




"""simple docstring"""




import os
from pickle import UnpicklingError
from typing import Dict, Tuple

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

import transformers

from .utils import logging


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



def     a__ (				snake_case__       , snake_case__       , snake_case__       , snake_case__=False				)						->					str:







			try:
						import torch  # noqa: F401
			except ImportError:
						logger.error(
						    """Loading a PyTorch model in Flax, 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

			if not is_sharded:
						lowerCamelCase			      =					os.path.abspath(_UpperCAmelCase				)
						logger.info(F'Loading PyTorch weights from {pt_path}'				)

						lowerCamelCase			      =					torch.load(_UpperCAmelCase       , map_location="""cpu"""				)
						logger.info(F'PyTorch checkpoint contains {sum(t.numel() for t in pt_state_dict.values()				):,} parameters.'				)

						lowerCamelCase			      =					convert_pytorch_state_dict_to_flax(_UpperCAmelCase       , _UpperCAmelCase				)
			else:
						# model is sharded and pytorch_checkpoint_path already contains the list of .pt shard files
						lowerCamelCase			      =					convert_pytorch_sharded_state_dict_to_flax(_UpperCAmelCase       , _UpperCAmelCase				)
			return flax_state_dict



def     a__ (				snake_case__       , snake_case__       , snake_case__       , snake_case__       , )						->					(Tuple[str], np.ndarray):







			def is_key_or_prefix_key_in_dict(snake_case__				) -> bool:
						return len(set(_UpperCAmelCase				) & {key, (model_prefix,) + key}				) > 0

			# layer norm
			lowerCamelCase			      =					pt_tuple_key[:-1] + ("scale",)
			if pt_tuple_key[-1] in ["weight", "gamma"] and is_key_or_prefix_key_in_dict(_UpperCAmelCase				):
						return renamed_pt_tuple_key, pt_tensor

			# batch norm layer mean
			lowerCamelCase			      =					pt_tuple_key[:-1] + ("mean",)
			if pt_tuple_key[-1] == "running_mean" and not is_key_or_prefix_key_in_dict(_UpperCAmelCase				):
						return renamed_pt_tuple_key, pt_tensor

			# batch norm layer var
			lowerCamelCase			      =					pt_tuple_key[:-1] + ("var",)
			if pt_tuple_key[-1] == "running_var" and not is_key_or_prefix_key_in_dict(_UpperCAmelCase				):
						return renamed_pt_tuple_key, pt_tensor

			# embedding
			lowerCamelCase			      =					pt_tuple_key[:-1] + ("embedding",)
			if pt_tuple_key[-1] == "weight" and is_key_or_prefix_key_in_dict(_UpperCAmelCase				):
						return renamed_pt_tuple_key, pt_tensor

			# conv layer
			lowerCamelCase			      =					pt_tuple_key[:-1] + ("kernel",)
			if pt_tuple_key[-1] == "weight" and pt_tensor.ndim == 4 and not is_key_or_prefix_key_in_dict(_UpperCAmelCase				):
						lowerCamelCase			      =					pt_tensor.transpose(2       , 3       , 1       , 0				)
						return renamed_pt_tuple_key, pt_tensor

			# linear layer
			lowerCamelCase			      =					pt_tuple_key[:-1] + ("kernel",)
			if pt_tuple_key[-1] == "weight" and not is_key_or_prefix_key_in_dict(_UpperCAmelCase				):
						lowerCamelCase			      =					pt_tensor.T
						return renamed_pt_tuple_key, pt_tensor

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

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

			# New `weight_norm` from https://github.com/huggingface/transformers/pull/24030
			lowerCamelCase			      =					None
			if pt_tuple_key[-3::2] == ("parametrizations", "original0"):
						lowerCamelCase			      =					pt_tuple_key[-2] + "_g"
			elif pt_tuple_key[-3::2] == ("parametrizations", "original1"):
						lowerCamelCase			      =					pt_tuple_key[-2] + "_v"
			if name is not None:
						lowerCamelCase			      =					pt_tuple_key[:-3] + (name,)
						return renamed_pt_tuple_key, pt_tensor

			return pt_tuple_key, pt_tensor



def     a__ (				snake_case__       , snake_case__				)						->					Tuple:
			lowerCamelCase			      =					{k: v.numpy() for k, v in pt_state_dict.items()}

			lowerCamelCase			      =					flax_model.base_model_prefix

			# use params dict if the model contains batch norm layers
			if "params" in flax_model.params:
						lowerCamelCase			      =					flax_model.params["params"]
			else:
						lowerCamelCase			      =					flax_model.params
			lowerCamelCase			      =					flatten_dict(_UpperCAmelCase				)

