"""simple docstring"""





import os
import tempfile
import unittest

from transformers import DistilBertConfig, is_torch_available
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
       import torch

       from transformers import (
           DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
           DistilBertForMaskedLM,
           DistilBertForMultipleChoice,
           DistilBertForQuestionAnswering,
           DistilBertForSequenceClassification,
           DistilBertForTokenClassification,
           DistilBertModel,
       )


class   UpperCAmelCase_			(					_a):


      def __init__(     self	,    a	,    a=1_3	,    a=7	,    a=True	,    a=True	,    a=False	,    a=True	,    a=9_9	,    a=3_2	,    a=5	,    a=4	,    a=3_7	,    a="gelu"	,    a=0.1	,    a=0.1	,    a=5_1_2	,    a=1_6	,    a=2	,    a=0.02	,    a=3	,    a=4	,    a=None	,    ) ->      Any:
             lowercase__    :	Tuple     					=			parent
             lowercase__    :	List[Any]     					=			batch_size
             lowercase__    :	List[Any]     					=			seq_length
             lowercase__    :	List[Any]     					=			is_training
             lowercase__    :	Optional[Any]     					=			use_input_mask
             lowercase__    :	Optional[int]     					=			use_token_type_ids
             lowercase__    :	int     					=			use_labels
             lowercase__    :	Tuple     					=			vocab_size
             lowercase__    :	int     					=			hidden_size
             lowercase__    :	Any     					=			num_hidden_layers
             lowercase__    :	List[str]     					=			num_attention_heads
             lowercase__    :	Optional[Any]     					=			intermediate_size
             lowercase__    :	Optional[Any]     					=			hidden_act
             lowercase__    :	List[str]     					=			hidden_dropout_prob
             lowercase__    :	List[Any]     					=			attention_probs_dropout_prob
             lowercase__    :	List[Any]     					=			max_position_embeddings
             lowercase__    :	List[str]     					=			type_vocab_size
             lowercase__    :	Tuple     					=			type_sequence_label_size
             lowercase__    :	List[Any]     					=			initializer_range
             lowercase__    :	str     					=			num_labels
             lowercase__    :	Tuple     					=			num_choices
             lowercase__    :	str     					=			scope


      def 	_UpperCAmelCase (     self							) ->      Any:
             lowercase__    :	List[Any]     					=			ids_tensor([self.batch_size, self.seq_length]	,    self.vocab_size							)

             lowercase__    :	str     					=			None
             if self.use_input_mask:
                    lowercase__    :	Any     					=			random_attention_mask([self.batch_size, self.seq_length]							)

             lowercase__    :	Dict     					=			None
             lowercase__    :	Optional[Any]     					=			None
             lowercase__    :	int     					=			None
             if self.use_labels:
                    lowercase__    :	Union[str, Any]     					=			ids_tensor([self.batch_size]	,    self.type_sequence_label_size							)
                    lowercase__    :	List[Any]     					=			ids_tensor([self.batch_size, self.seq_length]	,    self.num_labels							)
                    lowercase__    :	Dict     					=			ids_tensor([self.batch_size]	,    self.num_choices							)

             lowercase__    :	List[Any]     					=			self.get_config()

             return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels


      def 	_UpperCAmelCase (     self							) ->      Optional[int]:
             return 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	,    )


      def 	_UpperCAmelCase (     self	,    a	,    a	,    a	,    a	,    a	,    a							) ->      Dict:
             lowercase__    :	Tuple     					=			DistilBertModel(config=a							)
             model.to(a							)
             model.eval()
             lowercase__    :	Any     					=			model(a	,    a							)
             lowercase__    :	str     					=			model(a							)
             self.parent.assertEqual(result.last_hidden_state.shape	,    (self.batch_size, self.seq_length, self.hidden_size)							)


      def 	_UpperCAmelCase (     self	,    a	,    a	,    a	,    a	,    a	,    a							) ->      Dict:
             lowercase__    :	Optional[int]     					=			DistilBertForMaskedLM(config=a							)
             model.to(a							)
             model.eval()
             lowercase__    :	Union[str, Any]     					=			model(a	,    attention_mask=a	,    labels=a							)
             self.parent.assertEqual(result.logits.shape	,    (self.batch_size, self.seq_length, self.vocab_size)							)


