


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_   )						->str:
   if flax_key_tuple[-1] == "kernel" and flax_tensor.ndim == 3:
      # expert layer
      UpperCAmelCase							=     flax_key_tuple[:-1] + ("""weight""",)
      UpperCAmelCase							=     torch.permute(lowerCAmelCase_  ,						(0, 2, 1)   )
   elif flax_key_tuple[-1] == "kernel" and ".".join(lowerCAmelCase_   ):
      # 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   _UpperCamelCase				(						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_   )						->Dict:
   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   _UpperCamelCase				(						lowerCAmelCase_  ,						lowerCAmelCase_   )						->List[str]:
   UpperCAmelCase							=     rename_keys(lowerCAmelCase_   )
   UpperCAmelCase							=     {}
   for k, v in current_block.items():
      UpperCAmelCase							=     v
   UpperCAmelCase							=     new_current_block
   torch.save(lowerCAmelCase_  ,						lowerCAmelCase_   )
def   _UpperCamelCase				(						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_ = WEIGHTS_NAME   )						->int:
   UpperCAmelCase							=     convert_file_size_to_int(lowerCAmelCase_   )
   UpperCAmelCase							=     []
   UpperCAmelCase							=     {}
   UpperCAmelCase							=     0
   UpperCAmelCase							=     0

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

   UpperCAmelCase							=     {}
   for layer in checkpoint_info.keys():
      UpperCAmelCase				,  UpperCAmelCase				,  UpperCAmelCase							=     get_key_and_tensorstore_dict(
          lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_   )
      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(lowerCAmelCase_   )
      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("""/"""   )   )  ,						lowerCAmelCase_   )
      UpperCAmelCase							=     """/""".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:
         UpperCAmelCase							=     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
         UpperCAmelCase							=     {}
         UpperCAmelCase							=     0

      UpperCAmelCase							=     raw_weights.to(getattr(lowerCAmelCase_  ,						lowerCAmelCase_   )   )
      current_block_size += weight_size
      total_size += weight_size

   # Add the last block
   UpperCAmelCase							=     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
   UpperCAmelCase							=     {}
   UpperCAmelCase							=     {}
   for idx, shard in enumerate(lowerCAmelCase_   ):
      UpperCAmelCase							=     weights_name.replace(
          """.bin"""  ,						F"""-{idx+1:05d}-of-{len(lowerCAmelCase_   ):05d}.bin"""   )  # len(sharded_state_dicts):05d}
      UpperCAmelCase							=     os.path.join(lowerCAmelCase_  ,						weights_name.replace(""".bin"""  ,						F"""-{idx+1:05d}-of-???.bin"""   )   )
      os.rename(lowerCAmelCase_  ,						os.path.join(lowerCAmelCase_  ,						lowerCAmelCase_   )   )
      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(lowerCAmelCase_  ,						lowerCAmelCase_   )  ,						"""w"""  ,						encoding="""utf-8"""   ) as f:
      UpperCAmelCase							=     json.dumps(lowerCAmelCase_  ,						indent=2  ,						sort_keys=lowerCAmelCase_   ) + """\n"""
      f.write(lowerCAmelCase_   )

   return metadata, index


if __name__ == "__main__":
 __a     =			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.""",
 )
 __a     =			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]:
   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(lowerCAmelCase_  ,						return_tensors="""pt"""   ).input_ids
   UpperCAmelCase							=     model.generate(lowerCAmelCase_  ,						decoder_start_token_id=0   )
   print(tokenizer.decode(out[0]   )   )







import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
 from PIL import Image

 from transformers import OwlViTImageProcessor, OwlViTProcessor






@require_vision
class   __lowercase				(  unittest.TestCase  ):





      def        _lowercase				(		self						:					List[Any]       )    ->   Dict:






         """simple docstring"""







         UpperCAmelCase							=     tempfile.mkdtemp()

