






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

						return flax_key_tuple, flax_tensor
def 						A__			(				__lowerCAmelCase		:			str   ,    __lowerCAmelCase		:			Tuple   ,    __lowerCAmelCase		:			Union[str, Any]						):
						if "metadata" in layer:
												lowerCamelCase__         =  layer.split("""metadata"""						)
												lowerCamelCase__         =  """""".join(split_layer[0]						)[:-1]
												lowerCamelCase__         =  [tuple(("""metadata""" + split_layer[1]).split("""/"""						)						)]
						elif "kvstore" in layer:
												lowerCamelCase__         =  layer.split("""kvstore"""						)
												lowerCamelCase__         =  """""".join(split_layer[0]						)[:-1]
												lowerCamelCase__         =  [tuple(("""kvstore""" + split_layer[1]).split("""/"""						)						)]

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

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

						return curr_real_layer_name, split_layer, content
def 						A__			(				__lowerCAmelCase		:			Any   ,    __lowerCAmelCase		:			List[Any]						):
						lowerCamelCase__         =  rename_keys(__A						)
						lowerCamelCase__         =  {}
						for k, v in current_block.items():
												lowerCamelCase__         =  v
						lowerCamelCase__         =  new_current_block
						torch.save(__A   ,    __A						)
def 						A__			(				__lowerCAmelCase		:			Dict   ,    __lowerCAmelCase		:			Optional[Any]   ,    __lowerCAmelCase		:			List[Any]   ,    __lowerCAmelCase		:			Any   ,    __lowerCAmelCase		:			str = WEIGHTS_NAME						):
						lowerCamelCase__         =  convert_file_size_to_int(__A						)
						lowerCamelCase__         =  []
						lowerCamelCase__         =  {}
						lowerCamelCase__         =  0
						lowerCamelCase__         =  0

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

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

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

												# use the renaming pattern from the small conversion scripts
												lowerCamelCase__         =  rename_base_flax_keys(tuple(key.split("""/"""						)						)   ,    __A						)
												lowerCamelCase__         =  """/""".join(__A						)

												# If this weight is going to tip up over the maximal size, we split.
												if current_block_size + weight_size > max_shard_size:
																		lowerCamelCase__         =  os.path.join(
																		    __A   ,    weights_name.replace(""".bin"""   ,    F'''-{len(__A						)+1:05d}-of-???.bin'''						)						)
																		rename_and_save_block(__A   ,    __A						)
																		sharded_state_dicts.append(current_block.keys()						)
																		del current_block
																		lowerCamelCase__         =  {}
																		lowerCamelCase__         =  0

												lowerCamelCase__         =  raw_weights.to(getattr(__A   ,    __A						)						)
												current_block_size += weight_size
												total_size += weight_size

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

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

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

    # Add the metadata
						lowerCamelCase__         =  {"""total_size""": total_size}
						lowerCamelCase__         =  {"""metadata""": metadata, """weight_map""": weight_map}

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

						return metadata, index


if __name__ == "__main__":
							UpperCamelCase    :      Dict  			=		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    :      str  			=		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__			(				):
						from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration, TaTokenizer

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

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

						lowerCamelCase__         =  tokenizer(__A   ,    return_tensors="""pt"""						).input_ids
						lowerCamelCase__         =  model.generate(__A   ,    decoder_start_token_id=0						)
						print(tokenizer.decode(out[0]						)						)









'''simple docstring'''







import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
							import torch

if is_vision_available():
							from PIL import Image

							from transformers import LevitImageProcessor




class 		UpperCamelCase__    (unittest.TestCase			):




		'''simple docstring'''





		def __init__(      self   ,_lowerCAmelCase   ,_lowerCAmelCase=7   ,_lowerCAmelCase=3   ,_lowerCAmelCase=18   ,_lowerCAmelCase=30   ,_lowerCAmelCase=4_00   ,_lowerCAmelCase=True   ,_lowerCAmelCase=None   ,_lowerCAmelCase=True   ,_lowerCAmelCase=None   ,_lowerCAmelCase=True   ,_lowerCAmelCase=[0.5, 0.5, 0.5]   ,_lowerCAmelCase=[0.5, 0.5, 0.5]   ,):
								lowerCamelCase__         =  size if size is not None else {"""shortest_edge""": 18}
								lowerCamelCase__         =  crop_size if crop_size is not None else {"""height""": 18, """width""": 18}
								lowerCamelCase__         =  parent
								lowerCamelCase__         =  batch_size
								lowerCamelCase__         =  num_channels
								lowerCamelCase__         =  image_size
								lowerCamelCase__         =  min_resolution
								lowerCamelCase__         =  max_resolution
								lowerCamelCase__         =  do_resize
								lowerCamelCase__         =  size
								lowerCamelCase__         =  do_center_crop
								lowerCamelCase__         =  crop_size
								lowerCamelCase__         =  do_normalize
								lowerCamelCase__         =  image_mean
								lowerCamelCase__         =  image_std






		def 			UpperCamelCase_					(      self       ):
								return {
								    "image_mean": self.image_mean,
								    "image_std": self.image_std,
								    "do_normalize": self.do_normalize,
								    "do_resize": self.do_resize,
								    "do_center_crop": self.do_center_crop,
								    "size": self.size,
								    "crop_size": self.crop_size,
								}




@require_torch
@require_vision
class 		UpperCamelCase__    (a   ,unittest.TestCase			):




