

"""simple docstring"""





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()
__UpperCamelCase  :      str							      =		logging.get_logger(__name__)

__UpperCamelCase  :      Union[str, Any]							      =		{
    '''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''',
}
__UpperCamelCase  :      List[Any]							      =		[
    '''lm_head''',
    '''quantizer.weight_proj''',
    '''quantizer.codevectors''',
    '''project_q''',
    '''project_hid''',
]

def   __SCREAMING_SNAKE_CASE  (      A_						,     A_						,     A_						,     A_						,     A_ ):
	for attribute in key.split('''.''' ):
		lowerCAmelCase__	:						Any  				=	getattr(A_						,     A_ )

	if weight_type is not None:
		lowerCAmelCase__	:						Tuple  				=	getattr(A_						,     A_ ).shape
	else:
		lowerCAmelCase__	:						int  				=	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":
		lowerCAmelCase__	:						Any  				=	value
	elif weight_type == "weight_g":
		lowerCAmelCase__	:						List[str]  				=	value
	elif weight_type == "weight_v":
		lowerCAmelCase__	:						Union[str, Any]  				=	value
	elif weight_type == "bias":
		lowerCAmelCase__	:						Dict  				=	value
	else:
		lowerCAmelCase__	:						Union[str, Any]  				=	value

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

def   __SCREAMING_SNAKE_CASE  (      A_						,     A_ ):
	lowerCAmelCase__	:						Tuple  				=	[]
	lowerCAmelCase__	:						Union[str, Any]  				=	fairseq_model.state_dict()

	lowerCAmelCase__	:						List[str]  				=	hf_model.feature_extractor

	# if encoder has different dim to decoder -> use proj_weight
	lowerCAmelCase__	:						Any  				=	None

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

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

	return proj_weight

def   __SCREAMING_SNAKE_CASE  (      A_						,     A_						,     A_						,     A_						,     A_ ):
	lowerCAmelCase__	:						List[Any]  				=	full_name.split('''conv_layers.''' )[-1]
	lowerCAmelCase__	:						int  				=	name.split('''.''' )
	lowerCAmelCase__	:						Tuple  				=	int(items[0] )
	lowerCAmelCase__	:						Optional[Any]  				=	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.'
			)
			lowerCAmelCase__	:						Dict  				=	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.'
			)
			lowerCAmelCase__	:						Optional[int]  				=	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."
			)
			lowerCAmelCase__	:						Dict  				=	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.'
			)
			lowerCAmelCase__	:						Dict  				=	value
			logger.info(f'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.' )
	else:
		unused_weights.append(A_ )

def   __SCREAMING_SNAKE_CASE  (      A_ ):
	lowerCAmelCase__				,lowerCAmelCase__	:						List[Any]  				=	emb.weight.shape
	lowerCAmelCase__	:						List[str]  				=	nn.Linear(A_						,     A_						,     bias=A_ )
	lowerCAmelCase__	:						Optional[Any]  				=	emb.weight.data
	return lin_layer

def   __SCREAMING_SNAKE_CASE  (      A_ ):
	with open(A_						,     '''r'''						,     encoding='''utf-8''' ) as f:
		lowerCAmelCase__	:						str  				=	f.readlines()
		lowerCAmelCase__	:						Any  				=	[line.split(''' ''' )[0] for line in lines]

	lowerCAmelCase__	:						Tuple  				=	len(A_ )

	lowerCAmelCase__	:						Dict  				=	{
	    '''<s>''': 0,
	    '''<pad>''': 1,
	    '''</s>''': 2,
	    '''<unk>''': 3,
	}

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

@torch.no_grad()
def   __SCREAMING_SNAKE_CASE  (      A_						,     A_						,     A_						,     A_						,     A_						,     A_						,     A_						,     ):
	lowerCAmelCase__	:						Optional[int]  				=	WavaVecaConfig.from_pretrained(A_ )
	lowerCAmelCase__	:						Dict  				=	SpeechaTextaConfig.from_pretrained(
	    A_						,     vocab_size=A_						,     decoder_layers=A_						,     do_stable_layer_norm=A_ )

	lowerCAmelCase__	:						Optional[Any]  				=	WavaVecaFeatureExtractor(
	    feature_size=1						,     sampling_rate=1_60_00						,     padding_value=0						,     do_normalize=A_						,     return_attention_mask=A_						,     )

