






"""simple docstring"""






import functools
import operator

from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCAmelCase_   : Optional[int]   			= logging.get_logger(__name__)

UpperCAmelCase_   : List[Any]   			= {
    """asapp/sew-d-tiny-100k""": """https://huggingface.co/asapp/sew-d-tiny-100k/resolve/main/config.json""",
    # See all SEW-D models at https://huggingface.co/models?filter=sew-d
}




class 							lowerCAmelCase__				(			UpperCAmelCase__ ):






      '''simple docstring'''

      __UpperCamelCase	 =  "sew-d"
      def __init__(						self     :  Dict						,	lowercase_     :  Optional[Any]=32						,	lowercase_     :  List[Any]=768						,	lowercase_     :  int=12						,	lowercase_     :  Dict=12						,	lowercase_     :  Union[str, Any]=3072						,	lowercase_     :  Dict=2						,	lowercase_     :  List[Any]=512						,	lowercase_     :  Union[str, Any]=256						,	lowercase_     :  Optional[int]=True						,	lowercase_     :  List[str]=True						,	lowercase_     :  List[Any]=("p2c", "c2p")						,	lowercase_     :  Optional[int]="layer_norm"						,	lowercase_     :  List[Any]="gelu_python"						,	lowercase_     :  int=0.1						,	lowercase_     :  Optional[int]=0.1						,	lowercase_     :  Optional[int]=0.1						,	lowercase_     :  List[str]=0.0						,	lowercase_     :  Any=0.1						,	lowercase_     :  Dict=0.02						,	lowercase_     :  str=1e-7						,	lowercase_     :  Optional[int]=1e-5						,	lowercase_     :  int="group"						,	lowercase_     :  str="gelu"						,	lowercase_     :  List[str]=(64, 128, 128, 128, 128, 256, 256, 256, 256, 512, 512, 512, 512)						,	lowercase_     :  int=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1)						,	lowercase_     :  List[Any]=(10, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1)						,	lowercase_     :  List[Any]=False						,	lowercase_     :  int=128						,	lowercase_     :  List[Any]=16						,	lowercase_     :  Tuple=True						,	lowercase_     :  Any=0.05						,	lowercase_     :  Tuple=10						,	lowercase_     :  List[str]=2						,	lowercase_     :  Any=0.0						,	lowercase_     :  int=10						,	lowercase_     :  Optional[Any]=0						,	lowercase_     :  Optional[Any]="mean"						,	lowercase_     :  List[Any]=False						,	lowercase_     :  int=False						,	lowercase_     :  str=256						,	lowercase_     :  int=0						,	lowercase_     :  str=1						,	lowercase_     :  Any=2						,	**lowercase_     :  Union[str, Any]						,	):




          '''simple docstring'''


          super().__init__(**lowercase_						,	pad_token_id=lowercase_						,	bos_token_id=lowercase_						,	eos_token_id=lowercase_)
          SCREAMING_SNAKE_CASE_      :  Any    						=		hidden_size
          SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		feat_extract_norm
          SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		feat_extract_activation
          SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		list(lowercase_)
          SCREAMING_SNAKE_CASE_      :  str    						=		list(lowercase_)
          SCREAMING_SNAKE_CASE_      :  List[str]    						=		list(lowercase_)
          SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		conv_bias
          SCREAMING_SNAKE_CASE_      :  int    						=		num_conv_pos_embeddings
          SCREAMING_SNAKE_CASE_      :  Dict    						=		num_conv_pos_embedding_groups
          SCREAMING_SNAKE_CASE_      :  List[Any]    						=		len(self.conv_dim)
          SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		num_hidden_layers
          SCREAMING_SNAKE_CASE_      :  Dict    						=		intermediate_size
          SCREAMING_SNAKE_CASE_      :  int    						=		squeeze_factor
          SCREAMING_SNAKE_CASE_      :  Tuple    						=		max_position_embeddings
          SCREAMING_SNAKE_CASE_      :  Any    						=		position_buckets
          SCREAMING_SNAKE_CASE_      :  Tuple    						=		share_att_key
          SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		relative_attention
          SCREAMING_SNAKE_CASE_      :  Tuple    						=		norm_rel_ebd
          SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		list(lowercase_)
          SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		hidden_act
          SCREAMING_SNAKE_CASE_      :  int    						=		num_attention_heads
          SCREAMING_SNAKE_CASE_      :  Dict    						=		hidden_dropout
          SCREAMING_SNAKE_CASE_      :  List[str]    						=		attention_dropout
          SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		activation_dropout
          SCREAMING_SNAKE_CASE_      :  str    						=		feat_proj_dropout
          SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		final_dropout
          SCREAMING_SNAKE_CASE_      :  Any    						=		layer_norm_eps
          SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		feature_layer_norm_eps
          SCREAMING_SNAKE_CASE_      :  Dict    						=		initializer_range
          SCREAMING_SNAKE_CASE_      :  Any    						=		vocab_size

