

import argparse
import json
import os

import fairseq
import torch
from torch import nn

from transformers import (
    SpeechaTextaConfig,
    SpeechaTextaForCausalLM,
    SpeechaTextaTokenizer,
    SpeechEncoderDecoderConfig,
    SpeechEncoderDecoderModel,
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaModel,
    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_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': 'lm_head',
    'mask_emb': 'masked_spec_embed',
}
a_						     =    [
    'lm_head',
    'quantizer.weight_proj',
    'quantizer.codevectors',
    'project_q',
    'project_hid',
]

def  lowerCamelCase__ (					_a							,			_a							,			_a							,			_a							,			_a):
	for attribute in key.split("."):
		SCREAMING_SNAKE_CASE   :      Dict				     =       getattr(_a							,			_a)

	if weight_type is not None:
		SCREAMING_SNAKE_CASE   :      Tuple				     =       getattr(_a							,			_a).shape
	else:
		SCREAMING_SNAKE_CASE   :      Dict				     =       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":
		SCREAMING_SNAKE_CASE   :      List[Any]				     =       value
	elif weight_type == "weight_g":
		SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       value
	elif weight_type == "weight_v":
		SCREAMING_SNAKE_CASE   :      List[Any]				     =       value
	elif weight_type == "bias":
		SCREAMING_SNAKE_CASE   :      Tuple				     =       value
	else:
		SCREAMING_SNAKE_CASE   :      Tuple				     =       value

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

def  lowerCamelCase__ (					_a							,			_a):
	SCREAMING_SNAKE_CASE   :      Any				     =       []
	SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       fairseq_model.state_dict()

	SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       hf_model.feature_extractor

	# if encoder has different dim to decoder -> use proj_weight
	SCREAMING_SNAKE_CASE   :      Optional[int]				     =       None

	for name, value in fairseq_dict.items():
		SCREAMING_SNAKE_CASE   :      List[str]				     =       False
		if "conv_layers" in name:
			load_conv_layer(
			    _a							,			_a							,			_a							,			_a							,			hf_model.config.feat_extract_norm == "group"							,			)
			SCREAMING_SNAKE_CASE   :      List[str]				     =       True
		elif name.split(".")[0] == "proj":
			SCREAMING_SNAKE_CASE   :      int				     =       fairseq_model.proj
			SCREAMING_SNAKE_CASE   :      List[Any]				     =       True
		else:
			for key, mapped_key in MAPPING.items():
				if key in name or key.split("w2v_model.")[-1] == name.split(".")[0]:
					SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       True
					if "*" in mapped_key:
						SCREAMING_SNAKE_CASE   :      List[Any]				     =       name.split(_a)[0].split(".")[-2]
						SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       mapped_key.replace("*"							,			_a)
					if "weight_g" in name:
						SCREAMING_SNAKE_CASE   :      Any				     =       "weight_g"
					elif "weight_v" in name:
						SCREAMING_SNAKE_CASE   :      int				     =       "weight_v"
					elif "bias" in name:
						SCREAMING_SNAKE_CASE   :      Any				     =       "bias"
					elif "weight" in name:
						SCREAMING_SNAKE_CASE   :      int				     =       "weight"
					else:
						SCREAMING_SNAKE_CASE   :      List[Any]				     =       None
					set_recursively(_a							,			_a							,			_a							,			_a							,			_a)
				continue
		if not is_used:
			unused_weights.append(_a)

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

	return proj_weight

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

def  lowerCamelCase__ (					_a):
	SCREAMING_SNAKE_CASE     ,SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       emb.weight.shape
	SCREAMING_SNAKE_CASE   :      Optional[int]				     =       nn.Linear(_a							,			_a							,			bias=_a)
	SCREAMING_SNAKE_CASE   :      Tuple				     =       emb.weight.data
	return lin_layer

def  lowerCamelCase__ (					_a):
	with open(_a							,			"r"							,			encoding="utf-8") as f:
		SCREAMING_SNAKE_CASE   :      str				     =       f.readlines()
		SCREAMING_SNAKE_CASE   :      Optional[int]				     =       [line.split(" ")[0] for line in lines]

	SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       len(_a)

