

import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    UniSpeechConfig,
    UniSpeechForCTC,
    UniSpeechForPreTraining,
    WavaVecaFeatureExtractor,
    WavaVecaPhonemeCTCTokenizer,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
A_       : int         =			logging.get_logger(__name__)

A_       : str         =			{
    '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_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_       : List[Any]         =			[
    'ctc_proj',
    'quantizer.weight_proj',
    'quantizer.codevectors',
    'project_q',
    'project_hid',
]

def 						UpperCamelCase					(lowercase_: int    ,      lowercase_: Tuple    ,      lowercase_: List[Any]    ,      lowercase_: Optional[Any]    ,      lowercase_: Any    ,      lowercase_: Dict					)     ->      Dict:
	for attribute in key.split("""."""					):
		if is_finetuned:
			if attribute in ["quantizer", "project_q", "project_hid"]:
				# those layers are only relevant for pretraining and should be dropped
				return

			if attribute == "ctc_proj":
				# we should rename `ctc_proj` to `lm_head` for fine-tuned phoneme models
				A__							:    List[Any]        =		"""lm_head"""

		A__							:    List[str]        =		getattr(lowercase_    ,      lowercase_					)

	if weight_type is not None:
		A__							:    Optional[int]        =		getattr(lowercase_    ,      lowercase_					).shape
	else:
		A__							:    Optional[int]        =		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":
		A__							:    str        =		value
	elif weight_type == "weight_g":
		A__							:    Dict        =		value
	elif weight_type == "weight_v":
		A__							:    Tuple        =		value
	elif weight_type == "bias":
		A__							:    Optional[int]        =		value
	else:
		A__							:    Optional[Any]        =		value

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

def 						UpperCamelCase					(lowercase_: Any    ,      lowercase_: str    ,      lowercase_: Tuple					)     ->      List[Any]:
	A__							:    List[Any]        =		[]
	A__							:    List[str]        =		fairseq_model.state_dict()

	A__							:    Union[str, Any]        =		hf_model.unispeech.feature_extractor

	for name, value in fairseq_dict.items():
		A__							:    List[str]        =		False
		if "conv_layers" in name:
			load_conv_layer(
			    lowercase_    ,      lowercase_    ,      lowercase_    ,      lowercase_    ,      hf_model.config.feat_extract_norm == """group"""    ,      )
			A__							:    List[str]        =		True
		else:
			for key, mapped_key in MAPPING.items():
				A__							:    Tuple        =		"""unispeech.""" + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
				if key in name or key.split("""w2v_model."""					)[-1] == name.split("""."""					)[0]:
					A__							:    List[Any]        =		True
					if "*" in mapped_key:
						A__							:    Any        =		name.split(lowercase_					)[0].split("""."""					)[-2]
						A__							:    Optional[int]        =		mapped_key.replace("""*"""    ,      lowercase_					)
					if "weight_g" in name:
						A__							:    Dict        =		"""weight_g"""
					elif "weight_v" in name:
						A__							:    Optional[int]        =		"""weight_v"""
					elif "bias" in name:
						A__							:    Optional[int]        =		"""bias"""
					elif "weight" in name:
						# TODO: don't match quantizer.weight_proj
						A__							:    Any        =		"""weight"""
					else:
						A__							:    List[Any]        =		None
					set_recursively(lowercase_    ,      lowercase_    ,      lowercase_    ,      lowercase_    ,      lowercase_    ,      lowercase_					)
				continue
		if not is_used:
			unused_weights.append(lowercase_					)

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

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

@torch.no_grad()
def 						UpperCamelCase					(lowercase_: List[Any]    ,      lowercase_: List[str]    ,      lowercase_: Tuple=None    ,      lowercase_: int=None    ,      lowercase_: int=True					)     ->      List[Any]:
	if config_path is not None:
		A__							:    Optional[Any]        =		UniSpeechConfig.from_pretrained(lowercase_					)
	else:
		A__							:    Tuple        =		UniSpeechConfig()

	if is_finetuned:
		if dict_path:
			A__							:    List[Any]        =		Dictionary.load_from_json(lowercase_					)

			# important change bos & pad token id since CTC symbol is <pad> and
			# not <s> as in fairseq
			A__							:    Tuple        =		target_dict.pad_index
			A__							:    List[str]        =		target_dict.bos_index
			A__							:    Union[str, Any]        =		target_dict.eos_index
			A__							:    Optional[Any]        =		len(target_dict.symbols					)
			A__							:    str        =		os.path.join(lowercase_    ,      """vocab.json"""					)
			if not os.path.isdir(lowercase_					):
				logger.error("""--pytorch_dump_folder_path ({}) should be a directory""".format(lowercase_					)					)
				return
			os.makedirs(lowercase_    ,      exist_ok=lowercase_					)
			A__							:    str        =		target_dict.indices

			# fairseq has the <pad> and <s> switched
			A__							:    Any        =		42
			A__							:    Optional[Any]        =		43
			with open(lowercase_    ,      """w"""    ,      encoding="""utf-8"""					) as vocab_handle:
				json.dump(lowercase_    ,      lowercase_					)
			A__							:    int        =		WavaVecaPhonemeCTCTokenizer(
			    lowercase_    ,      unk_token=target_dict.unk_word    ,      pad_token=target_dict.pad_word    ,      bos_token=target_dict.bos_word    ,      eos_token=target_dict.eos_word    ,      word_delimiter_token="""|"""    ,      do_lower_case=lowercase_    ,      )
			A__							:    Union[str, Any]        =		True if config.feat_extract_norm == """layer""" else False
			A__							:    str        =		WavaVecaFeatureExtractor(
			    feature_size=1    ,      sampling_rate=16000    ,      padding_value=0    ,      do_normalize=lowercase_    ,      return_attention_mask=lowercase_    ,      )
			A__							:    Tuple        =		WavaVecaProcessor(feature_extractor=lowercase_    ,      tokenizer=lowercase_					)
			processor.save_pretrained(lowercase_					)

