
import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils_fast import AddedToken, PreTrainedTokenizerFast
from ...utils import logging


_lowerCamelCase        =logging.get_logger(__name__)

_lowerCamelCase        ={"""vocab_file""": """spiece.model""", """tokenizer_file""": """tokenizer.json"""}

_lowerCamelCase        ={
    """vocab_file""": {
        """TsinghuaAI/CPM-Generate""": """https://huggingface.co/TsinghuaAI/CPM-Generate/resolve/main/spiece.model""",
    },
    """tokenizer_file""": {
        """TsinghuaAI/CPM-Generate""": """https://huggingface.co/TsinghuaAI/CPM-Generate/resolve/main/tokenizer.json""",
    },
}





class 							A__		(     __SCREAMING_SNAKE_CASE):
						def __init__(				self   ,  __magic_name__=None   ,  __magic_name__=None   ,  __magic_name__=False   ,  __magic_name__=True   ,  __magic_name__=False   ,  __magic_name__="<s>"   ,  __magic_name__="</s>"   ,  __magic_name__="<unk>"   ,  __magic_name__="<sep>"   ,  __magic_name__="<pad>"   ,  __magic_name__="<cls>"   ,  __magic_name__="<mask>"   ,  __magic_name__=["<eop>", "<eod>"]   ,  **__magic_name__   ,  ):
							lowerCamelCase		:     List[str]          =				AddedToken(__magic_name__   ,  lstrip=__magic_name__   ,  rstrip=__magic_name__				) if isinstance(__magic_name__   ,  __magic_name__				) else mask_token

							super().__init__(
							    vocab_file=__magic_name__   ,  tokenizer_file=__magic_name__   ,  do_lower_case=__magic_name__   ,  remove_space=__magic_name__   ,  keep_accents=__magic_name__   ,  bos_token=__magic_name__   ,  eos_token=__magic_name__   ,  unk_token=__magic_name__   ,  sep_token=__magic_name__   ,  pad_token=__magic_name__   ,  cls_token=__magic_name__   ,  mask_token=__magic_name__   ,  additional_special_tokens=__magic_name__   ,  **__magic_name__   ,  )

							lowerCamelCase		:     Optional[Any]          =				3
							lowerCamelCase		:     str          =				do_lower_case
							lowerCamelCase		:     List[Any]          =				remove_space
							lowerCamelCase		:     str          =				keep_accents
							lowerCamelCase		:     Tuple          =				vocab_file
							lowerCamelCase		:     Union[str, Any]          =				False if not self.vocab_file else True

							try:
								import jieba
							except ModuleNotFoundError as error:
								raise error.__class__(
								    """You need to install jieba to use CpmTokenizer or CpmTokenizerFast. """
								    """See https://pypi.org/project/jieba/ for installation."""				)
							lowerCamelCase		:     Optional[Any]          =				jieba
							lowerCamelCase		:     str          =				str.maketrans(""" \n"""   ,  """\u2582\u2583"""				)
						def 						UpperCamelCase__							(				self   ,  __magic_name__   ,  __magic_name__ = None				):
							lowerCamelCase		:     Any          =				[self.sep_token_id]
							lowerCamelCase		:     List[Any]          =				[self.cls_token_id]
							if token_ids_a is None:
								return token_ids_a + sep + cls
							return token_ids_a + sep + token_ids_a + sep + cls
						def 						UpperCamelCase__							(				self   ,  __magic_name__   ,  __magic_name__ = None				):
							lowerCamelCase		:     Any          =				[self.sep_token_id]
							lowerCamelCase		:     Optional[Any]          =				[2]

							if token_ids_a is None:
								return len(token_ids_a + sep				) * [0] + cls_segment_id
							return len(token_ids_a + sep				) * [0] + len(token_ids_a + sep				) * [1] + cls_segment_id
						def 						UpperCamelCase__							(				self   ,  __magic_name__   ,  __magic_name__ = None				):
							if not self.can_save_slow_tokenizer:
								raise ValueError(
								    """Your fast tokenizer does not have the necessary information to save the vocabulary for a slow """
								    """tokenizer."""				)

							if not os.path.isdir(__magic_name__				):
								logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''				)
								return
							lowerCamelCase		:     int          =				os.path.join(
							    __magic_name__   ,  (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]				)

							if os.path.abspath(self.vocab_file				) != os.path.abspath(__magic_name__				):
								copyfile(self.vocab_file   ,  __magic_name__				)

