


import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import OPTConfig, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, slow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor


if is_flax_available():
   import os

   # The slow tests are often failing with OOM error on GPU
   # This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
   # but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
   _SCREAMING_SNAKE_CASE :					Tuple			=	"""platform"""

   import jax
   import jax.numpy as jnp

   from transformers import FlaxOPTForCausalLM, FlaxOPTModel, GPTaTokenizer







def 						_lowercase       (     __lowerCamelCase			:					int		,__lowerCamelCase			:					Optional[Any]		,__lowerCamelCase			:					int=None		,__lowerCamelCase			:					str=None			)  ->   Optional[int]:





     '''simple docstring'''





     if attention_mask is None:
          UpperCamelCase__							:  List[Any]								=     np.where(input_ids != config.pad_token_id		,1		,0			)
     return {
         "input_ids": input_ids,
         "attention_mask": attention_mask,
     }

@require_flax
class  UpperCamelCase__ :






      def __init__(     self					:   Dict,		__lowerCamelCase					:   Union[str, Any],		__lowerCamelCase					:   Any=13,		__lowerCamelCase					:   Union[str, Any]=7,		__lowerCamelCase					:   List[str]=True,		__lowerCamelCase					:   Any=False,		__lowerCamelCase					:   List[str]=99,		__lowerCamelCase					:   Optional[int]=16,		__lowerCamelCase					:   Union[str, Any]=2,		__lowerCamelCase					:   Optional[int]=4,		__lowerCamelCase					:   Dict=4,		__lowerCamelCase					:   Any="gelu",		__lowerCamelCase					:   str=0.1,		__lowerCamelCase					:   Union[str, Any]=0.1,		__lowerCamelCase					:   List[str]=20,		__lowerCamelCase					:   Optional[Any]=2,		__lowerCamelCase					:   Tuple=1,		__lowerCamelCase					:   Union[str, Any]=0,		__lowerCamelCase					:   Dict=16,		__lowerCamelCase					:   Optional[int]=16,		__lowerCamelCase					:   Any=0.02,		)  -> Any:
           UpperCamelCase__							:  Tuple								=     parent
           UpperCamelCase__							:  Optional[Any]								=     batch_size
           UpperCamelCase__							:  str								=     seq_length
           UpperCamelCase__							:  Any								=     is_training
           UpperCamelCase__							:  Dict								=     use_labels
           UpperCamelCase__							:  str								=     vocab_size
           UpperCamelCase__							:  Optional[Any]								=     hidden_size
           UpperCamelCase__							:  int								=     num_hidden_layers
           UpperCamelCase__							:  str								=     num_attention_heads
           UpperCamelCase__							:  Dict								=     intermediate_size
           UpperCamelCase__							:  Union[str, Any]								=     hidden_act
           UpperCamelCase__							:  List[str]								=     hidden_dropout_prob
           UpperCamelCase__							:  Optional[int]								=     attention_probs_dropout_prob
           UpperCamelCase__							:  List[str]								=     max_position_embeddings
           UpperCamelCase__							:  str								=     eos_token_id
           UpperCamelCase__							:  int								=     pad_token_id
           UpperCamelCase__							:  int								=     bos_token_id
           UpperCamelCase__							:  Union[str, Any]								=     embed_dim
           UpperCamelCase__							:  List[Any]								=     word_embed_proj_dim
           UpperCamelCase__							:  List[Any]								=     initializer_range
           UpperCamelCase__							:  List[str]								=     False






      def 	__lowercase(     self					:   Tuple   )  -> Union[str, Any]:
           UpperCamelCase__							:  str								=     np.clip(ids_tensor([self.batch_size, self.seq_length - 1],		self.vocab_size   ),		3,		self.vocab_size   )
           UpperCamelCase__							:  List[Any]								=     np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1),		dtype=np.intaa   )),		-1   )

