


'''simple docstring'''

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 :



		'''simple docstring'''







		def __init__(      self	:		Optional[Any]						,  _A	:		List[Any]						,  _A	:		Dict=13						,  _A	:		int=7						,  _A	:		Optional[int]=True						,  _A	:		str=True						,  _A	:		Optional[int]=True						,  _A	:		Tuple=99						,  _A	:		Tuple=32						,  _A	:		Any=5						,  _A	:		Optional[Any]=4						,  _A	:		int=37						,  _A	:		List[str]="gelu"						,  _A	:		List[Any]=0.1						,  _A	:		Dict=0.1						,  _A	:		Any=512						,  _A	:		Union[str, Any]=16						,  _A	:		Union[str, Any]=2						,  _A	:		str=0.02						,  _A	:		int=3						,  _A	:		List[Any]=4						,  _A	:		Tuple=None						,  )						->				List[str]:
			__magic_name__ :					Union[str, Any]  			=  parent
			__magic_name__ :					Optional[int]  			=  batch_size
			__magic_name__ :					Union[str, Any]  			=  seq_length
			__magic_name__ :					Union[str, Any]  			=  is_training
			__magic_name__ :					Optional[Any]  			=  use_input_mask
			__magic_name__ :					Union[str, Any]  			=  use_labels
			__magic_name__ :					int  			=  vocab_size
			__magic_name__ :					int  			=  hidden_size
			__magic_name__ :					Dict  			=  num_hidden_layers
			__magic_name__ :					Union[str, Any]  			=  num_attention_heads
			__magic_name__ :					int  			=  intermediate_size
			__magic_name__ :					Union[str, Any]  			=  hidden_act
			__magic_name__ :					Dict  			=  hidden_dropout_prob
			__magic_name__ :					Optional[int]  			=  attention_probs_dropout_prob
			__magic_name__ :					Tuple  			=  max_position_embeddings
			__magic_name__ :					Dict  			=  type_vocab_size
			__magic_name__ :					Dict  			=  type_sequence_label_size
			__magic_name__ :					Union[str, Any]  			=  initializer_range
			__magic_name__ :					Any  			=  num_labels
			__magic_name__ :					List[Any]  			=  num_choices
			__magic_name__ :					str  			=  scope







		def      __lowerCAmelCase	(      self	:		str    )						->				Tuple:
			__magic_name__ :					List[str]  			=  ids_tensor([self.batch_size, self.seq_length]						,  self.vocab_size    )

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

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

			__magic_name__ :					Dict  			=  self.get_config()

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







		def      __lowerCAmelCase	(      self	:		Union[str, Any]    )						->				Union[str, Any]:
			__magic_name__ :					List[Any]  			=  ids_tensor([self.batch_size, self.seq_length]						,  self.vocab_size    )

			__magic_name__ :					Optional[int]  			=  None
			if self.use_input_mask:
				__magic_name__ :					Tuple  			=  random_attention_mask([self.batch_size, self.seq_length]    )
			__magic_name__ :					Optional[Any]  			=  self.get_config()

			return config, input_ids, input_mask







		def      __lowerCAmelCase	(      self	:		Any    )						->				Any:
			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	:		int						,  _A	:		Union[str, Any]						,  _A	:		Optional[int]						,  _A	:		Any						,  _A	:		List[Any]						,  _A	:		int						,  _A	:		Any    )						->				Union[str, Any]:
			__magic_name__ :					str  			=  ErnieMModel(config=_A    )
			model.to(_A    )
			model.eval()
			__magic_name__ :					List[str]  			=  model(_A						,  return_dict=_A    )
			self.parent.assertEqual(result.last_hidden_state.shape						,  (self.batch_size, self.seq_length, self.hidden_size)    )







