
'''simple docstring'''




import argparse
import json
import os

import tensorstore as ts
import torch
from flax import serialization
from flax.traverse_util import flatten_dict, unflatten_dict
from tensorflow.io import gfile

from transformers.modeling_utils import dtype_byte_size
from transformers.models.switch_transformers.convert_switch_transformers_original_flax_checkpoint_to_pytorch import (
    rename_keys,
)
from transformers.utils import WEIGHTS_INDEX_NAME, WEIGHTS_NAME
from transformers.utils.hub import convert_file_size_to_int







def 		lowerCAmelCase_				(   snake_case__		,				snake_case__						):




    '''simple docstring'''



    if flax_key_tuple[-1] == "kernel" and flax_tensor.ndim == 3:
        # expert layer
        A       :   Union[str, Any]					=						flax_key_tuple[:-1] + ('''weight''',)
        A       :   Tuple					=						torch.permute(snake_case__		,				(0, 2, 1)						)
    elif flax_key_tuple[-1] == "kernel" and ".".join(snake_case__						):
        # linear layer
        A       :   Any					=						flax_key_tuple[:-1] + ('''weight''',)
        A       :   Union[str, Any]					=						flax_tensor.T
    elif flax_key_tuple[-1] in ["scale", "embedding"]:
        A       :   int					=						flax_key_tuple[:-1] + ('''weight''',)

    return flax_key_tuple, flax_tensor







def 		lowerCAmelCase_				(   snake_case__		,				snake_case__		,				snake_case__						):




    '''simple docstring'''



    if "metadata" in layer:
        A       :   Union[str, Any]					=						layer.split('''metadata'''						)
        A       :   List[Any]					=						''''''.join(split_layer[0]						)[:-1]
        A       :   int					=						[tuple(('''metadata''' + split_layer[1]).split('''/'''						)						)]
    elif "kvstore" in layer:
        A       :   Any					=						layer.split('''kvstore'''						)
        A       :   List[Any]					=						''''''.join(split_layer[0]						)[:-1]
        A       :   Optional[Any]					=						[tuple(('''kvstore''' + split_layer[1]).split('''/'''						)						)]

    else:
        A       :   Union[str, Any]					=						layer.split('''/'''						)
        A       :   Optional[int]					=						'''/'''.join(split_layer[:-1]						)
        A       :   Optional[Any]					=						(split_layer[-1],)

    if "kvstore/path" in layer:
        A       :   int					=						F'{switch_checkpoint_path}/{checkpoint_info[layer]}'
    elif "kvstore/driver" in layer:
        A       :   int					=						'''file'''
    else:
        A       :   Any					=						checkpoint_info[layer]

    return curr_real_layer_name, split_layer, content







def 		lowerCAmelCase_				(   snake_case__		,				snake_case__						):




    '''simple docstring'''



    A       :   Optional[int]					=						rename_keys(snake_case__						)
    A       :   Tuple					=						{}
    for k, v in current_block.items():
        A       :   Tuple					=						v
    A       :   List[str]					=						new_current_block
    torch.save(snake_case__		,				snake_case__						)







def 		lowerCAmelCase_				(   snake_case__		,				snake_case__		,				snake_case__		,				snake_case__		,				snake_case__ = WEIGHTS_NAME						):




    '''simple docstring'''



    A       :   Dict					=						convert_file_size_to_int(snake_case__						)
    A       :   str					=						[]
    A       :   str					=						{}
    A       :   Any					=						0
    A       :   List[str]					=						0

    os.makedirs(snake_case__		,				exist_ok=snake_case__						)
    with gfile.GFile(switch_checkpoint_path + '''/checkpoint'''		,				'''rb'''						) as fp:
        A       :   Tuple					=						serialization.msgpack_restore(fp.read()						)['''optimizer''']['''target''']
        A       :   List[Any]					=						flatten_dict(snake_case__		,				sep='''/'''						)

    A       :   List[str]					=						{}
    for layer in checkpoint_info.keys():
        A,  A,  A       :   Tuple					=						get_key_and_tensorstore_dict(
            snake_case__		,				snake_case__		,				snake_case__						)
        if curr_real_layer_name in all_layers:
            A       :   List[str]					=						content
        else:
            A       :   Optional[Any]					=						{split_layer[-1]: content}

    for key in all_layers.keys():
        # open tensorstore file
        A       :   Any					=						ts.open(unflatten_dict(all_layers[key]						)						).result().read().result()
        A       :   List[Any]					=						torch.tensor(snake_case__						)
        A       :   int					=						raw_weights.numel() * dtype_byte_size(raw_weights.dtype						)

        # use the renaming pattern from the small conversion scripts
        A,  A       :   int					=						rename_base_flax_keys(tuple(key.split('''/'''						)						)		,				snake_case__						)
        A       :   Union[str, Any]					=						'''/'''.join(snake_case__						)

        # If this weight is going to tip up over the maximal size, we split.
        if current_block_size + weight_size > max_shard_size:
            A       :   List[Any]					=						os.path.join(
                snake_case__		,				weights_name.replace('''.bin'''		,				F'-{len(snake_case__						)+1:05d}-of-???.bin'						)						)
            rename_and_save_block(snake_case__		,				snake_case__						)
            sharded_state_dicts.append(current_block.keys()						)
            del current_block
            A       :   Dict					=						{}
            A       :   List[Any]					=						0

        A       :   List[Any]					=						raw_weights.to(getattr(snake_case__		,				snake_case__						)						)
        current_block_size += weight_size
        total_size += weight_size

    # Add the last block
    A       :   Optional[int]					=						os.path.join(snake_case__		,				weights_name.replace('''.bin'''		,				F'-{len(snake_case__						)+1:05d}-of-???.bin'						)						)
    rename_and_save_block(snake_case__		,				snake_case__						)
    sharded_state_dicts.append(current_block.keys()						)

