import argparse
import collections

import torch
from flax import traverse_util
from tax import checkpoints

from transformers import TaConfig, TaEncoderModel, TaForConditionalGeneration
from transformers.utils import logging


logging.set_verbosity_info()



def lowerCamelCase						(     SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE="attention"			):
    '''simple docstring'''







    __UpperCamelCase  :int    						=	params[f"""{prefix}/layers_{i}/{layer_name}/key/kernel"""]
    __UpperCamelCase  :Optional[int]    						=	params[f"""{prefix}/layers_{i}/{layer_name}/out/kernel"""]
    __UpperCamelCase  :List[str]    						=	params[f"""{prefix}/layers_{i}/{layer_name}/query/kernel"""]
    __UpperCamelCase  :Union[str, Any]    						=	params[f"""{prefix}/layers_{i}/{layer_name}/value/kernel"""]
    return k, o, q, v



def lowerCamelCase						(     SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE=False			):
    '''simple docstring'''







    if split_mlp_wi:
        __UpperCamelCase  :List[str]    						=	params[f"""{prefix}/layers_{i}/mlp/wi_0/kernel"""]
        __UpperCamelCase  :Dict    						=	params[f"""{prefix}/layers_{i}/mlp/wi_1/kernel"""]
        __UpperCamelCase  :Optional[Any]    						=	(wi_a, wi_a)
    else:
        __UpperCamelCase  :List[str]    						=	params[f"""{prefix}/layers_{i}/mlp/wi/kernel"""]

    __UpperCamelCase  :int    						=	params[f"""{prefix}/layers_{i}/mlp/wo/kernel"""]
    return wi, wo



def lowerCamelCase						(     SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE			):
    '''simple docstring'''







    return params[f"""{prefix}/layers_{i}/{layer_name}/scale"""]



def lowerCamelCase						(     SCREAMING_SNAKE_CASE						,    *, SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE			):
    '''simple docstring'''







    __UpperCamelCase  :Tuple    						=	traverse_util.flatten_dict(variables['''target''']			)
    __UpperCamelCase  :Optional[int]    						=	{'''/'''.join(SCREAMING_SNAKE_CASE			): v for k, v in old.items()}

    # v1.1 models have a gated GeLU with wi_0 and wi_1 instead of wi
    __UpperCamelCase  :Union[str, Any]    						=	'''encoder/layers_0/mlp/wi_0/kernel''' in old
    print('''Split MLP:'''						,    SCREAMING_SNAKE_CASE			)

    __UpperCamelCase  :Optional[Any]    						=	collections.OrderedDict()

    # Shared embeddings.
    __UpperCamelCase  :Any    						=	old['''token_embedder/embedding''']

    # Encoder.
    for i in range(SCREAMING_SNAKE_CASE			):
        # Block i, layer 0 (Self Attention).
        __UpperCamelCase  :Tuple    						=	tax_layer_norm_lookup(SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    '''encoder'''						,    '''pre_attention_layer_norm'''			)
        __UpperCamelCase						, __UpperCamelCase						, __UpperCamelCase						, __UpperCamelCase  :Dict    						=	tax_attention_lookup(SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    '''encoder'''						,    '''attention'''			)
        __UpperCamelCase  :str    						=	layer_norm
        __UpperCamelCase  :str    						=	k.T
        __UpperCamelCase  :Optional[Any]    						=	o.T
        __UpperCamelCase  :Optional[int]    						=	q.T
        __UpperCamelCase  :Any    						=	v.T

        # Block i, layer 1 (MLP).
        __UpperCamelCase  :Union[str, Any]    						=	tax_layer_norm_lookup(SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    '''encoder'''						,    '''pre_mlp_layer_norm'''			)
        __UpperCamelCase						, __UpperCamelCase  :Optional[int]    						=	tax_mlp_lookup(SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    '''encoder'''						,    SCREAMING_SNAKE_CASE			)
        __UpperCamelCase  :Any    						=	layer_norm
        if split_mlp_wi:
            __UpperCamelCase  :Optional[Any]    						=	wi[0].T
            __UpperCamelCase  :int    						=	wi[1].T
        else:
            __UpperCamelCase  :Dict    						=	wi.T
        __UpperCamelCase  :str    						=	wo.T

