from __future__ import annotations

import unittest

from transformers import RoFormerConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
 import tensorflow as tf

 from transformers import (
     TFRoFormerForCausalLM,
     TFRoFormerForMaskedLM,
     TFRoFormerForMultipleChoice,
     TFRoFormerForQuestionAnswering,
     TFRoFormerForSequenceClassification,
     TFRoFormerForTokenClassification,
     TFRoFormerModel,
 )
 from transformers.models.roformer.modeling_tf_roformer import (
     TFRoFormerSelfAttention,
     TFRoFormerSinusoidalPositionalEmbedding,
 )






class 					__SCREAMING_SNAKE_CASE						:







  '''simple docstring'''






  def __init__(			self				,		snake_case_				,		snake_case_=1_3				,		snake_case_=7				,		snake_case_=True				,		snake_case_=True				,		snake_case_=True				,		snake_case_=True				,		snake_case_=9_9				,		snake_case_=3_2				,		snake_case_=2				,		snake_case_=4				,		snake_case_=3_7				,		snake_case_="gelu"				,		snake_case_=0.1				,		snake_case_=0.1				,		snake_case_=5_1_2				,		snake_case_=1_6				,		snake_case_=2				,		snake_case_=0.02				,		snake_case_=3				,		snake_case_=4				,		snake_case_=None				,		):




       '''simple docstring'''

       UpperCAmelCase_							:       List[str]       =				parent
       UpperCAmelCase_							:       List[str]       =				1_3
       UpperCAmelCase_							:       str       =				7
       UpperCAmelCase_							:       Any       =				True
       UpperCAmelCase_							:       Optional[Any]       =				True
       UpperCAmelCase_							:       Union[str, Any]       =				True
       UpperCAmelCase_							:       int       =				True
       UpperCAmelCase_							:       Optional[Any]       =				9_9
       UpperCAmelCase_							:       Optional[int]       =				3_2
       UpperCAmelCase_							:       Optional[Any]       =				2
       UpperCAmelCase_							:       List[str]       =				4
       UpperCAmelCase_							:       Dict       =				3_7
       UpperCAmelCase_							:       Dict       =				'gelu'
       UpperCAmelCase_							:       Optional[Any]       =				0.1
       UpperCAmelCase_							:       Dict       =				0.1
       UpperCAmelCase_							:       Dict       =				5_1_2
       UpperCAmelCase_							:       List[Any]       =				1_6
       UpperCAmelCase_							:       Optional[int]       =				2
       UpperCAmelCase_							:       List[str]       =				0.02
       UpperCAmelCase_							:       List[str]       =				3
       UpperCAmelCase_							:       str       =				4
       UpperCAmelCase_							:       Tuple       =				None


  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       UpperCAmelCase_							:       Any       =				ids_tensor([self.batch_size, self.seq_length]				,		self.vocab_size						)

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

       UpperCAmelCase_							:       Tuple       =				None
       if self.use_token_type_ids:
            UpperCAmelCase_							:       Any       =				ids_tensor([self.batch_size, self.seq_length]				,		self.type_vocab_size						)

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

       UpperCAmelCase_							:       Optional[Any]       =				RoFormerConfig(
           vocab_size=self.vocab_size				,		hidden_size=self.hidden_size				,		num_hidden_layers=self.num_hidden_layers				,		num_attention_heads=self.num_attention_heads				,		intermediate_size=self.intermediate_size				,		hidden_act=self.hidden_act				,		hidden_dropout_prob=self.hidden_dropout_prob				,		attention_probs_dropout_prob=self.attention_probs_dropout_prob				,		max_position_embeddings=self.max_position_embeddings				,		type_vocab_size=self.type_vocab_size				,		initializer_range=self.initializer_range				,		return_dict=__SCREAMING_SNAKE_CASE				,		)

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


  def _UpperCamelCase					(			self				,		snake_case_				,		snake_case_				,		snake_case_				,		snake_case_				,		snake_case_				,		snake_case_				,		snake_case_						):




