import argparse
import os
from pathlib import Path

import fairseq
import torch
from packaging import version
from torch import nn

from transformers import (
    BartConfig,
    BartForConditionalGeneration,
    BartForSequenceClassification,
    BartModel,
    BartTokenizer,
)
from transformers.utils import logging


__a  :     Dict       =  ["""bart.large""", """bart.large.mnli""", """bart.large.cnn""", """bart_xsum/model.pt"""]
__a  :     Union[str, Any]       =  {"""bart.large""": BartModel, """bart.large.mnli""": BartForSequenceClassification}
if version.parse(fairseq.__version__) < version.parse("""0.9.0"""):
		raise Exception("""requires fairseq >= 0.9.0""")


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

__a  :     str       =  """ Hello world! cécé herlolip"""

__a  :     int       =  [
    ("""model.classification_heads.mnli.dense.weight""", """classification_head.dense.weight"""),
    ("""model.classification_heads.mnli.dense.bias""", """classification_head.dense.bias"""),
    ("""model.classification_heads.mnli.out_proj.weight""", """classification_head.out_proj.weight"""),
    ("""model.classification_heads.mnli.out_proj.bias""", """classification_head.out_proj.bias"""),
]





def 				UpperCAmelCase						(    lowercase							):







				"""simple docstring"""



				__lowercase 			=							[
				    '''encoder.version''',
				    '''decoder.version''',
				    '''model.encoder.version''',
				    '''model.decoder.version''',
				    '''_float_tensor''',
				]
				for k in ignore_keys:
								state_dict.pop(lowercase							,			lowercase							)





def 				UpperCAmelCase						(    lowercase							,			lowercase							,			lowercase							):







				"""simple docstring"""



				__lowercase 			=							dct.pop(lowercase							)
				__lowercase 			=							val





def 				UpperCAmelCase						(    lowercase							):







				"""simple docstring"""



				__lowercase 			=							torch.load(lowercase							,			map_location='''cpu'''							)
				__lowercase 			=							torch.hub.load('''pytorch/fairseq'''							,			'''bart.large.cnn'''							).eval()
				hub_interface.model.load_state_dict(sd['''model''']							)
				return hub_interface





def 				UpperCAmelCase						(    lowercase							):







				"""simple docstring"""



				__lowercase			,     __lowercase 			=							emb.weight.shape
				__lowercase 			=							nn.Linear(lowercase							,			lowercase							,			bias=lowercase							)
				__lowercase 			=							emb.weight.data
				return lin_layer





@torch.no_grad()
def 				UpperCAmelCase						(    lowercase							,			lowercase							,			lowercase=None							):







				"""simple docstring"""



				if not os.path.exists(lowercase							):
								__lowercase 			=							torch.hub.load('''pytorch/fairseq'''							,			lowercase							).eval()
				else:
								__lowercase 			=							load_xsum_checkpoint(lowercase							)

				bart.model.upgrade_state_dict(bart.model.state_dict()							)
				if hf_checkpoint_name is None:
								__lowercase 			=							checkpoint_path.replace('''.'''							,			'''-'''							)
				__lowercase 			=							BartConfig.from_pretrained(lowercase							)
				__lowercase 			=							bart.encode(lowercase							).unsqueeze(0							)
				__lowercase 			=							BartTokenizer.from_pretrained(lowercase							).encode(lowercase							,			return_tensors='''pt'''							).unsqueeze(0							)
				if not torch.eq(lowercase							,			lowercase							).all():
								raise ValueError(
								    F"converted tokenizer and pretrained tokenizer returned different output: {tokens} != {tokensa}"							)

