


import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import MBartConfig, 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
							snake_case			:      str 							=   'platform'

							import jax
							import jax.numpy as jnp

							from transformers import AutoTokenizer
							from transformers.models.mbart.modeling_flax_mbart import (
							    FlaxMBartForConditionalGeneration,
							    FlaxMBartForQuestionAnswering,
							    FlaxMBartForSequenceClassification,
							    FlaxMBartModel,
							    shift_tokens_right,
							)


def 				SCREAMING_SNAKE_CASE (     UpperCAmelCase__   ,UpperCAmelCase__   ,UpperCAmelCase__=None   ,UpperCAmelCase__=None   ,UpperCAmelCase__=None   ,UpperCAmelCase__=None   ,UpperCAmelCase__=None   ,UpperCAmelCase__=None   ,):

				"""simple docstring"""






				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": decoder_attention_mask,
				}



class    __lowercase  (       unittest.TestCase							):

							"""simple docstring"""


							def __init__(    self				, A_				, A_=13				, A_=7				, A_=True				, A_=False				, A_=99				, A_=16				, A_=2				, A_=4				, A_=4				, A_="gelu"				, A_=0.1				, A_=0.1				, A_=32				, A_=2				, A_=1				, A_=0				, A_=2				, A_=0.02				, )->	Any:
											_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      = decoder_start_token_id
											_SCREAMING_SNAKE_CASE      = initializer_range





							def 		__magic_name__			(    self	)->	Optional[int]:
											_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(A_				, 1	)

											_SCREAMING_SNAKE_CASE      = MBartConfig(
											    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				, decoder_start_token_id=self.decoder_start_token_id				, initializer_range=self.initializer_range				, use_cache=A_				, )
											_SCREAMING_SNAKE_CASE      = prepare_mbart_inputs_dict(A_				, A_				, A_	)
											return config, inputs_dict





							def 		__magic_name__			(    self	)->	Tuple:
											_SCREAMING_SNAKE_CASE      ,      _SCREAMING_SNAKE_CASE      = self.prepare_config_and_inputs()
											return config, inputs_dict





							def 		__magic_name__			(    self				, A_				, A_				, A_	)->	int:
											_SCREAMING_SNAKE_CASE      = 20
											_SCREAMING_SNAKE_CASE      = model_class_name(A_	)

											_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]				, A_				, A_	)
											_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]				, A_				, decoder_attention_mask=A_				, past_key_values=A_				, decoder_position_ids=A_				, )

											_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:]				, A_				, decoder_attention_mask=A_				, past_key_values=outputs_cache.past_key_values				, decoder_position_ids=A_				, )

											_SCREAMING_SNAKE_CASE      = model.decode(A_				, A_	)

											_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 		__magic_name__			(    self				, A_				, A_				, A_	)->	Dict:
											_SCREAMING_SNAKE_CASE      = 20
											_SCREAMING_SNAKE_CASE      = model_class_name(A_	)

											_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]				, A_				, A_	)
											_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]				, A_				, decoder_attention_mask=A_				, past_key_values=A_				, decoder_position_ids=A_				, )
											_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:]				, A_				, past_key_values=outputs_cache.past_key_values				, decoder_attention_mask=A_				, decoder_position_ids=A_				, )

											_SCREAMING_SNAKE_CASE      = model.decode(A_				, A_				, decoder_attention_mask=A_	)

											_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    __lowercase  (       unittest.TestCase							):

							"""simple docstring"""


							SCREAMING_SNAKE_CASE   :					Tuple        =						99





							def 		__magic_name__			(    self	)->	Optional[Any]:
											_SCREAMING_SNAKE_CASE      = np.array(
											    [
											        [71, 82, 18, 33, 46, 91, 2],
											        [68, 34, 26, 58, 30, 82, 2],
											        [5, 97, 17, 39, 94, 40, 2],
											        [76, 83, 94, 25, 70, 78, 2],
											        [87, 59, 41, 35, 48, 66, 2],
											        [55, 13, 16, 58, 5, 2, 1],  # note padding
											        [64, 27, 31, 51, 12, 75, 2],
											        [52, 64, 86, 17, 83, 39, 2],
											        [48, 61, 9, 24, 71, 82, 2],
											        [26, 1, 60, 48, 22, 13, 2],
											        [21, 5, 62, 28, 14, 76, 2],
											        [45, 98, 37, 86, 59, 48, 2],
											        [70, 70, 50, 9, 28, 0, 2],
											    ]				, dtype=np.intaa				, )