      def 	_UpperCAmelCase (     self	,    a	,    a	,    a	,    a	,    a	,    a							) ->      int:
             lowercase__    :	Tuple     					=			DistilBertForQuestionAnswering(config=a							)
             model.to(a							)
             model.eval()
             lowercase__    :	Tuple     					=			model(
                 a	,    attention_mask=a	,    start_positions=a	,    end_positions=a							)
             self.parent.assertEqual(result.start_logits.shape	,    (self.batch_size, self.seq_length)							)
             self.parent.assertEqual(result.end_logits.shape	,    (self.batch_size, self.seq_length)							)


      def 	_UpperCAmelCase (     self	,    a	,    a	,    a	,    a	,    a	,    a							) ->      List[str]:
             lowercase__    :	int     					=			self.num_labels
             lowercase__    :	Dict     					=			DistilBertForSequenceClassification(a							)
             model.to(a							)
             model.eval()
             lowercase__    :	Optional[Any]     					=			model(a	,    attention_mask=a	,    labels=a							)
             self.parent.assertEqual(result.logits.shape	,    (self.batch_size, self.num_labels)							)


      def 	_UpperCAmelCase (     self	,    a	,    a	,    a	,    a	,    a	,    a							) ->      Any:
             lowercase__    :	Any     					=			self.num_labels
             lowercase__    :	List[str]     					=			DistilBertForTokenClassification(config=a							)
             model.to(a							)
             model.eval()

             lowercase__    :	Any     					=			model(a	,    attention_mask=a	,    labels=a							)
             self.parent.assertEqual(result.logits.shape	,    (self.batch_size, self.seq_length, self.num_labels)							)


      def 	_UpperCAmelCase (     self	,    a	,    a	,    a	,    a	,    a	,    a							) ->      Tuple:
             lowercase__    :	List[Any]     					=			self.num_choices
             lowercase__    :	Any     					=			DistilBertForMultipleChoice(config=a							)
             model.to(a							)
             model.eval()
             lowercase__    :	str     					=			input_ids.unsqueeze(1							).expand(-1	,    self.num_choices	,    -1							).contiguous()
             lowercase__    :	Optional[int]     					=			input_mask.unsqueeze(1							).expand(-1	,    self.num_choices	,    -1							).contiguous()
             lowercase__    :	int     					=			model(
                 a	,    attention_mask=a	,    labels=a	,    )
             self.parent.assertEqual(result.logits.shape	,    (self.batch_size, self.num_choices)							)


      def 	_UpperCAmelCase (     self							) ->      str:
             lowercase__    :	Union[str, Any]     					=			self.prepare_config_and_inputs()
             ((lowercase__)					,   (lowercase__)					,   (lowercase__)					,   (lowercase__)					,   (lowercase__)					,   (lowercase__))    :	List[str]     					=			config_and_inputs
             lowercase__    :	Optional[Any]     					=			{'input_ids': input_ids, 'attention_mask': input_mask}
             return config, inputs_dict





@require_torch
class   UpperCAmelCase_			(					_a				,							_a				,							unittest.TestCase):
      lowerCamelCase__	:					List[str]										=						(
          (
              DistilBertModel,
              DistilBertForMaskedLM,
              DistilBertForMultipleChoice,
              DistilBertForQuestionAnswering,
              DistilBertForSequenceClassification,
              DistilBertForTokenClassification,
          )
          if is_torch_available()
          else None
      )
      lowerCamelCase__	:					str										=						(
          {
              "feature-extraction": DistilBertModel,
              "fill-mask": DistilBertForMaskedLM,
              "question-answering": DistilBertForQuestionAnswering,
              "text-classification": DistilBertForSequenceClassification,
              "token-classification": DistilBertForTokenClassification,
              "zero-shot": DistilBertForSequenceClassification,
          }
          if is_torch_available()
          else {}
      )
      lowerCamelCase__	:					Optional[int]										=						True
      lowerCamelCase__	:					Any										=						True
      lowerCamelCase__	:					List[Any]										=						True
      lowerCamelCase__	:					Optional[Any]										=						True