    __UpperCamelCase  :Optional[Any]    						=	old[
        '''encoder/relpos_bias/rel_embedding'''
    ].T
    __UpperCamelCase  :Optional[int]    						=	old['''encoder/encoder_norm/scale''']

    if not is_encoder_only:
        # Decoder.
        for i in range(SCREAMING_SNAKE_CASE			):
            # Block i, layer 0 (Self Attention).
            __UpperCamelCase  :int    						=	tax_layer_norm_lookup(SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    '''decoder'''						,    '''pre_self_attention_layer_norm'''			)
            __UpperCamelCase						, __UpperCamelCase						, __UpperCamelCase						, __UpperCamelCase  :Dict    						=	tax_attention_lookup(SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    '''decoder'''						,    '''self_attention'''			)
            __UpperCamelCase  :str    						=	layer_norm
            __UpperCamelCase  :Optional[Any]    						=	k.T
            __UpperCamelCase  :List[str]    						=	o.T
            __UpperCamelCase  :Union[str, Any]    						=	q.T
            __UpperCamelCase  :int    						=	v.T

            # Block i, layer 1 (Cross Attention).
            __UpperCamelCase  :Optional[Any]    						=	tax_layer_norm_lookup(SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    '''decoder'''						,    '''pre_cross_attention_layer_norm'''			)
            __UpperCamelCase						, __UpperCamelCase						, __UpperCamelCase						, __UpperCamelCase  :int    						=	tax_attention_lookup(SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    '''decoder'''						,    '''encoder_decoder_attention'''			)
            __UpperCamelCase  :List[str]    						=	layer_norm
            __UpperCamelCase  :Any    						=	k.T
            __UpperCamelCase  :Any    						=	o.T
            __UpperCamelCase  :Optional[int]    						=	q.T
            __UpperCamelCase  :Union[str, Any]    						=	v.T

            # Block i, layer 2 (MLP).
            __UpperCamelCase  :Tuple    						=	tax_layer_norm_lookup(SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    '''decoder'''						,    '''pre_mlp_layer_norm'''			)
            __UpperCamelCase						, __UpperCamelCase  :Optional[Any]    						=	tax_mlp_lookup(SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    '''decoder'''						,    SCREAMING_SNAKE_CASE			)
            __UpperCamelCase  :Optional[Any]    						=	layer_norm
            if split_mlp_wi:
                __UpperCamelCase  :int    						=	wi[0].T
                __UpperCamelCase  :Any    						=	wi[1].T
            else:
                __UpperCamelCase  :Optional[Any]    						=	wi.T
            __UpperCamelCase  :Dict    						=	wo.T

        __UpperCamelCase  :Optional[int]    						=	old['''decoder/decoder_norm/scale''']
        __UpperCamelCase  :Optional[Any]    						=	old[
            '''decoder/relpos_bias/rel_embedding'''
        ].T

        # LM Head (only in v1.1 checkpoints, in v1.0 embeddings are used instead)
        if "decoder/logits_dense/kernel" in old:
            __UpperCamelCase  :List[str]    						=	old['''decoder/logits_dense/kernel'''].T

    return new



def lowerCamelCase						(     SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE			):
    '''simple docstring'''







    __UpperCamelCase  :Optional[Any]    						=	collections.OrderedDict([(k, torch.from_numpy(v.copy()			)) for (k, v) in converted_params.items()]			)

    # Add what is missing.
    if "encoder.embed_tokens.weight" not in state_dict:
        __UpperCamelCase  :Union[str, Any]    						=	state_dict['''shared.weight''']

    if not is_encoder_only:
        if "decoder.embed_tokens.weight" not in state_dict:
            __UpperCamelCase  :Any    						=	state_dict['''shared.weight''']

        if "lm_head.weight" not in state_dict:  # For old 1.0 models.
            print('''Using shared word embeddings as lm_head.'''			)
            __UpperCamelCase  :Optional[int]    						=	state_dict['''shared.weight''']

    return state_dict



def lowerCamelCase						(     SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE			):
    '''simple docstring'''







    __UpperCamelCase  :int    						=	checkpoints.load_tax_checkpoint(SCREAMING_SNAKE_CASE			)
    __UpperCamelCase  :str    						=	convert_tax_to_pytorch(SCREAMING_SNAKE_CASE						,    num_layers=config.num_layers						,    is_encoder_only=SCREAMING_SNAKE_CASE			)
    __UpperCamelCase  :Optional[int]    						=	make_state_dict(SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE			)
    model.load_state_dict(SCREAMING_SNAKE_CASE						,    strict=SCREAMING_SNAKE_CASE			)



def lowerCamelCase						(     SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE = False			):
    '''simple docstring'''