		'''simple docstring'''





		_UpperCamelCase				  =					LevitImageProcessor if is_vision_available() else None






		def 			UpperCamelCase_					(      self       ):
								lowerCamelCase__         =  LevitImageProcessingTester(self       )






		@property
		def 			UpperCamelCase_					(      self       ):
								return self.image_processor_tester.prepare_image_processor_dict()






		def 			UpperCamelCase_					(      self       ):
								lowerCamelCase__         =  self.image_processing_class(**self.image_processor_dict       )
								self.assertTrue(hasattr(_lowerCAmelCase   ,"""image_mean"""       )       )
								self.assertTrue(hasattr(_lowerCAmelCase   ,"""image_std"""       )       )
								self.assertTrue(hasattr(_lowerCAmelCase   ,"""do_normalize"""       )       )
								self.assertTrue(hasattr(_lowerCAmelCase   ,"""do_resize"""       )       )
								self.assertTrue(hasattr(_lowerCAmelCase   ,"""do_center_crop"""       )       )
								self.assertTrue(hasattr(_lowerCAmelCase   ,"""size"""       )       )






		def 			UpperCamelCase_					(      self       ):
								lowerCamelCase__         =  self.image_processing_class.from_dict(self.image_processor_dict       )
								self.assertEqual(image_processor.size   ,{"""shortest_edge""": 18}       )
								self.assertEqual(image_processor.crop_size   ,{"""height""": 18, """width""": 18}       )

								lowerCamelCase__         =  self.image_processing_class.from_dict(self.image_processor_dict   ,size=42   ,crop_size=84       )
								self.assertEqual(image_processor.size   ,{"""shortest_edge""": 42}       )
								self.assertEqual(image_processor.crop_size   ,{"""height""": 84, """width""": 84}       )






		def 			UpperCamelCase_					(      self       ):
								pass






		def 			UpperCamelCase_					(      self       ):
								# Initialize image_processing
								lowerCamelCase__         =  self.image_processing_class(**self.image_processor_dict       )
								# create random PIL images
								lowerCamelCase__         =  prepare_image_inputs(self.image_processor_tester   ,equal_resolution=_lowerCAmelCase       )
								for image in image_inputs:
														self.assertIsInstance(_lowerCAmelCase   ,Image.Image       )

								# Test not batched input
								lowerCamelCase__         =  image_processing(image_inputs[0]   ,return_tensors="""pt"""       ).pixel_values
								self.assertEqual(
								    encoded_images.shape   ,(
								        1,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.crop_size["""height"""],
								        self.image_processor_tester.crop_size["""width"""],
								    )   ,)

								# Test batched
								lowerCamelCase__         =  image_processing(_lowerCAmelCase   ,return_tensors="""pt"""       ).pixel_values
								self.assertEqual(
								    encoded_images.shape   ,(
								        self.image_processor_tester.batch_size,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.crop_size["""height"""],
								        self.image_processor_tester.crop_size["""width"""],
								    )   ,)






		def 			UpperCamelCase_					(      self       ):
								# Initialize image_processing
								lowerCamelCase__         =  self.image_processing_class(**self.image_processor_dict       )
								# create random numpy tensors
								lowerCamelCase__         =  prepare_image_inputs(self.image_processor_tester   ,equal_resolution=_lowerCAmelCase   ,numpify=_lowerCAmelCase       )
								for image in image_inputs:
														self.assertIsInstance(_lowerCAmelCase   ,np.ndarray       )

								# Test not batched input
								lowerCamelCase__         =  image_processing(image_inputs[0]   ,return_tensors="""pt"""       ).pixel_values
								self.assertEqual(
								    encoded_images.shape   ,(
								        1,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.crop_size["""height"""],
								        self.image_processor_tester.crop_size["""width"""],
								    )   ,)

								# Test batched
								lowerCamelCase__         =  image_processing(_lowerCAmelCase   ,return_tensors="""pt"""       ).pixel_values
								self.assertEqual(
								    encoded_images.shape   ,(
								        self.image_processor_tester.batch_size,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.crop_size["""height"""],
								        self.image_processor_tester.crop_size["""width"""],
								    )   ,)






		def 			UpperCamelCase_					(      self       ):
								# Initialize image_processing
								lowerCamelCase__         =  self.image_processing_class(**self.image_processor_dict       )
								# create random PyTorch tensors
								lowerCamelCase__         =  prepare_image_inputs(self.image_processor_tester   ,equal_resolution=_lowerCAmelCase   ,torchify=_lowerCAmelCase       )
								for image in image_inputs:
														self.assertIsInstance(_lowerCAmelCase   ,torch.Tensor       )

								# Test not batched input
								lowerCamelCase__         =  image_processing(image_inputs[0]   ,return_tensors="""pt"""       ).pixel_values
								self.assertEqual(
								    encoded_images.shape   ,(
								        1,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.crop_size["""height"""],
								        self.image_processor_tester.crop_size["""width"""],
								    )   ,)

								# Test batched
								lowerCamelCase__         =  image_processing(_lowerCAmelCase   ,return_tensors="""pt"""       ).pixel_values
								self.assertEqual(
								    encoded_images.shape   ,(
								        self.image_processor_tester.batch_size,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.crop_size["""height"""],
								        self.image_processor_tester.crop_size["""width"""],
								    )   ,)

