'''simple docstring'''




import tempfile
import unittest

import numpy as np

import transformers
from transformers import GPTaTokenizer, GPTNeoConfig, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, slow

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


if is_flax_available():
						import jax
						import jax.numpy as jnp

						from transformers.modeling_flax_pytorch_utils import (
						    convert_pytorch_state_dict_to_flax,
						    load_flax_weights_in_pytorch_model,
						)
						from transformers.models.gpt_neo.modeling_flax_gpt_neo import FlaxGPTNeoForCausalLM, FlaxGPTNeoModel

if is_torch_available():
						import torch






class     a__:


			'''simple docstring'''






			def __init__(       self	,   __lowerCAmelCase	,   __lowerCAmelCase=14	,   __lowerCAmelCase=7	,   __lowerCAmelCase=True	,   __lowerCAmelCase=True	,   __lowerCAmelCase=False	,   __lowerCAmelCase=True	,   __lowerCAmelCase=99	,   __lowerCAmelCase=32	,   __lowerCAmelCase=4	,   __lowerCAmelCase=4	,   __lowerCAmelCase=[[["global", "local"], 2]]	,   __lowerCAmelCase=37	,   __lowerCAmelCase="gelu"	,   __lowerCAmelCase=0.1	,   __lowerCAmelCase=0.1	,   __lowerCAmelCase=512	,   __lowerCAmelCase=7	,   __lowerCAmelCase=0.02	,   ):




					"""simple docstring"""


					lowerCAmelCase        =  parent
					lowerCAmelCase        =  batch_size
					lowerCAmelCase        =  seq_length
					lowerCAmelCase        =  is_training
					lowerCAmelCase        =  use_input_mask
					lowerCAmelCase        =  use_token_type_ids
					lowerCAmelCase        =  use_labels
					lowerCAmelCase        =  vocab_size
					lowerCAmelCase        =  hidden_size
					lowerCAmelCase        =  num_hidden_layers
					lowerCAmelCase        =  num_attention_heads
					lowerCAmelCase        =  attention_types
					lowerCAmelCase        =  intermediate_size
					lowerCAmelCase        =  hidden_act
					lowerCAmelCase        =  hidden_dropout_prob
					lowerCAmelCase        =  attention_probs_dropout_prob
					lowerCAmelCase        =  max_position_embeddings
					lowerCAmelCase        =  window_size
					lowerCAmelCase        =  initializer_range
					lowerCAmelCase        =  None
					lowerCAmelCase        =  vocab_size - 1
					lowerCAmelCase        =  vocab_size - 1
					lowerCAmelCase        =  vocab_size - 1



			def 							a_	(       self):




					"""simple docstring"""


					lowerCAmelCase        =  ids_tensor([self.batch_size, self.seq_length]	,   self.vocab_size)

					lowerCAmelCase        =  None
					if self.use_input_mask:
							lowerCAmelCase        =  random_attention_mask([self.batch_size, self.seq_length])

					lowerCAmelCase        =  GPTNeoConfig(
					    vocab_size=self.vocab_size	,   hidden_size=self.hidden_size	,   num_layers=self.num_hidden_layers	,   num_heads=self.num_attention_heads	,   max_position_embeddings=self.max_position_embeddings	,   use_cache=__lowerCAmelCase	,   bos_token_id=self.bos_token_id	,   eos_token_id=self.eos_token_id	,   pad_token_id=self.pad_token_id	,   window_size=self.window_size	,   attention_types=self.attention_types	,   )

					return (config, input_ids, input_mask)



			def 							a_	(       self):




					"""simple docstring"""


					lowerCAmelCase        =  self.prepare_config_and_inputs()
					lowerCAmelCase ,					lowerCAmelCase ,					lowerCAmelCase        =  config_and_inputs
					lowerCAmelCase        =  {"""input_ids""": input_ids, """attention_mask""": attention_mask}
					return config, inputs_dict



			def 							a_	(       self	,   __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase):




