






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__a       :	int             = {
    'configuration_blenderbot': [
        'BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'BlenderbotConfig',
        'BlenderbotOnnxConfig',
    ],
    'tokenization_blenderbot': ['BlenderbotTokenizer'],
}

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__a       :	Optional[Any]             = ['BlenderbotTokenizerFast']

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__a       :	Tuple             = [
	    'BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'BlenderbotForCausalLM',
	    'BlenderbotForConditionalGeneration',
	    'BlenderbotModel',
	    'BlenderbotPreTrainedModel',
	]


try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__a       :	Any             = [
	    'TFBlenderbotForConditionalGeneration',
	    'TFBlenderbotModel',
	    'TFBlenderbotPreTrainedModel',
	]


try:
	if not is_flax_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__a       :	int             = [
	    'FlaxBlenderbotForConditionalGeneration',
	    'FlaxBlenderbotModel',
	    'FlaxBlenderbotPreTrainedModel',
	]


if TYPE_CHECKING:
	from .configuration_blenderbot import (
	    BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP,
	    BlenderbotConfig,
	    BlenderbotOnnxConfig,
	)
	from .tokenization_blenderbot import BlenderbotTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_blenderbot_fast import BlenderbotTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_blenderbot import (
		    BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    BlenderbotForCausalLM,
		    BlenderbotForConditionalGeneration,
		    BlenderbotModel,
		    BlenderbotPreTrainedModel,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_blenderbot import (
		    TFBlenderbotForConditionalGeneration,
		    TFBlenderbotModel,
		    TFBlenderbotPreTrainedModel,
		)

	try:
		if not is_flax_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_flax_blenderbot import (
		    FlaxBlenderbotForConditionalGeneration,
		    FlaxBlenderbotModel,
		    FlaxBlenderbotPreTrainedModel,
		)

else:
	import sys

	__a       :	int             = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)










"""simple docstring"""







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        _SCREAMING_SNAKE_CASE						:





							"""simple docstring"""





							def __init__(   self:     Optional[Any]				,		__A:     List[Any]				,		__A:     Optional[int]=13				,		__A:     Tuple=7				,		__A:     Optional[Any]=True				,		__A:     Tuple=True				,		__A:     str=99				,		__A:     int=32				,		__A:     Union[str, Any]=5				,		__A:     Optional[Any]=4				,		__A:     Any=37				,		__A:     Union[str, Any]="gelu"				,		__A:     int=0.1				,		__A:     Optional[Any]=0.1				,		__A:     Optional[int]=50				,		__A:     str=0.0_2				,		__A:     Dict=True				,		__A:     Dict=None				,		):

												'''simple docstring'''

												a__          =  parent
												a__          =  batch_size
												a__          =  seq_length
												a__          =  is_training
												a__          =  use_input_mask
												a__          =  vocab_size
												a__          =  hidden_size
												a__          =  num_hidden_layers
												a__          =  num_attention_heads
												a__          =  intermediate_size
												a__          =  hidden_act
												a__          =  hidden_dropout_prob
												a__          =  attention_probs_dropout_prob
												a__          =  max_position_embeddings
												a__          =  initializer_range
												a__          =  use_labels
												a__          =  scope




							def  lowercase		(   self:     List[Any]    ):

												'''simple docstring'''

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

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

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

												a__          =  self.get_config()

												return config, input_ids, input_mask, token_labels




							def  lowercase		(   self:     int    ):

												'''simple docstring'''

												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  lowercase		(   self:     List[str]    ):

												'''simple docstring'''

												(
												    (
												    a__
												)       ,(
												    a__
												)       ,(
												    a__
												)       ,(
												    a__
												)       ,
												)          =  self.prepare_config_and_inputs()

												a__          =  True
												a__          =  floats_tensor([self.batch_size, self.seq_length, self.hidden_size]    )
												a__          =  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  lowercase		(   self:     str				,		__A:     List[str]				,		__A:     Optional[int]				,		__A:     Union[str, Any]				,		__A:     Optional[int]				,		**__A:     Union[str, Any]				,		):

												'''simple docstring'''

												a__          =  BertGenerationEncoder(config=__A    )
												model.to(__A    )
												model.eval()
												a__          =  model(__A				,		attention_mask=__A    )
												a__          =  model(__A    )
												self.parent.assertEqual(result.last_hidden_state.shape				,		(self.batch_size, self.seq_length, self.hidden_size)    )




							def  lowercase		(   self:     Optional[Any]				,		__A:     Any				,		__A:     int				,		__A:     Optional[int]				,		__A:     int				,		__A:     List[Any]				,		__A:     Any				,		**__A:     List[str]				,		):

												'''simple docstring'''

												a__          =  True
												a__          =  BertGenerationEncoder(config=__A    )
												model.to(__A    )
												model.eval()
												a__          =  model(
												    __A				,		attention_mask=__A				,		encoder_hidden_states=__A				,		encoder_attention_mask=__A				,		)
												a__          =  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  lowercase		(   self:     Dict				,		__A:     Tuple				,		__A:     str				,		__A:     str				,		__A:     Any				,		__A:     int				,		__A:     Any				,		**__A:     Dict				,		):

												'''simple docstring'''

												a__          =  True
												a__          =  True
												a__          =  BertGenerationDecoder(config=__A    ).to(__A    ).eval()

