
from __future__ import annotations

import unittest

from transformers import BlenderbotConfig, BlenderbotTokenizer, is_tf_available
from transformers.testing_utils import require_tf, require_tokenizers, slow
from transformers.utils import cached_property

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
		import tensorflow as tf

		from transformers import TFAutoModelForSeqaSeqLM, TFBlenderbotForConditionalGeneration, TFBlenderbotModel





@require_tf
class       __A:
					SCREAMING_SNAKE_CASE			    =    BlenderbotConfig
					SCREAMING_SNAKE_CASE			    =    {}
					SCREAMING_SNAKE_CASE			    =    '''gelu'''

					def __init__(	self					:     str   ,					__UpperCamelCase					:     Any   ,					__UpperCamelCase					:     Dict=1_3   ,					__UpperCamelCase					:     Dict=7   ,					__UpperCamelCase					:     Optional[Any]=True   ,					__UpperCamelCase					:     Dict=False   ,					__UpperCamelCase					:     int=9_9   ,					__UpperCamelCase					:     Optional[int]=3_2   ,					__UpperCamelCase					:     List[str]=2   ,					__UpperCamelCase					:     Union[str, Any]=4   ,					__UpperCamelCase					:     Optional[Any]=3_7   ,					__UpperCamelCase					:     List[Any]=0.1   ,					__UpperCamelCase					:     Union[str, Any]=0.1   ,					__UpperCamelCase					:     Dict=2_0   ,					__UpperCamelCase					:     Tuple=2   ,					__UpperCamelCase					:     Optional[int]=1   ,					__UpperCamelCase					:     str=0   ,					):
												lowerCamelCase_							  =					parent
												lowerCamelCase_							  =					batch_size
												lowerCamelCase_							  =					seq_length
												lowerCamelCase_							  =					is_training
												lowerCamelCase_							  =					use_labels
												lowerCamelCase_							  =					vocab_size
												lowerCamelCase_							  =					hidden_size
												lowerCamelCase_							  =					num_hidden_layers
												lowerCamelCase_							  =					num_attention_heads
												lowerCamelCase_							  =					intermediate_size

												lowerCamelCase_							  =					hidden_dropout_prob
												lowerCamelCase_							  =					attention_probs_dropout_prob
												lowerCamelCase_							  =					max_position_embeddings
												lowerCamelCase_							  =					eos_token_id
												lowerCamelCase_							  =					pad_token_id
												lowerCamelCase_							  =					bos_token_id

					def    lowercase__      (	self					:     Optional[Any]   ):
												lowerCamelCase_							  =					ids_tensor([self.batch_size, self.seq_length - 1]   ,					self.vocab_size   )
												lowerCamelCase_							  =					tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size   )   ,					1   )
												lowerCamelCase_							  =					tf.concat([input_ids, eos_tensor]   ,					axis=1   )

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

												lowerCamelCase_							  =					self.config_cls(
												    vocab_size=self.vocab_size   ,					d_model=self.hidden_size   ,					encoder_layers=self.num_hidden_layers   ,					decoder_layers=self.num_hidden_layers   ,					encoder_attention_heads=self.num_attention_heads   ,					decoder_attention_heads=self.num_attention_heads   ,					encoder_ffn_dim=self.intermediate_size   ,					decoder_ffn_dim=self.intermediate_size   ,					dropout=self.hidden_dropout_prob   ,					attention_dropout=self.attention_probs_dropout_prob   ,					max_position_embeddings=self.max_position_embeddings   ,					eos_token_ids=[2]   ,					bos_token_id=self.bos_token_id   ,					pad_token_id=self.pad_token_id   ,					decoder_start_token_id=self.pad_token_id   ,					**self.config_updates   ,					)
												lowerCamelCase_							  =					prepare_blenderbot_inputs_dict(__UpperCamelCase   ,					__UpperCamelCase   ,					__UpperCamelCase   )
												return config, inputs_dict



					def    lowercase__      (	self					:     str   ,					__UpperCamelCase					:     Optional[int]   ,					__UpperCamelCase					:     List[str]   ):
												lowerCamelCase_							  =					TFBlenderbotModel(config=__UpperCamelCase   ).get_decoder()
												lowerCamelCase_							  =					inputs_dict["""input_ids"""]

												lowerCamelCase_							  =					input_ids[:1, :]
												lowerCamelCase_							  =					inputs_dict["""attention_mask"""][:1, :]
												lowerCamelCase_							  =					inputs_dict["""head_mask"""]
												lowerCamelCase_							  =					1

												# first forward pass
												lowerCamelCase_							  =					model(__UpperCamelCase   ,					attention_mask=__UpperCamelCase   ,					head_mask=__UpperCamelCase   ,					use_cache=__UpperCamelCase   )

												lowerCamelCase_  ,   lowerCamelCase_							  =					outputs.to_tuple()

												# create hypothetical next token and extent to next_input_ids
												lowerCamelCase_							  =					ids_tensor((self.batch_size, 3)   ,					config.vocab_size   )
												lowerCamelCase_							  =					tf.cast(ids_tensor((self.batch_size, 3)   ,					2   )   ,					tf.inta   )

												# append to next input_ids and
												lowerCamelCase_							  =					tf.concat([input_ids, next_tokens]   ,					axis=-1   )
												lowerCamelCase_							  =					tf.concat([attention_mask, next_attn_mask]   ,					axis=-1   )

												lowerCamelCase_							  =					model(__UpperCamelCase   ,					attention_mask=__UpperCamelCase   )[0]
												lowerCamelCase_							  =					model(__UpperCamelCase   ,					attention_mask=__UpperCamelCase   ,					past_key_values=__UpperCamelCase   )[0]

												self.parent.assertEqual(next_tokens.shape[1]   ,					output_from_past.shape[1]   )