			# add batch_stats keys,values to dict
			if "batch_stats" in flax_model.params:
						lowerCamelCase			      =					flatten_dict(flax_model.params["""batch_stats"""]				)
						random_flax_state_dict.update(_UpperCAmelCase				)

			lowerCamelCase			      =					{}

			lowerCamelCase			      =					(model_prefix not in flax_model_params) and (
			    model_prefix in {k.split("""."""				)[0] for k in pt_state_dict.keys()}
			)
			lowerCamelCase			      =					(model_prefix in flax_model_params) and (
			    model_prefix not in {k.split("""."""				)[0] for k in pt_state_dict.keys()}
			)

			# Need to change some parameters name to match Flax names
			for pt_key, pt_tensor in pt_state_dict.items():
						lowerCamelCase			      =					tuple(pt_key.split("""."""				)				)

						# remove base model prefix if necessary
						lowerCamelCase			      =					pt_tuple_key[0] == model_prefix
						if load_model_with_head_into_base_model and has_base_model_prefix:
									lowerCamelCase			      =					pt_tuple_key[1:]

						# Correctly rename weight parameters
						lowerCamelCase			      =					rename_key_and_reshape_tensor(
						    _UpperCAmelCase       , _UpperCAmelCase       , _UpperCAmelCase       , _UpperCAmelCase				)

						# add model prefix if necessary
						lowerCamelCase			      =					(model_prefix,) + flax_key in random_flax_state_dict
						if load_base_model_into_model_with_head and require_base_model_prefix:
									lowerCamelCase			      =					(model_prefix,) + flax_key

						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}.'				)

        # add batch stats if the model contains batchnorm layers
						if "batch_stats" in flax_model.params:
									if "mean" in flax_key[-1] or "var" in flax_key[-1]:
												lowerCamelCase			      =					jnp.asarray(_UpperCAmelCase				)
												continue
									# remove num_batches_tracked key
									if "num_batches_tracked" in flax_key[-1]:
												flax_state_dict.pop(_UpperCAmelCase       , _UpperCAmelCase				)
												continue

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

						else:
									# also add unexpected weight so that warning is thrown
									lowerCamelCase			      =					jnp.asarray(_UpperCAmelCase				)

			return unflatten_dict(_UpperCAmelCase				)



def     a__ (				snake_case__       , snake_case__				)						->					Optional[int]:
			import torch

			# Load the index
			lowerCamelCase			      =					{}
			for shard_file in shard_filenames:
						# load using msgpack utils
						lowerCamelCase			      =					torch.load(_UpperCAmelCase				)
						lowerCamelCase			      =					{k: v.numpy() for k, v in pt_state_dict.items()}

						lowerCamelCase			      =					flax_model.base_model_prefix

						# use params dict if the model contains batch norm layers and then add batch_stats keys,values to dict
						if "batch_stats" in flax_model.params:
									lowerCamelCase			      =					flax_model.params["params"]

									lowerCamelCase			      =					flatten_dict(_UpperCAmelCase				)
									random_flax_state_dict.update(flatten_dict(flax_model.params["""batch_stats"""]				)				)
						else:
									lowerCamelCase			      =					flax_model.params
									lowerCamelCase			      =					flatten_dict(_UpperCAmelCase				)

						lowerCamelCase			      =					(model_prefix not in flax_model_params) and (
						    model_prefix in {k.split("""."""				)[0] for k in pt_state_dict.keys()}
						)
						lowerCamelCase			      =					(model_prefix in flax_model_params) and (
						    model_prefix not in {k.split("""."""				)[0] for k in pt_state_dict.keys()}
						)
						# Need to change some parameters name to match Flax names
						for pt_key, pt_tensor in pt_state_dict.items():
									lowerCamelCase			      =					tuple(pt_key.split("""."""				)				)

