


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 						_UpperCAmelCase      :






		def __init__(						self				:     Any   ,  UpperCAmelCase				:     Any   ,  UpperCAmelCase				:     int=13   ,  UpperCAmelCase				:     Optional[int]=32   ,  UpperCAmelCase				:     List[str]=2   ,  UpperCAmelCase				:     Any=3   ,  UpperCAmelCase				:     List[str]=16   ,  UpperCAmelCase				:     int=[1, 2, 1]   ,  UpperCAmelCase				:     List[str]=[2, 2, 4]   ,  UpperCAmelCase				:     List[str]=2   ,  UpperCAmelCase				:     Optional[Any]=2.0   ,  UpperCAmelCase				:     Tuple=True   ,  UpperCAmelCase				:     int=0.0   ,  UpperCAmelCase				:     Any=0.0   ,  UpperCAmelCase				:     Any=0.1   ,  UpperCAmelCase				:     Any="gelu"   ,  UpperCAmelCase				:     int=False   ,  UpperCAmelCase				:     Union[str, Any]=True   ,  UpperCAmelCase				:     Any=0.02   ,  UpperCAmelCase				:     Union[str, Any]=1E-5   ,  UpperCAmelCase				:     Optional[int]=True   ,  UpperCAmelCase				:     int=None   ,  UpperCAmelCase				:     Optional[int]=True   ,  UpperCAmelCase				:     str=10   ,  UpperCAmelCase				:     Union[str, Any]=8   ,  ):
				SCREAMING_SNAKE_CASE_				:int				     =						parent
				SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						batch_size
				SCREAMING_SNAKE_CASE_				:Dict				     =						image_size
				SCREAMING_SNAKE_CASE_				:List[str]				     =						patch_size
				SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						num_channels
				SCREAMING_SNAKE_CASE_				:Dict				     =						embed_dim
				SCREAMING_SNAKE_CASE_				:Dict				     =						depths
				SCREAMING_SNAKE_CASE_				:Dict				     =						num_heads
				SCREAMING_SNAKE_CASE_				:Tuple				     =						window_size
				SCREAMING_SNAKE_CASE_				:Optional[int]				     =						mlp_ratio
				SCREAMING_SNAKE_CASE_				:Dict				     =						qkv_bias
				SCREAMING_SNAKE_CASE_				:Optional[int]				     =						hidden_dropout_prob
				SCREAMING_SNAKE_CASE_				:str				     =						attention_probs_dropout_prob
				SCREAMING_SNAKE_CASE_				:Tuple				     =						drop_path_rate
				SCREAMING_SNAKE_CASE_				:Optional[int]				     =						hidden_act
				SCREAMING_SNAKE_CASE_				:str				     =						use_absolute_embeddings
				SCREAMING_SNAKE_CASE_				:List[str]				     =						patch_norm
				SCREAMING_SNAKE_CASE_				:List[Any]				     =						layer_norm_eps
				SCREAMING_SNAKE_CASE_				:Optional[int]				     =						initializer_range
				SCREAMING_SNAKE_CASE_				:List[Any]				     =						is_training
				SCREAMING_SNAKE_CASE_				:Tuple				     =						scope
				SCREAMING_SNAKE_CASE_				:Any				     =						use_labels
				SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						type_sequence_label_size
				SCREAMING_SNAKE_CASE_				:int				     =						encoder_stride






		def _snake_case		(						self				:     Optional[Any]):
				SCREAMING_SNAKE_CASE_				:str				     =						floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

				SCREAMING_SNAKE_CASE_				:Dict				     =						None
				if self.use_labels:
						SCREAMING_SNAKE_CASE_				:Any				     =						ids_tensor([self.batch_size]   ,  self.type_sequence_label_size)

