




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
			UpperCamelCase__		=						'platform'

			import jax
			import jax.numpy as jnp

			from transformers import FlaxOPTForCausalLM, FlaxOPTModel, GPTaTokenizer






def  snake_case (							A						,       A						,       A=None						,       A=None      )							->							Optional[Any]:




	'''simple docstring'''


	if attention_mask is None:
		lowerCamelCase_							:		Tuple							       =							np.where(input_ids != config.pad_token_id						,       1						,       0      )
	return {
	    "input_ids": input_ids,
	    "attention_mask": attention_mask,
	}


@require_flax
class     __SCREAMING_SNAKE_CASE							:

					def __init__(    self			,	__lowercase			,	__lowercase=13			,	__lowercase=7			,	__lowercase=True			,	__lowercase=False			,	__lowercase=99			,	__lowercase=16			,	__lowercase=2			,	__lowercase=4			,	__lowercase=4			,	__lowercase="gelu"			,	__lowercase=0.1			,	__lowercase=0.1			,	__lowercase=20			,	__lowercase=2			,	__lowercase=1			,	__lowercase=0			,	__lowercase=16			,	__lowercase=16			,	__lowercase=0.02			,	):







						'''simple docstring'''




						lowerCamelCase_							:		List[Any]							       =							parent
						lowerCamelCase_							:		Tuple							       =							batch_size
						lowerCamelCase_							:		Optional[int]							       =							seq_length
						lowerCamelCase_							:		Any							       =							is_training
						lowerCamelCase_							:		Any							       =							use_labels
						lowerCamelCase_							:		Any							       =							vocab_size
						lowerCamelCase_							:		List[str]							       =							hidden_size
						lowerCamelCase_							:		Optional[Any]							       =							num_hidden_layers
						lowerCamelCase_							:		List[Any]							       =							num_attention_heads
						lowerCamelCase_							:		Tuple							       =							intermediate_size
						lowerCamelCase_							:		Union[str, Any]							       =							hidden_act
						lowerCamelCase_							:		Any							       =							hidden_dropout_prob
						lowerCamelCase_							:		Optional[int]							       =							attention_probs_dropout_prob
						lowerCamelCase_							:		Optional[int]							       =							max_position_embeddings
						lowerCamelCase_							:		Any							       =							eos_token_id
						lowerCamelCase_							:		Optional[Any]							       =							pad_token_id
						lowerCamelCase_							:		Optional[Any]							       =							bos_token_id
						lowerCamelCase_							:		Union[str, Any]							       =							embed_dim
						lowerCamelCase_							:		Dict							       =							word_embed_proj_dim
						lowerCamelCase_							:		Any							       =							initializer_range
						lowerCamelCase_							:		Any							       =							False

					def      lowercase_							(    self ):







						'''simple docstring'''




						lowerCamelCase_							:		str							       =							np.clip(ids_tensor([self.batch_size, self.seq_length - 1]			,	self.vocab_size )			,	3			,	self.vocab_size )
						lowerCamelCase_							:		int							       =							np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)			,	dtype=np.intaa ))			,	-1 )

						lowerCamelCase_							:		Union[str, Any]							       =							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=UpperCamelCase__			,	word_embed_proj_dim=self.word_embed_proj_dim			,	initializer_range=self.initializer_range			,	use_cache=UpperCamelCase__			,	)
						lowerCamelCase_							:		Union[str, Any]							       =							prepare_opt_inputs_dict(UpperCamelCase__			,	UpperCamelCase__ )
						return config, inputs_dict

					def      lowercase_							(    self ):







						'''simple docstring'''




						lowerCamelCase_							:		Union[str, Any]							       =							self.prepare_config_and_inputs()
						return config, inputs_dict

					def      lowercase_							(    self			,	__lowercase			,	__lowercase			,	__lowercase ):