       '''simple docstring'''

       UpperCAmelCase_							:       Optional[Any]       =				TFRoFormerModel(config=__SCREAMING_SNAKE_CASE						)
       UpperCAmelCase_							:       Any       =				{'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}

       UpperCAmelCase_							:       Tuple       =				[input_ids, input_mask]
       UpperCAmelCase_							:       Optional[Any]       =				model(__SCREAMING_SNAKE_CASE						)

       UpperCAmelCase_							:       int       =				model(__SCREAMING_SNAKE_CASE						)

       self.parent.assertEqual(result.last_hidden_state.shape				,		(self.batch_size, self.seq_length, self.hidden_size)						)


  def _UpperCamelCase					(			self				,		snake_case_				,		snake_case_				,		snake_case_				,		snake_case_				,		snake_case_				,		snake_case_				,		snake_case_						):




       '''simple docstring'''

       UpperCAmelCase_							:       str       =				True
       UpperCAmelCase_							:       Union[str, Any]       =				TFRoFormerForCausalLM(config=__SCREAMING_SNAKE_CASE						)
       UpperCAmelCase_							:       Union[str, Any]       =				{
           'input_ids': input_ids,
           'attention_mask': input_mask,
           'token_type_ids': token_type_ids,
       }
       UpperCAmelCase_							:       Optional[Any]       =				model(__SCREAMING_SNAKE_CASE						)['logits']
       self.parent.assertListEqual(
           list(prediction_scores.numpy().shape						)				,		[self.batch_size, self.seq_length, self.vocab_size]						)


  def _UpperCamelCase					(			self				,		snake_case_				,		snake_case_				,		snake_case_				,		snake_case_				,		snake_case_				,		snake_case_				,		snake_case_						):




       '''simple docstring'''

       UpperCAmelCase_							:       Union[str, Any]       =				TFRoFormerForMaskedLM(config=__SCREAMING_SNAKE_CASE						)
       UpperCAmelCase_							:       Optional[Any]       =				{
           'input_ids': input_ids,
           'attention_mask': input_mask,
           'token_type_ids': token_type_ids,
       }
       UpperCAmelCase_							:       Optional[int]       =				model(__SCREAMING_SNAKE_CASE						)
       self.parent.assertEqual(result.logits.shape				,		(self.batch_size, self.seq_length, self.vocab_size)						)


  def _UpperCamelCase					(			self				,		snake_case_				,		snake_case_				,		snake_case_				,		snake_case_				,		snake_case_				,		snake_case_				,		snake_case_						):




       '''simple docstring'''

       UpperCAmelCase_							:       str       =				self.num_labels
       UpperCAmelCase_							:       Dict       =				TFRoFormerForSequenceClassification(config=__SCREAMING_SNAKE_CASE						)
       UpperCAmelCase_							:       List[str]       =				{
           'input_ids': input_ids,
           'attention_mask': input_mask,
           'token_type_ids': token_type_ids,
       }

       UpperCAmelCase_							:       Dict       =				model(__SCREAMING_SNAKE_CASE						)
       self.parent.assertEqual(result.logits.shape				,		(self.batch_size, self.num_labels)						)


  def _UpperCamelCase					(			self				,		snake_case_				,		snake_case_				,		snake_case_				,		snake_case_				,		snake_case_				,		snake_case_				,		snake_case_						):




       '''simple docstring'''

