



"""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 				A_      :



      """simple docstring"""
      __UpperCamelCase		    =							42
      __UpperCamelCase		    =							None
      # Automatically constructed
      __UpperCamelCase		    =							"dict"
      __UpperCamelCase		    =							None
      __UpperCamelCase		    =							field(default="""Translation"""     ,       init=_UpperCamelCase     ,       repr=_UpperCamelCase    )






      def __call__( self			:Optional[Any] )    ->      Tuple:
            return pa.struct({lang: pa.string() for lang in sorted(self.languages )} )






      def      UpperCAmelCase__					( self			:Optional[int] )    ->      Optional[int]:
            from .features import Value

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







@dataclass
class 				A_      :



      """simple docstring"""
      __UpperCamelCase		    =							None
      __UpperCamelCase		    =							None
      __UpperCamelCase		    =							None
      # Automatically constructed
      __UpperCamelCase		    =							"dict"
      __UpperCamelCase		    =							None
      __UpperCamelCase		    =							field(default="""TranslationVariableLanguages"""     ,       init=_UpperCamelCase     ,       repr=_UpperCamelCase    )






      def      UpperCAmelCase__					( self			:List[Any] )    ->      List[Any]:
            UpperCAmelCase					=			sorted(set(self.languages ) ) if self.languages else None
            UpperCAmelCase					=			len(self.languages ) if self.languages else None






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






      def      UpperCAmelCase__					( self			:Optional[int]       ,  lowercase_			:Tuple )    ->      Dict:
            UpperCAmelCase					=			set(self.languages )
            if self.languages and set(_UpperCAmelCase ) - lang_set:
                  raise ValueError(
                      f"""Some languages in example ({', '.join(sorted(set(_UpperCAmelCase ) - lang_set ) )}) are not in valid set ({', '.join(_UpperCAmelCase )}).""" )

            # 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(_UpperCAmelCase       ,  _UpperCAmelCase ):
                        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(_UpperCAmelCase ) )

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






      def      UpperCAmelCase__					( self			:Optional[Any] )    ->      List[str]:
            from .features import Sequence, Value

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










"""simple docstring"""


import re

import jax.numpy as jnp
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.random import PRNGKey

from ..utils import logging


snake_case_    					=	logging.get_logger(__name__)

def 			_lowerCAmelCase				(       lowercase_						):
						UpperCAmelCase					=			R'\w+[.]\d+'
						UpperCAmelCase					=			re.findall(lowercase_		,     lowercase_						)
						for pat in pats:
												UpperCAmelCase					=			key.replace(lowercase_		,     '_'.join(pat.split('.'						)						)						)
						return key

def 			_lowerCAmelCase				(       lowercase_		,     lowercase_		,     lowercase_						):
						UpperCAmelCase					=			pt_tuple_key[:-1] + ('scale',)
						if (
						    any('norm' in str_ for str_ in pt_tuple_key						)
						    and (pt_tuple_key[-1] == "bias")
						    and (pt_tuple_key[:-1] + ("bias",) not in random_flax_state_dict)
						    and (pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict)
						):
												UpperCAmelCase					=			pt_tuple_key[:-1] + ('scale',)
												return renamed_pt_tuple_key, pt_tensor
						elif pt_tuple_key[-1] in ["weight", "gamma"] and pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict:
												UpperCAmelCase					=			pt_tuple_key[:-1] + ('scale',)
												return renamed_pt_tuple_key, pt_tensor

						# embedding
						if pt_tuple_key[-1] == "weight" and pt_tuple_key[:-1] + ("embedding",) in random_flax_state_dict:
												UpperCAmelCase					=			pt_tuple_key[:-1] + ('embedding',)
												return renamed_pt_tuple_key, pt_tensor

						# conv layer
						UpperCAmelCase					=			pt_tuple_key[:-1] + ('kernel',)
						if pt_tuple_key[-1] == "weight" and pt_tensor.ndim == 4:
												UpperCAmelCase					=			pt_tensor.transpose(2		,     3		,     1		,     0						)
												return renamed_pt_tuple_key, pt_tensor

						# linear layer
						UpperCAmelCase					=			pt_tuple_key[:-1] + ('kernel',)
						if pt_tuple_key[-1] == "weight":
												UpperCAmelCase					=			pt_tensor.T
												return renamed_pt_tuple_key, pt_tensor

						# old PyTorch layer norm weight
						UpperCAmelCase					=			pt_tuple_key[:-1] + ('weight',)
						if pt_tuple_key[-1] == "gamma":
												return renamed_pt_tuple_key, pt_tensor

						# old PyTorch layer norm bias
						UpperCAmelCase					=			pt_tuple_key[:-1] + ('bias',)
						if pt_tuple_key[-1] == "beta":
												return renamed_pt_tuple_key, pt_tensor

						return pt_tuple_key, pt_tensor






def 			_lowerCAmelCase				(       lowercase_		,     lowercase_		,     lowercase_=42						):
						# Step 1: Convert pytorch tensor to numpy
						UpperCAmelCase					=			{k: v.numpy() for k, v in pt_state_dict.items()}

						# Step 2: Since the model is stateless, get random Flax params
						UpperCAmelCase					=			flax_model.init_weights(PRNGKey(lowercase_						)						)

						UpperCAmelCase					=			flatten_dict(lowercase_						)
						UpperCAmelCase					=			{}

						# Need to change some parameters name to match Flax names
						for pt_key, pt_tensor in pt_state_dict.items():
												UpperCAmelCase					=			rename_key(lowercase_						)
												UpperCAmelCase					=			tuple(renamed_pt_key.split('.'						)						)

												# Correctly rename weight parameters
												UpperCAmelCase				,   UpperCAmelCase					=			rename_key_and_reshape_tensor(lowercase_		,     lowercase_		,     lowercase_						)

												if flax_key in random_flax_state_dict:
																		if flax_tensor.shape != random_flax_state_dict[flax_key].shape:
																								raise ValueError(
																								    F"""PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape """
																								    F"""{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}."""						)

        # also add unexpected weight so that warning is thrown
												UpperCAmelCase					=			jnp.asarray(lowercase_						)

						return unflatten_dict(lowercase_						)