          if (
              (len(self.conv_stride) != self.num_feat_extract_layers)
              or (len(self.conv_kernel) != self.num_feat_extract_layers)
              or (len(self.conv_dim) != self.num_feat_extract_layers)
          ):
              raise ValueError(
                  '''Configuration for convolutional layers is incorrect.'''
                  '''It is required that `len(config.conv_dim)` == `len(config.conv_stride)` == `len(config.conv_kernel)`,'''
                  F'but is `len(config.conv_dim) = {len(self.conv_dim)}`, `len(config.conv_stride)'
                  F'= {len(self.conv_stride)}`, `len(config.conv_kernel) = {len(self.conv_kernel)}`.')

          # fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
          SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		apply_spec_augment
          SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		mask_time_prob
          SCREAMING_SNAKE_CASE_      :  Dict    						=		mask_time_length
          SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		mask_time_min_masks
          SCREAMING_SNAKE_CASE_      :  List[Any]    						=		mask_feature_prob
          SCREAMING_SNAKE_CASE_      :  Tuple    						=		mask_feature_length
          SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		mask_feature_min_masks

          # ctc loss
          SCREAMING_SNAKE_CASE_      :  int    						=		ctc_loss_reduction
          SCREAMING_SNAKE_CASE_      :  Any    						=		ctc_zero_infinity

          # sequence classification
          SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		use_weighted_layer_sum
          SCREAMING_SNAKE_CASE_      :  List[Any]    						=		classifier_proj_size



      @property
      def _SCREAMING_SNAKE_CASE (						self     :  Any):




          '''simple docstring'''


          return functools.reduce(operator.mul						,	self.conv_stride						,	1)










"""simple docstring"""






from pickle import UnpicklingError

import jax
import jax.numpy as jnp
import numpy as np
from flax.serialization import from_bytes
from flax.traverse_util import flatten_dict

from ..utils import logging


UpperCAmelCase_   : Optional[int]   			= logging.get_logger(__name__)



def 					_A			(__a    ,				__a							) ->       Tuple:
    """simple docstring"""






    try:
        with open(__a    ,				'''rb'''							) as flax_state_f:
            SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		from_bytes(__a    ,				flax_state_f.read()							)
    except UnpicklingError as e:
        try:
            with open(__a							) as f:
                if f.read().startswith('''version'''							):
                    raise OSError(
                        '''You seem to have cloned a repository without having git-lfs installed. Please'''
                        ''' install git-lfs and run `git lfs install` followed by `git lfs pull` in the'''
                        ''' folder you cloned.'''							)
                else:
                    raise ValueError from e
        except (UnicodeDecodeError, ValueError):
            raise EnvironmentError(f'Unable to convert {model_file} to Flax deserializable object. '							)

    return load_flax_weights_in_pytorch_model(__a    ,				__a							)





def 					_A			(__a    ,				__a							) ->       Tuple:
    """simple docstring"""






    try:
        import torch  # noqa: F401
    except ImportError:
        logger.error(
            '''Loading Flax weights in PyTorch requires both PyTorch and Flax to be installed. Please see'''
            ''' https://pytorch.org/ and https://flax.readthedocs.io/en/latest/installation.html for installation'''
            ''' instructions.'''							)
        raise

    # check if we have bf16 weights
    SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		flatten_dict(jax.tree_util.tree_map(lambda __a							: x.dtype == jnp.bfloataa    ,				__a							)							).values()
    if any(__a							):
        # convert all weights to fp32 if they are bf16 since torch.from_numpy can-not handle bf16