       UpperCAmelCase_							:       int       =				self.num_choices
       UpperCAmelCase_							:       List[str]       =				TFRoFormerForMultipleChoice(config=__SCREAMING_SNAKE_CASE						)
       UpperCAmelCase_							:       Optional[Any]       =				tf.tile(tf.expand_dims(__SCREAMING_SNAKE_CASE				,		1						)				,		(1, self.num_choices, 1)						)
       UpperCAmelCase_							:       Union[str, Any]       =				tf.tile(tf.expand_dims(__SCREAMING_SNAKE_CASE				,		1						)				,		(1, self.num_choices, 1)						)
       UpperCAmelCase_							:       Tuple       =				tf.tile(tf.expand_dims(__SCREAMING_SNAKE_CASE				,		1						)				,		(1, self.num_choices, 1)						)
       UpperCAmelCase_							:       str       =				{
           'input_ids': multiple_choice_inputs_ids,
           'attention_mask': multiple_choice_input_mask,
           'token_type_ids': multiple_choice_token_type_ids,
       }
       UpperCAmelCase_							:       Tuple       =				model(__SCREAMING_SNAKE_CASE						)
       self.parent.assertEqual(result.logits.shape				,		(self.batch_size, self.num_choices)						)


  def _UpperCamelCase					(			self				,		snake_case_				,		snake_case_				,		snake_case_				,		snake_case_				,		snake_case_				,		snake_case_				,		snake_case_						):




       '''simple docstring'''

       UpperCAmelCase_							:       Dict       =				self.num_labels
       UpperCAmelCase_							:       List[Any]       =				TFRoFormerForTokenClassification(config=__SCREAMING_SNAKE_CASE						)
       UpperCAmelCase_							:       Dict       =				{
           'input_ids': input_ids,
           'attention_mask': input_mask,
           'token_type_ids': token_type_ids,
       }
       UpperCAmelCase_							:       Optional[Any]       =				model(__SCREAMING_SNAKE_CASE						)
       self.parent.assertEqual(result.logits.shape				,		(self.batch_size, self.seq_length, self.num_labels)						)


  def _UpperCamelCase					(			self				,		snake_case_				,		snake_case_				,		snake_case_				,		snake_case_				,		snake_case_				,		snake_case_				,		snake_case_						):




       '''simple docstring'''

       UpperCAmelCase_							:       List[str]       =				TFRoFormerForQuestionAnswering(config=__SCREAMING_SNAKE_CASE						)
       UpperCAmelCase_							:       Tuple       =				{
           'input_ids': input_ids,
           'attention_mask': input_mask,
           'token_type_ids': token_type_ids,
       }

       UpperCAmelCase_							:       Any       =				model(__SCREAMING_SNAKE_CASE						)
       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						):




       '''simple docstring'''

       UpperCAmelCase_							:       List[Any]       =				self.prepare_config_and_inputs()
       (
           (
           UpperCAmelCase_   
       )       ,      (
           UpperCAmelCase_   
       )       ,      (
           UpperCAmelCase_   
       )       ,      (
           UpperCAmelCase_   
       )       ,      (
           UpperCAmelCase_   
       )       ,      (
           UpperCAmelCase_   
       )       ,      (
           UpperCAmelCase_   
       )       ,         
       )							:       Dict       =				config_and_inputs
       UpperCAmelCase_							:       Tuple       =				{'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
       return config, inputs_dict







@require_tf
class 					__SCREAMING_SNAKE_CASE						(	_A						,     _A						,     unittest.TestCase	):







  '''simple docstring'''






  lowerCamelCase_		:Tuple						   =		(
      (
          TFRoFormerModel,
          TFRoFormerForCausalLM,
          TFRoFormerForMaskedLM,
          TFRoFormerForQuestionAnswering,
          TFRoFormerForSequenceClassification,
          TFRoFormerForTokenClassification,
          TFRoFormerForMultipleChoice,
      )
      if is_tf_available()
      else ()
  )
  lowerCamelCase_		:Any						   =		(
      {
          '''feature-extraction''': TFRoFormerModel,
          '''fill-mask''': TFRoFormerForMaskedLM,
          '''question-answering''': TFRoFormerForQuestionAnswering,
          '''text-classification''': TFRoFormerForSequenceClassification,
          '''text-generation''': TFRoFormerForCausalLM,
          '''token-classification''': TFRoFormerForTokenClassification,
          '''zero-shot''': TFRoFormerForSequenceClassification,
      }
      if is_tf_available()
      else {}
  )

  lowerCamelCase_		:Tuple						   =		False
  lowerCamelCase_		:List[str]						   =		False


  def _UpperCamelCase					(			self				,		snake_case_				,		snake_case_				,		snake_case_				,		snake_case_				,		snake_case_						):