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

	# set weights for wav2vec2 encoder
	lowerCAmelCase__	:						str  				=	WavaVecaModel(A_ )
	lowerCAmelCase__	:						Dict  				=	recursively_load_weights_wavaveca(model.encoder						,     A_ )

	lowerCAmelCase__	:						List[Any]  				=	SpeechaTextaForCausalLM(A_ )
	lowerCAmelCase__				,lowerCAmelCase__	:						List[Any]  				=	hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict()						,     strict=A_ )

	# set output linear layer
	unexpected_keys.remove('''embed_out''' )
	lowerCAmelCase__	:						Optional[Any]  				=	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}' )

	lowerCAmelCase__	:						Union[str, Any]  				=	SpeechEncoderDecoderModel(encoder=A_						,     decoder=A_ )
	lowerCAmelCase__	:						Union[str, Any]  				=	False

	# add projection layer
	lowerCAmelCase__	:						Optional[int]  				=	nn.Parameter(projection_layer.weight )
	lowerCAmelCase__	:						List[Any]  				=	nn.Parameter(projection_layer.bias )

	lowerCAmelCase__	:						str  				=	create_vocab_dict(A_ )

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

	lowerCAmelCase__	:						str  				=	SpeechaTextaTokenizer(os.path.join(A_						,     '''vocab.json''' ) )
	tokenizer.save_pretrained(A_ )

	lowerCAmelCase__	:						Any  				=	hf_wavavec.config.to_dict()
	lowerCAmelCase__	:						Any  				=	tokenizer.pad_token_id
	lowerCAmelCase__	:						Optional[Any]  				=	tokenizer.bos_token_id
	lowerCAmelCase__	:						str  				=	tokenizer.eos_token_id
	lowerCAmelCase__	:						Optional[Any]  				=	'''speech_to_text_2'''
	lowerCAmelCase__	:						List[str]  				=	'''wav2vec2'''

	lowerCAmelCase__	:						Dict  				=	SpeechEncoderDecoderConfig.from_dict(A_ )

	hf_wavavec.save_pretrained(A_ )
	feature_extractor.save_pretrained(A_ )


if __name__ == "__main__":
							__UpperCamelCase  :      Dict							      =		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''')

							__UpperCamelCase  :      List[Any]							      =		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,
							)






'''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			:				Dict    		=			logging.get_logger(__name__)







def 		lowerCAmelCase_				(   snake_case__		,				snake_case__		,				snake_case__		,				snake_case__=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:
        A       :   Union[str, Any]					=						os.path.abspath(snake_case__						)
        logger.info(F'Loading PyTorch weights from {pt_path}'						)

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

        A       :   List[str]					=						convert_pytorch_state_dict_to_flax(snake_case__		,				snake_case__						)
    else:
        # model is sharded and pytorch_checkpoint_path already contains the list of .pt shard files
        A       :   Any					=						convert_pytorch_sharded_state_dict_to_flax(snake_case__		,				snake_case__						)
    return flax_state_dict







def 		lowerCAmelCase_				(   snake_case__		,				snake_case__		,				snake_case__		,				snake_case__		,				):




    '''simple docstring'''



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

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

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

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

    # embedding
    A       :   Any					=						pt_tuple_key[:-1] + ('''embedding''',)
    if pt_tuple_key[-1] == "weight" and is_key_or_prefix_key_in_dict(snake_case__						):
        return renamed_pt_tuple_key, pt_tensor

    # conv layer
    A       :   Optional[Any]					=						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(snake_case__						):
        A       :   List[Any]					=						pt_tensor.transpose(2		,				3		,				1		,				0						)
        return renamed_pt_tuple_key, pt_tensor

    # linear layer
    A       :   Optional[int]					=						pt_tuple_key[:-1] + ('''kernel''',)
    if pt_tuple_key[-1] == "weight" and not is_key_or_prefix_key_in_dict(snake_case__						):
        A       :   str					=						pt_tensor.T
        return renamed_pt_tuple_key, pt_tensor

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

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

    return pt_tuple_key, pt_tensor







def 		lowerCAmelCase_				(   snake_case__		,				snake_case__						):




    '''simple docstring'''



