




'''simple docstring'''





import argparse
from pathlib import Path

import torch

from transformers import OPTConfig, OPTModel
from transformers.utils import logging


logging.set_verbosity_info()
UpperCAmelCase__				     = logging.get_logger(__name__)







def 		_A(       UpperCamelCase__							:			Dict				)    -> List[str]:
							'''simple docstring'''


							__lowercase      =    torch.load(_lowerCamelCase				,       map_location='''cpu'''				)
							if "model" in sd.keys():
														__lowercase      =    torch.load(_lowerCamelCase				,       map_location='''cpu'''				)['model']

							# pop unnecessary weights
							__lowercase      =    [
							    'decoder.version',
							    'decoder.output_projection.weight',
							]
							for key in keys_to_delete:
														if key in sd:
																					sd.pop(_lowerCamelCase				)

							__lowercase      =    {
							    'decoder.project_in_dim.weight': 'decoder.project_in.weight',
							    'decoder.project_out_dim.weight': 'decoder.project_out.weight',
							    'decoder.layer_norm.weight': 'decoder.final_layer_norm.weight',
							    'decoder.layer_norm.bias': 'decoder.final_layer_norm.bias',
							}
							for old_key, new_key in keys_to_rename.items():
														if old_key in sd:
																					__lowercase      =    sd.pop(_lowerCamelCase				)

							__lowercase      =    list(sd.keys()				)
							for key in keys:
														if ".qkv_proj." in key:
																					__lowercase      =    sd[key]
																					# We split QKV in separate Q,K,V

																					__lowercase      =    key.replace('''.qkv_proj.'''				,       '''.q_proj.'''				)
																					__lowercase      =    key.replace('''.qkv_proj.'''				,       '''.k_proj.'''				)
																					__lowercase      =    key.replace('''.qkv_proj.'''				,       '''.v_proj.'''				)

																					__lowercase      =    value.shape[0]
																					assert depth % 3 == 0
																					# `SequeuceParallelTransformerBlock` has QKV weight is separated in K,V,Q despite the naming:
																					# https://cs.github.com/facebookresearch/metaseq/blob/51871bd73cd04c038f239ea2a26db1d7f6b37927/metaseq/modules/sequence_parallel_transformer_layer.py#L97
																					__lowercase      =    torch.split(_lowerCamelCase				,       depth // 3				,       dim=0				)

																					__lowercase      =    q
																					__lowercase      =    k
																					__lowercase      =    v
																					del sd[key]

							return sd







@torch.no_grad()
def 		_A(       UpperCamelCase__							:			Union[str, Any]				,       UpperCamelCase__							:			Optional[Any]				,       UpperCamelCase__							:			Union[str, Any]=None				)    -> Tuple:
							'''simple docstring'''


							__lowercase      =    load_checkpoint(_lowerCamelCase				)

							if config is not None:
														__lowercase      =    OPTConfig.from_pretrained(_lowerCamelCase				)
							else:
														__lowercase      =    OPTConfig()

							__lowercase      =    OPTModel(_lowerCamelCase				).half().eval()
							model.load_state_dict(_lowerCamelCase				)

							# Check results
							Path(_lowerCamelCase				).mkdir(exist_ok=_lowerCamelCase				)
							model.save_pretrained(_lowerCamelCase				)


if __name__ == "__main__":
			UpperCAmelCase__				     = argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    "--fairseq_path",
			    type=str,
			    help=(
			        "path to fairseq checkpoint in correct format. You can find all checkpoints in the correct format here:"
			        " https://huggingface.co/models?other=opt_metasq"
			    ),
			)
			parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
			parser.add_argument("--hf_config", default=None, type=str, help="Define HF config.")
			UpperCAmelCase__				     = parser.parse_args()
			convert_opt_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, config=args.hf_config)












from __future__ import annotations

from math import pi
from typing import Protocol

import matplotlib.pyplot as plt
import numpy as np







class a (     __SCREAMING_SNAKE_CASE  ):
						"""simple docstring"""

						def     UpperCAmelCase_      ( self :     List[str]				,							lowerCamelCase__ :     float				)    ->						float:
													"""simple docstring"""


													return 0.0







def 		_A(       UpperCamelCase__							:			np.ndarray				,       UpperCamelCase__							:			int				)    -> tuple[int | float, int | float]:
							'''simple docstring'''


							__lowercase      =    min([-20, np.min(fft_results[1 : samplerate // 2 - 1]				)]				)
							__lowercase      =    max([20, np.max(fft_results[1 : samplerate // 2 - 1]				)]				)
							return lowest, highest







def 		_A(       UpperCamelCase__							:			FilterType				,       UpperCamelCase__							:			int				)    -> None:
							'''simple docstring'''


							__lowercase      =    512
							__lowercase      =    [1] + [0] * (size - 1)
							__lowercase      =    [filter_type.process(UpperCamelCase__				) for item in inputs]

							__lowercase      =    [0] * (samplerate - size)  # zero-padding
							outputs += filler
							__lowercase      =    np.abs(np.fft.fft(UpperCamelCase__				)				)
							__lowercase      =    20 * np.logaa(UpperCamelCase__				)

							# Frequencies on log scale from 24 to nyquist frequency
							plt.xlim(24				,       samplerate / 2 - 1				)
							plt.xlabel('''Frequency (Hz)'''				)
							plt.xscale('''log'''				)

							# Display within reasonable bounds
							__lowercase      =    get_bounds(UpperCamelCase__				,       UpperCamelCase__				)
							plt.ylim(max([-80, bounds[0]]				)				,       min([80, bounds[1]]				)				)
							plt.ylabel('''Gain (dB)'''				)

							plt.plot(UpperCamelCase__				)
							plt.show()







def 		_A(       UpperCamelCase__							:			FilterType				,       UpperCamelCase__							:			int				)    -> None:
							'''simple docstring'''


							__lowercase      =    512
							__lowercase      =    [1] + [0] * (size - 1)
							__lowercase      =    [filter_type.process(UpperCamelCase__				) for item in inputs]

							__lowercase      =    [0] * (samplerate - size)  # zero-padding
							outputs += filler
							__lowercase      =    np.angle(np.fft.fft(UpperCamelCase__				)				)

							# Frequencies on log scale from 24 to nyquist frequency
							plt.xlim(24				,       samplerate / 2 - 1				)
							plt.xlabel('''Frequency (Hz)'''				)
							plt.xscale('''log'''				)

							plt.ylim(-2 * pi				,       2 * pi				)
							plt.ylabel('''Phase shift (Radians)'''				)
							plt.plot(np.unwrap(UpperCamelCase__				,       -2 * pi				)				)
							plt.show()