											_SCREAMING_SNAKE_CASE      = input_ids.shape[0]
											_SCREAMING_SNAKE_CASE      = MBartConfig(
											    vocab_size=self.vocab_size				, d_model=24				, encoder_layers=2				, decoder_layers=2				, encoder_attention_heads=2				, decoder_attention_heads=2				, encoder_ffn_dim=32				, decoder_ffn_dim=32				, max_position_embeddings=48				, eos_token_id=2				, pad_token_id=1				, bos_token_id=0				, )
											return config, input_ids, batch_size





							def 		__magic_name__			(    self	)->	Dict:
											_SCREAMING_SNAKE_CASE      ,      _SCREAMING_SNAKE_CASE      ,      _SCREAMING_SNAKE_CASE      = self._get_config_and_data()
											_SCREAMING_SNAKE_CASE      = FlaxMBartForSequenceClassification(A_	)
											_SCREAMING_SNAKE_CASE      = model(input_ids=A_				, decoder_input_ids=A_	)
											_SCREAMING_SNAKE_CASE      = (batch_size, config.num_labels)
											self.assertEqual(outputs['logits'].shape				, A_	)





							def 		__magic_name__			(    self	)->	Union[str, Any]:
											_SCREAMING_SNAKE_CASE      ,      _SCREAMING_SNAKE_CASE      ,      _SCREAMING_SNAKE_CASE      = self._get_config_and_data()
											_SCREAMING_SNAKE_CASE      = FlaxMBartForQuestionAnswering(A_	)
											_SCREAMING_SNAKE_CASE      = model(input_ids=A_	)

											self.assertEqual(outputs['start_logits'].shape				, input_ids.shape	)
											self.assertEqual(outputs['end_logits'].shape				, input_ids.shape	)





							def 		__magic_name__			(    self	)->	List[str]:
											_SCREAMING_SNAKE_CASE      ,      _SCREAMING_SNAKE_CASE      ,      _SCREAMING_SNAKE_CASE      = self._get_config_and_data()
											_SCREAMING_SNAKE_CASE      = FlaxMBartForConditionalGeneration(A_	)
											_SCREAMING_SNAKE_CASE      = lm_model(input_ids=A_	)
											_SCREAMING_SNAKE_CASE      = (batch_size, input_ids.shape[1], config.vocab_size)
											self.assertEqual(outputs['logits'].shape				, A_	)





							def 		__magic_name__			(    self	)->	int:
											_SCREAMING_SNAKE_CASE      = MBartConfig(
											    vocab_size=self.vocab_size				, d_model=14				, encoder_layers=2				, decoder_layers=2				, encoder_attention_heads=2				, decoder_attention_heads=2				, encoder_ffn_dim=8				, decoder_ffn_dim=8				, max_position_embeddings=48				, )
											_SCREAMING_SNAKE_CASE      = FlaxMBartForConditionalGeneration(A_	)
											_SCREAMING_SNAKE_CASE      = np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]]				, dtype=np.intaa	)
											_SCREAMING_SNAKE_CASE      = np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]]				, dtype=np.intaa	)
											_SCREAMING_SNAKE_CASE      = lm_model(input_ids=A_				, decoder_input_ids=A_	)
											_SCREAMING_SNAKE_CASE      = (*summary.shape, config.vocab_size)
											self.assertEqual(outputs['logits'].shape				, A_	)





							def 		__magic_name__			(    self	)->	str:
											_SCREAMING_SNAKE_CASE      = np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]]				, dtype=np.intaa	)
											_SCREAMING_SNAKE_CASE      = shift_tokens_right(A_				, 1	)
											_SCREAMING_SNAKE_CASE      = np.equal(A_				, 1	).astype(np.floataa	).sum()
											_SCREAMING_SNAKE_CASE      = np.equal(A_				, 1	).astype(np.floataa	).sum()
											self.assertEqual(shifted.shape				, input_ids.shape	)
											self.assertEqual(A_				, n_pad_before - 1	)
											self.assertTrue(np.equal(shifted[:, 0]				, 2	).all()	)