				if checkpoint_path == "bart.large.mnli":
								__lowercase 			=							bart.state_dict()
								remove_ignore_keys_(lowercase							)
								__lowercase 			=							state_dict['''model.decoder.embed_tokens.weight''']
								for src, dest in mnli_rename_keys:
												rename_key(lowercase							,			lowercase							,			lowercase							)
								__lowercase 			=							BartForSequenceClassification(lowercase							).eval()
								model.load_state_dict(lowercase							)
								__lowercase 			=							bart.predict('''mnli'''							,			lowercase							,			return_logits=lowercase							)
								__lowercase 			=							model(lowercase							)[0]  # logits
				else:  # no classification heads to worry about
								__lowercase 			=							bart.model.state_dict()
								remove_ignore_keys_(lowercase							)
								__lowercase 			=							state_dict['''decoder.embed_tokens.weight''']
								__lowercase 			=							bart.extract_features(lowercase							)
								if hf_checkpoint_name == "facebook/bart-large":
												__lowercase 			=							BartModel(lowercase							).eval()
												model.load_state_dict(lowercase							)
												__lowercase 			=							model(lowercase							).model[0]
								else:
												__lowercase 			=							BartForConditionalGeneration(lowercase							).eval()  # an existing summarization ckpt
												model.model.load_state_dict(lowercase							)
												if hasattr(lowercase							,			'''lm_head'''							):
																__lowercase 			=							make_linear_from_emb(model.model.shared							)
												__lowercase 			=							model.model(lowercase							)[0]

    # Check results
				if fairseq_output.shape != new_model_outputs.shape:
								raise ValueError(
								    F"`fairseq_output` shape and `new_model_output` shape are different: {fairseq_output.shape=}, {new_model_outputs.shape}"							)
				if (fairseq_output != new_model_outputs).any().item():
								raise ValueError('''Some values in `fairseq_output` are different from `new_model_outputs`'''							)
				Path(lowercase							).mkdir(exist_ok=lowercase							)
				model.save_pretrained(lowercase							)


if __name__ == "__main__":
		__a  :     Optional[int]       =  argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    """fairseq_path""", type=str, help="""bart.large, bart.large.cnn or a path to a model.pt on local filesystem."""
		)
		parser.add_argument("""pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
		parser.add_argument(
		    """--hf_config""", default=None, type=str, help="""Which huggingface architecture to use: bart-large-xsum"""
		)
		__a  :     Optional[int]       =  parser.parse_args()
		convert_bart_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, hf_checkpoint_name=args.hf_config)
import functools
import operator

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


__a  :     str       =  logging.get_logger(__name__)

__a  :     int       =  {
    """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    _UpperCamelCase      (						_UpperCAmelCase ):


			"""simple docstring"""






			__a  : Any			 =						'''wavlm'''





			def __init__(   self      ,   lowerCAmelCase__=32      ,   lowerCAmelCase__=7_68      ,   lowerCAmelCase__=12      ,   lowerCAmelCase__=12      ,   lowerCAmelCase__=30_72      ,   lowerCAmelCase__="gelu"      ,   lowerCAmelCase__=0.1      ,   lowerCAmelCase__=0.1      ,   lowerCAmelCase__=0.1      ,   lowerCAmelCase__=0.0      ,   lowerCAmelCase__=0.1      ,   lowerCAmelCase__=0.1      ,   lowerCAmelCase__=0.02      ,   lowerCAmelCase__=1E-5      ,   lowerCAmelCase__="group"      ,   lowerCAmelCase__="gelu"      ,   lowerCAmelCase__=(5_12, 5_12, 5_12, 5_12, 5_12, 5_12, 5_12)      ,   lowerCAmelCase__=(5, 2, 2, 2, 2, 2, 2)      ,   lowerCAmelCase__=(10, 3, 3, 3, 3, 2, 2)      ,   lowerCAmelCase__=False      ,   lowerCAmelCase__=1_28      ,   lowerCAmelCase__=16      ,   lowerCAmelCase__=3_20      ,   lowerCAmelCase__=8_00      ,   lowerCAmelCase__=False      ,   lowerCAmelCase__=True      ,   lowerCAmelCase__=0.05      ,   lowerCAmelCase__=10      ,   lowerCAmelCase__=2      ,   lowerCAmelCase__=0.0      ,   lowerCAmelCase__=10      ,   lowerCAmelCase__=3_20      ,   lowerCAmelCase__=2      ,   lowerCAmelCase__=0.1      ,   lowerCAmelCase__=1_00      ,   lowerCAmelCase__=2_56      ,   lowerCAmelCase__=2_56      ,   lowerCAmelCase__=0.1      ,   lowerCAmelCase__="mean"      ,   lowerCAmelCase__=False      ,   lowerCAmelCase__=False      ,   lowerCAmelCase__=2_56      ,   lowerCAmelCase__=(5_12, 5_12, 5_12, 5_12, 15_00)      ,   lowerCAmelCase__=(5, 3, 3, 1, 1)      ,   lowerCAmelCase__=(1, 2, 3, 1, 1)      ,   lowerCAmelCase__=5_12      ,   lowerCAmelCase__=80      ,   lowerCAmelCase__=0      ,   lowerCAmelCase__=1      ,   lowerCAmelCase__=2      ,   lowerCAmelCase__=False      ,   lowerCAmelCase__=3      ,   lowerCAmelCase__=2      ,   lowerCAmelCase__=3      ,   lowerCAmelCase__=None      ,   **lowerCAmelCase__      ,   )     ->					Tuple:

							'''simple docstring'''


							super().__init__(**lowerCAmelCase__      ,   pad_token_id=lowerCAmelCase__      ,   bos_token_id=lowerCAmelCase__      ,   eos_token_id=lowerCAmelCase__						)
							__lowercase 			=							hidden_size
							__lowercase 			=							feat_extract_norm
							__lowercase 			=							feat_extract_activation
							__lowercase 			=							list(lowerCAmelCase__						)
							__lowercase 			=							list(lowerCAmelCase__						)
							__lowercase 			=							list(lowerCAmelCase__						)
							__lowercase 			=							conv_bias
							__lowercase 			=							num_buckets
							__lowercase 			=							max_bucket_distance
							__lowercase 			=							num_conv_pos_embeddings
							__lowercase 			=							num_conv_pos_embedding_groups
							__lowercase 			=							len(self.conv_dim						)
							__lowercase 			=							num_hidden_layers
							__lowercase 			=							intermediate_size
							__lowercase 			=							hidden_act
							__lowercase 			=							num_attention_heads
							__lowercase 			=							hidden_dropout
							__lowercase 			=							attention_dropout
							__lowercase 			=							activation_dropout
							__lowercase 			=							feat_proj_dropout
							__lowercase 			=							final_dropout
							__lowercase 			=							layerdrop
							__lowercase 			=							layer_norm_eps
							__lowercase 			=							initializer_range
							__lowercase 			=							num_ctc_classes
							__lowercase 			=							vocab_size
							__lowercase 			=							do_stable_layer_norm
							__lowercase 			=							use_weighted_layer_sum
							__lowercase 			=							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
							__lowercase 			=							apply_spec_augment
							__lowercase 			=							mask_time_prob
							__lowercase 			=							mask_time_length
							__lowercase 			=							mask_time_min_masks
							__lowercase 			=							mask_feature_prob
							__lowercase 			=							mask_feature_length

							# parameters for pretraining with codevector quantized representations
							__lowercase 			=							num_codevectors_per_group
							__lowercase 			=							num_codevector_groups
							__lowercase 			=							contrastive_logits_temperature
							__lowercase 			=							num_negatives
							__lowercase 			=							codevector_dim
							__lowercase 			=							proj_codevector_dim
							__lowercase 			=							diversity_loss_weight

							# ctc loss
							__lowercase 			=							ctc_loss_reduction
							__lowercase 			=							ctc_zero_infinity

							# adapter
							__lowercase 			=							add_adapter
							__lowercase 			=							adapter_kernel_size
							__lowercase 			=							adapter_stride
							__lowercase 			=							num_adapter_layers
							__lowercase 			=							output_hidden_size or hidden_size

							# SequenceClassification-specific parameter. Feel free to ignore for other classes.
							__lowercase 			=							classifier_proj_size

							# XVector-specific parameters. Feel free to ignore for other classes.
							__lowercase 			=							list(lowerCAmelCase__						)
							__lowercase 			=							list(lowerCAmelCase__						)
							__lowercase 			=							list(lowerCAmelCase__						)
							__lowercase 			=							xvector_output_dim





			@property
			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					int:

							'''simple docstring'''


							return functools.reduce(operator.mul      ,   self.conv_stride      ,   1						)