					"""simple docstring"""


					lowerCAmelCase        =  20
					lowerCAmelCase        =  model_class_name(__lowerCAmelCase)

					lowerCAmelCase        =  model.init_cache(input_ids.shape[0]	,   __lowerCAmelCase)
					lowerCAmelCase        =  jnp.ones((input_ids.shape[0], max_decoder_length)	,   dtype="""i4""")

					lowerCAmelCase        =  jnp.broadcast_to(
					    jnp.arange(input_ids.shape[-1] - 1)[None, :]	,   (input_ids.shape[0], input_ids.shape[-1] - 1))
					lowerCAmelCase        =  model(
					    input_ids[:, :-1]	,   attention_mask=__lowerCAmelCase	,   past_key_values=__lowerCAmelCase	,   position_ids=__lowerCAmelCase	,   )

					lowerCAmelCase        =  jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]	,   dtype="""i4""")
					lowerCAmelCase        =  model(
					    input_ids[:, -1:]	,   attention_mask=__lowerCAmelCase	,   past_key_values=outputs_cache.past_key_values	,   position_ids=__lowerCAmelCase	,   )

					lowerCAmelCase        =  model(__lowerCAmelCase)

					lowerCAmelCase        =  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 							a_	(       self	,   __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase):




					"""simple docstring"""


					lowerCAmelCase        =  20
					lowerCAmelCase        =  model_class_name(__lowerCAmelCase)

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

					lowerCAmelCase        =  model.init_cache(input_ids.shape[0]	,   __lowerCAmelCase)
					lowerCAmelCase        =  jnp.broadcast_to(
					    jnp.arange(input_ids.shape[-1] - 1)[None, :]	,   (input_ids.shape[0], input_ids.shape[-1] - 1))

					lowerCAmelCase        =  model(
					    input_ids[:, :-1]	,   attention_mask=__lowerCAmelCase	,   past_key_values=__lowerCAmelCase	,   position_ids=__lowerCAmelCase	,   )
					lowerCAmelCase        =  jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]	,   dtype="""i4""")
					lowerCAmelCase        =  model(
					    input_ids[:, -1:]	,   past_key_values=outputs_cache.past_key_values	,   attention_mask=__lowerCAmelCase	,   position_ids=__lowerCAmelCase	,   )

					lowerCAmelCase        =  model(__lowerCAmelCase	,   attention_mask=__lowerCAmelCase)

					lowerCAmelCase        =  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     a__( lowerCAmelCase__			,						lowerCAmelCase__			,						unittest.TestCase     ):


			'''simple docstring'''






			UpperCAmelCase_      :					str								=	(FlaxGPTNeoModel, FlaxGPTNeoForCausalLM) if is_flax_available() else ()
			UpperCAmelCase_      :					Optional[Any]								=	(FlaxGPTNeoForCausalLM,) if is_flax_available() else ()



			def 							a_	(       self):




					"""simple docstring"""


					lowerCAmelCase        =  FlaxGPTNeoModelTester(self)



			def 							a_	(       self):




					"""simple docstring"""


					for model_class_name in self.all_model_classes:
							lowerCAmelCase ,					lowerCAmelCase ,					lowerCAmelCase        =  self.model_tester.prepare_config_and_inputs()
							self.model_tester.check_use_cache_forward(__lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase)



			def 							a_	(       self):




					"""simple docstring"""


					for model_class_name in self.all_model_classes:
							lowerCAmelCase ,					lowerCAmelCase ,					lowerCAmelCase        =  self.model_tester.prepare_config_and_inputs()
							self.model_tester.check_use_cache_forward_with_attn_mask(
							    __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase)



			@slow
			def 							a_	(       self):




