
import inspect
import unittest
import warnings
from typing import List

from transformers import EfficientFormerConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

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


if is_torch_available():
				import torch

				from transformers import (
				    MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
				    MODEL_MAPPING,
				    EfficientFormerForImageClassification,
				    EfficientFormerForImageClassificationWithTeacher,
				    EfficientFormerModel,
				)
				from transformers.models.efficientformer.modeling_efficientformer import (
				    EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
				)


if is_vision_available():
				from PIL import Image

				from transformers import EfficientFormerImageProcessor






class 							SCREAMING_SNAKE_CASE__     :

							"""simple docstring"""






							def __init__(	self  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_ = 13  ,						SCREAMING_SNAKE_CASE_ = 64  ,						SCREAMING_SNAKE_CASE_ = 2  ,						SCREAMING_SNAKE_CASE_ = 3  ,						SCREAMING_SNAKE_CASE_ = 3  ,						SCREAMING_SNAKE_CASE_ = True  ,						SCREAMING_SNAKE_CASE_ = True  ,						SCREAMING_SNAKE_CASE_ = 128  ,						SCREAMING_SNAKE_CASE_=[16, 32, 64, 128]  ,						SCREAMING_SNAKE_CASE_ = 7  ,						SCREAMING_SNAKE_CASE_ = 4  ,						SCREAMING_SNAKE_CASE_ = 37  ,						SCREAMING_SNAKE_CASE_ = "gelu"  ,						SCREAMING_SNAKE_CASE_ = 0.1  ,						SCREAMING_SNAKE_CASE_ = 0.1  ,						SCREAMING_SNAKE_CASE_ = 10  ,						SCREAMING_SNAKE_CASE_ = 0.0_2  ,						SCREAMING_SNAKE_CASE_ = 2  ,						SCREAMING_SNAKE_CASE_ = 1  ,						SCREAMING_SNAKE_CASE_ = 128  ,						SCREAMING_SNAKE_CASE_ = [2, 2, 2, 2]  ,						SCREAMING_SNAKE_CASE_ = 2  ,						SCREAMING_SNAKE_CASE_ = 2  ,						)-> Any:





													'''simple docstring'''






													__UpperCamelCase  	=      parent
													__UpperCamelCase  	=      batch_size
													__UpperCamelCase  	=      image_size
													__UpperCamelCase  	=      patch_size
													__UpperCamelCase  	=      num_channels
													__UpperCamelCase  	=      is_training
													__UpperCamelCase  	=      use_labels
													__UpperCamelCase  	=      hidden_size
													__UpperCamelCase  	=      num_hidden_layers
													__UpperCamelCase  	=      num_attention_heads
													__UpperCamelCase  	=      intermediate_size
													__UpperCamelCase  	=      hidden_act
													__UpperCamelCase  	=      hidden_dropout_prob
													__UpperCamelCase  	=      attention_probs_dropout_prob
													__UpperCamelCase  	=      type_sequence_label_size
													__UpperCamelCase  	=      initializer_range
													__UpperCamelCase  	=      encoder_stride
													__UpperCamelCase  	=      num_attention_outputs
													__UpperCamelCase  	=      embed_dim
													__UpperCamelCase  	=      embed_dim + 1
													__UpperCamelCase  	=      resolution
													__UpperCamelCase  	=      depths
													__UpperCamelCase  	=      hidden_sizes
													__UpperCamelCase  	=      dim
													__UpperCamelCase  	=      mlp_expansion_ratio



							def      A__		(	self      )-> str:





													'''simple docstring'''






													__UpperCamelCase  	=      floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]      )

													__UpperCamelCase  	=      None
													if self.use_labels:
																			__UpperCamelCase  	=      ids_tensor([self.batch_size]  ,						self.type_sequence_label_size      )

													__UpperCamelCase  	=      self.get_config()
													return config, pixel_values, labels



							def      A__		(	self      )-> Dict:





													'''simple docstring'''






													return EfficientFormerConfig(
													    image_size=self.image_size  ,						patch_size=self.patch_size  ,						num_channels=self.num_channels  ,						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  ,						is_decoder=SCREAMING_SNAKE_CASE_  ,						initializer_range=self.initializer_range  ,						encoder_stride=self.encoder_stride  ,						resolution=self.resolution  ,						depths=self.depths  ,						hidden_sizes=self.hidden_sizes  ,						dim=self.dim  ,						mlp_expansion_ratio=self.mlp_expansion_ratio  ,						)



							def      A__		(	self  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_      )-> Union[str, Any]:





													'''simple docstring'''






													__UpperCamelCase  	=      EfficientFormerModel(config=SCREAMING_SNAKE_CASE_      )
													model.to(SCREAMING_SNAKE_CASE_      )
													model.eval()
													__UpperCamelCase  	=      model(SCREAMING_SNAKE_CASE_      )
													self.parent.assertEqual(result.last_hidden_state.shape  ,						(self.batch_size, self.seq_length, self.hidden_size)      )



							def      A__		(	self  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_      )-> List[Any]:





													'''simple docstring'''






													__UpperCamelCase  	=      self.type_sequence_label_size
													__UpperCamelCase  	=      EfficientFormerForImageClassification(SCREAMING_SNAKE_CASE_      )
													model.to(SCREAMING_SNAKE_CASE_      )
													model.eval()
													__UpperCamelCase  	=      model(SCREAMING_SNAKE_CASE_  ,						labels=SCREAMING_SNAKE_CASE_      )
													self.parent.assertEqual(result.logits.shape  ,						(self.batch_size, self.type_sequence_label_size)      )

													# test greyscale images
													__UpperCamelCase  	=      1
													__UpperCamelCase  	=      EfficientFormerForImageClassification(SCREAMING_SNAKE_CASE_      )
													model.to(SCREAMING_SNAKE_CASE_      )
													model.eval()

													__UpperCamelCase  	=      floats_tensor([self.batch_size, 1, self.image_size, self.image_size]      )
													__UpperCamelCase  	=      model(SCREAMING_SNAKE_CASE_      )
													self.parent.assertEqual(result.logits.shape  ,						(self.batch_size, self.type_sequence_label_size)      )



							def      A__		(	self      )-> Tuple:





													'''simple docstring'''






													__UpperCamelCase  	=      self.prepare_config_and_inputs()
													(
													    (
													    __UpperCamelCase
													)	,				(
													    __UpperCamelCase
													)	,				(
													    __UpperCamelCase
													)	,				
													)  	=      config_and_inputs
													__UpperCamelCase  	=      {'''pixel_values''': pixel_values}
													return config, inputs_dict






@require_torch
class 							SCREAMING_SNAKE_CASE__     (				SCREAMING_SNAKE_CASE_ ,		SCREAMING_SNAKE_CASE_ ,		unittest.TestCase     ):

							"""simple docstring"""






							_snake_case  		=	(
							    (
							        EfficientFormerModel,
							        EfficientFormerForImageClassificationWithTeacher,
							        EfficientFormerForImageClassification,
							    )
							    if is_torch_available()
							    else ()
							)
							_snake_case  		=	(
							    {
							        'feature-extraction': EfficientFormerModel,
							        'image-classification': (
							            EfficientFormerForImageClassification,
							            EfficientFormerForImageClassificationWithTeacher,
							        ),
							    }
							    if is_torch_available()
							    else {}
							)
							_snake_case  		=	False

							_snake_case  		=	False
							_snake_case  		=	False
							_snake_case  		=	False



							def      A__		(	self      )-> Tuple:





													'''simple docstring'''






													__UpperCamelCase  	=      EfficientFormerModelTester(self      )
													__UpperCamelCase  	=      ConfigTester(
													    self  ,						config_class=SCREAMING_SNAKE_CASE_  ,						has_text_modality=SCREAMING_SNAKE_CASE_  ,						hidden_size=37      )



							def      A__		(	self      )-> Dict:





													'''simple docstring'''






													self.config_tester.run_common_tests()



							@unittest.skip(reason='''EfficientFormer does not use inputs_embeds'''      )
							def      A__		(	self      )-> List[Any]:





													'''simple docstring'''






													pass



							@unittest.skip(reason='''EfficientFormer does not support input and output embeddings'''      )
							def      A__		(	self      )-> int:





													'''simple docstring'''






													pass



							def      A__		(	self      )-> int:





													'''simple docstring'''






													__UpperCamelCase	,				__UpperCamelCase  	=      self.model_tester.prepare_config_and_inputs_for_common()

													for model_class in self.all_model_classes:
																			__UpperCamelCase  	=      model_class(SCREAMING_SNAKE_CASE_      )
																			__UpperCamelCase  	=      inspect.signature(model.forward      )
																			# signature.parameters is an OrderedDict => so arg_names order is deterministic
																			__UpperCamelCase  	=      [*signature.parameters.keys()]

																			__UpperCamelCase  	=      ['''pixel_values''']
																			self.assertListEqual(arg_names[:1]  ,						SCREAMING_SNAKE_CASE_      )



							def      A__		(	self      )-> Optional[Any]:





													'''simple docstring'''






													def check_hidden_states_output(SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_      ):
																			__UpperCamelCase  	=      model_class(SCREAMING_SNAKE_CASE_      )
																			model.to(SCREAMING_SNAKE_CASE_      )
																			model.eval()

																			with torch.no_grad():
																									__UpperCamelCase  	=      model(**self._prepare_for_class(SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_      )      )

																			__UpperCamelCase  	=      outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

																			__UpperCamelCase  	=      getattr(
																			    self.model_tester  ,						'''expected_num_hidden_layers'''  ,						self.model_tester.num_hidden_layers + 1      )
																			self.assertEqual(len(SCREAMING_SNAKE_CASE_      )  ,						SCREAMING_SNAKE_CASE_      )

																			if hasattr(self.model_tester  ,						'''encoder_seq_length'''      ):
																									__UpperCamelCase  	=      self.model_tester.encoder_seq_length
																									if hasattr(self.model_tester  ,						'''chunk_length'''      ) and self.model_tester.chunk_length > 1:
																															__UpperCamelCase  	=      seq_length * self.model_tester.chunk_length
																			else:
																									__UpperCamelCase  	=      self.model_tester.seq_length

																			self.assertListEqual(
																			    list(hidden_states[-1].shape[-2:]      )  ,						[seq_length, self.model_tester.hidden_size]  ,						)

																			if config.is_encoder_decoder:
																									__UpperCamelCase  	=      outputs.decoder_hidden_states

																									self.assertIsInstance(SCREAMING_SNAKE_CASE_  ,						(list, tuple)      )
																									self.assertEqual(len(SCREAMING_SNAKE_CASE_      )  ,						SCREAMING_SNAKE_CASE_      )
																									__UpperCamelCase  	=      getattr(self.model_tester  ,						'''seq_length'''  ,						SCREAMING_SNAKE_CASE_      )
																									__UpperCamelCase  	=      getattr(self.model_tester  ,						'''decoder_seq_length'''  ,						SCREAMING_SNAKE_CASE_      )

																									self.assertListEqual(
																									    list(hidden_states[-1].shape[-2:]      )  ,						[decoder_seq_length, self.model_tester.hidden_size]  ,						)

													__UpperCamelCase	,				__UpperCamelCase  	=      self.model_tester.prepare_config_and_inputs_for_common()

													for model_class in self.all_model_classes:
																			__UpperCamelCase  	=      True
																			check_hidden_states_output(SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_      )

																			# check that output_hidden_states also work using config
																			del inputs_dict["output_hidden_states"]
																			__UpperCamelCase  	=      True

																			check_hidden_states_output(SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_      )



							def      A__		(	self  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_=False      )-> str:





													'''simple docstring'''






													__UpperCamelCase  	=      super()._prepare_for_class(SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						return_labels=SCREAMING_SNAKE_CASE_      )

													if return_labels:
																			if model_class.__name__ == "EfficientFormerForImageClassificationWithTeacher":
																									del inputs_dict["labels"]

													return inputs_dict



							def      A__		(	self      )-> Optional[Any]:





													'''simple docstring'''






													__UpperCamelCase  	=      self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE_      )



							@unittest.skip(reason='''EfficientFormer does not implement masked image modeling yet'''      )
							def      A__		(	self      )-> Dict:





													'''simple docstring'''






													__UpperCamelCase  	=      self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_for_masked_image_modeling(*SCREAMING_SNAKE_CASE_      )



							def      A__		(	self      )-> Optional[int]:





													'''simple docstring'''






													__UpperCamelCase  	=      self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_for_image_classification(*SCREAMING_SNAKE_CASE_      )



							def      A__		(	self      )-> int:





													'''simple docstring'''






													if not self.model_tester.is_training:
																			return

													__UpperCamelCase	,				__UpperCamelCase  	=      self.model_tester.prepare_config_and_inputs_for_common()
													__UpperCamelCase  	=      True

													for model_class in self.all_model_classes:
																			# EfficientFormerForImageClassificationWithTeacher supports inference-only
																			if (
																			    model_class in get_values(SCREAMING_SNAKE_CASE_      )
																			    or model_class.__name__ == "EfficientFormerForImageClassificationWithTeacher"
																			):
																									continue
																			__UpperCamelCase  	=      model_class(SCREAMING_SNAKE_CASE_      )
																			model.to(SCREAMING_SNAKE_CASE_      )
																			model.train()
																			__UpperCamelCase  	=      self._prepare_for_class(SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						return_labels=SCREAMING_SNAKE_CASE_      )
																			__UpperCamelCase  	=      model(**SCREAMING_SNAKE_CASE_      ).loss
																			loss.backward()



							def      A__		(	self      )-> str:





													'''simple docstring'''






													__UpperCamelCase	,				__UpperCamelCase  	=      self.model_tester.prepare_config_and_inputs_for_common()

													__UpperCamelCase  	=      [
													    {'''title''': '''multi_label_classification''', '''num_labels''': 2, '''dtype''': torch.float},
													    {'''title''': '''single_label_classification''', '''num_labels''': 1, '''dtype''': torch.long},
													    {'''title''': '''regression''', '''num_labels''': 1, '''dtype''': torch.float},
													]

													for model_class in self.all_model_classes:
																			if (
																			    model_class
																			    not in [
																			        *get_values(SCREAMING_SNAKE_CASE_      ),
																			    ]
																			    or model_class.__name__ == "EfficientFormerForImageClassificationWithTeacher"
																			):
																									continue

																			for problem_type in problem_types:
																									with self.subTest(msg=F"Testing {model_class} with {problem_type['title']}"      ):
																															__UpperCamelCase  	=      problem_type['''title''']
																															__UpperCamelCase  	=      problem_type['''num_labels''']

																															__UpperCamelCase  	=      model_class(SCREAMING_SNAKE_CASE_      )
																															model.to(SCREAMING_SNAKE_CASE_      )
																															model.train()

																															__UpperCamelCase  	=      self._prepare_for_class(SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						return_labels=SCREAMING_SNAKE_CASE_      )

																															if problem_type["num_labels"] > 1:
																																					__UpperCamelCase  	=      inputs['''labels'''].unsqueeze(1      ).repeat(1  ,						problem_type['''num_labels''']      )

																															__UpperCamelCase  	=      inputs['''labels'''].to(problem_type['''dtype''']      )

																															# This tests that we do not trigger the warning form PyTorch "Using a target size that is different
																															# to the input size. This will likely lead to incorrect results due to broadcasting. Please ensure
																															# they have the same size." which is a symptom something in wrong for the regression problem.
																															# See https://github.com/huggingface/transformers/issues/11780
																															with warnings.catch_warnings(record=SCREAMING_SNAKE_CASE_      ) as warning_list:
																																					__UpperCamelCase  	=      model(**SCREAMING_SNAKE_CASE_      ).loss
																															for w in warning_list:
																																					if "Using a target size that is different to the input size" in str(w.message      ):
																																											raise ValueError(
																																											    F"Something is going wrong in the regression problem: intercepted {w.message}"      )

																															loss.backward()



							@slow
							def      A__		(	self      )-> Any:





													'''simple docstring'''






													for model_name in EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																			__UpperCamelCase  	=      EfficientFormerModel.from_pretrained(SCREAMING_SNAKE_CASE_      )
																			self.assertIsNotNone(SCREAMING_SNAKE_CASE_      )



							def      A__		(	self      )-> Union[str, Any]:





													'''simple docstring'''






													__UpperCamelCase	,				__UpperCamelCase  	=      self.model_tester.prepare_config_and_inputs_for_common()
													__UpperCamelCase  	=      True

													__UpperCamelCase  	=      getattr(self.model_tester  ,						'''seq_length'''  ,						SCREAMING_SNAKE_CASE_      )
													__UpperCamelCase  	=      getattr(self.model_tester  ,						'''encoder_seq_length'''  ,						SCREAMING_SNAKE_CASE_      )
													__UpperCamelCase  	=      getattr(self.model_tester  ,						'''key_length'''  ,						SCREAMING_SNAKE_CASE_      )
													__UpperCamelCase  	=      getattr(self.model_tester  ,						'''chunk_length'''  ,						SCREAMING_SNAKE_CASE_      )

													if chunk_length is not None and hasattr(self.model_tester  ,						'''num_hashes'''      ):
																			__UpperCamelCase  	=      encoder_seq_length * self.model_tester.num_hashes

													for model_class in self.all_model_classes:
																			__UpperCamelCase  	=      True
																			__UpperCamelCase  	=      False
																			__UpperCamelCase  	=      True
																			__UpperCamelCase  	=      model_class(SCREAMING_SNAKE_CASE_      )
																			model.to(SCREAMING_SNAKE_CASE_      )
																			model.eval()
																			with torch.no_grad():
																									__UpperCamelCase  	=      model(**self._prepare_for_class(SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_      )      )
																			__UpperCamelCase  	=      outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
																			self.assertEqual(len(SCREAMING_SNAKE_CASE_      )  ,						self.model_tester.num_attention_outputs      )