@require_flax
class    __lowercase  (       UpperCamelCase      ,     unittest.TestCase      ,     UpperCamelCase							):

							"""simple docstring"""


							SCREAMING_SNAKE_CASE   :					Optional[Any]        =						True
							SCREAMING_SNAKE_CASE   :					int        =						(
							    (
							        FlaxMBartModel,
							        FlaxMBartForConditionalGeneration,
							        FlaxMBartForSequenceClassification,
							        FlaxMBartForQuestionAnswering,
							    )
							    if is_flax_available()
							    else ()
							)
							SCREAMING_SNAKE_CASE   :					int        =						(FlaxMBartForConditionalGeneration,) if is_flax_available() else ()





							def 		__magic_name__			(    self	)->	Optional[Any]:
											_SCREAMING_SNAKE_CASE      = FlaxMBartModelTester(self	)





							def 		__magic_name__			(    self	)->	Union[str, 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(A_				, A_				, A_	)





							def 		__magic_name__			(    self	)->	List[str]:
											_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(A_				, A_				, A_	)





							def 		__magic_name__			(    self	)->	Dict:
											_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(A_				, A_	)
																			_SCREAMING_SNAKE_CASE      = model_class(A_	)

																			@jax.jit
																			def encode_jitted(A_				, A_=None				, **A_	):
																							return model.encode(input_ids=A_				, attention_mask=A_	)

																			with self.subTest('JIT Enabled'	):
																							_SCREAMING_SNAKE_CASE      = encode_jitted(**A_	).to_tuple()

																			with self.subTest('JIT Disabled'	):
																							with jax.disable_jit():
																											_SCREAMING_SNAKE_CASE      = encode_jitted(**A_	).to_tuple()

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





							def 		__magic_name__			(    self	)->	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(A_	)
																			_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_				, A_				, A_	):
																							return model.decode(
																							    decoder_input_ids=A_				, decoder_attention_mask=A_				, encoder_outputs=A_				, )

																			with self.subTest('JIT Enabled'	):
																							_SCREAMING_SNAKE_CASE      = decode_jitted(**A_	).to_tuple()

																			with self.subTest('JIT Disabled'	):
																							with jax.disable_jit():
																											_SCREAMING_SNAKE_CASE      = decode_jitted(**A_	).to_tuple()

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





							@slow
							def 		__magic_name__			(    self	)->	int:
											for model_class_name in self.all_model_classes:
															_SCREAMING_SNAKE_CASE      = model_class_name.from_pretrained('facebook/mbart-large-cc25'				, from_pt=A_	)
															# FlaxMBartForSequenceClassification expects eos token in input_ids
															_SCREAMING_SNAKE_CASE      = np.ones((1, 1)	) * model.config.eos_token_id
															_SCREAMING_SNAKE_CASE      = model(A_	)
															self.assertIsNotNone(A_	)







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

							"""simple docstring"""


							SCREAMING_SNAKE_CASE   :					List[Any]        =						[
							    " UN Chief Says There Is No Military Solution in Syria",
							]
							SCREAMING_SNAKE_CASE   :					Tuple        =						[
							    "Şeful ONU declară că nu există o soluţie militară în Siria",
							]
							SCREAMING_SNAKE_CASE   :					List[str]        =						"facebook/mbart-large-en-ro"





							@cached_property
							def 		__magic_name__			(    self	)->	str:
											return AutoTokenizer.from_pretrained(self.model_name	)