					"""simple docstring"""


					lowerCAmelCase        =  GPTaTokenizer.from_pretrained("""gpt2"""	,   pad_token="""<|endoftext|>"""	,   padding_side="""left""")
					lowerCAmelCase        =  tokenizer(["""Hello this is a long string""", """Hey"""]	,   return_tensors="""np"""	,   padding=__lowerCAmelCase	,   truncation=__lowerCAmelCase)

					lowerCAmelCase        =  FlaxGPTNeoForCausalLM.from_pretrained("""EleutherAI/gpt-neo-125M""")
					lowerCAmelCase        =  False
					lowerCAmelCase        =  model.config.eos_token_id

					lowerCAmelCase        =  jax.jit(model.generate)

					lowerCAmelCase        =  jit_generate(
					    inputs["""input_ids"""]	,   attention_mask=inputs["""attention_mask"""]	,   pad_token_id=tokenizer.pad_token_id).sequences

					lowerCAmelCase        =  tokenizer.batch_decode(__lowerCAmelCase	,   skip_special_tokens=__lowerCAmelCase)

					lowerCAmelCase        =  [
					    """Hello this is a long string of text.\n\nI'm trying to get the text of the""",
					    """Hey, I'm a little late to the party. I'm going to""",
					]

					self.assertListEqual(__lowerCAmelCase	,   __lowerCAmelCase)



			@is_pt_flax_cross_test
			def 							a_	(       self):




					"""simple docstring"""


					lowerCAmelCase ,					lowerCAmelCase        =  self.model_tester.prepare_config_and_inputs_for_common()

					for model_class in self.all_model_classes:
							with self.subTest(model_class.__name__):
									# prepare inputs
									lowerCAmelCase        =  self._prepare_for_class(__lowerCAmelCase	,   __lowerCAmelCase)
									lowerCAmelCase        =  {k: torch.tensor(v.tolist()) for k, v in prepared_inputs_dict.items()}

									# load corresponding PyTorch class
									lowerCAmelCase        =  model_class.__name__[4:]  # Skip the "Flax" at the beginning
									lowerCAmelCase        =  getattr(__lowerCAmelCase	,   __lowerCAmelCase)

									lowerCAmelCase ,					lowerCAmelCase        =  pt_inputs["""input_ids"""].shape
									lowerCAmelCase        =  np.random.randint(0	,   seq_length - 1	,   size=(batch_size,))
									for batch_idx, start_index in enumerate(__lowerCAmelCase):
											lowerCAmelCase        =  0
											lowerCAmelCase        =  1
											lowerCAmelCase        =  0
											lowerCAmelCase        =  1
									lowerCAmelCase        =  pt_model_class(__lowerCAmelCase).eval()
									lowerCAmelCase        =  model_class(__lowerCAmelCase	,   dtype=jnp.floataa)

									lowerCAmelCase        =  convert_pytorch_state_dict_to_flax(pt_model.state_dict()	,   __lowerCAmelCase)
									lowerCAmelCase        =  fx_state

									with torch.no_grad():
											lowerCAmelCase        =  pt_model(**__lowerCAmelCase).to_tuple()

									lowerCAmelCase        =  fx_model(**__lowerCAmelCase).to_tuple()
									self.assertEqual(len(__lowerCAmelCase)	,   len(__lowerCAmelCase)	,   """Output lengths differ between Flax and PyTorch""")
									for fx_output, pt_output in zip(__lowerCAmelCase	,   __lowerCAmelCase):
											self.assert_almost_equals(fx_output[:, -1]	,   pt_output[:, -1].numpy()	,   4E-2)

									with tempfile.TemporaryDirectory() as tmpdirname:
											pt_model.save_pretrained(__lowerCAmelCase)
											lowerCAmelCase        =  model_class.from_pretrained(__lowerCAmelCase	,   from_pt=__lowerCAmelCase)

									lowerCAmelCase        =  fx_model_loaded(**__lowerCAmelCase).to_tuple()
									self.assertEqual(
									    len(__lowerCAmelCase)	,   len(__lowerCAmelCase)	,   """Output lengths differ between Flax and PyTorch""")
									for fx_output_loaded, pt_output in zip(__lowerCAmelCase	,   __lowerCAmelCase):
											self.assert_almost_equals(fx_output_loaded[:, -1]	,   pt_output[:, -1].numpy()	,   4E-2)



