import json
from typing import Dict, List, Optional, Tuple, Union

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding, EncodedInput
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import PaddingStrategy, logging
from .tokenization_led import LEDTokenizer


lowerCamelCase__  =					logging.get_logger(__name__)


lowerCamelCase__  =					{'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}

lowerCamelCase__  =					{
    'vocab_file': {
        'allenai/led-base-16384': 'https://huggingface.co/allenai/led-base-16384/resolve/main/vocab.json',
    },
    'merges_file': {
        'allenai/led-base-16384': 'https://huggingface.co/allenai/led-base-16384/resolve/main/merges.txt',
    },
    'tokenizer_file': {
        'allenai/led-base-16384': 'https://huggingface.co/allenai/led-base-16384/resolve/main/tokenizer.json',
    },
}

lowerCamelCase__  =					{
    'allenai/led-base-16384': 1_6384,
}




class 					_UpperCAmelCase			(     lowerCAmelCase  ):



 '''simple docstring'''




 __A			       =					VOCAB_FILES_NAMES
 __A			       =					PRETRAINED_VOCAB_FILES_MAP
 __A			       =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 __A			       =					LEDTokenizer
 __A			       =					['''input_ids''', '''attention_mask''']







 def __init__(		self				:   int							, lowercase_				:   List[str]=None							, lowercase_				:   List[Any]=None							, lowercase_				:   Dict=None							, lowercase_				:   int="replace"							, lowercase_				:   List[Any]="<s>"							, lowercase_				:   Optional[int]="</s>"							, lowercase_				:   Tuple="</s>"							, lowercase_				:   List[str]="<s>"							, lowercase_				:   Tuple="<unk>"							, lowercase_				:   Optional[Any]="<pad>"							, lowercase_				:   Union[str, Any]="<mask>"							, lowercase_				:   List[str]=False							, lowercase_				:   List[str]=True							, **lowercase_				:   List[Any]							, )						->	Union[str, Any]:


   """simple docstring"""







   super().__init__(
       A_							, A_							, tokenizer_file=A_							, errors=A_							, bos_token=A_							, eos_token=A_							, sep_token=A_							, cls_token=A_							, unk_token=A_							, pad_token=A_							, mask_token=A_							, add_prefix_space=A_							, trim_offsets=A_							, **A_							, )

   _UpperCamelCase           =    json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__())
   if pre_tok_state.get("add_prefix_space"							, A_) != add_prefix_space:
     _UpperCamelCase           =    getattr(A_							, pre_tok_state.pop("type"))
     _UpperCamelCase           =    add_prefix_space
     _UpperCamelCase           =    pre_tok_class(**A_)

   _UpperCamelCase           =    add_prefix_space

   # the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
   _UpperCamelCase           =    """post_processor"""
   _UpperCamelCase           =    getattr(self.backend_tokenizer							, A_							, A_)
   if tokenizer_component_instance:
     _UpperCamelCase           =    json.loads(tokenizer_component_instance.__getstate__())

     # The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
     if "sep" in state:
       _UpperCamelCase           =    tuple(state["sep"])
     if "cls" in state:
       _UpperCamelCase           =    tuple(state["cls"])

     _UpperCamelCase           =    False

     if state.get("add_prefix_space"							, A_) != add_prefix_space:
       _UpperCamelCase           =    add_prefix_space
       _UpperCamelCase           =    True

     if state.get("trim_offsets"							, A_) != trim_offsets:
       _UpperCamelCase           =    trim_offsets
       _UpperCamelCase           =    True

     if changes_to_apply:
       _UpperCamelCase           =    getattr(A_							, state.pop("type"))
       _UpperCamelCase           =    component_class(**A_)
       setattr(self.backend_tokenizer							, A_							, A_)







 @property
 # Copied from transformers.models.bart.tokenization_bart_fast.BartTokenizerFast.mask_token with BART->LED
 def 		__UpperCAmelCase				(		self				:   List[Any])						->	Tuple:


   """simple docstring"""







   if self._mask_token is None:
     if self.verbose:
       logger.error("Using mask_token, but it is not set yet.")
     return None
   return str(self._mask_token)







