






"""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					(					lowerCAmelCase__       ,lowerCAmelCase__				):
    if flax_key_tuple[-1] == "kernel" and flax_tensor.ndim == 3:
        # expert layer
        A__							 =       flax_key_tuple[:-1] + ('weight',)
        A__							 =       torch.permute(lowerCAmelCase__       ,(0, 2, 1)				)
    elif flax_key_tuple[-1] == "kernel" and ".".join(lowerCAmelCase__				):
        # linear layer
        A__							 =       flax_key_tuple[:-1] + ('weight',)
        A__							 =       flax_tensor.T
    elif flax_key_tuple[-1] in ["scale", "embedding"]:
        A__							 =       flax_key_tuple[:-1] + ('weight',)

    return flax_key_tuple, flax_tensor



def  __lowerCamelCase					(					lowerCAmelCase__       ,lowerCAmelCase__       ,lowerCAmelCase__				):
    if "metadata" in layer:
        A__							 =       layer.split('metadata'				)
        A__							 =       ''.join(split_layer[0]				)[:-1]
        A__							 =       [tuple(('metadata' + split_layer[1]).split('/'				)				)]
    elif "kvstore" in layer:
        A__							 =       layer.split('kvstore'				)
        A__							 =       ''.join(split_layer[0]				)[:-1]
        A__							 =       [tuple(('kvstore' + split_layer[1]).split('/'				)				)]

    else:
        A__							 =       layer.split('/'				)
        A__							 =       '/'.join(split_layer[:-1]				)
        A__							 =       (split_layer[-1],)

    if "kvstore/path" in layer:
        A__							 =       f'''{switch_checkpoint_path}/{checkpoint_info[layer]}'''
    elif "kvstore/driver" in layer:
        A__							 =       'file'
    else:
        A__							 =       checkpoint_info[layer]

    return curr_real_layer_name, split_layer, content



def  __lowerCamelCase					(					lowerCAmelCase__       ,lowerCAmelCase__				):
    A__							 =       rename_keys(lowerCAmelCase__				)
    A__							 =       {}
    for k, v in current_block.items():
        A__							 =       v
    A__							 =       new_current_block
    torch.save(lowerCAmelCase__       ,lowerCAmelCase__				)



def  __lowerCamelCase					(					lowerCAmelCase__       ,lowerCAmelCase__       ,lowerCAmelCase__       ,lowerCAmelCase__       ,lowerCAmelCase__ = WEIGHTS_NAME				):
    A__							 =       convert_file_size_to_int(lowerCAmelCase__				)
    A__							 =       []
    A__							 =       {}
    A__							 =       0
    A__							 =       0

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

    A__							 =       {}
    for layer in checkpoint_info.keys():
        A__ ,			A__ ,			A__							 =       get_key_and_tensorstore_dict(
            lowerCAmelCase__       ,lowerCAmelCase__       ,lowerCAmelCase__				)
        if curr_real_layer_name in all_layers:
            A__							 =       content
        else:
            A__							 =       {split_layer[-1]: content}

    for key in all_layers.keys():
        # open tensorstore file
        A__							 =       ts.open(unflatten_dict(all_layers[key]				)				).result().read().result()
        A__							 =       torch.tensor(lowerCAmelCase__				)
        A__							 =       raw_weights.numel() * dtype_byte_size(raw_weights.dtype				)

        # use the renaming pattern from the small conversion scripts
        A__ ,			A__							 =       rename_base_flax_keys(tuple(key.split('/'				)				)       ,lowerCAmelCase__				)
        A__							 =       '/'.join(lowerCAmelCase__				)

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

        A__							 =       raw_weights.to(getattr(lowerCAmelCase__       ,lowerCAmelCase__				)				)
        current_block_size += weight_size
        total_size += weight_size

    # Add the last block
    A__							 =       os.path.join(lowerCAmelCase__       ,weights_name.replace('.bin'       ,f'''-{len(lowerCAmelCase__				)+1:05d}-of-???.bin'''				)				)
    rename_and_save_block(lowerCAmelCase__       ,lowerCAmelCase__				)
    sharded_state_dicts.append(current_block.keys()				)