							@cached_property
							def 		__magic_name__			(    self	)->	Optional[Any]:
											_SCREAMING_SNAKE_CASE      = FlaxMBartForConditionalGeneration.from_pretrained(self.model_name				, from_pt=A_	)
											return model





							def 		__magic_name__			(    self				, **A_	)->	Dict:
											_SCREAMING_SNAKE_CASE      = self.translate_src_text(**A_	)
											self.assertListEqual(self.expected_text				, A_	)





							def 		__magic_name__			(    self				, **A_	)->	Optional[Any]:
											_SCREAMING_SNAKE_CASE      = self.tokenizer(self.src_text				, **A_				, return_tensors='np'	)
											_SCREAMING_SNAKE_CASE      = self.model.generate(
											    model_inputs.input_ids				, attention_mask=model_inputs.attention_mask				, decoder_start_token_id=self.tokenizer.lang_code_to_id['ro_RO']				, early_stopping=A_				, num_beams=2				, ).sequences
											_SCREAMING_SNAKE_CASE      = self.tokenizer.batch_decode(A_				, skip_special_tokens=A_	)
											return generated_words





							@slow
							def 		__magic_name__			(    self	)->	Optional[int]:
											self._assert_generated_batch_equal_expected()




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

import regex as re

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


if TYPE_CHECKING:
							from transformers.pipelines.conversational import Conversation

snake_case			:      Any 							=   logging.get_logger(__name__)

snake_case			:      str 							=   {'vocab_file': 'vocab.json', 'merges_file': 'merges.txt'}

snake_case			:      Optional[int] 							=   {
    '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'
        ),
    },
}

snake_case			:      Tuple 							=   {
    'microsoft/deberta-base': 5_12,
    'microsoft/deberta-large': 5_12,
    'microsoft/deberta-xlarge': 5_12,
    'microsoft/deberta-base-mnli': 5_12,
    'microsoft/deberta-large-mnli': 5_12,
    'microsoft/deberta-xlarge-mnli': 5_12,
}

snake_case			:      Any 							=   {
    'microsoft/deberta-base': {'do_lower_case': False},
    'microsoft/deberta-large': {'do_lower_case': False},
}


def 				SCREAMING_SNAKE_CASE (     ):

				"""simple docstring"""






				_SCREAMING_SNAKE_CASE      = (
				    list(range(ord('!'							)   ,ord('~'							) + 1							)							) + list(range(ord('¡'							)   ,ord('¬'							) + 1							)							) + list(range(ord('®'							)   ,ord('ÿ'							) + 1							)							)
				)
				_SCREAMING_SNAKE_CASE      = bs[:]
				_SCREAMING_SNAKE_CASE      = 0
				for b in range(2**8							):
								if b not in bs:
												bs.append(UpperCAmelCase__							)
												cs.append(2**8 + n							)
												n += 1
				_SCREAMING_SNAKE_CASE      = [chr(UpperCAmelCase__							) for n in cs]
				return dict(zip(UpperCAmelCase__   ,UpperCAmelCase__							)							)


def 				SCREAMING_SNAKE_CASE (     UpperCAmelCase__							):

				"""simple docstring"""






				_SCREAMING_SNAKE_CASE      = set()
				_SCREAMING_SNAKE_CASE      = word[0]
				for char in word[1:]:
								pairs.add((prev_char, char)							)
								_SCREAMING_SNAKE_CASE      = char
				return pairs



class    __lowercase  (       UpperCamelCase							):

							"""simple docstring"""


							SCREAMING_SNAKE_CASE   :					int        =						VOCAB_FILES_NAMES
							SCREAMING_SNAKE_CASE   :					List[Any]        =						PRETRAINED_VOCAB_FILES_MAP
							SCREAMING_SNAKE_CASE   :					Any        =						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							SCREAMING_SNAKE_CASE   :					Optional[Any]        =						["input_ids", "attention_mask", "token_type_ids"]





