   raise ValueError('`tf` framework not supported.'					)

            snake_case__							:	Union[str, Any]   	=	[
                {'score': score, 'label': candidate_label}
                for score, candidate_label in sorted(zip(_snake_case, _snake_case					), key=lambda _snake_case					: -x[0]					)
            ]
            return result






