

import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import MarianConfig, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property

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
  A__										=      "platform"

  import jax
  import jax.numpy as jnp

  from transformers import MarianTokenizer
  from transformers.models.marian.modeling_flax_marian import FlaxMarianModel, FlaxMarianMTModel, shift_tokens_right





def 				__a							(       __A			,							__A			,							__A=None			,							__A=None			,							__A=None			,							__A=None			,							__A=None			,							__A=None			,							):





    if attention_mask is None:
        SCREAMING_SNAKE_CASE__         =    np.where(input_ids != config.pad_token_id			,							1			,							0						)
    if decoder_attention_mask is None:
        SCREAMING_SNAKE_CASE__         =    np.where(decoder_input_ids != config.pad_token_id			,							1			,							0						)
    if head_mask is None:
        SCREAMING_SNAKE_CASE__         =    np.ones((config.encoder_layers, config.encoder_attention_heads)						)
    if decoder_head_mask is None:
        SCREAMING_SNAKE_CASE__         =    np.ones((config.decoder_layers, config.decoder_attention_heads)						)
    if cross_attn_head_mask is None:
        SCREAMING_SNAKE_CASE__         =    np.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": attention_mask,
    }







class  UpperCamelCase_      :


  """simple docstring"""



  def __init__(						self     :					Dict  ,	A_     :					Tuple  ,	A_     :					Tuple=13  ,	A_     :					Tuple=7  ,	A_     :					Any=True  ,	A_     :					Dict=False  ,	A_     :					List[Any]=99  ,	A_     :					Dict=16  ,	A_     :					str=2  ,	A_     :					int=4  ,	A_     :					str=4  ,	A_     :					Optional[int]="gelu"  ,	A_     :					Any=0.1  ,	A_     :					List[str]=0.1  ,	A_     :					int=32  ,	A_     :					List[str]=2  ,	A_     :					Dict=1  ,	A_     :					List[str]=0  ,	A_     :					List[str]=0.02  ,	)       ->			int:
      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_act
      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
      SCREAMING_SNAKE_CASE__         =    initializer_range



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

      SCREAMING_SNAKE_CASE__         =    shift_tokens_right(lowerCamelCase_  ,	1  ,	2						)

      SCREAMING_SNAKE_CASE__         =    MarianConfig(
          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_id=self.eos_token_id  ,	bos_token_id=self.bos_token_id  ,	pad_token_id=self.pad_token_id  ,	initializer_range=self.initializer_range  ,	use_cache=lowerCamelCase_  ,	)
      SCREAMING_SNAKE_CASE__         =    prepare_marian_inputs_dict(lowerCamelCase_  ,	lowerCamelCase_  ,	lowerCamelCase_						)
      return config, inputs_dict



  def   _UpperCamelCase     (						self     :					List[Any]						)       ->			Optional[int]:
      SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__         =    self.prepare_config_and_inputs()
      return config, inputs_dict



  def   _UpperCamelCase     (						self     :					Tuple  ,	A_     :					Dict  ,	A_     :					str  ,	A_     :					List[Any]						)       ->			Union[str, Any]:
      SCREAMING_SNAKE_CASE__         =    20
      SCREAMING_SNAKE_CASE__         =    model_class_name(lowerCamelCase_						)

      SCREAMING_SNAKE_CASE__         =    model.encode(inputs_dict["input_ids"]						)

      SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__         =    (
          inputs_dict["decoder_input_ids"],
          inputs_dict["decoder_attention_mask"],
      )

      SCREAMING_SNAKE_CASE__         =    model.init_cache(decoder_input_ids.shape[0]  ,	lowerCamelCase_  ,	lowerCamelCase_						)
      SCREAMING_SNAKE_CASE__         =    jnp.ones((decoder_input_ids.shape[0], max_decoder_length)  ,	dtype="i4"						)

      SCREAMING_SNAKE_CASE__         =    jnp.broadcast_to(
          jnp.arange(decoder_input_ids.shape[-1] - 1						)[None, :]  ,	(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)  ,	)
      SCREAMING_SNAKE_CASE__         =    model.decode(
          decoder_input_ids[:, :-1]  ,	lowerCamelCase_  ,	decoder_attention_mask=lowerCamelCase_  ,	past_key_values=lowerCamelCase_  ,	decoder_position_ids=lowerCamelCase_  ,	)