												# select random slice
												lowerCamelCase_							  =					int(ids_tensor((1,)   ,					output_from_past.shape[-1]   )   )
												lowerCamelCase_							  =					output_from_no_past[:, -3:, random_slice_idx]
												lowerCamelCase_							  =					output_from_past[:, :, random_slice_idx]

												# test that outputs are equal for slice
												tf.debugging.assert_near(__UpperCamelCase   ,					__UpperCamelCase   ,					rtol=1E-3   )



def      __lowerCAmelCase						(							UpperCAmelCase__				:      Dict			,				UpperCAmelCase__				:      str			,				UpperCAmelCase__				:      Tuple			,				UpperCAmelCase__				:      List[str]=None			,				UpperCAmelCase__				:      Tuple=None			,				UpperCAmelCase__				:      Dict=None			,				UpperCAmelCase__				:      int=None			,				UpperCAmelCase__				:      int=None			,				)  ->    Union[str, Any]:
							if attention_mask is None:
														lowerCamelCase_							  =					tf.cast(tf.math.not_equal(UpperCAmelCase__			,				config.pad_token_id       )			,				tf.inta       )
							if decoder_attention_mask is None:
														lowerCamelCase_							  =					tf.concat(
														    [
														        tf.ones(decoder_input_ids[:, :1].shape			,				dtype=tf.inta       ),
														        tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:]			,				config.pad_token_id       )			,				tf.inta       ),
														    ]			,				axis=-1			,				)
							if head_mask is None:
														lowerCamelCase_							  =					tf.ones((config.encoder_layers, config.encoder_attention_heads)       )
							if decoder_head_mask is None:
														lowerCamelCase_							  =					tf.ones((config.decoder_layers, config.decoder_attention_heads)       )
							if cross_attn_head_mask is None:
														lowerCamelCase_							  =					tf.ones((config.decoder_layers, config.decoder_attention_heads)       )
							return {
							    "input_ids": input_ids,
							    "decoder_input_ids": decoder_input_ids,
							    "attention_mask": attention_mask,
							    "decoder_attention_mask": decoder_attention_mask,
							    "head_mask": head_mask,
							    "decoder_head_mask": decoder_head_mask,
							    "cross_attn_head_mask": cross_attn_head_mask,
							}





@require_tf
class       __A(				UpperCAmelCase     ,		UpperCAmelCase     ,		unittest.TestCase       ):
					SCREAMING_SNAKE_CASE			    =    (TFBlenderbotForConditionalGeneration, TFBlenderbotModel) if is_tf_available() else ()
					SCREAMING_SNAKE_CASE			    =    (TFBlenderbotForConditionalGeneration,) if is_tf_available() else ()
					SCREAMING_SNAKE_CASE			    =    (
					    {
					        '''conversational''': TFBlenderbotForConditionalGeneration,
					        '''feature-extraction''': TFBlenderbotModel,
					        '''summarization''': TFBlenderbotForConditionalGeneration,
					        '''text2text-generation''': TFBlenderbotForConditionalGeneration,
					        '''translation''': TFBlenderbotForConditionalGeneration,
					    }
					    if is_tf_available()
					    else {}
					)
					SCREAMING_SNAKE_CASE			    =    True
					SCREAMING_SNAKE_CASE			    =    False
					SCREAMING_SNAKE_CASE			    =    False

					def    lowercase__      (	self					:     int   ):
												lowerCamelCase_							  =					TFBlenderbotModelTester(self   )
												lowerCamelCase_							  =					ConfigTester(self   ,					config_class=__UpperCamelCase   )

					def    lowercase__      (	self					:     Optional[Any]   ):
												self.config_tester.run_common_tests()



					def    lowercase__      (	self					:     Optional[Any]   ):
												lowerCamelCase_							  =					self.model_tester.prepare_config_and_inputs_for_common()
												self.model_tester.check_decoder_model_past_large_inputs(*__UpperCamelCase   )





@require_tokenizers
@require_tf
class       __A(				unittest.TestCase       ):
					SCREAMING_SNAKE_CASE			    =    ['''My friends are cool but they eat too many carbs.''']
					SCREAMING_SNAKE_CASE			    =    '''facebook/blenderbot-400M-distill'''

					@cached_property
					def    lowercase__      (	self					:     int   ):
												return BlenderbotTokenizer.from_pretrained(self.model_name   )

					@cached_property
					def    lowercase__      (	self					:     Optional[Any]   ):
												lowerCamelCase_							  =					TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name   )
												return model



					@slow
					def    lowercase__      (	self					:     Dict   ):
												lowerCamelCase_							  =					self.tokenizer(self.src_text   ,					return_tensors="""tf"""   )
												lowerCamelCase_							  =					self.model.generate(
												    model_inputs.input_ids   ,					)
												lowerCamelCase_							  =					self.tokenizer.batch_decode(generated_ids.numpy()   ,					skip_special_tokens=__UpperCamelCase   )[0]
												assert (
												    generated_words
												    == " That's unfortunate. Are they trying to lose weight or are they just trying to be healthier?"
												)







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available


lowercase        =  {'''configuration_speech_encoder_decoder''': ['''SpeechEncoderDecoderConfig''']}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase        =  ['''SpeechEncoderDecoderModel''']

try:
		if not is_flax_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase        =  ['''FlaxSpeechEncoderDecoderModel''']

if TYPE_CHECKING:
		from .configuration_speech_encoder_decoder import SpeechEncoderDecoderConfig

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_speech_encoder_decoder import SpeechEncoderDecoderModel

		try:
				if not is_flax_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_flax_speech_encoder_decoder import FlaxSpeechEncoderDecoderModel

else:
		import sys

		lowercase        =  _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)





