



'''simple docstring'''






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

import numpy as np

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 tokenizers import pre_tokenizers

from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_codegen import CodeGenTokenizer


UpperCamelCase__:     List[Any]										=    logging.get_logger(__name__)

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

UpperCamelCase__:     Any										=    {
    "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",
    },
    "tokenizer_file": {
        "Salesforce/codegen-350M-mono": (
            "https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/tokenizer.json"
        ),
    },
}

UpperCamelCase__:     int										=    {
    "Salesforce/codegen-350M-mono": 2048,
}

class        SCREAMING_SNAKE_CASE(							A__    ):







	"""simple docstring"""






	lowerCamelCase__    				= VOCAB_FILES_NAMES
	lowerCamelCase__    				= PRETRAINED_VOCAB_FILES_MAP
	lowerCamelCase__    				= PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	lowerCamelCase__    				= ["""input_ids""", """attention_mask"""]
	lowerCamelCase__    				= CodeGenTokenizer







	def __init__(		self    :  Union[str, Any]	,    __snake_case    :  Any=None	,    __snake_case    :  int=None	,    __snake_case    :  List[str]=None	,    __snake_case    :  Optional[int]="<|endoftext|>"	,    __snake_case    :  Optional[int]="<|endoftext|>"	,    __snake_case    :  Optional[int]="<|endoftext|>"	,    __snake_case    :  List[Any]=False	,    **__snake_case    :  Optional[int]	,    )    ->	List[str]:
								super().__init__(
								    __snake_case	,    __snake_case	,    tokenizer_file=__snake_case	,    unk_token=__snake_case	,    bos_token=__snake_case	,    eos_token=__snake_case	,    add_prefix_space=__snake_case	,    **__snake_case	,    )

								if kwargs.pop('''add_bos_token'''	,    __snake_case	):
															UpperCAmelCase				: Optional[int]											=	kwargs.pop('''name_or_path'''	,    ''''''	)
															raise ValueError(
															    '''Currenty GPT2\'s fast tokenizer does NOT support adding a BOS token.'''
															    '''Instead you should use GPT2\'s slow tokenizer class `CodeGenTokenizer` as follows: \n'''
															    F"""`CodeGenTokenizer.from_pretrained('{model_id}')`\nor\n"""
															    F"""`AutoTokenizer.from_pretrained('{model_id}', use_fast=False)`\n"""
															    '''This issue will be fixed soon, see: https://github.com/huggingface/tokenizers/pull/1005.'''
															    ''' so that the fast tokenizer works correctly.'''	)

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

								UpperCAmelCase				: Any											=	add_prefix_space







	def   A			(		self    :  str	,    *__snake_case    :  Dict	,    **__snake_case    :  List[Any]	)    ->	BatchEncoding:
								UpperCAmelCase				: int											=	kwargs.get('''is_split_into_words'''	,    __snake_case	)
								assert self.add_prefix_space or not is_split_into_words, (
								    F"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
								    "to use it with pretokenized inputs."
								)

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







	def   A			(		self    :  Dict	,    *__snake_case    :  str	,    **__snake_case    :  Dict	)    ->	BatchEncoding:
								UpperCAmelCase				: Any											=	kwargs.get('''is_split_into_words'''	,    __snake_case	)

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

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







	def   A			(		self    :  Optional[Any]	,    __snake_case    :  str	,    __snake_case    :  Optional[str] = None	)    ->	Tuple[str]:
								UpperCAmelCase				: List[Any]											=	self._tokenizer.model.save(__snake_case	,    name=__snake_case	)
								return tuple(__snake_case	)







	def   A			(		self    :  int	,    __snake_case    :  Union[int, List[int], "np.ndarray", "torch.Tensor", "tf.Tensor"]	,    __snake_case    :  bool = False	,    __snake_case    :  bool = None	,    __snake_case    :  Optional[List[str]] = None	,    **__snake_case    :  Optional[Any]	,    )    ->	str:
								UpperCAmelCase				: List[str]											=	super().decode(
								    token_ids=__snake_case	,    skip_special_tokens=__snake_case	,    clean_up_tokenization_spaces=__snake_case	,    **__snake_case	,    )

								if truncate_before_pattern is not None and len(__snake_case	) > 0:
															UpperCAmelCase				: List[Any]											=	self.truncate(__snake_case	,    __snake_case	)

								return decoded_text







	def   A			(		self    :  int	,    __snake_case    :  int	,    __snake_case    :  Tuple	)    ->	List[str]:
								def find_re(__snake_case    :  List[str]	,    __snake_case    :  Optional[Any]	,    __snake_case    :  Tuple	):
															UpperCAmelCase				: str											=	pattern.search(__snake_case	,    __snake_case	)
															return m.start() if m else -1