				SCREAMING_SNAKE_CASE_				:List[Any]				     =						self.get_config()

				return config, pixel_values, labels






		def _snake_case		(						self				:     Tuple):
				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 _snake_case		(						self				:     Dict   ,  UpperCAmelCase				:     Union[str, Any]   ,  UpperCAmelCase				:     Union[str, Any]   ,  UpperCAmelCase				:     str):
				SCREAMING_SNAKE_CASE_				:int				     =						SwinvaModel(config=UpperCAmelCase)
				model.to(UpperCAmelCase)
				model.eval()
				SCREAMING_SNAKE_CASE_				:int				     =						model(UpperCAmelCase)

				SCREAMING_SNAKE_CASE_				:List[str]				     =						((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths) - 1))
				SCREAMING_SNAKE_CASE_				:List[str]				     =						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 _snake_case		(						self				:     List[str]   ,  UpperCAmelCase				:     Dict   ,  UpperCAmelCase				:     List[Any]   ,  UpperCAmelCase				:     Tuple):
				SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						SwinvaForMaskedImageModeling(config=UpperCAmelCase)
				model.to(UpperCAmelCase)
				model.eval()
				SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						model(UpperCAmelCase)
				self.parent.assertEqual(
				    result.logits.shape   ,  (self.batch_size, self.num_channels, self.image_size, self.image_size))

				# test greyscale images
				SCREAMING_SNAKE_CASE_				:str				     =						1
				SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						SwinvaForMaskedImageModeling(UpperCAmelCase)
				model.to(UpperCAmelCase)
				model.eval()

				SCREAMING_SNAKE_CASE_				:Tuple				     =						floats_tensor([self.batch_size, 1, self.image_size, self.image_size])
				SCREAMING_SNAKE_CASE_				:Optional[int]				     =						model(UpperCAmelCase)
				self.parent.assertEqual(result.logits.shape   ,  (self.batch_size, 1, self.image_size, self.image_size))






		def _snake_case		(						self				:     Dict   ,  UpperCAmelCase				:     str   ,  UpperCAmelCase				:     str   ,  UpperCAmelCase				:     Any):
				SCREAMING_SNAKE_CASE_				:Tuple				     =						self.type_sequence_label_size
				SCREAMING_SNAKE_CASE_				:List[str]				     =						SwinvaForImageClassification(UpperCAmelCase)
				model.to(UpperCAmelCase)
				model.eval()
				SCREAMING_SNAKE_CASE_				:str				     =						model(UpperCAmelCase   ,  labels=UpperCAmelCase)
				self.parent.assertEqual(result.logits.shape   ,  (self.batch_size, self.type_sequence_label_size))







		def _snake_case		(						self				:     int):
				SCREAMING_SNAKE_CASE_				:Optional[int]				     =						self.prepare_config_and_inputs()
				SCREAMING_SNAKE_CASE_     ,				SCREAMING_SNAKE_CASE_     ,				SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						config_and_inputs
				SCREAMING_SNAKE_CASE_				:List[str]				     =						{"pixel_values": pixel_values}
				return config, inputs_dict




@require_torch
class 						_UpperCAmelCase      (     lowercase		,		lowercase		,		unittest.TestCase							):
		lowerCamelCase_	:			Any       			=  (
		    (SwinvaModel, SwinvaForImageClassification, SwinvaForMaskedImageModeling) if is_torch_available() else ()
		)
		lowerCamelCase_	:			Dict       			=  (
		    {"""feature-extraction""": SwinvaModel, """image-classification""": SwinvaForImageClassification}
		    if is_torch_available()
		    else {}
		)

		lowerCamelCase_	:			Tuple       			=  False
		lowerCamelCase_	:			List[Any]       			=  False
		lowerCamelCase_	:			Dict       			=  False
		lowerCamelCase_	:			Union[str, Any]       			=  False






		def _snake_case		(						self				:     Union[str, Any]):
				SCREAMING_SNAKE_CASE_				:Any				     =						SwinvaModelTester(self)
				SCREAMING_SNAKE_CASE_				:Optional[int]				     =						ConfigTester(self   ,  config_class=UpperCAmelCase   ,  embed_dim=37)






		def _snake_case		(						self				:     int):
				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 _snake_case		(						self				:     List[str]):
				SCREAMING_SNAKE_CASE_				:Optional[int]				     =						self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_model(*UpperCAmelCase)






