




import argparse

import torch

# Step 1. clone https://github.com/microsoft/unilm
# Step 2. git checkout to https://github.com/microsoft/unilm/commit/b94ec76c36f02fb2b0bf0dcb0b8554a2185173cd
# Step 3. cd unilm
# Step 4. ln -s $(realpath wavlm/modules.py) ./  # create simlink
# import classes
from unilm.wavlm.WavLM import WavLM as WavLMOrig
from unilm.wavlm.WavLM import WavLMConfig as WavLMConfigOrig

from transformers import WavLMConfig, WavLMModel, 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.grep_linear": "encoder.layers.*.attention.gru_rel_pos_linear",
    "self_attn.relative_attention_bias": "encoder.layers.*.attention.rel_attn_embed",
    "self_attn.grep_a": "encoder.layers.*.attention.gru_rel_pos_const",
    "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": "ctc_proj",
    "mask_emb": "masked_spec_embed",
}
a													=			[
    "ctc_proj",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
]




def        _SCREAMING_SNAKE_CASE      (		snake_case			,     snake_case			,     snake_case			,     snake_case			,     snake_case       )						->				Dict:
						for attribute in key.split("""."""       ):
												_UpperCAmelCase      =			getattr(snake_case			,     snake_case       )

						if weight_type is not None:
												_UpperCAmelCase      =			getattr(snake_case			,     snake_case       ).shape
						else:
												_UpperCAmelCase      =			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":
												_UpperCAmelCase      =			value
						elif weight_type == "weight_g":
												_UpperCAmelCase      =			value
						elif weight_type == "weight_v":
												_UpperCAmelCase      =			value
						elif weight_type == "bias":
												_UpperCAmelCase      =			value
						else:
												_UpperCAmelCase      =			value

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




def        _SCREAMING_SNAKE_CASE      (		snake_case			,     snake_case       )						->				List[Any]:
						_UpperCAmelCase      =			[]
						_UpperCAmelCase      =			fairseq_model.state_dict()

						_UpperCAmelCase      =			hf_model.feature_extractor

						for name, value in fairseq_dict.items():
												_UpperCAmelCase      =			False
												if "conv_layers" in name:
																		load_conv_layer(
																		    snake_case			,     snake_case			,     snake_case			,     snake_case			,     hf_model.config.feat_extract_norm == """group"""			,     )
																		_UpperCAmelCase      =			True
												else:
																		for key, mapped_key in MAPPING.items():
																								if key in name or key.split("""w2v_model."""       )[-1] == name.split("""."""       )[0]:
																														_UpperCAmelCase      =			True
																														if "*" in mapped_key:
																																				_UpperCAmelCase      =			name.split(snake_case       )[0].split("""."""       )[-2]
																																				_UpperCAmelCase      =			mapped_key.replace("""*"""			,     snake_case       )
																														if "weight_g" in name:
																																				_UpperCAmelCase      =			"""weight_g"""
																														elif "weight_v" in name:
																																				_UpperCAmelCase      =			"""weight_v"""
																														elif "bias" in name and "relative_attention_bias" not in name:
																																				_UpperCAmelCase      =			"""bias"""
																														elif "weight" in name:
																																				# TODO: don't match quantizer.weight_proj
																																				_UpperCAmelCase      =			"""weight"""
																														else:
																																				_UpperCAmelCase      =			None

																														set_recursively(snake_case			,     snake_case			,     snake_case			,     snake_case			,     snake_case       )
																								continue
												if not is_used:
																		unused_weights.append(snake_case       )

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




def        _SCREAMING_SNAKE_CASE      (		snake_case			,     snake_case			,     snake_case			,     snake_case			,     snake_case       )						->				Any:
						_UpperCAmelCase      =			full_name.split("""conv_layers."""       )[-1]
						_UpperCAmelCase      =			name.split("""."""       )
						_UpperCAmelCase      =			int(items[0]       )
						_UpperCAmelCase      =			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."
																		)
																		_UpperCAmelCase      =			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."
																		)
																		_UpperCAmelCase      =			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."
																		)
																		_UpperCAmelCase      =			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."
																		)
																		_UpperCAmelCase      =			value
																		logger.info(f"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."       )
						else:
												unused_weights.append(snake_case       )




@torch.no_grad()
def        _SCREAMING_SNAKE_CASE      (		snake_case			,     snake_case			,     snake_case=None       )						->				List[Any]:
						# load the pre-trained checkpoints
						_UpperCAmelCase      =			torch.load(snake_case       )
						_UpperCAmelCase      =			WavLMConfigOrig(checkpoint["""cfg"""]       )
						_UpperCAmelCase      =			WavLMOrig(snake_case       )
						model.load_state_dict(checkpoint["""model"""]       )
						model.eval()

						if config_path is not None:
												_UpperCAmelCase      =			WavLMConfig.from_pretrained(snake_case       )
						else:
												_UpperCAmelCase      =			WavLMConfig()