         # fmt: off
         UpperCAmelCase							=     ["""""", """l""", """o""", """w""", """e""", """r""", """s""", """t""", """i""", """d""", """n""", """lo""", """l</w>""", """w</w>""", """r</w>""", """t</w>""", """low</w>""", """er</w>""", """lowest</w>""", """newer</w>""", """wider""", """<unk>""", """<|startoftext|>""", """<|endoftext|>"""]
         # fmt: on
         UpperCAmelCase							=     dict(zip(__lowerCamelCase					,       range(len(__lowerCamelCase       )       )       )       )
         UpperCAmelCase							=     ["""#version: 0.2""", """l o""", """lo w</w>""", """e r</w>""", """"""]
         UpperCAmelCase							=     {"""unk_token""": """<unk>"""}

         UpperCAmelCase							=     os.path.join(self.tmpdirname					,       VOCAB_FILES_NAMES["""vocab_file"""]       )
         UpperCAmelCase							=     os.path.join(self.tmpdirname					,       VOCAB_FILES_NAMES["""merges_file"""]       )
         with open(self.vocab_file					,       """w"""					,       encoding="""utf-8"""       ) as fp:
            fp.write(json.dumps(__lowerCamelCase       ) + """\n"""       )
         with open(self.merges_file					,       """w"""					,       encoding="""utf-8"""       ) as fp:
            fp.write("""\n""".join(__lowerCamelCase       )       )

         UpperCAmelCase							=     {
             """do_resize""": True,
             """size""": 2_0,
             """do_center_crop""": True,
             """crop_size""": 1_8,
             """do_normalize""": True,
             """image_mean""": [0.48_145_466, 0.4_578_275, 0.40_821_073],
             """image_std""": [0.26_862_954, 0.26_130_258, 0.27_577_711],
         }
         UpperCAmelCase							=     os.path.join(self.tmpdirname					,       __lowerCamelCase       )
         with open(self.image_processor_file					,       """w"""					,       encoding="""utf-8"""       ) as fp:
            json.dump(__lowerCamelCase					,       __lowerCamelCase       )





      def        _lowercase				(		self						:					List[Any]					,       **__lowerCamelCase						:					Union[str, Any]       )    ->   int:






         """simple docstring"""







         return CLIPTokenizer.from_pretrained(self.tmpdirname					,       pad_token="""!"""					,       **__lowerCamelCase       )





      def        _lowercase				(		self						:					Optional[Any]					,       **__lowerCamelCase						:					List[str]       )    ->   str:






         """simple docstring"""







         return CLIPTokenizerFast.from_pretrained(self.tmpdirname					,       pad_token="""!"""					,       **__lowerCamelCase       )





      def        _lowercase				(		self						:					Union[str, Any]					,       **__lowerCamelCase						:					Optional[Any]       )    ->   Optional[int]:






         """simple docstring"""







         return OwlViTImageProcessor.from_pretrained(self.tmpdirname					,       **__lowerCamelCase       )





      def        _lowercase				(		self						:					Any       )    ->   List[str]:






         """simple docstring"""







         shutil.rmtree(self.tmpdirname       )





      def        _lowercase				(		self						:					List[Any]       )    ->   Optional[Any]:






         """simple docstring"""







         UpperCAmelCase							=     [np.random.randint(2_5_5					,       size=(3, 3_0, 4_0_0)					,       dtype=np.uinta       )]

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

         return image_inputs





      def        _lowercase				(		self						:					int       )    ->   Optional[int]:






         """simple docstring"""







         UpperCAmelCase							=     self.get_tokenizer()
         UpperCAmelCase							=     self.get_rust_tokenizer()
         UpperCAmelCase							=     self.get_image_processor()

         UpperCAmelCase							=     OwlViTProcessor(tokenizer=__lowerCamelCase					,       image_processor=__lowerCamelCase       )
         processor_slow.save_pretrained(self.tmpdirname       )
         UpperCAmelCase							=     OwlViTProcessor.from_pretrained(self.tmpdirname					,       use_fast=__lowerCamelCase       )