		A__							:    Tuple        =		UniSpeechForCTC(lowercase_					)
	else:
		A__							:    str        =		UniSpeechForPreTraining(lowercase_					)

	if is_finetuned:
		A__   ,   A__   ,   A__							:    Optional[int]        =		fairseq.checkpoint_utils.load_model_ensemble_and_task(
		    [checkpoint_path]    ,      arg_overrides={"""data""": """/""".join(dict_path.split("""/"""					)[:-1]					), """w2v_path""": checkpoint_path}					)
	else:
		A__   ,   A__   ,   A__							:    Union[str, Any]        =		fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]					)

	A__							:    str        =		model[0].eval()

	recursively_load_weights(lowercase_    ,      lowercase_    ,      lowercase_					)

	hf_unispeech.save_pretrained(lowercase_					)


if __name__ == "__main__":
	A_       : Any         =			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('--dict_path', default=None, type=str, help='Path to dict of fine-tuned model')
	parser.add_argument('--config_path', default=None, type=str, help='Path to hf config.json of model to convert')
	parser.add_argument(
	    '--not_finetuned', action='store_true', help='Whether the model to convert is a fine-tuned model or not'
	)
	A_       : Tuple         =			parser.parse_args()
	convert_unispeech_checkpoint(
	    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
	)






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_       : List[Any]         =			logging.get_logger(__name__)



class     _a						:


					'''simple docstring'''






					def __init__( self    ,       A__ = None    ,       A__ = None    ,       A__=None    ,       A__=None			):
						if not conversation_id:
							A__							:    List[Any]        =		uuid.uuida()
						if past_user_inputs is None:
							A__							:    Dict        =		[]
						if generated_responses is None:
							A__							:    int        =		[]

						A__							:    uuid.UUID        =		conversation_id
						A__							:    List[str]        =		past_user_inputs
						A__							:    List[str]        =		generated_responses
						A__							:    Optional[str]        =		text






					def __eq__( self    ,       A__			):
						if not isinstance(A__    ,       A__			):
							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   __A			( self    ,       A__    ,       A__ = 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}\"."""			)
								A__							:    str        =		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:
							A__							:    Tuple        =		text






					def   __A			( self			):
						if self.new_user_input:
							self.past_user_inputs.append(self.new_user_input			)
						A__							:    Tuple        =		None






					def   __A			( self    ,       A__			):
						self.generated_responses.append(A__			)






					def   __A			( 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			):
						A__							:    Optional[Any]        =		F"""Conversation id: {self.uuid} \n"""
						for is_user, text in self.iter_texts():
							A__							:    str        =		"""user""" if is_user else """bot"""
							output += F"""{name} >> {text} \n"""
						return output



@add_end_docstrings(
    __magic_name__		,   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						(__magic_name__				):


					'''simple docstring'''






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






					def   __A			( self    ,       A__=None    ,       A__=None    ,       A__=None    ,       **A__			):
						A__							:    Tuple        =		{}
						A__							:    List[str]        =		{}
						A__							:    Union[str, Any]        =		{}

						if min_length_for_response is not None:
							A__							:    str        =		min_length_for_response
						if minimum_tokens is not None:
							A__							:    List[str]        =		minimum_tokens

						if "max_length" in generate_kwargs:
							A__							:    List[Any]        =		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:
							A__							:    Optional[int]        =		clean_up_tokenization_spaces

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






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






					def   __A			( self    ,       A__    ,       A__=32			):
						if not isinstance(A__    ,       A__			):
							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"""			):
							A__							:    List[str]        =		self.tokenizer._build_conversation_input_ids(A__			)
						else:
							# If the tokenizer cannot handle conversations, we default to only the old version
							A__							:    Tuple        =		self._legacy_parse_and_tokenize(A__			)

						if self.framework == "pt":
							A__							:    List[str]        =		torch.LongTensor([input_ids]			)
						elif self.framework == "tf":
							A__							:    Optional[Any]        =		tf.constant([input_ids]			)
						return {"input_ids": input_ids, "conversation": conversation}






					def   __A			( self    ,       A__    ,       A__=10    ,       **A__			):
						A__							:    List[Any]        =		generate_kwargs.get("""max_length"""    ,       self.model.config.max_length			)

						A__							:    Optional[int]        =		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})"""			)
							A__							:    Dict        =		max_length - minimum_tokens
							A__							:    Optional[int]        =		model_inputs["""input_ids"""][:, -trim:]
							if "attention_mask" in model_inputs:
								A__							:    str        =		model_inputs["""attention_mask"""][:, -trim:]
						A__							:    List[str]        =		model_inputs.pop("""conversation"""			)
						A__							:    Dict        =		max_length
						A__							:    str        =		self.model.generate(**A__    ,       **A__			)
						if self.model.config.is_encoder_decoder:
							A__							:    Union[str, Any]        =		1
						else:
							A__							:    Optional[Any]        =		n
						return {"output_ids": output_ids[:, start_position:], "conversation": conversation}






					def   __A			( self    ,       A__    ,       A__=True			):
						A__							:    Dict        =		model_outputs["""output_ids"""]
						A__							:    str        =		self.tokenizer.decode(
						    output_ids[0]    ,       skip_special_tokens=A__    ,       clean_up_tokenization_spaces=A__    ,       )
						A__							:    Optional[int]        =		model_outputs["""conversation"""]
						conversation.mark_processed()
						conversation.append_response(A__			)
						return conversation






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

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