						'''simple docstring'''




						lowerCamelCase_							:		Tuple							       =							20
						lowerCamelCase_							:		Optional[int]							       =							model_class_name(UpperCamelCase__ )

						lowerCamelCase_							:		Union[str, Any]							       =							inputs_dict["input_ids"]
						lowerCamelCase_							:		Tuple							       =							inputs_dict["attention_mask"]

						lowerCamelCase_							:		List[Any]							       =							model.init_cache(input_ids.shape[0]			,	UpperCamelCase__ )
						lowerCamelCase_							:		str							       =							jnp.ones((input_ids.shape[0], max_length)			,	dtype='i4' )

						lowerCamelCase_							:		Optional[Any]							       =							jnp.broadcast_to(
						    jnp.arange(input_ids.shape[-1] - 1 )[None, :]			,	(input_ids.shape[0], input_ids.shape[-1] - 1)			,	)
						lowerCamelCase_							:		int							       =							model(
						    input_ids[:, :-1]			,	attention_mask=UpperCamelCase__			,	past_key_values=UpperCamelCase__			,	position_ids=UpperCamelCase__			,	)

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

						lowerCamelCase_							:		Tuple							       =							model(UpperCamelCase__ )

						lowerCamelCase_							:		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}" )





					def      lowercase_							(    self			,	__lowercase			,	__lowercase			,	__lowercase ):







						'''simple docstring'''




						lowerCamelCase_							:		Union[str, Any]							       =							20
						lowerCamelCase_							:		Optional[Any]							       =							model_class_name(UpperCamelCase__ )

						lowerCamelCase_							:		List[str]							       =							(
						    inputs_dict["input_ids"],
						    inputs_dict["attention_mask"],
						)

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

						lowerCamelCase_							:		Dict							       =							model.init_cache(input_ids.shape[0]			,	UpperCamelCase__ )
						lowerCamelCase_							:		Optional[Any]							       =							jnp.broadcast_to(
						    jnp.arange(input_ids.shape[-1] - 1 )[None, :]			,	(input_ids.shape[0], input_ids.shape[-1] - 1)			,	)

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

						lowerCamelCase_							:		Dict							       =							model(UpperCamelCase__			,	attention_mask=UpperCamelCase__ )

						lowerCamelCase_							:		List[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     __SCREAMING_SNAKE_CASE							(		lowerCAmelCase__       ,					unittest.TestCase       ,					lowerCAmelCase__  ):
					_lowerCamelCase		  =			(FlaxOPTModel, FlaxOPTForCausalLM) if is_flax_available() else ()
					_lowerCamelCase		  =			() if is_flax_available() else ()

					def      lowercase_							(    self ):







						'''simple docstring'''




						lowerCamelCase_							:		Optional[Any]							       =							FlaxOPTModelTester(self )

					def      lowercase_							(    self ):







						'''simple docstring'''




						lowerCamelCase_							:		str							       =							self.model_tester.prepare_config_and_inputs()
						for model_class in self.all_model_classes:
							self.model_tester.check_use_cache_forward(UpperCamelCase__			,	UpperCamelCase__			,	UpperCamelCase__ )

					def      lowercase_							(    self ):







						'''simple docstring'''




						lowerCamelCase_							:		Any							       =							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(UpperCamelCase__			,	UpperCamelCase__			,	UpperCamelCase__ )





					@slow
					def      lowercase_							(    self ):







						'''simple docstring'''




						for model_class_name in self.all_model_classes:
							lowerCamelCase_							:		str							       =							model_class_name.from_pretrained('facebook/opt-125m' )
							lowerCamelCase_							:		Dict							       =							np.ones((1, 1) ) * model.config.eos_token_id
							lowerCamelCase_							:		Union[str, Any]							       =							model(UpperCamelCase__ )
							self.assertIsNotNone(UpperCamelCase__ )


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

					@slow
					def      lowercase_							(    self ):







