




"""simple docstring"""




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()
lowerCAmelCase							:			int     							=							logging.get_logger(__name__)

lowerCAmelCase							:			List[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""": """lm_head""",
    """mask_emb""": """masked_spec_embed""",
}
lowerCAmelCase							:			Optional[Any]     							=							[
    """lm_head""",
    """quantizer.weight_proj""",
    """quantizer.codevectors""",
    """project_q""",
    """project_hid""",
]



def     a__ (				snake_case__       , snake_case__       , snake_case__       , snake_case__       , snake_case__				)						->					Optional[int]:
			for attribute in key.split("""."""				):
						lowerCamelCase			      =					getattr(snake_case__       , snake_case__				)

			if weight_type is not None:
						lowerCamelCase			      =					getattr(snake_case__       , snake_case__				).shape
			else:
						lowerCamelCase			      =					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":
						lowerCamelCase			      =					value
			elif weight_type == "weight_g":
						lowerCamelCase			      =					value
			elif weight_type == "weight_v":
						lowerCamelCase			      =					value
			elif weight_type == "bias":
						lowerCamelCase			      =					value
			else:
						lowerCamelCase			      =					value

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



def     a__ (				snake_case__       , snake_case__				)						->					int:
			lowerCamelCase			      =					[]
			lowerCamelCase			      =					fairseq_model.state_dict()

			lowerCamelCase			      =					hf_model.feature_extractor

			# if encoder has different dim to decoder -> use proj_weight
			lowerCamelCase			      =					None

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

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

			return proj_weight



def     a__ (				snake_case__       , snake_case__       , snake_case__       , snake_case__       , snake_case__				)						->					Dict:
			lowerCamelCase			      =					full_name.split("""conv_layers."""				)[-1]
			lowerCamelCase			      =					name.split("""."""				)
			lowerCamelCase			      =					int(items[0]				)
			lowerCamelCase			      =					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.'
									)
									lowerCamelCase			      =					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.'
									)
									lowerCamelCase			      =					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."
									)
									lowerCamelCase			      =					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.'
									)
									lowerCamelCase			      =					value
									logger.info(F'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'				)
			else:
						unused_weights.append(snake_case__				)



def     a__ (				snake_case__				)						->					str:
			lowerCamelCase      ,   lowerCamelCase			      =					emb.weight.shape
			lowerCamelCase			      =					nn.Linear(snake_case__       , snake_case__       , bias=snake_case__				)
			lowerCamelCase			      =					emb.weight.data
			return lin_layer



def     a__ (				snake_case__				)						->					Optional[int]:
			with open(snake_case__       , """r"""       , encoding="""utf-8"""				) as f:
						lowerCamelCase			      =					f.readlines()
						lowerCamelCase			      =					[line.split(""" """				)[0] for line in lines]

			lowerCamelCase			      =					len(snake_case__				)

			lowerCamelCase			      =					{
			    """<s>""": 0,
			    """<pad>""": 1,
			    """</s>""": 2,
			    """<unk>""": 3,
			}

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



@torch.no_grad()
def     a__ (				snake_case__       , snake_case__       , snake_case__       , snake_case__       , snake_case__       , snake_case__       , snake_case__       , )						->					str:
			lowerCamelCase			      =					WavaVecaConfig.from_pretrained(snake_case__				)
			lowerCamelCase			      =					SpeechaTextaConfig.from_pretrained(
			    snake_case__       , vocab_size=snake_case__       , decoder_layers=snake_case__       , do_stable_layer_norm=snake_case__				)

			lowerCamelCase			      =					WavaVecaFeatureExtractor(
			    feature_size=1       , sampling_rate=1_60_00       , padding_value=0       , do_normalize=snake_case__       , return_attention_mask=snake_case__       , )

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

			# set weights for wav2vec2 encoder
			lowerCamelCase			      =					WavaVecaModel(snake_case__				)
			lowerCamelCase			      =					recursively_load_weights_wavaveca(model.encoder       , snake_case__				)

			lowerCamelCase			      =					SpeechaTextaForCausalLM(snake_case__				)
			lowerCamelCase      ,   lowerCamelCase			      =					hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict()       , strict=snake_case__				)

			# set output linear layer
			unexpected_keys.remove("""embed_out"""				)
			lowerCamelCase			      =					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}'				)

			lowerCamelCase			      =					SpeechEncoderDecoderModel(encoder=snake_case__       , decoder=snake_case__				)
			lowerCamelCase			      =					False

			# add projection layer
			lowerCamelCase			      =					nn.Parameter(projection_layer.weight				)
			lowerCamelCase			      =					nn.Parameter(projection_layer.bias				)

			lowerCamelCase			      =					create_vocab_dict(snake_case__				)

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

			lowerCamelCase			      =					SpeechaTextaTokenizer(os.path.join(snake_case__       , """vocab.json"""				)				)
			tokenizer.save_pretrained(snake_case__				)

			lowerCamelCase			      =					hf_wavavec.config.to_dict()
			lowerCamelCase			      =					tokenizer.pad_token_id
			lowerCamelCase			      =					tokenizer.bos_token_id
			lowerCamelCase			      =					tokenizer.eos_token_id
			lowerCamelCase			      =					"""speech_to_text_2"""
			lowerCamelCase			      =					"""wav2vec2"""

			lowerCamelCase			      =					SpeechEncoderDecoderConfig.from_dict(snake_case__				)

			hf_wavavec.save_pretrained(snake_case__				)
			feature_extractor.save_pretrained(snake_case__				)


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(
					    """--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""")

					lowerCAmelCase							:			int     							=							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,
					)









"""simple docstring"""




def     a__ (				snake_case__				)						->					list:

			if len(snake_case__				) < 2:
						return collection

			def circle_sort_util(snake_case__       , snake_case__       , snake_case__				) -> bool:
						lowerCamelCase			      =					False

						if low == high:
									return swapped

						lowerCamelCase			      =					low
						lowerCamelCase			      =					high

						while left < right:
									if collection[left] > collection[right]:
												lowerCamelCase      ,   lowerCamelCase			      =					(
												    collection[right],
												    collection[left],
												)
												lowerCamelCase			      =					True

									left += 1
									right -= 1

						if left == right and collection[left] > collection[right + 1]:
									lowerCamelCase      ,   lowerCamelCase			      =					(
									    collection[right + 1],
									    collection[left],
									)

									lowerCamelCase			      =					True

						lowerCamelCase			      =					low + int((high - low) / 2				)
						lowerCamelCase			      =					circle_sort_util(snake_case__       , snake_case__       , snake_case__				)
						lowerCamelCase			      =					circle_sort_util(snake_case__       , mid + 1       , snake_case__				)

						return swapped or left_swap or right_swap

			lowerCamelCase			      =					True

			while is_not_sorted is True:
						lowerCamelCase			      =					circle_sort_util(snake_case__       , 0       , len(snake_case__				) - 1				)

			return collection


if __name__ == "__main__":
					lowerCAmelCase							:			Tuple     							=							input("""Enter numbers separated by a comma:\n""").strip()
					lowerCAmelCase							:			List[Any]     							=							[int(item) for item in user_input.split(""",""")]
					print(circle_sort(unsorted))