									# remove base model prefix if necessary
									lowerCamelCase			      =					pt_tuple_key[0] == model_prefix
									if load_model_with_head_into_base_model and has_base_model_prefix:
												lowerCamelCase			      =					pt_tuple_key[1:]

									# Correctly rename weight parameters
									lowerCamelCase			      =					rename_key_and_reshape_tensor(
									    _UpperCAmelCase       , _UpperCAmelCase       , _UpperCAmelCase       , _UpperCAmelCase				)
									# add model prefix if necessary
									lowerCamelCase			      =					(model_prefix,) + flax_key in random_flax_state_dict
									if load_base_model_into_model_with_head and require_base_model_prefix:
												lowerCamelCase			      =					(model_prefix,) + flax_key

									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}.'				)

            # add batch stats if the model contains batchnorm layers
									if "batch_stats" in flax_model.params:
												if "mean" in flax_key[-1]:
															lowerCamelCase			      =					jnp.asarray(_UpperCAmelCase				)
															continue
												if "var" in flax_key[-1]:
															lowerCamelCase			      =					jnp.asarray(_UpperCAmelCase				)
															continue
												# remove num_batches_tracked key
												if "num_batches_tracked" in flax_key[-1]:
															flax_state_dict.pop(_UpperCAmelCase       , _UpperCAmelCase				)
															continue

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

									else:
												# also add unexpected weight so that warning is thrown
												lowerCamelCase			      =					jnp.asarray(_UpperCAmelCase				)
			return unflatten_dict(_UpperCAmelCase				)



def     a__ (				snake_case__       , snake_case__				)						->					str:
			lowerCamelCase			      =					os.path.abspath(_UpperCAmelCase				)
			logger.info(F'Loading Flax weights from {flax_checkpoint_path}'				)

			# import correct flax class
			lowerCamelCase			      =					getattr(_UpperCAmelCase       , """Flax""" + model.__class__.__name__				)

			# load flax weight dict
			with open(_UpperCAmelCase       , """rb"""				) as state_f:
						try:
									lowerCamelCase			      =					from_bytes(_UpperCAmelCase       , state_f.read()				)
						except UnpicklingError:
									raise EnvironmentError(F'Unable to convert {flax_checkpoint_path} to Flax deserializable object. '				)

			return load_flax_weights_in_pytorch_model(_UpperCAmelCase       , _UpperCAmelCase				)



def     a__ (				snake_case__       , snake_case__				)						->					int:







			try:
						import torch  # noqa: F401
			except ImportError:
						logger.error(
						    """Loading a 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
			lowerCamelCase			      =					flatten_dict(jax.tree_util.tree_map(lambda snake_case__				: x.dtype == jnp.bfloataa       , _UpperCAmelCase				)				).values()
			if any(_UpperCAmelCase				):
						# convert all weights to fp32 if the 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."""				)
						lowerCamelCase			      =					jax.tree_util.tree_map(
						    lambda snake_case__				: params.astype(np.floataa				) if params.dtype == jnp.bfloataa else params       , _UpperCAmelCase				)

			lowerCamelCase			      =					flatten_dict(_UpperCAmelCase				)
			lowerCamelCase			      =					pt_model.state_dict()

			lowerCamelCase			      =					(pt_model.base_model_prefix in flax_state) and (
			    pt_model.base_model_prefix not in {k.split("""."""				)[0] for k in pt_model_dict.keys()}
			)
			lowerCamelCase			      =					(pt_model.base_model_prefix not in flax_state) and (
			    pt_model.base_model_prefix in {k.split("""."""				)[0] for k in pt_model_dict.keys()}
			)

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

			for flax_key_tuple, flax_tensor in flax_state_dict.items():
						lowerCamelCase			      =					flax_key_tuple[0] == pt_model.base_model_prefix
						lowerCamelCase			      =					".".join((pt_model.base_model_prefix,) + flax_key_tuple				) in pt_model_dict

