
import unittest

from transformers import MraConfig, 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, floats_tensor, ids_tensor, random_attention_mask


if is_torch_available():
      import torch

      from transformers import (
          MraForMaskedLM,
          MraForMultipleChoice,
          MraForQuestionAnswering,
          MraForSequenceClassification,
          MraForTokenClassification,
          MraModel,
      )
      from transformers.models.mra.modeling_mra import MRA_PRETRAINED_MODEL_ARCHIVE_LIST

class 						lowercase_				:

 def __init__(					self   ,		__UpperCamelCase   ,		__UpperCamelCase=2   ,		__UpperCamelCase=8   ,		__UpperCamelCase=True   ,		__UpperCamelCase=True   ,		__UpperCamelCase=True   ,		__UpperCamelCase=True   ,		__UpperCamelCase=9_9   ,		__UpperCamelCase=1_6   ,		__UpperCamelCase=5   ,		__UpperCamelCase=2   ,		__UpperCamelCase=3_6   ,		__UpperCamelCase="gelu"   ,		__UpperCamelCase=0.0   ,		__UpperCamelCase=0.0   ,		__UpperCamelCase=5_1_2   ,		__UpperCamelCase=1_6   ,		__UpperCamelCase=2   ,		__UpperCamelCase=0.02   ,		__UpperCamelCase=3   ,		__UpperCamelCase=4   ,		__UpperCamelCase=None   ,		):




        """simple docstring"""


        UpperCamelCase_											=				parent
        UpperCamelCase_											=				batch_size
        UpperCamelCase_											=				seq_length
        UpperCamelCase_											=				is_training
        UpperCamelCase_											=				use_input_mask
        UpperCamelCase_											=				use_token_type_ids
        UpperCamelCase_											=				use_labels
        UpperCamelCase_											=				vocab_size
        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_											=				max_position_embeddings
        UpperCamelCase_											=				type_vocab_size
        UpperCamelCase_											=				type_sequence_label_size
        UpperCamelCase_											=				initializer_range
        UpperCamelCase_											=				num_labels
        UpperCamelCase_											=				num_choices
        UpperCamelCase_											=				scope

 def lowerCamelCase_ (					self   ):




        """simple docstring"""


        UpperCamelCase_											=				ids_tensor([self.batch_size, self.seq_length]   ,		self.vocab_size   )

        UpperCamelCase_											=				None
        if self.use_input_mask:
               UpperCamelCase_											=				random_attention_mask([self.batch_size, self.seq_length]   )

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

        UpperCamelCase_											=				None
        UpperCamelCase_											=				None
        UpperCamelCase_											=				None
        if self.use_labels:
               UpperCamelCase_											=				ids_tensor([self.batch_size]   ,		self.type_sequence_label_size   )
               UpperCamelCase_											=				ids_tensor([self.batch_size, self.seq_length]   ,		self.num_labels   )
               UpperCamelCase_											=				ids_tensor([self.batch_size]   ,		self.num_choices   )

        UpperCamelCase_											=				self.get_config()

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

 def lowerCamelCase_ (					self   ):




        """simple docstring"""


        return MraConfig(
            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   ,		is_decoder=__snake_case   ,		initializer_range=self.initializer_range   ,		)

 def lowerCamelCase_ (					self   ):




        """simple docstring"""


        UpperCamelCase_											=				self.get_config()
        UpperCamelCase_											=				3_0_0
        return config

 def lowerCamelCase_ (					self   ):




        """simple docstring"""


        (
            UpperCamelCase_		
        )											=				self.prepare_config_and_inputs()

        UpperCamelCase_											=				True
        UpperCamelCase_											=				floats_tensor([self.batch_size, self.seq_length, self.hidden_size]   )
        UpperCamelCase_											=				ids_tensor([self.batch_size, self.seq_length]   ,		vocab_size=2   )

        return (
            config,
            input_ids,
            token_type_ids,
            input_mask,
            sequence_labels,
            token_labels,
            choice_labels,
            encoder_hidden_states,
            encoder_attention_mask,
        )

 def lowerCamelCase_ (					self   ,		__UpperCamelCase   ,		__UpperCamelCase   ,		__UpperCamelCase   ,		__UpperCamelCase   ,		__UpperCamelCase   ,		__UpperCamelCase   ,		__UpperCamelCase   ):




        """simple docstring"""


