

import unittest

from transformers import BertGenerationConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
							import torch

							from transformers import BertGenerationDecoder, BertGenerationEncoder







class 	lowercase    :


		"""simple docstring"""




		def __init__(				self	,       __snake_case	,       __snake_case=13	,       __snake_case=7	,       __snake_case=True	,       __snake_case=True	,       __snake_case=99	,       __snake_case=32	,       __snake_case=5	,       __snake_case=4	,       __snake_case=37	,       __snake_case="gelu"	,       __snake_case=0.1	,       __snake_case=0.1	,       __snake_case=50	,       __snake_case=0.0_2	,       __snake_case=True	,       __snake_case=None	,       ):
			_UpperCamelCase   :		Tuple			=						parent
			_UpperCamelCase   :		Union[str, Any]			=						batch_size
			_UpperCamelCase   :		str			=						seq_length
			_UpperCamelCase   :		str			=						is_training
			_UpperCamelCase   :		Dict			=						use_input_mask
			_UpperCamelCase   :		Optional[int]			=						vocab_size
			_UpperCamelCase   :		Dict			=						hidden_size
			_UpperCamelCase   :		List[Any]			=						num_hidden_layers
			_UpperCamelCase   :		Optional[int]			=						num_attention_heads
			_UpperCamelCase   :		Union[str, Any]			=						intermediate_size
			_UpperCamelCase   :		List[str]			=						hidden_act
			_UpperCamelCase   :		str			=						hidden_dropout_prob
			_UpperCamelCase   :		str			=						attention_probs_dropout_prob
			_UpperCamelCase   :		Optional[int]			=						max_position_embeddings
			_UpperCamelCase   :		str			=						initializer_range
			_UpperCamelCase   :		Optional[int]			=						use_labels
			_UpperCamelCase   :		Optional[Any]			=						scope


		def 		A__					(				self):
			_UpperCamelCase   :		Union[str, Any]			=						ids_tensor([self.batch_size, self.seq_length]	,       self.vocab_size)

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

			if self.use_labels:
				_UpperCamelCase   :		Tuple			=						ids_tensor([self.batch_size, self.seq_length]	,       self.vocab_size)

			_UpperCamelCase   :		str			=						self.get_config()

			return config, input_ids, input_mask, token_labels


		def 		A__					(				self):
			return BertGenerationConfig(
			    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	,       is_decoder=__snake_case	,       initializer_range=self.initializer_range	,       )


		def 		A__					(				self):
			(
			    (
			    _UpperCamelCase
			)	,		(
			    _UpperCamelCase
			)	,		(
			    _UpperCamelCase
			)	,		(
			    _UpperCamelCase
			)	,		
			)   :		Any			=						self.prepare_config_and_inputs()

			_UpperCamelCase   :		List[str]			=						True
			_UpperCamelCase   :		str			=						floats_tensor([self.batch_size, self.seq_length, self.hidden_size])
			_UpperCamelCase   :		int			=						ids_tensor([self.batch_size, self.seq_length]	,       vocab_size=2)

			return (
			    config,
			    input_ids,
			    input_mask,
			    token_labels,
			    encoder_hidden_states,
			    encoder_attention_mask,
			)


		def 		A__					(				self	,       __snake_case	,       __snake_case	,       __snake_case	,       __snake_case	,       **__snake_case	,       ):
			_UpperCamelCase   :		Dict			=						BertGenerationEncoder(config=__snake_case)
			model.to(__snake_case)
			model.eval()
			_UpperCamelCase   :		List[str]			=						model(__snake_case	,       attention_mask=__snake_case)
			_UpperCamelCase   :		List[Any]			=						model(__snake_case)
			self.parent.assertEqual(result.last_hidden_state.shape	,       (self.batch_size, self.seq_length, self.hidden_size))


		def 		A__					(				self	,       __snake_case	,       __snake_case	,       __snake_case	,       __snake_case	,       __snake_case	,       __snake_case	,       **__snake_case	,       ):
			_UpperCamelCase   :		List[str]			=						True
			_UpperCamelCase   :		Optional[int]			=						BertGenerationEncoder(config=__snake_case)
			model.to(__snake_case)
			model.eval()
			_UpperCamelCase   :		Dict			=						model(
			    __snake_case	,       attention_mask=__snake_case	,       encoder_hidden_states=__snake_case	,       encoder_attention_mask=__snake_case	,       )
			_UpperCamelCase   :		Any			=						model(
			    __snake_case	,       attention_mask=__snake_case	,       encoder_hidden_states=__snake_case	,       )
			self.parent.assertEqual(result.last_hidden_state.shape	,       (self.batch_size, self.seq_length, self.hidden_size))