		def      __lowerCAmelCase	(      self	:		Union[str, Any]						,  _A	:		List[str]						,  _A	:		List[Any]						,  _A	:		Optional[int]						,  _A	:		Tuple						,  _A	:		Dict						,  _A	:		List[Any]    )						->				Tuple:
			__magic_name__ :					Any  			=  ErnieMForQuestionAnswering(config=_A    )
			model.to(_A    )
			model.eval()
			__magic_name__ :					List[Any]  			=  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      __lowerCAmelCase	(      self	:		Optional[Any]						,  _A	:		Union[str, Any]						,  _A	:		str						,  _A	:		List[str]						,  _A	:		Any						,  _A	:		Any						,  _A	:		str    )						->				Optional[Any]:
			__magic_name__ :					List[Any]  			=  ErnieMForInformationExtraction(config=_A    )
			model.to(_A    )
			model.eval()
			__magic_name__ :					Optional[int]  			=  torch.ones_like(_A						,  dtype=torch.floataa    )
			__magic_name__ :					Dict  			=  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      __lowerCAmelCase	(      self	:		int						,  _A	:		Union[str, Any]						,  _A	:		str						,  _A	:		Optional[Any]						,  _A	:		Optional[Any]						,  _A	:		List[str]						,  _A	:		List[Any]    )						->				int:
			__magic_name__ :					List[str]  			=  self.num_labels
			__magic_name__ :					int  			=  ErnieMForSequenceClassification(_A    )
			model.to(_A    )
			model.eval()
			__magic_name__ :					str  			=  model(_A						,  attention_mask=_A						,  labels=_A    )
			self.parent.assertEqual(result.logits.shape						,  (self.batch_size, self.num_labels)    )







		def      __lowerCAmelCase	(      self	:		Union[str, Any]						,  _A	:		List[str]						,  _A	:		Any						,  _A	:		Tuple						,  _A	:		Dict						,  _A	:		str						,  _A	:		List[str]    )						->				Dict:
			__magic_name__ :					Optional[Any]  			=  self.num_labels
			__magic_name__ :					Optional[int]  			=  ErnieMForTokenClassification(config=_A    )
			model.to(_A    )
			model.eval()
			input_ids.to(_A    )
			input_mask.to(_A    )
			token_labels.to(_A    )

			__magic_name__ :					List[Any]  			=  model(_A						,  attention_mask=_A						,  labels=_A    )

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







		def      __lowerCAmelCase	(      self	:		Dict						,  _A	:		Optional[int]						,  _A	:		Optional[Any]						,  _A	:		str						,  _A	:		int						,  _A	:		List[str]						,  _A	:		Dict    )						->				List[str]:
			__magic_name__ :					List[str]  			=  self.num_choices
			__magic_name__ :					Any  			=  ErnieMForMultipleChoice(config=_A    )
			model.to(_A    )
			model.eval()
			__magic_name__ :					List[Any]  			=  input_ids.unsqueeze(1    ).expand(-1						,  self.num_choices						,  -1    ).contiguous()
			__magic_name__ :					Tuple  			=  input_mask.unsqueeze(1    ).expand(-1						,  self.num_choices						,  -1    ).contiguous()
			__magic_name__ :					Union[str, Any]  			=  model(
			    _A						,  attention_mask=_A						,  labels=_A						,  )
			self.parent.assertEqual(result.logits.shape						,  (self.batch_size, self.num_choices)    )







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







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



		'''simple docstring'''







		A_			:    Optional[int]   					=       (
		    (
		        ErnieMModel,
		        ErnieMForMultipleChoice,
		        ErnieMForQuestionAnswering,
		        ErnieMForSequenceClassification,
		        ErnieMForTokenClassification,
		    )
		    if is_torch_available()
		    else ()
		)
		A_			:    str   					=       ()
		A_			:    Optional[int]   					=       (
		    {
		        """feature-extraction""": ErnieMModel,
		        """question-answering""": ErnieMForQuestionAnswering,
		        """text-classification""": ErnieMForSequenceClassification,
		        """token-classification""": ErnieMForTokenClassification,
		        """zero-shot""": ErnieMForSequenceClassification,
		    }
		    if is_torch_available()
		    else {}
		)
		A_			:    Tuple   					=       False







