


from pathlib import Path

import fire
from tqdm import tqdm

def 	lowerCAmelCase__			(      a__:		Dict="ro"      ,  a__:		Any="en"      ,  a__:		List[Any]="wmt16"      ,  a__:		Optional[Any]=None	)  ->		None:


							'''simple docstring'''





							try:
														import datasets
							except (ModuleNotFoundError, ImportError):
														raise ImportError('run pip install datasets'	)
							_UpperCAmelCase					=      F'''{src_lang}-{tgt_lang}'''
							print(F'''Converting {dataset}-{pair}'''	)
							_UpperCAmelCase					=      datasets.load_dataset(a__      ,  a__	)
							if save_dir is None:
														_UpperCAmelCase					=      F'''{dataset}-{pair}'''
							_UpperCAmelCase					=      Path(a__	)
							save_dir.mkdir(exist_ok=a__	)

							for split in ds.keys():
														print(F'''Splitting {split} with {ds[split].num_rows} records'''	)

														# to save to val.source, val.target like summary datasets
														_UpperCAmelCase					=      'val' if split == 'validation' else split
														_UpperCAmelCase					=      save_dir.joinpath(F'''{fn}.source'''	)
														_UpperCAmelCase					=      save_dir.joinpath(F'''{fn}.target'''	)
														_UpperCAmelCase					=      src_path.open('w+'	)
														_UpperCAmelCase					=      tgt_path.open('w+'	)

														# reader is the bottleneck so writing one record at a time doesn't slow things down
														for x in tqdm(ds[split]	):
																					_UpperCAmelCase					=      x['translation']
																					src_fp.write(ex[src_lang] + '\n'	)
																					tgt_fp.write(ex[tgt_lang] + '\n'	)

							print(F'''Saved {dataset} dataset to {save_dir}'''	)


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




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				:
		_a     :  Dict           =     BlenderbotConfig
		_a     :  Dict           =     {}
		_a     :  Union[str, Any]           =     'gelu'
		def __init__(      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE=13      ,   _SCREAMING_SNAKE_CASE=7      ,   _SCREAMING_SNAKE_CASE=True      ,   _SCREAMING_SNAKE_CASE=False      ,   _SCREAMING_SNAKE_CASE=99      ,   _SCREAMING_SNAKE_CASE=32      ,   _SCREAMING_SNAKE_CASE=2      ,   _SCREAMING_SNAKE_CASE=4      ,   _SCREAMING_SNAKE_CASE=37      ,   _SCREAMING_SNAKE_CASE=0.1      ,   _SCREAMING_SNAKE_CASE=0.1      ,   _SCREAMING_SNAKE_CASE=20      ,   _SCREAMING_SNAKE_CASE=2      ,   _SCREAMING_SNAKE_CASE=1      ,   _SCREAMING_SNAKE_CASE=0      ,   )			->	Optional[int]:




									"""simple docstring"""





									_UpperCAmelCase					=      parent
									_UpperCAmelCase					=      batch_size
									_UpperCAmelCase					=      seq_length
									_UpperCAmelCase					=      is_training
									_UpperCAmelCase					=      use_labels
									_UpperCAmelCase					=      vocab_size
									_UpperCAmelCase					=      hidden_size
									_UpperCAmelCase					=      num_hidden_layers
									_UpperCAmelCase					=      num_attention_heads
									_UpperCAmelCase					=      intermediate_size

									_UpperCAmelCase					=      hidden_dropout_prob
									_UpperCAmelCase					=      attention_probs_dropout_prob
									_UpperCAmelCase					=      max_position_embeddings
									_UpperCAmelCase					=      eos_token_id
									_UpperCAmelCase					=      pad_token_id
									_UpperCAmelCase					=      bos_token_id
		def      UpperCAmelCase__    (      self						)			->	Tuple:




									"""simple docstring"""





									_UpperCAmelCase					=      ids_tensor([self.batch_size, self.seq_length - 1]      ,   self.vocab_size						)
									_UpperCAmelCase					=      tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size						)      ,   1						)
									_UpperCAmelCase					=      tf.concat([input_ids, eos_tensor]      ,   axis=1						)

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

									_UpperCAmelCase					=      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      ,   )
									_UpperCAmelCase					=      prepare_blenderbot_inputs_dict(_SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						)
									return config, inputs_dict






		def      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						)			->	Any:




									"""simple docstring"""