							def __init__(    self				, A_				, A_				, A_="replace"				, A_="[CLS]"				, A_="[SEP]"				, A_="[SEP]"				, A_="[CLS]"				, A_="[UNK]"				, A_="[PAD]"				, A_="[MASK]"				, A_=False				, A_=False				, **A_				, )->	List[str]:
											_SCREAMING_SNAKE_CASE      = AddedToken(A_				, lstrip=A_				, rstrip=A_	) if isinstance(A_				, A_	) else bos_token
											_SCREAMING_SNAKE_CASE      = AddedToken(A_				, lstrip=A_				, rstrip=A_	) if isinstance(A_				, A_	) else eos_token
											_SCREAMING_SNAKE_CASE      = AddedToken(A_				, lstrip=A_				, rstrip=A_	) if isinstance(A_				, A_	) else sep_token
											_SCREAMING_SNAKE_CASE      = AddedToken(A_				, lstrip=A_				, rstrip=A_	) if isinstance(A_				, A_	) else cls_token
											_SCREAMING_SNAKE_CASE      = AddedToken(A_				, lstrip=A_				, rstrip=A_	) if isinstance(A_				, A_	) else unk_token
											_SCREAMING_SNAKE_CASE      = AddedToken(A_				, lstrip=A_				, rstrip=A_	) if isinstance(A_				, A_	) else pad_token

											# Mask token behave like a normal word, i.e. include the space before it
											_SCREAMING_SNAKE_CASE      = AddedToken(A_				, lstrip=A_				, rstrip=A_	) if isinstance(A_				, A_	) else mask_token

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

											with open(A_				, encoding='utf-8'	) as vocab_handle:
															_SCREAMING_SNAKE_CASE      = json.load(A_	)
											_SCREAMING_SNAKE_CASE      = {v: k for k, v in self.encoder.items()}
											_SCREAMING_SNAKE_CASE      = errors  # how to handle errors in decoding
											_SCREAMING_SNAKE_CASE      = bytes_to_unicode()
											_SCREAMING_SNAKE_CASE      = {v: k for k, v in self.byte_encoder.items()}
											with open(A_				, encoding='utf-8'	) as merges_handle:
															_SCREAMING_SNAKE_CASE      = merges_handle.read().split('\n'	)[1:-1]
											_SCREAMING_SNAKE_CASE      = [tuple(merge.split()	) for merge in bpe_merges]
											_SCREAMING_SNAKE_CASE      = dict(zip(A_				, range(len(A_	)	)	)	)
											_SCREAMING_SNAKE_CASE      = {}
											_SCREAMING_SNAKE_CASE      = add_prefix_space

											# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
											_SCREAMING_SNAKE_CASE      = re.compile(r'\'s|\'t|\'re|\'ve|\'m|\'ll|\'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+'	)





							@property
							# Copied from transformers.models.gpt2.tokenization_gpt2.GPT2Tokenizer.vocab_size
							def 		__magic_name__			(    self	)->	int:
											return len(self.encoder	)





							def 		__magic_name__			(    self	)->	Tuple:
											return dict(self.encoder				, **self.added_tokens_encoder	)





							def 		__magic_name__			(    self				, A_	)->	Optional[int]:
											if token in self.cache:
															return self.cache[token]
											_SCREAMING_SNAKE_CASE      = tuple(A_	)
											_SCREAMING_SNAKE_CASE      = get_pairs(A_	)

											if not pairs:
															return token

											while True:
															_SCREAMING_SNAKE_CASE      = min(A_				, key=lambda A_	: self.bpe_ranks.get(A_				, float('inf'	)	)	)
															if bigram not in self.bpe_ranks:
																			break
															_SCREAMING_SNAKE_CASE      ,      _SCREAMING_SNAKE_CASE      = bigram
															_SCREAMING_SNAKE_CASE      = []
															_SCREAMING_SNAKE_CASE      = 0
															while i < len(A_	):
																			try:
																							_SCREAMING_SNAKE_CASE      = word.index(A_				, A_	)
																			except ValueError:
																							new_word.extend(word[i:]	)
																							break
																			else:
																							new_word.extend(word[i:j]	)
																							_SCREAMING_SNAKE_CASE      = j

																			if word[i] == first and i < len(A_	) - 1 and word[i + 1] == second:
																							new_word.append(first + second	)
																							i += 2
																			else:
																							new_word.append(word[i]	)
																							i += 1
															_SCREAMING_SNAKE_CASE      = tuple(A_	)
															_SCREAMING_SNAKE_CASE      = new_word
															if len(A_	) == 1:
																			break
															else:
																			_SCREAMING_SNAKE_CASE      = get_pairs(A_	)
											_SCREAMING_SNAKE_CASE      = ' '.join(A_	)
											_SCREAMING_SNAKE_CASE      = word
											return word





