

import collections
import inspect
import unittest

from transformers import SwinvaConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
	import torch
	from torch import nn

	from transformers import SwinvaForImageClassification, SwinvaForMaskedImageModeling, SwinvaModel
	from transformers.models.swinva.modeling_swinva import SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST

if is_vision_available():
	from PIL import Image

	from transformers import AutoImageProcessor



class 					_a		:


					'''simple docstring'''






					def __init__(				self					, A__					, A__=13					, A__=32					, A__=2					, A__=3					, A__=16					, A__=[1, 2, 1]					, A__=[2, 2, 4]					, A__=2					, A__=2.0					, A__=True					, A__=0.0					, A__=0.0					, A__=0.1					, A__="gelu"					, A__=False					, A__=True					, A__=0.0_2					, A__=1e-5					, A__=True					, A__=None					, A__=True					, A__=10					, A__=8					, ):
						A__							:					str       =	parent
						A__							:					Union[str, Any]       =	batch_size
						A__							:					List[str]       =	image_size
						A__							:					Optional[Any]       =	patch_size
						A__							:					Tuple       =	num_channels
						A__							:					Optional[int]       =	embed_dim
						A__							:					Tuple       =	depths
						A__							:					List[str]       =	num_heads
						A__							:					List[Any]       =	window_size
						A__							:					Any       =	mlp_ratio
						A__							:					Optional[Any]       =	qkv_bias
						A__							:					Any       =	hidden_dropout_prob
						A__							:					Optional[int]       =	attention_probs_dropout_prob
						A__							:					int       =	drop_path_rate
						A__							:					Optional[Any]       =	hidden_act
						A__							:					int       =	use_absolute_embeddings
						A__							:					List[str]       =	patch_norm
						A__							:					Optional[int]       =	layer_norm_eps
						A__							:					List[str]       =	initializer_range
						A__							:					Optional[Any]       =	is_training
						A__							:					List[Any]       =	scope
						A__							:					Dict       =	use_labels
						A__							:					List[Any]       =	type_sequence_label_size
						A__							:					Optional[Any]       =	encoder_stride






					def 						__A    (				self    ):
						A__							:					Union[str, Any]       =	floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]    )

						A__							:					Optional[Any]       =	None
						if self.use_labels:
							A__							:					Tuple       =	ids_tensor([self.batch_size]					, self.type_sequence_label_size    )

						A__							:					int       =	self.get_config()

						return config, pixel_values, labels






					def 						__A    (				self    ):
						return SwinvaConfig(
						    image_size=self.image_size					, patch_size=self.patch_size					, num_channels=self.num_channels					, embed_dim=self.embed_dim					, depths=self.depths					, num_heads=self.num_heads					, window_size=self.window_size					, mlp_ratio=self.mlp_ratio					, qkv_bias=self.qkv_bias					, hidden_dropout_prob=self.hidden_dropout_prob					, attention_probs_dropout_prob=self.attention_probs_dropout_prob					, drop_path_rate=self.drop_path_rate					, hidden_act=self.hidden_act					, use_absolute_embeddings=self.use_absolute_embeddings					, path_norm=self.patch_norm					, layer_norm_eps=self.layer_norm_eps					, initializer_range=self.initializer_range					, encoder_stride=self.encoder_stride					, )






					def 						__A    (				self					, A__					, A__					, A__    ):
						A__							:					Optional[int]       =	SwinvaModel(config=A__    )
						model.to(A__    )
						model.eval()
						A__							:					Optional[int]       =	model(A__    )

						A__							:					str       =	((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths    ) - 1))
						A__							:					Tuple       =	int(config.embed_dim * 2 ** (len(config.depths    ) - 1)    )

						self.parent.assertEqual(result.last_hidden_state.shape					, (self.batch_size, expected_seq_len, expected_dim)    )






					def 						__A    (				self					, A__					, A__					, A__    ):
						A__							:					Any       =	SwinvaForMaskedImageModeling(config=A__    )
						model.to(A__    )
						model.eval()
						A__							:					str       =	model(A__    )
						self.parent.assertEqual(
						    result.logits.shape					, (self.batch_size, self.num_channels, self.image_size, self.image_size)    )

