






'''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       (  a_				:			str     , a_				:			str						)							->					Union[str, Any]:





      """simple docstring"""

      if flax_key_tuple[-1] == "kernel" and flax_tensor.ndim == 3:
            # expert layer
            UpperCamelCase_					: Tuple            =				flax_key_tuple[:-1] + ("""weight""",)
            UpperCamelCase_					: Tuple            =				torch.permute(__UpperCamelCase     , (0, 2, 1)						)
      elif flax_key_tuple[-1] == "kernel" and ".".join(__UpperCamelCase						):
            # linear layer
            UpperCamelCase_					: Union[str, Any]            =				flax_key_tuple[:-1] + ("""weight""",)
            UpperCamelCase_					: Any            =				flax_tensor.T
      elif flax_key_tuple[-1] in ["scale", "embedding"]:
            UpperCamelCase_					: List[Any]            =				flax_key_tuple[:-1] + ("""weight""",)

      return flax_key_tuple, flax_tensor



def 				snake_case       (  a_				:			Dict     , a_				:			List[str]     , a_				:			Optional[int]						)							->					List[Any]:





      """simple docstring"""

      if "metadata" in layer:
            UpperCamelCase_					: List[str]            =				layer.split("""metadata"""						)
            UpperCamelCase_					: Union[str, Any]            =				"""""".join(split_layer[0]						)[:-1]
            UpperCamelCase_					: int            =				[tuple(("""metadata""" + split_layer[1]).split("""/"""						)						)]
      elif "kvstore" in layer:
            UpperCamelCase_					: str            =				layer.split("""kvstore"""						)
            UpperCamelCase_					: List[str]            =				"""""".join(split_layer[0]						)[:-1]
            UpperCamelCase_					: Union[str, Any]            =				[tuple(("""kvstore""" + split_layer[1]).split("""/"""						)						)]

      else:
            UpperCamelCase_					: str            =				layer.split("""/"""						)
            UpperCamelCase_					: Optional[Any]            =				"""/""".join(split_layer[:-1]						)
            UpperCamelCase_					: Dict            =				(split_layer[-1],)

      if "kvstore/path" in layer:
            UpperCamelCase_					: Union[str, Any]            =				f"{switch_checkpoint_path}/{checkpoint_info[layer]}"
      elif "kvstore/driver" in layer:
            UpperCamelCase_					: List[Any]            =				"""file"""
      else:
            UpperCamelCase_					: Tuple            =				checkpoint_info[layer]

      return curr_real_layer_name, split_layer, content



def 				snake_case       (  a_				:			Tuple     , a_				:			int						)							->					Dict:





      """simple docstring"""

      UpperCamelCase_					: Dict            =				rename_keys(__UpperCamelCase						)
      UpperCamelCase_					: Tuple            =				{}
      for k, v in current_block.items():
            UpperCamelCase_					: Optional[Any]            =				v
      UpperCamelCase_					: Any            =				new_current_block
      torch.save(__UpperCamelCase     , __UpperCamelCase						)



def 				snake_case       (  a_				:			Optional[int]     , a_				:			Union[str, Any]     , a_				:			List[str]     , a_				:			str     , a_				:			Union[str, Any] = WEIGHTS_NAME						)							->					List[str]:





      """simple docstring"""

      UpperCamelCase_					: str            =				convert_file_size_to_int(__UpperCamelCase						)
      UpperCamelCase_					: str            =				[]
      UpperCamelCase_					: Any            =				{}
      UpperCamelCase_					: Dict            =				0
      UpperCamelCase_					: Any            =				0

      os.makedirs(__UpperCamelCase     , exist_ok=__UpperCamelCase						)
      with gfile.GFile(switch_checkpoint_path + """/checkpoint"""     , """rb"""						) as fp:
            UpperCamelCase_					: List[Any]            =				serialization.msgpack_restore(fp.read()						)["""optimizer"""]["""target"""]
            UpperCamelCase_					: Union[str, Any]            =				flatten_dict(__UpperCamelCase     , sep="""/"""						)

