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 __snake_case    :
   lowerCAmelCase_														=							BlenderbotConfig
   lowerCAmelCase_														=							{}
   lowerCAmelCase_														=							"gelu"




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



     """simple docstring"""






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

     SCREAMING_SNAKE_CASE__      							=				hidden_dropout_prob
     SCREAMING_SNAKE_CASE__      							=				attention_probs_dropout_prob
     SCREAMING_SNAKE_CASE__      							=				max_position_embeddings
     SCREAMING_SNAKE_CASE__      							=				eos_token_id
     SCREAMING_SNAKE_CASE__      							=				pad_token_id
     SCREAMING_SNAKE_CASE__      							=				bos_token_id




   def      __a						(   self      :    Union[str, Any]				):



     """simple docstring"""






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

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

     SCREAMING_SNAKE_CASE__      							=				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	,     )
     SCREAMING_SNAKE_CASE__      							=				prepare_blenderbot_inputs_dict(_lowercase	,     _lowercase	,     _lowercase				)
     return config, inputs_dict




   def      __a						(   self      :    Any	,     _lowercase      :    List[Any]	,     _lowercase      :    int				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				TFBlenderbotModel(config=_lowercase				).get_decoder()
     SCREAMING_SNAKE_CASE__      							=				inputs_dict["""input_ids"""]

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

     # first forward pass
     SCREAMING_SNAKE_CASE__      							=				model(_lowercase	,     attention_mask=_lowercase	,     head_mask=_lowercase	,     use_cache=_lowercase				)

     SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__      							=				outputs.to_tuple()

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

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

     SCREAMING_SNAKE_CASE__      							=				model(_lowercase	,     attention_mask=_lowercase				)[0]
     SCREAMING_SNAKE_CASE__      							=				model(_lowercase	,     attention_mask=_lowercase	,     past_key_values=_lowercase				)[0]

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

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

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





def      __SCREAMING_SNAKE_CASE  ( __UpperCamelCase   :	Any			,					__UpperCamelCase   :	str			,					__UpperCamelCase   :	Any			,					__UpperCamelCase   :	List[Any]=None			,					__UpperCamelCase   :	Any=None			,					__UpperCamelCase   :	Tuple=None			,					__UpperCamelCase   :	List[Any]=None			,					__UpperCamelCase   :	List[str]=None			,					)							-> List[Any]:







  """simple docstring"""





  if attention_mask is None:
    SCREAMING_SNAKE_CASE__      							=				tf.cast(tf.math.not_equal(__UpperCamelCase			,					config.pad_token_id     )			,					tf.inta     )
  if decoder_attention_mask is None:
    SCREAMING_SNAKE_CASE__      							=				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:
    SCREAMING_SNAKE_CASE__      							=				tf.ones((config.encoder_layers, config.encoder_attention_heads)     )
  if decoder_head_mask is None:
    SCREAMING_SNAKE_CASE__      							=				tf.ones((config.decoder_layers, config.decoder_attention_heads)     )
  if cross_attn_head_mask is None:
    SCREAMING_SNAKE_CASE__      							=				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    (						lowerCamelCase_       ,		lowerCamelCase_       ,		unittest.TestCase		):
   lowerCAmelCase_														=							(TFBlenderbotForConditionalGeneration, TFBlenderbotModel) if is_tf_available() else ()
   lowerCAmelCase_														=							(TFBlenderbotForConditionalGeneration,) if is_tf_available() else ()
   lowerCAmelCase_														=							(
       {
           "conversational": TFBlenderbotForConditionalGeneration,
           "feature-extraction": TFBlenderbotModel,
           "summarization": TFBlenderbotForConditionalGeneration,
           "text2text-generation": TFBlenderbotForConditionalGeneration,
           "translation": TFBlenderbotForConditionalGeneration,
       }
       if is_tf_available()
       else {}
   )
   lowerCAmelCase_														=							True
   lowerCAmelCase_														=							False
   lowerCAmelCase_														=							False




   def      __a						(   self      :    Optional[int]				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				TFBlenderbotModelTester(self				)
     SCREAMING_SNAKE_CASE__      							=				ConfigTester(self	,     config_class=_lowercase				)




   def      __a						(   self      :    Optional[Any]				):



     """simple docstring"""






     self.config_tester.run_common_tests()




   def      __a						(   self      :    Optional[Any]				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				self.model_tester.prepare_config_and_inputs_for_common()
     self.model_tester.check_decoder_model_past_large_inputs(*_lowercase				)







@require_tokenizers
@require_tf
class __snake_case    (						unittest.TestCase		):
   lowerCAmelCase_														=							["My friends are cool but they eat too many carbs."]
   lowerCAmelCase_														=							"facebook/blenderbot-400M-distill"




   @cached_property
   def      __a						(   self      :    Optional[Any]				):



     """simple docstring"""






     return BlenderbotTokenizer.from_pretrained(self.model_name				)




   @cached_property
   def      __a						(   self      :    Optional[int]				):



     """simple docstring"""






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




   @slow
   def      __a						(   self      :    Any				):



     """simple docstring"""






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

import argparse
import os

import transformers

from .convert_slow_tokenizer import SLOW_TO_FAST_CONVERTERS
from .utils import logging


logging.set_verbosity_info()