           UpperCamelCase__							:  int								=     OPTConfig(
               vocab_size=self.vocab_size,		hidden_size=self.hidden_size,		num_hidden_layers=self.num_hidden_layers,		num_attention_heads=self.num_attention_heads,		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_id=self.eos_token_id,		bos_token_id=self.bos_token_id,		pad_token_id=self.pad_token_id,		embed_dim=self.embed_dim,		is_encoder_decoder=__lowerCamelCase,		word_embed_proj_dim=self.word_embed_proj_dim,		initializer_range=self.initializer_range,		use_cache=__lowerCamelCase,		)
           UpperCamelCase__							:  str								=     prepare_opt_inputs_dict(__lowerCamelCase,		__lowerCamelCase   )
           return config, inputs_dict






      def 	__lowercase(     self					:   List[str]   )  -> List[Any]:
           UpperCamelCase__							:  int								=     self.prepare_config_and_inputs()
           return config, inputs_dict






      def 	__lowercase(     self					:   List[str],		__lowerCamelCase					:   Union[str, Any],		__lowerCamelCase					:   List[Any],		__lowerCamelCase					:   str   )  -> int:
           UpperCamelCase__							:  Optional[int]								=     20
           UpperCamelCase__							:  Dict								=     model_class_name(__lowerCamelCase   )

           UpperCamelCase__							:  Tuple								=     inputs_dict['''input_ids''']
           UpperCamelCase__							:  List[str]								=     inputs_dict['''attention_mask''']

           UpperCamelCase__							:  Optional[Any]								=     model.init_cache(input_ids.shape[0],		__lowerCamelCase   )
           UpperCamelCase__							:  Optional[int]								=     jnp.ones((input_ids.shape[0], max_length),		dtype='''i4'''   )

           UpperCamelCase__							:  int								=     jnp.broadcast_to(
               jnp.arange(input_ids.shape[-1] - 1   )[None, :],		(input_ids.shape[0], input_ids.shape[-1] - 1),		)
           UpperCamelCase__							:  List[str]								=     model(
               input_ids[:, :-1],		attention_mask=__lowerCamelCase,		past_key_values=__lowerCamelCase,		position_ids=__lowerCamelCase,		)

           UpperCamelCase__							:  Union[str, Any]								=     jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]],		dtype='''i4'''   )
           UpperCamelCase__							:  Optional[Any]								=     model(
               input_ids[:, -1:],		attention_mask=__lowerCamelCase,		past_key_values=outputs_cache.past_key_values,		position_ids=__lowerCamelCase,		)

           UpperCamelCase__							:  int								=     model(__lowerCamelCase   )

           UpperCamelCase__							:  Dict								=     np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])   )   )
           self.parent.assertTrue(diff < 1e-3,		msg=f'Max diff is {diff}'   )






      def 	__lowercase(     self					:   List[Any],		__lowerCamelCase					:   str,		__lowerCamelCase					:   List[Any],		__lowerCamelCase					:   List[Any]   )  -> int:
           UpperCamelCase__							:  List[Any]								=     20
           UpperCamelCase__							:  Tuple								=     model_class_name(__lowerCamelCase   )

           UpperCamelCase__							:  Tuple								=     (
               inputs_dict['''input_ids'''],
               inputs_dict['''attention_mask'''],
           )

           UpperCamelCase__							:  List[Any]								=     jnp.concatenate(
               [
                   attention_mask,
                   jnp.zeros((attention_mask.shape[0], max_length - attention_mask.shape[1])   ),
               ],		axis=-1,		)

           UpperCamelCase__							:  int								=     model.init_cache(input_ids.shape[0],		__lowerCamelCase   )
           UpperCamelCase__							:  Tuple								=     jnp.broadcast_to(
               jnp.arange(input_ids.shape[-1] - 1   )[None, :],		(input_ids.shape[0], input_ids.shape[-1] - 1),		)

           UpperCamelCase__							:  List[str]								=     model(
               input_ids[:, :-1],		attention_mask=__lowerCamelCase,		past_key_values=__lowerCamelCase,		position_ids=__lowerCamelCase,		)
           UpperCamelCase__							:  Tuple								=     jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]],		dtype='''i4'''   )
           UpperCamelCase__							:  Tuple								=     model(
               input_ids[:, -1:],		past_key_values=outputs_cache.past_key_values,		attention_mask=__lowerCamelCase,		position_ids=__lowerCamelCase,		)