        UpperCamelCase_											=				MraModel(config=__snake_case   )
        model.to(__snake_case   )
        model.eval()
        UpperCamelCase_											=				model(__snake_case   ,		attention_mask=__snake_case   ,		token_type_ids=__snake_case   )
        UpperCamelCase_											=				model(__snake_case   ,		token_type_ids=__snake_case   )
        UpperCamelCase_											=				model(__snake_case   )
        self.parent.assertEqual(result.last_hidden_state.shape   ,		(self.batch_size, self.seq_length, self.hidden_size)   )

 def lowerCamelCase_ (					self   ,		__UpperCamelCase   ,		__UpperCamelCase   ,		__UpperCamelCase   ,		__UpperCamelCase   ,		__UpperCamelCase   ,		__UpperCamelCase   ,		__UpperCamelCase   ,		__UpperCamelCase   ,		__UpperCamelCase   ,		):




        """simple docstring"""


        UpperCamelCase_											=				True
        UpperCamelCase_											=				MraModel(__snake_case   )
        model.to(__snake_case   )
        model.eval()
        UpperCamelCase_											=				model(
            __snake_case   ,		attention_mask=__snake_case   ,		token_type_ids=__snake_case   ,		encoder_hidden_states=__snake_case   ,		encoder_attention_mask=__snake_case   ,		)
        UpperCamelCase_											=				model(
            __snake_case   ,		attention_mask=__snake_case   ,		token_type_ids=__snake_case   ,		encoder_hidden_states=__snake_case   ,		)
        UpperCamelCase_											=				model(__snake_case   ,		attention_mask=__snake_case   ,		token_type_ids=__snake_case   )
        self.parent.assertEqual(result.last_hidden_state.shape   ,		(self.batch_size, self.seq_length, self.hidden_size)   )

 def lowerCamelCase_ (					self   ,		__UpperCamelCase   ,		__UpperCamelCase   ,		__UpperCamelCase   ,		__UpperCamelCase   ,		__UpperCamelCase   ,		__UpperCamelCase   ,		__UpperCamelCase   ):




        """simple docstring"""


        UpperCamelCase_											=				MraForMaskedLM(config=__snake_case   )
        model.to(__snake_case   )
        model.eval()
        UpperCamelCase_											=				model(__snake_case   ,		attention_mask=__snake_case   ,		token_type_ids=__snake_case   ,		labels=__snake_case   )
        self.parent.assertEqual(result.logits.shape   ,		(self.batch_size, self.seq_length, self.vocab_size)   )

 def lowerCamelCase_ (					self   ,		__UpperCamelCase   ,		__UpperCamelCase   ,		__UpperCamelCase   ,		__UpperCamelCase   ,		__UpperCamelCase   ,		__UpperCamelCase   ,		__UpperCamelCase   ):




        """simple docstring"""


        UpperCamelCase_											=				MraForQuestionAnswering(config=__snake_case   )
        model.to(__snake_case   )
        model.eval()
        UpperCamelCase_											=				model(
            __snake_case   ,		attention_mask=__snake_case   ,		token_type_ids=__snake_case   ,		start_positions=__snake_case   ,		end_positions=__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 lowerCamelCase_ (					self   ,		__UpperCamelCase   ,		__UpperCamelCase   ,		__UpperCamelCase   ,		__UpperCamelCase   ,		__UpperCamelCase   ,		__UpperCamelCase   ,		__UpperCamelCase   ):




        """simple docstring"""


        UpperCamelCase_											=				self.num_labels
        UpperCamelCase_											=				MraForSequenceClassification(__snake_case   )
        model.to(__snake_case   )
        model.eval()
        UpperCamelCase_											=				model(__snake_case   ,		attention_mask=__snake_case   ,		token_type_ids=__snake_case   ,		labels=__snake_case   )
        self.parent.assertEqual(result.logits.shape   ,		(self.batch_size, self.num_labels)   )

 def lowerCamelCase_ (					self   ,		__UpperCamelCase   ,		__UpperCamelCase   ,		__UpperCamelCase   ,		__UpperCamelCase   ,		__UpperCamelCase   ,		__UpperCamelCase   ,		__UpperCamelCase   ):




        """simple docstring"""


        UpperCamelCase_											=				self.num_labels
        UpperCamelCase_											=				MraForTokenClassification(config=__snake_case   )
        model.to(__snake_case   )
        model.eval()
        UpperCamelCase_											=				model(__snake_case   ,		attention_mask=__snake_case   ,		token_type_ids=__snake_case   ,		labels=__snake_case   )
        self.parent.assertEqual(result.logits.shape   ,		(self.batch_size, self.seq_length, self.num_labels)   )

 def lowerCamelCase_ (					self   ,		__UpperCamelCase   ,		__UpperCamelCase   ,		__UpperCamelCase   ,		__UpperCamelCase   ,		__UpperCamelCase   ,		__UpperCamelCase   ,		__UpperCamelCase   ):




        """simple docstring"""