       '''simple docstring'''

       if pipeline_test_casse_name == "TextGenerationPipelineTests":
            return True

       return False


  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       UpperCAmelCase_							:       Union[str, Any]       =				TFRoFormerModelTester(self						)
       UpperCAmelCase_							:       Optional[Any]       =				ConfigTester(self				,		config_class=__SCREAMING_SNAKE_CASE				,		hidden_size=3_7						)


  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       self.config_tester.run_common_tests()


  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       UpperCAmelCase_							:       Tuple       =				self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*__SCREAMING_SNAKE_CASE						)


  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       UpperCAmelCase_							:       Dict       =				self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_masked_lm(*__SCREAMING_SNAKE_CASE						)


  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       UpperCAmelCase_							:       List[Any]       =				self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_lm_head(*__SCREAMING_SNAKE_CASE						)


  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       UpperCAmelCase_							:       List[Any]       =				self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_multiple_choice(*__SCREAMING_SNAKE_CASE						)


  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       UpperCAmelCase_							:       Union[str, Any]       =				self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_question_answering(*__SCREAMING_SNAKE_CASE						)


  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       UpperCAmelCase_							:       str       =				self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_sequence_classification(*__SCREAMING_SNAKE_CASE						)


  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       UpperCAmelCase_							:       Tuple       =				self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_token_classification(*__SCREAMING_SNAKE_CASE						)






  @slow
  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       UpperCAmelCase_							:       Tuple       =				TFRoFormerModel.from_pretrained('junnyu/roformer_chinese_base'						)
       self.assertIsNotNone(__SCREAMING_SNAKE_CASE						)







@require_tf
class 					__SCREAMING_SNAKE_CASE						(	unittest.TestCase	):







  '''simple docstring'''






  @slow
  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       UpperCAmelCase_							:       List[str]       =				TFRoFormerForMaskedLM.from_pretrained('junnyu/roformer_chinese_base'						)
       UpperCAmelCase_							:       Dict       =				tf.constant([[0, 1, 2, 3, 4, 5]]						)
       UpperCAmelCase_							:       Any       =				model(__SCREAMING_SNAKE_CASE						)[0]

       # TODO Replace vocab size
       UpperCAmelCase_							:       List[str]       =				5_0_0_0_0

       UpperCAmelCase_							:       List[str]       =				[1, 6, vocab_size]
       self.assertEqual(output.shape				,		__SCREAMING_SNAKE_CASE						)

       print(output[:, :3, :3]						)

       # TODO Replace values below with what was printed above.
       UpperCAmelCase_							:       List[Any]       =				tf.constant(
           [
               [
                   [-0.12_05_33_41, -1.0_26_49_01, 0.29_22_19_46],
                   [-1.5_13_37_83, 0.19_74_33, 0.15_19_06_07],
                   [-5.0_13_54_03, -3.90_02_56, -0.84_03_87_64],
               ]
           ]						)
       tf.debugging.assert_near(output[:, :3, :3]				,		__SCREAMING_SNAKE_CASE				,		atol=1E-4						)







@require_tf
class 					__SCREAMING_SNAKE_CASE						(	unittest.TestCase	):







  '''simple docstring'''






  lowerCamelCase_		:Union[str, Any]						   =		1E-4


  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       UpperCAmelCase_							:       Any       =				tf.constant([[4, 1_0]]						)
       UpperCAmelCase_							:       Tuple       =				TFRoFormerSinusoidalPositionalEmbedding(num_positions=6				,		embedding_dim=6						)

