"""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								=		logging.get_logger(__name__)

def  _A(		lowerCAmelCase			,  lowerCAmelCase							):
							try:
														with open(lowerCAmelCase			,  """rb"""							) as flax_state_f:
																					A__       :     List[str]								=			from_bytes(lowerCAmelCase			,  flax_state_f.read()							)
							except UnpicklingError as e:
														try:
																					with open(lowerCAmelCase							) 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(lowerCAmelCase			,  lowerCAmelCase							)



def  _A(		lowerCAmelCase			,  lowerCAmelCase							):

							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
							A__       :     int								=			flatten_dict(jax.tree_util.tree_map(lambda lowerCAmelCase							: x.dtype == jnp.bfloataa			,  lowerCAmelCase							)							).values()
							if any(lowerCAmelCase							):
														# 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."""							)
														A__       :     Union[str, Any]								=			jax.tree_util.tree_map(
														    lambda lowerCAmelCase							: params.astype(np.floataa							) if params.dtype == jnp.bfloataa else params			,  lowerCAmelCase							)

							A__       :     int								=			""""""

							A__       :     int								=			flatten_dict(lowerCAmelCase			,  sep="""."""							)
							A__       :     Tuple								=			pt_model.state_dict()

							# keep track of unexpected & missing keys
							A__       :     Dict								=			[]
							A__       :     Dict								=			set(pt_model_dict.keys()							)

							for flax_key_tuple, flax_tensor in flax_state_dict.items():
														A__       :     Tuple								=			flax_key_tuple.split("""."""							)

														if flax_key_tuple_array[-1] == "kernel" and flax_tensor.ndim == 4:
																					A__       :     Dict								=			flax_key_tuple_array[:-1] + ["""weight"""]
																					A__       :     Tuple								=			jnp.transpose(lowerCAmelCase			,  (3, 2, 0, 1)							)
														elif flax_key_tuple_array[-1] == "kernel":
																					A__       :     Any								=			flax_key_tuple_array[:-1] + ["""weight"""]
																					A__       :     Any								=			flax_tensor.T
														elif flax_key_tuple_array[-1] == "scale":
																					A__       :     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(lowerCAmelCase							):
																												A__       :     Any								=			(
																												    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"""							)
																												)

														A__       :     str								=			""".""".join(lowerCAmelCase							)

														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
																												A__       :     List[str]								=			np.asarray(lowerCAmelCase							) if not isinstance(lowerCAmelCase			,  np.ndarray							) else flax_tensor
																												A__       :     List[Any]								=			torch.from_numpy(lowerCAmelCase							)
																												# remove from missing keys
																												missing_keys.remove(lowerCAmelCase							)
														else:
																					# weight is not expected by PyTorch model
																					unexpected_keys.append(lowerCAmelCase							)

							pt_model.load_state_dict(lowerCAmelCase							)

							# re-transform missing_keys to list
							A__       :     List[Any]								=			list(lowerCAmelCase							)

							if len(lowerCAmelCase							) > 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(lowerCAmelCase							) > 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






"""simple docstring"""



import functools
import operator

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


_UpperCamelCase								=		logging.get_logger(__name__)

_UpperCamelCase								=		{
    "asapp/sew-tiny-100k": "https://huggingface.co/asapp/sew-tiny-100k/resolve/main/config.json",
    # See all SEW models at https://huggingface.co/models?filter=sew
}
class 						__UpperCAmelCase    (__A     ):







			'''simple docstring'''
			_UpperCamelCase  :						Dict					     =				'sew'





			def __init__(			self   ,      snake_case_=32   ,      snake_case_=768   ,      snake_case_=12   ,      snake_case_=12   ,      snake_case_=3_072   ,      snake_case_=2   ,      snake_case_="gelu"   ,      snake_case_=0.1   ,      snake_case_=0.1   ,      snake_case_=0.1   ,      snake_case_=0.0   ,      snake_case_=0.1   ,      snake_case_=0.1   ,      snake_case_=0.02   ,      snake_case_=1E-5   ,      snake_case_="group"   ,      snake_case_="gelu"   ,      snake_case_=(64, 128, 128, 128, 128, 256, 256, 256, 256, 512, 512, 512, 512)   ,      snake_case_=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1)   ,      snake_case_=(10, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1)   ,      snake_case_=False   ,      snake_case_=128   ,      snake_case_=16   ,      snake_case_=True   ,      snake_case_=0.05   ,      snake_case_=10   ,      snake_case_=2   ,      snake_case_=0.0   ,      snake_case_=10   ,      snake_case_=0   ,      snake_case_="mean"   ,      snake_case_=False   ,      snake_case_=False   ,      snake_case_=256   ,      snake_case_=0   ,      snake_case_=1   ,      snake_case_=2   ,      **snake_case_   ,      ):







										'''simple docstring'''


										super().__init__(**snake_case_   ,      pad_token_id=snake_case_   ,      bos_token_id=snake_case_   ,      eos_token_id=snake_case_							)
										A__       :     Dict								=			hidden_size
										A__       :     Dict								=			feat_extract_norm
										A__       :     str								=			feat_extract_activation
										A__       :     Optional[Any]								=			list(snake_case_							)
										A__       :     str								=			list(snake_case_							)
										A__       :     Any								=			list(snake_case_							)
										A__       :     Any								=			conv_bias
										A__       :     Any								=			num_conv_pos_embeddings
										A__       :     Any								=			num_conv_pos_embedding_groups
										A__       :     str								=			len(self.conv_dim							)
										A__       :     Tuple								=			num_hidden_layers
										A__       :     int								=			intermediate_size
										A__       :     Union[str, Any]								=			squeeze_factor
										A__       :     Union[str, Any]								=			hidden_act
										A__       :     List[str]								=			num_attention_heads
										A__       :     List[str]								=			hidden_dropout
										A__       :     Dict								=			attention_dropout
										A__       :     Tuple								=			activation_dropout
										A__       :     Optional[int]								=			feat_proj_dropout
										A__       :     Optional[Any]								=			final_dropout
										A__       :     int								=			layerdrop
										A__       :     List[Any]								=			layer_norm_eps
										A__       :     int								=			initializer_range
										A__       :     Dict								=			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
										A__       :     Optional[Any]								=			apply_spec_augment
										A__       :     int								=			mask_time_prob
										A__       :     Tuple								=			mask_time_length
										A__       :     Optional[Any]								=			mask_time_min_masks
										A__       :     Any								=			mask_feature_prob
										A__       :     List[Any]								=			mask_feature_length
										A__       :     Any								=			mask_feature_min_masks

										# ctc loss
										A__       :     str								=			ctc_loss_reduction
										A__       :     List[Any]								=			ctc_zero_infinity

										# sequence classification
										A__       :     Union[str, Any]								=			use_weighted_layer_sum
										A__       :     str								=			classifier_proj_size





			@property
			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


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