			@is_pt_flax_cross_test
			def 							a_	(       self):




					"""simple docstring"""


					lowerCAmelCase ,					lowerCAmelCase        =  self.model_tester.prepare_config_and_inputs_for_common()
					for model_class in self.all_model_classes:
							with self.subTest(model_class.__name__):
									# prepare inputs
									lowerCAmelCase        =  self._prepare_for_class(__lowerCAmelCase	,   __lowerCAmelCase)
									lowerCAmelCase        =  {k: torch.tensor(v.tolist()) for k, v in prepared_inputs_dict.items()}

									# load corresponding PyTorch class
									lowerCAmelCase        =  model_class.__name__[4:]  # Skip the "Flax" at the beginning
									lowerCAmelCase        =  getattr(__lowerCAmelCase	,   __lowerCAmelCase)

									lowerCAmelCase        =  pt_model_class(__lowerCAmelCase).eval()
									lowerCAmelCase        =  model_class(__lowerCAmelCase	,   dtype=jnp.floataa)

									lowerCAmelCase        =  load_flax_weights_in_pytorch_model(__lowerCAmelCase	,   fx_model.params)
									lowerCAmelCase ,					lowerCAmelCase        =  pt_inputs["""input_ids"""].shape
									lowerCAmelCase        =  np.random.randint(0	,   seq_length - 1	,   size=(batch_size,))
									for batch_idx, start_index in enumerate(__lowerCAmelCase):
											lowerCAmelCase        =  0
											lowerCAmelCase        =  1
											lowerCAmelCase        =  0
											lowerCAmelCase        =  1

									# make sure weights are tied in PyTorch
									pt_model.tie_weights()

									with torch.no_grad():
											lowerCAmelCase        =  pt_model(**__lowerCAmelCase).to_tuple()

									lowerCAmelCase        =  fx_model(**__lowerCAmelCase).to_tuple()
									self.assertEqual(len(__lowerCAmelCase)	,   len(__lowerCAmelCase)	,   """Output lengths differ between Flax and PyTorch""")
									for fx_output, pt_output in zip(__lowerCAmelCase	,   __lowerCAmelCase):
											self.assert_almost_equals(fx_output[:, -1]	,   pt_output[:, -1].numpy()	,   4E-2)

									with tempfile.TemporaryDirectory() as tmpdirname:
											fx_model.save_pretrained(__lowerCAmelCase)
											lowerCAmelCase        =  pt_model_class.from_pretrained(__lowerCAmelCase	,   from_flax=__lowerCAmelCase)

									with torch.no_grad():
											lowerCAmelCase        =  pt_model_loaded(**__lowerCAmelCase).to_tuple()

									self.assertEqual(
									    len(__lowerCAmelCase)	,   len(__lowerCAmelCase)	,   """Output lengths differ between Flax and PyTorch""")
									for fx_output, pt_output in zip(__lowerCAmelCase	,   __lowerCAmelCase):
											self.assert_almost_equals(fx_output[:, -1]	,   pt_output[:, -1].numpy()	,   4E-2)







			@slow
			def 							a_	(       self):




					"""simple docstring"""


					for model_class_name in self.all_model_classes:
							lowerCAmelCase        =  model_class_name.from_pretrained("""EleutherAI/gpt-neo-125M""")
							lowerCAmelCase        =  model(np.ones((1, 1)))
							self.assertIsNotNone(__lowerCAmelCase)





'''simple docstring'''




