





from ...configuration_utils import PretrainedConfig
from ...utils import logging


snake_case_           =      logging.get_logger(__name__)

snake_case_           =      {
    'facebook/s2t-wav2vec2-large-en-de': (
        'https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/config.json'
    ),
    # See all Speech2Text models at https://huggingface.co/models?filter=speech2text2
}

class 		SCREAMING_SNAKE_CASE__    (	_UpperCAmelCase  ):
						A_						:    Tuple							=   'speech_to_text_2'
						A_						:    Tuple							=   ['past_key_values']
						A_						:    Dict							=   {'num_attention_heads': 'decoder_attention_heads', 'hidden_size': 'd_model'}


						def __init__(self	:				int    ,      a__	:				List[str]=1_0000    ,      a__	:				List[Any]=6    ,      a__	:				Optional[Any]=2048    ,      a__	:				List[Any]=4    ,      a__	:				Optional[Any]=0.0    ,      a__	:				Dict=True    ,      a__	:				Optional[int]="relu"    ,      a__	:				Optional[int]=256    ,      a__	:				Union[str, Any]=0.1    ,      a__	:				int=0.0    ,      a__	:				Optional[int]=0.0    ,      a__	:				List[str]=0.0_2    ,      a__	:				Union[str, Any]=2    ,      a__	:				int=True    ,      a__	:				List[Any]=1    ,      a__	:				str=0    ,      a__	:				List[Any]=2    ,      a__	:				Any=1024    ,      **a__	:				Dict    ,      ):





									"""simple docstring"""



									__snake_case								=       vocab_size
									__snake_case								=       d_model
									__snake_case								=       decoder_ffn_dim
									__snake_case								=       decoder_layers
									__snake_case								=       decoder_attention_heads
									__snake_case								=       dropout
									__snake_case								=       attention_dropout
									__snake_case								=       activation_dropout
									__snake_case								=       activation_function
									__snake_case								=       init_std
									__snake_case								=       decoder_layerdrop
									__snake_case								=       use_cache
									__snake_case								=       decoder_layers
									__snake_case								=       scale_embedding  # scale factor will be sqrt(d_model) if True
									__snake_case								=       max_target_positions

									super().__init__(
									    pad_token_id=a__    ,      bos_token_id=a__    ,      eos_token_id=a__    ,      decoder_start_token_id=a__    ,      **a__    ,      )








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

snake_case_           =      logging.get_logger(__name__)

@add_end_docstrings(_UpperCAmelCase  )
class 		SCREAMING_SNAKE_CASE__    (	_UpperCAmelCase  ):


      def __init__(self	:				int    ,      *a__	:				List[Any]    ,      **a__	:				Dict				):





         """simple docstring"""



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


      def __call__(self	:				Optional[Any]    ,      a__	:				Union[str, List[str], "Image.Image", List["Image.Image"]]    ,      **a__	:				List[str]				):





         """simple docstring"""



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


      def        a			(self	:				int    ,      **a__	:				int				):





         """simple docstring"""



         return {}, {}, {}


      def        a			(self	:				Optional[int]    ,      a__	:				Optional[int]				):





         """simple docstring"""



         __snake_case								=       load_image(a__				)
         __snake_case								=       image.size
         __snake_case								=       self.image_processor(images=a__    ,      return_tensors=self.framework				)
         return model_inputs


      def        a			(self	:				List[Any]    ,      a__	:				Union[str, Any]				):





         """simple docstring"""



         __snake_case								=       self.model(**a__				)
         return model_outputs


      def        a			(self	:				int    ,      a__	:				str				):





         """simple docstring"""



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