						'''simple docstring'''




						lowerCamelCase_							:		List[Any]							       =							FlaxOPTModel.from_pretrained('facebook/opt-350m' )
						lowerCamelCase_							:		Dict							       =							jnp.array([[0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]] )
						lowerCamelCase_							:		Tuple							       =							model(input_ids=UpperCamelCase__ ).last_hidden_state
						lowerCamelCase_							:		List[str]							       =							(1, 11, 512)
						self.assertEqual(output.shape			,	UpperCamelCase__ )
						lowerCamelCase_							:		Optional[Any]							       =							jnp.array(
						    [[-0.28_67, -1.92_56, -0.30_62], [-1.27_11, -0.13_37, -0.18_97], [0.41_09, 0.11_87, -1.31_42]] )
						self.assertTrue(jnp.allclose(output[:, :3, :3]			,	UpperCamelCase__			,	atol=4E-2 ) )


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

					def      lowercase_							(    self ):







						'''simple docstring'''




						super().setUp()
						lowerCamelCase_							:		Dict							       =							"facebook/opt-350m"





					def      lowercase_							(    self ):







						'''simple docstring'''




						lowerCamelCase_							:		int							       =							FlaxOPTForCausalLM.from_pretrained(self.path_model )
						lowerCamelCase_							:		Optional[Any]							       =							GPTaTokenizer.from_pretrained(self.path_model )

						lowerCamelCase_							:		Optional[Any]							       =							[
						    "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
						lowerCamelCase_							:		Dict							       =							tokenizer(UpperCamelCase__			,	return_tensors='jax'			,	padding=UpperCamelCase__			,	add_special_tokens=UpperCamelCase__ )
						lowerCamelCase_							:		int							       =							model(inputs.input_ids			,	attention_mask=inputs.attention_mask )[0].mean(axis=-1 )
						lowerCamelCase_							:		Any							       =							jnp.array(
						    [
						        [1.38_51, -13.89_23, -10.52_29, -10.75_33, -0.23_09, -10.23_84, -0.53_65, -9.09_47, -5.16_70],
						        [-4.70_73, -10.62_76, -3.94_15, -21.52_42, -0.28_22, -0.28_22, -0.28_22, -0.28_22, -0.28_22],
						        [0.62_47, -3.42_29, -8.91_79, -1.42_97, -14.16_50, 1.41_46, -9.02_18, -0.27_03, -0.27_03],
						        [6.47_83, -1.99_13, -10.79_26, -2.33_36, 1.50_92, -0.99_74, -6.82_13, 1.34_77, 1.34_77],
						    ] )
						self.assertTrue(jnp.allclose(UpperCamelCase__			,	UpperCamelCase__			,	atol=4E-2 ) )

						lowerCamelCase_							:		Dict							       =							jax.jit(UpperCamelCase__ )
						lowerCamelCase_							:		Tuple							       =							model(inputs.input_ids			,	attention_mask=inputs.attention_mask )[0].mean(axis=-1 )
						self.assertTrue(jnp.allclose(UpperCamelCase__			,	UpperCamelCase__			,	atol=4E-2 ) )


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

					@property
					def      lowercase_							(    self ):







						'''simple docstring'''




						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 ):







						'''simple docstring'''




						lowerCamelCase_							:		Any							       =							"facebook/opt-125m"

						lowerCamelCase_							:		List[Any]							       =							[
						    "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",
						]

						lowerCamelCase_							:		Optional[int]							       =							[]

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

						for prompt in self.prompts:
							lowerCamelCase_							:		Optional[Any]							       =							tokenizer(UpperCamelCase__			,	return_tensors='jax' ).input_ids

							lowerCamelCase_							:		Union[str, Any]							       =							model.generate(UpperCamelCase__			,	max_length=10 )
							lowerCamelCase_							:		Optional[int]							       =							generated_ids[0]

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

						self.assertListEqual(UpperCamelCase__			,	UpperCamelCase__ )

					def      lowercase_							(    self ):







						'''simple docstring'''




						lowerCamelCase_							:		Dict							       =							"facebook/opt-350m"

						lowerCamelCase_							:		List[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",
						]

						lowerCamelCase_							:		Optional[int]							       =							[]
						lowerCamelCase_							:		Any							       =							FlaxOPTForCausalLM.from_pretrained(UpperCamelCase__ )
						lowerCamelCase_							:		List[Any]							       =							GPTaTokenizer.from_pretrained(UpperCamelCase__ )