      SCREAMING_SNAKE_CASE__         =    jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]  ,	dtype="i4"						)
      SCREAMING_SNAKE_CASE__         =    model.decode(
          decoder_input_ids[:, -1:]  ,	lowerCamelCase_  ,	decoder_attention_mask=lowerCamelCase_  ,	past_key_values=outputs_cache.past_key_values  ,	decoder_position_ids=lowerCamelCase_  ,	)

      SCREAMING_SNAKE_CASE__         =    model.decode(lowerCamelCase_  ,	lowerCamelCase_						)

      SCREAMING_SNAKE_CASE__         =    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   _UpperCamelCase     (						self     :					List[str]  ,	A_     :					Any  ,	A_     :					List[Any]  ,	A_     :					List[str]						)       ->			Dict:
      SCREAMING_SNAKE_CASE__         =    20
      SCREAMING_SNAKE_CASE__         =    model_class_name(lowerCamelCase_						)

      SCREAMING_SNAKE_CASE__         =    model.encode(inputs_dict["input_ids"]						)

      SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__         =    (
          inputs_dict["decoder_input_ids"],
          inputs_dict["decoder_attention_mask"],
      )

      SCREAMING_SNAKE_CASE__         =    jnp.concatenate(
          [
              decoder_attention_mask,
              jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])						),
          ]  ,	axis=-1  ,	)

      SCREAMING_SNAKE_CASE__         =    model.init_cache(decoder_input_ids.shape[0]  ,	lowerCamelCase_  ,	lowerCamelCase_						)
      SCREAMING_SNAKE_CASE__         =    jnp.broadcast_to(
          jnp.arange(decoder_input_ids.shape[-1] - 1						)[None, :]  ,	(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)  ,	)

      SCREAMING_SNAKE_CASE__         =    model.decode(
          decoder_input_ids[:, :-1]  ,	lowerCamelCase_  ,	decoder_attention_mask=lowerCamelCase_  ,	past_key_values=lowerCamelCase_  ,	decoder_position_ids=lowerCamelCase_  ,	)
      SCREAMING_SNAKE_CASE__         =    jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]  ,	dtype="i4"						)
      SCREAMING_SNAKE_CASE__         =    model.decode(
          decoder_input_ids[:, -1:]  ,	lowerCamelCase_  ,	past_key_values=outputs_cache.past_key_values  ,	decoder_attention_mask=lowerCamelCase_  ,	decoder_position_ids=lowerCamelCase_  ,	)

      SCREAMING_SNAKE_CASE__         =    model.decode(lowerCamelCase_  ,	lowerCamelCase_  ,	decoder_attention_mask=lowerCamelCase_						)

      SCREAMING_SNAKE_CASE__         =    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_      (	_snake_case		,  unittest.TestCase		,  _snake_case      ):


  """simple docstring"""
  SCREAMING_SNAKE_CASE__					:			Tuple					    =   True
  SCREAMING_SNAKE_CASE__					:			Union[str, Any]					    =   (FlaxMarianModel, FlaxMarianMTModel) if is_flax_available() else ()
  SCREAMING_SNAKE_CASE__					:			Dict					    =   (FlaxMarianMTModel,) if is_flax_available() else ()



  def   _UpperCamelCase     (						self     :					Any						)       ->			int:
      SCREAMING_SNAKE_CASE__         =    FlaxMarianModelTester(self						)



  def   _UpperCamelCase     (						self     :					Dict						)       ->			Any:
      SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__         =    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   _UpperCamelCase     (						self     :					Dict						)       ->			Dict:
      SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__         =    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_						)



  def   _UpperCamelCase     (						self     :					Any						)       ->			Any:
      SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__         =    self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
          with self.subTest(model_class.__name__						):
              SCREAMING_SNAKE_CASE__         =    self._prepare_for_class(lowerCamelCase_  ,	lowerCamelCase_						)
              SCREAMING_SNAKE_CASE__         =    model_class(lowerCamelCase_						)