         UpperCAmelCase							=     OwlViTProcessor(tokenizer=__lowerCamelCase					,       image_processor=__lowerCamelCase       )
         processor_fast.save_pretrained(self.tmpdirname       )
         UpperCAmelCase							=     OwlViTProcessor.from_pretrained(self.tmpdirname       )

         self.assertEqual(processor_slow.tokenizer.get_vocab()					,       tokenizer_slow.get_vocab()       )
         self.assertEqual(processor_fast.tokenizer.get_vocab()					,       tokenizer_fast.get_vocab()       )
         self.assertEqual(tokenizer_slow.get_vocab()					,       tokenizer_fast.get_vocab()       )
         self.assertIsInstance(processor_slow.tokenizer					,       __lowerCamelCase       )
         self.assertIsInstance(processor_fast.tokenizer					,       __lowerCamelCase       )

         self.assertEqual(processor_slow.image_processor.to_json_string()					,       image_processor.to_json_string()       )
         self.assertEqual(processor_fast.image_processor.to_json_string()					,       image_processor.to_json_string()       )
         self.assertIsInstance(processor_slow.image_processor					,       __lowerCamelCase       )
         self.assertIsInstance(processor_fast.image_processor					,       __lowerCamelCase       )





      def        _lowercase				(		self						:					str       )    ->   Dict:






         """simple docstring"""







         UpperCAmelCase							=     OwlViTProcessor(tokenizer=self.get_tokenizer()					,       image_processor=self.get_image_processor()       )
         processor.save_pretrained(self.tmpdirname       )

         UpperCAmelCase							=     self.get_tokenizer(bos_token="""(BOS)"""					,       eos_token="""(EOS)"""       )
         UpperCAmelCase							=     self.get_image_processor(do_normalize=__lowerCamelCase       )

         UpperCAmelCase							=     OwlViTProcessor.from_pretrained(
             self.tmpdirname					,       bos_token="""(BOS)"""					,       eos_token="""(EOS)"""					,       do_normalize=__lowerCamelCase       )

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

         self.assertEqual(processor.image_processor.to_json_string()					,       image_processor_add_kwargs.to_json_string()       )
         self.assertIsInstance(processor.image_processor					,       __lowerCamelCase       )





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






         """simple docstring"""







         UpperCAmelCase							=     self.get_image_processor()
         UpperCAmelCase							=     self.get_tokenizer()

         UpperCAmelCase							=     OwlViTProcessor(tokenizer=__lowerCamelCase					,       image_processor=__lowerCamelCase       )

         UpperCAmelCase							=     self.prepare_image_inputs()

         UpperCAmelCase							=     image_processor(__lowerCamelCase					,       return_tensors="""np"""       )
         UpperCAmelCase							=     processor(images=__lowerCamelCase					,       return_tensors="""np"""       )

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





      def        _lowercase				(		self						:					Optional[int]       )    ->   Any:






         """simple docstring"""







         UpperCAmelCase							=     self.get_image_processor()
         UpperCAmelCase							=     self.get_tokenizer()

         UpperCAmelCase							=     OwlViTProcessor(tokenizer=__lowerCamelCase					,       image_processor=__lowerCamelCase       )

         UpperCAmelCase							=     """lower newer"""

         UpperCAmelCase							=     processor(text=__lowerCamelCase					,       return_tensors="""np"""       )

         UpperCAmelCase							=     tokenizer(__lowerCamelCase					,       return_tensors="""np"""       )

         for key in encoded_tok.keys():
            self.assertListEqual(encoded_tok[key][0].tolist()					,       encoded_processor[key][0].tolist()       )





      def        _lowercase				(		self						:					Union[str, Any]       )    ->   List[str]:






         """simple docstring"""







         UpperCAmelCase							=     self.get_image_processor()
         UpperCAmelCase							=     self.get_tokenizer()

         UpperCAmelCase							=     OwlViTProcessor(tokenizer=__lowerCamelCase					,       image_processor=__lowerCamelCase       )

         UpperCAmelCase							=     """lower newer"""
         UpperCAmelCase							=     self.prepare_image_inputs()

         UpperCAmelCase							=     processor(text=__lowerCamelCase					,       images=__lowerCamelCase       )

         self.assertListEqual(list(inputs.keys()       )					,       ["""input_ids""", """attention_mask""", """pixel_values"""]       )