        UpperCamelCase_											=				self.num_choices
        UpperCamelCase_											=				MraForMultipleChoice(config=__snake_case   )
        model.to(__snake_case   )
        model.eval()
        UpperCamelCase_											=				input_ids.unsqueeze(1   ).expand(-1   ,		self.num_choices   ,		-1   ).contiguous()
        UpperCamelCase_											=				token_type_ids.unsqueeze(1   ).expand(-1   ,		self.num_choices   ,		-1   ).contiguous()
        UpperCamelCase_											=				input_mask.unsqueeze(1   ).expand(-1   ,		self.num_choices   ,		-1   ).contiguous()
        UpperCamelCase_											=				model(
            __snake_case   ,		attention_mask=__snake_case   ,		token_type_ids=__snake_case   ,		labels=__snake_case   ,		)
        self.parent.assertEqual(result.logits.shape   ,		(self.batch_size, self.num_choices)   )

 def lowerCamelCase_ (					self   ):




        """simple docstring"""


        UpperCamelCase_											=				self.prepare_config_and_inputs()
        (
            UpperCamelCase_		
        )											=				config_and_inputs
        UpperCamelCase_											=				{'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
        return config, inputs_dict



@require_torch
class 						lowercase_				(				A__							,		unittest.TestCase ):
 A__  : List[str]      					=			(
     (
         MraModel,
         MraForMaskedLM,
         MraForMultipleChoice,
         MraForQuestionAnswering,
         MraForSequenceClassification,
         MraForTokenClassification,
     )
     if is_torch_available()
     else ()
 )
 A__  : List[str]      					=			False
 A__  : Union[str, Any]      					=			False
 A__  : Dict      					=			False
 A__  : Optional[int]      					=			False

 A__  : Optional[Any]      					=			()

 def lowerCamelCase_ (					self   ):




        """simple docstring"""


        UpperCamelCase_											=				MraModelTester(self   )
        UpperCamelCase_											=				ConfigTester(self   ,		config_class=__snake_case   ,		hidden_size=3_7   )

 def lowerCamelCase_ (					self   ):




        """simple docstring"""


        self.config_tester.run_common_tests()

 def lowerCamelCase_ (					self   ):




        """simple docstring"""


        UpperCamelCase_											=				self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*__snake_case   )

 def lowerCamelCase_ (					self   ):




        """simple docstring"""


        UpperCamelCase_											=				self.model_tester.prepare_config_and_inputs()
        for type in ["absolute", "relative_key", "relative_key_query"]:
               UpperCamelCase_											=				type
               self.model_tester.create_and_check_model(*__snake_case   )

 def lowerCamelCase_ (					self   ):




        """simple docstring"""


        UpperCamelCase_											=				self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_masked_lm(*__snake_case   )

 def lowerCamelCase_ (					self   ):




        """simple docstring"""


        UpperCamelCase_											=				self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_multiple_choice(*__snake_case   )

 def lowerCamelCase_ (					self   ):




        """simple docstring"""


        UpperCamelCase_											=				self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_question_answering(*__snake_case   )

 def lowerCamelCase_ (					self   ):




        """simple docstring"""


        UpperCamelCase_											=				self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_sequence_classification(*__snake_case   )

 def lowerCamelCase_ (					self   ):




        """simple docstring"""


        UpperCamelCase_											=				self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_token_classification(*__snake_case   )

 @slow
 def lowerCamelCase_ (					self   ):




        """simple docstring"""


        for model_name in MRA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               UpperCamelCase_											=				MraModel.from_pretrained(__snake_case   )
               self.assertIsNotNone(__snake_case   )

 @unittest.skip(reason="""MRA does not output attentions"""   )
 def lowerCamelCase_ (					self   ):




        """simple docstring"""


        return



@require_torch
class 						lowercase_				(				unittest.TestCase ):

 @slow
 def lowerCamelCase_ (					self   ):




        """simple docstring"""


        UpperCamelCase_											=				MraModel.from_pretrained("""uw-madison/mra-base-512-4"""   )
        UpperCamelCase_											=				torch.arange(2_5_6   ).unsqueeze(0   )

        with torch.no_grad():
               UpperCamelCase_											=				model(__snake_case   )[0]

        UpperCamelCase_											=				torch.Size((1, 2_5_6, 7_6_8)   )
        self.assertEqual(output.shape   ,		__snake_case   )

        UpperCamelCase_											=				torch.tensor(
            [[[-0.0_140, 0.0_830, -0.0_381], [0.1_546, 0.1_402, 0.0_220], [0.1_162, 0.0_851, 0.0_165]]]   )

        self.assertTrue(torch.allclose(output[:, :3, :3]   ,		__snake_case   ,		atol=1e-4   )   )

 @slow
 def lowerCamelCase_ (					self   ):




        """simple docstring"""