        # and bf16 is not fully supported in PT yet.
        logger.warning(
            '''Found ``bfloat16`` weights in Flax model. Casting all ``bfloat16`` weights to ``float32`` '''
            '''before loading those in PyTorch model.'''							)
        SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		jax.tree_util.tree_map(
            lambda __a							: params.astype(np.floataa							) if params.dtype == jnp.bfloataa else params    ,				__a							)

    SCREAMING_SNAKE_CASE_      :  int    						=		''''''

    SCREAMING_SNAKE_CASE_      :  str    						=		flatten_dict(__a    ,				sep='''.'''							)
    SCREAMING_SNAKE_CASE_      :  List[Any]    						=		pt_model.state_dict()

    # keep track of unexpected & missing keys
    SCREAMING_SNAKE_CASE_      :  str    						=		[]
    SCREAMING_SNAKE_CASE_      :  Any    						=		set(pt_model_dict.keys()							)

    for flax_key_tuple, flax_tensor in flax_state_dict.items():
        SCREAMING_SNAKE_CASE_      :  Any    						=		flax_key_tuple.split('''.'''							)

        if flax_key_tuple_array[-1] == "kernel" and flax_tensor.ndim == 4:
            SCREAMING_SNAKE_CASE_      :  Any    						=		flax_key_tuple_array[:-1] + ['''weight''']
            SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		jnp.transpose(__a    ,				(3, 2, 0, 1)							)
        elif flax_key_tuple_array[-1] == "kernel":
            SCREAMING_SNAKE_CASE_      :  Tuple    						=		flax_key_tuple_array[:-1] + ['''weight''']
            SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		flax_tensor.T
        elif flax_key_tuple_array[-1] == "scale":
            SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		flax_key_tuple_array[:-1] + ['''weight''']

        if "time_embedding" not in flax_key_tuple_array:
            for i, flax_key_tuple_string in enumerate(__a							):
                SCREAMING_SNAKE_CASE_      :  List[str]    						=		(
                    flax_key_tuple_string.replace('''_0'''    ,				'''.0'''							)
                    .replace('''_1'''    ,				'''.1'''							)
                    .replace('''_2'''    ,				'''.2'''							)
                    .replace('''_3'''    ,				'''.3'''							)
                    .replace('''_4'''    ,				'''.4'''							)
                    .replace('''_5'''    ,				'''.5'''							)
                    .replace('''_6'''    ,				'''.6'''							)
                    .replace('''_7'''    ,				'''.7'''							)
                    .replace('''_8'''    ,				'''.8'''							)
                    .replace('''_9'''    ,				'''.9'''							)
                )

        SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		'''.'''.join(__a							)

        if flax_key in pt_model_dict:
            if flax_tensor.shape != pt_model_dict[flax_key].shape:
                raise ValueError(
                    f'Flax checkpoint seems to be incorrect. Weight {flax_key_tuple} was expected '
                    f'to be of shape {pt_model_dict[flax_key].shape}, but is {flax_tensor.shape}.'							)
            else:
                # add weight to pytorch dict
                SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		np.asarray(__a							) if not isinstance(__a    ,				np.ndarray							) else flax_tensor
                SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		torch.from_numpy(__a							)
                # remove from missing keys
                missing_keys.remove(__a							)
        else:
            # weight is not expected by PyTorch model
            unexpected_keys.append(__a							)

    pt_model.load_state_dict(__a							)

    # re-transform missing_keys to list
    SCREAMING_SNAKE_CASE_      :  int    						=		list(__a							)

    if len(__a							) > 0:
        logger.warning(
            '''Some weights of the Flax model were not used when initializing the PyTorch model'''
            f' {pt_model.__class__.__name__}: {unexpected_keys}\n- This IS expected if you are initializing'
            f' {pt_model.__class__.__name__} from a Flax model trained on another task or with another architecture'
            ''' (e.g. initializing a BertForSequenceClassification model from a FlaxBertForPreTraining model).\n- This'''
            f' IS NOT expected if you are initializing {pt_model.__class__.__name__} from a Flax model that you expect'
            ''' to be exactly identical (e.g. initializing a BertForSequenceClassification model from a'''
            ''' FlaxBertForSequenceClassification model).'''							)
    if len(__a							) > 0:
        logger.warning(
            f'Some weights of {pt_model.__class__.__name__} were not initialized from the Flax model and are newly'
            f' initialized: {missing_keys}\nYou should probably TRAIN this model on a down-stream task to be able to'
            ''' use it for predictions and inference.'''							)

    return pt_model


