

from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


a		={
    """configuration_blenderbot_small""": [
        """BLENDERBOT_SMALL_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """BlenderbotSmallConfig""",
        """BlenderbotSmallOnnxConfig""",
    ],
    """tokenization_blenderbot_small""": ["""BlenderbotSmallTokenizer"""],
}

try:
					if not is_tokenizers_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					a		=["""BlenderbotSmallTokenizerFast"""]

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					a		=[
					    """BLENDERBOT_SMALL_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """BlenderbotSmallForCausalLM""",
					    """BlenderbotSmallForConditionalGeneration""",
					    """BlenderbotSmallModel""",
					    """BlenderbotSmallPreTrainedModel""",
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					a		=[
					    """TFBlenderbotSmallForConditionalGeneration""",
					    """TFBlenderbotSmallModel""",
					    """TFBlenderbotSmallPreTrainedModel""",
					]

try:
					if not is_flax_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					a		=[
					    """FlaxBlenderbotSmallForConditionalGeneration""",
					    """FlaxBlenderbotSmallModel""",
					    """FlaxBlenderbotSmallPreTrainedModel""",
					]

if TYPE_CHECKING:
					from .configuration_blenderbot_small import (
					    BLENDERBOT_SMALL_PRETRAINED_CONFIG_ARCHIVE_MAP,
					    BlenderbotSmallConfig,
					    BlenderbotSmallOnnxConfig,
					)
					from .tokenization_blenderbot_small import BlenderbotSmallTokenizer

					try:
										if not is_tokenizers_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_blenderbot_small_fast import BlenderbotSmallTokenizerFast

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_blenderbot_small import (
										    BLENDERBOT_SMALL_PRETRAINED_MODEL_ARCHIVE_LIST,
										    BlenderbotSmallForCausalLM,
										    BlenderbotSmallForConditionalGeneration,
										    BlenderbotSmallModel,
										    BlenderbotSmallPreTrainedModel,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_blenderbot_small import (
										    TFBlenderbotSmallForConditionalGeneration,
										    TFBlenderbotSmallModel,
										    TFBlenderbotSmallPreTrainedModel,
										)

					try:
										if not is_flax_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_flax_blenderbot_small import (
										    FlaxBlenderbotSmallForConditionalGeneration,
										    FlaxBlenderbotSmallModel,
										    FlaxBlenderbotSmallPreTrainedModel,
										)

else:
					import sys

					a		=_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)









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






