


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


a					  =     logging.get_logger(__name__)


def 			UpperCAmelCase_				(      UpperCAmelCase__						,							UpperCAmelCase__						,							UpperCAmelCase__						,							UpperCAmelCase__=False  ):
					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:
										lowercase_  							=			os.path.abspath(UpperCAmelCase__  )
										logger.info(F'''Loading PyTorch weights from {pt_path}'''  )

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

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


def 			UpperCAmelCase_				(      UpperCAmelCase__						,							UpperCAmelCase__						,							UpperCAmelCase__						,							UpperCAmelCase__						,							):

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

					# layer norm
					lowercase_  							=			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
					lowercase_  							=			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
					lowercase_  							=			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
					lowercase_  							=			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
					lowercase_  							=			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__  ):
										lowercase_  							=			pt_tensor.transpose(2						,							3						,							1						,							0  )
										return renamed_pt_tuple_key, pt_tensor

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

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

					# old PyTorch layer norm bias
					lowercase_  							=			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
					lowercase_  							=			None
					if pt_tuple_key[-3::2] == ("parametrizations", "original0"):
										lowercase_  							=			pt_tuple_key[-2] + """_g"""
					elif pt_tuple_key[-3::2] == ("parametrizations", "original1"):
										lowercase_  							=			pt_tuple_key[-2] + """_v"""
					if name is not None:
										lowercase_  							=			pt_tuple_key[:-3] + (name,)
										return renamed_pt_tuple_key, pt_tensor

					return pt_tuple_key, pt_tensor


def 			UpperCAmelCase_				(      UpperCAmelCase__						,							UpperCAmelCase__  ):
					# convert pytorch tensor to numpy
					lowercase_  							=			{k: v.numpy() for k, v in pt_state_dict.items()}

					lowercase_  							=			flax_model.base_model_prefix

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

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

					lowercase_  							=			{}

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

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

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

										# add model prefix if necessary
										lowercase_  							=			(model_prefix,) + flax_key in random_flax_state_dict
										if load_base_model_into_model_with_head and require_base_model_prefix:
															lowercase_  							=			(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]:
																				lowercase_  							=			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
															lowercase_  							=			jnp.asarray(UpperCAmelCase__  )

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

					return unflatten_dict(UpperCAmelCase__  )


def 			UpperCAmelCase_				(      UpperCAmelCase__						,							UpperCAmelCase__  ):
					import torch

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

										lowercase_  							=			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:
															lowercase_  							=			flax_model.params["""params"""]

															lowercase_  							=			flatten_dict(UpperCAmelCase__  )
															random_flax_state_dict.update(flatten_dict(flax_model.params["""batch_stats"""]  )  )
										else:
															lowercase_  							=			flax_model.params
															lowercase_  							=			flatten_dict(UpperCAmelCase__  )

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

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

															# Correctly rename weight parameters
															lowercase_							,					lowercase_  							=			rename_key_and_reshape_tensor(
															    UpperCAmelCase__						,							UpperCAmelCase__						,							UpperCAmelCase__						,							UpperCAmelCase__  )
															# add model prefix if necessary
															lowercase_  							=			(model_prefix,) + flax_key in random_flax_state_dict
															if load_base_model_into_model_with_head and require_base_model_prefix:
																				lowercase_  							=			(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]:
																									lowercase_  							=			jnp.asarray(UpperCAmelCase__  )
																									continue
																				if "var" in flax_key[-1]:
																									lowercase_  							=			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
																				lowercase_  							=			jnp.asarray(UpperCAmelCase__  )

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


def 			UpperCAmelCase_				(      UpperCAmelCase__						,							UpperCAmelCase__  ):
					lowercase_  							=			os.path.abspath(UpperCAmelCase__  )
					logger.info(F'''Loading Flax weights from {flax_checkpoint_path}'''  )

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

					# load flax weight dict
					with open(UpperCAmelCase__						,							"""rb"""  ) as state_f:
										try:
															lowercase_  							=			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 			UpperCAmelCase_				(      UpperCAmelCase__						,							UpperCAmelCase__  ):