		def      __lowerCAmelCase	(      self	:		List[str]						,  _A	:		Optional[int]						,  _A	:		Any						,  _A	:		Optional[int]						,  _A	:		str						,  _A	:		List[str]    )						->				Any:
			if pipeline_test_casse_name == "QAPipelineTests":
				return True

			return False







		def      __lowerCAmelCase	(      self	:		Optional[Any]    )						->				Tuple:
			__magic_name__ :					Any  			=  ErnieMModelTester(self    )
			__magic_name__ :					Optional[int]  			=  ConfigTester(self						,  config_class=_A						,  hidden_size=37    )







		def      __lowerCAmelCase	(      self	:		List[Any]    )						->				int:
			self.config_tester.run_common_tests()







		def      __lowerCAmelCase	(      self	:		int    )						->				Optional[int]:
			__magic_name__ :					Union[str, Any]  			=  self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_model(*_A    )







		def      __lowerCAmelCase	(      self	:		Optional[Any]    )						->				Dict:
			__magic_name__ :					Tuple  			=  self.model_tester.prepare_config_and_inputs()
			for type in ["absolute", "relative_key", "relative_key_query"]:
				__magic_name__ :					int  			=  type
				self.model_tester.create_and_check_model(*_A    )







		def      __lowerCAmelCase	(      self	:		int    )						->				int:
			__magic_name__ :					Optional[int]  			=  self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_for_multiple_choice(*_A    )







		def      __lowerCAmelCase	(      self	:		List[Any]    )						->				Any:
			__magic_name__ :					str  			=  self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_for_question_answering(*_A    )







		def      __lowerCAmelCase	(      self	:		Optional[Any]    )						->				str:
			__magic_name__ :					List[str]  			=  self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_for_sequence_classification(*_A    )







		def      __lowerCAmelCase	(      self	:		Dict    )						->				Dict:
			__magic_name__ :					int  			=  self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_for_information_extraction(*_A    )







		def      __lowerCAmelCase	(      self	:		Dict    )						->				Dict:
			__magic_name__ :					Dict  			=  self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_for_token_classification(*_A    )







		@slow
		def      __lowerCAmelCase	(      self	:		int    )						->				Any:
			for model_name in ERNIE_M_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
				__magic_name__ :					Dict  			=  ErnieMModel.from_pretrained(_A    )
				self.assertIsNotNone(_A    )







@require_torch
class _lowerCamelCase (		unittest.TestCase							):



		'''simple docstring'''







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

			# TODO Replace vocab size
			__magic_name__ :					int  			=  768

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

			__magic_name__ :					Tuple  			=  torch.tensor(
			    [[[-0.0012, 0.1245, -0.0214], [-0.0742, 0.0244, -0.0771], [-0.0333, 0.1164, -0.1554]]]    )

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



'''simple docstring'''

import random
import unittest

import numpy as np
import torch

from diffusers import DDIMScheduler, LDMSuperResolutionPipeline, UNetaDModel, VQModel
from diffusers.utils import PIL_INTERPOLATION, floats_tensor, load_image, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch


enable_full_determinism()
class _lowerCamelCase (		unittest.TestCase							):



		'''simple docstring'''







		@property
		def      __lowerCAmelCase	(      self	:		Union[str, Any]    )						->				List[Any]:
			__magic_name__ :					Union[str, Any]  			=  1
			__magic_name__ :					str  			=  3
			__magic_name__ :					Optional[Any]  			=  (32, 32)

			__magic_name__ :					Dict  			=  floats_tensor((batch_size, num_channels) + sizes						,  rng=random.Random(0    )    ).to(_A    )
			return image







		@property
		def      __lowerCAmelCase	(      self	:		Optional[Any]    )						->				List[str]:
			torch.manual_seed(0    )
			__magic_name__ :					Dict  			=  UNetaDModel(
			    block_out_channels=(32, 64)						,  layers_per_block=2						,  sample_size=32						,  in_channels=6						,  out_channels=3						,  down_block_types=('DownBlock2D', 'AttnDownBlock2D')						,  up_block_types=('AttnUpBlock2D', 'UpBlock2D')						,  )
			return model