						# test greyscale images
						A__							:					Any       =	1
						A__							:					Dict       =	SwinvaForMaskedImageModeling(A__    )
						model.to(A__    )
						model.eval()

						A__							:					Optional[int]       =	floats_tensor([self.batch_size, 1, self.image_size, self.image_size]    )
						A__							:					List[str]       =	model(A__    )
						self.parent.assertEqual(result.logits.shape					, (self.batch_size, 1, self.image_size, self.image_size)    )






					def 						__A    (				self					, A__					, A__					, A__    ):
						A__							:					str       =	self.type_sequence_label_size
						A__							:					List[Any]       =	SwinvaForImageClassification(A__    )
						model.to(A__    )
						model.eval()
						A__							:					Union[str, Any]       =	model(A__					, labels=A__    )
						self.parent.assertEqual(result.logits.shape					, (self.batch_size, self.type_sequence_label_size)    )






					def 						__A    (				self    ):
						A__							:					Tuple       =	self.prepare_config_and_inputs()
						A__							:					Optional[int]       =	config_and_inputs
						A__							:					str       =	{'pixel_values': pixel_values}
						return config, inputs_dict



@require_torch
class 					_a		(__magic_name__	,     __magic_name__	,     unittest.TestCase       ):


					'''simple docstring'''






					UpperCAmelCase__:     int													= (
					    (SwinvaModel, SwinvaForImageClassification, SwinvaForMaskedImageModeling) if is_torch_available() else ()
					)
					UpperCAmelCase__:     int													= (
					    {'''feature-extraction''': SwinvaModel, '''image-classification''': SwinvaForImageClassification}
					    if is_torch_available()
					    else {}
					)

					UpperCAmelCase__:     Tuple													= False
					UpperCAmelCase__:     Any													= False
					UpperCAmelCase__:     str													= False
					UpperCAmelCase__:     Optional[Any]													= False






					def 						__A    (				self    ):
						A__							:					Optional[Any]       =	SwinvaModelTester(self    )
						A__							:					int       =	ConfigTester(self					, config_class=A__					, embed_dim=37    )






					def 						__A    (				self    ):
						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    ):
						A__							:					Any       =	self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(*A__    )






					@unittest.skip(reason="""Got `CUDA error: misaligned address` with PyTorch 2.0.0."""    )
					def 						__A    (				self    ):
						pass






					@unittest.skip(reason="""Swinv2 does not use inputs_embeds"""    )
					def 						__A    (				self    ):
						pass






					def 						__A    (				self    ):
						A__							:					int       =	self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
							A__							:					int       =	model_class(A__    )
							self.assertIsInstance(model.get_input_embeddings()					, (nn.Module)    )
							A__							:					Optional[int]       =	model.get_output_embeddings()
							self.assertTrue(x is None or isinstance(A__					, nn.Linear    )    )






					def 						__A    (				self    ):
						A__							:					Optional[int]       =	self.model_tester.prepare_config_and_inputs_for_common()

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

							A__							:					Dict       =	['pixel_values']
							self.assertListEqual(arg_names[:1]					, A__    )






					def 						__A    (				self    ):
						A__							:					List[Any]       =	self.model_tester.prepare_config_and_inputs_for_common()
						A__							:					Optional[int]       =	True

						for model_class in self.all_model_classes:
							A__							:					Tuple       =	True
							A__							:					str       =	False
							A__							:					List[Any]       =	True
							A__							:					Dict       =	model_class(A__    )
							model.to(A__    )
							model.eval()
							with torch.no_grad():
								A__							:					int       =	model(**self._prepare_for_class(A__					, A__    )    )
							A__							:					Dict       =	outputs.attentions
							A__							:					Dict       =	len(self.model_tester.depths    )
							self.assertEqual(len(A__    )					, A__    )