					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
					lowercase_  							=			flatten_dict(jax.tree_util.tree_map(lambda UpperCAmelCase__  : 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."""  )
										lowercase_  							=			jax.tree_util.tree_map(
										    lambda UpperCAmelCase__  : params.astype(np.floataa  ) if params.dtype == jnp.bfloataa else params						,							UpperCAmelCase__  )

					lowercase_  							=			flatten_dict(UpperCAmelCase__  )
					lowercase_  							=			pt_model.state_dict()

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

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

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

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

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

										if flax_key in special_pt_names:
															lowercase_  							=			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
																				lowercase_  							=			np.asarray(UpperCAmelCase__  ) if not isinstance(UpperCAmelCase__						,							np.ndarray  ) else flax_tensor
																				lowercase_  							=			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
					lowercase_  							=			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






import argparse
import json
import os

import fairseq
import torch
from torch import nn

from transformers import (
    SpeechaTextaConfig,
    SpeechaTextaForCausalLM,
    SpeechaTextaTokenizer,
    SpeechEncoderDecoderConfig,
    SpeechEncoderDecoderModel,
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaModel,
    logging,
)


logging.set_verbosity_info()
a					  =     logging.get_logger(__name__)

a					  =     {
    'post_extract_proj': 'feature_projection.projection',
    'encoder.pos_conv.0': 'encoder.pos_conv_embed.conv',
    'self_attn.k_proj': 'encoder.layers.*.attention.k_proj',
    'self_attn.v_proj': 'encoder.layers.*.attention.v_proj',
    'self_attn.q_proj': 'encoder.layers.*.attention.q_proj',
    'self_attn.out_proj': 'encoder.layers.*.attention.out_proj',
    'self_attn_layer_norm': 'encoder.layers.*.layer_norm',
    'fc1': 'encoder.layers.*.feed_forward.intermediate_dense',
    'fc2': 'encoder.layers.*.feed_forward.output_dense',
    'final_layer_norm': 'encoder.layers.*.final_layer_norm',
    'encoder.layer_norm': 'encoder.layer_norm',
    'w2v_model.layer_norm': 'feature_projection.layer_norm',
    'quantizer.weight_proj': 'quantizer.weight_proj',
    'quantizer.vars': 'quantizer.codevectors',
    'project_q': 'project_q',
    'final_proj': 'project_hid',
    'w2v_encoder.proj': 'lm_head',
    'mask_emb': 'masked_spec_embed',
}
a					  =     [
    'lm_head',
    'quantizer.weight_proj',
    'quantizer.codevectors',
    'project_q',
    'project_hid',
]


def 			UpperCAmelCase_				(      UpperCAmelCase__						,							UpperCAmelCase__						,							UpperCAmelCase__						,							UpperCAmelCase__						,							UpperCAmelCase__  ):
					for attribute in key.split("""."""  ):
										lowercase_  							=			getattr(UpperCAmelCase__						,							UpperCAmelCase__  )

					if weight_type is not None:
										lowercase_  							=			getattr(UpperCAmelCase__						,							UpperCAmelCase__  ).shape
					else:
										lowercase_  							=			hf_pointer.shape

					assert hf_shape == value.shape, (
					    F'''Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be'''
					    F''' {value.shape} for {full_name}'''
					)

					if weight_type == "weight":
										lowercase_  							=			value
					elif weight_type == "weight_g":
										lowercase_  							=			value
					elif weight_type == "weight_v":
										lowercase_  							=			value
					elif weight_type == "bias":
										lowercase_  							=			value
					else:
										lowercase_  							=			value

					logger.info(F'''{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.'''  )


def 			UpperCAmelCase_				(      UpperCAmelCase__						,							UpperCAmelCase__  ):
					lowercase_  							=			[]
					lowercase_  							=			fairseq_model.state_dict()