							return (out_vocab_file,)
						def 						UpperCamelCase__							(				self   ,  __magic_name__   ,  *__magic_name__   ,  **__magic_name__				):
							lowerCamelCase		:     Union[str, Any]          =				[
							    """ """.join([x.translate(self.translator				) for x in self.jieba.cut(__magic_name__   ,  cut_all=__magic_name__				)]				)
							    for text in batch_text_or_text_pairs
							]
							return super()._batch_encode_plus(__magic_name__   ,  *__magic_name__   ,  **__magic_name__				)






						def 						UpperCamelCase__							(				self   ,  *__magic_name__   ,  **__magic_name__				):
							lowerCamelCase		:     str          =				super()._decode(*__magic_name__   ,  **__magic_name__				)
							lowerCamelCase		:     Optional[int]          =				text.replace(""" """   ,  """"""				).replace("""\u2582"""   ,  """ """				).replace("""\u2583"""   ,  """\n"""				)
							return text






import unittest

import pytest

from transformers import (
    MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    MBartaaTokenizerFast,
    MBartConfig,
    MBartForConditionalGeneration,
    TranslationPipeline,
    pipeline,
)
from transformers.testing_utils import is_pipeline_test, require_tf, require_torch, slow

from .test_pipelines_common import ANY





@is_pipeline_test
class 							A__		(     unittest.TestCase):
      _UpperCAmelCase    :   Tuple   	=      MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
      _UpperCAmelCase    :   Optional[Any]   	=      TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
      def 						UpperCamelCase__							(				self   ,  __magic_name__   ,  __magic_name__   ,  __magic_name__				):
       if isinstance(model.config   ,  __magic_name__				):
        lowerCamelCase      ,   lowerCamelCase		:     Union[str, Any]          =				list(tokenizer.lang_code_to_id.keys()				)[:2]
        lowerCamelCase		:     Tuple          =				TranslationPipeline(model=__magic_name__   ,  tokenizer=__magic_name__   ,  src_lang=__magic_name__   ,  tgt_lang=__magic_name__				)
       else:
        lowerCamelCase		:     Tuple          =				TranslationPipeline(model=__magic_name__   ,  tokenizer=__magic_name__				)
       return translator, ["Some string", "Some other text"]
      def 						UpperCamelCase__							(				self   ,  __magic_name__   ,  __magic_name__				):
       lowerCamelCase		:     Optional[Any]          =				translator("""Some string"""				)
       self.assertEqual(__magic_name__   ,  [{"""translation_text""": ANY(__magic_name__				)}]				)

       lowerCamelCase		:     Dict          =				translator(["""Some string"""]				)
       self.assertEqual(__magic_name__   ,  [{"""translation_text""": ANY(__magic_name__				)}]				)

       lowerCamelCase		:     Any          =				translator(["""Some string""", """other string"""]				)
       self.assertEqual(__magic_name__   ,  [{"""translation_text""": ANY(__magic_name__				)}, {"""translation_text""": ANY(__magic_name__				)}]				)
      @require_torch
      def 						UpperCamelCase__							(				self				):
       lowerCamelCase		:     Any          =				pipeline("""translation_en_to_ro"""   ,  model="""patrickvonplaten/t5-tiny-random"""   ,  framework="""pt"""				)
       lowerCamelCase		:     Union[str, Any]          =				translator("""This is a test string"""   ,  max_length=2_0				)
       self.assertEqual(
           __magic_name__   ,  [
               {
                   """translation_text""": (
                       """Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide"""
                       """ Beide Beide"""
                   )
               }
           ]   ,  )
      @require_tf
      def 						UpperCamelCase__							(				self				):
       lowerCamelCase		:     str          =				pipeline("""translation_en_to_ro"""   ,  model="""patrickvonplaten/t5-tiny-random"""   ,  framework="""tf"""				)
       lowerCamelCase		:     Tuple          =				translator("""This is a test string"""   ,  max_length=2_0				)
       self.assertEqual(
           __magic_name__   ,  [
               {
                   """translation_text""": (
                       """Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide"""
                       """ Beide Beide"""
                   )
               }
           ]   ,  )
      @require_torch
      def 						UpperCamelCase__							(				self				):
       lowerCamelCase		:     int          =				pipeline("""translation_en_to_de"""   ,  model="""patrickvonplaten/t5-tiny-random"""   ,  framework="""pt"""				)
       lowerCamelCase		:     List[Any]          =				translator("""This is a test string"""   ,  max_length=2_0				)
       self.assertEqual(
           __magic_name__   ,  [
               {
                   """translation_text""": (
                       """monoton monoton monoton monoton monoton monoton monoton monoton monoton monoton urine urine"""
                       """ urine urine urine urine urine urine urine"""
                   )
               }
           ]   ,  )






