

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"""






  lowerCamelCase       :     str		 =BlenderbotConfig
  lowerCamelCase       :     Union[str, Any]		 ={}
  lowerCamelCase       :     Optional[Any]		 ="gelu"







  def __init__(     self					:       Union[str, Any]			,				lowerCAmelCase					:       Union[str, Any]			,				lowerCAmelCase					:       Union[str, Any]=13			,				lowerCAmelCase					:       Optional[Any]=7			,				lowerCAmelCase					:       Optional[int]=True			,				lowerCAmelCase					:       Optional[Any]=False			,				lowerCAmelCase					:       Dict=99			,				lowerCAmelCase					:       List[str]=32			,				lowerCAmelCase					:       List[str]=2			,				lowerCAmelCase					:       List[str]=4			,				lowerCAmelCase					:       List[str]=37			,				lowerCAmelCase					:       Any=0.1			,				lowerCAmelCase					:       List[str]=0.1			,				lowerCAmelCase					:       Union[str, Any]=20			,				lowerCAmelCase					:       int=2			,				lowerCAmelCase					:       Dict=1			,				lowerCAmelCase					:       Any=0			,				)				->      int:






     """simple docstring"""







     __lowerCAmelCase    :     Dict          =		parent
     __lowerCAmelCase    :     Dict          =		batch_size
     __lowerCAmelCase    :     Dict          =		seq_length
     __lowerCAmelCase    :     Union[str, Any]          =		is_training
     __lowerCAmelCase    :     int          =		use_labels
     __lowerCAmelCase    :     str          =		vocab_size
     __lowerCAmelCase    :     Optional[int]          =		hidden_size
     __lowerCAmelCase    :     List[Any]          =		num_hidden_layers
     __lowerCAmelCase    :     str          =		num_attention_heads
     __lowerCAmelCase    :     Dict          =		intermediate_size

     __lowerCAmelCase    :     int          =		hidden_dropout_prob
     __lowerCAmelCase    :     Tuple          =		attention_probs_dropout_prob
     __lowerCAmelCase    :     Union[str, Any]          =		max_position_embeddings
     __lowerCAmelCase    :     int          =		eos_token_id
     __lowerCAmelCase    :     Optional[int]          =		pad_token_id
     __lowerCAmelCase    :     Optional[Any]          =		bos_token_id







  def 			SCREAMING_SNAKE_CASE       (     self					:       Dict       )				->      Dict:






     """simple docstring"""







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

     __lowerCAmelCase    :     Optional[Any]          =		ids_tensor([self.batch_size, self.seq_length]			,				self.vocab_size       )

     __lowerCAmelCase    :     Dict          =		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    :     List[str]          =		prepare_blenderbot_inputs_dict(__snake_case			,				__snake_case			,				__snake_case       )
     return config, inputs_dict







  def 			SCREAMING_SNAKE_CASE       (     self					:       List[str]			,				lowerCAmelCase					:       Optional[Any]			,				lowerCAmelCase					:       Dict       )				->      Tuple:






     """simple docstring"""







     __lowerCAmelCase    :     List[str]          =		TFBlenderbotModel(config=__snake_case       ).get_decoder()
     __lowerCAmelCase    :     Union[str, Any]          =		inputs_dict["""input_ids"""]

     __lowerCAmelCase    :     Any          =		input_ids[:1, :]
     __lowerCAmelCase    :     List[str]          =		inputs_dict["""attention_mask"""][:1, :]
     __lowerCAmelCase    :     Dict          =		inputs_dict["""head_mask"""]
     __lowerCAmelCase    :     Optional[Any]          =		1

     # first forward pass
     __lowerCAmelCase    :     Tuple          =		model(__snake_case			,				attention_mask=__snake_case			,				head_mask=__snake_case			,				use_cache=__snake_case       )

     __lowerCAmelCase    :     List[str]          =		outputs.to_tuple()

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

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

     __lowerCAmelCase    :     Optional[int]          =		model(__snake_case			,				attention_mask=__snake_case       )[0]
     __lowerCAmelCase    :     Dict          =		model(__snake_case			,				attention_mask=__snake_case			,				past_key_values=__snake_case       )[0]