           UpperCamelCase__							:  Tuple								=     model(__lowerCamelCase,		attention_mask=__lowerCamelCase   )

           UpperCamelCase__							:  str								=     np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])   )   )
           self.parent.assertTrue(diff < 1e-3,		msg=f'Max diff is {diff}'   )

@require_flax
class  UpperCamelCase__ ( __lowerCamelCase							,						unittest.TestCase							,						__lowerCamelCase			):
      a__   :     Tuple								=						(FlaxOPTModel, FlaxOPTForCausalLM) if is_flax_available() else ()
      a__   :     Tuple								=						() if is_flax_available() else ()






      def 	__lowercase(     self					:   List[str]   )  -> List[Any]:
           UpperCamelCase__							:  List[str]								=     FlaxOPTModelTester(self   )






      def 	__lowercase(     self					:   Optional[int]   )  -> List[Any]:
           UpperCamelCase__							:  List[str]								=     self.model_tester.prepare_config_and_inputs()
           for model_class in self.all_model_classes:
                self.model_tester.check_use_cache_forward(__lowerCamelCase,		__lowerCamelCase,		__lowerCamelCase   )






      def 	__lowercase(     self					:   int   )  -> Optional[Any]:
           UpperCamelCase__							:  str								=     self.model_tester.prepare_config_and_inputs()
           for model_class in self.all_model_classes:
                self.model_tester.check_use_cache_forward_with_attn_mask(__lowerCamelCase,		__lowerCamelCase,		__lowerCamelCase   )






      @slow
      def 	__lowercase(     self					:   Dict   )  -> Tuple:
           for model_class_name in self.all_model_classes:
                UpperCamelCase__							:  Optional[int]								=     model_class_name.from_pretrained('''facebook/opt-125m'''   )
                UpperCamelCase__							:  Optional[Any]								=     np.ones((1, 1)   ) * model.config.eos_token_id
                UpperCamelCase__							:  Union[str, Any]								=     model(__lowerCamelCase   )
                self.assertIsNotNone(__lowerCamelCase   )

@require_sentencepiece
@require_flax
class  UpperCamelCase__ ( unittest.TestCase			):






      @slow
      def 	__lowercase(     self					:   str   )  -> Optional[Any]:
           UpperCamelCase__							:  Any								=     FlaxOPTModel.from_pretrained('''facebook/opt-350m'''   )
           UpperCamelCase__							:  str								=     jnp.array([[0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69, 4_60_78, 15_88, 2]]   )
           UpperCamelCase__							:  Optional[Any]								=     model(input_ids=__lowerCamelCase   ).last_hidden_state
           UpperCamelCase__							:  Optional[Any]								=     (1, 11, 5_12)
           self.assertEqual(output.shape,		__lowerCamelCase   )
           UpperCamelCase__							:  str								=     jnp.array(
               [[-0.2867, -1.9256, -0.3062], [-1.2711, -0.1337, -0.1897], [0.4109, 0.1187, -1.3142]]   )
           self.assertTrue(jnp.allclose(output[:, :3, :3],		__lowerCamelCase,		atol=4e-2   )   )

@require_flax
@slow
class  UpperCamelCase__ ( unittest.TestCase			):






      def 	__lowercase(     self					:   str   )  -> Dict:
           super().setUp()
           UpperCamelCase__							:  Optional[Any]								=     '''facebook/opt-350m'''






      def 	__lowercase(     self					:   Any   )  -> List[Any]:
           UpperCamelCase__							:  str								=     FlaxOPTForCausalLM.from_pretrained(self.path_model   )
           UpperCamelCase__							:  int								=     GPTaTokenizer.from_pretrained(self.path_model   )