    __UpperCamelCase  :Union[str, Any]    						=	TaConfig.from_json_file(SCREAMING_SNAKE_CASE			)
    print(f"""Building PyTorch model from configuration: {config}"""			)
    # Non-v1.1 checkpoints could also use T5Model, but this works for all.
    # The v1.0 checkpoints will simply have an LM head that is the word embeddings.
    if is_encoder_only:
        __UpperCamelCase  :Dict    						=	TaEncoderModel(SCREAMING_SNAKE_CASE			)
    else:
        __UpperCamelCase  :str    						=	TaForConditionalGeneration(SCREAMING_SNAKE_CASE			)

    # Load weights from tf checkpoint
    load_tax_weights_in_ta(SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE			)

    # Save pytorch-model
    print(f"""Save PyTorch model to {pytorch_dump_path}"""			)
    model.save_pretrained(SCREAMING_SNAKE_CASE			)

    # Verify that we can load the checkpoint.
    model.from_pretrained(SCREAMING_SNAKE_CASE			)
    print('''Done'''			)


if __name__ == "__main__":
    __lowercase											=      argparse.ArgumentParser(description='''Converts a native T5X checkpoint into a PyTorch checkpoint.''')
    # Required parameters
    parser.add_argument(
        '''--t5x_checkpoint_path''', default=None, type=str, required=True, help='''Path to the T5X checkpoint.'''
    )
    parser.add_argument(
        '''--config_file''',
        default=None,
        type=str,
        required=True,
        help='''The config json file corresponding to the pre-trained T5 model.\nThis specifies the model architecture.''',
    )
    parser.add_argument(
        '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
    )
    parser.add_argument(
        '''--is_encoder_only''', action='''store_true''', help='''Check if the model is encoder-decoder model''', default=False
    )
    __lowercase											=      parser.parse_args()
    convert_tax_checkpoint_to_pytorch(
        args.tax_checkpoint_path, args.config_file, args.pytorch_dump_path, args.is_encoder_only
    )






import functools
import operator

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


__lowercase											=      logging.get_logger(__name__)

__lowercase											=      {
    '''microsoft/wavlm-base''': '''https://huggingface.co/microsoft/wavlm-base/resolve/main/config.json''',
    # See all WavLM models at https://huggingface.co/models?filter=wavlm
}


class 			lowerCamelCase_  (     UpperCAmelCase_				):






    '''simple docstring'''






    a__			:		List[Any]      			=   """wavlm"""







    def __init__(			self				,				__lowercase=32				,				__lowercase=768				,				__lowercase=12				,				__lowercase=12				,				__lowercase=3_072				,				__lowercase="gelu"				,				__lowercase=0.1				,				__lowercase=0.1				,				__lowercase=0.1				,				__lowercase=0.0				,				__lowercase=0.1				,				__lowercase=0.1				,				__lowercase=0.02				,				__lowercase=1E-5				,				__lowercase="group"				,				__lowercase="gelu"				,				__lowercase=(512, 512, 512, 512, 512, 512, 512)				,				__lowercase=(5, 2, 2, 2, 2, 2, 2)				,				__lowercase=(10, 3, 3, 3, 3, 2, 2)				,				__lowercase=False				,				__lowercase=128				,				__lowercase=16				,				__lowercase=320				,				__lowercase=800				,				__lowercase=False				,				__lowercase=True				,				__lowercase=0.05				,				__lowercase=10				,				__lowercase=2				,				__lowercase=0.0				,				__lowercase=10				,				__lowercase=320				,				__lowercase=2				,				__lowercase=0.1				,				__lowercase=100				,				__lowercase=256				,				__lowercase=256				,				__lowercase=0.1				,				__lowercase="mean"				,				__lowercase=False				,				__lowercase=False				,				__lowercase=256				,				__lowercase=(512, 512, 512, 512, 1_500)				,				__lowercase=(5, 3, 3, 1, 1)				,				__lowercase=(1, 2, 3, 1, 1)				,				__lowercase=512				,				__lowercase=80				,				__lowercase=0				,				__lowercase=1				,				__lowercase=2				,				__lowercase=False				,				__lowercase=3				,				__lowercase=2				,				__lowercase=3				,				__lowercase=None				,				**__lowercase				,				) ->			Dict:
        super().__init__(**__lowercase				,				pad_token_id=__lowercase				,				bos_token_id=__lowercase				,				eos_token_id=__lowercase)
        __UpperCamelCase  :Any    						=	hidden_size
        __UpperCamelCase  :Tuple    						=	feat_extract_norm
        __UpperCamelCase  :List[str]    						=	feat_extract_activation
        __UpperCamelCase  :int    						=	list(__lowercase)
        __UpperCamelCase  :List[Any]    						=	list(__lowercase)
        __UpperCamelCase  :Union[str, Any]    						=	list(__lowercase)
        __UpperCamelCase  :Optional[Any]    						=	conv_bias
        __UpperCamelCase  :Tuple    						=	num_buckets
        __UpperCamelCase  :Optional[int]    						=	max_bucket_distance
        __UpperCamelCase  :Union[str, Any]    						=	num_conv_pos_embeddings
        __UpperCamelCase  :Optional[Any]    						=	num_conv_pos_embedding_groups
        __UpperCamelCase  :List[Any]    						=	len(self.conv_dim)
        __UpperCamelCase  :Tuple    						=	num_hidden_layers
        __UpperCamelCase  :str    						=	intermediate_size
        __UpperCamelCase  :Union[str, Any]    						=	hidden_act
        __UpperCamelCase  :Optional[int]    						=	num_attention_heads
        __UpperCamelCase  :str    						=	hidden_dropout
        __UpperCamelCase  :int    						=	attention_dropout
        __UpperCamelCase  :Optional[int]    						=	activation_dropout
        __UpperCamelCase  :str    						=	feat_proj_dropout
        __UpperCamelCase  :List[Any]    						=	final_dropout
        __UpperCamelCase  :int    						=	layerdrop
        __UpperCamelCase  :List[Any]    						=	layer_norm_eps
        __UpperCamelCase  :Optional[int]    						=	initializer_range
        __UpperCamelCase  :Any    						=	num_ctc_classes
        __UpperCamelCase  :Optional[int]    						=	vocab_size
        __UpperCamelCase  :List[Any]    						=	do_stable_layer_norm
        __UpperCamelCase  :str    						=	use_weighted_layer_sum
        __UpperCamelCase  :Any    						=	classifier_proj_size