		@unittest.skip(reason="Got `CUDA error: misaligned address` with PyTorch 2.0.0.")
		def _snake_case		(						self				:     Union[str, Any]):
				pass






		@unittest.skip(reason="Swinv2 does not use inputs_embeds")
		def _snake_case		(						self				:     Union[str, Any]):
				pass






		def _snake_case		(						self				:     int):
				SCREAMING_SNAKE_CASE_     ,				SCREAMING_SNAKE_CASE_				:int				     =						self.model_tester.prepare_config_and_inputs_for_common()

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






		def _snake_case		(						self				:     str):
				SCREAMING_SNAKE_CASE_     ,				SCREAMING_SNAKE_CASE_				:Optional[int]				     =						self.model_tester.prepare_config_and_inputs_for_common()

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

						SCREAMING_SNAKE_CASE_				:Any				     =						["pixel_values"]
						self.assertListEqual(arg_names[:1]   ,  UpperCAmelCase)






		def _snake_case		(						self				:     List[str]):
				SCREAMING_SNAKE_CASE_     ,				SCREAMING_SNAKE_CASE_				:str				     =						self.model_tester.prepare_config_and_inputs_for_common()
				SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						True

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

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

						self.assertListEqual(
						    list(attentions[0].shape[-3:])   ,  [self.model_tester.num_heads[0], window_size_squared, window_size_squared]   ,  )
						SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						len(UpperCAmelCase)

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

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

						SCREAMING_SNAKE_CASE_				:Dict				     =						outputs.attentions

						self.assertEqual(len(UpperCAmelCase)   ,  UpperCAmelCase)

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






		def _snake_case		(						self				:     Optional[int]   ,  UpperCAmelCase				:     Optional[int]   ,  UpperCAmelCase				:     Dict   ,  UpperCAmelCase				:     int   ,  UpperCAmelCase				:     int):
				SCREAMING_SNAKE_CASE_				:Optional[int]				     =						model_class(UpperCAmelCase)
				model.to(UpperCAmelCase)
				model.eval()

				with torch.no_grad():
						SCREAMING_SNAKE_CASE_				:str				     =						model(**self._prepare_for_class(UpperCAmelCase   ,  UpperCAmelCase))

				SCREAMING_SNAKE_CASE_				:int				     =						outputs.hidden_states

				SCREAMING_SNAKE_CASE_				:List[str]				     =						getattr(
				    self.model_tester   ,  "expected_num_hidden_layers"   ,  len(self.model_tester.depths) + 1)
				self.assertEqual(len(UpperCAmelCase)   ,  UpperCAmelCase)

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

				SCREAMING_SNAKE_CASE_				: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]   ,  )

				SCREAMING_SNAKE_CASE_				:str				     =						outputs.reshaped_hidden_states
				self.assertEqual(len(UpperCAmelCase)   ,  UpperCAmelCase)

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






		def _snake_case		(						self				:     Optional[int]):
				SCREAMING_SNAKE_CASE_     ,				SCREAMING_SNAKE_CASE_				:Optional[int]				     =						self.model_tester.prepare_config_and_inputs_for_common()

				SCREAMING_SNAKE_CASE_				:Tuple				     =						(
				    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:
						SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						True
						self.check_hidden_states_output(UpperCAmelCase   ,  UpperCAmelCase   ,  UpperCAmelCase   ,  UpperCAmelCase)

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

						self.check_hidden_states_output(UpperCAmelCase   ,  UpperCAmelCase   ,  UpperCAmelCase   ,  UpperCAmelCase)






		def _snake_case		(						self				:     List[Any]):
				SCREAMING_SNAKE_CASE_     ,				SCREAMING_SNAKE_CASE_				:List[Any]				     =						self.model_tester.prepare_config_and_inputs_for_common()
				SCREAMING_SNAKE_CASE_				:List[str]				     =						3