           UpperCamelCase__							:  List[str]								=     [
               '''Today is a beautiful day and I want to''',
               '''In the city of''',
               '''Paris is the capital of France and''',
               '''Computers and mobile phones have taken''',
           ]
           # verify that prompt without BOS token is identical to Metaseq -> add_special_tokens=False
           UpperCamelCase__							:  List[Any]								=     tokenizer(__lowerCamelCase,		return_tensors='''jax''',		padding=__lowerCamelCase,		add_special_tokens=__lowerCamelCase   )
           UpperCamelCase__							:  Tuple								=     model(inputs.input_ids,		attention_mask=inputs.attention_mask   )[0].mean(axis=-1   )
           UpperCamelCase__							:  Optional[Any]								=     jnp.array(
               [
                   [1.3851, -13.8923, -10.5229, -10.7533, -0.2309, -10.2384, -0.5365, -9.0947, -5.1670],
                   [-4.7073, -10.6276, -3.9415, -21.5242, -0.2822, -0.2822, -0.2822, -0.2822, -0.2822],
                   [0.6247, -3.4229, -8.9179, -1.4297, -14.1650, 1.4146, -9.0218, -0.2703, -0.2703],
                   [6.4783, -1.9913, -10.7926, -2.3336, 1.5092, -0.9974, -6.8213, 1.3477, 1.3477],
               ]   )
           self.assertTrue(jnp.allclose(__lowerCamelCase,		__lowerCamelCase,		atol=4e-2   )   )

           UpperCamelCase__							:  Union[str, Any]								=     jax.jit(__lowerCamelCase   )
           UpperCamelCase__							:  List[Any]								=     model(inputs.input_ids,		attention_mask=inputs.attention_mask   )[0].mean(axis=-1   )
           self.assertTrue(jnp.allclose(__lowerCamelCase,		__lowerCamelCase,		atol=4e-2   )   )

@require_flax
@slow
class  UpperCamelCase__ ( unittest.TestCase			):






      @property
      def 	__lowercase(     self					:   Tuple   )  -> List[str]:
           return [
               "Today is a beautiful day and I want",
               "In the city of",
               "Paris is the capital of France and",
               "Computers and mobile phones have taken",
           ]






      def 	__lowercase(     self					:   Optional[Any]   )  -> Any:
           UpperCamelCase__							:  List[Any]								=     '''facebook/opt-125m'''

           UpperCamelCase__							:  int								=     [
               '''Today is a beautiful day and I want to''',
               '''In the city of New York, the city''',
               '''Paris is the capital of France and the capital''',
               '''Computers and mobile phones have taken over the''',
           ]

           UpperCamelCase__							:  Optional[Any]								=     []

           UpperCamelCase__							:  List[Any]								=     FlaxOPTForCausalLM.from_pretrained(__lowerCamelCase   )
           UpperCamelCase__							:  List[Any]								=     GPTaTokenizer.from_pretrained(__lowerCamelCase   )

           for prompt in self.prompts:
                UpperCamelCase__							:  int								=     tokenizer(__lowerCamelCase,		return_tensors='''jax'''   ).input_ids

                UpperCamelCase__							:  Optional[Any]								=     model.generate(__lowerCamelCase,		max_length=10   )
                UpperCamelCase__							:  Dict								=     generated_ids[0]

                UpperCamelCase__							:  Tuple								=     tokenizer.batch_decode(__lowerCamelCase,		skip_special_tokens=__lowerCamelCase   )
                predicted_outputs += generated_string

           self.assertListEqual(__lowerCamelCase,		__lowerCamelCase   )






      def 	__lowercase(     self					:   Dict   )  -> int:
           UpperCamelCase__							:  str								=     '''facebook/opt-350m'''

           UpperCamelCase__							:  Optional[Any]								=     [
               '''Today is a beautiful day and I want to''',
               '''In the city of San Francisco, the city''',
               '''Paris is the capital of France and the capital''',
               '''Computers and mobile phones have taken over the''',
           ]

           UpperCamelCase__							:  List[str]								=     []
           UpperCamelCase__							:  List[Any]								=     FlaxOPTForCausalLM.from_pretrained(__lowerCamelCase   )
           UpperCamelCase__							:  Any								=     GPTaTokenizer.from_pretrained(__lowerCamelCase   )

           for prompt in self.prompts:
                UpperCamelCase__							:  Dict								=     tokenizer(__lowerCamelCase,		return_tensors='''jax'''   ).input_ids