						# adapt flax_key to prepare for loading from/to base model only
						if load_model_with_head_into_base_model and has_base_model_prefix:
									lowerCamelCase			      =					flax_key_tuple[1:]
						elif load_base_model_into_model_with_head and require_base_model_prefix:
									lowerCamelCase			      =					(pt_model.base_model_prefix,) + flax_key_tuple

						# rename flax weights to PyTorch format
						if flax_key_tuple[-1] == "kernel" and flax_tensor.ndim == 4 and ".".join(_UpperCAmelCase				) not in pt_model_dict:
									# conv layer
									lowerCamelCase			      =					flax_key_tuple[:-1] + ("weight",)
									lowerCamelCase			      =					jnp.transpose(_UpperCAmelCase       , (3, 2, 0, 1)				)
						elif flax_key_tuple[-1] == "kernel" and ".".join(_UpperCAmelCase				) not in pt_model_dict:
									# linear layer
									lowerCamelCase			      =					flax_key_tuple[:-1] + ("weight",)
									lowerCamelCase			      =					flax_tensor.T
						elif flax_key_tuple[-1] in ["scale", "embedding"]:
									lowerCamelCase			      =					flax_key_tuple[:-1] + ("weight",)

						# adding batch stats from flax batch norm to pt
						elif "mean" in flax_key_tuple[-1]:
									lowerCamelCase			      =					flax_key_tuple[:-1] + ("running_mean",)
						elif "var" in flax_key_tuple[-1]:
									lowerCamelCase			      =					flax_key_tuple[:-1] + ("running_var",)

						if "batch_stats" in flax_state:
									lowerCamelCase			      =					".".join(flax_key_tuple[1:]				)  # Remove the params/batch_stats header
						else:
									lowerCamelCase			      =					".".join(_UpperCAmelCase				)

						# We also need to look at `pt_model_dict` and see if there are keys requiring further transformation.
						lowerCamelCase			      =					{}
						# New `weight_norm` from https://github.com/huggingface/transformers/pull/24030
						for key in pt_model_dict:
									lowerCamelCase			      =					key.split("""."""				)
									lowerCamelCase			      =					None
									if key_components[-3::2] == ["parametrizations", "original0"]:
												lowerCamelCase			      =					key_components[-2] + "_g"
									elif key_components[-3::2] == ["parametrizations", "original1"]:
												lowerCamelCase			      =					key_components[-2] + "_v"
									if name is not None:
												lowerCamelCase			      =					key_components[:-3] + [name]
												lowerCamelCase			      =					".".join(_UpperCAmelCase				)
												lowerCamelCase			      =					key

						if flax_key in special_pt_names:
									lowerCamelCase			      =					special_pt_names[flax_key]

						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
												lowerCamelCase			      =					np.asarray(_UpperCAmelCase				) if not isinstance(_UpperCAmelCase       , np.ndarray				) else flax_tensor
												lowerCamelCase			      =					torch.from_numpy(_UpperCAmelCase				)
												# remove from missing keys
												missing_keys.remove(_UpperCAmelCase				)
						else:
									# weight is not expected by PyTorch model
									unexpected_keys.append(_UpperCAmelCase				)

			pt_model.load_state_dict(_UpperCAmelCase				)

			# re-transform missing_keys to list
			lowerCamelCase			      =					list(_UpperCAmelCase				)

			if len(_UpperCAmelCase				) > 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)."""				)
			else:
						logger.warning(F'All Flax model weights were used when initializing {pt_model.__class__.__name__}.\n'				)
			if len(_UpperCAmelCase				) > 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."""				)
			else:
						logger.warning(
						    F'All the weights of {pt_model.__class__.__name__} were initialized from the Flax model.\n'
						    """If your task is similar to the task the model of the checkpoint was trained on, """
						    F'you can already use {pt_model.__class__.__name__} for predictions without further training.'				)

			return pt_model




'''simple docstring'''




import math
from typing import Optional

import numpy as np

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


__SCREAMING_SNAKE_CASE       :     Dict     =  logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE       :     List[Any]     =  {
    """facebook/encodec_24khz""": """https://huggingface.co/facebook/encodec_24khz/resolve/main/config.json""",
    """facebook/encodec_48khz""": """https://huggingface.co/facebook/encodec_48khz/resolve/main/config.json""",
}

class 	lowerCamelCase_	(snake_case__    ):
      '''simple docstring'''