      @require_tf
      def 						UpperCamelCase__							(				self				):
       lowerCamelCase		:     Union[str, Any]          =				pipeline("""translation_en_to_de"""   ,  model="""patrickvonplaten/t5-tiny-random"""   ,  framework="""tf"""				)
       lowerCamelCase		:     Union[str, Any]          =				translator("""This is a test string"""   ,  max_length=2_0				)
       self.assertEqual(
           __magic_name__   ,  [
               {
                   """translation_text""": (
                       """monoton monoton monoton monoton monoton monoton monoton monoton monoton monoton urine urine"""
                       """ urine urine urine urine urine urine urine"""
                   )
               }
           ]   ,  )





class 							A__		(     unittest.TestCase):
      @require_torch
      @slow
      def 						UpperCamelCase__							(				self				):
       # We don't provide a default for this pair
       with self.assertRaises(__magic_name__				):
        pipeline(task="""translation_cn_to_ar"""				)

       # but we do for this one
       lowerCamelCase		:     Optional[Any]          =				pipeline(task="""translation_en_to_de"""				)
       self.assertEqual(translator._preprocess_params["""src_lang"""]   ,  """en"""				)
       self.assertEqual(translator._preprocess_params["""tgt_lang"""]   ,  """de"""				)
      @require_torch
      @slow
      def 						UpperCamelCase__							(				self				):
       lowerCamelCase		:     Optional[int]          =				MBartForConditionalGeneration.from_pretrained("""facebook/mbart-large-50-many-to-many-mmt"""				)
       lowerCamelCase		:     List[Any]          =				MBartaaTokenizerFast.from_pretrained("""facebook/mbart-large-50-many-to-many-mmt"""				)

       lowerCamelCase		:     Any          =				pipeline(task="""translation"""   ,  model=__magic_name__   ,  tokenizer=__magic_name__				)
       # Missing src_lang, tgt_lang
       with self.assertRaises(__magic_name__				):
        translator("""This is a test"""				)

       lowerCamelCase		:     Optional[int]          =				translator("""This is a test"""   ,  src_lang="""en_XX"""   ,  tgt_lang="""ar_AR"""				)
       self.assertEqual(__magic_name__   ,  [{"""translation_text""": """هذا إختبار"""}]				)

       lowerCamelCase		:     Optional[Any]          =				translator("""This is a test"""   ,  src_lang="""en_XX"""   ,  tgt_lang="""hi_IN"""				)
       self.assertEqual(__magic_name__   ,  [{"""translation_text""": """यह एक परीक्षण है"""}]				)

       # src_lang, tgt_lang can be defined at pipeline call time
       lowerCamelCase		:     str          =				pipeline(task="""translation"""   ,  model=__magic_name__   ,  tokenizer=__magic_name__   ,  src_lang="""en_XX"""   ,  tgt_lang="""ar_AR"""				)
       lowerCamelCase		:     Union[str, Any]          =				translator("""This is a test"""				)
       self.assertEqual(__magic_name__   ,  [{"""translation_text""": """هذا إختبار"""}]				)
      @require_torch
      def 						UpperCamelCase__							(				self				):
       lowerCamelCase		:     List[str]          =				"""patrickvonplaten/t5-tiny-random"""
       lowerCamelCase		:     Tuple          =				pipeline(task="""translation_cn_to_ar"""   ,  model=__magic_name__				)
       self.assertEqual(translator._preprocess_params["""src_lang"""]   ,  """cn"""				)
       self.assertEqual(translator._preprocess_params["""tgt_lang"""]   ,  """ar"""				)
      @require_torch
      def 						UpperCamelCase__							(				self				):
       lowerCamelCase		:     Dict          =				"""patrickvonplaten/t5-tiny-random"""
       with pytest.warns(__magic_name__   ,  match=r""".*translation_en_to_de.*"""				):
        lowerCamelCase		:     Optional[int]          =				pipeline(task="""translation"""   ,  model=__magic_name__				)
       self.assertEqual(translator.task   ,  """translation_en_to_de"""				)
       self.assertEqual(translator._preprocess_params["""src_lang"""]   ,  """en"""				)
       self.assertEqual(translator._preprocess_params["""tgt_lang"""]   ,  """de"""				)






      @require_torch
      def 						UpperCamelCase__							(				self				):
       with self.assertRaises(__magic_name__				):
        pipeline(task="""translation"""				)