                UpperCamelCase__							:  List[Any]								=     model.generate(__lowerCamelCase,		max_length=10   )
                UpperCamelCase__							:  Union[str, Any]								=     generated_ids[0]

                UpperCamelCase__							:  List[Any]								=     tokenizer.batch_decode(__lowerCamelCase,		skip_special_tokens=__lowerCamelCase   )
                predicted_outputs += generated_string

           self.assertListEqual(__lowerCamelCase,		__lowerCamelCase   )






      def 	__lowercase(     self					:   List[Any]   )  -> Optional[Any]:
           UpperCamelCase__							:  List[Any]								=     '''facebook/opt-125m'''
           UpperCamelCase__							:  int								=     [
               '''Today is a beautiful day and I want to thank''',
               '''In the city of Rome Canaver Canaver Canaver Canaver''',
           ]
           UpperCamelCase__							:  str								=     FlaxOPTForCausalLM.from_pretrained(__lowerCamelCase   )
           UpperCamelCase__							:  Optional[Any]								=     GPTaTokenizer.from_pretrained(__lowerCamelCase   )
           UpperCamelCase__							:  Optional[Any]								=     tokenizer(
               [
                   '''Today is a beautiful day and I want to''',
                   '''In the city of''',
               ],		return_tensors='''jax''',		padding=__lowerCamelCase,		)

           UpperCamelCase__							:  Tuple								=     jax.jit(model.generate   )

           UpperCamelCase__							:  int								=     jit_generate(inputs['''input_ids'''],		attention_mask=inputs['''attention_mask''']   ).sequences

           UpperCamelCase__							:  List[Any]								=     tokenizer.batch_decode(__lowerCamelCase,		skip_special_tokens=__lowerCamelCase   )

           self.assertIsNotNone(__lowerCamelCase,		__lowerCamelCase   )






      def 	__lowercase(     self					:   Union[str, Any]   )  -> str:
           UpperCamelCase__							:  Union[str, Any]								=     '''facebook/opt-350m'''

           UpperCamelCase__							:  Optional[int]								=     GPTaTokenizer.from_pretrained(__lowerCamelCase   )
           UpperCamelCase__							:  Tuple								=     FlaxOPTForCausalLM.from_pretrained(__lowerCamelCase   )

           UpperCamelCase__							:  List[str]								=     '''left'''

           # use different length sentences to test batching
           UpperCamelCase__							:  Tuple								=     [
               '''Hello, my dog is a little''',
               '''Today, I''',
           ]

           UpperCamelCase__							:  Optional[Any]								=     tokenizer(__lowerCamelCase,		return_tensors='''jax''',		padding=__lowerCamelCase   )
           UpperCamelCase__							:  Dict								=     inputs['''input_ids''']

           UpperCamelCase__							:  Tuple								=     model.generate(input_ids=__lowerCamelCase,		attention_mask=inputs['''attention_mask'''],		trace=__lowerCamelCase   )

           UpperCamelCase__							:  str								=     tokenizer(sentences[0],		return_tensors='''jax'''   ).input_ids
           UpperCamelCase__							:  Optional[int]								=     model.generate(input_ids=__lowerCamelCase   )

           UpperCamelCase__							:  Tuple								=     inputs_non_padded.shape[-1] - inputs['''attention_mask'''][-1].sum()
           UpperCamelCase__							:  List[str]								=     tokenizer(sentences[1],		return_tensors='''jax'''   ).input_ids
           UpperCamelCase__							:  Tuple								=     model.generate(input_ids=__lowerCamelCase,		max_length=model.config.max_length - num_paddings   )

           UpperCamelCase__							:  int								=     tokenizer.batch_decode(outputs[0],		skip_special_tokens=__lowerCamelCase   )
           UpperCamelCase__							:  Optional[int]								=     tokenizer.decode(output_non_padded[0][0],		skip_special_tokens=__lowerCamelCase   )
           UpperCamelCase__							:  List[str]								=     tokenizer.decode(output_padded[0][0],		skip_special_tokens=__lowerCamelCase   )

