





'''simple docstring'''






import argparse
import re

from flax.traverse_util import flatten_dict, unflatten_dict
from tax import checkpoints

from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration
from transformers.modeling_flax_pytorch_utils import load_flax_weights_in_pytorch_model
from transformers.utils import logging


logging.set_verbosity_info()


# should not include what is already done by the `from_pt` argument
A   : Tuple             =			{
    '/attention/': '/0/SelfAttention/',
    '/self_attention/': '/0/SelfAttention/',
    '/encoder_decoder_attention/': '/1/EncDecAttention/',
    'value': 'v',
    'query': 'q',
    'key': 'k',
    'out': 'o',
    'pre_self_attention_layer_norm': '0/layer_norm',
    'pre_cross_attention_layer_norm': '1/layer_norm',
    'pre_attention_layer_norm': '0/layer_norm',  # previously 1, but seems wrong
    'token_embedder': 'shared',
    'encoder_norm': 'final_layer_norm',
    'decoder_norm': 'final_layer_norm',
    'relpos_bias/rel_embedding': 'block/0/layer/0/SelfAttention/relative_attention_bias/weight',
    'router/router_weights/w/': 'router/classifier/',
    'roer/roer_weights/w/': 'router/classifier/',
    'logits_dense': 'lm_head',
}





def       lowercase_      (		lowercase__						)   ->Dict:
						# 1. in HF T5, we have block.{x}.layer.{y}. which corresponds to layer.{x} in
						# the original model
						_snake_case:   str  					=		list(s_dict.keys()						)
						for key in keys:
												_snake_case:   List[Any]  					=		R'.*/layers_(\d+)'
												_snake_case:   Union[str, Any]  					=		key
												if re.match(lowercase__				,  lowercase__						):
																		_snake_case:   Union[str, Any]  					=		re.sub(R'layers_(\d+)'				,  R'block/\1/layer'				,  lowercase__						)

												_snake_case:   Union[str, Any]  					=		R'(encoder|decoder)\/'

												if re.match(lowercase__				,  lowercase__						):
																		_snake_case:   str  					=		re.match(lowercase__				,  lowercase__						).groups()
																		if groups[0] == "encoder":
																								_snake_case:   Tuple  					=		re.sub(R'/mlp/'				,  R'/1/mlp/'				,  lowercase__						)
																								_snake_case:   int  					=		re.sub(R'/pre_mlp_layer_norm/'				,  R'/1/layer_norm/'				,  lowercase__						)

																		elif groups[0] == "decoder":
																								_snake_case:   int  					=		re.sub(R'/mlp/'				,  R'/2/mlp/'				,  lowercase__						)
																								_snake_case:   str  					=		re.sub(R'/pre_mlp_layer_norm/'				,  R'/2/layer_norm/'				,  lowercase__						)

        # 2. Convert other classic mappings
												for old_key, temp_key in MOE_LAYER_NAME_MAPPING.items():
																		if old_key in new_key:
																								_snake_case:   Optional[int]  					=		new_key.replace(lowercase__				,  lowercase__						)

												print(F'''{key} -> {new_key}'''						)
												_snake_case:   Optional[Any]  					=		s_dict.pop(lowercase__						)

						if "encoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight" in s_dict:
												_snake_case:   int  					=		s_dict[
												    'encoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight'
												].T
						if "decoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight" in s_dict:
												_snake_case:   Tuple  					=		s_dict[
												    'decoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight'
												].T

						# 3. Take extra care of the EXPERTS layer
						for key in list(s_dict.keys()						):
												if "expert" in key:
																		_snake_case:   List[str]  					=		s_dict[key].shape[0]
																		_snake_case:   Optional[int]  					=		s_dict[key]
																		for idx in range(lowercase__						):
																								_snake_case:   Optional[int]  					=		expert_weihts[idx]
																								print(F'''{key} -> {key.replace("expert/"				,  "nested fstring"						)}'''						)

																		s_dict.pop(lowercase__						)

						return s_dict


A   : Optional[int]             =			{
    'NUM_ENCODER_LAYERS': 'num_layers',
    'NUM_DECODER_LAYERS': 'num_decoder_layers',
    'NUM_HEADS': 'num_heads',
    'HEAD_DIM': 'd_kv',
    'EMBED_DIM': 'd_model',
    'MLP_DIM': 'd_ff',
    'NUM_SELECTED_EXPERTS': 'num_selected_experts',
    'NUM_ENCODER_SPARSE_LAYERS': 'num_sparse_encoder_layers',
    'NUM_DECODER_SPARSE_LAYERS': 'num_sparse_decoder_layers',
    'dense.MlpBlock.activations': 'feed_forward_proj',
}





def       lowercase_      (		lowercase__				,  lowercase__						)   ->str:
						# Convert a google style config to the hugging face fromat
						import regex as re

						with open(lowercase__				,  'r'						) as f:
												_snake_case:   Optional[Any]  					=		f.read()