																			# check that output_attentions also work using config
																			del inputs_dict["output_attentions"]
																			__UpperCamelCase  	=      True
																			__UpperCamelCase  	=      model_class(SCREAMING_SNAKE_CASE_      )
																			model.to(SCREAMING_SNAKE_CASE_      )
																			model.eval()
																			with torch.no_grad():
																									__UpperCamelCase  	=      model(**self._prepare_for_class(SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_      )      )
																			__UpperCamelCase  	=      outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
																			self.assertEqual(len(SCREAMING_SNAKE_CASE_      )  ,						self.model_tester.num_attention_outputs      )

																			if chunk_length is not None:
																									self.assertListEqual(
																									    list(attentions[0].shape[-4:]      )  ,						[self.model_tester.num_attention_heads, encoder_seq_length, chunk_length, encoder_key_length]  ,						)
																			else:
																									self.assertListEqual(
																									    list(attentions[0].shape[-3:]      )  ,						[self.model_tester.num_attention_heads, encoder_seq_length, encoder_key_length]  ,						)





def 						A_			(				)   ->     Any:



						'''simple docstring'''

						__UpperCamelCase  	=      Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''			)
						return image






@require_torch
@require_vision
class 							SCREAMING_SNAKE_CASE__     (				unittest.TestCase     ):

							"""simple docstring"""






							@cached_property
							def      A__		(	self      )-> int:





													'''simple docstring'''






													return (
													    EfficientFormerImageProcessor.from_pretrained('''snap-research/efficientformer-l1-300'''      )
													    if is_vision_available()
													    else None
													)



							@slow
							def      A__		(	self      )-> str:





													'''simple docstring'''






													__UpperCamelCase  	=      EfficientFormerForImageClassification.from_pretrained('''snap-research/efficientformer-l1-300'''      ).to(
													    SCREAMING_SNAKE_CASE_      )

													__UpperCamelCase  	=      self.default_image_processor
													__UpperCamelCase  	=      prepare_img()
													__UpperCamelCase  	=      image_processor(images=SCREAMING_SNAKE_CASE_  ,						return_tensors='''pt'''      ).to(SCREAMING_SNAKE_CASE_      )

													# forward pass
													with torch.no_grad():
																			__UpperCamelCase  	=      model(**SCREAMING_SNAKE_CASE_      )

													# verify the logits
													__UpperCamelCase  	=      (1, 1000)
													self.assertEqual(outputs.logits.shape  ,						SCREAMING_SNAKE_CASE_      )

													__UpperCamelCase  	=      torch.tensor([-0.0_5_5_5, 0.4_8_2_5, -0.0_8_5_2]      ).to(SCREAMING_SNAKE_CASE_      )
													self.assertTrue(torch.allclose(outputs.logits[0][:3]  ,						SCREAMING_SNAKE_CASE_  ,						atol=1E-4      )      )



							@slow
							def      A__		(	self      )-> List[Any]:





													'''simple docstring'''






													__UpperCamelCase  	=      EfficientFormerForImageClassificationWithTeacher.from_pretrained(
													    '''snap-research/efficientformer-l1-300'''      ).to(SCREAMING_SNAKE_CASE_      )

													__UpperCamelCase  	=      self.default_image_processor
													__UpperCamelCase  	=      prepare_img()
													__UpperCamelCase  	=      image_processor(images=SCREAMING_SNAKE_CASE_  ,						return_tensors='''pt'''      ).to(SCREAMING_SNAKE_CASE_      )

													# forward pass
													with torch.no_grad():
																			__UpperCamelCase  	=      model(**SCREAMING_SNAKE_CASE_      )

													# verify the logits
													__UpperCamelCase  	=      (1, 1000)
													self.assertEqual(outputs.logits.shape  ,						SCREAMING_SNAKE_CASE_      )

													__UpperCamelCase  	=      torch.tensor([-0.1_3_1_2, 0.4_3_5_3, -1.0_4_9_9]      ).to(SCREAMING_SNAKE_CASE_      )
													self.assertTrue(torch.allclose(outputs.logits[0][:3]  ,						SCREAMING_SNAKE_CASE_  ,						atol=1E-4      )      )








import unittest

from transformers import ErnieMConfig, is_torch_available
from transformers.testing_utils import require_torch, 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 (
         ErnieMForInformationExtraction,
         ErnieMForMultipleChoice,
         ErnieMForQuestionAnswering,
         ErnieMForSequenceClassification,
         ErnieMForTokenClassification,
         ErnieMModel,
     )
     from transformers.models.ernie_m.modeling_ernie_m import ERNIE_M_PRETRAINED_MODEL_ARCHIVE_LIST



class 							lowercase__:






      '''simple docstring'''







      def __init__(		self   ,			__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE=13   ,			__SCREAMING_SNAKE_CASE=7   ,			__SCREAMING_SNAKE_CASE=True   ,			__SCREAMING_SNAKE_CASE=True   ,			__SCREAMING_SNAKE_CASE=True   ,			__SCREAMING_SNAKE_CASE=99   ,			__SCREAMING_SNAKE_CASE=32   ,			__SCREAMING_SNAKE_CASE=5   ,			__SCREAMING_SNAKE_CASE=4   ,			__SCREAMING_SNAKE_CASE=37   ,			__SCREAMING_SNAKE_CASE="gelu"   ,			__SCREAMING_SNAKE_CASE=0.1   ,			__SCREAMING_SNAKE_CASE=0.1   ,			__SCREAMING_SNAKE_CASE=5_12   ,			__SCREAMING_SNAKE_CASE=16   ,			__SCREAMING_SNAKE_CASE=2   ,			__SCREAMING_SNAKE_CASE=0.02   ,			__SCREAMING_SNAKE_CASE=3   ,			__SCREAMING_SNAKE_CASE=4   ,			__SCREAMING_SNAKE_CASE=None   ,			)					->						Any:







         """simple docstring"""







         UpperCamelCase__ :			Optional[Any] 						=parent
         UpperCamelCase__ :			Tuple 						=batch_size
         UpperCamelCase__ :			Union[str, Any] 						=seq_length
         UpperCamelCase__ :			Union[str, Any] 						=is_training
         UpperCamelCase__ :			Tuple 						=use_input_mask
         UpperCamelCase__ :			Union[str, Any] 						=use_labels
         UpperCamelCase__ :			Union[str, Any] 						=vocab_size
         UpperCamelCase__ :			List[Any] 						=hidden_size
         UpperCamelCase__ :			Dict 						=num_hidden_layers
         UpperCamelCase__ :			Any 						=num_attention_heads
         UpperCamelCase__ :			List[str] 						=intermediate_size
         UpperCamelCase__ :			List[Any] 						=hidden_act
         UpperCamelCase__ :			Tuple 						=hidden_dropout_prob
         UpperCamelCase__ :			Any 						=attention_probs_dropout_prob
         UpperCamelCase__ :			Tuple 						=max_position_embeddings
         UpperCamelCase__ :			Dict 						=type_vocab_size
         UpperCamelCase__ :			Tuple 						=type_sequence_label_size
         UpperCamelCase__ :			Tuple 						=initializer_range
         UpperCamelCase__ :			List[str] 						=num_labels
         UpperCamelCase__ :			Any 						=num_choices
         UpperCamelCase__ :			Tuple 						=scope




      def    UpperCAmelCase							(		self)					->						int:







         """simple docstring"""







         UpperCamelCase__ :			List[str] 						=ids_tensor([self.batch_size, self.seq_length]   ,			self.vocab_size)

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

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

         UpperCamelCase__ :			Tuple 						=self.get_config()

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




      def    UpperCAmelCase							(		self)					->						str:







         """simple docstring"""







         UpperCamelCase__ :			Tuple 						=ids_tensor([self.batch_size, self.seq_length]   ,			self.vocab_size)

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

         return config, input_ids, input_mask




      def    UpperCAmelCase							(		self)					->						Optional[int]:







         """simple docstring"""







         return ErnieMConfig(
             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   ,			)




      def    UpperCAmelCase							(		self   ,			__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE)					->						str:







         """simple docstring"""







         UpperCamelCase__ :			Tuple 						=ErnieMModel(config=__SCREAMING_SNAKE_CASE)
         model.to(__SCREAMING_SNAKE_CASE)
         model.eval()
         UpperCamelCase__ :			int 						=model(__SCREAMING_SNAKE_CASE   ,			return_dict=__SCREAMING_SNAKE_CASE)
         self.parent.assertEqual(result.last_hidden_state.shape   ,			(self.batch_size, self.seq_length, self.hidden_size))




      def    UpperCAmelCase							(		self   ,			__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE)					->						List[Any]:







         """simple docstring"""