		def 		A__					(				self	,       __snake_case	,       __snake_case	,       __snake_case	,       __snake_case	,       __snake_case	,       __snake_case	,       **__snake_case	,       ):
			_UpperCamelCase   :		Tuple			=						True
			_UpperCamelCase   :		List[str]			=						True
			_UpperCamelCase   :		Dict			=						BertGenerationDecoder(config=__snake_case).to(__snake_case).eval()

			# first forward pass
			_UpperCamelCase   :		List[Any]			=						model(
			    __snake_case	,       attention_mask=__snake_case	,       encoder_hidden_states=__snake_case	,       encoder_attention_mask=__snake_case	,       use_cache=__snake_case	,       )
			_UpperCamelCase   :		Union[str, Any]			=						outputs.past_key_values

			# create hypothetical multiple next token and extent to next_input_ids
			_UpperCamelCase   :		str			=						ids_tensor((self.batch_size, 3)	,       config.vocab_size)
			_UpperCamelCase   :		Any			=						ids_tensor((self.batch_size, 3)	,       vocab_size=2)

			# append to next input_ids and
			_UpperCamelCase   :		int			=						torch.cat([input_ids, next_tokens]	,       dim=-1)
			_UpperCamelCase   :		List[Any]			=						torch.cat([input_mask, next_mask]	,       dim=-1)

			_UpperCamelCase   :		List[Any]			=						model(
			    __snake_case	,       attention_mask=__snake_case	,       encoder_hidden_states=__snake_case	,       encoder_attention_mask=__snake_case	,       output_hidden_states=__snake_case	,       )['hidden_states'][0]
			_UpperCamelCase   :		Any			=						model(
			    __snake_case	,       attention_mask=__snake_case	,       encoder_hidden_states=__snake_case	,       encoder_attention_mask=__snake_case	,       past_key_values=__snake_case	,       output_hidden_states=__snake_case	,       )['hidden_states'][0]

			# select random slice
			_UpperCamelCase   :		int			=						ids_tensor((1,)	,       output_from_past.shape[-1]).item()
			_UpperCamelCase   :		Tuple			=						output_from_no_past[:, -3:, random_slice_idx].detach()
			_UpperCamelCase   :		Optional[int]			=						output_from_past[:, :, random_slice_idx].detach()

			self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1])

			# test that outputs are equal for slice
			self.parent.assertTrue(torch.allclose(__snake_case	,       __snake_case	,       atol=1e-3))


		def 		A__					(				self	,       __snake_case	,       __snake_case	,       __snake_case	,       __snake_case	,       *__snake_case	,       ):
			_UpperCamelCase   :		Tuple			=						BertGenerationDecoder(__snake_case)
			model.to(__snake_case)
			model.eval()
			_UpperCamelCase   :		str			=						model(__snake_case	,       attention_mask=__snake_case	,       labels=__snake_case)
			self.parent.assertEqual(result.logits.shape	,       (self.batch_size, self.seq_length, self.vocab_size))






		def 		A__					(				self):
			_UpperCamelCase	,		_UpperCamelCase	,		_UpperCamelCase	,		_UpperCamelCase   :		Dict			=						self.prepare_config_and_inputs()
			_UpperCamelCase   :		Tuple			=						{'input_ids': input_ids, 'attention_mask': input_mask}
			return config, inputs_dict







@require_torch
class 	lowercase    (				_lowercase    ,							_lowercase    ,							_lowercase    ,							unittest.TestCase							):