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

import numpy as np
import regex as re

from ...utils import is_tf_available, is_torch_available, logging


if TYPE_CHECKING:
						if is_torch_available():
												import torch
						if is_tf_available():
												import tensorflow as tf

from ...tokenization_utils import AddedToken, PreTrainedTokenizer


__lowercase				   =  logging.get_logger(__name__)

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

__lowercase				   =  {
    '''vocab_file''': {
        '''Salesforce/codegen-350M-mono''': '''https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/vocab.json''',
    },
    '''merges_file''': {
        '''Salesforce/codegen-350M-mono''': '''https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/merges.txt''',
    },
}

__lowercase				   =  {
    '''Salesforce/codegen-350M-mono''': 2_0_4_8,
}






@lru_cache()
def     snake_case__							(      )   ->     int:

		'''simple docstring'''


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






def     snake_case__							(      _A: int			)   ->     int:

		'''simple docstring'''


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






class     a__( lowerCAmelCase__     ):


			'''simple docstring'''






			UpperCAmelCase_      :					Tuple								=	VOCAB_FILES_NAMES
			UpperCAmelCase_      :					List[str]								=	PRETRAINED_VOCAB_FILES_MAP
			UpperCAmelCase_      :					Any								=	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			UpperCAmelCase_      :					str								=	['''input_ids''', '''attention_mask''']



			def __init__(       self	,   __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase="replace"	,   __lowerCAmelCase="<|endoftext|>"	,   __lowerCAmelCase="<|endoftext|>"	,   __lowerCAmelCase="<|endoftext|>"	,   __lowerCAmelCase=None	,   __lowerCAmelCase=False	,   __lowerCAmelCase=False	,   **__lowerCAmelCase	,   ):




					"""simple docstring"""


					lowerCAmelCase        =  AddedToken(__lowerCAmelCase	,   lstrip=__lowerCAmelCase	,   rstrip=__lowerCAmelCase) if isinstance(__lowerCAmelCase	,   __lowerCAmelCase) else bos_token
					lowerCAmelCase        =  AddedToken(__lowerCAmelCase	,   lstrip=__lowerCAmelCase	,   rstrip=__lowerCAmelCase) if isinstance(__lowerCAmelCase	,   __lowerCAmelCase) else eos_token
					lowerCAmelCase        =  AddedToken(__lowerCAmelCase	,   lstrip=__lowerCAmelCase	,   rstrip=__lowerCAmelCase) if isinstance(__lowerCAmelCase	,   __lowerCAmelCase) else unk_token
					lowerCAmelCase        =  AddedToken(__lowerCAmelCase	,   lstrip=__lowerCAmelCase	,   rstrip=__lowerCAmelCase) if isinstance(__lowerCAmelCase	,   __lowerCAmelCase) else pad_token
					super().__init__(
					    errors=__lowerCAmelCase	,   unk_token=__lowerCAmelCase	,   bos_token=__lowerCAmelCase	,   eos_token=__lowerCAmelCase	,   pad_token=__lowerCAmelCase	,   add_prefix_space=__lowerCAmelCase	,   add_bos_token=__lowerCAmelCase	,   **__lowerCAmelCase	,   )
					lowerCAmelCase        =  add_bos_token

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

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



			@property
			def 							a_	(       self):




					"""simple docstring"""


					return len(self.encoder)



			def 							a_	(       self):




					"""simple docstring"""


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



			def 							a_	(       self	,   __lowerCAmelCase):




					"""simple docstring"""


					if token in self.cache:
							return self.cache[token]
					lowerCAmelCase        =  tuple(__lowerCAmelCase)
					lowerCAmelCase        =  get_pairs(__lowerCAmelCase)

					if not pairs:
							return token

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

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



			def 							a_	(       self	,   __lowerCAmelCase	,   __lowerCAmelCase=None):




					"""simple docstring"""


					if self.add_bos_token:
							lowerCAmelCase        =  [self.bos_token_id]
					else:
							lowerCAmelCase        =  []

					lowerCAmelCase        =  bos_token_ids + token_ids_a

					if token_ids_a is None:
							return output

					return output + bos_token_ids + token_ids_a



			def 							a_	(       self	,   __lowerCAmelCase):