       UpperCAmelCase_							:       int       =				emba(input_ids.shape						)
       UpperCAmelCase_							:       Any       =				tf.constant(
           [[0.00_00, 0.00_00, 0.00_00, 1.00_00, 1.00_00, 1.00_00], [0.84_15, 0.04_64, 0.00_22, 0.54_03, 0.99_89, 1.00_00]]						)

       tf.debugging.assert_near(__SCREAMING_SNAKE_CASE				,		__SCREAMING_SNAKE_CASE				,		atol=self.tolerance						)






  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       UpperCAmelCase_							:       List[str]       =				tf.constant(
           [
               [0.00_00, 0.00_00, 0.00_00, 0.00_00, 0.00_00],
               [0.84_15, 0.82_19, 0.80_20, 0.78_19, 0.76_17],
               [0.90_93, 0.93_64, 0.95_81, 0.97_49, 0.98_70],
           ]						)
       UpperCAmelCase_							:       List[Any]       =				TFRoFormerSinusoidalPositionalEmbedding(num_positions=5_1_2				,		embedding_dim=5_1_2						)
       emba([2, 1_6, 5_1_2]						)
       UpperCAmelCase_							:       Any       =				emba.weight[:3, :5]

       tf.debugging.assert_near(__SCREAMING_SNAKE_CASE				,		__SCREAMING_SNAKE_CASE				,		atol=self.tolerance						)







@require_tf
class 					__SCREAMING_SNAKE_CASE						(	unittest.TestCase	):







  '''simple docstring'''






  lowerCamelCase_		:Optional[Any]						   =		1E-4


  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       UpperCAmelCase_							:       Any       =				tf.reshape(tf.range(2 * 1_2 * 1_6 * 6_4				,		dtype=tf.floataa						)				,		shape=(2, 1_2, 1_6, 6_4)						) / 1_0_0

       UpperCAmelCase_							:       List[Any]       =				-tf.reshape(tf.range(2 * 1_2 * 1_6 * 6_4				,		dtype=tf.floataa						)				,		shape=(2, 1_2, 1_6, 6_4)						) / 1_0_0

       UpperCAmelCase_							:       Dict       =				TFRoFormerSinusoidalPositionalEmbedding(num_positions=3_2				,		embedding_dim=6_4						)
       UpperCAmelCase_							:       Optional[int]       =				embed_positions([2, 1_6, 7_6_8]						)[None, None, :, :]

       UpperCAmelCase_       ,      UpperCAmelCase_							:       Dict       =				TFRoFormerSelfAttention.apply_rotary_position_embeddings(
           __SCREAMING_SNAKE_CASE				,		__SCREAMING_SNAKE_CASE				,		__SCREAMING_SNAKE_CASE						)

       UpperCAmelCase_							:       List[str]       =				tf.constant(
           [
               [0.00_00, 0.01_00, 0.02_00, 0.03_00, 0.04_00, 0.05_00, 0.06_00, 0.07_00],
               [-0.20_12, 0.88_97, 0.02_63, 0.94_01, 0.20_74, 0.94_63, 0.34_81, 0.93_43],
               [-1.70_57, 0.62_71, -1.21_45, 1.38_97, -0.63_03, 1.76_47, -0.11_73, 1.89_85],
               [-2.17_31, -1.63_97, -2.73_58, 0.28_54, -2.18_40, 1.71_83, -1.30_18, 2.48_71],
               [0.27_17, -3.61_73, -2.92_06, -2.19_88, -3.66_38, 0.38_58, -2.91_55, 2.29_80],
               [3.98_59, -2.15_80, -0.79_84, -4.49_04, -4.11_81, -2.02_52, -4.47_82, 1.12_53],
           ]						)
       UpperCAmelCase_							:       Union[str, Any]       =				tf.constant(
           [
               [0.00_00, -0.01_00, -0.02_00, -0.03_00, -0.04_00, -0.05_00, -0.06_00, -0.07_00],
               [0.20_12, -0.88_97, -0.02_63, -0.94_01, -0.20_74, -0.94_63, -0.34_81, -0.93_43],
               [1.70_57, -0.62_71, 1.21_45, -1.38_97, 0.63_03, -1.76_47, 0.11_73, -1.89_85],
               [2.17_31, 1.63_97, 2.73_58, -0.28_54, 2.18_40, -1.71_83, 1.30_18, -2.48_71],
               [-0.27_17, 3.61_73, 2.92_06, 2.19_88, 3.66_38, -0.38_58, 2.91_55, -2.29_80],
               [-3.98_59, 2.15_80, 0.79_84, 4.49_04, 4.11_81, 2.02_52, 4.47_82, -1.12_53],
           ]						)

