



'''simple docstring'''





from dataclasses import dataclass, field
from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional, Union

import pyarrow as pa


if TYPE_CHECKING:
   from .features import FeatureType



@dataclass
class     _UpperCAmelCase			:
     UpperCamelCase__		    =				42
     UpperCamelCase__		    =				None
     # Automatically constructed
     UpperCamelCase__		    =				"dict"
     UpperCamelCase__		    =				None
     UpperCamelCase__		    =				field(default='''Translation'''			,      init=__lowercase			,      repr=__lowercase     )




     def __call__(				self):
         return pa.struct({lang: pa.string() for lang in sorted(self.languages)})




     def 		snake_case_		(				self):
         from .features import Value

         return {k: Value('''string''') for k in sorted(self.languages)}







@dataclass
class     _UpperCAmelCase			:
     UpperCamelCase__		    =				None
     UpperCamelCase__		    =				None
     UpperCamelCase__		    =				None
     # Automatically constructed
     UpperCamelCase__		    =				"dict"
     UpperCamelCase__		    =				None
     UpperCamelCase__		    =				field(default='''TranslationVariableLanguages'''			,      init=__lowercase			,      repr=__lowercase     )




     def 		snake_case_		(				self):
         A__		 =					sorted(set(self.languages)) if self.languages else None
         A__		 =					len(self.languages) if self.languages else None




     def __call__(				self):
         return pa.struct({'''language''': pa.list_(pa.string()), '''translation''': pa.list_(pa.string())})




     def 		snake_case_		(				self ,						a__):
         A__		 =					set(self.languages)
         if self.languages and set(__a) - lang_set:
             raise ValueError(
                 F"Some languages in example ({', '.join(sorted(set(__a) - lang_set))}) are not in valid set ({', '.join(__a)}).")

         # Convert dictionary into tuples, splitting out cases where there are
         # multiple translations for a single language.
         A__		 =					[]
         for lang, text in translation_dict.items():
             if isinstance(__a ,						__a):
                 translation_tuples.append((lang, text))
             else:
                 translation_tuples.extend([(lang, el) for el in text])

        # Ensure translations are in ascending order by language code.
         A__		 =					zip(*sorted(__a))

         return {"language": languages, "translation": translations}




     def 		snake_case_		(				self):
         from .features import Sequence, Value

         return {
             "language": Sequence(Value('''string''')),
             "translation": Sequence(Value('''string''')),
         }





# Copyright 2022 The HuggingFace Team and The OpenBMB Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


_lowercase						  =		{
    "configuration_cpmant": ["CPMANT_PRETRAINED_CONFIG_ARCHIVE_MAP", "CpmAntConfig"],
    "tokenization_cpmant": ["CpmAntTokenizer"],
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _lowercase						  =		[
       "CPMANT_PRETRAINED_MODEL_ARCHIVE_LIST",
       "CpmAntForCausalLM",
       "CpmAntModel",
       "CpmAntPreTrainedModel",
   ]


if TYPE_CHECKING:
   from .configuration_cpmant import CPMANT_PRETRAINED_CONFIG_ARCHIVE_MAP, CpmAntConfig
   from .tokenization_cpmant import CpmAntTokenizer

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_cpmant import (
          CPMANT_PRETRAINED_MODEL_ARCHIVE_LIST,
          CpmAntForCausalLM,
          CpmAntModel,
          CpmAntPreTrainedModel,
      )


else:
   import sys

   _lowercase						  =		_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