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

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

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






def       snake_case_ (__A	:		Dict   ,  __A	:		List[str]   ,  __A	:		Optional[int]   ,  __A	:		Any=None   ,  __A	:		int=None   ,  __A	:		int=None   ,  __A	:		Union[str, Any]=None   ,  __A	:		List[Any]=None   ,  )     ->      Tuple:
   if attention_mask is None:
      __lowerCAmelCase    :     Optional[int]          =		tf.cast(tf.math.not_equal(__A   ,  config.pad_token_id	)   ,  tf.inta	)
   if decoder_attention_mask is None:
      __lowerCAmelCase    :     List[Any]          =		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    :     Optional[Any]          =		tf.ones((config.encoder_layers, config.encoder_attention_heads)	)
   if decoder_head_mask is None:
      __lowerCAmelCase    :     Tuple          =		tf.ones((config.decoder_layers, config.decoder_attention_heads)	)
   if cross_attn_head_mask is None:
      __lowerCAmelCase    :     Any          =		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      (    UpperCamelCase__  ,				UpperCamelCase__  ,				unittest.TestCase					):
  """simple docstring"""






  lowerCamelCase       :     List[Any]		 =(TFBlenderbotForConditionalGeneration, TFBlenderbotModel) if is_tf_available() else ()
  lowerCamelCase       :     List[Any]		 =(TFBlenderbotForConditionalGeneration,) if is_tf_available() else ()
  lowerCamelCase       :     List[Any]		 =(
      {
          "conversational": TFBlenderbotForConditionalGeneration,
          "feature-extraction": TFBlenderbotModel,
          "summarization": TFBlenderbotForConditionalGeneration,
          "text2text-generation": TFBlenderbotForConditionalGeneration,
          "translation": TFBlenderbotForConditionalGeneration,
      }
      if is_tf_available()
      else {}
  )
  lowerCamelCase       :     Optional[Any]		 =True
  lowerCamelCase       :     Dict		 =False
  lowerCamelCase       :     List[Any]		 =False







  def 			SCREAMING_SNAKE_CASE       (     self					:       Union[str, Any]       )				->      List[Any]:






     """simple docstring"""







     __lowerCAmelCase    :     str          =		TFBlenderbotModelTester(self       )
     __lowerCAmelCase    :     Dict          =		ConfigTester(self			,				config_class=__snake_case       )







  def 			SCREAMING_SNAKE_CASE       (     self					:       Union[str, Any]       )				->      str:






     """simple docstring"""







     self.config_tester.run_common_tests()







  def 			SCREAMING_SNAKE_CASE       (     self					:       List[str]       )				->      Any:






     """simple docstring"""







     __lowerCAmelCase    :     Optional[int]          =		self.model_tester.prepare_config_and_inputs_for_common()
     self.model_tester.check_decoder_model_past_large_inputs(*__snake_case       )






@require_tokenizers
@require_tf
class SCREAMING_SNAKE_CASE      (    unittest.TestCase					):
  """simple docstring"""






  lowerCamelCase       :     Optional[Any]		 =["My friends are cool but they eat too many carbs."]
  lowerCamelCase       :     Dict		 ="facebook/blenderbot-400M-distill"







  @cached_property
  def 			SCREAMING_SNAKE_CASE       (     self					:       List[str]       )				->      List[Any]:






     """simple docstring"""







     return BlenderbotTokenizer.from_pretrained(self.model_name       )







  @cached_property
  def 			SCREAMING_SNAKE_CASE       (     self					:       Optional[Any]       )				->      List[str]:






     """simple docstring"""







     __lowerCAmelCase    :     Tuple          =		TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name       )
     return model







  @slow
  def 			SCREAMING_SNAKE_CASE       (     self					:       Union[str, Any]       )				->      Optional[Any]:






     """simple docstring"""







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








from pathlib import Path

import fire
from tqdm import tqdm




def 		lowerCAmelCase_ (						lowerCamelCase="ro"						,  lowerCamelCase="en"						,  lowerCamelCase="wmt16"						,  lowerCamelCase=None ):
				try:
								import datasets
				except (ModuleNotFoundError, ImportError):
								raise ImportError("""run pip install datasets""" )
				__magic_name__						:						Dict										=F"{src_lang}-{tgt_lang}"
				print(F"Converting {dataset}-{pair}" )
				__magic_name__						:						Dict										=datasets.load_dataset(lowerCamelCase						,  lowerCamelCase )
				if save_dir is None:
								__magic_name__						:						Optional[int]										=F"{dataset}-{pair}"
				__magic_name__						:						int										=Path(lowerCamelCase )
				save_dir.mkdir(exist_ok=lowerCamelCase )

				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
								__magic_name__						:						Dict										="""val""" if split == """validation""" else split
								__magic_name__						:						List[Any]										=save_dir.joinpath(F"{fn}.source" )
								__magic_name__						:						Optional[int]										=save_dir.joinpath(F"{fn}.target" )
								__magic_name__						:						Optional[Any]										=src_path.open("""w+""" )
								__magic_name__						:						List[Any]										=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] ):
												__magic_name__						:						str										=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)




