





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       __lowerCAmelCase :






			def __init__(self      ,							__magic_name__      ,							__magic_name__=13      ,							__magic_name__=7      ,							__magic_name__=True      ,							__magic_name__=True      ,							__magic_name__=True      ,							__magic_name__=99      ,							__magic_name__=32      ,							__magic_name__=5      ,							__magic_name__=4      ,							__magic_name__=37      ,							__magic_name__="gelu"      ,							__magic_name__=0.1      ,							__magic_name__=0.1      ,							__magic_name__=512      ,							__magic_name__=16      ,							__magic_name__=2      ,							__magic_name__=0.02      ,							__magic_name__=3      ,							__magic_name__=4      ,							__magic_name__=None      ,							)  ->     Optional[Any]:


							'''simple docstring'''



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






			def     lowerCamelCase						(self					)  ->     int:


							'''simple docstring'''



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

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

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

							snake_case_		:					List[str]			    =						self.get_config()

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






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


							'''simple docstring'''



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

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

							return config, input_ids, input_mask






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


							'''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     lowerCamelCase						(self      ,							__magic_name__      ,							__magic_name__      ,							__magic_name__      ,							__magic_name__      ,							__magic_name__      ,							__magic_name__					)  ->     Union[str, Any]:


							'''simple docstring'''



							snake_case_		:					Union[str, Any]			    =						ErnieMModel(config=__magic_name__					)
							model.to(__magic_name__					)
							model.eval()
							snake_case_		:					Optional[Any]			    =						model(__magic_name__      ,							return_dict=__magic_name__					)
							self.parent.assertEqual(result.last_hidden_state.shape      ,							(self.batch_size, self.seq_length, self.hidden_size)					)






			def     lowerCamelCase						(self      ,							__magic_name__      ,							__magic_name__      ,							__magic_name__      ,							__magic_name__      ,							__magic_name__      ,							__magic_name__					)  ->     Tuple:


							'''simple docstring'''



							snake_case_		:					List[Any]			    =						ErnieMForQuestionAnswering(config=__magic_name__					)
							model.to(__magic_name__					)
							model.eval()
							snake_case_		:					Optional[int]			    =						model(
							    __magic_name__      ,							attention_mask=__magic_name__      ,							start_positions=__magic_name__      ,							end_positions=__magic_name__      ,							)
							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     lowerCamelCase						(self      ,							__magic_name__      ,							__magic_name__      ,							__magic_name__      ,							__magic_name__      ,							__magic_name__      ,							__magic_name__					)  ->     List[Any]:


							'''simple docstring'''



							snake_case_		:					int			    =						ErnieMForInformationExtraction(config=__magic_name__					)
							model.to(__magic_name__					)
							model.eval()
							snake_case_		:					List[Any]			    =						torch.ones_like(__magic_name__      ,							dtype=torch.floataa					)
							snake_case_		:					Optional[Any]			    =						model(
							    __magic_name__      ,							attention_mask=__magic_name__      ,							start_positions=__magic_name__      ,							end_positions=__magic_name__      ,							)
							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     lowerCamelCase						(self      ,							__magic_name__      ,							__magic_name__      ,							__magic_name__      ,							__magic_name__      ,							__magic_name__      ,							__magic_name__					)  ->     Optional[Any]:


							'''simple docstring'''



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






			def     lowerCamelCase						(self      ,							__magic_name__      ,							__magic_name__      ,							__magic_name__      ,							__magic_name__      ,							__magic_name__      ,							__magic_name__					)  ->     str:


							'''simple docstring'''



							snake_case_		:					Optional[int]			    =						self.num_labels
							snake_case_		:					int			    =						ErnieMForTokenClassification(config=__magic_name__					)
							model.to(__magic_name__					)
							model.eval()
							input_ids.to(__magic_name__					)
							input_mask.to(__magic_name__					)
							token_labels.to(__magic_name__					)

							snake_case_		:					Optional[int]			    =						model(__magic_name__      ,							attention_mask=__magic_name__      ,							labels=__magic_name__					)

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






			def     lowerCamelCase						(self      ,							__magic_name__      ,							__magic_name__      ,							__magic_name__      ,							__magic_name__      ,							__magic_name__      ,							__magic_name__					)  ->     Tuple:


							'''simple docstring'''



							snake_case_		:					Tuple			    =						self.num_choices
							snake_case_		:					int			    =						ErnieMForMultipleChoice(config=__magic_name__					)
							model.to(__magic_name__					)
							model.eval()
							snake_case_		:					int			    =						input_ids.unsqueeze(1					).expand(-1      ,							self.num_choices      ,							-1					).contiguous()
							snake_case_		:					Any			    =						input_mask.unsqueeze(1					).expand(-1      ,							self.num_choices      ,							-1					).contiguous()
							snake_case_		:					Tuple			    =						model(
							    __magic_name__      ,							attention_mask=__magic_name__      ,							labels=__magic_name__      ,							)
							self.parent.assertEqual(result.logits.shape      ,							(self.batch_size, self.num_choices)					)






