





from typing import List, Union

import numpy as np

from ..utils import add_end_docstrings, 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_torch_available():
			import torch

			from ..models.auto.modeling_auto import MODEL_FOR_DEPTH_ESTIMATION_MAPPING

_a						=   logging.get_logger(__name__)






@add_end_docstrings(lowerCAmelCase				)
class       __A					(					lowerCAmelCase				):

			'''simple docstring'''







			def __init__(					self ,						*__lowerCAmelCase ,						**__lowerCAmelCase							):



				'''simple docstring'''







				super().__init__(*__lowerCAmelCase ,						**__lowerCAmelCase							)
				requires_backends(self ,						'''vision'''							)
				self.check_model_type(__lowerCAmelCase							)





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



				'''simple docstring'''







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





			def 	__lowerCamelCase       (					self ,						**__lowerCAmelCase							):



				'''simple docstring'''







				return {}, {}, {}





			def 	__lowerCamelCase       (					self ,						__lowerCAmelCase							):



				'''simple docstring'''







				lowerCamelCase__              =	load_image(__lowerCAmelCase							)
				lowerCamelCase__              =	image.size
				lowerCamelCase__              =	self.image_processor(images=__lowerCAmelCase ,						return_tensors=self.framework							)
				return model_inputs





			def 	__lowerCamelCase       (					self ,						__lowerCAmelCase							):



				'''simple docstring'''







				lowerCamelCase__              =	self.model(**__lowerCAmelCase							)
				return model_outputs





			def 	__lowerCamelCase       (					self ,						__lowerCAmelCase							):



				'''simple docstring'''







				lowerCamelCase__              =	model_outputs.predicted_depth
				lowerCamelCase__              =	torch.nn.functional.interpolate(
				    predicted_depth.unsqueeze(1							) ,						size=self.image_size[::-1] ,						mode='''bicubic''' ,						align_corners=__lowerCAmelCase							)
				lowerCamelCase__              =	prediction.squeeze().cpu().numpy()
				lowerCamelCase__              =	(output * 2_5_5 / np.max(__lowerCAmelCase							)).astype('''uint8'''							)
				lowerCamelCase__              =	Image.fromarray(__lowerCAmelCase							)
				lowerCamelCase__              =	{}
				lowerCamelCase__              =	predicted_depth
				lowerCamelCase__              =	depth
				return output_dict










from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding






class       __A					(					lowerCAmelCase				):

			'''simple docstring'''







			lowerCAmelCase_							  =		"""ClapFeatureExtractor"""
			lowerCAmelCase_							  =		("""RobertaTokenizer""", """RobertaTokenizerFast""")





			def __init__(					self ,						__lowerCAmelCase ,						__lowerCAmelCase							):



				'''simple docstring'''







				super().__init__(__lowerCAmelCase ,						__lowerCAmelCase							)





			def __call__(					self ,						__lowerCAmelCase=None ,						__lowerCAmelCase=None ,						__lowerCAmelCase=None ,						**__lowerCAmelCase							):



				'''simple docstring'''







				lowerCamelCase__              =	kwargs.pop('''sampling_rate''' ,						__lowerCAmelCase							)

				if text is None and audios is None:
					raise ValueError('''You have to specify either text or audios. Both cannot be none.'''							)

				if text is not None:
					lowerCamelCase__              =	self.tokenizer(__lowerCAmelCase ,						return_tensors=__lowerCAmelCase ,						**__lowerCAmelCase							)

				if audios is not None:
					lowerCamelCase__              =	self.feature_extractor(
					    __lowerCAmelCase ,						sampling_rate=__lowerCAmelCase ,						return_tensors=__lowerCAmelCase ,						**__lowerCAmelCase							)

				if text is not None and audios is not None:
					lowerCamelCase__              =	audio_features.input_features
					return encoding
				elif text is not None:
					return encoding
				else:
					return BatchEncoding(data=dict(**__lowerCAmelCase							) ,						tensor_type=__lowerCAmelCase							)





			def 	__lowerCamelCase       (					self ,						*__lowerCAmelCase ,						**__lowerCAmelCase							):



				'''simple docstring'''







				return self.tokenizer.batch_decode(*__lowerCAmelCase ,						**__lowerCAmelCase							)





			def 	__lowerCamelCase       (					self ,						*__lowerCAmelCase ,						**__lowerCAmelCase							):



				'''simple docstring'''







				return self.tokenizer.decode(*__lowerCAmelCase ,						**__lowerCAmelCase							)





			@property
			def 	__lowerCamelCase       (					self							):



				'''simple docstring'''







				lowerCamelCase__              =	self.tokenizer.model_input_names
				lowerCamelCase__              =	self.feature_extractor.model_input_names
				return list(dict.fromkeys(tokenizer_input_names + feature_extractor_input_names							)							)