							def 		__magic_name__			(    self				, A_				, A_ = None	)->	List[int]:
											if token_ids_a is None:
															return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
											_SCREAMING_SNAKE_CASE      = [self.cls_token_id]
											_SCREAMING_SNAKE_CASE      = [self.sep_token_id]
											return cls + token_ids_a + sep + token_ids_a + sep





							def 		__magic_name__			(    self				, A_				, A_ = None				, A_ = False	)->	List[int]:
											if already_has_special_tokens:
															return super().get_special_tokens_mask(
															    token_ids_a=A_				, token_ids_a=A_				, already_has_special_tokens=A_	)

											if token_ids_a is None:
															return [1] + ([0] * len(A_	)) + [1]
											return [1] + ([0] * len(A_	)) + [1] + ([0] * len(A_	)) + [1]





							def 		__magic_name__			(    self				, A_				, A_ = None	)->	List[int]:
											_SCREAMING_SNAKE_CASE      = [self.sep_token_id]
											_SCREAMING_SNAKE_CASE      = [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 		__magic_name__			(    self				, A_	)->	Dict:
											_SCREAMING_SNAKE_CASE      = []
											for token in re.findall(self.pat				, A_	):
															_SCREAMING_SNAKE_CASE      = ''.join(
															    self.byte_encoder[b] for b in token.encode('utf-8'	)	)  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
															bpe_tokens.extend(bpe_token for bpe_token in self.bpe(A_	).split(' '	)	)
											return bpe_tokens





							def 		__magic_name__			(    self				, A_	)->	Optional[Any]:
											return self.encoder.get(A_				, self.encoder.get(self.unk_token	)	)





							def 		__magic_name__			(    self				, A_	)->	Dict:
											return self.decoder.get(A_	)





							def 		__magic_name__			(    self				, A_	)->	Dict:
											_SCREAMING_SNAKE_CASE      = ''.join(A_	)
											_SCREAMING_SNAKE_CASE      = bytearray([self.byte_decoder[c] for c in text]	).decode('utf-8'				, errors=self.errors	)
											return text





							def 		__magic_name__			(    self				, A_				, A_ = None	)->	Tuple[str]:
											if not os.path.isdir(A_	):
															logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''	)
															return
											_SCREAMING_SNAKE_CASE      = os.path.join(
											    A_				, (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']	)
											_SCREAMING_SNAKE_CASE      = os.path.join(
											    A_				, (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['merges_file']	)

											with open(A_				, 'w'				, encoding='utf-8'	) as f:
															f.write(json.dumps(self.encoder				, indent=2				, sort_keys=A_				, ensure_ascii=A_	) + '\n'	)

											_SCREAMING_SNAKE_CASE      = 0
											with open(A_				, 'w'				, encoding='utf-8'	) as writer:
															writer.write('#version: 0.2\n'	)
															for bpe_tokens, token_index in sorted(self.bpe_ranks.items()				, key=lambda A_	: kv[1]	):
																			if index != token_index:
																							logger.warning(
																							    F'''Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.'''
																							    ' Please check that the tokenizer is not corrupted!'	)
																							_SCREAMING_SNAKE_CASE      = token_index
																			writer.write(' '.join(A_	) + '\n'	)
																			index += 1

											return vocab_file, merge_file





							def 		__magic_name__			(    self				, A_				, A_=False				, **A_	)->	str:
											_SCREAMING_SNAKE_CASE      = kwargs.pop('add_prefix_space'				, self.add_prefix_space	)
											if (is_split_into_words or add_prefix_space) and (len(A_	) > 0 and not text[0].isspace()):
															_SCREAMING_SNAKE_CASE      = ' ' + text
											return (text, kwargs)





							def 		__magic_name__			(    self				, A_	)->	List[int]:
											_SCREAMING_SNAKE_CASE      = []
											for is_user, text in conversation.iter_texts():
															input_ids.extend(self.encode(A_				, add_special_tokens=A_	) + [self.eos_token_id]	)
											if len(A_	) > self.model_max_length:
															_SCREAMING_SNAKE_CASE      = input_ids[-self.model_max_length :]
											return input_ids
