




'''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 						snake_case_       (UpperCamelCase			:    List[Any]					,		UpperCamelCase			:    str ):





							'''simple docstring'''


							if flax_key_tuple[-1] == "kernel" and flax_tensor.ndim == 3:
														# expert layer
														_a      	=							flax_key_tuple[:-1] + ('''weight''',)
														_a      	=							torch.permute(UpperCamelCase					,		(0, 2, 1) )
							elif flax_key_tuple[-1] == "kernel" and ".".join(UpperCamelCase ):
														# 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 						snake_case_       (UpperCamelCase			:    Union[str, Any]					,		UpperCamelCase			:    Optional[int]					,		UpperCamelCase			:    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 						snake_case_       (UpperCamelCase			:    Dict					,		UpperCamelCase			:    Optional[Any] ):





							'''simple docstring'''


							_a      	=							rename_keys(UpperCamelCase )
							_a      	=							{}
							for k, v in current_block.items():
														_a      	=							v
							_a      	=							new_current_block
							torch.save(UpperCamelCase					,		UpperCamelCase )
def 						snake_case_       (UpperCamelCase			:    Dict					,		UpperCamelCase			:    Any					,		UpperCamelCase			:    List[str]					,		UpperCamelCase			:    Tuple					,		UpperCamelCase			:    str = WEIGHTS_NAME ):





							'''simple docstring'''


							_a      	=							convert_file_size_to_int(UpperCamelCase )
							_a      	=							[]
							_a      	=							{}
							_a      	=							0
							_a      	=							0

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

							_a      	=							{}
							for layer in checkpoint_info.keys():
														_a	,				_a	,				_a      	=							get_key_and_tensorstore_dict(
														    UpperCamelCase					,		UpperCamelCase					,		UpperCamelCase )
														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(UpperCamelCase )
														_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('''/''' ) )					,		UpperCamelCase )
														_a      	=							'''/'''.join(UpperCamelCase )

														# 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(
																					    UpperCamelCase					,		weights_name.replace('''.bin'''					,		f'-{len(UpperCamelCase )+1:05d}-of-???.bin' ) )
																					rename_and_save_block(UpperCamelCase					,		UpperCamelCase )
																					sharded_state_dicts.append(current_block.keys() )
																					del current_block
																					_a      	=							{}
																					_a      	=							0

														_a      	=							raw_weights.to(getattr(UpperCamelCase					,		UpperCamelCase ) )
														current_block_size += weight_size
														total_size += weight_size

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

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

							# Otherwise, let's build the index
							_a      	=							{}
							_a      	=							{}
							for idx, shard in enumerate(UpperCamelCase ):
														_a      	=							weights_name.replace(
														    '''.bin'''					,		f'-{idx+1:05d}-of-{len(UpperCamelCase ):05d}.bin' )  # len(sharded_state_dicts):05d}
														_a      	=							os.path.join(UpperCamelCase					,		weights_name.replace('''.bin'''					,		f'-{idx+1:05d}-of-???.bin' ) )
														os.rename(UpperCamelCase					,		os.path.join(UpperCamelCase					,		UpperCamelCase ) )
														_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(UpperCamelCase					,		UpperCamelCase )					,		'''w'''					,		encoding='''utf-8''' ) as f:
														_a      	=							json.dumps(UpperCamelCase					,		indent=2					,		sort_keys=UpperCamelCase ) + '''\n'''
														f.write(UpperCamelCase )

							return metadata, index


if __name__ == "__main__":
				_snake_case : Any  				=						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.',
				)
				_snake_case : Tuple  				=						parser.parse_args()
				shard_on_the_fly(
				    args.switch_tax_checkpoint_path,
				    args.pytorch_dump_folder_path,
				    args.max_shard_size,
				    args.dtype,
				)

def 						snake_case_       ():





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









import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available


if is_vision_available():
    from PIL import Image

    from transformers import (
        AutoProcessor,
        BertTokenizerFast,
        BlipImageProcessor,
        GPTaTokenizer,
        InstructBlipProcessor,
        PreTrainedTokenizerFast,
    )






@require_vision
class 					SCREAMING_SNAKE_CASE_  (       unittest.TestCase ):
       """simple docstring"""


       def 				UpperCamelCase__						(				self	:List[str]):



          """simple docstring"""

          _lowercase              =tempfile.mkdtemp()

          _lowercase              =BlipImageProcessor()
          _lowercase              =GPTaTokenizer.from_pretrained('hf-internal-testing/tiny-random-GPT2Model')
          _lowercase              =BertTokenizerFast.from_pretrained('hf-internal-testing/tiny-random-bert')

          _lowercase              =InstructBlipProcessor(snake_case,      snake_case,      snake_case)

          processor.save_pretrained(self.tmpdirname)





       def 				UpperCamelCase__						(				self	:List[str],      **snake_case	:str):



          """simple docstring"""

          return AutoProcessor.from_pretrained(self.tmpdirname,      **snake_case).tokenizer





       def 				UpperCamelCase__						(				self	:Optional[Any],      **snake_case	:List[Any]):



          """simple docstring"""

          return AutoProcessor.from_pretrained(self.tmpdirname,      **snake_case).image_processor





       def 				UpperCamelCase__						(				self	:Tuple,      **snake_case	:Any):



          """simple docstring"""

          return AutoProcessor.from_pretrained(self.tmpdirname,      **snake_case).qformer_tokenizer





       def 				UpperCamelCase__						(				self	:Optional[int]):