							# check that output_attentions also work using config
							del inputs_dict["output_attentions"]
							A__							:					List[str]       =	True
							A__							:					Optional[int]       =	config.window_size**2
							A__							:					str       =	model_class(A__    )
							model.to(A__    )
							model.eval()
							with torch.no_grad():
								A__							:					Optional[Any]       =	model(**self._prepare_for_class(A__					, A__    )    )
							A__							:					Optional[Any]       =	outputs.attentions
							self.assertEqual(len(A__    )					, A__    )

							self.assertListEqual(
							    list(attentions[0].shape[-3:]    )					, [self.model_tester.num_heads[0], window_size_squared, window_size_squared]					, )
							A__							:					Tuple       =	len(A__    )

							# Check attention is always last and order is fine
							A__							:					str       =	True
							A__							:					Union[str, Any]       =	True
							A__							:					str       =	model_class(A__    )
							model.to(A__    )
							model.eval()
							with torch.no_grad():
								A__							:					List[Any]       =	model(**self._prepare_for_class(A__					, A__    )    )

							if hasattr(self.model_tester					, """num_hidden_states_types"""    ):
								A__							:					Optional[Any]       =	self.model_tester.num_hidden_states_types
							else:
								# also another +1 for reshaped_hidden_states
								A__							:					Any       =	2
							self.assertEqual(out_len + added_hidden_states					, len(A__    )    )

							A__							:					Dict       =	outputs.attentions

							self.assertEqual(len(A__    )					, A__    )

							self.assertListEqual(
							    list(self_attentions[0].shape[-3:]    )					, [self.model_tester.num_heads[0], window_size_squared, window_size_squared]					, )






					def 						__A    (				self					, A__					, A__					, A__					, A__    ):
						A__							:					int       =	model_class(A__    )
						model.to(A__    )
						model.eval()

						with torch.no_grad():
							A__							:					Optional[Any]       =	model(**self._prepare_for_class(A__					, A__    )    )

						A__							:					Optional[Any]       =	outputs.hidden_states

						A__							:					str       =	getattr(
						    self.model_tester					, """expected_num_hidden_layers"""					, len(self.model_tester.depths    ) + 1    )
						self.assertEqual(len(A__    )					, A__    )

						# Swinv2 has a different seq_length
						A__							:					int       =	(
						    config.patch_size
						    if isinstance(config.patch_size					, collections.abc.Iterable    )
						    else (config.patch_size, config.patch_size)
						)

						A__							:					List[str]       =	(image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])

						self.assertListEqual(
						    list(hidden_states[0].shape[-2:]    )					, [num_patches, self.model_tester.embed_dim]					, )

						A__							:					Union[str, Any]       =	outputs.reshaped_hidden_states
						self.assertEqual(len(A__    )					, A__    )

						A__							:					Any       =	reshaped_hidden_states[0].shape
						A__							:					List[str]       =	(
						    reshaped_hidden_states[0].view(A__					, A__					, height * width    ).permute(0					, 2					, 1    )
						)
						self.assertListEqual(
						    list(reshaped_hidden_states.shape[-2:]    )					, [num_patches, self.model_tester.embed_dim]					, )






					def 						__A    (				self    ):
						A__							:					Optional[Any]       =	self.model_tester.prepare_config_and_inputs_for_common()

						A__							:					str       =	(
						    self.model_tester.image_size
						    if isinstance(self.model_tester.image_size					, collections.abc.Iterable    )
						    else (self.model_tester.image_size, self.model_tester.image_size)
						)

						for model_class in self.all_model_classes:
							A__							:					Any       =	True
							self.check_hidden_states_output(A__					, A__					, A__					, A__    )

							# check that output_hidden_states also work using config
							del inputs_dict["output_hidden_states"]
							A__							:					List[str]       =	True

							self.check_hidden_states_output(A__					, A__					, A__					, A__    )






					def 						__A    (				self    ):
						A__							:					Dict       =	self.model_tester.prepare_config_and_inputs_for_common()
						A__							:					Any       =	3