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

							lowerCamelCase_							:		Optional[int]							       =							model.generate(UpperCamelCase__			,	max_length=10 )
							lowerCamelCase_							:		List[str]							       =							generated_ids[0]

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

						self.assertListEqual(UpperCamelCase__			,	UpperCamelCase__ )

					def      lowercase_							(    self ):







						'''simple docstring'''




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

						lowerCamelCase_							:		str							       =							jax.jit(model.generate )

						lowerCamelCase_							:		List[Any]							       =							jit_generate(inputs['input_ids']			,	attention_mask=inputs['attention_mask'] ).sequences

						lowerCamelCase_							:		Dict							       =							tokenizer.batch_decode(UpperCamelCase__			,	skip_special_tokens=UpperCamelCase__ )

						self.assertIsNotNone(UpperCamelCase__			,	UpperCamelCase__ )





					def      lowercase_							(    self ):







						'''simple docstring'''




						lowerCamelCase_							:		int							       =							"facebook/opt-350m"

						lowerCamelCase_							:		Dict							       =							GPTaTokenizer.from_pretrained(UpperCamelCase__ )
						lowerCamelCase_							:		Optional[int]							       =							FlaxOPTForCausalLM.from_pretrained(UpperCamelCase__ )

						lowerCamelCase_							:		Any							       =							"left"

						# use different length sentences to test batching
						lowerCamelCase_							:		Union[str, Any]							       =							[
						    "Hello, my dog is a little",
						    "Today, I",
						]

						lowerCamelCase_							:		str							       =							tokenizer(UpperCamelCase__			,	return_tensors='jax'			,	padding=UpperCamelCase__ )
						lowerCamelCase_							:		Dict							       =							inputs["input_ids"]

						lowerCamelCase_							:		Union[str, Any]							       =							model.generate(input_ids=UpperCamelCase__			,	attention_mask=inputs['attention_mask']			,	trace=UpperCamelCase__ )

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

						lowerCamelCase_							:		Union[str, Any]							       =							inputs_non_padded.shape[-1] - inputs["attention_mask"][-1].sum()
						lowerCamelCase_							:		int							       =							tokenizer(sentences[1]			,	return_tensors='jax' ).input_ids
						lowerCamelCase_							:		Optional[int]							       =							model.generate(input_ids=UpperCamelCase__			,	max_length=model.config.max_length - num_paddings )

						lowerCamelCase_							:		str							       =							tokenizer.batch_decode(outputs[0]			,	skip_special_tokens=UpperCamelCase__ )
						lowerCamelCase_							:		Union[str, Any]							       =							tokenizer.decode(output_non_padded[0][0]			,	skip_special_tokens=UpperCamelCase__ )
						lowerCamelCase_							:		List[Any]							       =							tokenizer.decode(output_padded[0][0]			,	skip_special_tokens=UpperCamelCase__ )

						lowerCamelCase_							:		Tuple							       =							[
						    "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(UpperCamelCase__			,	UpperCamelCase__ )
						self.assertListEqual(UpperCamelCase__			,	[non_padded_sentence, padded_sentence] )






import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple

import regex as re

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


UpperCamelCase__		=						logging.get_logger(__name__)

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

UpperCamelCase__		=						{
    'vocab_file': {
        'roberta-base': 'https://huggingface.co/roberta-base/resolve/main/vocab.json',
        'roberta-large': 'https://huggingface.co/roberta-large/resolve/main/vocab.json',
        'roberta-large-mnli': 'https://huggingface.co/roberta-large-mnli/resolve/main/vocab.json',
        'distilroberta-base': 'https://huggingface.co/distilroberta-base/resolve/main/vocab.json',
        'roberta-base-openai-detector': 'https://huggingface.co/roberta-base-openai-detector/resolve/main/vocab.json',
        'roberta-large-openai-detector': (
            'https://huggingface.co/roberta-large-openai-detector/resolve/main/vocab.json'
        ),
    },
    'merges_file': {
        'roberta-base': 'https://huggingface.co/roberta-base/resolve/main/merges.txt',
        'roberta-large': 'https://huggingface.co/roberta-large/resolve/main/merges.txt',
        'roberta-large-mnli': 'https://huggingface.co/roberta-large-mnli/resolve/main/merges.txt',
        'distilroberta-base': 'https://huggingface.co/distilroberta-base/resolve/main/merges.txt',
        'roberta-base-openai-detector': 'https://huggingface.co/roberta-base-openai-detector/resolve/main/merges.txt',
        'roberta-large-openai-detector': (
            'https://huggingface.co/roberta-large-openai-detector/resolve/main/merges.txt'
        ),
    },
}