	SCREAMING_SNAKE_CASE   :      Optional[int]				     =       {
	    "<s>": 0,
	    "<pad>": 1,
	    "</s>": 2,
	    "<unk>": 3,
	}

	vocab_dict.update(dict(zip(_a							,			range(4							,			num_words + 4))))
	return vocab_dict

@torch.no_grad()
def  lowerCamelCase__ (					_a							,			_a							,			_a							,			_a							,			_a							,			_a							,			_a							,			):
	SCREAMING_SNAKE_CASE   :      Optional[int]				     =       WavaVecaConfig.from_pretrained(_a)
	SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       SpeechaTextaConfig.from_pretrained(
	    _a							,			vocab_size=_a							,			decoder_layers=_a							,			do_stable_layer_norm=_a)

	SCREAMING_SNAKE_CASE   :      Any				     =       WavaVecaFeatureExtractor(
	    feature_size=1							,			sampling_rate=16000							,			padding_value=0							,			do_normalize=_a							,			return_attention_mask=_a							,			)

	SCREAMING_SNAKE_CASE     ,SCREAMING_SNAKE_CASE     ,SCREAMING_SNAKE_CASE   :      Dict				     =       fairseq.checkpoint_utils.load_model_ensemble_and_task(
	    [checkpoint_path]							,			arg_overrides={"data": "/".join(dict_path.split("/")[:-1])})
	SCREAMING_SNAKE_CASE   :      Optional[int]				     =       model[0].eval()

	# set weights for wav2vec2 encoder
	SCREAMING_SNAKE_CASE   :      Any				     =       WavaVecaModel(_a)
	SCREAMING_SNAKE_CASE   :      Dict				     =       recursively_load_weights_wavaveca(model.encoder							,			_a)

	SCREAMING_SNAKE_CASE   :      Dict				     =       SpeechaTextaForCausalLM(_a)
	SCREAMING_SNAKE_CASE     ,SCREAMING_SNAKE_CASE   :      List[str]				     =       hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict()							,			strict=_a)

	# set output linear layer
	unexpected_keys.remove("embed_out")
	SCREAMING_SNAKE_CASE   :      Tuple				     =       nn.Parameter(model.decoder.embed_out.detach())

	# layer norm is init to identity matrix so leaving it is fine
	logger.warning(f"The following keys are missing when loading the decoder weights: {missing_keys}")
	logger.warning(f"The following keys are unexpected when loading the decoder weights: {unexpected_keys}")

	SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       SpeechEncoderDecoderModel(encoder=_a							,			decoder=_a)
	SCREAMING_SNAKE_CASE   :      Optional[int]				     =       False

	# add projection layer
	SCREAMING_SNAKE_CASE   :      Dict				     =       nn.Parameter(projection_layer.weight)
	SCREAMING_SNAKE_CASE   :      Optional[int]				     =       nn.Parameter(projection_layer.bias)

	SCREAMING_SNAKE_CASE   :      int				     =       create_vocab_dict(_a)

	with open(os.path.join(_a							,			"vocab.json")							,			"w") as fp:
		json.dump(_a							,			_a)

	SCREAMING_SNAKE_CASE   :      Any				     =       SpeechaTextaTokenizer(os.path.join(_a							,			"vocab.json"))
	tokenizer.save_pretrained(_a)

	SCREAMING_SNAKE_CASE   :      Optional[int]				     =       hf_wavavec.config.to_dict()
	SCREAMING_SNAKE_CASE   :      Dict				     =       tokenizer.pad_token_id
	SCREAMING_SNAKE_CASE   :      str				     =       tokenizer.bos_token_id
	SCREAMING_SNAKE_CASE   :      Dict				     =       tokenizer.eos_token_id
	SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       "speech_to_text_2"
	SCREAMING_SNAKE_CASE   :      Any				     =       "wav2vec2"

	SCREAMING_SNAKE_CASE   :      str				     =       SpeechEncoderDecoderConfig.from_dict(_a)

	hf_wavavec.save_pretrained(_a)
	feature_extractor.save_pretrained(_a)