						_snake_case:   Any  					=		re.findall(R'(.*) = ([0-9.]*)'				,  lowercase__						)
						_snake_case:   List[str]  					=		{}
						for param, value in regex_match:
												if param in GIN_TO_CONFIG_MAPPING and value != "":
																		_snake_case:   Union[str, Any]  					=		float(lowercase__						) if '.' in value else int(lowercase__						)

						_snake_case:   Dict  					=		re.findall(R'(.*activations) = \(\'(.*)\',\)'				,  lowercase__						)[0]
						_snake_case:   Optional[int]  					=		str(activation[1]						)

						_snake_case:   Optional[Any]  					=		num_experts
						_snake_case:   str  					=		SwitchTransformersConfig(**lowercase__						)
						return config





def       lowercase_      (		lowercase__				,  lowercase__				,  lowercase__=None				,  lowercase__="./"				,  lowercase__=8						)   ->Tuple:
						# Initialise PyTorch model

						print(F'''Loading flax weights from : {flax_checkpoint_path}'''						)
						_snake_case:   Union[str, Any]  					=		checkpoints.load_tax_checkpoint(lowercase__						)

						if gin_file is not None:
												_snake_case:   Tuple  					=		convert_gin_to_config(lowercase__				,  lowercase__						)
						else:
												_snake_case:   List[Any]  					=		SwitchTransformersConfig.from_pretrained(lowercase__						)

						_snake_case:   Any  					=		SwitchTransformersForConditionalGeneration(lowercase__						)

						_snake_case:   List[Any]  					=		flax_params['target']
						_snake_case:   List[str]  					=		flatten_dict(lowercase__				,  sep='/'						)
						_snake_case:   int  					=		rename_keys(lowercase__						)
						_snake_case:   Tuple  					=		unflatten_dict(lowercase__				,  sep='/'						)

						# Load the flax params in the PT model
						load_flax_weights_in_pytorch_model(lowercase__				,  lowercase__						)

						print(F'''Save PyTorch model to {pytorch_dump_path}'''						)
						pt_model.save_pretrained(lowercase__						)


if __name__ == "__main__":
		A   : str             =			argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '--switch_t5x_checkpoint_path',
		    default=None,
		    type=str,
		    required=True,
		    help=(
		        'The config json file corresponding to the pre-trained SwitchTransformers model. \nThis specifies the'
		        ' model architecture. If not provided, a `gin_file` has to be provided.'
		    ),
		)
		parser.add_argument(
		    '--gin_file',
		    default=None,
		    type=str,
		    required=False,
		    help='Path to the gin config file. If not provided, a `config_file` has to be passed   ',
		)
		parser.add_argument(
		    '--config_name', default=None, type=str, required=False, help='Config name of SwitchTransformers model.'
		)
		parser.add_argument(
		    '--pytorch_dump_folder_path', default=None, type=str, required=True, help='Path to the output pytorch model.'
		)
		parser.add_argument('--num_experts', default=8, type=int, required=False, help='Number of experts')
		A   : List[str]             =			parser.parse_args()
		convert_flax_checkpoint_to_pytorch(
		    args.switch_tax_checkpoint_path,
		    args.config_name,
		    args.gin_file,
		    args.pytorch_dump_folder_path,
		    args.num_experts,
		)












