


'''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 				lowerCamelCase__				(	A       :      List[str]				,       A       :      Union[str, Any]					):




						'''simple docstring'''






						if flax_key_tuple[-1] == "kernel" and flax_tensor.ndim == 3:
												# expert layer
												UpperCAmelCase					    =     flax_key_tuple[:-1] + ('''weight''',)
												UpperCAmelCase					    =     torch.permute(A				,       (0, 2, 1)					)
						elif flax_key_tuple[-1] == "kernel" and ".".join(A					):
												# 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 				lowerCamelCase__				(	A       :      str				,       A       :      Tuple				,       A       :      Any					):




						'''simple docstring'''






						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 				lowerCamelCase__				(	A       :      Any				,       A       :      Dict					):




						'''simple docstring'''






						UpperCAmelCase					    =     rename_keys(A					)
						UpperCAmelCase					    =     {}
						for k, v in current_block.items():
												UpperCAmelCase					    =     v
						UpperCAmelCase					    =     new_current_block
						torch.save(A				,       A					)


def 				lowerCamelCase__				(	A       :      int				,       A       :      Optional[Any]				,       A       :      int				,       A       :      Optional[Any]				,       A       :      str = WEIGHTS_NAME					):




						'''simple docstring'''






						UpperCAmelCase					    =     convert_file_size_to_int(A					)
						UpperCAmelCase					    =     []
						UpperCAmelCase					    =     {}
						UpperCAmelCase					    =     0
						UpperCAmelCase					    =     0

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

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

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

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

						return metadata, index


if __name__ == "__main__":
	_lowercase      :      Union[str, 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.""",
	)
	_lowercase      :      List[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 				lowerCamelCase__				(	):




						'''simple docstring'''






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









'''simple docstring'''



import copy
import inspect
import unittest

from transformers import AutoBackbone
from transformers.configuration_utils import PretrainedConfig
from transformers.testing_utils import require_timm, require_torch, torch_device
from transformers.utils.import_utils import is_torch_available

from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor


if is_torch_available():
	import torch

	from transformers import TimmBackbone, TimmBackboneConfig

from ...test_pipeline_mixin import PipelineTesterMixin






class 	UpperCamelCase__:




				def __init__(		self      :			List[str]				,   lowerCAmelCase      :			int				,   lowerCAmelCase      :			Optional[int]=None				,   lowerCAmelCase      :			Optional[int]=None				,   lowerCAmelCase      :			List[str]=None				,   lowerCAmelCase      :			int="resnet50"				,   lowerCAmelCase      :			Optional[int]=3				,   lowerCAmelCase      :			Union[str, Any]=32				,   lowerCAmelCase      :			List[Any]=3				,   lowerCAmelCase      :			Tuple=True				,   lowerCAmelCase      :			str=True				,   )->					Union[str, Any]:

										"""simple docstring"""




										UpperCAmelCase					    =     parent
										UpperCAmelCase					    =     out_indices if out_indices is not None else [4]
										UpperCAmelCase					    =     stage_names
										UpperCAmelCase					    =     out_features
										UpperCAmelCase					    =     backbone
										UpperCAmelCase					    =     batch_size
										UpperCAmelCase					    =     image_size
										UpperCAmelCase					    =     num_channels
										UpperCAmelCase					    =     use_pretrained_backbone
										UpperCAmelCase					    =     is_training




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

										"""simple docstring"""




										UpperCAmelCase					    =     floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]				)
										UpperCAmelCase					    =     self.get_config()

										return config, pixel_values




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

										"""simple docstring"""




										return TimmBackboneConfig(
										    image_size=self.image_size				,   num_channels=self.num_channels				,   out_features=self.out_features				,   out_indices=self.out_indices				,   stage_names=self.stage_names				,   use_pretrained_backbone=self.use_pretrained_backbone				,   backbone=self.backbone				,   )




				def 					a__(		self      :			Dict				,   lowerCAmelCase      :			str				,   lowerCAmelCase      :			Dict				)->					Tuple:

										"""simple docstring"""




										UpperCAmelCase					    =     TimmBackbone(config=lowerCAmelCase				)
										model.to(lowerCAmelCase				)
										model.eval()
										with torch.no_grad():
																UpperCAmelCase					    =     model(lowerCAmelCase				)
										self.parent.assertEqual(
										    result.feature_map[-1].shape				,   (self.batch_size, model.channels[-1], 14, 14)				,   )





				def 					a__(		self      :			str				)->					Optional[Any]:

										"""simple docstring"""