      UpperCamelCase_					: Dict            =				{}
      for layer in checkpoint_info.keys():
            UpperCamelCase_     ,      UpperCamelCase_     ,      UpperCamelCase_					: Tuple            =				get_key_and_tensorstore_dict(
                __UpperCamelCase     , __UpperCamelCase     , __UpperCamelCase						)
            if curr_real_layer_name in all_layers:
                  UpperCamelCase_					: int            =				content
            else:
                  UpperCamelCase_					: Dict            =				{split_layer[-1]: content}

      for key in all_layers.keys():
            # open tensorstore file
            UpperCamelCase_					: str            =				ts.open(unflatten_dict(all_layers[key]						)						).result().read().result()
            UpperCamelCase_					: List[str]            =				torch.tensor(__UpperCamelCase						)
            UpperCamelCase_					: Union[str, Any]            =				raw_weights.numel() * dtype_byte_size(raw_weights.dtype						)

            # use the renaming pattern from the small conversion scripts
            UpperCamelCase_     ,      UpperCamelCase_					: Union[str, Any]            =				rename_base_flax_keys(tuple(key.split("""/"""						)						)     , __UpperCamelCase						)
            UpperCamelCase_					: Optional[int]            =				"""/""".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:
                  UpperCamelCase_					: Dict            =				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
                  UpperCamelCase_					: Dict            =				{}
                  UpperCamelCase_					: Any            =				0

            UpperCamelCase_					: Optional[Any]            =				raw_weights.to(getattr(__UpperCamelCase     , __UpperCamelCase						)						)
            current_block_size += weight_size
            total_size += weight_size

      # Add the last block
      UpperCamelCase_					: Tuple            =				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
      UpperCamelCase_					: Any            =				{}
      UpperCamelCase_					: Union[str, Any]            =				{}
      for idx, shard in enumerate(__UpperCamelCase						):
            UpperCamelCase_					: List[str]            =				weights_name.replace(
                """.bin"""     , f"-{idx+1:05d}-of-{len(__UpperCamelCase						):05d}.bin"						)  # len(sharded_state_dicts):05d}
            UpperCamelCase_					: Union[str, Any]            =				os.path.join(__UpperCamelCase     , weights_name.replace(""".bin"""     , f"-{idx+1:05d}-of-???.bin"						)						)
            os.rename(__UpperCamelCase     , os.path.join(__UpperCamelCase     , __UpperCamelCase						)						)
            UpperCamelCase_					: int            =				shard
            for key in shard:
                  UpperCamelCase_					: Optional[Any]            =				shard_file

    # Add the metadata
      UpperCamelCase_					: List[Any]            =				{"""total_size""": total_size}
      UpperCamelCase_					: Optional[int]            =				{"""metadata""": metadata, """weight_map""": weight_map}

      with open(os.path.join(__UpperCamelCase     , __UpperCamelCase						)     , """w"""     , encoding="""utf-8"""						) as f:
            UpperCamelCase_					: Any            =				json.dumps(__UpperCamelCase     , indent=2     , sort_keys=__UpperCamelCase						) + """\n"""
            f.write(__UpperCamelCase						)

      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 				snake_case       (  )							->					Optional[Any]:





      """simple docstring"""

      from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration, TaTokenizer

      UpperCamelCase_					: List[Any]            =				SwitchTransformersConfig.from_pretrained("""google/switch-base-8"""						)
      config.save_pretrained("""/home/arthur_huggingface_co/transformers/switch_converted"""						)
      UpperCamelCase_					: Optional[Any]            =				SwitchTransformersForConditionalGeneration.from_pretrained(
          """/home/arthur_huggingface_co/transformers/switch_converted"""     , device_map="""auto"""						)

      UpperCamelCase_					: Optional[Any]            =				TaTokenizer.from_pretrained("""t5-small"""						)
      UpperCamelCase_					: int            =				"""A <extra_id_0> walks into a bar a orders a <extra_id_1> with <extra_id_2> pinch of <extra_id_3>."""