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

    # Otherwise, let's build the index
    A__							 =       {}
    A__							 =       {}
    for idx, shard in enumerate(lowerCAmelCase__				):
        A__							 =       weights_name.replace(
            '.bin'       ,f'''-{idx+1:05d}-of-{len(lowerCAmelCase__				):05d}.bin'''				)  # len(sharded_state_dicts):05d}
        A__							 =       os.path.join(lowerCAmelCase__       ,weights_name.replace('.bin'       ,f'''-{idx+1:05d}-of-???.bin'''				)				)
        os.rename(lowerCAmelCase__       ,os.path.join(lowerCAmelCase__       ,lowerCAmelCase__				)				)
        A__							 =       shard
        for key in shard:
            A__							 =       shard_file

    # Add the metadata
    A__							 =       {'total_size': total_size}
    A__							 =       {'metadata': metadata, 'weight_map': weight_map}

    with open(os.path.join(lowerCAmelCase__       ,lowerCAmelCase__				)       ,'w'       ,encoding='utf-8'				) as f:
        A__							 =       json.dumps(lowerCAmelCase__       ,indent=2       ,sort_keys=lowerCAmelCase__				) + '\n'
        f.write(lowerCAmelCase__				)

    return metadata, index


if __name__ == "__main__":
     SCREAMING_SNAKE_CASE						:						str					   =       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.''',
     )
     SCREAMING_SNAKE_CASE						:						Optional[Any]					   =       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					(					):
    from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration, TaTokenizer

    A__							 =       SwitchTransformersConfig.from_pretrained('google/switch-base-8'				)
    config.save_pretrained('/home/arthur_huggingface_co/transformers/switch_converted'				)
    A__							 =       SwitchTransformersForConditionalGeneration.from_pretrained(
        '/home/arthur_huggingface_co/transformers/switch_converted'       ,device_map='auto'				)

    A__							 =       TaTokenizer.from_pretrained('t5-small'				)
    A__							 =       'A <extra_id_0> walks into a bar a orders a <extra_id_1> with <extra_id_2> pinch of <extra_id_3>.'

    A__							 =       tokenizer(lowerCAmelCase__       ,return_tensors='pt'				).input_ids
    A__							 =       model.generate(lowerCAmelCase__       ,decoder_start_token_id=0				)
    print(tokenizer.decode(out[0]				)				)










"""simple docstring"""






import torch

from transformers import AutoModel





class       snake_case_       (    torch.nn.Module       ):



	"""simple docstring"""

	def __init__(      self  ,					__a="sayef/fsner-bert-base-uncased"						):


					"""simple docstring"""

					super(__a  ,					self						).__init__()

					A__							 =       AutoModel.from_pretrained(__a  ,					return_dict=__a						)
					A__							 =       torch.nn.CosineSimilarity(3  ,					1E-08						)
					A__							 =       torch.nn.Softmax(dim=1						)


	def       _UpperCAmelCase						(      self  ,					**__a						):


					"""simple docstring"""

					return self.bert(**__a						).last_hidden_state


	def       _UpperCAmelCase						(      self  ,					__a						):


					"""simple docstring"""

					return token_embeddings.sum(2  ,					keepdim=__a						)


	def       _UpperCAmelCase						(      self  ,					__a  ,					__a  ,					__a=1						):


					"""simple docstring"""

					return self.softmax(T * self.cos(__a  ,					__a						)						)






	def       _UpperCAmelCase						(      self  ,					__a  ,					__a						):


					"""simple docstring"""

					A__							 =       W_supports['sizes'].tolist()
					A__							 =       W_supports['start_token_id'].item()
					A__							 =       W_supports['end_token_id'].item()

					del W_supports["sizes"]
					del W_supports["start_token_id"]
					del W_supports["end_token_id"]

					A__							 =       self.BERT(**__a						)
					A__							 =       self.BERT(**__a						)

					A__							 =       None
					A__							 =       None

					A__							 =       W_supports['input_ids'] == start_token_id
					A__							 =       W_supports['input_ids'] == end_token_id

					for i, size in enumerate(__a						):
									if i == 0:
													A__							 =       0
									else:
													A__							 =       support_sizes[i - 1]

									A__							 =       S[s : s + size][start_token_masks[s : s + size]]
									A__							 =       S[s : s + size][end_token_masks[s : s + size]]

									A__							 =       torch.matmul(q[i]  ,					s_start.T						).sum(1						).softmax(0						)
									A__							 =       torch.matmul(q[i]  ,					s_end.T						).sum(1						).softmax(0						)

									if p_starts is not None:
													A__							 =       torch.vstack((p_starts, p_start)						)
													A__							 =       torch.vstack((p_ends, p_end)						)
									else:
													A__							 =       p_start
													A__							 =       p_end

					return p_starts, p_ends