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





      def        _lowercase				(		self						:					Optional[int]       )    ->   Any:






         """simple docstring"""







         UpperCAmelCase							=     """google/owlvit-base-patch32"""
         UpperCAmelCase							=     OwlViTProcessor.from_pretrained(__lowerCamelCase       )

         UpperCAmelCase							=     ["""cat""", """nasa badge"""]
         UpperCAmelCase							=     processor(text=__lowerCamelCase       )

         UpperCAmelCase							=     1_6
         self.assertListEqual(list(inputs.keys()       )					,       ["""input_ids""", """attention_mask"""]       )
         self.assertEqual(inputs["""input_ids"""].shape					,       (2, seq_length)       )

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





      def        _lowercase				(		self						:					Any       )    ->   int:






         """simple docstring"""







         UpperCAmelCase							=     """google/owlvit-base-patch32"""
         UpperCAmelCase							=     OwlViTProcessor.from_pretrained(__lowerCamelCase       )

         UpperCAmelCase							=     [["""cat""", """nasa badge"""], ["""person"""]]
         UpperCAmelCase							=     processor(text=__lowerCamelCase       )

         UpperCAmelCase							=     1_6
         UpperCAmelCase							=     len(__lowerCamelCase       )
         UpperCAmelCase							=     max([len(__lowerCamelCase       ) for texts in input_texts]       )

         self.assertListEqual(list(inputs.keys()       )					,       ["""input_ids""", """attention_mask"""]       )
         self.assertEqual(inputs["""input_ids"""].shape					,       (batch_size * num_max_text_queries, seq_length)       )

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





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






         """simple docstring"""







         UpperCAmelCase							=     """google/owlvit-base-patch32"""
         UpperCAmelCase							=     OwlViTProcessor.from_pretrained(__lowerCamelCase       )

         UpperCAmelCase							=     ["""cat""", """nasa badge"""]
         UpperCAmelCase							=     processor(text=__lowerCamelCase       )

         UpperCAmelCase							=     1_6
         UpperCAmelCase							=     inputs["""input_ids"""]
         UpperCAmelCase							=     [
             [4_9_4_0_6, 2_3_6_8, 4_9_4_0_7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
             [4_9_4_0_6, 6_8_4_1, 1_1_3_0_1, 4_9_4_0_7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
         ]

         self.assertListEqual(list(inputs.keys()       )					,       ["""input_ids""", """attention_mask"""]       )
         self.assertEqual(inputs["""input_ids"""].shape					,       (2, seq_length)       )
         self.assertListEqual(list(input_ids[0]       )					,       predicted_ids[0]       )
         self.assertListEqual(list(input_ids[1]       )					,       predicted_ids[1]       )





      def        _lowercase				(		self						:					List[Any]       )    ->   Tuple:






         """simple docstring"""







         UpperCAmelCase							=     self.get_image_processor()
         UpperCAmelCase							=     self.get_tokenizer()

         UpperCAmelCase							=     OwlViTProcessor(tokenizer=__lowerCamelCase					,       image_processor=__lowerCamelCase       )

         UpperCAmelCase							=     self.prepare_image_inputs()
         UpperCAmelCase							=     self.prepare_image_inputs()

         UpperCAmelCase							=     processor(images=__lowerCamelCase					,       query_images=__lowerCamelCase       )

         self.assertListEqual(list(inputs.keys()       )					,       ["""query_pixel_values""", """pixel_values"""]       )

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





      def        _lowercase				(		self						:					Tuple       )    ->   Any:






         """simple docstring"""







         UpperCAmelCase							=     self.get_image_processor()
         UpperCAmelCase							=     self.get_tokenizer()

         UpperCAmelCase							=     OwlViTProcessor(tokenizer=__lowerCamelCase					,       image_processor=__lowerCamelCase       )

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

         UpperCAmelCase							=     processor.batch_decode(__lowerCamelCase       )
         UpperCAmelCase							=     tokenizer.batch_decode(__lowerCamelCase       )

         self.assertListEqual(__lowerCamelCase					,       __lowerCamelCase       )