						_UpperCAmelCase      =			WavLMModel(snake_case       )

						recursively_load_weights(snake_case			,     snake_case       )

						hf_wavlm.save_pretrained(snake_case       )


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("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
				a													=			parser.parse_args()
				convert_wavlm_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)





import uuid
from typing import Any, Dict, List, Optional, Union

from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_tf_available():
				import tensorflow as tf

if is_torch_available():
				import torch


a													=			logging.get_logger(__name__)




class       _A				:






		def __init__(    self						,		_SCREAMING_SNAKE_CASE = None						,		_SCREAMING_SNAKE_CASE = None						,		_SCREAMING_SNAKE_CASE=None						,		_SCREAMING_SNAKE_CASE=None			):
								if not conversation_id:
														_UpperCAmelCase      =			uuid.uuida()
								if past_user_inputs is None:
														_UpperCAmelCase      =			[]
								if generated_responses is None:
														_UpperCAmelCase      =			[]

								_UpperCAmelCase      =			conversation_id
								_UpperCAmelCase      =			past_user_inputs
								_UpperCAmelCase      =			generated_responses
								_UpperCAmelCase      =			text






		def __eq__(    self						,		_SCREAMING_SNAKE_CASE			):
								if not isinstance(_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE			):
														return False
								if self.uuid == other.uuid:
														return True
								return (
								    self.new_user_input == other.new_user_input
								    and self.past_user_inputs == other.past_user_inputs
								    and self.generated_responses == other.generated_responses
								)






		def 				UpperCAmelCase (    self						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE = False			):
								if self.new_user_input:
														if overwrite:
																				logger.warning(
																				    F"User input added while unprocessed input was existing: \"{self.new_user_input}\" was overwritten "
																				    F"with: \"{text}\"."			)
																				_UpperCAmelCase      =			text
														else:
																				logger.warning(
																				    F"User input added while unprocessed input was existing: \"{self.new_user_input}\" new input "
																				    F"ignored: \"{text}\". Set `overwrite` to True to overwrite unprocessed user input"			)
								else:
														_UpperCAmelCase      =			text






		def 				UpperCAmelCase (    self			):
								if self.new_user_input:
														self.past_user_inputs.append(self.new_user_input			)
								_UpperCAmelCase      =			None






		def 				UpperCAmelCase (    self						,		_SCREAMING_SNAKE_CASE			):
								self.generated_responses.append(_SCREAMING_SNAKE_CASE			)






		def 				UpperCAmelCase (    self			):
								for user_input, generated_response in zip(self.past_user_inputs						,		self.generated_responses			):
														yield True, user_input
														yield False, generated_response
								if self.new_user_input:
														yield True, self.new_user_input






		def __repr__(    self			):
								_UpperCAmelCase      =			F"Conversation id: {self.uuid} \n"
								for is_user, text in self.iter_texts():
														_UpperCAmelCase      =			"""user""" if is_user else """bot"""
														output += F"{name} >> {text} \n"
								return output




@add_end_docstrings(
    __lowercase       ,     R"""
        min_length_for_response (`int`, *optional*, defaults to 32):
            The minimum length (in number of tokens) for a response.
        minimum_tokens (`int`, *optional*, defaults to 10):
            The minimum length of tokens to leave for a response.
    """       ,     )
class       _A				(		__lowercase     ):






		def __init__(    self						,		*_SCREAMING_SNAKE_CASE						,		**_SCREAMING_SNAKE_CASE			):
								super().__init__(*_SCREAMING_SNAKE_CASE						,		**_SCREAMING_SNAKE_CASE			)
								if self.tokenizer.pad_token_id is None:
														_UpperCAmelCase      =			self.tokenizer.eos_token






		def 				UpperCAmelCase (    self						,		_SCREAMING_SNAKE_CASE=None						,		_SCREAMING_SNAKE_CASE=None						,		_SCREAMING_SNAKE_CASE=None						,		**_SCREAMING_SNAKE_CASE			):
								_UpperCAmelCase      =			{}
								_UpperCAmelCase      =			{}
								_UpperCAmelCase      =			{}

								if min_length_for_response is not None:
														_UpperCAmelCase      =			min_length_for_response
								if minimum_tokens is not None:
														_UpperCAmelCase      =			minimum_tokens