		"""simple docstring"""




		a__					       =  (BertGenerationEncoder, BertGenerationDecoder) if is_torch_available() else ()
		a__					       =  (BertGenerationDecoder,) if is_torch_available() else ()
		a__					       =  (
		    {"feature-extraction": BertGenerationEncoder, "text-generation": BertGenerationDecoder}
		    if is_torch_available()
		    else {}
		)


		def 		A__					(				self):
			_UpperCamelCase   :		Tuple			=						BertGenerationEncoderTester(self)
			_UpperCamelCase   :		Tuple			=						ConfigTester(self	,       config_class=__snake_case	,       hidden_size=37)


		def 		A__					(				self):
			self.config_tester.run_common_tests()


		def 		A__					(				self):
			_UpperCamelCase   :		List[str]			=						self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_model(*__snake_case)


		def 		A__					(				self):
			_UpperCamelCase	,		_UpperCamelCase	,		_UpperCamelCase	,		_UpperCamelCase   :		Optional[int]			=						self.model_tester.prepare_config_and_inputs()
			_UpperCamelCase   :		Optional[Any]			=						'bert'
			self.model_tester.create_and_check_model(__snake_case	,       __snake_case	,       __snake_case	,       __snake_case)


		def 		A__					(				self):
			_UpperCamelCase   :		Dict			=						self.model_tester.prepare_config_and_inputs_for_decoder()
			self.model_tester.create_and_check_model_as_decoder(*__snake_case)


		def 		A__					(				self):
			_UpperCamelCase   :		Any			=						self.model_tester.prepare_config_and_inputs_for_decoder()
			self.model_tester.create_and_check_decoder_model_past_large_inputs(*__snake_case)


		def 		A__					(				self):
			# This regression test was failing with PyTorch < 1.3
			(
			    (
			    _UpperCamelCase
			)	,		(
			    _UpperCamelCase
			)	,		(
			    _UpperCamelCase
			)	,		(
			    _UpperCamelCase
			)	,		(
			    _UpperCamelCase
			)	,		(
			    _UpperCamelCase
			)	,		
			)   :		Dict			=						self.model_tester.prepare_config_and_inputs_for_decoder()

			_UpperCamelCase   :		str			=						None

			self.model_tester.create_and_check_model_as_decoder(
			    __snake_case	,       __snake_case	,       __snake_case	,       __snake_case	,       __snake_case	,       __snake_case	,       )


		def 		A__					(				self):
			_UpperCamelCase   :		Dict			=						self.model_tester.prepare_config_and_inputs_for_decoder()
			self.model_tester.create_and_check_for_causal_lm(*__snake_case)






		@slow
		def 		A__					(				self):
			_UpperCamelCase   :		List[str]			=						BertGenerationEncoder.from_pretrained('google/bert_for_seq_generation_L-24_bbc_encoder')
			self.assertIsNotNone(__snake_case)







@require_torch
class 	lowercase    (				unittest.TestCase							):


		"""simple docstring"""




		@slow
		def 		A__					(				self):
			_UpperCamelCase   :		int			=						BertGenerationEncoder.from_pretrained('google/bert_for_seq_generation_L-24_bbc_encoder')
			_UpperCamelCase   :		str			=						torch.tensor([[1_01, 75_92, 10_10, 20_26, 38_99, 20_03, 1_01_40, 1_02]])
			with torch.no_grad():
				_UpperCamelCase   :		List[str]			=						model(__snake_case)[0]
			_UpperCamelCase   :		str			=						torch.Size([1, 8, 10_24])
			self.assertEqual(output.shape	,       __snake_case)
			_UpperCamelCase   :		Dict			=						torch.tensor(
			    [[[0.1_7_7_5, 0.0_0_8_3, -0.0_3_2_1], [1.6_0_0_2, 0.1_2_8_7, 0.3_9_1_2], [2.1_4_7_3, 0.5_7_9_1, 0.6_0_6_6]]])
			self.assertTrue(torch.allclose(output[:, :3, :3]	,       __snake_case	,       atol=1e-4))







@require_torch
class 	lowercase    (				unittest.TestCase							):


		"""simple docstring"""




