'''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


_a   :	Union[str, Any] 							=   logging.get_logger(__name__)

_a   :	Any 							=   {"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}

_a   :	Optional[int] 							=   {
    "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"
        ),
    },
}

_a   :	Optional[Any] 							=   {
    "Salesforce/codegen-350M-mono": 2048,
}






class 		__A       (__a   ):
       snake_case       :str				    =					VOCAB_FILES_NAMES
       snake_case       :Tuple				    =					PRETRAINED_VOCAB_FILES_MAP
       snake_case       :Dict				    =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       snake_case       :str				    =					["input_ids", "attention_mask"]
       snake_case       :Tuple				    =					CodeGenTokenizer






       def __init__(      self				,     UpperCamelCase_=None				,     UpperCamelCase_=None				,     UpperCamelCase_=None				,     UpperCamelCase_="<|endoftext|>"				,     UpperCamelCase_="<|endoftext|>"				,     UpperCamelCase_="<|endoftext|>"				,     UpperCamelCase_=False				,     **UpperCamelCase_				,     ):
              super().__init__(
                  a_				,     a_				,     tokenizer_file=a_				,     unk_token=a_				,     bos_token=a_				,     eos_token=a_				,     add_prefix_space=a_				,     **a_				,     )

              if kwargs.pop("add_bos_token"				,     a_      ):
                     __UpperCAmelCase  :				Union[str, Any]       				=		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  :				Union[str, Any]       				=		json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()      )
              if pre_tok_state.get("add_prefix_space"				,     a_      ) != add_prefix_space:
                     __UpperCAmelCase  :				str       				=		getattr(a_				,     pre_tok_state.pop("type"      )      )
                     __UpperCAmelCase  :				Dict       				=		add_prefix_space
                     __UpperCAmelCase  :				List[Any]       				=		pre_tok_class(**a_      )

              __UpperCAmelCase  :				List[str]       				=		add_prefix_space






       def 			_snake_case						(      self				,     *UpperCamelCase_				,     **UpperCamelCase_      ):
              __UpperCAmelCase  :				Union[str, Any]       				=		kwargs.get("is_split_into_words"				,     a_      )
              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(*a_				,     **a_      )






       def 			_snake_case						(      self				,     *UpperCamelCase_				,     **UpperCamelCase_      ):
              __UpperCAmelCase  :				int       				=		kwargs.get("is_split_into_words"				,     a_      )

              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(*a_				,     **a_      )






       def 			_snake_case						(      self				,     UpperCamelCase_				,     UpperCamelCase_ = None      ):
              __UpperCAmelCase  :				int       				=		self._tokenizer.model.save(a_				,     name=a_      )
              return tuple(a_      )






       def 			_snake_case						(      self				,     UpperCamelCase_				,     UpperCamelCase_ = False				,     UpperCamelCase_ = None				,     UpperCamelCase_ = None				,     **UpperCamelCase_				,     ):
              __UpperCAmelCase  :				str       				=		super().decode(
                  token_ids=a_				,     skip_special_tokens=a_				,     clean_up_tokenization_spaces=a_				,     **a_				,     )

              if truncate_before_pattern is not None and len(a_      ) > 0:
                     __UpperCAmelCase  :				int       				=		self.truncate(a_				,     a_      )

              return decoded_text







       def 			_snake_case						(      self				,     UpperCamelCase_				,     UpperCamelCase_      ):



              def find_re(UpperCamelCase_				,     UpperCamelCase_				,     UpperCamelCase_      ):
                     __UpperCAmelCase  :				str       				=		pattern.search(a_				,     a_      )
                     return m.start() if m else -1

              __UpperCAmelCase  :				Optional[int]       				=		[re.compile(a_				,     re.MULTILINE      ) for pattern in truncate_before_pattern]

              __UpperCAmelCase  :				List[str]       				=		list(re.finditer("^print"				,     a_				,     re.MULTILINE      )      )

              if len(a_      ) > 1:
                     __UpperCAmelCase  :				List[str]       				=		completion[: prints[1].start()]

              __UpperCAmelCase  :				List[Any]       				=		list(re.finditer("^def"				,     a_				,     re.MULTILINE      )      )

              if len(a_      ) > 1:
                     __UpperCAmelCase  :				Optional[int]       				=		completion[: defs[1].start()]

              __UpperCAmelCase  :				str       				=		0

              __UpperCAmelCase  :				str       				=		[
                  pos for pos in [find_re(a_				,     a_				,     a_      ) for terminal in terminals] if pos != -1
              ]

