


"""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__					=logging.get_logger(__name__)

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

UpperCAmelCase__					={
    "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__					={
    "Salesforce/codegen-350M-mono": 2048,
}





class  lowerCamelCase__    (		_a     ):
      a			:  List[Any]           =		VOCAB_FILES_NAMES
      a			:  Any           =		PRETRAINED_VOCAB_FILES_MAP
      a			:  List[Any]           =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      a			:  List[str]           =		["""input_ids""", """attention_mask"""]
      a			:  str           =		CodeGenTokenizer
      def __init__(		self						:						Any   ,							A_						:						Dict=None   ,							A_						:						Union[str, Any]=None   ,							A_						:						Dict=None   ,							A_						:						List[Any]="<|endoftext|>"   ,							A_						:						Union[str, Any]="<|endoftext|>"   ,							A_						:						Optional[Any]="<|endoftext|>"   ,							A_						:						List[str]=False   ,							**A_						:						Dict   ,							):






       '''simple docstring'''


       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_				):
        __lowercase						=						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."""				)

       __lowercase						=						json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()				)
       if pre_tok_state.get("""add_prefix_space"""   ,							A_				) != add_prefix_space:
        __lowercase						=						getattr(A_   ,							pre_tok_state.pop("""type"""				)				)
        __lowercase						=						add_prefix_space
        __lowercase						=						pre_tok_class(**A_				)

       __lowercase						=						add_prefix_space
      def SCREAMING_SNAKE_CASE_						(		self						:						int   ,							*A_						:						Tuple   ,							**A_						:						Dict				):






       '''simple docstring'''


       __lowercase						=						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 SCREAMING_SNAKE_CASE_						(		self						:						str   ,							*A_						:						List[Any]   ,							**A_						:						List[str]				):






       '''simple docstring'''


       __lowercase						=						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 SCREAMING_SNAKE_CASE_						(		self						:						List[Any]   ,							A_						:						str   ,							A_						:						Optional[str] = None				):






       '''simple docstring'''


       __lowercase						=						self._tokenizer.model.save(A_   ,							name=A_				)
       return tuple(A_				)
      def SCREAMING_SNAKE_CASE_						(		self						:						Any   ,							A_						:						Union[int, List[int], "np.ndarray", "torch.Tensor", "tf.Tensor"]   ,							A_						:						bool = False   ,							A_						:						bool = None   ,							A_						:						Optional[List[str]] = None   ,							**A_						:						Tuple   ,							):






       '''simple docstring'''


       __lowercase						=						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:
        __lowercase						=						self.truncate(A_   ,							A_				)

       return decoded_text






      def SCREAMING_SNAKE_CASE_						(		self						:						Optional[int]   ,							A_						:						str   ,							A_						:						Dict				):






       '''simple docstring'''


       def find_re(A_						:						Dict   ,							A_						:						Dict   ,							A_						:						str				):
        __lowercase						=						pattern.search(A_   ,							A_				)
        return m.start() if m else -1

       __lowercase						=						[re.compile(A_   ,							re.MULTILINE				) for pattern in truncate_before_pattern]

       __lowercase						=						list(re.finditer("""^print"""   ,							A_   ,							re.MULTILINE				)				)

       if len(A_				) > 1:
        __lowercase						=						completion[: prints[1].start()]

       __lowercase						=						list(re.finditer("""^def"""   ,							A_   ,							re.MULTILINE				)				)

       if len(A_				) > 1:
        __lowercase						=						completion[: defs[1].start()]

       __lowercase						=						0

       __lowercase						=						[
           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 unittest

from transformers import BarthezTokenizer, BarthezTokenizerFast, BatchEncoding
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow

from ...test_tokenization_common import TokenizerTesterMixin





@require_tokenizers
@require_sentencepiece
@slow  # see https://github.com/huggingface/transformers/issues/11457
class  lowerCamelCase__    (		_a      , unittest.TestCase     ):
      a			:  Union[str, Any]           =		BarthezTokenizer
      a			:  Any           =		BarthezTokenizerFast
      a			:  Tuple           =		True
      a			:  List[Any]           =		True
      def SCREAMING_SNAKE_CASE_						(		self						:						List[str]				):






       '''simple docstring'''


       super().setUp()

       __lowercase						=						BarthezTokenizerFast.from_pretrained("""moussaKam/mbarthez"""				)
       tokenizer.save_pretrained(self.tmpdirname				)
       tokenizer.save_pretrained(self.tmpdirname   ,							legacy_format=A_				)
       __lowercase						=						tokenizer
      def SCREAMING_SNAKE_CASE_						(		self						:						Tuple				):






       '''simple docstring'''


       __lowercase						=						"""<pad>"""
       __lowercase						=						1

       self.assertEqual(self.get_tokenizer()._convert_token_to_id(A_				)   ,							A_				)
       self.assertEqual(self.get_tokenizer()._convert_id_to_token(A_				)   ,							A_				)
      def SCREAMING_SNAKE_CASE_						(		self						:						List[str]				):






       '''simple docstring'''