'''simple docstring'''






import inspect
import unittest

from transformers import ViTConfig
from transformers.testing_utils import (
    require_accelerate,
    require_torch,
    require_torch_gpu,
    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, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
		import torch
		from torch import nn

		from transformers import ViTForImageClassification, ViTForMaskedImageModeling, ViTModel
		from transformers.models.vit.modeling_vit import VIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
		from PIL import Image

		from transformers import ViTImageProcessor







class 			lowerCamelCase					:


					def __init__(					self	:  Union[str, Any]       ,     __snake_case	:  Union[str, Any]       ,     __snake_case	:  str=13       ,     __snake_case	:  Union[str, Any]=30       ,     __snake_case	:  Union[str, Any]=2       ,     __snake_case	:  Dict=3       ,     __snake_case	:  Optional[Any]=True       ,     __snake_case	:  Optional[int]=True       ,     __snake_case	:  Optional[int]=32       ,     __snake_case	:  Optional[int]=5       ,     __snake_case	:  Any=4       ,     __snake_case	:  int=37       ,     __snake_case	:  int="gelu"       ,     __snake_case	:  Union[str, Any]=0.1       ,     __snake_case	:  Optional[int]=0.1       ,     __snake_case	:  int=10       ,     __snake_case	:  Any=0.02       ,     __snake_case	:  List[str]=None       ,     __snake_case	:  Tuple=2       ,     ):





											'''simple docstring'''





											_snake_case:   Optional[Any]  					=		parent
											_snake_case:   Tuple  					=		batch_size
											_snake_case:   str  					=		image_size
											_snake_case:   int  					=		patch_size
											_snake_case:   Union[str, Any]  					=		num_channels
											_snake_case:   Dict  					=		is_training
											_snake_case:   Optional[Any]  					=		use_labels
											_snake_case:   Optional[Any]  					=		hidden_size
											_snake_case:   Tuple  					=		num_hidden_layers
											_snake_case:   List[Any]  					=		num_attention_heads
											_snake_case:   Union[str, Any]  					=		intermediate_size
											_snake_case:   List[str]  					=		hidden_act
											_snake_case:   Tuple  					=		hidden_dropout_prob
											_snake_case:   List[Any]  					=		attention_probs_dropout_prob
											_snake_case:   str  					=		type_sequence_label_size
											_snake_case:   Any  					=		initializer_range
											_snake_case:   str  					=		scope
											_snake_case:   Union[str, Any]  					=		encoder_stride

											# in ViT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
											_snake_case:   Tuple  					=		(image_size // patch_size) ** 2
											_snake_case:   List[str]  					=		num_patches + 1


					def    SCREAMING_SNAKE_CASE_		(					self	:  str       ):





											'''simple docstring'''





											_snake_case:   List[str]  					=		floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]       )

											_snake_case:   List[str]  					=		None
											if self.use_labels:
																	_snake_case:   Any  					=		ids_tensor([self.batch_size]       ,     self.type_sequence_label_size       )

											_snake_case:   Union[str, Any]  					=		self.get_config()

											return config, pixel_values, labels


					def    SCREAMING_SNAKE_CASE_		(					self	:  Any       ):





											'''simple docstring'''





											return ViTConfig(
											    image_size=self.image_size       ,     patch_size=self.patch_size       ,     num_channels=self.num_channels       ,     hidden_size=self.hidden_size       ,     num_hidden_layers=self.num_hidden_layers       ,     num_attention_heads=self.num_attention_heads       ,     intermediate_size=self.intermediate_size       ,     hidden_act=self.hidden_act       ,     hidden_dropout_prob=self.hidden_dropout_prob       ,     attention_probs_dropout_prob=self.attention_probs_dropout_prob       ,     is_decoder=__snake_case       ,     initializer_range=self.initializer_range       ,     encoder_stride=self.encoder_stride       ,     )


					def    SCREAMING_SNAKE_CASE_		(					self	:  List[str]       ,     __snake_case	:  int       ,     __snake_case	:  Optional[int]       ,     __snake_case	:  List[str]       ):





											'''simple docstring'''





											_snake_case:   Dict  					=		ViTModel(config=__snake_case       )
											model.to(__snake_case       )
											model.eval()
											_snake_case:   Tuple  					=		model(__snake_case       )
											self.parent.assertEqual(result.last_hidden_state.shape       ,     (self.batch_size, self.seq_length, self.hidden_size)       )


					def    SCREAMING_SNAKE_CASE_		(					self	:  List[Any]       ,     __snake_case	:  Union[str, Any]       ,     __snake_case	:  Union[str, Any]       ,     __snake_case	:  int       ):





											'''simple docstring'''





											_snake_case:   int  					=		ViTForMaskedImageModeling(config=__snake_case       )
											model.to(__snake_case       )
											model.eval()
											_snake_case:   Dict  					=		model(__snake_case       )
											self.parent.assertEqual(
											    result.reconstruction.shape       ,     (self.batch_size, self.num_channels, self.image_size, self.image_size)       )