         UpperCamelCase__ :			str 						=ErnieMForQuestionAnswering(config=__SCREAMING_SNAKE_CASE)
         model.to(__SCREAMING_SNAKE_CASE)
         model.eval()
         UpperCamelCase__ :			Dict 						=model(
             __SCREAMING_SNAKE_CASE   ,			attention_mask=__SCREAMING_SNAKE_CASE   ,			start_positions=__SCREAMING_SNAKE_CASE   ,			end_positions=__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   ,			__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE)					->						Dict:







         """simple docstring"""







         UpperCamelCase__ :			Any 						=ErnieMForInformationExtraction(config=__SCREAMING_SNAKE_CASE)
         model.to(__SCREAMING_SNAKE_CASE)
         model.eval()
         UpperCamelCase__ :			int 						=torch.ones_like(__SCREAMING_SNAKE_CASE   ,			dtype=torch.floataa)
         UpperCamelCase__ :			Union[str, Any] 						=model(
             __SCREAMING_SNAKE_CASE   ,			attention_mask=__SCREAMING_SNAKE_CASE   ,			start_positions=__SCREAMING_SNAKE_CASE   ,			end_positions=__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   ,			__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE)					->						List[str]:







         """simple docstring"""







         UpperCamelCase__ :			List[Any] 						=self.num_labels
         UpperCamelCase__ :			List[Any] 						=ErnieMForSequenceClassification(__SCREAMING_SNAKE_CASE)
         model.to(__SCREAMING_SNAKE_CASE)
         model.eval()
         UpperCamelCase__ :			Union[str, Any] 						=model(__SCREAMING_SNAKE_CASE   ,			attention_mask=__SCREAMING_SNAKE_CASE   ,			labels=__SCREAMING_SNAKE_CASE)
         self.parent.assertEqual(result.logits.shape   ,			(self.batch_size, self.num_labels))




      def    UpperCAmelCase							(		self   ,			__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE)					->						Tuple:







         """simple docstring"""







         UpperCamelCase__ :			List[str] 						=self.num_labels
         UpperCamelCase__ :			int 						=ErnieMForTokenClassification(config=__SCREAMING_SNAKE_CASE)
         model.to(__SCREAMING_SNAKE_CASE)
         model.eval()
         input_ids.to(__SCREAMING_SNAKE_CASE)
         input_mask.to(__SCREAMING_SNAKE_CASE)
         token_labels.to(__SCREAMING_SNAKE_CASE)

         UpperCamelCase__ :			Tuple 						=model(__SCREAMING_SNAKE_CASE   ,			attention_mask=__SCREAMING_SNAKE_CASE   ,			labels=__SCREAMING_SNAKE_CASE)

         self.parent.assertEqual(result.logits.shape   ,			(self.batch_size, self.seq_length, self.num_labels))




      def    UpperCAmelCase							(		self   ,			__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE)					->						Optional[Any]:







         """simple docstring"""







         UpperCamelCase__ :			List[Any] 						=self.num_choices
         UpperCamelCase__ :			List[str] 						=ErnieMForMultipleChoice(config=__SCREAMING_SNAKE_CASE)
         model.to(__SCREAMING_SNAKE_CASE)
         model.eval()
         UpperCamelCase__ :			Optional[Any] 						=input_ids.unsqueeze(1).expand(-1   ,			self.num_choices   ,			-1).contiguous()
         UpperCamelCase__ :			int 						=input_mask.unsqueeze(1).expand(-1   ,			self.num_choices   ,			-1).contiguous()
         UpperCamelCase__ :			Optional[Any] 						=model(
             __SCREAMING_SNAKE_CASE   ,			attention_mask=__SCREAMING_SNAKE_CASE   ,			labels=__SCREAMING_SNAKE_CASE   ,			)
         self.parent.assertEqual(result.logits.shape   ,			(self.batch_size, self.num_choices))




      def    UpperCAmelCase							(		self)					->						Union[str, Any]:







         """simple docstring"""







         UpperCamelCase__ :			Tuple 						=self.prepare_config_and_inputs()
         (
             (
             UpperCamelCase__
         )						,	(
             UpperCamelCase__
         )						,	(
             UpperCamelCase__
         )						,	(
             UpperCamelCase__
         )						,	(
             UpperCamelCase__
         )						,	(
             UpperCamelCase__
         )						,	
         ) :			int 						=config_and_inputs
         UpperCamelCase__ :			Tuple 						={"input_ids": input_ids, "attention_mask": input_mask}
         return config, inputs_dict




@require_torch
class 							lowercase__(						snake_case__			,							snake_case__			,							unittest.TestCase			):