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

    A       :   int					=						flax_model.base_model_prefix

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

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

    A       :   int					=						{}

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

        # remove base model prefix if necessary
        A       :   Union[str, Any]					=						pt_tuple_key[0] == model_prefix
        if load_model_with_head_into_base_model and has_base_model_prefix:
            A       :   Any					=						pt_tuple_key[1:]

        # Correctly rename weight parameters
        A,  A       :   Dict					=						rename_key_and_reshape_tensor(
            snake_case__		,				snake_case__		,				snake_case__		,				snake_case__						)

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

            # also add unexpected weight so that warning is thrown
            A       :   List[str]					=						jnp.asarray(snake_case__						)

        else:
            # also add unexpected weight so that warning is thrown
            A       :   Optional[Any]					=						jnp.asarray(snake_case__						)

    return unflatten_dict(snake_case__						)







def 		lowerCAmelCase_				(   snake_case__		,				snake_case__						):




    '''simple docstring'''



    import torch

    # Load the index
    A       :   Union[str, Any]					=						{}
    for shard_file in shard_filenames:
        # load using msgpack utils
        A       :   List[str]					=						torch.load(snake_case__						)
        A       :   int					=						{k: v.numpy() for k, v in pt_state_dict.items()}

        A       :   Tuple					=						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:
            A       :   Optional[int]					=						flax_model.params['''params''']

            A       :   List[Any]					=						flatten_dict(snake_case__						)
            random_flax_state_dict.update(flatten_dict(flax_model.params['''batch_stats''']						)						)
        else:
            A       :   Dict					=						flax_model.params
            A       :   Tuple					=						flatten_dict(snake_case__						)

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

            # remove base model prefix if necessary
            A       :   List[str]					=						pt_tuple_key[0] == model_prefix
            if load_model_with_head_into_base_model and has_base_model_prefix:
                A       :   Union[str, Any]					=						pt_tuple_key[1:]

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

                # also add unexpected weight so that warning is thrown
                A       :   Optional[Any]					=						jnp.asarray(snake_case__						)

            else:
                # also add unexpected weight so that warning is thrown
                A       :   Optional[Any]					=						jnp.asarray(snake_case__						)
    return unflatten_dict(snake_case__						)







def 		lowerCAmelCase_				(   snake_case__		,				snake_case__						):




    '''simple docstring'''



    A       :   Dict					=						os.path.abspath(snake_case__						)
    logger.info(F'Loading Flax weights from {flax_checkpoint_path}'						)

    # import correct flax class
    A       :   List[str]					=						getattr(snake_case__		,				'''Flax''' + model.__class__.__name__						)

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







def 		lowerCAmelCase_				(   snake_case__		,				snake_case__						):




    '''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
    A       :   List[str]					=						flatten_dict(jax.tree_util.tree_map(lambda snake_case__						: x.dtype == jnp.bfloataa		,				snake_case__						)						).values()
    if any(snake_case__						):
        # 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.'''						)
        A       :   Optional[Any]					=						jax.tree_util.tree_map(
            lambda snake_case__						: params.astype(np.floataa						) if params.dtype == jnp.bfloataa else params		,				snake_case__						)

    A       :   Union[str, Any]					=						flatten_dict(snake_case__						)
    A       :   List[Any]					=						pt_model.state_dict()

    A       :   Union[str, Any]					=						(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()}
    )
    A       :   Tuple					=						(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
    A       :   int					=						[]
    A       :   Any					=						set(pt_model_dict.keys()						)

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

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

        if "batch_stats" in flax_state:
            A       :   List[Any]					=						'''.'''.join(flax_key_tuple[1:]						)  # Remove the params/batch_stats header
        else:
            A       :   Union[str, Any]					=						'''.'''.join(snake_case__						)

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

        if flax_key in special_pt_names:
            A       :   Optional[Any]					=						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
                A       :   Dict					=						np.asarray(snake_case__						) if not isinstance(snake_case__		,				np.ndarray						) else flax_tensor
                A       :   Dict					=						torch.from_numpy(snake_case__						)
                # remove from missing keys
                missing_keys.remove(snake_case__						)
        else:
            # weight is not expected by PyTorch model
            unexpected_keys.append(snake_case__						)

    pt_model.load_state_dict(snake_case__						)

    # re-transform missing_keys to list
    A       :   List[Any]					=						list(snake_case__						)

    if len(snake_case__						) > 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(snake_case__						) > 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




