

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   :      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   :      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   :      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,
			)


import numpy as np






class _UpperCamelCase							:
			'''simple docstring'''







			def __init__(			self							: Dict	)							->       str:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      List[str]				     =       (0, 0)
				SCREAMING_SNAKE_CASE   :      Any				     =       None
				SCREAMING_SNAKE_CASE   :      Any				     =       0
				SCREAMING_SNAKE_CASE   :      List[str]				     =       0
				SCREAMING_SNAKE_CASE   :      Any				     =       0
			def __eq__(			self							: List[str]      ,      a							: Any	)							->       Tuple:

				"""simple docstring"""

				return self.position == cell.position






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

				"""simple docstring"""

				print(self.position	)






class _UpperCamelCase							:
			'''simple docstring'''







			def __init__(			self							: List[Any]      ,      a							: List[Any]=(5, 5)	)							->       Optional[int]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       np.zeros(a	)
				SCREAMING_SNAKE_CASE   :      str				     =       world_size[0]
				SCREAMING_SNAKE_CASE   :      int				     =       world_size[1]
			def 							__UpperCamelCase (			self							: Union[str, Any]	)							->       Optional[int]:

				"""simple docstring"""

				print(self.w	)






			def 							__UpperCamelCase (			self							: Dict      ,      a							: Dict	)							->       Tuple:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       [
				    (-1, -1),
				    (-1, 0),
				    (-1, 1),
				    (0, -1),
				    (0, 1),
				    (1, -1),
				    (1, 0),
				    (1, 1),
				]
				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       cell.position[0]
				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       cell.position[1]
				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       []
				for n in neughbour_cord:
					SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       current_x + n[0]
					SCREAMING_SNAKE_CASE   :      int				     =       current_y + n[1]
					if 0 <= x < self.world_x_limit and 0 <= y < self.world_y_limit:
						SCREAMING_SNAKE_CASE   :      int				     =       Cell()
						SCREAMING_SNAKE_CASE   :      str				     =       (x, y)
						SCREAMING_SNAKE_CASE   :      Any				     =       cell
						neighbours.append(a	)
				return neighbours

def  lowerCamelCase__ (					_a							,			_a							,			_a):
	SCREAMING_SNAKE_CASE   :      Tuple				     =       []
	SCREAMING_SNAKE_CASE   :      Tuple				     =       []
	_open.append(_a)

	while _open:
		SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       np.argmin([n.f for n in _open])
		SCREAMING_SNAKE_CASE   :      List[str]				     =       _open[min_f]
		_closed.append(_open.pop(_a))
		if current == goal:
			break
		for n in world.get_neigbours(_a):
			for c in _closed:
				if c == n:
					continue
			SCREAMING_SNAKE_CASE   :      Tuple				     =       current.g + 1
			SCREAMING_SNAKE_CASE     ,SCREAMING_SNAKE_CASE   :      List[str]				     =       n.position
			SCREAMING_SNAKE_CASE     ,SCREAMING_SNAKE_CASE   :      str				     =       goal.position
			SCREAMING_SNAKE_CASE   :      Any				     =       (ya - ya) ** 2 + (xa - xa) ** 2
			SCREAMING_SNAKE_CASE   :      List[str]				     =       n.h + n.g

			for c in _open:
				if c == n and c.f < n.f:
					continue
			_open.append(_a)
	SCREAMING_SNAKE_CASE   :      Tuple				     =       []
	while current.parent is not None:
		path.append(current.position)
		SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       current.parent
	path.append(current.position)
	return path[::-1]


if __name__ == "__main__":
			a_						     =    Gridworld()
			# Start position and goal
			a_						     =    Cell()
			a_						     =    (0, 0)
			a_						     =    Cell()
			a_						     =    (4, 4)
			print(F'''path from {start.position} to {goal.position}''')
			a_						     =    astar(world, start, goal)
			#   Just for visual reasons.
			for i in s:
						a_						     =    1
			print(world.w)