              @jax.jit
              def encode_jitted(A_     :					Tuple  ,	A_     :					Optional[int]=None  ,	**A_     :					Any						):
                  return model.encode(input_ids=lowerCamelCase_  ,	attention_mask=lowerCamelCase_						)

              with self.subTest("JIT Enabled"						):
                  SCREAMING_SNAKE_CASE__         =    encode_jitted(**lowerCamelCase_						).to_tuple()

              with self.subTest("JIT Disabled"						):
                  with jax.disable_jit():
                      SCREAMING_SNAKE_CASE__         =    encode_jitted(**lowerCamelCase_						).to_tuple()

              self.assertEqual(len(lowerCamelCase_						)  ,	len(lowerCamelCase_						)						)
              for jitted_output, output in zip(lowerCamelCase_  ,	lowerCamelCase_						):
                  self.assertEqual(jitted_output.shape  ,	output.shape						)



  def   _UpperCamelCase     (						self     :					List[Any]						)       ->			Union[str, Any]:
      SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__         =    self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
          with self.subTest(model_class.__name__						):
              SCREAMING_SNAKE_CASE__         =    model_class(lowerCamelCase_						)
              SCREAMING_SNAKE_CASE__         =    model.encode(inputs_dict["input_ids"]  ,	inputs_dict["attention_mask"]						)

              SCREAMING_SNAKE_CASE__         =    {
                  "decoder_input_ids": inputs_dict["decoder_input_ids"],
                  "decoder_attention_mask": inputs_dict["decoder_attention_mask"],
                  "encoder_outputs": encoder_outputs,
              }

              @jax.jit
              def decode_jitted(A_     :					Union[str, Any]  ,	A_     :					int  ,	A_     :					Optional[Any]						):
                  return model.decode(
                      decoder_input_ids=lowerCamelCase_  ,	decoder_attention_mask=lowerCamelCase_  ,	encoder_outputs=lowerCamelCase_  ,	)

              with self.subTest("JIT Enabled"						):
                  SCREAMING_SNAKE_CASE__         =    decode_jitted(**lowerCamelCase_						).to_tuple()

              with self.subTest("JIT Disabled"						):
                  with jax.disable_jit():
                      SCREAMING_SNAKE_CASE__         =    decode_jitted(**lowerCamelCase_						).to_tuple()

              self.assertEqual(len(lowerCamelCase_						)  ,	len(lowerCamelCase_						)						)
              for jitted_output, output in zip(lowerCamelCase_  ,	lowerCamelCase_						):
                  self.assertEqual(jitted_output.shape  ,	output.shape						)







  @slow
  def   _UpperCamelCase     (						self     :					Tuple						)       ->			int:

      for model_class_name in self.all_model_classes:
          SCREAMING_SNAKE_CASE__         =    model_class_name.from_pretrained("Helsinki-NLP/opus-mt-en-de"						)
          # FlaxMarianForSequenceClassification expects eos token in input_ids
          SCREAMING_SNAKE_CASE__         =    np.ones((1, 1)						) * model.config.eos_token_id
          SCREAMING_SNAKE_CASE__         =    model(lowerCamelCase_						)
          self.assertIsNotNone(lowerCamelCase_						)







@require_flax
@require_sentencepiece
@require_tokenizers
class  UpperCamelCase_      (	unittest.TestCase      ):


  """simple docstring"""
  SCREAMING_SNAKE_CASE__					:			str					    =   None
  SCREAMING_SNAKE_CASE__					:			Optional[Any]					    =   None



  @classmethod
  def   _UpperCamelCase     (						cls     :					List[str]						)       ->			Union[str, Any]:
      SCREAMING_SNAKE_CASE__         =    f"Helsinki-NLP/opus-mt-{cls.src}-{cls.tgt}"
      return cls



  @cached_property
  def   _UpperCamelCase     (						self     :					Optional[Any]						)       ->			Dict:
      return MarianTokenizer.from_pretrained(self.model_name						)



  @property
  def   _UpperCamelCase     (						self     :					str						)       ->			Optional[int]:
      return self.tokenizer.eos_token_id



  @cached_property
  def   _UpperCamelCase     (						self     :					Optional[Any]						)       ->			Optional[Any]:
      SCREAMING_SNAKE_CASE__         =    FlaxMarianMTModel.from_pretrained(self.model_name						)
      self.assertEqual(model.config.decoder_start_token_id  ,	model.config.pad_token_id						)
      return model



  def   _UpperCamelCase     (						self     :					Optional[Any]  ,	**A_     :					int						)       ->			List[Any]:
      SCREAMING_SNAKE_CASE__         =    self.translate_src_text(**lowerCamelCase_						)
      self.assertListEqual(self.expected_text  ,	lowerCamelCase_						)







  def   _UpperCamelCase     (						self     :					Optional[int]  ,	**A_     :					Optional[int]						)       ->			Union[str, Any]:
      SCREAMING_SNAKE_CASE__         =    self.tokenizer(self.src_text  ,	padding=lowerCamelCase_  ,	return_tensors="np"  ,	**lowerCamelCase_						)
      SCREAMING_SNAKE_CASE__         =    self.model.generate(
          model_inputs.input_ids  ,	attention_mask=model_inputs.attention_mask  ,	num_beams=2  ,	max_length=128  ,	).sequences
      SCREAMING_SNAKE_CASE__         =    self.tokenizer.batch_decode(lowerCamelCase_  ,	skip_special_tokens=lowerCamelCase_						)
      return generated_words