        if (
            (len(self.conv_stride) != self.num_feat_extract_layers)
            or (len(self.conv_kernel) != self.num_feat_extract_layers)
            or (len(self.conv_dim) != self.num_feat_extract_layers)
        ):
            raise ValueError(
                '''Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =='''
                ''' `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ='''
                f""" {len(self.conv_dim)}`, `len(config.conv_stride) = {len(self.conv_stride)}`,"""
                f""" `len(config.conv_kernel) = {len(self.conv_kernel)}`.""")

        # fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
        __UpperCamelCase  :Union[str, Any]    						=	apply_spec_augment
        __UpperCamelCase  :Optional[Any]    						=	mask_time_prob
        __UpperCamelCase  :Union[str, Any]    						=	mask_time_length
        __UpperCamelCase  :Optional[int]    						=	mask_time_min_masks
        __UpperCamelCase  :str    						=	mask_feature_prob
        __UpperCamelCase  :Tuple    						=	mask_feature_length

        # parameters for pretraining with codevector quantized representations
        __UpperCamelCase  :Optional[Any]    						=	num_codevectors_per_group
        __UpperCamelCase  :List[Any]    						=	num_codevector_groups
        __UpperCamelCase  :str    						=	contrastive_logits_temperature
        __UpperCamelCase  :Tuple    						=	num_negatives
        __UpperCamelCase  :Any    						=	codevector_dim
        __UpperCamelCase  :Union[str, Any]    						=	proj_codevector_dim
        __UpperCamelCase  :Tuple    						=	diversity_loss_weight

        # ctc loss
        __UpperCamelCase  :int    						=	ctc_loss_reduction
        __UpperCamelCase  :Any    						=	ctc_zero_infinity

        # adapter
        __UpperCamelCase  :List[Any]    						=	add_adapter
        __UpperCamelCase  :Dict    						=	adapter_kernel_size
        __UpperCamelCase  :Any    						=	adapter_stride
        __UpperCamelCase  :Optional[int]    						=	num_adapter_layers
        __UpperCamelCase  :Union[str, Any]    						=	output_hidden_size or hidden_size

        # SequenceClassification-specific parameter. Feel free to ignore for other classes.
        __UpperCamelCase  :int    						=	classifier_proj_size

        # XVector-specific parameters. Feel free to ignore for other classes.
        __UpperCamelCase  :Optional[Any]    						=	list(__lowercase)
        __UpperCamelCase  :Optional[Any]    						=	list(__lowercase)
        __UpperCamelCase  :List[str]    						=	list(__lowercase)
        __UpperCamelCase  :List[Any]    						=	xvector_output_dim







    @property
    def     UpperCamelCase__     (			self) ->			Any:
        return functools.reduce(operator.mul				,				self.conv_stride				,				1)





