




"""simple docstring"""






import unittest

import numpy as np

from transformers import DistilBertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
    import jax.numpy as jnp

    from transformers.models.distilbert.modeling_flax_distilbert import (
        FlaxDistilBertForMaskedLM,
        FlaxDistilBertForMultipleChoice,
        FlaxDistilBertForQuestionAnswering,
        FlaxDistilBertForSequenceClassification,
        FlaxDistilBertForTokenClassification,
        FlaxDistilBertModel,
    )




class   a__			(       unittest.TestCase  ):





      def __init__(							self      :	Dict     ,a__      :	List[Any]     ,a__      :	Optional[int]=13     ,a__      :	Dict=7     ,a__      :	Any=True     ,a__      :	Tuple=True     ,a__      :	Union[str, Any]=True     ,a__      :	Dict=True     ,a__      :	Dict=99     ,a__      :	Optional[Any]=32     ,a__      :	Dict=5     ,a__      :	List[Any]=4     ,a__      :	Union[str, Any]=37     ,a__      :	str="gelu"     ,a__      :	Any=0.1     ,a__      :	Optional[int]=0.1     ,a__      :	Tuple=512     ,a__      :	Any=16     ,a__      :	Any=2     ,a__      :	Dict=0.02     ,a__      :	int=4     ,)						->       Union[str, Any]:




           """simple docstring"""







           _lowerCAmelCase:Optional[int]									=  parent
           _lowerCAmelCase:List[str]									=  batch_size
           _lowerCAmelCase:Tuple									=  seq_length
           _lowerCAmelCase:List[Any]									=  is_training
           _lowerCAmelCase:Dict									=  use_attention_mask
           _lowerCAmelCase:Dict									=  use_token_type_ids
           _lowerCAmelCase:Any									=  use_labels
           _lowerCAmelCase:str									=  vocab_size
           _lowerCAmelCase:int									=  hidden_size
           _lowerCAmelCase:Union[str, Any]									=  num_hidden_layers
           _lowerCAmelCase:List[str]									=  num_attention_heads
           _lowerCAmelCase:Optional[int]									=  intermediate_size
           _lowerCAmelCase:str									=  hidden_act
           _lowerCAmelCase:Optional[int]									=  hidden_dropout_prob
           _lowerCAmelCase:Optional[int]									=  attention_probs_dropout_prob
           _lowerCAmelCase:Any									=  max_position_embeddings
           _lowerCAmelCase:Optional[int]									=  type_vocab_size
           _lowerCAmelCase:Optional[int]									=  type_sequence_label_size
           _lowerCAmelCase:Tuple									=  initializer_range
           _lowerCAmelCase:Dict									=  num_choices





      def       __UpperCamelCase     (							self      :	List[str])						->       int:




           """simple docstring"""







           _lowerCAmelCase:int									=  ids_tensor([self.batch_size, self.seq_length]     ,self.vocab_size)

           _lowerCAmelCase:List[Any]									=  None
           if self.use_attention_mask:
                _lowerCAmelCase:str									=  random_attention_mask([self.batch_size, self.seq_length])

           _lowerCAmelCase:Dict									=  DistilBertConfig(
               vocab_size=self.vocab_size     ,dim=self.hidden_size     ,n_layers=self.num_hidden_layers     ,n_heads=self.num_attention_heads     ,hidden_dim=self.intermediate_size     ,hidden_act=self.hidden_act     ,dropout=self.hidden_dropout_prob     ,attention_dropout=self.attention_probs_dropout_prob     ,max_position_embeddings=self.max_position_embeddings     ,initializer_range=self.initializer_range     ,tie_weights_=a__     ,)

           return config, input_ids, attention_mask







      def       __UpperCamelCase     (							self      :	Any)						->       Any:




           """simple docstring"""







           _lowerCAmelCase:Optional[Any]									=  self.prepare_config_and_inputs()
           _lowerCAmelCase:Optional[Any]									=  config_and_inputs
           _lowerCAmelCase:Any									=  {'''input_ids''': input_ids, '''attention_mask''': attention_mask}
           return config, inputs_dict




@require_flax
class   a__			(       UpperCamelCase_					,	unittest.TestCase  ):
      snake_case__             = (
          (
              FlaxDistilBertModel,
              FlaxDistilBertForMaskedLM,
              FlaxDistilBertForMultipleChoice,
              FlaxDistilBertForQuestionAnswering,
              FlaxDistilBertForSequenceClassification,
              FlaxDistilBertForTokenClassification,
              FlaxDistilBertForQuestionAnswering,
          )
          if is_flax_available()
          else ()
      )





      def       __UpperCamelCase     (							self      :	str)						->       Dict:




           """simple docstring"""







           _lowerCAmelCase:str									=  FlaxDistilBertModelTester(self)







      @slow
      def       __UpperCamelCase     (							self      :	List[str])						->       int:




           """simple docstring"""







           for model_class_name in self.all_model_classes:
                _lowerCAmelCase:List[str]									=  model_class_name.from_pretrained('''distilbert-base-uncased''')
                _lowerCAmelCase:Tuple									=  model(np.ones((1, 1)))
                self.assertIsNotNone(a__)




@require_flax
class   a__			(       unittest.TestCase  ):





      @slow
      def       __UpperCamelCase     (							self      :	Union[str, Any])						->       Optional[Any]:




           """simple docstring"""







           _lowerCAmelCase:Any									=  FlaxDistilBertModel.from_pretrained('''distilbert-base-uncased''')
           _lowerCAmelCase:Optional[int]									=  np.array([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]])
           _lowerCAmelCase:Tuple									=  np.array([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]])
           _lowerCAmelCase:List[str]									=  model(a__     ,attention_mask=a__)[0]
           _lowerCAmelCase:List[str]									=  (1, 11, 768)
           self.assertEqual(output.shape     ,a__)
           _lowerCAmelCase:int									=  np.array([[[-0.1639, 0.3299, 0.1648], [-0.1746, 0.3289, 0.1710], [-0.1884, 0.3357, 0.1810]]])

           self.assertTrue(jnp.allclose(output[:, 1:4, 1:4]     ,a__     ,atol=1E-4))












"""simple docstring"""






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__						=			logging.get_logger(__name__)




@add_end_docstrings(UpperCamelCase_  )
class   a__			(       UpperCamelCase_  ):





						def __init__(							self      :	int     ,*a__      :	Optional[Any]     ,**a__      :	Union[str, Any])						->       Tuple:




											"""simple docstring"""







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





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




											"""simple docstring"""







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





						def       __UpperCamelCase     (							self      :	Union[str, Any]     ,**a__      :	List[Any])						->       Any:




											"""simple docstring"""







											return {}, {}, {}





						def       __UpperCamelCase     (							self      :	Tuple     ,a__      :	Optional[int])						->       Optional[Any]:




											"""simple docstring"""







											_lowerCAmelCase:List[str]									=  load_image(a__)
											_lowerCAmelCase:int									=  image.size
											_lowerCAmelCase:int									=  self.image_processor(images=a__     ,return_tensors=self.framework)
											return model_inputs





						def       __UpperCamelCase     (							self      :	Dict     ,a__      :	List[str])						->       Union[str, Any]:




											"""simple docstring"""







											_lowerCAmelCase:Any									=  self.model(**a__)
											return model_outputs







						def       __UpperCamelCase     (							self      :	List[Any]     ,a__      :	Dict)						->       Any:




											"""simple docstring"""







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