				SCREAMING_SNAKE_CASE_				: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)
				)
				SCREAMING_SNAKE_CASE_				:int				     =						(
				    config.patch_size
				    if isinstance(config.patch_size   ,  collections.abc.Iterable)
				    else (config.patch_size, config.patch_size)
				)

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

				for model_class in self.all_model_classes:
						SCREAMING_SNAKE_CASE_				:Dict				     =						True
						self.check_hidden_states_output(UpperCAmelCase   ,  UpperCAmelCase   ,  UpperCAmelCase   ,  (padded_height, padded_width))

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






		def _snake_case		(						self				:     Union[str, Any]):
				SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_masked_image_modeling(*UpperCAmelCase)






		def _snake_case		(						self				:     int):
				SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_image_classification(*UpperCAmelCase)






		@slow
		def _snake_case		(						self				:     Dict):
				for model_name in SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
						SCREAMING_SNAKE_CASE_				:Optional[int]				     =						SwinvaModel.from_pretrained(UpperCAmelCase)
						self.assertIsNotNone(UpperCAmelCase)







		def _snake_case		(						self				:     Tuple):
				SCREAMING_SNAKE_CASE_     ,				SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						self.model_tester.prepare_config_and_inputs_for_common()

				SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						_config_zero_init(UpperCAmelCase)
				for model_class in self.all_model_classes:
						SCREAMING_SNAKE_CASE_				:List[str]				     =						model_class(config=UpperCAmelCase)
						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 						_UpperCAmelCase      (     unittest.TestCase							):






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







		@slow
		def _snake_case		(						self				:     Tuple):
				SCREAMING_SNAKE_CASE_				:Optional[int]				     =						SwinvaForImageClassification.from_pretrained("microsoft/swinv2-tiny-patch4-window8-256").to(
				    UpperCAmelCase)
				SCREAMING_SNAKE_CASE_				:Tuple				     =						self.default_image_processor

				SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
				SCREAMING_SNAKE_CASE_				:Optional[int]				     =						image_processor(images=UpperCAmelCase   ,  return_tensors="pt").to(UpperCAmelCase)

				# forward pass
				with torch.no_grad():
						SCREAMING_SNAKE_CASE_				:Tuple				     =						model(**UpperCAmelCase)

				# verify the logits
				SCREAMING_SNAKE_CASE_				:int				     =						torch.Size((1, 10_00))
				self.assertEqual(outputs.logits.shape   ,  UpperCAmelCase)
				SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						torch.tensor([-0.3947, -0.4306, 0.0026]).to(UpperCAmelCase)
				self.assertTrue(torch.allclose(outputs.logits[0, :3]   ,  UpperCAmelCase   ,  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     lowercase	(    a     , a ):



		'''simple docstring'''




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

		return flax_key_tuple, flax_tensor


def     lowercase	(    a     , a     , a ):



		'''simple docstring'''




		if "metadata" in layer:
				SCREAMING_SNAKE_CASE_				:Dict				     =						layer.split("metadata" )
				SCREAMING_SNAKE_CASE_				:Optional[int]				     =						"".join(split_layer[0] )[:-1]
				SCREAMING_SNAKE_CASE_				:str				     =						[tuple(("metadata" + split_layer[1]).split("/" ) )]
		elif "kvstore" in layer:
				SCREAMING_SNAKE_CASE_				:str				     =						layer.split("kvstore" )
				SCREAMING_SNAKE_CASE_				:str				     =						"".join(split_layer[0] )[:-1]
				SCREAMING_SNAKE_CASE_				:str				     =						[tuple(("kvstore" + split_layer[1]).split("/" ) )]

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

		if "kvstore/path" in layer:
				SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						F"{switch_checkpoint_path}/{checkpoint_info[layer]}"
		elif "kvstore/driver" in layer:
				SCREAMING_SNAKE_CASE_				:Tuple				     =						"file"
		else:
				SCREAMING_SNAKE_CASE_				:str				     =						checkpoint_info[layer]

		return curr_real_layer_name, split_layer, content


def     lowercase	(    a     , a ):



		'''simple docstring'''