						A__							:					int       =	(
						    self.model_tester.image_size
						    if isinstance(self.model_tester.image_size					, collections.abc.Iterable    )
						    else (self.model_tester.image_size, self.model_tester.image_size)
						)
						A__							:					str       =	(
						    config.patch_size
						    if isinstance(config.patch_size					, collections.abc.Iterable    )
						    else (config.patch_size, config.patch_size)
						)

						A__							:					str       =	image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
						A__							:					Any       =	image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

						for model_class in self.all_model_classes:
							A__							:					str       =	True
							self.check_hidden_states_output(A__					, A__					, A__					, (padded_height, padded_width)    )

							# check that output_hidden_states also work using config
							del inputs_dict["output_hidden_states"]
							A__							:					Any       =	True
							self.check_hidden_states_output(A__					, A__					, A__					, (padded_height, padded_width)    )






					def 						__A    (				self    ):
						A__							:					Any       =	self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_masked_image_modeling(*A__    )






					def 						__A    (				self    ):
						A__							:					Dict       =	self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_image_classification(*A__    )






					@slow
					def 						__A    (				self    ):







						for model_name in SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
							A__							:					List[str]       =	SwinvaModel.from_pretrained(A__    )
							self.assertIsNotNone(A__    )






					def 						__A    (				self    ):
						A__							:					Optional[int]       =	self.model_tester.prepare_config_and_inputs_for_common()

						A__							:					Optional[int]       =	_config_zero_init(A__    )
						for model_class in self.all_model_classes:
							A__							:					int       =	model_class(config=A__    )
							for name, param in model.named_parameters():
								if "embeddings" not in name and "logit_scale" not in name and param.requires_grad:
									self.assertIn(
									    ((param.data.mean() * 1e9).round() / 1e9).item()					, [0.0, 1.0]					, msg=F"""Parameter {name} of model {model_class} seems not properly initialized"""					, )



@require_vision
@require_torch
class 					_a		(unittest.TestCase       ):


					'''simple docstring'''






					@cached_property
					def 						__A    (				self    ):
						return (
						    AutoImageProcessor.from_pretrained("""microsoft/swinv2-tiny-patch4-window8-256"""    )
						    if is_vision_available()
						    else None
						)






					@slow
					def 						__A    (				self    ):
						A__							:					Tuple       =	SwinvaForImageClassification.from_pretrained("""microsoft/swinv2-tiny-patch4-window8-256"""    ).to(
						    A__    )
						A__							:					Dict       =	self.default_image_processor

						A__							:					List[str]       =	Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""    )
						A__							:					Any       =	image_processor(images=A__					, return_tensors="""pt"""    ).to(A__    )

						# forward pass
						with torch.no_grad():
							A__							:					Union[str, Any]       =	model(**A__    )

						# verify the logits
						A__							:					List[str]       =	torch.Size((1, 1000)    )
						self.assertEqual(outputs.logits.shape					, A__    )
						A__							:					Optional[Any]       =	torch.tensor([-0.3_9_4_7, -0.4_3_0_6, 0.0_0_2_6]    ).to(A__    )
						self.assertTrue(torch.allclose(outputs.logits[0, :3]					, A__					, atol=1e-4    )    )