									_UpperCAmelCase					=      TFBlenderbotModel(config=_SCREAMING_SNAKE_CASE						).get_decoder()
									_UpperCAmelCase					=      inputs_dict['input_ids']

									_UpperCAmelCase					=      input_ids[:1, :]
									_UpperCAmelCase					=      inputs_dict['attention_mask'][:1, :]
									_UpperCAmelCase					=      inputs_dict['head_mask']
									_UpperCAmelCase					=      1

									# first forward pass
									_UpperCAmelCase					=      model(_SCREAMING_SNAKE_CASE      ,   attention_mask=_SCREAMING_SNAKE_CASE      ,   head_mask=_SCREAMING_SNAKE_CASE      ,   use_cache=_SCREAMING_SNAKE_CASE						)

									_UpperCAmelCase		,	_UpperCAmelCase					=      outputs.to_tuple()

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

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

									_UpperCAmelCase					=      model(_SCREAMING_SNAKE_CASE      ,   attention_mask=_SCREAMING_SNAKE_CASE						)[0]
									_UpperCAmelCase					=      model(_SCREAMING_SNAKE_CASE      ,   attention_mask=_SCREAMING_SNAKE_CASE      ,   past_key_values=_SCREAMING_SNAKE_CASE						)[0]

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

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

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



def 	lowerCAmelCase__			(      a__:		Dict      ,  a__:		Dict      ,  a__:		Any      ,  a__:		Any=None      ,  a__:		List[Any]=None      ,  a__:		Union[str, Any]=None      ,  a__:		Tuple=None      ,  a__:		Union[str, Any]=None      ,  )  ->		Any:


							'''simple docstring'''





							if attention_mask is None:
														_UpperCAmelCase					=      tf.cast(tf.math.not_equal(a__      ,  config.pad_token_id	)      ,  tf.inta	)
							if decoder_attention_mask is None:
														_UpperCAmelCase					=      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:
														_UpperCAmelCase					=      tf.ones((config.encoder_layers, config.encoder_attention_heads)	)
							if decoder_head_mask is None:
														_UpperCAmelCase					=      tf.ones((config.decoder_layers, config.decoder_attention_heads)	)
							if cross_attn_head_mask is None:
														_UpperCAmelCase					=      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      ):
		_a     :  List[Any]           =     (TFBlenderbotForConditionalGeneration, TFBlenderbotModel) if is_tf_available() else ()
		_a     :  List[str]           =     (TFBlenderbotForConditionalGeneration,) if is_tf_available() else ()
		_a     :  List[str]           =     (
		    {
		        'conversational': TFBlenderbotForConditionalGeneration,
		        'feature-extraction': TFBlenderbotModel,
		        'summarization': TFBlenderbotForConditionalGeneration,
		        'text2text-generation': TFBlenderbotForConditionalGeneration,
		        'translation': TFBlenderbotForConditionalGeneration,
		    }
		    if is_tf_available()
		    else {}
		)
		_a     :  Dict           =     True
		_a     :  int           =     False
		_a     :  Union[str, Any]           =     False
		def      UpperCAmelCase__    (      self						)			->	List[Any]:




									"""simple docstring"""





									_UpperCAmelCase					=      TFBlenderbotModelTester(self						)
									_UpperCAmelCase					=      ConfigTester(self      ,   config_class=_SCREAMING_SNAKE_CASE						)
		def      UpperCAmelCase__    (      self						)			->	str:




									"""simple docstring"""





									self.config_tester.run_common_tests()






		def      UpperCAmelCase__    (      self						)			->	List[str]:




									"""simple docstring"""





									_UpperCAmelCase					=      self.model_tester.prepare_config_and_inputs_for_common()
									self.model_tester.check_decoder_model_past_large_inputs(*_SCREAMING_SNAKE_CASE						)



@require_tokenizers
@require_tf
class 		__a				(    unittest.TestCase      ):
		_a     :  int           =     ['My friends are cool but they eat too many carbs.']
		_a     :  List[Any]           =     'facebook/blenderbot-400M-distill'
		@cached_property
		def      UpperCAmelCase__    (      self						)			->	List[Any]:




									"""simple docstring"""





									return BlenderbotTokenizer.from_pretrained(self.model_name						)
		@cached_property
		def      UpperCAmelCase__    (      self						)			->	Any:




									"""simple docstring"""





									_UpperCAmelCase					=      TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name						)
									return model






		@slow
		def      UpperCAmelCase__    (      self						)			->	str:




									"""simple docstring"""





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