		@property
		def      __lowerCAmelCase	(      self	:		Any    )						->				Optional[int]:
			torch.manual_seed(0    )
			__magic_name__ :					Union[str, Any]  			=  VQModel(
			    block_out_channels=[32, 64]						,  in_channels=3						,  out_channels=3						,  down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']						,  up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']						,  latent_channels=3						,  )
			return model







		def      __lowerCAmelCase	(      self	:		Optional[int]    )						->				int:
			__magic_name__ :					Dict  			=  'cpu'
			__magic_name__ :					str  			=  self.dummy_uncond_unet
			__magic_name__ :					Optional[int]  			=  DDIMScheduler()
			__magic_name__ :					Optional[Any]  			=  self.dummy_vq_model

			__magic_name__ :					Tuple  			=  LDMSuperResolutionPipeline(unet=_A						,  vqvae=_A						,  scheduler=_A    )
			ldm.to(_A    )
			ldm.set_progress_bar_config(disable=_A    )

			__magic_name__ :					Optional[int]  			=  self.dummy_image.to(_A    )

			__magic_name__ :					List[Any]  			=  torch.Generator(device=_A    ).manual_seed(0    )
			__magic_name__ :					str  			=  ldm(image=_A						,  generator=_A						,  num_inference_steps=2						,  output_type='numpy'    ).images

			__magic_name__ :					List[Any]  			=  image[0, -3:, -3:, -1]

			assert image.shape == (1, 64, 64, 3)
			__magic_name__ :					Optional[Any]  			=  np.array([0.8678, 0.8245, 0.6381, 0.6830, 0.4385, 0.5599, 0.4641, 0.6201, 0.5150]    )

			assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1E-2







		@unittest.skipIf(torch_device != 'cuda'						,  'This test requires a GPU'    )
		def      __lowerCAmelCase	(      self	:		List[Any]    )						->				List[Any]:
			__magic_name__ :					Union[str, Any]  			=  self.dummy_uncond_unet
			__magic_name__ :					Tuple  			=  DDIMScheduler()
			__magic_name__ :					Union[str, Any]  			=  self.dummy_vq_model

			# put models in fp16
			__magic_name__ :					Optional[int]  			=  unet.half()
			__magic_name__ :					int  			=  vqvae.half()

			__magic_name__ :					Optional[Any]  			=  LDMSuperResolutionPipeline(unet=_A						,  vqvae=_A						,  scheduler=_A    )
			ldm.to(_A    )
			ldm.set_progress_bar_config(disable=_A    )

			__magic_name__ :					str  			=  self.dummy_image.to(_A    )

			__magic_name__ :					Union[str, Any]  			=  ldm(_A						,  num_inference_steps=2						,  output_type='numpy'    ).images

			assert image.shape == (1, 64, 64, 3)







@slow
@require_torch
class _lowerCamelCase (		unittest.TestCase							):



		'''simple docstring'''







		def      __lowerCAmelCase	(      self	:		str    )						->				Dict:
			__magic_name__ :					Tuple  			=  load_image(
			    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
			    '/vq_diffusion/teddy_bear_pool.png'    )
			__magic_name__ :					Dict  			=  init_image.resize((64, 64)						,  resample=PIL_INTERPOLATION['lanczos']    )

			__magic_name__ :					List[Any]  			=  LDMSuperResolutionPipeline.from_pretrained('duongna/ldm-super-resolution'						,  device_map='auto'    )
			ldm.set_progress_bar_config(disable=_A    )

			__magic_name__ :					int  			=  torch.manual_seed(0    )
			__magic_name__ :					Optional[Any]  			=  ldm(image=_A						,  generator=_A						,  num_inference_steps=20						,  output_type='numpy'    ).images

			__magic_name__ :					List[str]  			=  image[0, -3:, -3:, -1]

			assert image.shape == (1, 256, 256, 3)
			__magic_name__ :					int  			=  np.array([0.7644, 0.7679, 0.7642, 0.7633, 0.7666, 0.7560, 0.7425, 0.7257, 0.6907]    )

			assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1E-2