
'''simple docstring'''



import os
import tempfile
import unittest

from transformers.models.marian.convert_marian_tatoeba_to_pytorch import DEFAULT_REPO, TatoebaConverter
from transformers.testing_utils import slow
from transformers.utils import cached_property




@unittest.skipUnless(os.path.exists(_SCREAMING_SNAKE_CASE					)			,				'''Tatoeba directory does not exist.'''					)
class     UpperCAmelCase_	(						unittest.TestCase					):






						'''simple docstring'''






						@cached_property
						def    _lowercase      (   self ):

												"""simple docstring"""



												_lowerCAmelCase    		=					tempfile.mkdtemp()
												return TatoebaConverter(save_dir=_lowercase )




						@slow
						def    _lowercase      (   self ):

												"""simple docstring"""



												self.resolver.convert_models(["""heb-eng"""] )




						@slow
						def    _lowercase      (   self ):

												"""simple docstring"""



												_lowerCAmelCase		,       _lowerCAmelCase    		=					self.resolver.write_model_card("""opus-mt-he-en"""       ,  dry_run=_lowercase )
												assert mmeta["long_pair"] == "heb-eng"


'''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


_lowercase          =						logging.get_logger(__name__)




@add_end_docstrings(_SCREAMING_SNAKE_CASE					)
class     UpperCAmelCase_	(						_SCREAMING_SNAKE_CASE					):






						'''simple docstring'''






						def __init__(   self       ,  **_lowercase ):

												"""simple docstring"""



												super().__init__(**_lowercase )

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




						def __call__(   self       ,  _lowercase       ,  **_lowercase ):

												"""simple docstring"""



												return super().__call__(_lowercase       ,  **_lowercase )




						def    _lowercase      (   self       ,  **_lowercase ):

												"""simple docstring"""



												_lowerCAmelCase    		=					{}
												if "candidate_labels" in kwargs:
																		_lowerCAmelCase    		=					kwargs["""candidate_labels"""]
												if "hypothesis_template" in kwargs:
																		_lowerCAmelCase    		=					kwargs["""hypothesis_template"""]

												return preprocess_params, {}, {}




						def    _lowercase      (   self       ,  _lowercase       ,  _lowercase=None       ,  _lowercase="This is a sound of {}." ):

												"""simple docstring"""



												if isinstance(_lowercase       ,  _lowercase ):
																		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
																								_lowerCAmelCase    		=					requests.get(_lowercase ).content
																		else:
																								with open(_lowercase       ,  """rb""" ) as f:
																														_lowerCAmelCase    		=					f.read()

												if isinstance(_lowercase       ,  _lowercase ):
																		_lowerCAmelCase    		=					ffmpeg_read(_lowercase       ,  self.feature_extractor.sampling_rate )

												if not isinstance(_lowercase       ,  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""" )

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




						def    _lowercase      (   self       ,  _lowercase ):

												"""simple docstring"""



												_lowerCAmelCase    		=					model_inputs.pop("""candidate_labels""" )
												_lowerCAmelCase    		=					model_inputs.pop("""text_inputs""" )
												if isinstance(text_inputs[0]       ,  _lowercase ):
																		_lowerCAmelCase    		=					text_inputs[0]
												else:
																		# Batching case.
																		_lowerCAmelCase    		=					text_inputs[0][0]

												_lowerCAmelCase    		=					self.model(**_lowercase       ,  **_lowercase )

												_lowerCAmelCase    		=					{
												    """candidate_labels""": candidate_labels,
												    """logits""": outputs.logits_per_audio,
												}
												return model_outputs




						def    _lowercase      (   self       ,  _lowercase ):

												"""simple docstring"""



												_lowerCAmelCase    		=					model_outputs.pop("""candidate_labels""" )
												_lowerCAmelCase    		=					model_outputs["""logits"""][0]

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

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