UpperCamelCase__		=						{
    'roberta-base': 5_1_2,
    'roberta-large': 5_1_2,
    'roberta-large-mnli': 5_1_2,
    'distilroberta-base': 5_1_2,
    'roberta-base-openai-detector': 5_1_2,
    'roberta-large-openai-detector': 5_1_2,
}






@lru_cache()
def  snake_case (							)							->							List[Any]:




	'''simple docstring'''


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






def  snake_case (							A      )							->							int:




	'''simple docstring'''


	lowerCamelCase_							:		Union[str, Any]							       =							set()
	lowerCamelCase_							:		Optional[int]							       =							word[0]
	for char in word[1:]:
		pairs.add((prev_char, char)      )
		lowerCamelCase_							:		Optional[Any]							       =							char
	return pairs



class     __SCREAMING_SNAKE_CASE							(		lowerCamelCase  ):
					_lowerCamelCase		  =			VOCAB_FILES_NAMES
					_lowerCamelCase		  =			PRETRAINED_VOCAB_FILES_MAP
					_lowerCamelCase		  =			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					_lowerCamelCase		  =			["""input_ids""", """attention_mask"""]

					def __init__(    self			,	__lowercase			,	__lowercase			,	__lowercase="replace"			,	__lowercase="<s>"			,	__lowercase="</s>"			,	__lowercase="</s>"			,	__lowercase="<s>"			,	__lowercase="<unk>"			,	__lowercase="<pad>"			,	__lowercase="<mask>"			,	__lowercase=False			,	**__lowercase			,	):







						'''simple docstring'''




						lowerCamelCase_							:		Optional[int]							       =							AddedToken(__lowercase			,	lstrip=__lowercase			,	rstrip=__lowercase ) if isinstance(__lowercase			,	__lowercase ) else bos_token
						lowerCamelCase_							:		Dict							       =							AddedToken(__lowercase			,	lstrip=__lowercase			,	rstrip=__lowercase ) if isinstance(__lowercase			,	__lowercase ) else eos_token
						lowerCamelCase_							:		Union[str, Any]							       =							AddedToken(__lowercase			,	lstrip=__lowercase			,	rstrip=__lowercase ) if isinstance(__lowercase			,	__lowercase ) else sep_token
						lowerCamelCase_							:		Union[str, Any]							       =							AddedToken(__lowercase			,	lstrip=__lowercase			,	rstrip=__lowercase ) if isinstance(__lowercase			,	__lowercase ) else cls_token
						lowerCamelCase_							:		Any							       =							AddedToken(__lowercase			,	lstrip=__lowercase			,	rstrip=__lowercase ) if isinstance(__lowercase			,	__lowercase ) else unk_token
						lowerCamelCase_							:		Tuple							       =							AddedToken(__lowercase			,	lstrip=__lowercase			,	rstrip=__lowercase ) if isinstance(__lowercase			,	__lowercase ) else pad_token

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

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

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

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

					@property
					def      lowercase_							(    self ):







						'''simple docstring'''




						return len(self.encoder )

					def      lowercase_							(    self ):







						'''simple docstring'''




						return dict(self.encoder			,	**self.added_tokens_encoder )

					def      lowercase_							(    self			,	__lowercase ):