        UpperCamelCase_											=				MraForMaskedLM.from_pretrained("""uw-madison/mra-base-512-4"""   )
        UpperCamelCase_											=				torch.arange(2_5_6   ).unsqueeze(0   )

        with torch.no_grad():
               UpperCamelCase_											=				model(__snake_case   )[0]

        UpperCamelCase_											=				5_0_2_6_5

        UpperCamelCase_											=				torch.Size((1, 2_5_6, vocab_size)   )
        self.assertEqual(output.shape   ,		__snake_case   )

        UpperCamelCase_											=				torch.tensor(
            [[[9.2_595, -3.6_038, 11.8_819], [9.3_869, -3.2_693, 11.0_956], [11.8_524, -3.4_938, 13.1_210]]]   )

        self.assertTrue(torch.allclose(output[:, :3, :3]   ,		__snake_case   ,		atol=1e-4   )   )

 @slow
 def lowerCamelCase_ (					self   ):




        """simple docstring"""


        UpperCamelCase_											=				MraForMaskedLM.from_pretrained("""uw-madison/mra-base-4096-8-d3"""   )
        UpperCamelCase_											=				torch.arange(4_0_9_6   ).unsqueeze(0   )

        with torch.no_grad():
               UpperCamelCase_											=				model(__snake_case   )[0]

        UpperCamelCase_											=				5_0_2_6_5

        UpperCamelCase_											=				torch.Size((1, 4_0_9_6, vocab_size)   )
        self.assertEqual(output.shape   ,		__snake_case   )

        UpperCamelCase_											=				torch.tensor(
            [[[5.4_789, -2.3_564, 7.5_064], [7.9_067, -1.3_369, 9.9_668], [9.0_712, -1.8_106, 7.0_380]]]   )

        self.assertTrue(torch.allclose(output[:, :3, :3]   ,		__snake_case   ,		atol=1e-4   )   )








'''simple docstring'''






from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import ScoreSdeVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput

class 				SCREAMING_SNAKE_CASE(							A__			):







	"""simple docstring"""






	lowerCamelCase__			 =       42
	lowerCamelCase__			 =       42







	def __init__(						self			:     Union[str, Any]		,			__snake_case			:     UNetaDModel		,			__snake_case			:     ScoreSdeVeScheduler		)			->		int:
								super().__init__()
								self.register_modules(unet=__snake_case		,			scheduler=__snake_case		)







	@torch.no_grad()
	def __call__(						self			:     Optional[int]		,			__snake_case			:     int = 1		,			__snake_case			:     int = 2000		,			__snake_case			:     Optional[Union[torch.Generator, List[torch.Generator]]] = None		,			__snake_case			:     Optional[str] = "pil"		,			__snake_case			:     bool = True		,			**__snake_case			:     Optional[int]		,			)			->		Union[ImagePipelineOutput, Tuple]:
								UpperCAmelCase :       str 							=		self.unet.config.sample_size
								UpperCAmelCase :       Union[str, Any] 							=		(batch_size, 3, img_size, img_size)

								UpperCAmelCase :       int 							=		self.unet

								UpperCAmelCase :       Any 							=		randn_tensor(__snake_case		,			generator=__snake_case		) * self.scheduler.init_noise_sigma
								UpperCAmelCase :       List[Any] 							=		sample.to(self.device		)

								self.scheduler.set_timesteps(__snake_case		)
								self.scheduler.set_sigmas(__snake_case		)

								for i, t in enumerate(self.progress_bar(self.scheduler.timesteps		)		):
															UpperCAmelCase :       Any 							=		self.scheduler.sigmas[i] * torch.ones(shape[0]		,			device=self.device		)

															# correction step
															for _ in range(self.scheduler.config.correct_steps		):
																						UpperCAmelCase :       Union[str, Any] 							=		self.unet(__snake_case		,			__snake_case		).sample
																						UpperCAmelCase :       Optional[Any] 							=		self.scheduler.step_correct(__snake_case		,			__snake_case		,			generator=__snake_case		).prev_sample

															# prediction step
															UpperCAmelCase :       Optional[Any] 							=		model(__snake_case		,			__snake_case		).sample
															UpperCAmelCase :       List[str] 							=		self.scheduler.step_pred(__snake_case		,			__snake_case		,			__snake_case		,			generator=__snake_case		)

															UpperCAmelCase							,		UpperCAmelCase :       Optional[Any] 							=		output.prev_sample, output.prev_sample_mean

								UpperCAmelCase :       int 							=		sample_mean.clamp(0		,			1		)
								UpperCAmelCase :       Union[str, Any] 							=		sample.cpu().permute(0		,			2		,			3		,			1		).numpy()
								if output_type == "pil":
															UpperCAmelCase :       Optional[Any] 							=		self.numpy_to_pil(__snake_case		)

								if not return_dict:
															return (sample,)

								return ImagePipelineOutput(images=__snake_case		)