											# test greyscale images
											_snake_case:   List[str]  					=		1
											_snake_case:   Tuple  					=		ViTForMaskedImageModeling(__snake_case       )
											model.to(__snake_case       )
											model.eval()

											_snake_case:   List[str]  					=		floats_tensor([self.batch_size, 1, self.image_size, self.image_size]       )
											_snake_case:   Union[str, Any]  					=		model(__snake_case       )
											self.parent.assertEqual(result.reconstruction.shape       ,     (self.batch_size, 1, self.image_size, self.image_size)       )


					def    SCREAMING_SNAKE_CASE_		(					self	:  Dict       ,     __snake_case	:  Union[str, Any]       ,     __snake_case	:  Optional[int]       ,     __snake_case	:  Dict       ):





											'''simple docstring'''





											_snake_case:   Optional[int]  					=		self.type_sequence_label_size
											_snake_case:   Union[str, Any]  					=		ViTForImageClassification(__snake_case       )
											model.to(__snake_case       )
											model.eval()
											_snake_case:   List[Any]  					=		model(__snake_case       ,     labels=__snake_case       )
											self.parent.assertEqual(result.logits.shape       ,     (self.batch_size, self.type_sequence_label_size)       )

											# test greyscale images
											_snake_case:   Tuple  					=		1
											_snake_case:   Optional[int]  					=		ViTForImageClassification(__snake_case       )
											model.to(__snake_case       )
											model.eval()

											_snake_case:   List[Any]  					=		floats_tensor([self.batch_size, 1, self.image_size, self.image_size]       )
											_snake_case:   Any  					=		model(__snake_case       )
											self.parent.assertEqual(result.logits.shape       ,     (self.batch_size, self.type_sequence_label_size)       )




					def    SCREAMING_SNAKE_CASE_		(					self	:  Tuple       ):





											'''simple docstring'''





											_snake_case:   Any  					=		self.prepare_config_and_inputs()
											(
											    (
											    _snake_case
											)					,						(
											    _snake_case
											)					,						(
											    _snake_case
											)					,						
											):   int  					=		config_and_inputs
											_snake_case:   Dict  					=		{'pixel_values': pixel_values}
											return config, inputs_dict







@require_torch
class 			lowerCamelCase					(						__UpperCAmelCase     ,							__UpperCAmelCase     ,							unittest.TestCase  ):
					_SCREAMING_SNAKE_CASE							   =    (
					    (
					        ViTModel,
					        ViTForImageClassification,
					        ViTForMaskedImageModeling,
					    )
					    if is_torch_available()
					    else ()
					)
					_SCREAMING_SNAKE_CASE							   =    (
					    {"feature-extraction": ViTModel, "image-classification": ViTForImageClassification}
					    if is_torch_available()
					    else {}
					)
					_SCREAMING_SNAKE_CASE							   =    True

					_SCREAMING_SNAKE_CASE							   =    False
					_SCREAMING_SNAKE_CASE							   =    False
					_SCREAMING_SNAKE_CASE							   =    False


					def    SCREAMING_SNAKE_CASE_		(					self	:  Tuple       ):





											'''simple docstring'''





											_snake_case:   Optional[int]  					=		ViTModelTester(self       )
											_snake_case:   Union[str, Any]  					=		ConfigTester(self       ,     config_class=__snake_case       ,     has_text_modality=__snake_case       ,     hidden_size=37       )


					def    SCREAMING_SNAKE_CASE_		(					self	:  int       ):





											'''simple docstring'''





											self.config_tester.run_common_tests()


					@unittest.skip(reason='ViT does not use inputs_embeds'       )
					def    SCREAMING_SNAKE_CASE_		(					self	:  List[str]       ):





											'''simple docstring'''





											pass


					def    SCREAMING_SNAKE_CASE_		(					self	:  str       ):





											'''simple docstring'''





											_snake_case					,						_snake_case:   Dict  					=		self.model_tester.prepare_config_and_inputs_for_common()

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


					def    SCREAMING_SNAKE_CASE_		(					self	:  Any       ):





											'''simple docstring'''





											_snake_case					,						_snake_case:   List[Any]  					=		self.model_tester.prepare_config_and_inputs_for_common()

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

																	_snake_case:   str  					=		['pixel_values']
																	self.assertListEqual(arg_names[:1]       ,     __snake_case       )


					def    SCREAMING_SNAKE_CASE_		(					self	:  Optional[Any]       ):





											'''simple docstring'''





