


'''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


_lowercase    :    Optional[Any]			     =  logging.get_logger(__name__)


def    lowerCamelCase__   ( A     :    Any   ,     A     :    Dict   ,     A     :    str   ,     A     :    Optional[Any]=False  ):




						'''simple docstring'''






						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:
												UpperCAmelCase         =			os.path.abspath(A  )
												logger.info(f"""Loading PyTorch weights from {pt_path}"""  )

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

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


def    lowerCamelCase__   ( A     :    Tuple[str]   ,     A     :    np.ndarray   ,     A     :    Dict[str, jnp.ndarray]   ,     A     :    str   ,     ):




						'''simple docstring'''






						def is_key_or_prefix_key_in_dict(A     :    Tuple[str]  ) -> bool:
												return len(set(A  ) & {key, (model_prefix,) + key}  ) > 0

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

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

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

						# embedding
						UpperCAmelCase         =			pt_tuple_key[:-1] + ('''embedding''',)
						if pt_tuple_key[-1] == "weight" and is_key_or_prefix_key_in_dict(A  ):
												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 and not is_key_or_prefix_key_in_dict(A  ):
												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" and not is_key_or_prefix_key_in_dict(A  ):
												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

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

						return pt_tuple_key, pt_tensor


def    lowerCamelCase__   ( A     :    int   ,     A     :    int  ):




						'''simple docstring'''






						UpperCAmelCase         =			{k: v.numpy() for k, v in pt_state_dict.items()}

						UpperCAmelCase         =			flax_model.base_model_prefix

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

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

						UpperCAmelCase         =			{}

						UpperCAmelCase         =			(model_prefix not in flax_model_params) and (
						    model_prefix in {k.split('''.'''  )[0] for k in pt_state_dict.keys()}
						)
						UpperCAmelCase         =			(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():
												UpperCAmelCase         =			tuple(pt_key.split('''.'''  )  )

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

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

												# add model prefix if necessary
												UpperCAmelCase         =			(model_prefix,) + flax_key in random_flax_state_dict
												if load_base_model_into_model_with_head and require_base_model_prefix:
																		UpperCAmelCase         =			(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]:
																								UpperCAmelCase         =			jnp.asarray(A  )
																								continue
																		# remove num_batches_tracked key
																		if "num_batches_tracked" in flax_key[-1]:
																								flax_state_dict.pop(A   ,     A  )
																								continue

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

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

						return unflatten_dict(A  )


def    lowerCamelCase__   ( A     :    Optional[int]   ,     A     :    str  ):




						'''simple docstring'''






						import torch

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

												UpperCAmelCase         =			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:
																		UpperCAmelCase         =			flax_model.params['''params''']

																		UpperCAmelCase         =			flatten_dict(A  )
																		random_flax_state_dict.update(flatten_dict(flax_model.params['''batch_stats''']  )  )
												else:
																		UpperCAmelCase         =			flax_model.params
																		UpperCAmelCase         =			flatten_dict(A  )

												UpperCAmelCase         =			(model_prefix not in flax_model_params) and (
												    model_prefix in {k.split('''.'''  )[0] for k in pt_state_dict.keys()}
												)
												UpperCAmelCase         =			(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():
																		UpperCAmelCase         =			tuple(pt_key.split('''.'''  )  )

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

																		# Correctly rename weight parameters
																		UpperCAmelCase       ,       UpperCAmelCase         =			rename_key_and_reshape_tensor(
																		    A   ,     A   ,     A   ,     A  )
																		# add model prefix if necessary
																		UpperCAmelCase         =			(model_prefix,) + flax_key in random_flax_state_dict
																		if load_base_model_into_model_with_head and require_base_model_prefix:
																								UpperCAmelCase         =			(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]:
																														UpperCAmelCase         =			jnp.asarray(A  )
																														continue
																								if "var" in flax_key[-1]:
																														UpperCAmelCase         =			jnp.asarray(A  )
																														continue
																								# remove num_batches_tracked key
																								if "num_batches_tracked" in flax_key[-1]:
																														flax_state_dict.pop(A   ,     A  )
																														continue

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

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


def    lowerCamelCase__   ( A     :    List[str]   ,     A     :    List[Any]  ):




						'''simple docstring'''






						UpperCAmelCase         =			os.path.abspath(A  )
						logger.info(f"""Loading Flax weights from {flax_checkpoint_path}"""  )

						# import correct flax class
						UpperCAmelCase         =			getattr(A   ,     '''Flax''' + model.__class__.__name__  )

						# load flax weight dict
						with open(A   ,     '''rb'''  ) as state_f:
												try:
																		UpperCAmelCase         =			from_bytes(A   ,     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(A   ,     A  )




def    lowerCamelCase__   ( A     :    Tuple   ,     A     :    List[Any]  ):




						'''simple docstring'''






						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
						UpperCAmelCase         =			flatten_dict(jax.tree_util.tree_map(lambda A  : x.dtype == jnp.bfloataa   ,     A  )  ).values()
						if any(A  ):
												# 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.'''  )
												UpperCAmelCase         =			jax.tree_util.tree_map(
												    lambda A  : params.astype(np.floataa  ) if params.dtype == jnp.bfloataa else params   ,     A  )

						UpperCAmelCase         =			flatten_dict(A  )
						UpperCAmelCase         =			pt_model.state_dict()

