import argparse

import torch

from transformers import BlenderbotConfig, BlenderbotForConditionalGeneration
from transformers.utils import logging


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

__lowercase											=      [
    ['''attention''', '''attn'''],
    ['''encoder_attention''', '''encoder_attn'''],
    ['''q_lin''', '''q_proj'''],
    ['''k_lin''', '''k_proj'''],
    ['''v_lin''', '''v_proj'''],
    ['''out_lin''', '''out_proj'''],
    ['''norm_embeddings''', '''layernorm_embedding'''],
    ['''position_embeddings''', '''embed_positions'''],
    ['''embeddings''', '''embed_tokens'''],
    ['''ffn.lin''', '''fc'''],
]



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







				if k == "embeddings.weight":
								return "shared.weight"

				for parlai_name, hf_name in PATTERNS:
								__UpperCamelCase  :Union[str, Any]    						=	k.replace(SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE			)

				if k.startswith('''encoder'''			):
								__UpperCamelCase  :Any    						=	k.replace('''.attn'''						,    '''.self_attn'''			)
								__UpperCamelCase  :Optional[int]    						=	k.replace('''norm1'''						,    '''self_attn_layer_norm'''			)
								__UpperCamelCase  :int    						=	k.replace('''norm2'''						,    '''final_layer_norm'''			)
				elif k.startswith('''decoder'''			):
								__UpperCamelCase  :Optional[int]    						=	k.replace('''norm1'''						,    '''self_attn_layer_norm'''			)
								__UpperCamelCase  :Optional[Any]    						=	k.replace('''norm2'''						,    '''encoder_attn_layer_norm'''			)
								__UpperCamelCase  :str    						=	k.replace('''norm3'''						,    '''final_layer_norm'''			)
				return k



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







				__UpperCamelCase  :Optional[Any]    						=	[
				    '''model.encoder.layernorm_embedding.weight''',
				    '''model.encoder.layernorm_embedding.bias''',
				    '''model.decoder.layernorm_embedding.weight''',
				    '''model.decoder.layernorm_embedding.bias''',
				]
				for k in keys:
								__UpperCamelCase  :Union[str, Any]    						=	sd.pop(SCREAMING_SNAKE_CASE			)
								__UpperCamelCase  :int    						=	k.replace('''layernorm_embedding'''						,    '''layer_norm'''			)
								assert new_k not in sd
								__UpperCamelCase  :List[Any]    						=	v


__lowercase											=      ['''START''']



@torch.no_grad()
def lowerCamelCase						(     SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE			):
				'''simple docstring'''







				__UpperCamelCase  :List[Any]    						=	torch.load(SCREAMING_SNAKE_CASE						,    map_location='''cpu'''			)
				__UpperCamelCase  :List[Any]    						=	model['''model''']
				__UpperCamelCase  :List[str]    						=	BlenderbotConfig.from_json_file(SCREAMING_SNAKE_CASE			)
				__UpperCamelCase  :Optional[Any]    						=	BlenderbotForConditionalGeneration(SCREAMING_SNAKE_CASE			)
				__UpperCamelCase  :int    						=	m.model.state_dict().keys()
				__UpperCamelCase  :Optional[int]    						=	[]
				__UpperCamelCase  :List[str]    						=	{}
				for k, v in sd.items():
								if k in IGNORE_KEYS:
												continue

								__UpperCamelCase  :Optional[int]    						=	rename_state_dict_key(SCREAMING_SNAKE_CASE			)
								if new_k not in valid_keys:
												failures.append([k, new_k]			)
								else:
												__UpperCamelCase  :int    						=	v
				if cfg.normalize_before:  # Blenderbot-3B checkpoints. Rename layernorm_embedding -> layer_norm
								rename_layernorm_keys(SCREAMING_SNAKE_CASE			)
				m.model.load_state_dict(SCREAMING_SNAKE_CASE						,    strict=SCREAMING_SNAKE_CASE			)
				m.half()
				m.save_pretrained(SCREAMING_SNAKE_CASE			)


if __name__ == "__main__":
				__lowercase											=      argparse.ArgumentParser()
				# Required parameters
				parser.add_argument('''--src_path''', type=str, help='''like blenderbot-model.bin''')
				parser.add_argument('''--save_dir''', default='''hf_blenderbot''', type=str, help='''Where to save converted model.''')
				parser.add_argument(
				    '''--hf_config_json''', default='''blenderbot-3b-config.json''', type=str, help='''Path to config to use'''
				)
				__lowercase											=      parser.parse_args()
				convert_parlai_checkpoint(args.src_path, args.save_dir, args.hf_config_json)






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)