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







'''simple docstring'''




import time
import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch, torch_device

from ..test_modeling_common import ids_tensor


if is_torch_available():
   import torch

   from transformers.generation import (
       MaxLengthCriteria,
       MaxNewTokensCriteria,
       MaxTimeCriteria,
       StoppingCriteriaList,
       validate_stopping_criteria,
   )






@require_torch
class 		__A       (unittest.TestCase   ):






       def 			_snake_case						(      self				,     UpperCamelCase_      ):
              __UpperCAmelCase  :				List[str]       				=		3
              __UpperCAmelCase  :				Tuple       				=		2_50

              __UpperCAmelCase  :				str       				=		ids_tensor((batch_size, length)				,     UpperCamelCase_      )
              __UpperCAmelCase  :				Any       				=		torch.ones((batch_size, length)				,     device=UpperCamelCase_				,     dtype=torch.float      ) / length
              return input_ids, scores






       def 			_snake_case						(      self      ):
              __UpperCAmelCase				, __UpperCAmelCase  :				Tuple       				=		self._get_tensors(5      )

              __UpperCAmelCase  :				Tuple       				=		StoppingCriteriaList(
                  [
                      MaxLengthCriteria(max_length=10      ),
                      MaxTimeCriteria(max_time=0.1      ),
                  ]      )

              self.assertFalse(criteria(UpperCamelCase_				,     UpperCamelCase_      )      )

              __UpperCAmelCase				, __UpperCAmelCase  :				int       				=		self._get_tensors(9      )
              self.assertFalse(criteria(UpperCamelCase_				,     UpperCamelCase_      )      )

              __UpperCAmelCase				, __UpperCAmelCase  :				Optional[int]       				=		self._get_tensors(10      )
              self.assertTrue(criteria(UpperCamelCase_				,     UpperCamelCase_      )      )






       def 			_snake_case						(      self      ):
              __UpperCAmelCase  :				int       				=		MaxLengthCriteria(max_length=10      )

              __UpperCAmelCase				, __UpperCAmelCase  :				Tuple       				=		self._get_tensors(5      )
              self.assertFalse(criteria(UpperCamelCase_				,     UpperCamelCase_      )      )

              __UpperCAmelCase				, __UpperCAmelCase  :				Dict       				=		self._get_tensors(9      )
              self.assertFalse(criteria(UpperCamelCase_				,     UpperCamelCase_      )      )

              __UpperCAmelCase				, __UpperCAmelCase  :				Optional[int]       				=		self._get_tensors(10      )
              self.assertTrue(criteria(UpperCamelCase_				,     UpperCamelCase_      )      )






       def 			_snake_case						(      self      ):
              __UpperCAmelCase  :				Optional[Any]       				=		MaxNewTokensCriteria(start_length=5				,     max_new_tokens=5      )

              __UpperCAmelCase				, __UpperCAmelCase  :				List[str]       				=		self._get_tensors(5      )
              self.assertFalse(criteria(UpperCamelCase_				,     UpperCamelCase_      )      )

              __UpperCAmelCase				, __UpperCAmelCase  :				Dict       				=		self._get_tensors(9      )
              self.assertFalse(criteria(UpperCamelCase_				,     UpperCamelCase_      )      )

              __UpperCAmelCase				, __UpperCAmelCase  :				Optional[Any]       				=		self._get_tensors(10      )
              self.assertTrue(criteria(UpperCamelCase_				,     UpperCamelCase_      )      )

              __UpperCAmelCase  :				Union[str, Any]       				=		StoppingCriteriaList([criteria]      )
              self.assertEqual(criteria_list.max_length				,     10      )






       def 			_snake_case						(      self      ):
              __UpperCAmelCase				, __UpperCAmelCase  :				Optional[Any]       				=		self._get_tensors(5      )

              __UpperCAmelCase  :				str       				=		MaxTimeCriteria(max_time=0.1      )
              self.assertFalse(criteria(UpperCamelCase_				,     UpperCamelCase_      )      )

              __UpperCAmelCase  :				str       				=		MaxTimeCriteria(max_time=0.1				,     initial_timestamp=time.time() - 0.2      )
              self.assertTrue(criteria(UpperCamelCase_				,     UpperCamelCase_      )      )







       def 			_snake_case						(      self      ):
              validate_stopping_criteria(StoppingCriteriaList([MaxLengthCriteria(10      )]      )				,     10      )

              with self.assertWarns(UpperCamelCase_      ):
                     validate_stopping_criteria(StoppingCriteriaList([MaxLengthCriteria(10      )]      )				,     11      )

              __UpperCAmelCase  :				Optional[int]       				=		validate_stopping_criteria(StoppingCriteriaList()				,     11      )

              self.assertEqual(len(UpperCamelCase_      )				,     1      )