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('--dict_path', default=None, type=str, help='Path to dict of fine-tuned model')
			parser.add_argument(
			    '--encoder_config_path',
			    default='facebook/wav2vec2-large-lv60',
			    type=str,
			    help='Path to hf encoder wav2vec2 checkpoint config',
			)
			parser.add_argument(
			    '--decoder_config_path',
			    default='facebook/s2t-small-mustc-en-fr-st',
			    type=str,
			    help='Path to hf decoder s2t checkpoint config',
			)
			parser.add_argument('--vocab_size', default=1_0224, type=int, help='Vocab size of decoder')
			parser.add_argument('--num_decoder_layers', default=7, type=int, help='Number of decoder layers')

			a_						     =    parser.parse_args()
			convert_wavaveca_checkpoint(
			    args.checkpoint_path,
			    args.pytorch_dump_folder_path,
			    args.dict_path,
			    encoder_config_path=args.encoder_config_path,
			    decoder_config_path=args.decoder_config_path,
			    vocab_size=args.vocab_size,
			    num_decoder_layers=args.num_decoder_layers,
			)


from collections.abc import Iterable
from typing import Any






class _UpperCamelCase							:
			'''simple docstring'''







			def __init__(			self							: Tuple      ,      a							: int | None = None	)							->       List[Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Any				     =       value
				SCREAMING_SNAKE_CASE   :      Node | None				     =       None  # Added in order to delete a node easier
				SCREAMING_SNAKE_CASE   :      Node | None				     =       None
				SCREAMING_SNAKE_CASE   :      Node | None				     =       None






			def __repr__(			self							: Optional[Any]	)							->       str:

				"""simple docstring"""

				from pprint import pformat

				if self.left is None and self.right is None:
					return str(self.value	)
				return pformat({F"{self.value}": (self.left, self.right)}      ,      indent=1	)






class _UpperCamelCase							:
			'''simple docstring'''







			def __init__(			self							: Optional[int]      ,      a							: Node | None = None	)							->       Union[str, Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      List[str]				     =       root
			def __str__(			self							: Any	)							->       str:

				"""simple docstring"""

				return str(self.root	)
			def 							__UpperCamelCase (			self							: List[str]      ,      a							: Node      ,      a							: Node | None	)							->       None:

				"""simple docstring"""

				if new_children is not None:  # reset its kids
					SCREAMING_SNAKE_CASE   :      Dict				     =       node.parent
				if node.parent is not None:  # reset its parent
					if self.is_right(a	):  # If it is the right children
						SCREAMING_SNAKE_CASE   :      Dict				     =       new_children
					else:
						SCREAMING_SNAKE_CASE   :      Any				     =       new_children
				else:
					SCREAMING_SNAKE_CASE   :      List[Any]				     =       new_children
			def 							__UpperCamelCase (			self							: Optional[Any]      ,      a							: Node	)							->       bool:

				"""simple docstring"""

				if node.parent and node.parent.right:
					return node == node.parent.right
				return False
			def 							__UpperCamelCase (			self							: Union[str, Any]	)							->       bool:

				"""simple docstring"""

				return self.root is None
			def 							__UpperCamelCase (			self							: List[str]      ,      a							: int	)							->       None:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Dict				     =       Node(a	)  # create a new Node
				if self.empty():  # if Tree is empty
					SCREAMING_SNAKE_CASE   :      Tuple				     =       new_node  # set its root
				else:  # Tree is not empty
					SCREAMING_SNAKE_CASE   :      Optional[int]				     =       self.root  # from root
					if parent_node is None:
						return
					while True:  # While we don't get to a leaf
						if value < parent_node.value:  # We go left
							if parent_node.left is None:
								SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       new_node  # We insert the new node in a leaf
								break
							else:
								SCREAMING_SNAKE_CASE   :      Any				     =       parent_node.left
						else:
							if parent_node.right is None:
								SCREAMING_SNAKE_CASE   :      str				     =       new_node
								break
							else:
								SCREAMING_SNAKE_CASE   :      Optional[int]				     =       parent_node.right
					SCREAMING_SNAKE_CASE   :      int				     =       parent_node
			def 							__UpperCamelCase (			self							: Any      ,      *a							: str	)							->       None:

				"""simple docstring"""

				for value in values:
					self.__insert(a	)
			def 							__UpperCamelCase (			self							: Tuple      ,      a							: List[Any]	)							->       Node | None:

				"""simple docstring"""

				if self.empty():
					raise IndexError("Warning: Tree is empty! please use another."	)
				else:
					SCREAMING_SNAKE_CASE   :      str				     =       self.root
					# use lazy evaluation here to avoid NoneType Attribute error
					while node is not None and node.value is not value:
						SCREAMING_SNAKE_CASE   :      Optional[int]				     =       node.left if value < node.value else node.right
					return node
			def 							__UpperCamelCase (			self							: int      ,      a							: Node | None = None	)							->       Node | None:

				"""simple docstring"""

				if node is None:
					if self.root is None:
						return None
					SCREAMING_SNAKE_CASE   :      Tuple				     =       self.root

				if not self.empty():
					while node.right is not None:
						SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       node.right
				return node
			def 							__UpperCamelCase (			self							: int      ,      a							: Node | None = None	)							->       Node | None:

				"""simple docstring"""

				if node is None:
					SCREAMING_SNAKE_CASE   :      List[Any]				     =       self.root
				if self.root is None:
					return None
				if not self.empty():
					SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       self.root
					while node.left is not None:
						SCREAMING_SNAKE_CASE   :      str				     =       node.left
				return node
			def 							__UpperCamelCase (			self							: Dict      ,      a							: int	)							->       None:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Dict				     =       self.search(a	)  # Look for the node with that label
				if node is not None:
					if node.left is None and node.right is None:  # If it has no children
						self.__reassign_nodes(a      ,      a	)
					elif node.left is None:  # Has only right children
						self.__reassign_nodes(a      ,      node.right	)
					elif node.right is None:  # Has only left children
						self.__reassign_nodes(a      ,      node.left	)
					else:
						SCREAMING_SNAKE_CASE   :      Tuple				     =       self.get_max(
						    node.left	)  # Gets the max value of the left branch
						self.remove(tmp_node.value	)  # type: ignore
						SCREAMING_SNAKE_CASE   :      str				     =       (
						    tmp_node.value  # type: ignore
						)  # Assigns the value to the node to delete and keep tree structure
			def 							__UpperCamelCase (			self							: List[Any]      ,      a							: Node | None	)							->       Iterable:

				"""simple docstring"""

				if node is not None:
					yield node  # Preorder Traversal
					yield from self.preorder_traverse(node.left	)
					yield from self.preorder_traverse(node.right	)
			def 							__UpperCamelCase (			self							: str      ,      a							: Union[str, Any]=None	)							->       Any:

				"""simple docstring"""

				if traversal_function is None:
					return self.preorder_traverse(self.root	)
				else:
					return traversal_function(self.root	)
			def 							__UpperCamelCase (			self							: Optional[Any]      ,      a							: list      ,      a							: Node | None	)							->       None:

				"""simple docstring"""

				if node:
					self.inorder(a      ,      node.left	)
					arr.append(node.value	)
					self.inorder(a      ,      node.right	)






			def 							__UpperCamelCase (			self							: int      ,      a							: int      ,      a							: Node	)							->       int:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      list[int]				     =       []
				self.inorder(a      ,      a	)  # append all values to list using inorder traversal
				return arr[k - 1]

def  lowerCamelCase__ (					_a):
	SCREAMING_SNAKE_CASE   :      int				     =       []
	if curr_node is not None:
		SCREAMING_SNAKE_CASE   :      List[str]				     =       postorder(curr_node.left) + postorder(curr_node.right) + [curr_node]
	return node_list

def  lowerCamelCase__ (					):
	SCREAMING_SNAKE_CASE   :      int				     =       (8, 3, 6, 1, 10, 14, 13, 4, 7)
	SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       BinarySearchTree()
	for i in testlist:
		t.insert(_a)

	# Prints all the elements of the list in order traversal
	print(_a)

	if t.search(6) is not None:
		print("The value 6 exists")
	else:
		print("The value 6 doesn't exist")

	if t.search(-1) is not None:
		print("The value -1 exists")
	else:
		print("The value -1 doesn't exist")

	if not t.empty():
		print("Max Value: "							,			t.get_max().value)  # type: ignore
		print("Min Value: "							,			t.get_min().value)  # type: ignore

	for i in testlist:
		t.remove(_a)
		print(_a)


if __name__ == "__main__":
			import doctest

			doctest.testmod(verbose=True)