								UpperCAmelCase				: List[str]											=	[re.compile(__snake_case	,    re.MULTILINE	) for pattern in truncate_before_pattern]

								UpperCAmelCase				: List[Any]											=	list(re.finditer('''^print'''	,    __snake_case	,    re.MULTILINE	)	)

								if len(__snake_case	) > 1:
															UpperCAmelCase				: Union[str, Any]											=	completion[: prints[1].start()]

								UpperCAmelCase				: List[Any]											=	list(re.finditer('''^def'''	,    __snake_case	,    re.MULTILINE	)	)

								if len(__snake_case	) > 1:
															UpperCAmelCase				: Optional[Any]											=	completion[: defs[1].start()]

								UpperCAmelCase				: Tuple											=	0

								UpperCAmelCase				: int											=	[
								    pos for pos in [find_re(__snake_case	,    __snake_case	,    __snake_case	) for terminal in terminals] if pos != -1
								]

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










'''simple docstring'''






import tempfile
import unittest

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from transformers.testing_utils import (
    is_torch_available,
    require_optimum,
    require_torch,
    slow,
)


if is_torch_available():
						import torch

@require_torch
@require_optimum
@slow
class        SCREAMING_SNAKE_CASE(							unittest.TestCase    ):







	"""simple docstring"""






	def   A			(		self    :  Union[str, Any]	)    ->	Any:
								UpperCAmelCase				: Optional[int]											=	'''hf-internal-testing/tiny-random-t5'''
								UpperCAmelCase				: int											=	AutoTokenizer.from_pretrained(__snake_case	)
								UpperCAmelCase				: Tuple											=	AutoModelForSeqaSeqLM.from_pretrained(__snake_case	)

								UpperCAmelCase				: Optional[Any]											=	tokenizer('''This is me'''	,    return_tensors='''pt'''	)

								UpperCAmelCase				: Union[str, Any]											=	model.to_bettertransformer()

								self.assertTrue(any('''BetterTransformer''' in mod.__class__.__name__ for _, mod in model.named_modules()	)	)

								UpperCAmelCase				: Dict											=	model.generate(**__snake_case	)

								UpperCAmelCase				: Union[str, Any]											=	model.reverse_bettertransformer()

								self.assertFalse(any('''BetterTransformer''' in mod.__class__.__name__ for _, mod in model.named_modules()	)	)

								with tempfile.TemporaryDirectory() as tmpdirname:
															model.save_pretrained(__snake_case	)

															UpperCAmelCase				: Optional[int]											=	AutoModelForSeqaSeqLM.from_pretrained(__snake_case	)

															self.assertFalse(
															    any('''BetterTransformer''' in mod.__class__.__name__ for _, mod in model_reloaded.named_modules()	)	)

															UpperCAmelCase				: Dict											=	model_reloaded.generate(**__snake_case	)
															self.assertTrue(torch.allclose(__snake_case	,    __snake_case	)	)







	def   A			(		self    :  Optional[int]	)    ->	List[Any]:
								UpperCAmelCase				: Dict											=	'''hf-internal-testing/tiny-random-t5'''
								UpperCAmelCase				: Optional[int]											=	AutoModelForSeqaSeqLM.from_pretrained(__snake_case	)

								UpperCAmelCase				: List[Any]											=	model.to_bettertransformer()

								with tempfile.TemporaryDirectory() as tmpdirname:
															with self.assertRaises(__snake_case	):
																						model.save_pretrained(__snake_case	)

															UpperCAmelCase				: Any											=	model.reverse_bettertransformer()
															model.save_pretrained(__snake_case	)