      def 	_UpperCAmelCase (     self							) ->      Union[str, Any]:
             lowercase__    :	str     					=			DistilBertModelTester(self							)
             lowercase__    :	int     					=			ConfigTester(self	,    config_class=a	,    dim=3_7							)


      def 	_UpperCAmelCase (     self							) ->      Dict:
             self.config_tester.run_common_tests()


      def 	_UpperCAmelCase (     self							) ->      Optional[Any]:
             lowercase__    :	int     					=			self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_distilbert_model(*a							)


      def 	_UpperCAmelCase (     self							) ->      Any:
             lowercase__    :	int     					=			self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_distilbert_for_masked_lm(*a							)


      def 	_UpperCAmelCase (     self							) ->      Optional[int]:
             lowercase__    :	str     					=			self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_distilbert_for_question_answering(*a							)


      def 	_UpperCAmelCase (     self							) ->      int:
             lowercase__    :	List[str]     					=			self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_distilbert_for_sequence_classification(*a							)


      def 	_UpperCAmelCase (     self							) ->      List[str]:
             lowercase__    :	Dict     					=			self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_distilbert_for_token_classification(*a							)


      def 	_UpperCAmelCase (     self							) ->      str:
             lowercase__    :	Any     					=			self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_distilbert_for_multiple_choice(*a							)


      @slow
      def 	_UpperCAmelCase (     self							) ->      str:
             for model_name in DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                    lowercase__    :	str     					=			DistilBertModel.from_pretrained(a							)
                    self.assertIsNotNone(a							)


      @slow
      @require_torch_gpu
      def 	_UpperCAmelCase (     self							) ->      Any:
             lowercase__					,   lowercase__    :	Dict     					=			self.model_tester.prepare_config_and_inputs_for_common()
             for model_class in self.all_model_classes:
                    # BertForMultipleChoice behaves incorrectly in JIT environments.
                    if model_class == DistilBertForMultipleChoice:
                           return

                    lowercase__    :	Optional[int]     					=			True
                    lowercase__    :	Union[str, Any]     					=			model_class(config=a							)

                    lowercase__    :	int     					=			self._prepare_for_class(a	,    a							)
                    lowercase__    :	Tuple     					=			torch.jit.trace(
                        a	,    (inputs_dict['input_ids'].to('cpu'							), inputs_dict['attention_mask'].to('cpu'							))							)

                    with tempfile.TemporaryDirectory() as tmp:
                           torch.jit.save(a	,    os.path.join(a	,    'traced_model.pt'							)							)
                           lowercase__    :	Optional[int]     					=			torch.jit.load(os.path.join(a	,    'traced_model.pt'							)	,    map_location=a							)
                           loaded(inputs_dict['input_ids'].to(a							)	,    inputs_dict['attention_mask'].to(a							)							)





@require_torch
class   UpperCAmelCase_			(					unittest.TestCase):


      @slow
      def 	_UpperCAmelCase (     self							) ->      List[str]:
             lowercase__    :	int     					=			DistilBertModel.from_pretrained('distilbert-base-uncased'							)
             lowercase__    :	Union[str, Any]     					=			torch.tensor([[0, 3_4_5, 2_3_2, 3_2_8, 7_4_0, 1_4_0, 1_6_9_5, 6_9, 6_0_7_8, 1_5_8_8, 2]]							)
             lowercase__    :	Optional[Any]     					=			torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]							)
             with torch.no_grad():
                    lowercase__    :	Optional[Any]     					=			model(a	,    attention_mask=a							)[0]
             lowercase__    :	Tuple     					=			torch.Size((1, 1_1, 7_6_8)							)
             self.assertEqual(output.shape	,    a							)
             lowercase__    :	List[Any]     					=			torch.tensor(
                 [[[-0.1_639, 0.3_299, 0.1_648], [-0.1_746, 0.3_289, 0.1_710], [-0.1_884, 0.3_357, 0.1_810]]]							)

             self.assertTrue(torch.allclose(output[:, 1:4, 1:4]	,    a	,    atol=1e-4							)							)