						'''simple docstring'''




						if token in self.cache:
							return self.cache[token]
						lowerCamelCase_							:		Any							       =							tuple(__lowercase )
						lowerCamelCase_							:		Tuple							       =							get_pairs(__lowercase )

						if not pairs:
							return token

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

								if word[i] == first and i < len(__lowercase ) - 1 and word[i + 1] == second:
									new_word.append(first + second )
									i += 2
								else:
									new_word.append(word[i] )
									i += 1
							lowerCamelCase_							:		List[Any]							       =							tuple(__lowercase )
							lowerCamelCase_							:		Optional[Any]							       =							new_word
							if len(__lowercase ) == 1:
								break
							else:
								lowerCamelCase_							:		Optional[Any]							       =							get_pairs(__lowercase )
						lowerCamelCase_							:		Optional[int]							       =							' '.join(__lowercase )
						lowerCamelCase_							:		Union[str, Any]							       =							word
						return word

					def      lowercase_							(    self			,	__lowercase ):







						'''simple docstring'''




						lowerCamelCase_							:		Union[str, Any]							       =							[]
						for token in re.findall(self.pat			,	__lowercase ):
							lowerCamelCase_							:		int							       =							''.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(__lowercase ).split(' ' ) )
						return bpe_tokens

					def      lowercase_							(    self			,	__lowercase ):







						'''simple docstring'''




						return self.encoder.get(__lowercase			,	self.encoder.get(self.unk_token ) )

					def      lowercase_							(    self			,	__lowercase ):







						'''simple docstring'''




						return self.decoder.get(__lowercase )

					def      lowercase_							(    self			,	__lowercase ):







						'''simple docstring'''




						lowerCamelCase_							:		int							       =							''.join(__lowercase )
						lowerCamelCase_							:		Tuple							       =							bytearray([self.byte_decoder[c] for c in text] ).decode('utf-8'			,	errors=self.errors )
						return text

					def      lowercase_							(    self			,	__lowercase			,	__lowercase = None ):







						'''simple docstring'''




						if not os.path.isdir(__lowercase ):
							logger.error(f"Vocabulary path ({save_directory}) should be a directory" )
							return
						lowerCamelCase_							:		List[str]							       =							os.path.join(
						    __lowercase			,	(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'] )
						lowerCamelCase_							:		Union[str, Any]							       =							os.path.join(
						    __lowercase			,	(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['merges_file'] )

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

						lowerCamelCase_							:		Dict							       =							0
						with open(__lowercase			,	'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 __lowercase : 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!' )
									lowerCamelCase_							:		Tuple							       =							token_index
								writer.write(' '.join(__lowercase ) + '\n' )
								index += 1

						return vocab_file, merge_file

					def      lowercase_							(    self			,	__lowercase			,	__lowercase = None ):







						'''simple docstring'''




						if token_ids_a is None:
							return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
						lowerCamelCase_							:		Optional[Any]							       =							[self.cls_token_id]
						lowerCamelCase_							:		Optional[Any]							       =							[self.sep_token_id]
						return cls + token_ids_a + sep + sep + token_ids_a + sep

					def      lowercase_							(    self			,	__lowercase			,	__lowercase = None			,	__lowercase = False ):







						'''simple docstring'''




						if already_has_special_tokens:
							return super().get_special_tokens_mask(
							    token_ids_a=__lowercase			,	token_ids_a=__lowercase			,	already_has_special_tokens=__lowercase )

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

					def      lowercase_							(    self			,	__lowercase			,	__lowercase = None ):







						'''simple docstring'''




						lowerCamelCase_							:		List[Any]							       =							[self.sep_token_id]
						lowerCamelCase_							:		Any							       =							[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      lowercase_							(    self			,	__lowercase			,	__lowercase=False			,	**__lowercase ):







						'''simple docstring'''




						lowerCamelCase_							:		Any							       =							kwargs.pop('add_prefix_space'			,	self.add_prefix_space )
						if (is_split_into_words or add_prefix_space) and (len(__lowercase ) > 0 and not text[0].isspace()):
							lowerCamelCase_							:		List[Any]							       =							' ' + text
						return (text, kwargs)
