





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


_SCREAMING_SNAKE_CASE					=    logging.get_logger(__name__)






class 							__lowercase       :







	'''simple docstring'''

	def __init__(self     ,_lowerCamelCase = None     ,_lowerCamelCase = None     ,_lowerCamelCase=None     ,_lowerCamelCase=None )							->							Any:







				'''simple docstring'''





				if not conversation_id:
							__lowercase										=							uuid.uuida()
				if past_user_inputs is None:
							__lowercase										=							[]
				if generated_responses is None:
							__lowercase										=							[]

				__lowercase										=							conversation_id
				__lowercase										=							past_user_inputs
				__lowercase										=							generated_responses
				__lowercase										=							text


	def __eq__(self     ,_lowerCamelCase )							->							str:







				'''simple docstring'''





				if not isinstance(_lowerCamelCase     ,_lowerCamelCase ):
							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     ,_lowerCamelCase     ,_lowerCamelCase = False )							->							Optional[int]:







				'''simple docstring'''





				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}\"." )
										__lowercase										=							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:
							__lowercase										=							text


	def 						_UpperCAmelCase     (self )							->							Optional[Any]:







				'''simple docstring'''





				if self.new_user_input:
							self.past_user_inputs.append(self.new_user_input )
				__lowercase										=							None


	def 						_UpperCAmelCase     (self     ,_lowerCamelCase )							->							Any:







				'''simple docstring'''





				self.generated_responses.append(_lowerCamelCase )


	def 						_UpperCAmelCase     (self )							->							Tuple:







				'''simple docstring'''





				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 )							->							int:







				'''simple docstring'''





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






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







	'''simple docstring'''

	def __init__(self     ,*_lowerCamelCase     ,**_lowerCamelCase )							->							Tuple:







				'''simple docstring'''





				super().__init__(*_lowerCamelCase     ,**_lowerCamelCase )
				if self.tokenizer.pad_token_id is None:
							__lowercase										=							self.tokenizer.eos_token


	def 						_UpperCAmelCase     (self     ,_lowerCamelCase=None     ,_lowerCamelCase=None     ,_lowerCamelCase=None     ,**_lowerCamelCase )							->							Union[str, Any]:







				'''simple docstring'''





				__lowercase										=							{}
				__lowercase										=							{}
				__lowercase										=							{}

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

				if "max_length" in generate_kwargs:
							__lowercase										=							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:
							__lowercase										=							clean_up_tokenization_spaces

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


	def __call__(self     ,_lowerCamelCase     ,_lowerCamelCase=0     ,**_lowerCamelCase )							->							List[str]:







				'''simple docstring'''





				__lowercase										=							super().__call__(_lowerCamelCase     ,num_workers=_lowerCamelCase     ,**_lowerCamelCase )
				if isinstance(_lowerCamelCase     ,_lowerCamelCase ) and len(_lowerCamelCase ) == 1:
							return outputs[0]
				return outputs


	def 						_UpperCAmelCase     (self     ,_lowerCamelCase     ,_lowerCamelCase=32 )							->							Dict[str, Any]:







				'''simple docstring'''





				if not isinstance(_lowerCamelCase     ,_lowerCamelCase ):
							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''' ):
							__lowercase										=							self.tokenizer._build_conversation_input_ids(_lowerCamelCase )
				else:
							# If the tokenizer cannot handle conversations, we default to only the old version
							__lowercase										=							self._legacy_parse_and_tokenize(_lowerCamelCase )

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


	def 						_UpperCAmelCase     (self     ,_lowerCamelCase     ,_lowerCamelCase=10     ,**_lowerCamelCase )							->							Optional[Any]:







				'''simple docstring'''





				__lowercase										=							generate_kwargs.get('''max_length'''     ,self.model.config.max_length )

				__lowercase										=							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})" )
							__lowercase										=							max_length - minimum_tokens
							__lowercase										=							model_inputs['''input_ids'''][:, -trim:]
							if "attention_mask" in model_inputs:
										__lowercase										=							model_inputs['''attention_mask'''][:, -trim:]
				__lowercase										=							model_inputs.pop('''conversation''' )
				__lowercase										=							max_length
				__lowercase										=							self.model.generate(**_lowerCamelCase     ,**_lowerCamelCase )
				if self.model.config.is_encoder_decoder:
							__lowercase										=							1
				else:
							__lowercase										=							n
				return {"output_ids": output_ids[:, start_position:], "conversation": conversation}


	def 						_UpperCAmelCase     (self     ,_lowerCamelCase     ,_lowerCamelCase=True )							->							Dict:







				'''simple docstring'''





				__lowercase										=							model_outputs['''output_ids''']
				__lowercase										=							self.tokenizer.decode(
				    output_ids[0]     ,skip_special_tokens=_lowerCamelCase     ,clean_up_tokenization_spaces=_lowerCamelCase     ,)
				__lowercase										=							model_outputs['''conversation''']
				conversation.mark_processed()
				conversation.append_response(_lowerCamelCase )
				return conversation


	def 						_UpperCAmelCase     (self     ,_lowerCamelCase )							->							Dict:







				'''simple docstring'''





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

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












'''simple docstring'''



import argparse

import torch

from transformers import (
    UniSpeechSatConfig,
    UniSpeechSatForAudioFrameClassification,
    UniSpeechSatForSequenceClassification,
    UniSpeechSatForXVector,
    WavaVecaFeatureExtractor,
    logging,
)


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




def    _lowerCAmelCase						(				lowerCamelCase_			:  List[str]		,       lowerCamelCase_			:  List[str]		,       lowerCamelCase_			:  List[str]				):
			__lowercase										=							UniSpeechSatForSequenceClassification.from_pretrained(lowerCamelCase_		,       config=lowerCamelCase_				)
			__lowercase										=							downstream_dict['''projector.weight''']
			__lowercase										=							downstream_dict['''projector.bias''']
			__lowercase										=							downstream_dict['''model.post_net.linear.weight''']
			__lowercase										=							downstream_dict['''model.post_net.linear.bias''']
			return model




def    _lowerCAmelCase						(				lowerCamelCase_			:  str		,       lowerCamelCase_			:  Optional[int]		,       lowerCamelCase_			:  Optional[Any]				):
			__lowercase										=							UniSpeechSatForAudioFrameClassification.from_pretrained(lowerCamelCase_		,       config=lowerCamelCase_				)
			__lowercase										=							downstream_dict['''model.linear.weight''']
			__lowercase										=							downstream_dict['''model.linear.bias''']
			return model




def    _lowerCAmelCase						(				lowerCamelCase_			:  Optional[Any]		,       lowerCamelCase_			:  Dict		,       lowerCamelCase_			:  Optional[int]				):
			__lowercase										=							UniSpeechSatForXVector.from_pretrained(lowerCamelCase_		,       config=lowerCamelCase_				)
			__lowercase										=							downstream_dict['''connector.weight''']
			__lowercase										=							downstream_dict['''connector.bias''']
			for i, kernel_size in enumerate(hf_config.tdnn_kernel				):
						__lowercase										=							downstream_dict[
						    f"model.framelevel_feature_extractor.module.{i}.kernel.weight"
						]
						__lowercase										=							downstream_dict[f"model.framelevel_feature_extractor.module.{i}.kernel.bias"]

			__lowercase										=							downstream_dict['''model.utterancelevel_feature_extractor.linear1.weight''']
			__lowercase										=							downstream_dict['''model.utterancelevel_feature_extractor.linear1.bias''']
			__lowercase										=							downstream_dict['''model.utterancelevel_feature_extractor.linear2.weight''']
			__lowercase										=							downstream_dict['''model.utterancelevel_feature_extractor.linear2.bias''']
			__lowercase										=							downstream_dict['''objective.W''']
			return model




@torch.no_grad()
def    _lowerCAmelCase						(				lowerCamelCase_			:  Tuple		,       lowerCamelCase_			:  List[str]		,       lowerCamelCase_			:  Dict		,       lowerCamelCase_			:  Optional[int]				):
			__lowercase										=							torch.load(lowerCamelCase_		,       map_location='''cpu'''				)

			__lowercase										=							checkpoint['''Downstream''']

			__lowercase										=							UniSpeechSatConfig.from_pretrained(lowerCamelCase_				)
			__lowercase										=							WavaVecaFeatureExtractor.from_pretrained(
			    lowerCamelCase_		,       return_attention_mask=lowerCamelCase_		,       do_normalize=lowerCamelCase_				)

			__lowercase										=							hf_config.architectures[0]
			if arch.endswith('''ForSequenceClassification'''				):
						__lowercase										=							convert_classification(lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_				)
			elif arch.endswith('''ForAudioFrameClassification'''				):
						__lowercase										=							convert_diarization(lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_				)
			elif arch.endswith('''ForXVector'''				):
						__lowercase										=							convert_xvector(lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_				)
			else:
						raise NotImplementedError(f"S3PRL weights conversion is not supported for {arch}"				)

			if hf_config.use_weighted_layer_sum:
						__lowercase										=							checkpoint['''Featurizer''']['''weights''']

			hf_feature_extractor.save_pretrained(lowerCamelCase_				)
			hf_model.save_pretrained(lowerCamelCase_				)


if __name__ == "__main__":
	_SCREAMING_SNAKE_CASE					=    argparse.ArgumentParser()
	parser.add_argument(
	    '''--base_model_name''', default=None, type=str, help='''Name of the huggingface pretrained base model.'''
	)
	parser.add_argument('''--config_path''', default=None, type=str, help='''Path to the huggingface classifier config.''')
	parser.add_argument('''--checkpoint_path''', default=None, type=str, help='''Path to the s3prl checkpoint.''')
	parser.add_argument('''--model_dump_path''', default=None, type=str, help='''Path to the final converted model.''')
	_SCREAMING_SNAKE_CASE					=    parser.parse_args()
	convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)