					lowercase_  							=			hf_model.feature_extractor

					# if encoder has different dim to decoder -> use proj_weight
					lowercase_  							=			None

					for name, value in fairseq_dict.items():
										lowercase_  							=			False
										if "conv_layers" in name:
															load_conv_layer(
															    UpperCAmelCase__						,							UpperCAmelCase__						,							UpperCAmelCase__						,							UpperCAmelCase__						,							hf_model.config.feat_extract_norm == """group"""						,							)
															lowercase_  							=			True
										elif name.split("""."""  )[0] == "proj":
															lowercase_  							=			fairseq_model.proj
															lowercase_  							=			True
										else:
															for key, mapped_key in MAPPING.items():
																				if key in name or key.split("""w2v_model."""  )[-1] == name.split("""."""  )[0]:
																									lowercase_  							=			True
																									if "*" in mapped_key:
																														lowercase_  							=			name.split(UpperCAmelCase__  )[0].split("""."""  )[-2]
																														lowercase_  							=			mapped_key.replace("""*"""						,							UpperCAmelCase__  )
																									if "weight_g" in name:
																														lowercase_  							=			"""weight_g"""
																									elif "weight_v" in name:
																														lowercase_  							=			"""weight_v"""
																									elif "bias" in name:
																														lowercase_  							=			"""bias"""
																									elif "weight" in name:
																														lowercase_  							=			"""weight"""
																									else:
																														lowercase_  							=			None
																									set_recursively(UpperCAmelCase__						,							UpperCAmelCase__						,							UpperCAmelCase__						,							UpperCAmelCase__						,							UpperCAmelCase__  )
																				continue
										if not is_used:
															unused_weights.append(UpperCAmelCase__  )

					logger.warning(F'''Unused weights: {unused_weights}'''  )

					return proj_weight


def 			UpperCAmelCase_				(      UpperCAmelCase__						,							UpperCAmelCase__						,							UpperCAmelCase__						,							UpperCAmelCase__						,							UpperCAmelCase__  ):
					lowercase_  							=			full_name.split("""conv_layers."""  )[-1]
					lowercase_  							=			name.split("""."""  )
					lowercase_  							=			int(items[0]  )
					lowercase_  							=			int(items[1]  )

					if type_id == 0:
										if "bias" in name:
															assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
															    F'''{full_name} has size {value.shape}, but'''
															    F''' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'''
															)
															lowercase_  							=			value
															logger.info(F'''Feat extract conv layer {layer_id} was initialized from {full_name}.'''  )
										elif "weight" in name:
															assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
															    F'''{full_name} has size {value.shape}, but'''
															    F''' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'''
															)
															lowercase_  							=			value
															logger.info(F'''Feat extract conv layer {layer_id} was initialized from {full_name}.'''  )
					elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
										if "bias" in name:
															assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
															    F'''{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was'''
															    " found."
															)
															lowercase_  							=			value
															logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''  )
										elif "weight" in name:
															assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
															    F'''{full_name} has size {value.shape}, but'''
															    F''' {feature_extractor[layer_id].layer_norm.weight.data.shape} was found.'''
															)
															lowercase_  							=			value
															logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''  )
					else:
										unused_weights.append(UpperCAmelCase__  )


def 			UpperCAmelCase_				(      UpperCAmelCase__  ):
					lowercase_							,					lowercase_  							=			emb.weight.shape
					lowercase_  							=			nn.Linear(UpperCAmelCase__						,							UpperCAmelCase__						,							bias=UpperCAmelCase__  )
					lowercase_  							=			emb.weight.data
					return lin_layer


def 			UpperCAmelCase_				(      UpperCAmelCase__  ):
					with open(UpperCAmelCase__						,							"""r"""						,							encoding="""utf-8"""  ) as f:
										lowercase_  							=			f.readlines()
										lowercase_  							=			[line.split(""" """  )[0] for line in lines]

					lowercase_  							=			len(UpperCAmelCase__  )

					lowercase_  							=			{
					    """<s>""": 0,
					    """<pad>""": 1,
					    """</s>""": 2,
					    """<unk>""": 3,
					}

					vocab_dict.update(dict(zip(UpperCAmelCase__						,							range(4						,							num_words + 4  )  )  )  )
					return vocab_dict


@torch.no_grad()
def 			UpperCAmelCase_				(      UpperCAmelCase__						,							UpperCAmelCase__						,							UpperCAmelCase__						,							UpperCAmelCase__						,							UpperCAmelCase__						,							UpperCAmelCase__						,							UpperCAmelCase__						,							):
					lowercase_  							=			WavaVecaConfig.from_pretrained(UpperCAmelCase__  )
					lowercase_  							=			SpeechaTextaConfig.from_pretrained(
					    UpperCAmelCase__						,							vocab_size=UpperCAmelCase__						,							decoder_layers=UpperCAmelCase__						,							do_stable_layer_norm=UpperCAmelCase__  )

					lowercase_  							=			WavaVecaFeatureExtractor(
					    feature_size=1						,							sampling_rate=1_6_0_0_0						,							padding_value=0						,							do_normalize=UpperCAmelCase__						,							return_attention_mask=UpperCAmelCase__						,							)

					lowercase_							,					lowercase_							,					lowercase_  							=			fairseq.checkpoint_utils.load_model_ensemble_and_task(
					    [checkpoint_path]						,							arg_overrides={"""data""": """/""".join(dict_path.split("""/"""  )[:-1]  )}  )
					lowercase_  							=			model[0].eval()

