



"""simple docstring"""


# Copyright 2023 The HuggingFace Inc. 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.
from typing import TYPE_CHECKING

import torch

from ..models.auto import AutoModelForVisualQuestionAnswering, AutoProcessor
from ..utils import requires_backends
from .base import PipelineTool


if TYPE_CHECKING:
					from PIL import Image
class 			_a (					lowerCAmelCase):
			"""simple docstring"""
			UpperCamelCase__			      =	"""dandelin/vilt-b32-finetuned-vqa"""
			UpperCamelCase__			      =	(
			    """This is a tool that answers a question about an image. It takes an input named `image` which should be the """
			    """image containing the information, as well as a `question` which should be the question in English. It """
			    """returns a text that is the answer to the question."""
			)
			UpperCamelCase__			      =	"""image_qa"""
			UpperCamelCase__			      =	AutoProcessor
			UpperCamelCase__			      =	AutoModelForVisualQuestionAnswering

			UpperCamelCase__			      =	["""image""", """text"""]
			UpperCamelCase__			      =	["""text"""]



			def __init__(		self     :    Union[str, Any]				,   *__UpperCamelCase     :    Optional[int]				,   **__UpperCamelCase     :    List[Any]   )->Dict:
							requires_backends(self				,   ['''vision''']   )
							super().__init__(*__UpperCamelCase				,   **__UpperCamelCase   )



			def  lowercase__						(		self     :    Union[str, Any]				,   __UpperCamelCase     :    "Image"				,   __UpperCamelCase     :    str   )->Optional[Any]:
							return self.pre_processor(__UpperCamelCase				,   __UpperCamelCase				,   return_tensors='''pt'''   )



			def  lowercase__						(		self     :    str				,   __UpperCamelCase     :    Tuple   )->Optional[int]:
							with torch.no_grad():
											return self.model(**__UpperCamelCase   ).logits



			def  lowercase__						(		self     :    Any				,   __UpperCamelCase     :    Union[str, Any]   )->Dict:
							_UpperCAmelCase					=    outputs.argmax(-1   ).item()
							return self.model.config.idalabel[idx]








"""simple docstring"""


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[str]										=   logging.get_logger(__name__)
@add_end_docstrings(lowerCAmelCase)
class 			_a (					lowerCAmelCase):
			"""simple docstring"""



			def __init__(		self     :    Union[str, Any]				,   **__UpperCamelCase     :    Optional[Any]   )->List[Any]:
							super().__init__(**__UpperCamelCase   )

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



			def __call__(		self     :    Optional[int]				,   __UpperCamelCase     :    Union[np.ndarray, bytes, str]				,   **__UpperCamelCase     :    Tuple   )->List[str]:
							return super().__call__(__UpperCamelCase				,   **__UpperCamelCase   )



			def  lowercase__						(		self     :    Any				,   **__UpperCamelCase     :    Any   )->Union[str, Any]:
							_UpperCAmelCase					=    {}
							if "candidate_labels" in kwargs:
											_UpperCAmelCase					=    kwargs['''candidate_labels''']
							if "hypothesis_template" in kwargs:
											_UpperCAmelCase					=    kwargs['''hypothesis_template''']

							return preprocess_params, {}, {}



			def  lowercase__						(		self     :    Optional[int]				,   __UpperCamelCase     :    Union[str, Any]				,   __UpperCamelCase     :    List[Any]=None				,   __UpperCamelCase     :    Tuple="This is a sound of {}."   )->int:
							if isinstance(__UpperCamelCase				,   __UpperCamelCase   ):
											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
															_UpperCAmelCase					=    requests.get(__UpperCamelCase   ).content
											else:
															with open(__UpperCamelCase				,   '''rb'''   ) as f:
																			_UpperCAmelCase					=    f.read()

							if isinstance(__UpperCamelCase				,   __UpperCamelCase   ):
											_UpperCAmelCase					=    ffmpeg_read(__UpperCamelCase				,   self.feature_extractor.sampling_rate   )

							if not isinstance(__UpperCamelCase				,   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'''   )

							_UpperCAmelCase					=    self.feature_extractor(
							    [audio]				,   sampling_rate=self.feature_extractor.sampling_rate				,   return_tensors='''pt'''   )
							_UpperCAmelCase					=    candidate_labels
							_UpperCAmelCase					=    [hypothesis_template.format(__UpperCamelCase   ) for x in candidate_labels]
							_UpperCAmelCase					=    self.tokenizer(__UpperCamelCase				,   return_tensors=self.framework				,   padding=__UpperCamelCase   )
							_UpperCAmelCase					=    [text_inputs]
							return inputs



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

							_UpperCAmelCase					=    self.model(**__UpperCamelCase				,   **__UpperCamelCase   )

							_UpperCAmelCase					=    {
							    '''candidate_labels''': candidate_labels,
							    '''logits''': outputs.logits_per_audio,
							}
							return model_outputs



			def  lowercase__						(		self     :    List[str]				,   __UpperCamelCase     :    Any   )->List[Any]:
							_UpperCAmelCase					=    model_outputs.pop('''candidate_labels'''   )
							_UpperCAmelCase					=    model_outputs['''logits'''][0]

							if self.framework == "pt":
											_UpperCAmelCase					=    logits.softmax(dim=0   )
											_UpperCAmelCase					=    probs.tolist()
							else:
											raise ValueError('''`tf` framework not supported.'''   )

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