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

	return flax_key_tuple, flax_tensor

def 		UpperCamelCase      (lowercase_:				Tuple					,   lowercase_:				Optional[int]					,   lowercase_:				str      )  ->   Union[str, Any]:
	if "metadata" in layer:
		A__							:					Tuple       =	layer.split("""metadata"""      )
		A__							:					Optional[Any]       =	"""""".join(split_layer[0]      )[:-1]
		A__							:					Optional[Any]       =	[tuple(("""metadata""" + split_layer[1]).split("""/"""      )      )]
	elif "kvstore" in layer:
		A__							:					str       =	layer.split("""kvstore"""      )
		A__							:					int       =	"""""".join(split_layer[0]      )[:-1]
		A__							:					Optional[int]       =	[tuple(("""kvstore""" + split_layer[1]).split("""/"""      )      )]

	else:
		A__							:					Any       =	layer.split("""/"""      )
		A__							:					int       =	"""/""".join(split_layer[:-1]      )
		A__							:					str       =	(split_layer[-1],)

	if "kvstore/path" in layer:
		A__							:					Dict       =	f"""{switch_checkpoint_path}/{checkpoint_info[layer]}"""
	elif "kvstore/driver" in layer:
		A__							:					Optional[int]       =	"""file"""
	else:
		A__							:					str       =	checkpoint_info[layer]

	return curr_real_layer_name, split_layer, content

def 		UpperCamelCase      (lowercase_:				str					,   lowercase_:				List[Any]      )  ->   int:
	A__							:					int       =	rename_keys(lowercase_      )
	A__							:					Any       =	{}
	for k, v in current_block.items():
		A__							:					Dict       =	v
	A__							:					str       =	new_current_block
	torch.save(lowercase_					,   lowercase_      )

def 		UpperCamelCase      (lowercase_:				Dict					,   lowercase_:				Optional[Any]					,   lowercase_:				Optional[Any]					,   lowercase_:				Optional[int]					,   lowercase_:				str = WEIGHTS_NAME      )  ->   Tuple:
	A__							:					Optional[int]       =	convert_file_size_to_int(lowercase_      )
	A__							:					List[Any]       =	[]
	A__							:					int       =	{}
	A__							:					List[str]       =	0
	A__							:					Any       =	0

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

	A__							:					Any       =	{}
	for layer in checkpoint_info.keys():
		A__     ,     A__     ,     A__							:					Union[str, Any]       =	get_key_and_tensorstore_dict(
		    lowercase_					,   lowercase_					,   lowercase_      )
		if curr_real_layer_name in all_layers:
			A__							:					Optional[int]       =	content
		else:
			A__							:					List[Any]       =	{split_layer[-1]: content}

	for key in all_layers.keys():
		# open tensorstore file
		A__							:					Optional[Any]       =	ts.open(unflatten_dict(all_layers[key]      )      ).result().read().result()
		A__							:					List[Any]       =	torch.tensor(lowercase_      )
		A__							:					List[Any]       =	raw_weights.numel() * dtype_byte_size(raw_weights.dtype      )

		# use the renaming pattern from the small conversion scripts
		A__     ,     A__							:					Any       =	rename_base_flax_keys(tuple(key.split("""/"""      )      )					,   lowercase_      )
		A__							:					Any       =	"""/""".join(lowercase_      )

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

		A__							:					List[str]       =	raw_weights.to(getattr(lowercase_					,   lowercase_      )      )
		current_block_size += weight_size
		total_size += weight_size

	# Add the last block
	A__							:					Union[str, Any]       =	os.path.join(lowercase_					,   weights_name.replace(""".bin"""					,   f"""-{len(lowercase_      )+1:05d}-of-???.bin"""      )      )
	rename_and_save_block(lowercase_					,   lowercase_      )
	sharded_state_dicts.append(current_block.keys()      )

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

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

    # Add the metadata
	A__							:					Tuple       =	{"""total_size""": total_size}
	A__							:					Union[str, Any]       =	{"""metadata""": metadata, """weight_map""": weight_map}

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

	return metadata, index


if __name__ == "__main__":
	A_ :				Optional[int]						 =    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_ :				Dict						 =    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      ()  ->   int:
	from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration, TaTokenizer

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

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

	A__							:					Union[str, Any]       =	tokenizer(lowercase_					,   return_tensors="""pt"""      ).input_ids
	A__							:					Tuple       =	model.generate(lowercase_					,   decoder_start_token_id=0      )
	print(tokenizer.decode(out[0]      )      )