      '''simple docstring'''







      snake_case__       =					(
          (
              ErnieMModel,
              ErnieMForMultipleChoice,
              ErnieMForQuestionAnswering,
              ErnieMForSequenceClassification,
              ErnieMForTokenClassification,
          )
          if is_torch_available()
          else ()
      )
      snake_case__       =					()
      snake_case__       =					(
          {
              '''feature-extraction''': ErnieMModel,
              '''question-answering''': ErnieMForQuestionAnswering,
              '''text-classification''': ErnieMForSequenceClassification,
              '''token-classification''': ErnieMForTokenClassification,
              '''zero-shot''': ErnieMForSequenceClassification,
          }
          if is_torch_available()
          else {}
      )
      snake_case__       =					False




      def    UpperCAmelCase							(		self   ,			__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE)					->						Optional[Any]:







         """simple docstring"""







         if pipeline_test_casse_name == "QAPipelineTests":
            return True

         return False




      def    UpperCAmelCase							(		self)					->						Optional[int]:







         """simple docstring"""







         UpperCamelCase__ :			Dict 						=ErnieMModelTester(self)
         UpperCamelCase__ :			int 						=ConfigTester(self   ,			config_class=__SCREAMING_SNAKE_CASE   ,			hidden_size=37)




      def    UpperCAmelCase							(		self)					->						List[Any]:







         """simple docstring"""







         self.config_tester.run_common_tests()




      def    UpperCAmelCase							(		self)					->						List[str]:







         """simple docstring"""







         UpperCamelCase__ :			Any 						=self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*__SCREAMING_SNAKE_CASE)




      def    UpperCAmelCase							(		self)					->						Union[str, Any]:







         """simple docstring"""







         UpperCamelCase__ :			int 						=self.model_tester.prepare_config_and_inputs()
         for type in ["absolute", "relative_key", "relative_key_query"]:
            UpperCamelCase__ :			List[str] 						=type
            self.model_tester.create_and_check_model(*__SCREAMING_SNAKE_CASE)




      def    UpperCAmelCase							(		self)					->						List[str]:







         """simple docstring"""







         UpperCamelCase__ :			Optional[Any] 						=self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_multiple_choice(*__SCREAMING_SNAKE_CASE)




      def    UpperCAmelCase							(		self)					->						Tuple:







         """simple docstring"""







         UpperCamelCase__ :			Optional[Any] 						=self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_question_answering(*__SCREAMING_SNAKE_CASE)




      def    UpperCAmelCase							(		self)					->						Any:







         """simple docstring"""







         UpperCamelCase__ :			Optional[int] 						=self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_sequence_classification(*__SCREAMING_SNAKE_CASE)




      def    UpperCAmelCase							(		self)					->						List[str]:







         """simple docstring"""







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




      def    UpperCAmelCase							(		self)					->						Union[str, Any]:







         """simple docstring"""







         UpperCamelCase__ :			Optional[Any] 						=self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_token_classification(*__SCREAMING_SNAKE_CASE)




      @slow
      def    UpperCAmelCase							(		self)					->						Tuple:







         """simple docstring"""







         for model_name in ERNIE_M_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
            UpperCamelCase__ :			Optional[Any] 						=ErnieMModel.from_pretrained(__SCREAMING_SNAKE_CASE)
            self.assertIsNotNone(__SCREAMING_SNAKE_CASE)




@require_torch
class 							lowercase__(						unittest.TestCase			):






      '''simple docstring'''







      @slow
      def    UpperCAmelCase							(		self)					->						Any:







         """simple docstring"""







         UpperCamelCase__ :			Optional[int] 						=ErnieMModel.from_pretrained("susnato/ernie-m-base_pytorch")
         model.eval()
         UpperCamelCase__ :			int 						=torch.tensor([[0, 1, 2, 3, 4, 5]])
         UpperCamelCase__ :			List[Any] 						=model(__SCREAMING_SNAKE_CASE)[0]

         # TODO Replace vocab size
         UpperCamelCase__ :			List[Any] 						=7_68

         UpperCamelCase__ :			Dict 						=torch.Size((1, 6, hidden_size))
         self.assertEqual(output.shape   ,			__SCREAMING_SNAKE_CASE)

         UpperCamelCase__ :			Union[str, Any] 						=torch.tensor(
             [[[-0.00_12, 0.12_45, -0.02_14], [-0.07_42, 0.02_44, -0.07_71], [-0.03_33, 0.11_64, -0.15_54]]])

         self.assertTrue(torch.allclose(output[:, :3, :3]   ,			__SCREAMING_SNAKE_CASE   ,			atol=1E-3))