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


							'''simple docstring'''



							snake_case_		:					List[str]			    =						self.prepare_config_and_inputs()
							(
							    (
							    snake_case_
							)    ,							(
							    snake_case_
							)    ,							(
							    snake_case_
							)    ,							(
							    snake_case_
							)    ,							(
							    snake_case_
							)    ,							(
							    snake_case_
							)    ,							
							)		:					Dict			    =						config_and_inputs
							snake_case_		:					Union[str, Any]			    =						{'''input_ids''': input_ids, '''attention_mask''': input_mask}
							return config, inputs_dict




@require_torch
class       __lowerCAmelCase (    _a,  _a,  unittest.TestCase  ):
			lowerCamelCase_  :  Optional[Any]     =   (
			    (
			        ErnieMModel,
			        ErnieMForMultipleChoice,
			        ErnieMForQuestionAnswering,
			        ErnieMForSequenceClassification,
			        ErnieMForTokenClassification,
			    )
			    if is_torch_available()
			    else ()
			)
			lowerCamelCase_  :  str     =   ()
			lowerCamelCase_  :  Optional[Any]     =   (
			    {
			        '''feature-extraction''': ErnieMModel,
			        '''question-answering''': ErnieMForQuestionAnswering,
			        '''text-classification''': ErnieMForSequenceClassification,
			        '''token-classification''': ErnieMForTokenClassification,
			        '''zero-shot''': ErnieMForSequenceClassification,
			    }
			    if is_torch_available()
			    else {}
			)
			lowerCamelCase_  :  Optional[Any]     =   False






			def     lowerCamelCase						(self      ,							__magic_name__      ,							__magic_name__      ,							__magic_name__      ,							__magic_name__      ,							__magic_name__					)  ->     List[str]:


							'''simple docstring'''



							if pipeline_test_casse_name == "QAPipelineTests":
											return True

							return False






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


							'''simple docstring'''



							snake_case_		:					Any			    =						ErnieMModelTester(self					)
							snake_case_		:					Tuple			    =						ConfigTester(self      ,							config_class=__magic_name__      ,							hidden_size=37					)






			def     lowerCamelCase						(self					)  ->     Tuple:


							'''simple docstring'''



							self.config_tester.run_common_tests()






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


							'''simple docstring'''



							snake_case_		:					Tuple			    =						self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*__magic_name__					)






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


							'''simple docstring'''



							snake_case_		:					Dict			    =						self.model_tester.prepare_config_and_inputs()
							for type in ["absolute", "relative_key", "relative_key_query"]:
											snake_case_		:					List[str]			    =						type
											self.model_tester.create_and_check_model(*__magic_name__					)






			def     lowerCamelCase						(self					)  ->     Dict:


							'''simple docstring'''



							snake_case_		:					Dict			    =						self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_multiple_choice(*__magic_name__					)






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


							'''simple docstring'''



							snake_case_		:					List[Any]			    =						self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_question_answering(*__magic_name__					)






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


							'''simple docstring'''



							snake_case_		:					str			    =						self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_sequence_classification(*__magic_name__					)






			def     lowerCamelCase						(self					)  ->     int:


							'''simple docstring'''



							snake_case_		:					Tuple			    =						self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_information_extraction(*__magic_name__					)






			def     lowerCamelCase						(self					)  ->     str:


							'''simple docstring'''



							snake_case_		:					List[str]			    =						self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_token_classification(*__magic_name__					)






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


							'''simple docstring'''



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




@require_torch
class       __lowerCAmelCase (    unittest.TestCase  ):






			@slow
			def     lowerCamelCase						(self					)  ->     Dict:


							'''simple docstring'''



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

							# TODO Replace vocab size
							snake_case_		:					Union[str, Any]			    =						768

							snake_case_		:					Optional[int]			    =						torch.Size((1, 6, hidden_size)					)
							self.assertEqual(output.shape      ,							__magic_name__					)

							snake_case_		:					Tuple			    =						torch.tensor(
							    [[[-0.0_012, 0.1_245, -0.0_214], [-0.0_742, 0.0_244, -0.0_771], [-0.0_333, 0.1_164, -0.1_554]]]					)

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







import warnings

from ...utils import logging
from .image_processing_levit import LevitImageProcessor


lowerCAmelCase_												=       logging.get_logger(__name__)



class       __lowerCAmelCase (    _a  ):






			def __init__(self      ,							*__magic_name__      ,							**__magic_name__					)  ->     None:


							'''simple docstring'''



							warnings.warn(
							    '''The class LevitFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'''
							    ''' use LevitImageProcessor instead.'''      ,							__magic_name__      ,							)
							super().__init__(*__magic_name__      ,							**__magic_name__					)
