
'''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]						)						)






'''simple docstring'''




from scipy.stats import pearsonr

import datasets


lowercase			:				Optional[int]    		=			'\nPearson correlation coefficient and p-value for testing non-correlation.\nThe Pearson correlation coefficient measures the linear relationship between two datasets. The calculation of the p-value relies on the assumption that each dataset is normally distributed. Like other correlation coefficients, this one varies between -1 and +1 with 0 implying no correlation. Correlations of -1 or +1 imply an exact linear relationship. Positive correlations imply that as x increases, so does y. Negative correlations imply that as x increases, y decreases.\nThe p-value roughly indicates the probability of an uncorrelated system producing datasets that have a Pearson correlation at least as extreme as the one computed from these datasets.\n'


lowercase			:				Optional[Any]    		=			'\nArgs:\n    predictions (`list` of `int`): Predicted class labels, as returned by a model.\n    references (`list` of `int`): Ground truth labels.\n    return_pvalue (`boolean`): If `True`, returns the p-value, along with the correlation coefficient. If `False`, returns only the correlation coefficient. Defaults to `False`.\n\nReturns:\n    pearsonr (`float`): Pearson correlation coefficient. Minimum possible value is -1. Maximum possible value is 1. Values of 1 and -1 indicate exact linear positive and negative relationships, respectively. A value of 0 implies no correlation.\n    p-value (`float`): P-value, which roughly indicates the probability of an The p-value roughly indicates the probability of an uncorrelated system producing datasets that have a Pearson correlation at least as extreme as the one computed from these datasets. Minimum possible value is 0. Maximum possible value is 1. Higher values indicate higher probabilities.\n\nExamples:\n\n    Example 1-A simple example using only predictions and references.\n        >>> pearsonr_metric = datasets.load_metric("pearsonr")\n        >>> results = pearsonr_metric.compute(predictions=[10, 9, 2.5, 6, 4], references=[1, 2, 3, 4, 5])\n        >>> print(round(results[\'pearsonr\'], 2))\n        -0.74\n\n    Example 2-The same as Example 1, but that also returns the `p-value`.\n        >>> pearsonr_metric = datasets.load_metric("pearsonr")\n        >>> results = pearsonr_metric.compute(predictions=[10, 9, 2.5, 6, 4], references=[1, 2, 3, 4, 5], return_pvalue=True)\n        >>> print(sorted(list(results.keys())))\n        [\'p-value\', \'pearsonr\']\n        >>> print(round(results[\'pearsonr\'], 2))\n        -0.74\n        >>> print(round(results[\'p-value\'], 2))\n        0.15\n'


lowercase			:				str    		=			'\n@article{2020SciPy-NMeth,\nauthor  = {Virtanen, Pauli and Gommers, Ralf and Oliphant, Travis E. and\n      Haberland, Matt and Reddy, Tyler and Cournapeau, David and\n      Burovski, Evgeni and Peterson, Pearu and Weckesser, Warren and\n      Bright, Jonathan and {van der Walt}, St{\'e}fan J. and\n      Brett, Matthew and Wilson, Joshua and Millman, K. Jarrod and\n      Mayorov, Nikolay and Nelson, Andrew R. J. and Jones, Eric and\n      Kern, Robert and Larson, Eric and Carey, C J and\n      Polat, Ilhan and Feng, Yu and Moore, Eric W. and\n      {VanderPlas}, Jake and Laxalde, Denis and Perktold, Josef and\n      Cimrman, Robert and Henriksen, Ian and Quintero, E. A. and\n      Harris, Charles R. and Archibald, Anne M. and\n      Ribeiro, Antonio H. and Pedregosa, Fabian and\n      {van Mulbregt}, Paul and {SciPy 1.0 Contributors}},\ntitle   = {{{SciPy} 1.0: Fundamental Algorithms for Scientific\n      Computing in Python}},\njournal = {Nature Methods},\nyear    = {2020},\nvolume  = {17},\npages   = {261--272},\nadsurl  = {https://rdcu.be/b08Wh},\ndoi = {10.1038/s41592-019-0686-2},\n}\n'





@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION    ,			_KWARGS_DESCRIPTION						)
class  A				(							datasets.Metric						):





     def 				__lowerCAmelCase							(							self	) ->				List[str]:






         """simple docstring"""






         return datasets.MetricInfo(
             description=_DESCRIPTION						,			citation=_CITATION						,			inputs_description=_KWARGS_DESCRIPTION						,			features=datasets.Features(
                 {
                     '''predictions''': datasets.Value('''float'''	),
                     '''references''': datasets.Value('''float'''	),
                 }	)						,			reference_urls=['''https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.pearsonr.html''']						,			)





     def 				__lowerCAmelCase							(							self						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE=False	) ->				Union[str, Any]:






         """simple docstring"""






         if return_pvalue:
             A       :   Union[str, Any]					=						pearsonr(SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE	)
             return {"pearsonr": results[0], "p-value": results[1]}
         else:
             return {"pearsonr": float(pearsonr(SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE	)[0]	)}