 @mask_token.setter
 def 		__UpperCAmelCase				(		self				:   int							, lowercase_				:   str)						->	Any:


   """simple docstring"""







   _UpperCamelCase           =    AddedToken(A_							, lstrip=A_							, rstrip=A_) if isinstance(A_							, A_) else value
   _UpperCamelCase           =    value







 def 		__UpperCAmelCase				(		self				:   Optional[int]							, *lowercase_				:   Tuple							, **lowercase_				:   Optional[Any])						->	Optional[Any]:


   """simple docstring"""







   _UpperCamelCase           =    kwargs.get("is_split_into_words"							, A_)

   if is_split_into_words and not self.add_prefix_space:
     raise ValueError(
         f'You need to instantiate {self.__class__.__name__} with add_prefix_space=True '
         "to use it with pretokenized inputs.")

   return super()._batch_encode_plus(*A_							, **A_)







 def 		__UpperCAmelCase				(		self				:   Dict							, *lowercase_				:   Optional[int]							, **lowercase_				:   List[str])						->	List[Any]:


   """simple docstring"""







   _UpperCamelCase           =    kwargs.get("is_split_into_words"							, A_)

   if is_split_into_words and not self.add_prefix_space:
     raise ValueError(
         f'You need to instantiate {self.__class__.__name__} with add_prefix_space=True '
         "to use it with pretokenized inputs.")

   return super()._encode_plus(*A_							, **A_)







 def 		__UpperCAmelCase				(		self				:   int							, lowercase_				:   Tuple							, lowercase_				:   List[str] = None)						->	List[Any]:


   """simple docstring"""







   _UpperCamelCase           =    self._tokenizer.model.save(A_							, name=A_)
   return tuple(A_)







 def 		__UpperCAmelCase				(		self				:   List[Any]							, lowercase_				:   int							, lowercase_				:   str=None)						->	Optional[Any]:


   """simple docstring"""







   _UpperCamelCase           =    [self.bos_token_id] + token_ids_a + [self.eos_token_id]
   if token_ids_a is None:
     return output

   return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]







 def 		__UpperCAmelCase				(		self				:   Union[str, Any]							, lowercase_				:   int							, lowercase_				:   Union[str, Any] = None)						->	Optional[int]:


   """simple docstring"""







   _UpperCamelCase           =    [self.sep_token_id]
   _UpperCamelCase           =    [self.cls_token_id]

   if token_ids_a is None:
     return len(cls + token_ids_a + sep) * [0]
   return len(cls + token_ids_a + sep + sep + token_ids_a + sep) * [0]







 def 		__UpperCAmelCase				(		self				:   Optional[Any]							, lowercase_				:   Any							, lowercase_				:   str = None							, lowercase_				:   List[str] = PaddingStrategy.DO_NOT_PAD							, lowercase_				:   Any = None							, lowercase_				:   Union[str, Any] = None							, )						->	Dict:


   """simple docstring"""







   _UpperCamelCase           =    super()._pad(
       encoded_inputs=A_							, max_length=A_							, padding_strategy=A_							, pad_to_multiple_of=A_							, return_attention_mask=A_							, )

   # Load from model defaults
   if return_attention_mask is None:
     _UpperCamelCase           =    """attention_mask""" in self.model_input_names

   if return_attention_mask and "global_attention_mask" in encoded_inputs:
     _UpperCamelCase           =    encoded_inputs[self.model_input_names[0]]
     # `global_attention_mask` need to have the same length as other (sequential) inputs.
     _UpperCamelCase           =    len(encoded_inputs["global_attention_mask"]) != len(A_)

     if needs_to_be_padded:
       _UpperCamelCase           =    len(A_) - len(encoded_inputs["global_attention_mask"])

       if self.padding_side == "right":
         # Use `-1` since `0` in `global_attention_mask` means `local attention` instead of `not to attend`
         _UpperCamelCase           =    (
             encoded_inputs["""global_attention_mask"""] + [-1] * difference
         )
       elif self.padding_side == "left":
         _UpperCamelCase           =    [-1] * difference + encoded_inputs[
             """global_attention_mask"""
         ]
       else:
         raise ValueError("Invalid padding strategy:" + str(self.padding_side))