          """simple docstring"""

          shutil.rmtree(self.tmpdirname)





       def 				UpperCamelCase__						(				self	:List[str]):



          """simple docstring"""

          _lowercase              =[np.random.randint(255,      size=(3, 30, 400),      dtype=np.uinta)]

          _lowercase              =[Image.fromarray(np.moveaxis(snake_case,      0,      -1)) for x in image_inputs]

          return image_inputs





       def 				UpperCamelCase__						(				self	:List[Any]):



          """simple docstring"""

          _lowercase              =InstructBlipProcessor(
              tokenizer=self.get_tokenizer(),      image_processor=self.get_image_processor(),      qformer_tokenizer=self.get_qformer_tokenizer(),      )
          processor.save_pretrained(self.tmpdirname)

          _lowercase              =self.get_tokenizer(bos_token='(BOS)',      eos_token='(EOS)')
          _lowercase              =self.get_image_processor(do_normalize=snake_case,      padding_value=1.0)

          _lowercase              =InstructBlipProcessor.from_pretrained(
              self.tmpdirname,      bos_token='(BOS)',      eos_token='(EOS)',      do_normalize=snake_case,      padding_value=1.0)

          self.assertEqual(processor.tokenizer.get_vocab(),      tokenizer_add_kwargs.get_vocab())
          self.assertIsInstance(processor.tokenizer,      snake_case)

          self.assertEqual(processor.image_processor.to_json_string(),      image_processor_add_kwargs.to_json_string())
          self.assertIsInstance(processor.image_processor,      snake_case)
          self.assertIsInstance(processor.qformer_tokenizer,      snake_case)





       def 				UpperCamelCase__						(				self	:Tuple):



          """simple docstring"""

          _lowercase              =self.get_image_processor()
          _lowercase              =self.get_tokenizer()
          _lowercase              =self.get_qformer_tokenizer()

          _lowercase              =InstructBlipProcessor(
              tokenizer=snake_case,      image_processor=snake_case,      qformer_tokenizer=snake_case)

          _lowercase              =self.prepare_image_inputs()

          _lowercase              =image_processor(snake_case,      return_tensors='np')
          _lowercase              =processor(images=snake_case,      return_tensors='np')

          for key in input_feat_extract.keys():
             self.assertAlmostEqual(input_feat_extract[key].sum(),      input_processor[key].sum(),      delta=1e-2)





       def 				UpperCamelCase__						(				self	:List[Any]):



          """simple docstring"""

          _lowercase              =self.get_image_processor()
          _lowercase              =self.get_tokenizer()
          _lowercase              =self.get_qformer_tokenizer()

          _lowercase              =InstructBlipProcessor(
              tokenizer=snake_case,      image_processor=snake_case,      qformer_tokenizer=snake_case)

          _lowercase              ='lower newer'

          _lowercase              =processor(text=snake_case)

          _lowercase              =tokenizer(snake_case,      return_token_type_ids=snake_case)
          _lowercase              =qformer_tokenizer(snake_case,      return_token_type_ids=snake_case)

          for key in encoded_tokens.keys():
             self.assertListEqual(encoded_tokens[key],      encoded_processor[key])

          for key in encoded_tokens_qformer.keys():
             self.assertListEqual(encoded_tokens_qformer[key],      encoded_processor['qformer_' + key])





       def 				UpperCamelCase__						(				self	:Any):



          """simple docstring"""

          _lowercase              =self.get_image_processor()
          _lowercase              =self.get_tokenizer()
          _lowercase              =self.get_qformer_tokenizer()

          _lowercase              =InstructBlipProcessor(
              tokenizer=snake_case,      image_processor=snake_case,      qformer_tokenizer=snake_case)

          _lowercase              ='lower newer'
          _lowercase              =self.prepare_image_inputs()

          _lowercase              =processor(text=snake_case,      images=snake_case)

          self.assertListEqual(
              list(inputs.keys()),      ['input_ids', 'attention_mask', 'qformer_input_ids', 'qformer_attention_mask', 'pixel_values'],      )

          # test if it raises when no input is passed
          with pytest.raises(snake_case):
             processor()





       def 				UpperCamelCase__						(				self	:Dict):



          """simple docstring"""

          _lowercase              =self.get_image_processor()
          _lowercase              =self.get_tokenizer()
          _lowercase              =self.get_qformer_tokenizer()

          _lowercase              =InstructBlipProcessor(
              tokenizer=snake_case,      image_processor=snake_case,      qformer_tokenizer=snake_case)

          _lowercase              =[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

          _lowercase              =processor.batch_decode(snake_case)
          _lowercase              =tokenizer.batch_decode(snake_case)

          self.assertListEqual(snake_case,      snake_case)





       def 				UpperCamelCase__						(				self	:int):



          """simple docstring"""

          _lowercase              =self.get_image_processor()
          _lowercase              =self.get_tokenizer()
          _lowercase              =self.get_qformer_tokenizer()

          _lowercase              =InstructBlipProcessor(
              tokenizer=snake_case,      image_processor=snake_case,      qformer_tokenizer=snake_case)

          _lowercase              ='lower newer'
          _lowercase              =self.prepare_image_inputs()

          _lowercase              =processor(text=snake_case,      images=snake_case)

          self.assertListEqual(
              list(inputs.keys()),      ['input_ids', 'attention_mask', 'qformer_input_ids', 'qformer_attention_mask', 'pixel_values'],      )