       __lowercase						=						list(self.get_tokenizer().get_vocab().keys()				)

       self.assertEqual(vocab_keys[0]   ,							"""<s>"""				)
       self.assertEqual(vocab_keys[1]   ,							"""<pad>"""				)
       self.assertEqual(vocab_keys[-1]   ,							"""<mask>"""				)
       self.assertEqual(len(A_				)   ,							1_0_1_1_2_2				)
      def SCREAMING_SNAKE_CASE_						(		self						:						Union[str, Any]				):






       '''simple docstring'''


       self.assertEqual(self.get_tokenizer().vocab_size   ,							1_0_1_1_2_2				)
      @require_torch
      def SCREAMING_SNAKE_CASE_						(		self						:						str				):






       '''simple docstring'''


       __lowercase						=						["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
       __lowercase						=						[0, 5_7, 3_0_1_8, 7_0_3_0_7, 9_1, 2]

       __lowercase						=						self.tokenizer(
           A_   ,							max_length=len(A_				)   ,							padding=A_   ,							truncation=A_   ,							return_tensors="""pt"""				)
       self.assertIsInstance(A_   ,							A_				)

       self.assertEqual((2, 6)   ,							batch.input_ids.shape				)
       self.assertEqual((2, 6)   ,							batch.attention_mask.shape				)
       __lowercase						=						batch.input_ids.tolist()[0]
       self.assertListEqual(A_   ,							A_				)
      def SCREAMING_SNAKE_CASE_						(		self						:						int				):






       '''simple docstring'''


       if not self.test_rust_tokenizer:
        return

       __lowercase						=						self.get_tokenizer()
       __lowercase						=						self.get_rust_tokenizer()

       __lowercase						=						"""I was born in 92000, and this is falsé."""

       __lowercase						=						tokenizer.tokenize(A_				)
       __lowercase						=						rust_tokenizer.tokenize(A_				)
       self.assertListEqual(A_   ,							A_				)

       __lowercase						=						tokenizer.encode(A_   ,							add_special_tokens=A_				)
       __lowercase						=						rust_tokenizer.encode(A_   ,							add_special_tokens=A_				)
       self.assertListEqual(A_   ,							A_				)

       __lowercase						=						self.get_rust_tokenizer()
       __lowercase						=						tokenizer.encode(A_				)
       __lowercase						=						rust_tokenizer.encode(A_				)
       self.assertListEqual(A_   ,							A_				)






      @slow
      def SCREAMING_SNAKE_CASE_						(		self						:						Union[str, Any]				):






       '''simple docstring'''


       __lowercase						=						{"""input_ids""": [[0, 4_9_0, 1_4_3_2_8, 4_5_0_7, 3_5_4, 4_7, 4_3_6_6_9, 9_5, 2_5, 7_8_1_1_7, 2_0_2_1_5, 1_9_7_7_9, 1_9_0, 2_2, 4_0_0, 4, 3_5_3_4_3, 8_0_3_1_0, 6_0_3, 8_6, 2_4_9_3_7, 1_0_5, 3_3_4_3_8, 9_4_7_6_2, 1_9_6, 3_9_6_4_2, 7, 1_5, 1_5_9_3_3, 1_7_3, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 1_0_5_3_4, 8_7, 2_5, 6_6, 3_3_5_8, 1_9_6, 5_5_2_8_9, 8, 8_2_9_6_1, 8_1, 2_2_0_4, 7_5_2_0_3, 7, 1_5, 7_6_3, 1_2_9_5_6, 2_1_6, 1_7_8, 1_4_3_2_8, 9_5_9_5, 1_3_7_7, 6_9_6_9_3, 7, 4_4_8, 7_1_0_2_1, 1_9_6, 1_8_1_0_6, 1_4_3_7, 1_3_9_7_4, 1_0_8, 9_0_8_3, 4, 4_9_3_1_5, 7, 3_9, 8_6, 1_3_2_6, 2_7_9_3, 4_6_3_3_3, 4, 4_4_8, 1_9_6, 7_4_5_8_8, 7, 4_9_3_1_5, 7, 3_9, 2_1, 8_2_2, 3_8_4_7_0, 7_4, 2_1, 6_6_7_2_3, 6_2_4_8_0, 8, 2_2_0_5_0, 5, 2]], """attention_mask""": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}  # noqa: E501
       # fmt: on

       # moussaKam/mbarthez is a french model. So we also use french texts.
       __lowercase						=						[
           """Le transformeur est un modèle d'apprentissage profond introduit en 2017, """
           """utilisé principalement dans le domaine du traitement automatique des langues (TAL).""",
           """À l'instar des réseaux de neurones récurrents (RNN), les transformeurs sont conçus """
           """pour gérer des données séquentielles, telles que le langage naturel, pour des tâches """
           """telles que la traduction et la synthèse de texte.""",
       ]

       self.tokenizer_integration_test_util(
           expected_encoding=A_   ,							model_name="""moussaKam/mbarthez"""   ,							revision="""c2e4ecbca5e3cd2c37fe1ac285ca4fbdf1366fb6"""   ,							sequences=A_   ,							)