__lowerCamelCase      :			int							       =		logging.get_logger(__name__)


__lowerCamelCase      :			Tuple							       =		{name: getattr(transformers, name + '''Fast''') for name in SLOW_TO_FAST_CONVERTERS}



def      __SCREAMING_SNAKE_CASE  ( __UpperCamelCase   :	int			,					__UpperCamelCase   :	Optional[int]			,					__UpperCamelCase   :	str			,					__UpperCamelCase   :	Optional[Any]     )							-> Union[str, Any]:







  """simple docstring"""





  if tokenizer_name is not None and tokenizer_name not in TOKENIZER_CLASSES:
    raise ValueError(f"""Unrecognized tokenizer name, should be one of {list(TOKENIZER_CLASSES.keys()     )}."""     )

  if tokenizer_name is None:
    SCREAMING_SNAKE_CASE__      							=				TOKENIZER_CLASSES
  else:
    SCREAMING_SNAKE_CASE__      							=				{tokenizer_name: getattr(__UpperCamelCase			,					tokenizer_name + """Fast"""     )}

  logger.info(f"""Loading tokenizer classes: {tokenizer_names}"""     )

  for tokenizer_name in tokenizer_names:
    SCREAMING_SNAKE_CASE__      							=				TOKENIZER_CLASSES[tokenizer_name]

    SCREAMING_SNAKE_CASE__      							=				True
    if checkpoint_name is None:
      SCREAMING_SNAKE_CASE__      							=				list(tokenizer_class.max_model_input_sizes.keys()     )
    else:
      SCREAMING_SNAKE_CASE__      							=				[checkpoint_name]

    logger.info(f"""For tokenizer {tokenizer_class.__class__.__name__} loading checkpoints: {checkpoint_names}"""     )

    for checkpoint in checkpoint_names:
      logger.info(f"""Loading {tokenizer_class.__class__.__name__} {checkpoint}"""     )

      # Load tokenizer
      SCREAMING_SNAKE_CASE__      							=				tokenizer_class.from_pretrained(__UpperCamelCase			,					force_download=__UpperCamelCase     )

      # Save fast tokenizer
      logger.info(f"""Save fast tokenizer to {dump_path} with prefix {checkpoint} add_prefix {add_prefix}"""     )

      # For organization names we create sub-directories
      if "/" in checkpoint:
        SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__      							=				checkpoint.split("""/"""     )
        SCREAMING_SNAKE_CASE__      							=				os.path.join(__UpperCamelCase			,					__UpperCamelCase     )
      elif add_prefix:
        SCREAMING_SNAKE_CASE__      							=				checkpoint
        SCREAMING_SNAKE_CASE__      							=				dump_path
      else:
        SCREAMING_SNAKE_CASE__      							=				None
        SCREAMING_SNAKE_CASE__      							=				dump_path

      logger.info(f"""=> {dump_path_full} with prefix {checkpoint_prefix_name}, add_prefix {add_prefix}"""     )

      if checkpoint in list(tokenizer.pretrained_vocab_files_map.values()     )[0]:
        SCREAMING_SNAKE_CASE__      							=				list(tokenizer.pretrained_vocab_files_map.values()     )[0][checkpoint]
        SCREAMING_SNAKE_CASE__      							=				file_path.split(__UpperCamelCase     )[-1][0]
        if next_char == "/":
          SCREAMING_SNAKE_CASE__      							=				os.path.join(__UpperCamelCase			,					__UpperCamelCase     )
          SCREAMING_SNAKE_CASE__      							=				None

        logger.info(f"""=> {dump_path_full} with prefix {checkpoint_prefix_name}, add_prefix {add_prefix}"""     )

      SCREAMING_SNAKE_CASE__      							=				tokenizer.save_pretrained(
          __UpperCamelCase			,					legacy_format=__UpperCamelCase			,					filename_prefix=__UpperCamelCase     )
      logger.info(f"""=> File names {file_names}"""     )

      for file_name in file_names:
        if not file_name.endswith("""tokenizer.json"""     ):
          os.remove(__UpperCamelCase     )
          logger.info(f"""=> removing {file_name}"""     )


if __name__ == "__main__":
   __lowerCamelCase      :			str							       =		argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       '''--dump_path''', default=None, type=str, required=True, help='''Path to output generated fast tokenizer files.'''
   )
   parser.add_argument(
       '''--tokenizer_name''',
       default=None,
       type=str,
       help=(
           F"""Optional tokenizer type selected in the list of {list(TOKENIZER_CLASSES.keys())}. If not given, will """
           '''download and convert all the checkpoints from AWS.'''
       ),
   )
   parser.add_argument(
       '''--checkpoint_name''',
       default=None,
       type=str,
       help='''Optional checkpoint name. If not given, will download and convert the canonical checkpoints from AWS.''',
   )
   parser.add_argument(
       '''--force_download''',
       action='''store_true''',
       help='''Re-download checkpoints.''',
   )
   __lowerCamelCase      :			Any							       =		parser.parse_args()

   convert_slow_checkpoint_to_fast(args.tokenizer_name, args.checkpoint_name, args.dump_path, args.force_download)