										UpperCAmelCase					    =     self.prepare_config_and_inputs()
										UpperCAmelCase    ,    UpperCAmelCase					    =     config_and_inputs
										UpperCAmelCase					    =     {'''pixel_values''': pixel_values}
										return config, inputs_dict







@require_torch
@require_timm
class 	UpperCamelCase__(       lowerCAmelCase						,    lowerCAmelCase						,    lowerCAmelCase						,    unittest.TestCase	):
				__magic_name__    :				List[str]  		=	(TimmBackbone,) if is_torch_available() else ()
				__magic_name__    :				Any  		=	{"feature-extraction": TimmBackbone} if is_torch_available() else {}
				__magic_name__    :				Union[str, Any]  		=	False
				__magic_name__    :				int  		=	False
				__magic_name__    :				Tuple  		=	False
				__magic_name__    :				List[str]  		=	False




				def 					a__(		self      :			int				)->					str:

										"""simple docstring"""




										UpperCAmelCase					    =     TimmBackboneModelTester(self				)
										UpperCAmelCase					    =     ConfigTester(self				,   config_class=lowerCAmelCase				,   has_text_modality=lowerCAmelCase				)




				def 					a__(		self      :			List[str]				)->					Dict:

										"""simple docstring"""




										self.config_tester.create_and_test_config_to_json_string()
										self.config_tester.create_and_test_config_to_json_file()
										self.config_tester.create_and_test_config_from_and_save_pretrained()
										self.config_tester.create_and_test_config_with_num_labels()
										self.config_tester.check_config_can_be_init_without_params()
										self.config_tester.check_config_arguments_init()




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

										"""simple docstring"""




										UpperCAmelCase					    =     '''resnet18'''
										UpperCAmelCase					    =     '''microsoft/resnet-18'''

										UpperCAmelCase					    =     AutoBackbone.from_pretrained(lowerCAmelCase				,   use_timm_backbone=lowerCAmelCase				)
										UpperCAmelCase					    =     AutoBackbone.from_pretrained(lowerCAmelCase				)

										self.assertEqual(len(timm_model.out_features				)				,   len(transformers_model.out_features				)				)
										self.assertEqual(len(timm_model.stage_names				)				,   len(transformers_model.stage_names				)				)
										self.assertEqual(timm_model.channels				,   transformers_model.channels				)
										# Out indices are set to the last layer by default. For timm models, we don't know
										# the number of layers in advance, so we set it to (-1,), whereas for transformers
										# models, we set it to [len(stage_names) - 1] (kept for backward compatibility).
										self.assertEqual(timm_model.out_indices				,   (-1,)				)
										self.assertEqual(transformers_model.out_indices				,   [len(timm_model.stage_names				) - 1]				)

										UpperCAmelCase					    =     AutoBackbone.from_pretrained(lowerCAmelCase				,   use_timm_backbone=lowerCAmelCase				,   out_indices=[1, 2, 3]				)
										UpperCAmelCase					    =     AutoBackbone.from_pretrained(lowerCAmelCase				,   out_indices=[1, 2, 3]				)

										self.assertEqual(timm_model.out_indices				,   transformers_model.out_indices				)
										self.assertEqual(len(timm_model.out_features				)				,   len(transformers_model.out_features				)				)
										self.assertEqual(timm_model.channels				,   transformers_model.channels				)




				@unittest.skip('''TimmBackbone doesn\'t support feed forward chunking'''				)
				def 					a__(		self      :			Union[str, Any]				)->					Tuple:

										"""simple docstring"""




										pass




				@unittest.skip('''TimmBackbone doesn\'t have num_hidden_layers attribute'''				)
				def 					a__(		self      :			Dict				)->					Dict:

										"""simple docstring"""




										pass




				@unittest.skip('''TimmBackbone initialization is managed on the timm side'''				)
				def 					a__(		self      :			Tuple				)->					List[Any]:

										"""simple docstring"""




										pass




				@unittest.skip('''TimmBackbone models doesn\'t have inputs_embeds'''				)
				def 					a__(		self      :			List[str]				)->					Optional[Any]:

										"""simple docstring"""




										pass




				@unittest.skip('''TimmBackbone models doesn\'t have inputs_embeds'''				)
				def 					a__(		self      :			Optional[Any]				)->					Optional[Any]:

										"""simple docstring"""




										pass




				@unittest.skip('''TimmBackbone model cannot be created without specifying a backbone checkpoint'''				)
				def 					a__(		self      :			Any				)->					Optional[int]:

										"""simple docstring"""




										pass




				@unittest.skip('''Only checkpoints on timm can be loaded into TimmBackbone'''				)
				def 					a__(		self      :			Optional[int]				)->					Optional[int]:

										"""simple docstring"""




										pass




				@unittest.skip('''model weights aren\'t tied in TimmBackbone.'''				)
				def 					a__(		self      :			str				)->					str:

										"""simple docstring"""




										pass




				@unittest.skip('''model weights aren\'t tied in TimmBackbone.'''				)
				def 					a__(		self      :			Any				)->					int:

										"""simple docstring"""




										pass




				@unittest.skip('''Only checkpoints on timm can be loaded into TimmBackbone'''				)
				def 					a__(		self      :			List[Any]				)->					int:

										"""simple docstring"""




										pass




				@unittest.skip('''Only checkpoints on timm can be loaded into TimmBackbone'''				)
				def 					a__(		self      :			List[Any]				)->					List[str]:

										"""simple docstring"""




										pass




				@unittest.skip('''TimmBackbone doesn\'t have hidden size info in its configuration.'''				)
				def 					a__(		self      :			List[str]				)->					Dict:

										"""simple docstring"""




										pass




				@unittest.skip('''TimmBackbone doesn\'t support output_attentions.'''				)
				def 					a__(		self      :			str				)->					Tuple:

										"""simple docstring"""




										pass




				@unittest.skip('''Safetensors is not supported by timm.'''				)
				def 					a__(		self      :			List[Any]				)->					List[Any]:

										"""simple docstring"""




										pass




				@unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.'''				)
				def 					a__(		self      :			int				)->					Tuple:

										"""simple docstring"""




										pass




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

										"""simple docstring"""




										UpperCAmelCase    ,    UpperCAmelCase					    =     self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
																UpperCAmelCase					    =     model_class(lowerCAmelCase				)
																UpperCAmelCase					    =     inspect.signature(model.forward				)
																# signature.parameters is an OrderedDict => so arg_names order is deterministic
																UpperCAmelCase					    =     [*signature.parameters.keys()]

																UpperCAmelCase					    =     ['''pixel_values''']
																self.assertListEqual(arg_names[:1]				,   lowerCAmelCase				)




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

										"""simple docstring"""




										UpperCAmelCase    ,    UpperCAmelCase					    =     self.model_tester.prepare_config_and_inputs_for_common()
										UpperCAmelCase					    =     True
										UpperCAmelCase					    =     self.has_attentions

										# no need to test all models as different heads yield the same functionality
										UpperCAmelCase					    =     self.all_model_classes[0]
										UpperCAmelCase					    =     model_class(lowerCAmelCase				)
										model.to(lowerCAmelCase				)

										UpperCAmelCase					    =     self._prepare_for_class(lowerCAmelCase				,   lowerCAmelCase				)
										UpperCAmelCase					    =     model(**lowerCAmelCase				)
										UpperCAmelCase					    =     outputs[0][-1]

										# Encoder-/Decoder-only models
										UpperCAmelCase					    =     outputs.hidden_states[0]
										hidden_states.retain_grad()

										if self.has_attentions:
																UpperCAmelCase					    =     outputs.attentions[0]
																attentions.retain_grad()

										output.flatten()[0].backward(retain_graph=lowerCAmelCase				)

										self.assertIsNotNone(hidden_states.grad				)

										if self.has_attentions:
																self.assertIsNotNone(attentions.grad				)





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

										"""simple docstring"""




										UpperCAmelCase    ,    UpperCAmelCase					    =     self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
																UpperCAmelCase					    =     model_class(lowerCAmelCase				)
																model.to(lowerCAmelCase				)
																model.eval()
																UpperCAmelCase					    =     model(**lowerCAmelCase				)

																self.assertEqual(len(result.feature_maps				)				,   len(config.out_indices				)				)
																self.assertEqual(len(model.channels				)				,   len(config.out_indices				)				)

																# Check output of last stage is taken if out_features=None, out_indices=None
																UpperCAmelCase					    =     copy.deepcopy(lowerCAmelCase				)
																UpperCAmelCase					    =     None
																UpperCAmelCase					    =     model_class(lowerCAmelCase				)
																model.to(lowerCAmelCase				)
																model.eval()
																UpperCAmelCase					    =     model(**lowerCAmelCase				)

																self.assertEqual(len(result.feature_maps				)				,   1				)
																self.assertEqual(len(model.channels				)				,   1				)

																# Check backbone can be initialized with fresh weights
																UpperCAmelCase					    =     copy.deepcopy(lowerCAmelCase				)
																UpperCAmelCase					    =     False
																UpperCAmelCase					    =     model_class(lowerCAmelCase				)
																model.to(lowerCAmelCase				)
																model.eval()
																UpperCAmelCase					    =     model(**lowerCAmelCase				)