						UpperCAmelCase         =			(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()}
						)
						UpperCAmelCase         =			(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
						UpperCAmelCase         =			[]
						UpperCAmelCase         =			set(pt_model_dict.keys()  )

						for flax_key_tuple, flax_tensor in flax_state_dict.items():
												UpperCAmelCase         =			flax_key_tuple[0] == pt_model.base_model_prefix
												UpperCAmelCase         =			'''.'''.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:
																		UpperCAmelCase         =			flax_key_tuple[1:]
												elif load_base_model_into_model_with_head and require_base_model_prefix:
																		UpperCAmelCase         =			(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(A  ) not in pt_model_dict:
																		# conv layer
																		UpperCAmelCase         =			flax_key_tuple[:-1] + ('''weight''',)
																		UpperCAmelCase         =			jnp.transpose(A   ,     (3, 2, 0, 1)  )
												elif flax_key_tuple[-1] == "kernel" and ".".join(A  ) not in pt_model_dict:
																		# linear layer
																		UpperCAmelCase         =			flax_key_tuple[:-1] + ('''weight''',)
																		UpperCAmelCase         =			flax_tensor.T
												elif flax_key_tuple[-1] in ["scale", "embedding"]:
																		UpperCAmelCase         =			flax_key_tuple[:-1] + ('''weight''',)

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

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

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

												if flax_key in special_pt_names:
																		UpperCAmelCase         =			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
																								UpperCAmelCase         =			np.asarray(A  ) if not isinstance(A   ,     np.ndarray  ) else flax_tensor
																								UpperCAmelCase         =			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
						UpperCAmelCase         =			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).'''  )
						else:
												logger.warning(f"""All Flax model weights were used when initializing {pt_model.__class__.__name__}.\n"""  )
						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.'''  )
						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


_lowercase    :    Union[str, Any]			     =  logging.get_logger(__name__)

_lowercase    :    List[str]			     =  {
    """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  UpperCamelCase__(     lowerCAmelCase ):
				__magic_name__       :   List[str]							= "encodec"




				def __init__(	self    :				List[str]   ,					lowerCAmelCase    :				int=[1.5, 3.0, 6.0, 12.0, 24.0]   ,					lowerCAmelCase    :				Tuple=24000   ,					lowerCAmelCase    :				List[Any]=1   ,					lowerCAmelCase    :				Union[str, Any]=False   ,					lowerCAmelCase    :				str=None   ,					lowerCAmelCase    :				Dict=None   ,					lowerCAmelCase    :				str=128   ,					lowerCAmelCase    :				Any=32   ,					lowerCAmelCase    :				Any=1   ,					lowerCAmelCase    :				List[Any]=[8, 5, 4, 2]   ,					lowerCAmelCase    :				Union[str, Any]="weight_norm"   ,					lowerCAmelCase    :				str=7   ,					lowerCAmelCase    :				Optional[int]=7   ,					lowerCAmelCase    :				Any=3   ,					lowerCAmelCase    :				Tuple=2   ,					lowerCAmelCase    :				Optional[Any]=True   ,					lowerCAmelCase    :				List[str]="reflect"   ,					lowerCAmelCase    :				Optional[int]=2   ,					lowerCAmelCase    :				List[Any]=2   ,					lowerCAmelCase    :				Union[str, Any]=1.0   ,					lowerCAmelCase    :				Optional[Any]=1024   ,					lowerCAmelCase    :				Optional[Any]=None   ,					lowerCAmelCase    :				str=True   ,					**lowerCAmelCase    :				str   ,					)->  List[str]:

										"""simple docstring"""




										UpperCAmelCase         =			target_bandwidths
										UpperCAmelCase         =			sampling_rate
										UpperCAmelCase         =			audio_channels
										UpperCAmelCase         =			normalize
										UpperCAmelCase         =			chunk_length_s
										UpperCAmelCase         =			overlap
										UpperCAmelCase         =			hidden_size
										UpperCAmelCase         =			num_filters
										UpperCAmelCase         =			num_residual_layers
										UpperCAmelCase         =			upsampling_ratios
										UpperCAmelCase         =			norm_type
										UpperCAmelCase         =			kernel_size
										UpperCAmelCase         =			last_kernel_size
										UpperCAmelCase         =			residual_kernel_size
										UpperCAmelCase         =			dilation_growth_rate
										UpperCAmelCase         =			use_causal_conv
										UpperCAmelCase         =			pad_mode
										UpperCAmelCase         =			compress
										UpperCAmelCase         =			num_lstm_layers
										UpperCAmelCase         =			trim_right_ratio
										UpperCAmelCase         =			codebook_size
										UpperCAmelCase         =			codebook_dim if codebook_dim is not None else hidden_size
										UpperCAmelCase         =			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__(**lowerCAmelCase   )




				@property
				def   a__(	self    :				str   )->  Optional[int]:

										"""simple docstring"""




										if self.chunk_length_s is None:
																return None
										else:
																return int(self.chunk_length_s * self.sampling_rate   )




				@property
				def   a__(	self    :				List[str]   )->  Optional[int]:

										"""simple docstring"""




										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    :				List[Any]   )->  int:

										"""simple docstring"""




										UpperCAmelCase         =			np.prod(self.upsampling_ratios   )
										return math.ceil(self.sampling_rate / hop_length   )





				@property
				def   a__(	self    :				int   )->  int:

										"""simple docstring"""




										return int(1000 * self.target_bandwidths[-1] // (self.frame_rate * 10)   )





