

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    _a						:


					'''simple docstring'''






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






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

						A__							:   Dict					       =		None
						if self.use_input_mask:
							A__							:   Optional[Any]					       =		random_attention_mask([self.batch_size, self.seq_length]      )

						if self.use_labels:
							A__							:   int					       =		ids_tensor([self.batch_size, self.seq_length]   ,	self.vocab_size      )

						A__							:   Optional[Any]					       =		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=A__   ,	initializer_range=self.initializer_range   ,	)






					def   __A      (			self      ):
						(
						    A__       
						)							:   Any					       =		self.prepare_config_and_inputs()

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






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






					def   __A      (			self   ,	A__   ,	A__   ,	A__   ,	A__   ,	A__   ,	A__   ,	**A__   ,	):
						A__							:   Union[str, Any]					       =		True
						A__							:   List[Any]					       =		True
						A__							:   Union[str, Any]					       =		BertGenerationDecoder(config=A__      ).to(A__      ).eval()

						# first forward pass
						A__							:   Optional[int]					       =		model(
						    A__   ,	attention_mask=A__   ,	encoder_hidden_states=A__   ,	encoder_attention_mask=A__   ,	use_cache=A__   ,	)
						A__							:   Union[str, Any]					       =		outputs.past_key_values

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

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

						A__							:   str					       =		model(
						    A__   ,	attention_mask=A__   ,	encoder_hidden_states=A__   ,	encoder_attention_mask=A__   ,	output_hidden_states=A__   ,	)["""hidden_states"""][0]
						A__							:   Optional[int]					       =		model(
						    A__   ,	attention_mask=A__   ,	encoder_hidden_states=A__   ,	encoder_attention_mask=A__   ,	past_key_values=A__   ,	output_hidden_states=A__   ,	)["""hidden_states"""][0]

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






					def   __A      (			self   ,	A__   ,	A__   ,	A__   ,	A__   ,	*A__   ,	):
						A__							:   str					       =		BertGenerationDecoder(A__      )
						model.to(A__      )
						model.eval()
						A__							:   List[Any]					       =		model(A__   ,	attention_mask=A__   ,	labels=A__      )
						self.parent.assertEqual(result.logits.shape   ,	(self.batch_size, self.seq_length, self.vocab_size)      )






					def   __A      (			self      ):
						A__							:   List[str]					       =		self.prepare_config_and_inputs()
						A__							:   int					       =		{"""input_ids""": input_ids, """attention_mask""": input_mask}
						return config, inputs_dict



@require_torch
class    _a						(__magic_name__		,       __magic_name__		,       __magic_name__		,       unittest.TestCase    ):


					'''simple docstring'''






					UpperCAmelCase__:       List[str]  							=	(BertGenerationEncoder, BertGenerationDecoder) if is_torch_available() else ()
					UpperCAmelCase__:       int  							=	(BertGenerationDecoder,) if is_torch_available() else ()
					UpperCAmelCase__:       str  							=	(
					    {'''feature-extraction''': BertGenerationEncoder, '''text-generation''': BertGenerationDecoder}
					    if is_torch_available()
					    else {}
					)






					def   __A      (			self      ):
						A__							:   Any					       =		BertGenerationEncoderTester(self      )
						A__							:   List[Any]					       =		ConfigTester(self   ,	config_class=A__   ,	hidden_size=37      )






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






					def   __A      (			self      ):
						A__							:   Tuple					       =		self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(*A__      )






					def   __A      (			self      ):
						A__							:   Optional[int]					       =		self.model_tester.prepare_config_and_inputs()
						A__							:   List[Any]					       =		"""bert"""
						self.model_tester.create_and_check_model(A__   ,	A__   ,	A__   ,	A__      )






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






					def   __A      (			self      ):
						A__							:   Optional[Any]					       =		self.model_tester.prepare_config_and_inputs_for_decoder()
						self.model_tester.create_and_check_decoder_model_past_large_inputs(*A__      )






					def   __A      (			self      ):
						# This regression test was failing with PyTorch < 1.3
						(
						    A__       
						)							:   Union[str, Any]					       =		self.model_tester.prepare_config_and_inputs_for_decoder()

						A__							:   str					       =		None

						self.model_tester.create_and_check_model_as_decoder(
						    A__   ,	A__   ,	A__   ,	A__   ,	A__   ,	A__   ,	)






					def   __A      (			self      ):
						A__							:   Any					       =		self.model_tester.prepare_config_and_inputs_for_decoder()
						self.model_tester.create_and_check_for_causal_lm(*A__      )






					@slow
					def   __A      (			self      ):
						A__							:   Dict					       =		BertGenerationEncoder.from_pretrained("""google/bert_for_seq_generation_L-24_bbc_encoder"""      )
						self.assertIsNotNone(A__      )



@require_torch
class    _a						(unittest.TestCase    ):


					'''simple docstring'''






					@slow
					def   __A      (			self      ):
						A__							:   List[str]					       =		BertGenerationEncoder.from_pretrained("""google/bert_for_seq_generation_L-24_bbc_encoder"""      )
						A__							:   Optional[Any]					       =		torch.tensor([[101, 7592, 1010, 2026, 3899, 2003, 1_0140, 102]]      )
						with torch.no_grad():
							A__							:   Union[str, Any]					       =		model(A__      )[0]
						A__							:   Dict					       =		torch.Size([1, 8, 1024]      )
						self.assertEqual(output.shape   ,	A__      )
						A__							:   str					       =		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]   ,	A__   ,	atol=1e-4      )      )



@require_torch
class    _a						(unittest.TestCase    ):


					'''simple docstring'''






					@slow
					def   __A      (			self      ):
						A__							:   int					       =		BertGenerationDecoder.from_pretrained("""google/bert_for_seq_generation_L-24_bbc_encoder"""      )
						A__							:   Tuple					       =		torch.tensor([[101, 7592, 1010, 2026, 3899, 2003, 1_0140, 102]]      )
						with torch.no_grad():
							A__							:   Optional[int]					       =		model(A__      )[0]
						A__							:   str					       =		torch.Size([1, 8, 5_0358]      )
						self.assertEqual(output.shape   ,	A__      )
						A__							:   int					       =		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]   ,	A__   ,	atol=1e-4      )      )






import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device


if is_torch_available():
	from transformers import AutoModelForSeqaSeqLM, AutoTokenizer



@require_torch
@require_sentencepiece
@require_tokenizers
class    _a						(unittest.TestCase    ):


					'''simple docstring'''






					@slow
					def   __A      (			self      ):
						A__							:   Optional[Any]					       =		AutoModelForSeqaSeqLM.from_pretrained("""google/mt5-small"""   ,	return_dict=A__      ).to(A__      )
						A__							:   str					       =		AutoTokenizer.from_pretrained("""google/mt5-small"""      )

						A__							:   int					       =		tokenizer("""Hello there"""   ,	return_tensors="""pt"""      ).input_ids
						A__							:   List[Any]					       =		tokenizer("""Hi I am"""   ,	return_tensors="""pt"""      ).input_ids

						A__							:   Union[str, Any]					       =		model(input_ids.to(A__      )   ,	labels=labels.to(A__      )      ).loss
						A__							:   Union[str, Any]					       =		-(labels.shape[-1] * loss.item())

						A__							:   Any					       =		-8_4.9_1_2_7
						self.assertTrue(abs(mtf_score - EXPECTED_SCORE      ) < 1e-4      )