           UpperCamelCase__							:  str								=     [
               '''Hello, my dog is a little bit of a dork.\nI\'m a little bit''',
               '''Today, I was in the middle of a conversation with a friend about the''',
           ]
           self.assertListEqual(__lowerCamelCase,		__lowerCamelCase   )
           self.assertListEqual(__lowerCamelCase,		[non_padded_sentence, padded_sentence]   )








import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_deberta import DebertaTokenizer


if TYPE_CHECKING:
   from transformers.pipelines.conversational import Conversation


_SCREAMING_SNAKE_CASE :					Tuple			=	logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE :					Tuple			=	{"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt""", """tokenizer_file""": """tokenizer.json"""}

_SCREAMING_SNAKE_CASE :					str			=	{
    """vocab_file""": {
        """microsoft/deberta-base""": """https://huggingface.co/microsoft/deberta-base/resolve/main/vocab.json""",
        """microsoft/deberta-large""": """https://huggingface.co/microsoft/deberta-large/resolve/main/vocab.json""",
        """microsoft/deberta-xlarge""": """https://huggingface.co/microsoft/deberta-xlarge/resolve/main/vocab.json""",
        """microsoft/deberta-base-mnli""": """https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/vocab.json""",
        """microsoft/deberta-large-mnli""": """https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/vocab.json""",
        """microsoft/deberta-xlarge-mnli""": (
            """https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/vocab.json"""
        ),
    },
    """merges_file""": {
        """microsoft/deberta-base""": """https://huggingface.co/microsoft/deberta-base/resolve/main/merges.txt""",
        """microsoft/deberta-large""": """https://huggingface.co/microsoft/deberta-large/resolve/main/merges.txt""",
        """microsoft/deberta-xlarge""": """https://huggingface.co/microsoft/deberta-xlarge/resolve/main/merges.txt""",
        """microsoft/deberta-base-mnli""": """https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/merges.txt""",
        """microsoft/deberta-large-mnli""": """https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/merges.txt""",
        """microsoft/deberta-xlarge-mnli""": (
            """https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/merges.txt"""
        ),
    },
}

_SCREAMING_SNAKE_CASE :					Any			=	{
    """microsoft/deberta-base""": 512,
    """microsoft/deberta-large""": 512,
    """microsoft/deberta-xlarge""": 512,
    """microsoft/deberta-base-mnli""": 512,
    """microsoft/deberta-large-mnli""": 512,
    """microsoft/deberta-xlarge-mnli""": 512,
}

_SCREAMING_SNAKE_CASE :					Any			=	{
    """microsoft/deberta-base""": {"""do_lower_case""": False},
    """microsoft/deberta-large""": {"""do_lower_case""": False},
}

class  UpperCamelCase__ ( __lowerCamelCase			):
      a__   :     int								=						VOCAB_FILES_NAMES
      a__   :     Optional[Any]								=						PRETRAINED_VOCAB_FILES_MAP
      a__   :     int								=						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      a__   :     str								=						['input_ids', 'attention_mask', 'token_type_ids']
      a__   :     int								=						DebertaTokenizer






      def __init__(     self					:   Optional[int],		__lowerCamelCase					:   List[str]=None,		__lowerCamelCase					:   Optional[Any]=None,		__lowerCamelCase					:   Union[str, Any]=None,		__lowerCamelCase					:   str="replace",		__lowerCamelCase					:   Any="[CLS]",		__lowerCamelCase					:   Optional[int]="[SEP]",		__lowerCamelCase					:   List[Any]="[SEP]",		__lowerCamelCase					:   Optional[Any]="[CLS]",		__lowerCamelCase					:   List[Any]="[UNK]",		__lowerCamelCase					:   Optional[Any]="[PAD]",		__lowerCamelCase					:   Dict="[MASK]",		__lowerCamelCase					:   List[Any]=False,		**__lowerCamelCase					:   Union[str, Any],		)  -> Optional[int]:
           super().__init__(
               __lowerCamelCase,		__lowerCamelCase,		tokenizer_file=__lowerCamelCase,		errors=__lowerCamelCase,		bos_token=__lowerCamelCase,		eos_token=__lowerCamelCase,		unk_token=__lowerCamelCase,		sep_token=__lowerCamelCase,		cls_token=__lowerCamelCase,		pad_token=__lowerCamelCase,		mask_token=__lowerCamelCase,		add_prefix_space=__lowerCamelCase,		**__lowerCamelCase,		)
           UpperCamelCase__							:  Any								=     kwargs.pop('''add_bos_token''',		__lowerCamelCase   )

