'''simple docstring'''

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   SCREAMING_SNAKE_CASE			:







		"""simple docstring"""
		lowercase   :      Tuple       =							BlenderbotSmallConfig
		lowercase   :      List[Any]       =							{}
		lowercase   :      Tuple       =							'gelu'






		def __init__(   self		,						__A		,						__A=13		,						__A=7		,						__A=True		,						__A=False		,						__A=99		,						__A=32		,						__A=2		,						__A=4		,						__A=37		,						__A=0.1		,						__A=0.1		,						__A=20		,						__A=2		,						__A=1		,						__A=0		,						) ->     Dict:
								_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 		UpperCamelCase__       (   self				) ->     Optional[int]:
								_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(__A		,						__A		,						__A				)
								return config, inputs_dict






		def 		UpperCamelCase__       (   self		,						__A		,						__A				) ->     List[Any]:
								_lowerCAmelCase										=TFBlenderbotSmallModel(config=__A				).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(__A		,						attention_mask=__A		,						head_mask=__A		,						use_cache=__A				)

								_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(__A		,						attention_mask=__A				)[0]
								_lowerCAmelCase										=model(__A		,						attention_mask=__A		,						past_key_values=__A				)[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(__A		,						__A		,						rtol=1E-3				)



def 							UpperCamelCase__			(			a__			,      a__			,      a__			,      a__=None			,      a__=None			,      a__=None			,      a__=None			,      a__=None			,      ):


						'''simple docstring'''






						if attention_mask is None:
												_lowerCAmelCase										=tf.cast(tf.math.not_equal(a__			,      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   SCREAMING_SNAKE_CASE			(						__lowercase    ,			__lowercase    ,			unittest.TestCase):







		"""simple docstring"""
		lowercase   :      List[str]       =							(
		    (TFBlenderbotSmallForConditionalGeneration, TFBlenderbotSmallModel) if is_tf_available() else ()
		)
		lowercase   :      Optional[Any]       =							(TFBlenderbotSmallForConditionalGeneration,) if is_tf_available() else ()
		lowercase   :      Union[str, Any]       =							(
		    {
		        'conversational': TFBlenderbotSmallForConditionalGeneration,
		        'feature-extraction': TFBlenderbotSmallModel,
		        'summarization': TFBlenderbotSmallForConditionalGeneration,
		        'text2text-generation': TFBlenderbotSmallForConditionalGeneration,
		        'translation': TFBlenderbotSmallForConditionalGeneration,
		    }
		    if is_tf_available()
		    else {}
		)
		lowercase   :      List[str]       =							True
		lowercase   :      Optional[Any]       =							False
		lowercase   :      Any       =							False






		def 		UpperCamelCase__       (   self				) ->     Optional[Any]:
								_lowerCAmelCase										=TFBlenderbotSmallModelTester(self				)
								_lowerCAmelCase										=ConfigTester(self		,						config_class=__A				)






		def 		UpperCamelCase__       (   self				) ->     str:
								self.config_tester.run_common_tests()






		def 		UpperCamelCase__       (   self				) ->     Optional[Any]:
								_lowerCAmelCase										=self.model_tester.prepare_config_and_inputs_for_common()
								self.model_tester.check_decoder_model_past_large_inputs(*__A				)

@require_tokenizers
@require_tf
class   SCREAMING_SNAKE_CASE			(						unittest.TestCase):







		"""simple docstring"""
		lowercase   :      Any       =							[
		    '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?'
		]
		lowercase   :      Any       =							'facebook/blenderbot_small-90M'






		@cached_property
		def 		UpperCamelCase__       (   self				) ->     Tuple:
								# use "old" tokenizer here because of bug when downloading new tokenizer
								return BlenderbotSmallTokenizer.from_pretrained('facebook/blenderbot-90M'				)






		@cached_property
		def 		UpperCamelCase__       (   self				) ->     Tuple:
								_lowerCAmelCase										=TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name				)
								return model






		@slow
		def 		UpperCamelCase__       (   self				) ->     str:
								_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=__A		,						)
								_lowerCAmelCase										=self.tokenizer.batch_decode(generated_ids.numpy()		,						skip_special_tokens=__A				)[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.",
								)






'''simple docstring'''

lowercase_     		= '''
# Installazione di Transformers
! pip install transformers datasets
# Per installare dalla fonte invece dell\'ultima versione rilasciata, commenta il comando sopra e
# rimuovi la modalità commento al comando seguente.
# ! pip install git+https://github.com/huggingface/transformers.git
'''

lowercase_     		= [{'''type''': '''code''', '''content''': INSTALL_CONTENT}]
lowercase_     		= {
    '''{processor_class}''': '''FakeProcessorClass''',
    '''{model_class}''': '''FakeModelClass''',
    '''{object_class}''': '''FakeObjectClass''',
}