@require_flax
@require_sentencepiece
@require_tokenizers
class  UpperCamelCase_      (	_snake_case      ):


  """simple docstring"""
  SCREAMING_SNAKE_CASE__					:			int					    =   """en"""
  SCREAMING_SNAKE_CASE__					:			Any					    =   """fr"""
  SCREAMING_SNAKE_CASE__					:			List[Any]					    =   [
      """I am a small frog.""",
      """Now I can forget the 100 words of german that I know.""",
  ]
  SCREAMING_SNAKE_CASE__					:			Tuple					    =   [
      """Je suis une petite grenouille.""",
      """Maintenant, je peux oublier les 100 mots d'allemand que je connais.""",
  ]



  @slow
  def   _UpperCamelCase     (						self     :					Any						)       ->			List[Any]:
      self._assert_generated_batch_equal_expected()







@require_flax
@require_sentencepiece
@require_tokenizers
class  UpperCamelCase_      (	_snake_case      ):


  """simple docstring"""
  SCREAMING_SNAKE_CASE__					:			Any					    =   """fr"""
  SCREAMING_SNAKE_CASE__					:			int					    =   """en"""
  SCREAMING_SNAKE_CASE__					:			List[str]					    =   [
      """Donnez moi le micro.""",
      """Tom et Mary étaient assis à une table.""",  # Accents
  ]
  SCREAMING_SNAKE_CASE__					:			Tuple					    =   [
      """Give me the microphone.""",
      """Tom and Mary were sitting at a table.""",
  ]



  @slow
  def   _UpperCamelCase     (						self     :					Dict						)       ->			List[Any]:
      self._assert_generated_batch_equal_expected()







@require_flax
@require_sentencepiece
@require_tokenizers
class  UpperCamelCase_      (	_snake_case      ):


  """simple docstring"""
  SCREAMING_SNAKE_CASE__					:			List[str]					    =   """mt"""
  SCREAMING_SNAKE_CASE__					:			List[Any]					    =   """en"""
  SCREAMING_SNAKE_CASE__					:			List[Any]					    =   ["""Billi messu b'mod ġentili, Ġesù fejjaq raġel li kien milqut bil - marda kerha tal - ġdiem."""]
  SCREAMING_SNAKE_CASE__					:			Union[str, Any]					    =   ["""Touching gently, Jesus healed a man who was affected by the sad disease of leprosy."""]



  @slow
  def   _UpperCamelCase     (						self     :					List[str]						)       ->			int:
      self._assert_generated_batch_equal_expected()







@require_flax
@require_sentencepiece
@require_tokenizers
class  UpperCamelCase_      (	_snake_case      ):


  """simple docstring"""
  SCREAMING_SNAKE_CASE__					:			int					    =   """en"""
  SCREAMING_SNAKE_CASE__					:			Dict					    =   """de"""
  SCREAMING_SNAKE_CASE__					:			Optional[Any]					    =   [
      """I am a small frog.""",
      """Now I can forget the 100 words of german that I know.""",
      """Tom asked his teacher for advice.""",
      """That's how I would do it.""",
      """Tom really admired Mary's courage.""",
      """Turn around and close your eyes.""",
  ]
  SCREAMING_SNAKE_CASE__					:			Optional[Any]					    =   [
      """Ich bin ein kleiner Frosch.""",
      """Jetzt kann ich die 100 Wörter des Deutschen vergessen, die ich kenne.""",
      """Tom bat seinen Lehrer um Rat.""",
      """So würde ich das machen.""",
      """Tom bewunderte Marias Mut wirklich.""",
      """Drehen Sie sich um und schließen Sie die Augen.""",
  ]



  @slow
  def   _UpperCamelCase     (						self     :					Tuple						)       ->			List[str]:
      self._assert_generated_batch_equal_expected()







