





"""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    (       SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE							)		->       Optional[int]:






       """simple docstring"""




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

       return flax_key_tuple, flax_tensor

def       __lowerCamelCase    (       SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE							)		->       Union[str, Any]:






       """simple docstring"""




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

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

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

       return curr_real_layer_name, split_layer, content

def       __lowerCamelCase    (       SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE							)		->       Dict:






       """simple docstring"""




       _UpperCAmelCase     =     rename_keys(snake_case__							)
       _UpperCAmelCase     =     {}
       for k, v in current_block.items():
              _UpperCAmelCase     =     v
       _UpperCAmelCase     =     new_current_block
       torch.save(snake_case__,snake_case__							)

def       __lowerCamelCase    (       SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE = WEIGHTS_NAME							)		->       Dict:






       """simple docstring"""




       _UpperCAmelCase     =     convert_file_size_to_int(snake_case__							)
       _UpperCAmelCase     =     []
       _UpperCAmelCase     =     {}
       _UpperCAmelCase     =     0
       _UpperCAmelCase     =     0

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

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

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

              # use the renaming pattern from the small conversion scripts
              _UpperCAmelCase    ,							_UpperCAmelCase     =     rename_base_flax_keys(tuple(key.split('/'							)							),snake_case__							)
              _UpperCAmelCase     =     '/'.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:
                     _UpperCAmelCase     =     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
                     _UpperCAmelCase     =     {}
                     _UpperCAmelCase     =     0

              _UpperCAmelCase     =     raw_weights.to(getattr(snake_case__,snake_case__							)							)
              current_block_size += weight_size
              total_size += weight_size

       # Add the last block
       _UpperCAmelCase     =     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
       _UpperCAmelCase     =     {}
       _UpperCAmelCase     =     {}
       for idx, shard in enumerate(snake_case__							):
              _UpperCAmelCase     =     weights_name.replace(
                  '.bin',F"""-{idx+1:05d}-of-{len(snake_case__							):05d}.bin"""							)  # len(sharded_state_dicts):05d}
              _UpperCAmelCase     =     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__							)							)
              _UpperCAmelCase     =     shard
              for key in shard:
                     _UpperCAmelCase     =     shard_file

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

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

       return metadata, index


if __name__ == "__main__":
       lowerCAmelCase_			=							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.''',
       )
       lowerCAmelCase_			=							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    (       )		->       Union[str, Any]:






       """simple docstring"""




       from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration, TaTokenizer

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

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

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













"""simple docstring"""






# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from ..models.auto import AutoModelForSeqaSeqLM, AutoTokenizer
from .base import PipelineTool



class 	lowerCAmelCase					(      snake_case							):
       lowerCAmelCase__						 =  """philschmid/bart-large-cnn-samsum"""
       lowerCAmelCase__						 =  (
           """This is a tool that summarizes an English text. It takes an input `text` containing the text to summarize, """
           """and returns a summary of the text."""
       )
       lowerCAmelCase__						 =  """summarizer"""
       lowerCAmelCase__						 =  AutoTokenizer
       lowerCAmelCase__						 =  AutoModelForSeqaSeqLM

       lowerCAmelCase__						 =  ["""text"""]
       lowerCAmelCase__						 =  ["""text"""]






       def 							__A (   self    ,  a__     ):
              return self.pre_processor(a__    ,  return_tensors='pt'    ,  truncation=a__     )






       def 							__A (   self    ,  a__     ):
              return self.model.generate(**a__     )[0]






       def 							__A (   self    ,  a__     ):
              return self.pre_processor.decode(a__    ,  skip_special_tokens=a__    ,  clean_up_tokenization_spaces=a__     )