      UpperCamelCase_					: str            =				tokenizer(__UpperCamelCase     , return_tensors="""pt"""						).input_ids
      UpperCamelCase_					: Dict            =				model.generate(__UpperCamelCase     , decoder_start_token_id=0						)
      print(tokenizer.decode(out[0]						)						)











import unittest

from transformers import MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING, is_vision_available
from transformers.pipelines import pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_vision_available():
							from PIL import Image
else:


							class   __lowerCAmelCase  :
												"""simple docstring"""






												@staticmethod
												def      _a				(  *_snake_case       :							Any					,       **_snake_case       :							Optional[int]       ):






															"""simple docstring"""


															pass


@is_pipeline_test
@require_torch
@require_vision
class   __lowerCAmelCase  (		unittest.TestCase   ):
					"""simple docstring"""






					A__					:		Union[str, Any]      							=     MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING



					def      _a				(  self       :							List[Any]					,       _snake_case       :							Union[str, Any]					,       _snake_case       :							Tuple					,       _snake_case       :							Union[str, Any]       ):






								"""simple docstring"""


								A__	  =     pipeline('visual-question-answering'					,       model='hf-internal-testing/tiny-vilt-random-vqa'       )
								A__	  =     [
								    {
								        'image': Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'       ),
								        'question': 'How many cats are there?',
								    },
								    {
								        'image': './tests/fixtures/tests_samples/COCO/000000039769.png',
								        'question': 'How many cats are there?',
								    },
								]
								return vqa_pipeline, examples



					def      _a				(  self       :							Optional[Any]					,       _snake_case       :							Union[str, Any]					,       _snake_case       :							List[str]       ):






								"""simple docstring"""


								A__	  =     vqa_pipeline(_snake_case					,       top_k=1       )
								self.assertEqual(
								    _snake_case					,       [
								        [{'score': ANY(_snake_case       ), 'answer': ANY(_snake_case       )}],
								        [{'score': ANY(_snake_case       ), 'answer': ANY(_snake_case       )}],
								    ]					,       )



					@require_torch
					def      _a				(  self       :							Any       ):






								"""simple docstring"""


								A__	  =     pipeline('visual-question-answering'					,       model='hf-internal-testing/tiny-vilt-random-vqa'       )
								A__	  =     './tests/fixtures/tests_samples/COCO/000000039769.png'
								A__	  =     'How many cats are there?'

								A__	  =     vqa_pipeline(image=_snake_case					,       question='How many cats are there?'					,       top_k=2       )
								self.assertEqual(
								    _snake_case					,       [{'score': ANY(_snake_case       ), 'answer': ANY(_snake_case       )}, {'score': ANY(_snake_case       ), 'answer': ANY(_snake_case       )}]       )

								A__	  =     vqa_pipeline({'image': image, 'question': question}					,       top_k=2       )
								self.assertEqual(
								    _snake_case					,       [{'score': ANY(_snake_case       ), 'answer': ANY(_snake_case       )}, {'score': ANY(_snake_case       ), 'answer': ANY(_snake_case       )}]       )



					@slow
					@require_torch
					def      _a				(  self       :							Any       ):






								"""simple docstring"""


								A__	  =     pipeline('visual-question-answering'					,       model='dandelin/vilt-b32-finetuned-vqa'       )
								A__	  =     './tests/fixtures/tests_samples/COCO/000000039769.png'
								A__	  =     'How many cats are there?'

								A__	  =     vqa_pipeline(image=_snake_case					,       question=_snake_case					,       top_k=2       )
								self.assertEqual(
								    nested_simplify(_snake_case					,       decimals=4       )					,       [{'score': 0.8799, 'answer': '2'}, {'score': 0.296, 'answer': '1'}]       )

								A__	  =     vqa_pipeline({'image': image, 'question': question}					,       top_k=2       )
								self.assertEqual(
								    nested_simplify(_snake_case					,       decimals=4       )					,       [{'score': 0.8799, 'answer': '2'}, {'score': 0.296, 'answer': '1'}]       )

								A__	  =     vqa_pipeline(
								    [{'image': image, 'question': question}, {'image': image, 'question': question}]					,       top_k=2       )
								self.assertEqual(
								    nested_simplify(_snake_case					,       decimals=4       )					,       [[{'score': 0.8799, 'answer': '2'}, {'score': 0.296, 'answer': '1'}]] * 2					,       )



					@require_tf
					@unittest.skip('Visual question answering not implemented in TF'       )
					def      _a				(  self       :							Dict       ):






								"""simple docstring"""


								pass




