

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

SCREAMING_SNAKE_CASE		:Tuple						=     logging.get_logger(__name__)






@add_end_docstrings(snake_case				)
class 						__magic_name__					(							snake_case				):






     def __init__(				self		,						*_lowercase		,						**_lowercase	)->     List[Any]:
         super().__init__(*_lowercase		,						**_lowercase	)
         requires_backends(self		,						"vision"	)
         self.check_model_type(_lowercase	)






     def __call__(				self		,						_lowercase		,						**_lowercase	)->     Any:
         return super().__call__(_lowercase		,						**_lowercase	)






     def 	UpperCAmelCase_				(				self		,						**_lowercase	)->     List[str]:
         return {}, {}, {}






     def 	UpperCAmelCase_				(				self		,						_lowercase	)->     Union[str, Any]:
         UpperCamelCase_			  =						load_image(_lowercase	)
         UpperCamelCase_			  =						image.size
         UpperCamelCase_			  =						self.image_processor(images=_lowercase		,						return_tensors=self.framework	)
         return model_inputs






     def 	UpperCAmelCase_				(				self		,						_lowercase	)->     List[str]:
         UpperCamelCase_			  =						self.model(**_lowercase	)
         return model_outputs






     def 	UpperCAmelCase_				(				self		,						_lowercase	)->     str:
         UpperCamelCase_			  =						model_outputs.predicted_depth
         UpperCamelCase_			  =						torch.nn.functional.interpolate(
             predicted_depth.unsqueeze(1	)		,						size=self.image_size[::-1]		,						mode="bicubic"		,						align_corners=_lowercase	)
         UpperCamelCase_			  =						prediction.squeeze().cpu().numpy()
         UpperCamelCase_			  =						(output * 255 / np.max(_lowercase	)).astype("uint8"	)
         UpperCamelCase_			  =						Image.fromarray(_lowercase	)
         UpperCamelCase_			  =						{}
         UpperCamelCase_			  =						predicted_depth
         UpperCamelCase_			  =						depth
         return output_dict



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_torch_available,
)


SCREAMING_SNAKE_CASE		:Union[str, Any]						=     {
    """configuration_speecht5""": [
        """SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """SPEECHT5_PRETRAINED_HIFIGAN_CONFIG_ARCHIVE_MAP""",
        """SpeechT5Config""",
        """SpeechT5HifiGanConfig""",
    ],
    """feature_extraction_speecht5""": ["""SpeechT5FeatureExtractor"""],
    """processing_speecht5""": ["""SpeechT5Processor"""],
}

try:
 if not is_sentencepiece_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE		:Union[str, Any]						=     ["""SpeechT5Tokenizer"""]

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE		:Any						=     [
     """SPEECHT5_PRETRAINED_MODEL_ARCHIVE_LIST""",
     """SpeechT5ForSpeechToText""",
     """SpeechT5ForSpeechToSpeech""",
     """SpeechT5ForTextToSpeech""",
     """SpeechT5Model""",
     """SpeechT5PreTrainedModel""",
     """SpeechT5HifiGan""",
 ]

if TYPE_CHECKING:
 from .configuration_speechta import (
     SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP,
     SPEECHT5_PRETRAINED_HIFIGAN_CONFIG_ARCHIVE_MAP,
     SpeechTaConfig,
     SpeechTaHifiGanConfig,
 )
 from .feature_extraction_speechta import SpeechTaFeatureExtractor
 from .processing_speechta import SpeechTaProcessor

 try:
  if not is_sentencepiece_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_speechta import SpeechTaTokenizer

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_speechta import (
      SPEECHT5_PRETRAINED_MODEL_ARCHIVE_LIST,
      SpeechTaForSpeechToSpeech,
      SpeechTaForSpeechToText,
      SpeechTaForTextToSpeech,
      SpeechTaHifiGan,
      SpeechTaModel,
      SpeechTaPreTrainedModel,
  )

else:
 import sys

 SCREAMING_SNAKE_CASE		:List[str]						=     _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