       tf.debugging.assert_near(query_layer[0, 0, :6, :8]				,		__SCREAMING_SNAKE_CASE				,		atol=self.tolerance						)
       tf.debugging.assert_near(key_layer[0, 0, :6, :8]				,		__SCREAMING_SNAKE_CASE				,		atol=self.tolerance						)







'''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 					__SCREAMING_SNAKE_CASE						:







   '''simple docstring'''






   @staticmethod
   def _UpperCamelCase					(			*snake_case_				,		**snake_case_						):




        '''simple docstring'''

        pass


def  _lowerCamelCase   (				lowerCamelCase_ :							Image       ):



     """simple docstring"""





     UpperCAmelCase_							:       Dict       =				hashlib.mda(image.tobytes()       )
     return m.hexdigest()






@is_pipeline_test
@require_vision
@require_timm
@require_torch
class 					__SCREAMING_SNAKE_CASE						(	unittest.TestCase	):







  '''simple docstring'''






  lowerCamelCase_		:Optional[int]						   =		MODEL_FOR_DEPTH_ESTIMATION_MAPPING


  def _UpperCamelCase					(			self				,		snake_case_				,		snake_case_				,		snake_case_						):




       '''simple docstring'''

       UpperCAmelCase_							:       str       =				DepthEstimationPipeline(model=snake_case_				,		image_processor=snake_case_						)
       return depth_estimator, [
           "./tests/fixtures/tests_samples/COCO/000000039769.png",
           "./tests/fixtures/tests_samples/COCO/000000039769.png",
       ]


  def _UpperCamelCase					(			self				,		snake_case_				,		snake_case_						):




       '''simple docstring'''

       UpperCAmelCase_							:       Dict       =				depth_estimator('./tests/fixtures/tests_samples/COCO/000000039769.png'						)
       self.assertEqual({'predicted_depth': ANY(torch.Tensor						), 'depth': ANY(Image.Image						)}				,		snake_case_						)
       import datasets

       UpperCAmelCase_							:       Optional[int]       =				datasets.load_dataset('hf-internal-testing/fixtures_image_utils'				,		'image'				,		split='test'						)
       UpperCAmelCase_							:       Union[str, 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						)},
           ]				,		snake_case_				,		)


  @require_tf
  @unittest.skip('Depth estimation is not implemented in TF'						)
  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       pass


  @slow
  @require_torch
  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       UpperCAmelCase_							:       Any       =				'Intel/dpt-large'
       UpperCAmelCase_							:       Dict       =				pipeline('depth-estimation'				,		model=snake_case_						)
       UpperCAmelCase_							:       Optional[int]       =				depth_estimator('http://images.cocodataset.org/val2017/000000039769.jpg'						)
       UpperCAmelCase_							:       Union[str, Any]       =				hashimage(outputs['depth']						)

       # This seems flaky.
       # self.assertEqual(outputs["depth"], "1a39394e282e9f3b0741a90b9f108977")
       self.assertEqual(nested_simplify(outputs['predicted_depth'].max().item()						)				,		29.3_04						)
       self.assertEqual(nested_simplify(outputs['predicted_depth'].min().item()						)				,		2.6_62						)






  @require_torch
  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       self.skipTest('There is not hf-internal-testing tiny model for either GLPN nor DPT'						)






