
"""simple docstring"""





from typing import List, Union

from ..utils import (
    add_end_docstrings,
    is_tf_available,
    is_torch_available,
    is_vision_available,
    logging,
    requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
							from PIL import Image

							from ..image_utils import load_image

if is_tf_available():
							import tensorflow as tf

							from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
							from ..tf_utils import stable_softmax

if is_torch_available():
							from ..models.auto.modeling_auto import MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING

__magic_name__       =			logging.get_logger(__name__)
@add_end_docstrings(__a )
class 						SCREAMING_SNAKE_CASE_ (						__a ):




		"""simple docstring"""





		def __init__(     self							,  *lowerCAmelCase__							,  **lowerCAmelCase__):
				super().__init__(*lowerCAmelCase__							,  **lowerCAmelCase__)
				requires_backends(self							,  """vision""")
				self.check_model_type(
				    TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
				    if self.framework == """tf"""
				    else MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING)




		def 	snake_case_     (     self							,  lowerCAmelCase__=None):
				__SCREAMING_SNAKE_CASE            = {}
				if top_k is not None:
						__SCREAMING_SNAKE_CASE            = top_k
				return {}, {}, postprocess_params




		def __call__(     self							,  lowerCAmelCase__							,  **lowerCAmelCase__):
				return super().__call__(lowerCAmelCase__							,  **lowerCAmelCase__)




		def 	snake_case_     (     self							,  lowerCAmelCase__):
				__SCREAMING_SNAKE_CASE            = load_image(lowerCAmelCase__)
				__SCREAMING_SNAKE_CASE            = self.image_processor(images=lowerCAmelCase__							,  return_tensors=self.framework)
				return model_inputs




		def 	snake_case_     (     self							,  lowerCAmelCase__):
				__SCREAMING_SNAKE_CASE            = self.model(**lowerCAmelCase__)
				return model_outputs




		def 	snake_case_     (     self							,  lowerCAmelCase__							,  lowerCAmelCase__=5):
				if top_k > self.model.config.num_labels:
						__SCREAMING_SNAKE_CASE            = self.model.config.num_labels

				if self.framework == "pt":
						__SCREAMING_SNAKE_CASE            = model_outputs.logits.softmax(-1)[0]
						__SCREAMING_SNAKE_CASE       ,__SCREAMING_SNAKE_CASE            = probs.topk(lowerCAmelCase__)
				elif self.framework == "tf":
						__SCREAMING_SNAKE_CASE            = stable_softmax(model_outputs.logits							,  axis=-1)[0]
						__SCREAMING_SNAKE_CASE            = tf.math.top_k(lowerCAmelCase__							,  k=lowerCAmelCase__)
						__SCREAMING_SNAKE_CASE       ,__SCREAMING_SNAKE_CASE            = topk.values.numpy(), topk.indices.numpy()
				else:
						raise ValueError(f"Unsupported framework: {self.framework}")

				__SCREAMING_SNAKE_CASE            = scores.tolist()
				__SCREAMING_SNAKE_CASE            = ids.tolist()
				return [{"score": score, "label": self.model.config.idalabel[_id]} for score, _id in zip(lowerCAmelCase__							,  lowerCAmelCase__)]






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__A				={'''configuration_wavlm''': ['''WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''WavLMConfig''']}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__A				=[
			    '''WAVLM_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''WavLMForAudioFrameClassification''',
			    '''WavLMForCTC''',
			    '''WavLMForSequenceClassification''',
			    '''WavLMForXVector''',
			    '''WavLMModel''',
			    '''WavLMPreTrainedModel''',
			]

if TYPE_CHECKING:
			from .configuration_wavlm import WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP, WavLMConfig

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_wavlm import (
						    WAVLM_PRETRAINED_MODEL_ARCHIVE_LIST,
						    WavLMForAudioFrameClassification,
						    WavLMForCTC,
						    WavLMForSequenceClassification,
						    WavLMForXVector,
						    WavLMModel,
						    WavLMPreTrainedModel,
						)

else:
			import sys

			__A				=_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)