"""simple docstring"""





import hashlib
import unittest

from transformers import MODEL_FOR_DEPTH_ESTIMATION_MAPPING, is_torch_available, is_vision_available
from transformers.pipelines import DepthEstimationPipeline, pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_timm,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_torch_available():
       import torch

if is_vision_available():
       from PIL import Image
else:


       class   UpperCAmelCase_			:


             @staticmethod
             def 	_UpperCAmelCase (     *a	,    **a							) ->      int:
                    pass





def        a_  (   _lowerCAmelCase						:				Image  ):





       '''simple docstring'''

       lowercase__    :	List[str]     					=			hashlib.mda(image.tobytes()  )
       return m.hexdigest()






@is_pipeline_test
@require_vision
@require_timm
@require_torch
class   UpperCAmelCase_			(					unittest.TestCase):
      lowerCamelCase__	:					Union[str, Any]										=						MODEL_FOR_DEPTH_ESTIMATION_MAPPING


      def 	_UpperCAmelCase (     self	,    a	,    a	,    a							) ->      Dict:
             lowercase__    :	Union[str, Any]     					=			DepthEstimationPipeline(model=a	,    image_processor=a							)
             return depth_estimator, [
                 "./tests/fixtures/tests_samples/COCO/000000039769.png",
                 "./tests/fixtures/tests_samples/COCO/000000039769.png",
             ]


      def 	_UpperCAmelCase (     self	,    a	,    a							) ->      Optional[int]:
             lowercase__    :	Tuple     					=			depth_estimator('./tests/fixtures/tests_samples/COCO/000000039769.png'							)
             self.assertEqual({'predicted_depth': ANY(torch.Tensor							), 'depth': ANY(Image.Image							)}	,    a							)
             import datasets

             lowercase__    :	Tuple     					=			datasets.load_dataset('hf-internal-testing/fixtures_image_utils'	,    'image'	,    split='test'							)
             lowercase__    :	List[Any]     					=			depth_estimator(
                 [
                     Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'							),
                     'http://images.cocodataset.org/val2017/000000039769.jpg',
                     # RGBA
                     dataset[0]['file'],
                     # LA
                     dataset[1]['file'],
                     # L
                     dataset[2]['file'],
                 ]							)
             self.assertEqual(
                 [
                     {'predicted_depth': ANY(torch.Tensor							), 'depth': ANY(Image.Image							)},
                     {'predicted_depth': ANY(torch.Tensor							), 'depth': ANY(Image.Image							)},
                     {'predicted_depth': ANY(torch.Tensor							), 'depth': ANY(Image.Image							)},
                     {'predicted_depth': ANY(torch.Tensor							), 'depth': ANY(Image.Image							)},
                     {'predicted_depth': ANY(torch.Tensor							), 'depth': ANY(Image.Image							)},
                 ]	,    a	,    )


      @require_tf
      @unittest.skip('Depth estimation is not implemented in TF'							)
      def 	_UpperCAmelCase (     self							) ->      Optional[int]:
             pass


      @slow
      @require_torch
      def 	_UpperCAmelCase (     self							) ->      Tuple:
             lowercase__    :	Tuple     					=			'Intel/dpt-large'
             lowercase__    :	Optional[int]     					=			pipeline('depth-estimation'	,    model=a							)
             lowercase__    :	List[Any]     					=			depth_estimator('http://images.cocodataset.org/val2017/000000039769.jpg'							)
             lowercase__    :	Optional[Any]     					=			hashimage(outputs['depth']							)

             # This seems flaky.
             # self.assertEqual(outputs["depth"], "1a39394e282e9f3b0741a90b9f108977")
             self.assertEqual(nested_simplify(outputs['predicted_depth'].max().item()							)	,    29.304							)
             self.assertEqual(nested_simplify(outputs['predicted_depth'].min().item()							)	,    2.662							)


      @require_torch
      def 	_UpperCAmelCase (     self							) ->      Optional[int]:
             # This is highly irregular to have no small tests.
             self.skipTest('There is not hf-internal-testing tiny model for either GLPN nor DPT'							)