		SCREAMING_SNAKE_CASE_				:int				     =						rename_keys(a )
		SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						{}
		for k, v in current_block.items():
				SCREAMING_SNAKE_CASE_				:List[str]				     =						v
		SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						new_current_block
		torch.save(a     , a )


def     lowercase	(    a     , a     , a     , a     , a = WEIGHTS_NAME ):



		'''simple docstring'''




		SCREAMING_SNAKE_CASE_				:Optional[int]				     =						convert_file_size_to_int(a )
		SCREAMING_SNAKE_CASE_				:int				     =						[]
		SCREAMING_SNAKE_CASE_				:str				     =						{}
		SCREAMING_SNAKE_CASE_				:List[str]				     =						0
		SCREAMING_SNAKE_CASE_				:Optional[int]				     =						0

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

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

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

				# use the renaming pattern from the small conversion scripts
				SCREAMING_SNAKE_CASE_     ,				SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						rename_base_flax_keys(tuple(key.split("/" ) )     , a )
				SCREAMING_SNAKE_CASE_				:Any				     =						"/".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:
						SCREAMING_SNAKE_CASE_				:str				     =						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
						SCREAMING_SNAKE_CASE_				:Tuple				     =						{}
						SCREAMING_SNAKE_CASE_				:Dict				     =						0

				SCREAMING_SNAKE_CASE_				:Optional[int]				     =						raw_weights.to(getattr(a     , a ) )
				current_block_size += weight_size
				total_size += weight_size

		# Add the last block
		SCREAMING_SNAKE_CASE_				:Dict				     =						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
		SCREAMING_SNAKE_CASE_				:Optional[int]				     =						{}
		SCREAMING_SNAKE_CASE_				:int				     =						{}
		for idx, shard in enumerate(a ):
				SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						weights_name.replace(
				    ".bin"     , F"-{idx+1:05d}-of-{len(a ):05d}.bin" )  # len(sharded_state_dicts):05d}
				SCREAMING_SNAKE_CASE_				:Any				     =						os.path.join(a     , weights_name.replace(".bin"     , F"-{idx+1:05d}-of-???.bin" ) )
				os.rename(a     , os.path.join(a     , a ) )
				SCREAMING_SNAKE_CASE_				:List[Any]				     =						shard
				for key in shard:
						SCREAMING_SNAKE_CASE_				:str				     =						shard_file

    # Add the metadata
		SCREAMING_SNAKE_CASE_				:List[str]				     =						{"total_size": total_size}
		SCREAMING_SNAKE_CASE_				:Optional[int]				     =						{"metadata": metadata, "weight_map": weight_map}

		with open(os.path.join(a     , a )     , "w"     , encoding="utf-8" ) as f:
				SCREAMING_SNAKE_CASE_				:Optional[int]				     =						json.dumps(a     , indent=2     , sort_keys=a ) + "\n"
				f.write(a )

		return metadata, index


if __name__ == "__main__":
		SCREAMING_SNAKE_CASE__     					=			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.",
		)
		SCREAMING_SNAKE_CASE__     					=			parser.parse_args()
		shard_on_the_fly(
		    args.switch_tax_checkpoint_path,
		    args.pytorch_dump_folder_path,
		    args.max_shard_size,
		    args.dtype,
		)


def     lowercase	(    ):



		'''simple docstring'''




		from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration, TaTokenizer

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

		SCREAMING_SNAKE_CASE_				:List[Any]				     =						TaTokenizer.from_pretrained("t5-small" )
		SCREAMING_SNAKE_CASE_				:Optional[int]				     =						"A <extra_id_0> walks into a bar a orders a <extra_id_1> with <extra_id_2> pinch of <extra_id_3>."

		SCREAMING_SNAKE_CASE_				:List[Any]				     =						tokenizer(a     , return_tensors="pt" ).input_ids
		SCREAMING_SNAKE_CASE_				:List[str]				     =						model.generate(a     , decoder_start_token_id=0 )
		print(tokenizer.decode(out[0] ) )






