import argparse

import fairseq
import torch

from transformers import UniSpeechSatConfig, UniSpeechSatForCTC, UniSpeechSatForPreTraining, logging


logging.set_verbosity_info()
_lowerCamelCase   :				Any     			=						logging.get_logger(__name__)

_lowerCamelCase   :				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''',
    '''encoder.layer_norm_for_extract''': '''layer_norm_for_extract''',
    '''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''',
    '''label_embs_concat''': '''label_embeddings_concat''',
    '''mask_emb''': '''masked_spec_embed''',
    '''spk_proj''': '''speaker_proj''',
}
_lowerCamelCase   :				Any     			=						[
    '''lm_head''',
    '''quantizer.weight_proj''',
    '''quantizer.codevectors''',
    '''project_q''',
    '''project_hid''',
    '''label_embeddings_concat''',
    '''speaker_proj''',
    '''layer_norm_for_extract''',
]





def 		__lowerCamelCase							(UpperCAmelCase__       :							Optional[Any] ,	UpperCAmelCase__       :							Dict ,	UpperCAmelCase__       :							Dict ,	UpperCAmelCase__       :							Tuple ,	UpperCAmelCase__       :							List[str]					):
					for attribute in key.split("."					):
										SCREAMING_SNAKE_CASE           =    getattr(UpperCAmelCase__ ,	UpperCAmelCase__					)

					if weight_type is not None:
										SCREAMING_SNAKE_CASE           =    getattr(UpperCAmelCase__ ,	UpperCAmelCase__					).shape
					else:
										SCREAMING_SNAKE_CASE           =    hf_pointer.shape

					if hf_shape != value.shape:
										raise ValueError(
										    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           =    value
					elif weight_type == "weight_g":
										SCREAMING_SNAKE_CASE           =    value
					elif weight_type == "weight_v":
										SCREAMING_SNAKE_CASE           =    value
					elif weight_type == "bias":
										SCREAMING_SNAKE_CASE           =    value
					else:
										SCREAMING_SNAKE_CASE           =    value

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





def 		__lowerCamelCase							(UpperCAmelCase__       :							Optional[Any] ,	UpperCAmelCase__       :							Union[str, Any]					):
					SCREAMING_SNAKE_CASE           =    []
					SCREAMING_SNAKE_CASE           =    fairseq_model.state_dict()

					SCREAMING_SNAKE_CASE           =    hf_model.unispeech_sat.feature_extractor

					for name, value in fairseq_dict.items():
										SCREAMING_SNAKE_CASE           =    False
										if "conv_layers" in name:
															load_conv_layer(
															    UpperCAmelCase__ ,	UpperCAmelCase__ ,	UpperCAmelCase__ ,	UpperCAmelCase__ ,	hf_model.config.feat_extract_norm == "group" ,	)
															SCREAMING_SNAKE_CASE           =    True
										else:
															for key, mapped_key in MAPPING.items():
																				SCREAMING_SNAKE_CASE           =    "unispeech_sat." + 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]:
																									if "layer_norm_for_extract" in name and (".".join(name.split("."					)[:-1]					) != key):
																														# special case since naming is very similar
																														continue
																									SCREAMING_SNAKE_CASE           =    True
																									if "*" in mapped_key:
																														SCREAMING_SNAKE_CASE           =    name.split(UpperCAmelCase__					)[0].split("."					)[-2]
																														SCREAMING_SNAKE_CASE           =    mapped_key.replace("*" ,	UpperCAmelCase__					)
																									if "weight_g" in name:
																														SCREAMING_SNAKE_CASE           =    "weight_g"
																									elif "weight_v" in name:
																														SCREAMING_SNAKE_CASE           =    "weight_v"
																									elif "bias" in name:
																														SCREAMING_SNAKE_CASE           =    "bias"
																									elif "weight" in name:
																														# TODO: don't match quantizer.weight_proj
																														SCREAMING_SNAKE_CASE           =    "weight"
																									else:
																														SCREAMING_SNAKE_CASE           =    None
																									set_recursively(UpperCAmelCase__ ,	UpperCAmelCase__ ,	UpperCAmelCase__ ,	UpperCAmelCase__ ,	UpperCAmelCase__					)
																				continue
										if not is_used:
															unused_weights.append(UpperCAmelCase__					)

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





def 		__lowerCamelCase							(UpperCAmelCase__       :							Optional[Any] ,	UpperCAmelCase__       :							Tuple ,	UpperCAmelCase__       :							List[str] ,	UpperCAmelCase__       :							str ,	UpperCAmelCase__       :							Optional[Any]					):
					SCREAMING_SNAKE_CASE           =    full_name.split("conv_layers."					)[-1]
					SCREAMING_SNAKE_CASE           =    name.split("."					)
					SCREAMING_SNAKE_CASE           =    int(items[0]					)
					SCREAMING_SNAKE_CASE           =    int(items[1]					)

					if type_id == 0:
										if "bias" in name:
															if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
																				raise ValueError(
																				    F"{full_name} has size {value.shape}, but"
																				    F" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found."					)
															SCREAMING_SNAKE_CASE           =    value
															logger.info(F"Feat extract conv layer {layer_id} was initialized from {full_name}."					)
										elif "weight" in name:
															if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
																				raise ValueError(
																				    F"{full_name} has size {value.shape}, but"
																				    F" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found."					)
															SCREAMING_SNAKE_CASE           =    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:
															if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
																				raise ValueError(
																				    F"{full_name} has size {value.shape}, but"
																				    F" {feature_extractor[layer_id].layer_norm.bias.data.shape} was found."					)
															SCREAMING_SNAKE_CASE           =    value
															logger.info(F"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."					)
										elif "weight" in name:
															if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
																				raise ValueError(
																				    F"{full_name} has size {value.shape}, but"
																				    F" {feature_extractor[layer_id].layer_norm.weight.data.shape} was found."					)
															SCREAMING_SNAKE_CASE           =    value
															logger.info(F"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."					)
					else:
										unused_weights.append(UpperCAmelCase__					)





