


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

UpperCamelCase					:       str			    =     logging.get_logger(__name__)



@add_end_docstrings(A__  )
class A__     ( A__  ):



  """simple docstring"""



  def __init__( self			:  Optional[Any]				,				*lowerCamelCase__			:  Any				,				**lowerCamelCase__			:  int       ):
        super().__init__(*lowerCamelCase__				,				**lowerCamelCase__       )
        requires_backends(self				,				"vision"       )
        self.check_model_type(lowerCamelCase__       )

  def __call__( self			:  List[Any]				,				lowerCamelCase__			:  Union[str, List[str], "Image.Image", List["Image.Image"]]				,				**lowerCamelCase__			:  Optional[int]       ):
        return super().__call__(lowerCamelCase__				,				**lowerCamelCase__       )

  def    _UpperCamelCase( self			:  List[str]				,				**lowerCamelCase__			:  int       ):
        return {}, {}, {}

  def    _UpperCamelCase( self			:  int				,				lowerCamelCase__			:  Any       ):
        a__	:  str     =   load_image(lowerCamelCase__       )
        a__	:  int     =   image.size
        a__	:  Optional[Any]     =   self.image_processor(images=lowerCamelCase__				,				return_tensors=self.framework       )
        return model_inputs

  def    _UpperCamelCase( self			:  str				,				lowerCamelCase__			:  int       ):
        a__	:  List[Any]     =   self.model(**lowerCamelCase__       )
        return model_outputs

  def    _UpperCamelCase( self			:  Union[str, Any]				,				lowerCamelCase__			:  int       ):
        a__	:  Tuple     =   model_outputs.predicted_depth
        a__	:  Tuple     =   torch.nn.functional.interpolate(
            predicted_depth.unsqueeze(1       )				,				size=self.image_size[::-1]				,				mode="bicubic"				,				align_corners=lowerCamelCase__       )
        a__	:  Optional[Any]     =   prediction.squeeze().cpu().numpy()
        a__	:  Tuple     =   (output * 255 / np.max(lowerCamelCase__       )).astype("uint8"       )
        a__	:  List[Any]     =   Image.fromarray(lowerCamelCase__       )
        a__	:  Optional[Any]     =   {}
        a__	:  List[str]     =   predicted_depth
        a__	:  Tuple     =   depth
        return output_dict




"""simple docstring"""
import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device


if is_torch_available():
    from transformers import AutoModelForSeqaSeqLM, AutoTokenizer

@require_torch
@require_sentencepiece
@require_tokenizers
class       lowerCamelCase	(       unittest.TestCase   ):


  '''simple docstring'''







  @slow
  def  _a      (self	):


        """simple docstring"""

        UpperCAmelCase__			:							Dict							=    AutoModelForSeqaSeqLM.from_pretrained("""google/mt5-small"""							,       return_dict=_lowerCamelCase	).to(_lowerCamelCase	)
        UpperCAmelCase__			:							Optional[Any]							=    AutoTokenizer.from_pretrained("""google/mt5-small"""	)

        UpperCAmelCase__			:							Any							=    tokenizer("""Hello there"""							,       return_tensors="""pt"""	).input_ids
        UpperCAmelCase__			:							str							=    tokenizer("""Hi I am"""							,       return_tensors="""pt"""	).input_ids

        UpperCAmelCase__			:							Tuple							=    model(input_ids.to(_lowerCamelCase	)							,       labels=labels.to(_lowerCamelCase	)	).loss
        UpperCAmelCase__			:							Union[str, Any]							=    -(labels.shape[-1] * loss.item())

        UpperCAmelCase__			:							List[str]							=    -84.9_127
        self.assertTrue(abs(mtf_score - EXPECTED_SCORE	) < 1e-4	)

