





'''simple docstring'''







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 				__SCREAMING_SNAKE_CASE  :






				"""simple docstring"""







				__a            =BlenderbotConfig
				__a            ={}
				__a            ='gelu'




				def __init__(  self     :					Dict					,				__a     :					str					,				__a     :					Dict=13					,				__a     :					List[str]=7					,				__a     :					List[Any]=True					,				__a     :					Tuple=False					,				__a     :					Union[str, Any]=99					,				__a     :					List[str]=32					,				__a     :					int=2					,				__a     :					Any=4					,				__a     :					str=37					,				__a     :					Tuple=0.1					,				__a     :					Dict=0.1					,				__a     :					int=20					,				__a     :					List[Any]=2					,				__a     :					int=1					,				__a     :					Optional[int]=0					,				):
								_a								= parent
								_a								= batch_size
								_a								= seq_length
								_a								= is_training
								_a								= use_labels
								_a								= vocab_size
								_a								= hidden_size
								_a								= num_hidden_layers
								_a								= num_attention_heads
								_a								= intermediate_size

								_a								= hidden_dropout_prob
								_a								= attention_probs_dropout_prob
								_a								= max_position_embeddings
								_a								= eos_token_id
								_a								= pad_token_id
								_a								= bos_token_id




				def   UpperCamelCase__   (  self     :					Tuple					):
								_a								= ids_tensor([self.batch_size, self.seq_length - 1]					,				self.vocab_size					)
								_a								= tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size					)					,				1					)
								_a								= tf.concat([input_ids, eos_tensor]					,				axis=1					)

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

								_a								= 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					,				)
								_a								= prepare_blenderbot_inputs_dict(__a					,				__a					,				__a					)
								return config, inputs_dict




				def   UpperCamelCase__   (  self     :					str					,				__a     :					int					,				__a     :					Tuple					):
								_a								= TFBlenderbotModel(config=__a					).get_decoder()
								_a								= inputs_dict["input_ids"]

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

								# first forward pass
								_a								= model(__a					,				attention_mask=__a					,				head_mask=__a					,				use_cache=__a					)

								_a	,					_a								= outputs.to_tuple()

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

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

								_a								= model(__a					,				attention_mask=__a					)[0]
								_a								= 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
								_a								= int(ids_tensor((1,)					,				output_from_past.shape[-1]					)					)
								_a								= output_from_no_past[:, -3:, random_slice_idx]
								_a								= output_from_past[:, :, random_slice_idx]

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






def  _lowerCamelCase				( lowercase						:      Union[str, Any]    ,							lowercase						:      Union[str, Any]    ,							lowercase						:      Dict    ,							lowercase						:      Union[str, Any]=None    ,							lowercase						:      Optional[int]=None    ,							lowercase						:      Tuple=None    ,							lowercase						:      Any=None    ,							lowercase						:      Union[str, Any]=None    ,							)	->	List[Any]:
				if attention_mask is None:
								_a								= tf.cast(tf.math.not_equal(lowercase    ,							config.pad_token_id						)    ,							tf.inta						)
				if decoder_attention_mask is None:
								_a								= 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:
								_a								= tf.ones((config.encoder_layers, config.encoder_attention_heads)						)
				if decoder_head_mask is None:
								_a								= tf.ones((config.decoder_layers, config.decoder_attention_heads)						)
				if cross_attn_head_mask is None:
								_a								= 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  (lowerCamelCase_       ,							lowerCamelCase_       ,							unittest.TestCase      ):






				"""simple docstring"""







				__a            =(TFBlenderbotForConditionalGeneration, TFBlenderbotModel) if is_tf_available() else ()
				__a            =(TFBlenderbotForConditionalGeneration,) if is_tf_available() else ()
				__a            =(
				    {
				        'conversational': TFBlenderbotForConditionalGeneration,
				        'feature-extraction': TFBlenderbotModel,
				        'summarization': TFBlenderbotForConditionalGeneration,
				        'text2text-generation': TFBlenderbotForConditionalGeneration,
				        'translation': TFBlenderbotForConditionalGeneration,
				    }
				    if is_tf_available()
				    else {}
				)
				__a            =True
				__a            =False
				__a            =False




				def   UpperCamelCase__   (  self     :					Tuple					):
								_a								= TFBlenderbotModelTester(self					)
								_a								= ConfigTester(self					,				config_class=__a					)




				def   UpperCamelCase__   (  self     :					int					):
								self.config_tester.run_common_tests()




				def   UpperCamelCase__   (  self     :					int					):
								_a								= 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"""







				__a            =['My friends are cool but they eat too many carbs.']
				__a            ='facebook/blenderbot-400M-distill'




				@cached_property
				def   UpperCamelCase__   (  self     :					Optional[int]					):
								return BlenderbotTokenizer.from_pretrained(self.model_name					)




				@cached_property
				def   UpperCamelCase__   (  self     :					Union[str, Any]					):
								_a								= TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name					)
								return model




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







'''simple docstring'''







import fire
from torch.utils.data import DataLoader
from tqdm import tqdm

from transformers import AutoTokenizer
from utils import SeqaSeqDataset, pickle_save






def  _lowerCamelCase				( lowercase						:      Union[str, Any]    ,							lowercase						:      int    ,							lowercase						:      int=1024    ,							lowercase						:      int=1024    ,							lowercase						:      Tuple=False    ,							**lowercase						:      Optional[int]						)	->	Union[str, Any]:
				_a								= AutoTokenizer.from_pretrained(lowercase						)
				_a								= SeqaSeqDataset(lowercase    ,							lowercase    ,							lowercase    ,							lowercase    ,							type_path="train"    ,							**lowercase						)
				_a								= tok.pad_token_id

				def get_lens(lowercase						:      Optional[int]						):
								_a								= tqdm(
								    DataLoader(lowercase    ,							batch_size=512    ,							num_workers=8    ,							shuffle=lowercase    ,							collate_fn=ds.collate_fn						)    ,							desc=str(ds.len_file						)    ,							)
								_a								= []
								for batch in dl:
												_a								= batch["input_ids"].ne(lowercase						).sum(1						).tolist()
												_a								= batch["labels"].ne(lowercase						).sum(1						).tolist()
												if consider_target:
																for src, tgt in zip(lowercase    ,							lowercase						):
																				max_lens.append(max(lowercase    ,							lowercase						)						)
												else:
																max_lens.extend(lowercase						)
								return max_lens

				_a								= get_lens(lowercase						)
				_a								= SeqaSeqDataset(lowercase    ,							lowercase    ,							lowercase    ,							lowercase    ,							type_path="val"    ,							**lowercase						)
				_a								= get_lens(lowercase						)
				pickle_save(lowercase    ,							train_ds.len_file						)
				pickle_save(lowercase    ,							val_ds.len_file						)


if __name__ == "__main__":
					fire.Fire(save_len_file)