					"""simple docstring"""


					lowerCAmelCase        =  []
					for token in re.findall(self.pat	,   __lowerCAmelCase):
							lowerCAmelCase        =  """""".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(__lowerCAmelCase).split(""" """))
					return bpe_tokens



			def 							a_	(       self	,   __lowerCAmelCase):




					"""simple docstring"""


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



			def 							a_	(       self	,   __lowerCAmelCase):




					"""simple docstring"""


					return self.decoder.get(__lowerCAmelCase)



			def 							a_	(       self	,   __lowerCAmelCase):




					"""simple docstring"""


					lowerCAmelCase        =  """""".join(__lowerCAmelCase)
					lowerCAmelCase        =  bytearray([self.byte_decoder[c] for c in text]).decode("""utf-8"""	,   errors=self.errors)
					return text



			def 							a_	(       self	,   __lowerCAmelCase	,   __lowerCAmelCase = None):




					"""simple docstring"""


					if not os.path.isdir(__lowerCAmelCase):
							logger.error(f"Vocabulary path ({save_directory}) should be a directory")
							return
					lowerCAmelCase        =  os.path.join(
					    __lowerCAmelCase	,   (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""])
					lowerCAmelCase        =  os.path.join(
					    __lowerCAmelCase	,   (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""merges_file"""])

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

					lowerCAmelCase        =  0
					with open(__lowerCAmelCase	,   """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 __lowerCAmelCase: 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        =  token_index
									writer.write(""" """.join(__lowerCAmelCase) + """\n""")
									index += 1

					return vocab_file, merge_file



			def 							a_	(       self	,   __lowerCAmelCase	,   __lowerCAmelCase=False	,   **__lowerCAmelCase):




					"""simple docstring"""


					lowerCAmelCase        =  kwargs.pop("""add_prefix_space"""	,   self.add_prefix_space)
					if is_split_into_words or add_prefix_space:
							lowerCAmelCase        =  """ """ + text
					return (text, kwargs)



			def 							a_	(       self	,   __lowerCAmelCase	,   __lowerCAmelCase = False	,   __lowerCAmelCase = None	,   __lowerCAmelCase = None	,   **__lowerCAmelCase	,   ):




					"""simple docstring"""


					lowerCAmelCase        =  super()._decode(
					    token_ids=__lowerCAmelCase	,   skip_special_tokens=__lowerCAmelCase	,   clean_up_tokenization_spaces=__lowerCAmelCase	,   **__lowerCAmelCase	,   )

					if truncate_before_pattern is not None and len(__lowerCAmelCase) > 0:
							lowerCAmelCase        =  self.truncate(__lowerCAmelCase	,   __lowerCAmelCase)

					return decoded_text







			def 							a_	(       self	,   __lowerCAmelCase	,   __lowerCAmelCase):




					"""simple docstring"""


					def find_re(__lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase):
							lowerCAmelCase        =  pattern.search(__lowerCAmelCase	,   __lowerCAmelCase)
							return m.start() if m else -1

					lowerCAmelCase        =  [re.compile(__lowerCAmelCase	,   re.MULTILINE) for pattern in truncate_before_pattern]

					lowerCAmelCase        =  list(re.finditer("""^print"""	,   __lowerCAmelCase	,   re.MULTILINE))

					if len(__lowerCAmelCase) > 1:
							lowerCAmelCase        =  completion[: prints[1].start()]

					lowerCAmelCase        =  list(re.finditer("""^def"""	,   __lowerCAmelCase	,   re.MULTILINE))

					if len(__lowerCAmelCase) > 1:
							lowerCAmelCase        =  completion[: defs[1].start()]

					lowerCAmelCase        =  0

					lowerCAmelCase        =  [
					    pos for pos in [find_re(__lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase) for terminal in terminals] if pos != -1
					]

					if len(__lowerCAmelCase) > 0:
							return completion[: min(__lowerCAmelCase)]
					else:
							return completion