@torch.no_grad()
def 		__lowerCamelCase							(UpperCAmelCase__       :							Dict ,	UpperCAmelCase__       :							Optional[Any] ,	UpperCAmelCase__       :							Dict=None ,	UpperCAmelCase__       :							Tuple=None ,	UpperCAmelCase__       :							Optional[Any]=True					):
					if config_path is not None:
										SCREAMING_SNAKE_CASE           =    UniSpeechSatConfig.from_pretrained(UpperCAmelCase__					)
					else:
										SCREAMING_SNAKE_CASE           =    UniSpeechSatConfig()

					SCREAMING_SNAKE_CASE           =    ""

					if is_finetuned:
										SCREAMING_SNAKE_CASE           =    UniSpeechSatForCTC(UpperCAmelCase__					)
					else:
										SCREAMING_SNAKE_CASE           =    UniSpeechSatForPreTraining(UpperCAmelCase__					)

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

					recursively_load_weights(UpperCAmelCase__ ,	UpperCAmelCase__					)

					hf_wavavec.save_pretrained(UpperCAmelCase__					)


if __name__ == "__main__":
	_lowerCamelCase   :				Optional[int]     			=						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'''
	)
	_lowerCamelCase   :				Union[str, Any]     			=						parser.parse_args()
	convert_unispeech_sat_checkpoint(
	    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
	)





from random import randint, random





def 		__lowerCamelCase							(UpperCAmelCase__       :							int ,	UpperCAmelCase__       :							int ,	UpperCAmelCase__       :							int ,	UpperCAmelCase__       :							bool = False ,	UpperCAmelCase__       :							bool = False ,	UpperCAmelCase__       :							int = 5 ,	):
					SCREAMING_SNAKE_CASE           =    [[-1] * number_of_cells]  # Create a highway without any car
					SCREAMING_SNAKE_CASE           =    0
					SCREAMING_SNAKE_CASE           =    max(UpperCAmelCase__ ,	0					)
					while i < number_of_cells:
										SCREAMING_SNAKE_CASE           =    (
										    randint(0 ,	UpperCAmelCase__					) if random_speed else initial_speed
										)  # Place the cars
										i += (
										    randint(1 ,	max_speed * 2					) if random_frequency else frequency
										)  # Arbitrary number, may need tuning
					return highway





def 		__lowerCamelCase							(UpperCAmelCase__       :							list ,	UpperCAmelCase__       :							int					):
					SCREAMING_SNAKE_CASE           =    0
					SCREAMING_SNAKE_CASE           =    highway_now[car_index + 1 :]
					for cell in range(len(UpperCAmelCase__					)					):  # May need a better name for this
										if cells[cell] != -1:  # If the cell is not empty then
															return distance  # we have the distance we wanted
										distance += 1
					# Here if the car is near the end of the highway
					return distance + get_distance(UpperCAmelCase__ ,	-1					)





def 		__lowerCamelCase							(UpperCAmelCase__       :							list ,	UpperCAmelCase__       :							float ,	UpperCAmelCase__       :							int					):
					SCREAMING_SNAKE_CASE           =    len(UpperCAmelCase__					)
					# Beforce calculations, the highway is empty
					SCREAMING_SNAKE_CASE           =    [-1] * number_of_cells

					for car_index in range(UpperCAmelCase__					):
										if highway_now[car_index] != -1:
															# Add 1 to the current speed of the car and cap the speed
															SCREAMING_SNAKE_CASE           =    min(highway_now[car_index] + 1 ,	UpperCAmelCase__					)
															# Number of empty cell before the next car
															SCREAMING_SNAKE_CASE           =    get_distance(UpperCAmelCase__ ,	UpperCAmelCase__					) - 1
															# We can't have the car causing an accident
															SCREAMING_SNAKE_CASE           =    min(next_highway[car_index] ,	UpperCAmelCase__					)
															if random() < probability:
																				# Randomly, a driver will slow down
																				SCREAMING_SNAKE_CASE           =    max(next_highway[car_index] - 1 ,	0					)
					return next_highway





def 		__lowerCamelCase							(UpperCAmelCase__       :							list ,	UpperCAmelCase__       :							int ,	UpperCAmelCase__       :							float ,	UpperCAmelCase__       :							int					):
					SCREAMING_SNAKE_CASE           =    len(highway[0]					)

					for i in range(UpperCAmelCase__					):
										SCREAMING_SNAKE_CASE           =    update(highway[i] ,	UpperCAmelCase__ ,	UpperCAmelCase__					)
										SCREAMING_SNAKE_CASE           =    [-1] * number_of_cells

										for car_index in range(UpperCAmelCase__					):
															SCREAMING_SNAKE_CASE           =    next_speeds_calculated[car_index]
															if speed != -1:
																				# Change the position based on the speed (with % to create the loop)
																				SCREAMING_SNAKE_CASE           =    (car_index + speed) % number_of_cells
																				# Commit the change of position
																				SCREAMING_SNAKE_CASE           =    speed
										highway.append(UpperCAmelCase__					)

					return highway


if __name__ == "__main__":
							import doctest

							doctest.testmod()