   return encoded_inputs








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     :
     __SCREAMING_SNAKE_CASE     					=    BlenderbotConfig
     __SCREAMING_SNAKE_CASE     					=    {}
     __SCREAMING_SNAKE_CASE     					=    '''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							,					):
       _UpperCAmelCase		:							List[str]							=     parent
       _UpperCAmelCase		:							Union[str, Any]							=     batch_size
       _UpperCAmelCase		:							Optional[Any]							=     seq_length
       _UpperCAmelCase		:							List[Any]							=     is_training
       _UpperCAmelCase		:							Union[str, Any]							=     use_labels
       _UpperCAmelCase		:							Tuple							=     vocab_size
       _UpperCAmelCase		:							Optional[int]							=     hidden_size
       _UpperCAmelCase		:							int							=     num_hidden_layers
       _UpperCAmelCase		:							List[Any]							=     num_attention_heads
       _UpperCAmelCase		:							List[Any]							=     intermediate_size

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


     def        __snake_case(  self    ):
       _UpperCAmelCase		:							Optional[Any]							=     ids_tensor([self.batch_size, self.seq_length - 1]							,					self.vocab_size    )
       _UpperCAmelCase		:							Tuple							=     tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size    )							,					1    )
       _UpperCAmelCase		:							List[Any]							=     tf.concat([input_ids, eos_tensor]							,					axis=1    )

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

       _UpperCAmelCase		:							List[Any]							=     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		:							Dict							=     prepare_blenderbot_inputs_dict(A_							,					A_							,					A_    )
       return config, inputs_dict




     def        __snake_case(  self							,					A_							,					A_    ):
       _UpperCAmelCase		:							Union[str, Any]							=     TFBlenderbotModel(config=A_    ).get_decoder()
       _UpperCAmelCase		:							List[Any]							=     inputs_dict["""input_ids"""]

       _UpperCAmelCase		:							Any							=     input_ids[:1, :]
       _UpperCAmelCase		:							List[str]							=     inputs_dict["""attention_mask"""][:1, :]
       _UpperCAmelCase		:							Tuple							=     inputs_dict["""head_mask"""]
       _UpperCAmelCase		:							Union[str, Any]							=     1

       # first forward pass
       _UpperCAmelCase		:							List[str]							=     model(A_							,					attention_mask=A_							,					head_mask=A_							,					use_cache=A_    )

       _UpperCAmelCase,_UpperCAmelCase		:							List[str]							=     outputs.to_tuple()

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

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

       _UpperCAmelCase		:							Tuple							=     model(A_							,					attention_mask=A_    )[0]
       _UpperCAmelCase		:							int							=     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
       _UpperCAmelCase		:							Tuple							=     int(ids_tensor((1,)							,					output_from_past.shape[-1]    )    )
       _UpperCAmelCase		:							str							=     output_from_no_past[:, -3:, random_slice_idx]
       _UpperCAmelCase		:							str							=     output_from_past[:, :, random_slice_idx]

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




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


     def        __snake_case(  self    ):
       _UpperCAmelCase		:							Union[str, Any]							=     TFBlenderbotModelTester(self    )
       _UpperCAmelCase		:							Any							=     ConfigTester(self							,					config_class=A_    )


     def        __snake_case(  self    ):
       self.config_tester.run_common_tests()




     def        __snake_case(  self    ):
       _UpperCAmelCase		:							Tuple							=     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     ):
     __SCREAMING_SNAKE_CASE     					=    ['''My friends are cool but they eat too many carbs.''']
     __SCREAMING_SNAKE_CASE     					=    '''facebook/blenderbot-400M-distill'''


     @cached_property
     def        __snake_case(  self    ):
       return BlenderbotTokenizer.from_pretrained(self.model_name    )


     @cached_property
     def        __snake_case(  self    ):
       _UpperCAmelCase		:							List[Any]							=     TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name    )
       return model




     @slow
     def        __snake_case(  self    ):
       _UpperCAmelCase		:							Any							=     self.tokenizer(self.src_text							,					return_tensors="""tf"""    )
       _UpperCAmelCase		:							List[Any]							=     self.model.generate(
           model_inputs.input_ids							,					)
       _UpperCAmelCase		:							List[str]							=     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?"
       )