								if "max_length" in generate_kwargs:
														_UpperCAmelCase      =			generate_kwargs["""max_length"""]
														# self.max_length = generate_kwargs.get("max_length", self.model.config.max_length)
								if clean_up_tokenization_spaces is not None:
														_UpperCAmelCase      =			clean_up_tokenization_spaces

								if generate_kwargs:
														forward_params.update(_SCREAMING_SNAKE_CASE			)
								return preprocess_params, forward_params, postprocess_params






		def __call__(    self						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE=0						,		**_SCREAMING_SNAKE_CASE			):
								_UpperCAmelCase      =			super().__call__(_SCREAMING_SNAKE_CASE						,		num_workers=_SCREAMING_SNAKE_CASE						,		**_SCREAMING_SNAKE_CASE			)
								if isinstance(_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE			) and len(_SCREAMING_SNAKE_CASE			) == 1:
														return outputs[0]
								return outputs






		def 				UpperCAmelCase (    self						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE=32			):
								if not isinstance(_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE			):
														raise ValueError("""ConversationalPipeline, expects Conversation as inputs"""			)
								if conversation.new_user_input is None:
														raise ValueError(
														    F"Conversation with UUID {type(conversation.uuid			)} does not contain new user input to process. "
														    """Add user inputs with the conversation's `add_user_input` method"""			)
								if hasattr(self.tokenizer						,		"""_build_conversation_input_ids"""			):
														_UpperCAmelCase      =			self.tokenizer._build_conversation_input_ids(_SCREAMING_SNAKE_CASE			)
								else:
														# If the tokenizer cannot handle conversations, we default to only the old version
														_UpperCAmelCase      =			self._legacy_parse_and_tokenize(_SCREAMING_SNAKE_CASE			)

								if self.framework == "pt":
														_UpperCAmelCase      =			torch.LongTensor([input_ids]			)
								elif self.framework == "tf":
														_UpperCAmelCase      =			tf.constant([input_ids]			)
								return {"input_ids": input_ids, "conversation": conversation}






		def 				UpperCAmelCase (    self						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE=10						,		**_SCREAMING_SNAKE_CASE			):
								_UpperCAmelCase      =			generate_kwargs.get("""max_length"""						,		self.model.config.max_length			)

								_UpperCAmelCase      =			model_inputs["""input_ids"""].shape[1]
								if max_length - minimum_tokens < n:
														logger.warning(F"Conversation input is to long ({n}), trimming it to ({max_length} - {minimum_tokens})"			)
														_UpperCAmelCase      =			max_length - minimum_tokens
														_UpperCAmelCase      =			model_inputs["""input_ids"""][:, -trim:]
														if "attention_mask" in model_inputs:
																				_UpperCAmelCase      =			model_inputs["""attention_mask"""][:, -trim:]
								_UpperCAmelCase      =			model_inputs.pop("""conversation"""			)
								_UpperCAmelCase      =			max_length
								_UpperCAmelCase      =			self.model.generate(**_SCREAMING_SNAKE_CASE						,		**_SCREAMING_SNAKE_CASE			)
								if self.model.config.is_encoder_decoder:
														_UpperCAmelCase      =			1
								else:
														_UpperCAmelCase      =			n
								return {"output_ids": output_ids[:, start_position:], "conversation": conversation}






		def 				UpperCAmelCase (    self						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE=True			):
								_UpperCAmelCase      =			model_outputs["""output_ids"""]
								_UpperCAmelCase      =			self.tokenizer.decode(
								    output_ids[0]						,		skip_special_tokens=_SCREAMING_SNAKE_CASE						,		clean_up_tokenization_spaces=_SCREAMING_SNAKE_CASE						,		)
								_UpperCAmelCase      =			model_outputs["""conversation"""]
								conversation.mark_processed()
								conversation.append_response(_SCREAMING_SNAKE_CASE			)
								return conversation






		def 				UpperCAmelCase (    self						,		_SCREAMING_SNAKE_CASE			):
								_UpperCAmelCase      =			self.tokenizer.eos_token_id
								_UpperCAmelCase      =			[]
								for is_user, text in conversation.iter_texts():
														if eos_token_id is not None:
																				input_ids.extend(self.tokenizer.encode(_SCREAMING_SNAKE_CASE						,		add_special_tokens=_SCREAMING_SNAKE_CASE			) + [eos_token_id]			)
														else:
																				input_ids.extend(self.tokenizer.encode(_SCREAMING_SNAKE_CASE						,		add_special_tokens=_SCREAMING_SNAKE_CASE			)			)

								if len(_SCREAMING_SNAKE_CASE			) > self.tokenizer.model_max_length:
														_UpperCAmelCase      =			input_ids[-self.tokenizer.model_max_length :]
								return input_ids