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 							UpperCamelCase				(    lowerCAmelCase_       ,					lowerCAmelCase_   )		->						Dict:




       '''simple docstring'''






       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 							UpperCamelCase				(    lowerCAmelCase_       ,					lowerCAmelCase_       ,					lowerCAmelCase_   )		->						List[Any]:




       '''simple docstring'''






       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 							UpperCamelCase				(    lowerCAmelCase_       ,					lowerCAmelCase_   )		->						Tuple:




       '''simple docstring'''






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

def 							UpperCamelCase				(    lowerCAmelCase_       ,					lowerCAmelCase_       ,					lowerCAmelCase_       ,					lowerCAmelCase_       ,					lowerCAmelCase_ = WEIGHTS_NAME   )		->						List[str]:




       '''simple docstring'''






       _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__":
      UpperCAmelCase_								=							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.''',
      )
      UpperCAmelCase_								=							parser.parse_args()
      shard_on_the_fly(
          args.switch_tax_checkpoint_path,
          args.pytorch_dump_folder_path,
          args.max_shard_size,
          args.dtype,
      )





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




       '''simple docstring'''






       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]   )   )
import os
import tempfile
import unittest
import uuid
from pathlib import Path

from transformers.testing_utils import get_tests_dir, require_soundfile, require_torch, require_vision
from transformers.tools.agent_types import AgentAudio, AgentImage, AgentText
from transformers.utils import is_soundfile_availble, is_torch_available, is_vision_available


if is_torch_available():
      import torch

if is_soundfile_availble():
      import soundfile as sf

if is_vision_available():
      from PIL import Image

def 							UpperCamelCase				(    lowerCAmelCase_=""   )		->						str:




       '''simple docstring'''






       _A=	tempfile.mkdtemp()
       return os.path.join(lowerCAmelCase_       ,					str(uuid.uuida()   ) + suffix   )





@require_soundfile
@require_torch
class      lowerCAmelCase						(					unittest.TestCase			):



     def 			a__  (					self       ):
            _A=	torch.rand(12				,      dtype=torch.floataa       ) - 0.5
            _A=	AgentAudio(lowerCAmelCase__       )
            _A=	str(agent_type.to_string()       )

            # Ensure that the tensor and the agent_type's tensor are the same
            self.assertTrue(torch.allclose(lowerCAmelCase__				,      agent_type.to_raw()				,      atol=1E-4       )       )

            del agent_type

            # Ensure the path remains even after the object deletion
            self.assertTrue(os.path.exists(lowerCAmelCase__       )       )

            # Ensure that the file contains the same value as the original tensor
            _A,			_A=	sf.read(lowerCAmelCase__       )
            self.assertTrue(torch.allclose(lowerCAmelCase__				,      torch.tensor(lowerCAmelCase__       )				,      atol=1E-4       )       )





     def 			a__  (					self       ):
            _A=	torch.rand(12				,      dtype=torch.floataa       ) - 0.5
            _A=	get_new_path(suffix='.wav'       )
            sf.write(lowerCAmelCase__				,      lowerCAmelCase__				,      16000       )

            _A=	AgentAudio(lowerCAmelCase__       )

            self.assertTrue(torch.allclose(lowerCAmelCase__				,      agent_type.to_raw()				,      atol=1E-4       )       )
            self.assertEqual(agent_type.to_string()				,      lowerCAmelCase__       )





@require_vision
@require_torch
class      lowerCAmelCase						(					unittest.TestCase			):



     def 			a__  (					self       ):
            _A=	torch.randint(0				,      256				,      (64, 64, 3)       )
            _A=	AgentImage(lowerCAmelCase__       )
            _A=	str(agent_type.to_string()       )

            # Ensure that the tensor and the agent_type's tensor are the same
            self.assertTrue(torch.allclose(lowerCAmelCase__				,      agent_type._tensor				,      atol=1E-4       )       )

            self.assertIsInstance(agent_type.to_raw()				,      Image.Image       )

            # Ensure the path remains even after the object deletion
            del agent_type
            self.assertTrue(os.path.exists(lowerCAmelCase__       )       )



     def 			a__  (					self       ):
            _A=	Path(get_tests_dir('fixtures/tests_samples/COCO'       )       ) / '000000039769.png'
            _A=	Image.open(lowerCAmelCase__       )
            _A=	AgentImage(lowerCAmelCase__       )

            self.assertTrue(path.samefile(agent_type.to_string()       )       )
            self.assertTrue(image == agent_type.to_raw()       )

            # Ensure the path remains even after the object deletion
            del agent_type
            self.assertTrue(os.path.exists(lowerCAmelCase__       )       )





     def 			a__  (					self       ):
            _A=	Path(get_tests_dir('fixtures/tests_samples/COCO'       )       ) / '000000039769.png'
            _A=	Image.open(lowerCAmelCase__       )
            _A=	AgentImage(lowerCAmelCase__       )

            self.assertFalse(path.samefile(agent_type.to_string()       )       )
            self.assertTrue(image == agent_type.to_raw()       )

            # Ensure the path remains even after the object deletion
            del agent_type
            self.assertTrue(os.path.exists(lowerCAmelCase__       )       )





class      lowerCAmelCase						(					unittest.TestCase			):



     def 			a__  (					self       ):
            _A=	'Hey!'
            _A=	AgentText(lowerCAmelCase__       )

            self.assertEqual(lowerCAmelCase__				,      agent_type.to_string()       )
            self.assertEqual(lowerCAmelCase__				,      agent_type.to_raw()       )
            self.assertEqual(lowerCAmelCase__				,      lowerCAmelCase__       )