		@slow
		def 		A__					(				self):
			_UpperCamelCase   :		Tuple			=						BertGenerationDecoder.from_pretrained('google/bert_for_seq_generation_L-24_bbc_encoder')
			_UpperCamelCase   :		Tuple			=						torch.tensor([[1_01, 75_92, 10_10, 20_26, 38_99, 20_03, 1_01_40, 1_02]])
			with torch.no_grad():
				_UpperCamelCase   :		str			=						model(__snake_case)[0]
			_UpperCamelCase   :		Optional[int]			=						torch.Size([1, 8, 5_03_58])
			self.assertEqual(output.shape	,       __snake_case)
			_UpperCamelCase   :		List[Any]			=						torch.tensor(
			    [[[-0.5_7_8_8, -2.5_9_9_4, -3.7_0_5_4], [0.0_4_3_8, 4.7_9_9_7, 1.8_7_9_5], [1.5_8_6_2, 6.6_4_0_9, 4.4_6_3_8]]])
			self.assertTrue(torch.allclose(output[:, :3, :3]	,       __snake_case	,       atol=1e-4))







import unittest

import torch

from diffusers import DDIMScheduler, DDPMScheduler, UNetaDModel
from diffusers.training_utils import set_seed
from diffusers.utils.testing_utils import slow


lowerCAmelCase__								=		False







class 	lowercase    (				unittest.TestCase							):


		"""simple docstring"""




		def 		A__					(				self	,       __snake_case=32):
			set_seed(0)
			_UpperCamelCase   :		int			=						UNetaDModel(sample_size=__snake_case	,       in_channels=3	,       out_channels=3)
			_UpperCamelCase   :		str			=						torch.optim.SGD(model.parameters()	,       lr=0.0_0_0_1)
			return model, optimizer






		@slow
		def 		A__					(				self):
			_UpperCamelCase   :		Tuple			=						'cpu'  # ensure full determinism without setting the CUBLAS_WORKSPACE_CONFIG env variable
			_UpperCamelCase   :		List[Any]			=						DDPMScheduler(
			    num_train_timesteps=10_00	,       beta_start=0.0_0_0_1	,       beta_end=0.0_2	,       beta_schedule='linear'	,       clip_sample=__snake_case	,       )
			_UpperCamelCase   :		List[Any]			=						DDIMScheduler(
			    num_train_timesteps=10_00	,       beta_start=0.0_0_0_1	,       beta_end=0.0_2	,       beta_schedule='linear'	,       clip_sample=__snake_case	,       )

			assert ddpm_scheduler.config.num_train_timesteps == ddim_scheduler.config.num_train_timesteps

			# shared batches for DDPM and DDIM
			set_seed(0)
			_UpperCamelCase   :		Optional[Any]			=						[torch.randn((4, 3, 32, 32)).clip(-1	,       1).to(__snake_case) for _ in range(4)]
			_UpperCamelCase   :		str			=						[torch.randn((4, 3, 32, 32)).to(__snake_case) for _ in range(4)]
			_UpperCamelCase   :		int			=						[torch.randint(0	,       10_00	,       (4,)).long().to(__snake_case) for _ in range(4)]

			# train with a DDPM scheduler
			_UpperCamelCase	,		_UpperCamelCase   :		List[Any]			=						self.get_model_optimizer(resolution=32)
			model.train().to(__snake_case)
			for i in range(4):
				optimizer.zero_grad()
				_UpperCamelCase   :		int			=						ddpm_scheduler.add_noise(clean_images[i]	,       noise[i]	,       timesteps[i])
				_UpperCamelCase   :		Any			=						model(__snake_case	,       timesteps[i]).sample
				_UpperCamelCase   :		str			=						torch.nn.functional.mse_loss(__snake_case	,       noise[i])
				loss.backward()
				optimizer.step()
			del model, optimizer

			# recreate the model and optimizer, and retry with DDIM
			_UpperCamelCase	,		_UpperCamelCase   :		Union[str, Any]			=						self.get_model_optimizer(resolution=32)
			model.train().to(__snake_case)
			for i in range(4):
				optimizer.zero_grad()
				_UpperCamelCase   :		Dict			=						ddim_scheduler.add_noise(clean_images[i]	,       noise[i]	,       timesteps[i])
				_UpperCamelCase   :		Dict			=						model(__snake_case	,       timesteps[i]).sample
				_UpperCamelCase   :		Tuple			=						torch.nn.functional.mse_loss(__snake_case	,       noise[i])
				loss.backward()
				optimizer.step()
			del model, optimizer

			self.assertTrue(torch.allclose(__snake_case	,       __snake_case	,       atol=1e-5))
			self.assertTrue(torch.allclose(__snake_case	,       __snake_case	,       atol=1e-5))