												# first forward pass
												a__          =  model(
												    __A				,		attention_mask=__A				,		encoder_hidden_states=__A				,		encoder_attention_mask=__A				,		use_cache=__A				,		)
												a__          =  outputs.past_key_values

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

												# append to next input_ids and
												a__          =  torch.cat([input_ids, next_tokens]				,		dim=-1    )
												a__          =  torch.cat([input_mask, next_mask]				,		dim=-1    )

												a__          =  model(
												    __A				,		attention_mask=__A				,		encoder_hidden_states=__A				,		encoder_attention_mask=__A				,		output_hidden_states=__A				,		)['''hidden_states'''][0]
												a__          =  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__          =  ids_tensor((1,)				,		output_from_past.shape[-1]    ).item()
												a__          =  output_from_no_past[:, -3:, random_slice_idx].detach()
												a__          =  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  lowercase		(   self:     Optional[Any]				,		__A:     List[Any]				,		__A:     List[Any]				,		__A:     Union[str, Any]				,		__A:     Optional[Any]				,		*__A:     Optional[int]				,		):

												'''simple docstring'''

												a__          =  BertGenerationDecoder(__A    )
												model.to(__A    )
												model.eval()
												a__          =  model(__A				,		attention_mask=__A				,		labels=__A    )
												self.parent.assertEqual(result.logits.shape				,		(self.batch_size, self.seq_length, self.vocab_size)    )





							def  lowercase		(   self:     Dict    ):

												'''simple docstring'''

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







@require_torch
class        _SCREAMING_SNAKE_CASE						(  __snake_case       , __snake_case       , __snake_case       , unittest.TestCase							):





							"""simple docstring"""





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




							def  lowercase		(   self:     Optional[int]    ):

												'''simple docstring'''

												a__          =  BertGenerationEncoderTester(self    )
												a__          =  ConfigTester(self				,		config_class=__A				,		hidden_size=37    )




							def  lowercase		(   self:     Optional[int]    ):

												'''simple docstring'''

												self.config_tester.run_common_tests()




							def  lowercase		(   self:     Union[str, Any]    ):

												'''simple docstring'''

												a__          =  self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_model(*__A    )




							def  lowercase		(   self:     Any    ):

												'''simple docstring'''

												a__       ,a__       ,a__       ,a__          =  self.model_tester.prepare_config_and_inputs()
												a__          =  '''bert'''
												self.model_tester.create_and_check_model(__A				,		__A				,		__A				,		__A    )




							def  lowercase		(   self:     int    ):

												'''simple docstring'''

												a__          =  self.model_tester.prepare_config_and_inputs_for_decoder()
												self.model_tester.create_and_check_model_as_decoder(*__A    )




							def  lowercase		(   self:     List[str]    ):

												'''simple docstring'''

												a__          =  self.model_tester.prepare_config_and_inputs_for_decoder()
												self.model_tester.create_and_check_decoder_model_past_large_inputs(*__A    )




							def  lowercase		(   self:     Union[str, Any]    ):

												'''simple docstring'''

												(
												    (
												    a__
												)       ,(
												    a__
												)       ,(
												    a__
												)       ,(
												    a__
												)       ,(
												    a__
												)       ,(
												    a__
												)       ,
												)          =  self.model_tester.prepare_config_and_inputs_for_decoder()

												a__          =  None

												self.model_tester.create_and_check_model_as_decoder(
												    __A				,		__A				,		__A				,		__A				,		__A				,		__A				,		)




							def  lowercase		(   self:     str    ):

												'''simple docstring'''

												a__          =  self.model_tester.prepare_config_and_inputs_for_decoder()
												self.model_tester.create_and_check_for_causal_lm(*__A    )





							@slow
							def  lowercase		(   self:     Dict    ):

												'''simple docstring'''

												a__          =  BertGenerationEncoder.from_pretrained('''google/bert_for_seq_generation_L-24_bbc_encoder'''    )
												self.assertIsNotNone(__A    )







@require_torch
class        _SCREAMING_SNAKE_CASE						(  unittest.TestCase							):





							"""simple docstring"""





							@slow
							def  lowercase		(   self:     Optional[Any]    ):

												'''simple docstring'''

												a__          =  BertGenerationEncoder.from_pretrained('''google/bert_for_seq_generation_L-24_bbc_encoder'''    )
												a__          =  torch.tensor([[101, 7592, 1010, 2026, 3899, 2003, 10140, 102]]    )
												with torch.no_grad():
																	a__          =  model(__A    )[0]
												a__          =  torch.Size([1, 8, 1024]    )
												self.assertEqual(output.shape				,		__A    )
												a__          =  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        _SCREAMING_SNAKE_CASE						(  unittest.TestCase							):





							"""simple docstring"""





							@slow
							def  lowercase		(   self:     List[Any]    ):

												'''simple docstring'''

												a__          =  BertGenerationDecoder.from_pretrained('''google/bert_for_seq_generation_L-24_bbc_encoder'''    )
												a__          =  torch.tensor([[101, 7592, 1010, 2026, 3899, 2003, 10140, 102]]    )
												with torch.no_grad():
																	a__          =  model(__A    )[0]
												a__          =  torch.Size([1, 8, 50358]    )
												self.assertEqual(output.shape				,		__A    )
												a__          =  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    )    )