@require_flax
@require_sentencepiece
@require_tokenizers
class  UpperCamelCase_      (	_snake_case      ):


  """simple docstring"""
  SCREAMING_SNAKE_CASE__					:			Optional[Any]					    =   """en"""
  SCREAMING_SNAKE_CASE__					:			Optional[Any]					    =   """zh"""
  SCREAMING_SNAKE_CASE__					:			Tuple					    =   ["""My name is Wolfgang and I live in Berlin"""]
  SCREAMING_SNAKE_CASE__					:			Dict					    =   ["""我叫沃尔夫冈 我住在柏林"""]



  @slow
  def   _UpperCamelCase     (						self     :					Optional[int]						)       ->			List[str]:
      self._assert_generated_batch_equal_expected()







@require_flax
@require_sentencepiece
@require_tokenizers
class  UpperCamelCase_      (	_snake_case      ):


  """simple docstring"""
  SCREAMING_SNAKE_CASE__					:			Tuple					    =   """ru"""
  SCREAMING_SNAKE_CASE__					:			Optional[Any]					    =   """fr"""
  SCREAMING_SNAKE_CASE__					:			Any					    =   ["""Он показал мне рукопись своей новой пьесы."""]
  SCREAMING_SNAKE_CASE__					:			Dict					    =   ["""Il m'a montré le manuscrit de sa nouvelle pièce."""]



  @slow
  def   _UpperCamelCase     (						self     :					str						)       ->			Dict:
      self._assert_generated_batch_equal_expected()







@require_flax
@require_sentencepiece
@require_tokenizers
class  UpperCamelCase_      (	_snake_case      ):


  """simple docstring"""
  SCREAMING_SNAKE_CASE__					:			List[Any]					    =   """en"""
  SCREAMING_SNAKE_CASE__					:			Union[str, Any]					    =   """ROMANCE"""
  SCREAMING_SNAKE_CASE__					:			Union[str, Any]					    =   [
      """>>fr<< Don't spend so much time watching TV.""",
      """>>pt<< Your message has been sent.""",
      """>>es<< He's two years older than me.""",
  ]
  SCREAMING_SNAKE_CASE__					:			List[str]					    =   [
      """Ne passez pas autant de temps à regarder la télé.""",
      """A sua mensagem foi enviada.""",
      """Es dos años más viejo que yo.""",
  ]



  @slow
  def   _UpperCamelCase     (						self     :					Union[str, Any]						)       ->			str:
      self._assert_generated_batch_equal_expected()







from typing import Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_openai import OpenAIGPTTokenizer


_lowercase:					List[str]      =						logging.get_logger(__name__)

_lowercase:					str      =						{"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}

_lowercase:					Union[str, Any]      =						{
    "vocab_file": {"openai-gpt": "https://huggingface.co/openai-gpt/resolve/main/vocab.json"},
    "merges_file": {"openai-gpt": "https://huggingface.co/openai-gpt/resolve/main/merges.txt"},
    "tokenizer_file": {"openai-gpt": "https://huggingface.co/openai-gpt/resolve/main/tokenizer.json"},
}

_lowercase:					int      =						{
    "openai-gpt": 512,
}



class       _lowercase				(  lowerCAmelCase      ):





	"""simple docstring"""
	__A		      =				VOCAB_FILES_NAMES
	__A		      =				PRETRAINED_VOCAB_FILES_MAP
	__A		      =				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	__A		      =				["input_ids", "attention_mask"]
	__A		      =				OpenAIGPTTokenizer




	def __init__(self						,			lowerCamelCase_=None						,			lowerCamelCase_=None						,			lowerCamelCase_=None						,			lowerCamelCase_="<unk>"						,			**lowerCamelCase_						):
						"""simple docstring"""

						super().__init__(lowerCamelCase_						,			lowerCamelCase_						,			tokenizer_file=lowerCamelCase_						,			unk_token=lowerCamelCase_						,			**lowerCamelCase_						)




	@property
	def 	UpperCamelCase_			(self						):
						"""simple docstring"""

						return True




	def 	UpperCamelCase_			(self						,			lowerCamelCase_						,			lowerCamelCase_ = None						):
						"""simple docstring"""

						a   	=  self._tokenizer.model.save(lowerCamelCase_						,			name=lowerCamelCase_						)
						return tuple(lowerCamelCase_						)