           UpperCamelCase__							:  List[Any]								=     json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()   )
           if pre_tok_state.get('''add_prefix_space''',		__lowerCamelCase   ) != add_prefix_space:
                UpperCamelCase__							:  Tuple								=     getattr(__lowerCamelCase,		pre_tok_state.pop('''type'''   )   )
                UpperCamelCase__							:  Tuple								=     add_prefix_space
                UpperCamelCase__							:  Union[str, Any]								=     pre_tok_class(**__lowerCamelCase   )

           UpperCamelCase__							:  Optional[int]								=     add_prefix_space






      @property
      def 	__lowercase(     self					:   Dict   )  -> str:
           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 	__lowercase(     self					:   List[Any],		__lowerCamelCase					:   Union[str, Any]   )  -> List[str]:
           UpperCamelCase__							:  List[Any]								=     AddedToken(__lowerCamelCase,		lstrip=__lowerCamelCase,		rstrip=__lowerCamelCase   ) if isinstance(__lowerCamelCase,		__lowerCamelCase   ) else value
           UpperCamelCase__							:  List[str]								=     value






      def 	__lowercase(     self					:   List[str],		__lowerCamelCase					:   List[int],		__lowerCamelCase					:   Optional[List[int]] = None   )  -> List[int]:
           if token_ids_a is None:
                return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
           UpperCamelCase__							:  Union[str, Any]								=     [self.cls_token_id]
           UpperCamelCase__							:  Union[str, Any]								=     [self.sep_token_id]
           return cls + token_ids_a + sep + token_ids_a + sep






      def 	__lowercase(     self					:   str,		__lowerCamelCase					:   List[int],		__lowerCamelCase					:   Optional[List[int]] = None   )  -> List[int]:
           UpperCamelCase__							:  Dict								=     [self.sep_token_id]
           UpperCamelCase__							:  int								=     [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   ) * [0] + len(token_ids_a + sep   ) * [1]






      def 	__lowercase(     self					:   Union[str, Any],		*__lowerCamelCase					:   Any,		**__lowerCamelCase					:   List[Any]   )  -> BatchEncoding:
           UpperCamelCase__							:  str								=     kwargs.get('''is_split_into_words''',		__lowerCamelCase   )
           assert self.add_prefix_space or not is_split_into_words, (
               f'You need to instantiate {self.__class__.__name__} with add_prefix_space=True '
               "to use it with pretokenized inputs."
           )

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






      def 	__lowercase(     self					:   Optional[Any],		*__lowerCamelCase					:   str,		**__lowerCamelCase					:   int   )  -> BatchEncoding:
           UpperCamelCase__							:  str								=     kwargs.get('''is_split_into_words''',		__lowerCamelCase   )

           assert self.add_prefix_space or not is_split_into_words, (
               f'You need to instantiate {self.__class__.__name__} with add_prefix_space=True '
               "to use it with pretokenized inputs."
           )

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






      def 	__lowercase(     self					:   Union[str, Any],		__lowerCamelCase					:   str,		__lowerCamelCase					:   Optional[str] = None   )  -> Tuple[str]:
           UpperCamelCase__							:  Dict								=     self._tokenizer.model.save(__lowerCamelCase,		name=__lowerCamelCase   )
           return tuple(__lowerCamelCase   )






      def 	__lowercase(     self					:   int,		__lowerCamelCase					:   "Conversation"   )  -> List[int]:
           UpperCamelCase__							:  List[Any]								=     []
           for is_user, text in conversation.iter_texts():
                input_ids.extend(self.encode(__lowerCamelCase,		add_special_tokens=__lowerCamelCase   ) + [self.eos_token_id]   )

           if len(__lowerCamelCase   ) > self.model_max_length:
                UpperCamelCase__							:  List[Any]								=     input_ids[-self.model_max_length :]
           return input_ids