    # If we only have one shard, we return it
    if len(snake_case__						) == 1:
        return {weights_name: sharded_state_dicts[0]}, None

    # Otherwise, let's build the index
    A       :   Union[str, Any]					=						{}
    A       :   List[str]					=						{}
    for idx, shard in enumerate(snake_case__						):
        A       :   int					=						weights_name.replace(
            '''.bin'''		,				F'-{idx+1:05d}-of-{len(snake_case__						):05d}.bin'						)  # len(sharded_state_dicts):05d}
        A       :   Union[str, Any]					=						os.path.join(snake_case__		,				weights_name.replace('''.bin'''		,				F'-{idx+1:05d}-of-???.bin'						)						)
        os.rename(snake_case__		,				os.path.join(snake_case__		,				snake_case__						)						)
        A       :   str					=						shard
        for key in shard:
            A       :   Tuple					=						shard_file

    # Add the metadata
    A       :   Tuple					=						{'''total_size''': total_size}
    A       :   Optional[int]					=						{'''metadata''': metadata, '''weight_map''': weight_map}

    with open(os.path.join(snake_case__		,				snake_case__						)		,				'''w'''		,				encoding='''utf-8'''						) as f:
        A       :   Union[str, Any]					=						json.dumps(snake_case__		,				indent=2		,				sort_keys=snake_case__						) + '''\n'''
        f.write(snake_case__						)

    return metadata, index


if __name__ == "__main__":
      lowercase			:				Optional[int]    		=			argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          '--switch_t5x_checkpoint_path',
          default='/mnt/disks/disk_switch/original_checkpoints/switch-xxl-128/checkpoint_634600',
          type=str,
          required=False,
          help='Path to a directory containing a folder per layer. Follows the original Google format.',
      )
      parser.add_argument('--max_shard_size', default='10GB', required=False, help='Max shard size')
      parser.add_argument('--dtype', default='bfloat16', type=str, required=False, help='dtype of the saved model')
      parser.add_argument(
          '--pytorch_dump_folder_path',
          default='/mnt/disks/disk_switch/original_checkpoints/switch-xxl-128-converted',
          type=str,
          required=False,
          help='Path to the output pytorch model.',
      )
      lowercase			:				List[str]    		=			parser.parse_args()
      shard_on_the_fly(
          args.switch_tax_checkpoint_path,
          args.pytorch_dump_folder_path,
          args.max_shard_size,
          args.dtype,
      )







def 		lowerCAmelCase_				(   ):




    '''simple docstring'''



    from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration, TaTokenizer

    A       :   Optional[Any]					=						SwitchTransformersConfig.from_pretrained('''google/switch-base-8'''						)
    config.save_pretrained('''/home/arthur_huggingface_co/transformers/switch_converted'''						)
    A       :   Any					=						SwitchTransformersForConditionalGeneration.from_pretrained(
        '''/home/arthur_huggingface_co/transformers/switch_converted'''		,				device_map='''auto'''						)

    A       :   Any					=						TaTokenizer.from_pretrained('''t5-small'''						)
    A       :   Union[str, Any]					=						'''A <extra_id_0> walks into a bar a orders a <extra_id_1> with <extra_id_2> pinch of <extra_id_3>.'''

    A       :   List[Any]					=						tokenizer(snake_case__		,				return_tensors='''pt'''						).input_ids
    A       :   Optional[Any]					=						model.generate(snake_case__		,				decoder_start_token_id=0						)
    print(tokenizer.decode(out[0]						)						)









from __future__ import annotations

from math import pi
from typing import Protocol

import matplotlib.pyplot as plt
import numpy as np



class   UpperCAmelCase_    (       a):







							def    snake_case__      (   self,			__a):

														'''simple docstring'''

														return 0.0






def 				A				(					_lowerCamelCase				, _lowerCamelCase  ):





							'''simple docstring'''




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






def 				A				(					_lowerCamelCase				, _lowerCamelCase  ):





							'''simple docstring'''




							_lowerCAmelCase     :							int    =    512
							_lowerCAmelCase     :							Union[str, Any]    =    [1] + [0] * (size - 1)
							_lowerCAmelCase     :							Optional[Any]    =    [filter_type.process(_lowerCamelCase  ) for item in inputs]

							_lowerCAmelCase     :							int    =    [0] * (samplerate - size)  # zero-padding
							outputs += filler
							_lowerCAmelCase     :							str    =    np.abs(np.fft.fft(_lowerCamelCase  )  )
							_lowerCAmelCase     :							Union[str, Any]    =    20 * np.logaa(_lowerCamelCase  )

							# 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
							_lowerCAmelCase     :							List[Any]    =    get_bounds(_lowerCamelCase				, _lowerCamelCase  )
							plt.ylim(max([-80, bounds[0]]  )				, min([80, bounds[1]]  )  )
							plt.ylabel("Gain (dB)"  )

							plt.plot(_lowerCamelCase  )
							plt.show()






def 				A				(					_lowerCamelCase				, _lowerCamelCase  ):





							'''simple docstring'''




							_lowerCAmelCase     :							Union[str, Any]    =    512
							_lowerCAmelCase     :							Optional[Any]    =    [1] + [0] * (size - 1)
							_lowerCAmelCase     :							str    =    [filter_type.process(_lowerCamelCase  ) for item in inputs]

							_lowerCAmelCase     :							Optional[Any]    =    [0] * (samplerate - size)  # zero-padding
							outputs += filler
							_lowerCAmelCase     :							Optional[Any]    =    np.angle(np.fft.fft(_lowerCamelCase  )  )

							# 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(_lowerCamelCase				, -2 * pi  )  )
							plt.show()