					# set weights for wav2vec2 encoder
					lowercase_  							=			WavaVecaModel(UpperCAmelCase__  )
					lowercase_  							=			recursively_load_weights_wavaveca(model.encoder						,							UpperCAmelCase__  )

					lowercase_  							=			SpeechaTextaForCausalLM(UpperCAmelCase__  )
					lowercase_							,					lowercase_  							=			hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict()						,							strict=UpperCAmelCase__  )

					# set output linear layer
					unexpected_keys.remove("""embed_out"""  )
					lowercase_  							=			nn.Parameter(model.decoder.embed_out.detach()  )

					# layer norm is init to identity matrix so leaving it is fine
					logger.warning(F'''The following keys are missing when loading the decoder weights: {missing_keys}'''  )
					logger.warning(F'''The following keys are unexpected when loading the decoder weights: {unexpected_keys}'''  )

					lowercase_  							=			SpeechEncoderDecoderModel(encoder=UpperCAmelCase__						,							decoder=UpperCAmelCase__  )
					lowercase_  							=			False

					# add projection layer
					lowercase_  							=			nn.Parameter(projection_layer.weight  )
					lowercase_  							=			nn.Parameter(projection_layer.bias  )

					lowercase_  							=			create_vocab_dict(UpperCAmelCase__  )

					with open(os.path.join(UpperCAmelCase__						,							"""vocab.json"""  )						,							"""w"""  ) as fp:
										json.dump(UpperCAmelCase__						,							UpperCAmelCase__  )

					lowercase_  							=			SpeechaTextaTokenizer(os.path.join(UpperCAmelCase__						,							"""vocab.json"""  )  )
					tokenizer.save_pretrained(UpperCAmelCase__  )

					lowercase_  							=			hf_wavavec.config.to_dict()
					lowercase_  							=			tokenizer.pad_token_id
					lowercase_  							=			tokenizer.bos_token_id
					lowercase_  							=			tokenizer.eos_token_id
					lowercase_  							=			"""speech_to_text_2"""
					lowercase_  							=			"""wav2vec2"""

					lowercase_  							=			SpeechEncoderDecoderConfig.from_dict(UpperCAmelCase__  )

					hf_wavavec.save_pretrained(UpperCAmelCase__  )
					feature_extractor.save_pretrained(UpperCAmelCase__  )


if __name__ == "__main__":
							a					  =     argparse.ArgumentParser()
							parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
							parser.add_argument('--checkpoint_path', default=None, type=str, help='Path to fairseq checkpoint')
							parser.add_argument('--dict_path', default=None, type=str, help='Path to dict of fine-tuned model')
							parser.add_argument(
							    '--encoder_config_path',
							    default='facebook/wav2vec2-large-lv60',
							    type=str,
							    help='Path to hf encoder wav2vec2 checkpoint config',
							)
							parser.add_argument(
							    '--decoder_config_path',
							    default='facebook/s2t-small-mustc-en-fr-st',
							    type=str,
							    help='Path to hf decoder s2t checkpoint config',
							)
							parser.add_argument('--vocab_size', default=1_0_2_2_4, type=int, help='Vocab size of decoder')
							parser.add_argument('--num_decoder_layers', default=7, type=int, help='Number of decoder layers')

							a					  =     parser.parse_args()
							convert_wavaveca_checkpoint(
							    args.checkpoint_path,
							    args.pytorch_dump_folder_path,
							    args.dict_path,
							    encoder_config_path=args.encoder_config_path,
							    decoder_config_path=args.decoder_config_path,
							    vocab_size=args.vocab_size,
							    num_decoder_layers=args.num_decoder_layers,
							)


