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 SCREAMING_SNAKE_CASE_		:
   __lowerCAmelCase           =				42
   __lowerCAmelCase           =				None
   # Automatically constructed
   __lowerCAmelCase           =				"dict"
   __lowerCAmelCase           =				None
   __lowerCAmelCase           =				field(default="""Translation"""  ,     init=_UpperCamelCase  ,     repr=_UpperCamelCase		)







   def __call__(     self						:						List[Any]				):





    """simple docstring"""


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







   def 						lowerCamelCase_	(     self						:						Any				):





    """simple docstring"""


    from .features import Value

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





@dataclass
class SCREAMING_SNAKE_CASE_		:
   __lowerCAmelCase           =				None
   __lowerCAmelCase           =				None
   __lowerCAmelCase           =				None
   # Automatically constructed
   __lowerCAmelCase           =				"dict"
   __lowerCAmelCase           =				None
   __lowerCAmelCase           =				field(default="""TranslationVariableLanguages"""  ,     init=_UpperCamelCase  ,     repr=_UpperCamelCase		)







   def 						lowerCamelCase_	(     self						:						str				):





    """simple docstring"""


    UpperCamelCase			  =      sorted(set(self.languages				)				) if self.languages else None
    UpperCamelCase			  =      len(self.languages				) if self.languages else None







   def __call__(     self						:						Union[str, Any]				):





    """simple docstring"""


    return pa.struct({"""language""": pa.list_(pa.string()				), """translation""": pa.list_(pa.string()				)}				)







   def 						lowerCamelCase_	(     self						:						List[str]   ,   lowerCamelCase_						:						Tuple				):





    """simple docstring"""


    UpperCamelCase			  =      set(self.languages				)
    if self.languages and set(lowerCamelCase_				) - lang_set:
     raise ValueError(
         f"""Some languages in example ({", ".join(sorted(set(lowerCamelCase_				) - lang_set				)				)}) are not in valid set ({", ".join(lowerCamelCase_				)})."""				)

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

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

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







   def 						lowerCamelCase_	(     self						:						Optional[Any]				):





    """simple docstring"""


    from .features import Sequence, Value

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





"""simple docstring"""




# We ignore warnings about stepping the scheduler since we step it ourselves during gradient accumulation

import warnings

from .state import AcceleratorState, GradientState


warnings.filterwarnings("ignore", category=UserWarning, module="torch.optim.lr_scheduler")






class       UpperCAmelCase_    :



      def __init__(	self	:      Optional[Any]	,      A	:      List[str]	,      A	:      List[str]	,      A	:      bool = True	,      A	:      bool = False		):
           _UpperCAmelCase     :      Dict														=       scheduler
           _UpperCAmelCase     :      str														=       optimizers if isinstance(A	,      (list, tuple)		) else [optimizers]
           _UpperCAmelCase     :      List[str]														=       split_batches
           _UpperCAmelCase     :      Dict														=       step_with_optimizer
           _UpperCAmelCase     :      Tuple														=       GradientState()



      def 		snake_case_   (	self	:      Optional[int]	,      *A	:      List[Any]	,      **A	:      Optional[int]		):
           if not self.step_with_optimizer:
                # No link between scheduler and optimizer -> just step
                self.scheduler.step(*A	,      **A		)
                return

           # Otherwise, first make sure the optimizer was stepped.
           if not self.gradient_state.sync_gradients:
                if self.gradient_state.adjust_scheduler:
                     self.scheduler._step_count += 1
                return

           for opt in self.optimizers:
                if opt.step_was_skipped:
                     return
           if self.split_batches:
                # Split batches -> the training dataloader batch size is not changed so one step per training step
                self.scheduler.step(*A	,      **A		)
           else:
                # Otherwise the training dataloader batch size was multiplied by `num_processes`, so we need to do
                # num_processes steps per training step
                _UpperCAmelCase     :      int														=       AcceleratorState().num_processes
                for _ in range(A		):
                     # Special case when using OneCycle and `drop_last` was not used
                     if hasattr(self.scheduler	,      "total_steps"		):
                          if self.scheduler._step_count <= self.scheduler.total_steps:
                               self.scheduler.step(*A	,      **A		)
                     else:
                          self.scheduler.step(*A	,      **A		)



      def 		snake_case_   (	self	:      Union[str, Any]		):
           return self.scheduler.get_last_lr()



      def 		snake_case_   (	self	:      str		):
           return self.scheduler.state_dict()



      def 		snake_case_   (	self	:      str	,      A	:      Optional[Any]		):
           self.scheduler.load_state_dict(A		)



      def 		snake_case_   (	self	:      Tuple		):
           return self.scheduler.get_lr()



      def 		snake_case_   (	self	:      Optional[int]	,      *A	:      List[str]	,      **A	:      Optional[Any]		):
           return self.scheduler.print_lr(*A	,      **A		)
