

from __future__ import annotations

import unittest

from transformers import BlenderbotSmallConfig, BlenderbotSmallTokenizer, 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, TFBlenderbotSmallForConditionalGeneration, TFBlenderbotSmallModel





@require_tf
class     snake_case_			:


							"""simple docstring"""





							__UpperCAmelCase       =BlenderbotSmallConfig
							__UpperCAmelCase       ={}
							__UpperCAmelCase       ="""gelu"""




							def __init__( self					,						_A					,						_A=1_3					,						_A=7					,						_A=True					,						_A=False					,						_A=9_9					,						_A=3_2					,						_A=2					,						_A=4					,						_A=3_7					,						_A=0.1					,						_A=0.1					,						_A=2_0					,						_A=2					,						_A=1					,						_A=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        A__  ( self							):
												__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_small_inputs_dict(UpperCamelCase_					,						UpperCamelCase_					,						UpperCamelCase_							)
												return config, inputs_dict




							def        A__  ( self					,						_A					,						_A							):
												__lowerCAmelCase					=							TFBlenderbotSmallModel(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					=							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 		__lowercase    (  UpperCAmelCase__		,					UpperCAmelCase__		,					UpperCAmelCase__		,					UpperCAmelCase__=None		,					UpperCAmelCase__=None		,					UpperCAmelCase__=None		,					UpperCAmelCase__=None		,					UpperCAmelCase__=None		,					):

					"""simple docstring"""






					if attention_mask is None:
										__lowerCAmelCase					=							tf.cast(tf.math.not_equal(_lowercase		,					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     snake_case_			(				_a			, _a			, unittest.TestCase							):


							"""simple docstring"""





							__UpperCAmelCase       =(
							    (TFBlenderbotSmallForConditionalGeneration, TFBlenderbotSmallModel) if is_tf_available() else ()
							)
							__UpperCAmelCase       =(TFBlenderbotSmallForConditionalGeneration,) if is_tf_available() else ()
							__UpperCAmelCase       =(
							    {
							        """conversational""": TFBlenderbotSmallForConditionalGeneration,
							        """feature-extraction""": TFBlenderbotSmallModel,
							        """summarization""": TFBlenderbotSmallForConditionalGeneration,
							        """text2text-generation""": TFBlenderbotSmallForConditionalGeneration,
							        """translation""": TFBlenderbotSmallForConditionalGeneration,
							    }
							    if is_tf_available()
							    else {}
							)
							__UpperCAmelCase       =True
							__UpperCAmelCase       =False
							__UpperCAmelCase       =False




							def        A__  ( self							):
												__lowerCAmelCase					=							TFBlenderbotSmallModelTester(self							)
												__lowerCAmelCase					=							ConfigTester(self					,						config_class=UpperCamelCase_							)




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




							def        A__  ( self							):
												__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     snake_case_			(				unittest.TestCase							):


							"""simple docstring"""





							__UpperCAmelCase       =[
							    """Social anxiety\nWow, I am never shy. Do you have anxiety?\nYes. I end up sweating and blushing and feel like """
							    """  i'm going to throw up.\nand why is that?"""
							]
							__UpperCAmelCase       ="""facebook/blenderbot_small-90M"""




							@cached_property
							def        A__  ( self							):
												return BlenderbotSmallTokenizer.from_pretrained('facebook/blenderbot-90M'							)




							@cached_property
							def        A__  ( self							):
												__lowerCAmelCase					=							TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name							)
												return model




							@slow
							def        A__  ( self							):
												__lowerCAmelCase					=							self.tokenizer(self.src_text					,						return_tensors='tf'							)
												__lowerCAmelCase					=							self.model.generate(
												    model_inputs.input_ids					,						attention_mask=model_inputs.attention_mask					,						num_beams=2					,						use_cache=UpperCamelCase_					,						)
												__lowerCAmelCase					=							self.tokenizer.batch_decode(generated_ids.numpy()					,						skip_special_tokens=UpperCamelCase_							)[0]
												assert generated_words in (
												    "i don't know. i just feel like i'm going to throw up. it's not fun.",
												    "i'm not sure. i just feel like i've been feeling like i have to be in a certain place",
												    "i'm not sure. i just feel like i've been in a bad situation.",
												)









from typing import TYPE_CHECKING

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


lowerCamelCase      				=     {'''configuration_unispeech''': ['''UNISPEECH_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''UniSpeechConfig''']}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowerCamelCase      				=     [
		    '''UNISPEECH_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''UniSpeechForCTC''',
		    '''UniSpeechForPreTraining''',
		    '''UniSpeechForSequenceClassification''',
		    '''UniSpeechModel''',
		    '''UniSpeechPreTrainedModel''',
		]

if TYPE_CHECKING:
		from .configuration_unispeech import UNISPEECH_PRETRAINED_CONFIG_ARCHIVE_MAP, UniSpeechConfig

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_unispeech import (
				    UNISPEECH_PRETRAINED_MODEL_ARCHIVE_LIST,
				    UniSpeechForCTC,
				    UniSpeechForPreTraining,
				    UniSpeechForSequenceClassification,
				    UniSpeechModel,
				    UniSpeechPreTrainedModel,
				)

else:
		import sys

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