      __UpperCamelCase:  str						=						"encodec"




      def __init__(       self						:				Optional[int]						,					A						:				Union[str, Any]=[1.5, 3.0, 6.0, 12.0, 24.0]						,					A						:				List[Any]=24000						,					A						:				Union[str, Any]=1						,					A						:				List[Any]=False						,					A						:				Optional[int]=None						,					A						:				int=None						,					A						:				str=128						,					A						:				List[Any]=32						,					A						:				List[Any]=1						,					A						:				int=[8, 5, 4, 2]						,					A						:				Optional[int]="weight_norm"						,					A						:				List[Any]=7						,					A						:				Any=7						,					A						:				Dict=3						,					A						:				Optional[int]=2						,					A						:				Dict=True						,					A						:				Dict="reflect"						,					A						:				Any=2						,					A						:				Dict=2						,					A						:				str=1.0						,					A						:				Optional[int]=1024						,					A						:				Any=None						,					A						:				Any=True						,					**A						:				str						,					):
             _UpperCAmelCase     :  Optional[int]							      =    target_bandwidths
             _UpperCAmelCase     :  List[str]							      =    sampling_rate
             _UpperCAmelCase     :  Optional[int]							      =    audio_channels
             _UpperCAmelCase     :  str							      =    normalize
             _UpperCAmelCase     :  int							      =    chunk_length_s
             _UpperCAmelCase     :  str							      =    overlap
             _UpperCAmelCase     :  Optional[Any]							      =    hidden_size
             _UpperCAmelCase     :  int							      =    num_filters
             _UpperCAmelCase     :  Optional[Any]							      =    num_residual_layers
             _UpperCAmelCase     :  Optional[int]							      =    upsampling_ratios
             _UpperCAmelCase     :  int							      =    norm_type
             _UpperCAmelCase     :  List[Any]							      =    kernel_size
             _UpperCAmelCase     :  List[Any]							      =    last_kernel_size
             _UpperCAmelCase     :  List[Any]							      =    residual_kernel_size
             _UpperCAmelCase     :  List[str]							      =    dilation_growth_rate
             _UpperCAmelCase     :  Dict							      =    use_causal_conv
             _UpperCAmelCase     :  Tuple							      =    pad_mode
             _UpperCAmelCase     :  Tuple							      =    compress
             _UpperCAmelCase     :  List[str]							      =    num_lstm_layers
             _UpperCAmelCase     :  List[Any]							      =    trim_right_ratio
             _UpperCAmelCase     :  int							      =    codebook_size
             _UpperCAmelCase     :  Optional[Any]							      =    codebook_dim if codebook_dim is not None else hidden_size
             _UpperCAmelCase     :  Optional[int]							      =    use_conv_shortcut

             if self.norm_type not in ["weight_norm", "time_group_norm"]:
                    raise ValueError(
                        F"""self.norm_type must be one of `\"weight_norm\"`, `\"time_group_norm\"`), got {self.norm_type}"""       )

             super().__init__(**A       )




      @property
      def      _A	(       self						:				Any       ):
             if self.chunk_length_s is None:
                    return None
             else:
                    return int(self.chunk_length_s * self.sampling_rate       )




      @property
      def      _A	(       self						:				Union[str, Any]       ):
             if self.chunk_length_s is None or self.overlap is None:
                    return None
             else:
                    return max(1						,					int((1.0 - self.overlap) * self.chunk_length       )       )




      @property
      def      _A	(       self						:				Union[str, Any]       ):
             _UpperCAmelCase     :  Dict							      =    np.prod(self.upsampling_ratios       )
             return math.ceil(self.sampling_rate / hop_length       )





      @property
      def      _A	(       self						:				str       ):
             return int(1000 * self.target_bandwidths[-1] // (self.frame_rate * 10)       )

