


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      _a					( SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE	):


		"""simple docstring"""




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

		return flax_key_tuple, flax_tensor
def      _a					( SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE	):


		"""simple docstring"""




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

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

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

		return curr_real_layer_name, split_layer, content
def      _a					( SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE	):


		"""simple docstring"""




		lowercase__				= rename_keys(SCREAMING_SNAKE_CASE	)
		lowercase__				= {}
		for k, v in current_block.items():
				lowercase__				= v
		lowercase__				= new_current_block
		torch.save(SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE	)
def      _a					( SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE = WEIGHTS_NAME	):


		"""simple docstring"""




		lowercase__				= convert_file_size_to_int(SCREAMING_SNAKE_CASE	)
		lowercase__				= []
		lowercase__				= {}
		lowercase__				= 0
		lowercase__				= 0

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

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

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

				# use the renaming pattern from the small conversion scripts
				lowercase__	,						lowercase__				= rename_base_flax_keys(tuple(key.split('''/'''	)	)		,							SCREAMING_SNAKE_CASE	)
				lowercase__				= '''/'''.join(SCREAMING_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:
						lowercase__				= os.path.join(
						    SCREAMING_SNAKE_CASE		,							weights_name.replace('''.bin'''		,							f'-{len(SCREAMING_SNAKE_CASE	)+1:05d}-of-???.bin'	)	)
						rename_and_save_block(SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE	)
						sharded_state_dicts.append(current_block.keys()	)
						del current_block
						lowercase__				= {}
						lowercase__				= 0

				lowercase__				= raw_weights.to(getattr(SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE	)	)
				current_block_size += weight_size
				total_size += weight_size

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

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

		# Otherwise, let's build the index
		lowercase__				= {}
		lowercase__				= {}
		for idx, shard in enumerate(SCREAMING_SNAKE_CASE	):
				lowercase__				= weights_name.replace(
				    '''.bin'''		,							f'-{idx+1:05d}-of-{len(SCREAMING_SNAKE_CASE	):05d}.bin'	)  # len(sharded_state_dicts):05d}
				lowercase__				= os.path.join(SCREAMING_SNAKE_CASE		,							weights_name.replace('''.bin'''		,							f'-{idx+1:05d}-of-???.bin'	)	)
				os.rename(SCREAMING_SNAKE_CASE		,							os.path.join(SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE	)	)
				lowercase__				= shard
				for key in shard:
						lowercase__				= shard_file

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

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


		"""simple docstring"""




		from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration, TaTokenizer

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

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

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









import unittest

import torch

from diffusers import VQModel
from diffusers.utils import floats_tensor, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin


enable_full_determinism()

class 							__SCREAMING_SNAKE_CASE       (  _a ,		_a ,		unittest.TestCase							):
							snake_case  :   List[str]      =							VQModel
							snake_case  :   Dict      =							"""sample"""






							@property
							def 					_lowerCamelCase		(    self	,			__lowerCAmelCase=(32, 32)	):
														UpperCamelCase__					  =      4
														UpperCamelCase__					  =      3

														UpperCamelCase__					  =      floats_tensor((batch_size, num_channels) + sizes	).to(__lowerCAmelCase	)

														return {"sample": image}






							@property
							def 					_lowerCamelCase		(    self	):
														return (3, 32, 32)






							@property
							def 					_lowerCamelCase		(    self	):
														return (3, 32, 32)






							def 					_lowerCamelCase		(    self	):
														UpperCamelCase__					  =      {
														    """block_out_channels""": [32, 64],
														    """in_channels""": 3,
														    """out_channels""": 3,
														    """down_block_types""": ["""DownEncoderBlock2D""", """DownEncoderBlock2D"""],
														    """up_block_types""": ["""UpDecoderBlock2D""", """UpDecoderBlock2D"""],
														    """latent_channels""": 3,
														}
														UpperCamelCase__					  =      self.dummy_input
														return init_dict, inputs_dict






							def 					_lowerCamelCase		(    self	):
														pass






							def 					_lowerCamelCase		(    self	):
														pass






							def 					_lowerCamelCase		(    self	):
														UpperCamelCase__  ,  UpperCamelCase__					  =      VQModel.from_pretrained("""fusing/vqgan-dummy"""	,			output_loading_info=__lowerCAmelCase	)
														self.assertIsNotNone(__lowerCAmelCase	)
														self.assertEqual(len(loading_info["""missing_keys"""]	)	,			0	)

														model.to(__lowerCAmelCase	)
														UpperCamelCase__					  =      model(**self.dummy_input	)

														assert image is not None, "Make sure output is not None"







							def 					_lowerCamelCase		(    self	):
														UpperCamelCase__					  =      VQModel.from_pretrained("""fusing/vqgan-dummy"""	)
														model.to(__lowerCAmelCase	).eval()

														torch.manual_seed(0	)
														if torch.cuda.is_available():
																					torch.cuda.manual_seed_all(0	)

														UpperCamelCase__					  =      torch.randn(1	,			model.config.in_channels	,			model.config.sample_size	,			model.config.sample_size	)
														UpperCamelCase__					  =      image.to(__lowerCAmelCase	)
														with torch.no_grad():
																					UpperCamelCase__					  =      model(__lowerCAmelCase	).sample

														UpperCamelCase__					  =      output[0, -1, -3:, -3:].flatten().cpu()
														# fmt: off
														UpperCamelCase__					  =      torch.tensor([-0.0153, -0.4044, -0.1880, -0.5161, -0.2418, -0.4072, -0.1612, -0.0633, -0.0143]	)
														# fmt: on
														self.assertTrue(torch.allclose(__lowerCAmelCase	,			__lowerCAmelCase	,			atol=1E-3	)	)