											_snake_case:   List[Any]  					=		self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_model(*__snake_case       )


					def    SCREAMING_SNAKE_CASE_		(					self	:  List[str]       ):





											'''simple docstring'''





											_snake_case:   int  					=		self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_masked_image_modeling(*__snake_case       )


					def    SCREAMING_SNAKE_CASE_		(					self	:  Union[str, Any]       ):





											'''simple docstring'''





											_snake_case:   str  					=		self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_image_classification(*__snake_case       )




					@slow
					def    SCREAMING_SNAKE_CASE_		(					self	:  List[str]       ):





											'''simple docstring'''





											for model_name in VIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																	_snake_case:   Any  					=		ViTModel.from_pretrained(__snake_case       )
																	self.assertIsNotNone(__snake_case       )





def       lowercase_      (		)   ->List[Any]:
						_snake_case:   Dict  					=		Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'						)
						return image







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


					@cached_property
					def    SCREAMING_SNAKE_CASE_		(					self	:  Optional[Any]       ):





											'''simple docstring'''





											return ViTImageProcessor.from_pretrained('google/vit-base-patch16-224'       ) if is_vision_available() else None


					@slow
					def    SCREAMING_SNAKE_CASE_		(					self	:  Dict       ):





											'''simple docstring'''





											_snake_case:   Optional[int]  					=		ViTForImageClassification.from_pretrained('google/vit-base-patch16-224'       ).to(__snake_case       )

											_snake_case:   Dict  					=		self.default_image_processor
											_snake_case:   Optional[Any]  					=		prepare_img()
											_snake_case:   List[str]  					=		image_processor(images=__snake_case       ,     return_tensors='pt'       ).to(__snake_case       )

											# forward pass
											with torch.no_grad():
																	_snake_case:   Optional[int]  					=		model(**__snake_case       )

											# verify the logits
											_snake_case:   Union[str, Any]  					=		torch.Size((1, 10_00)       )
											self.assertEqual(outputs.logits.shape       ,     __snake_case       )

											_snake_case:   Dict  					=		torch.tensor([-0.2_744, 0.8_215, -0.0_836]       ).to(__snake_case       )

											self.assertTrue(torch.allclose(outputs.logits[0, :3]       ,     __snake_case       ,     atol=1e-4       )       )


					@slow
					def    SCREAMING_SNAKE_CASE_		(					self	:  Union[str, Any]       ):





											'''simple docstring'''





											_snake_case:   str  					=		ViTModel.from_pretrained('facebook/dino-vits8'       ).to(__snake_case       )

											_snake_case:   Any  					=		ViTImageProcessor.from_pretrained('facebook/dino-vits8'       ,     size=4_80       )
											_snake_case:   Optional[int]  					=		prepare_img()
											_snake_case:   Dict  					=		image_processor(images=__snake_case       ,     return_tensors='pt'       )
											_snake_case:   Optional[Any]  					=		inputs.pixel_values.to(__snake_case       )

											# forward pass
											with torch.no_grad():
																	_snake_case:   str  					=		model(__snake_case       ,     interpolate_pos_encoding=__snake_case       )

											# verify the logits
											_snake_case:   List[str]  					=		torch.Size((1, 36_01, 3_84)       )
											self.assertEqual(outputs.last_hidden_state.shape       ,     __snake_case       )

											_snake_case:   Any  					=		torch.tensor(
											    [[4.2_340, 4.3_906, -6.6_692], [4.5_463, 1.8_928, -6.7_257], [4.4_429, 0.8_496, -5.8_585]]       ).to(__snake_case       )

											self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :3, :3]       ,     __snake_case       ,     atol=1e-4       )       )




					@slow
					@require_accelerate
					@require_torch_gpu
					def    SCREAMING_SNAKE_CASE_		(					self	:  int       ):





											'''simple docstring'''





											_snake_case:   List[Any]  					=		ViTModel.from_pretrained('facebook/dino-vits8'       ,     torch_dtype=torch.floataa       ,     device_map='auto'       )
											_snake_case:   Dict  					=		self.default_image_processor

											_snake_case:   Any  					=		prepare_img()
											_snake_case:   str  					=		image_processor(images=__snake_case       ,     return_tensors='pt'       )
											_snake_case:   Any  					=		inputs.pixel_values.to(__snake_case       )

											# forward pass to make sure inference works in fp16
											with torch.no_grad():
																	_snake_case:   int  					=		model(__snake_case       )





