






"""simple docstring"""







import collections
import inspect
import unittest

from transformers import FocalNetConfig
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_backbone_common import BackboneTesterMixin
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 (
			    FocalNetBackbone,
			    FocalNetForImageClassification,
			    FocalNetForMaskedImageModeling,
			    FocalNetModel,
			)
			from transformers.models.focalnet.modeling_focalnet import FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST

if is_vision_available():
			from PIL import Image

			from transformers import AutoImageProcessor


class    __UpperCAmelCase:




						"""simple docstring"""




						def __init__(       self	,  snake_case__	,  snake_case__=13	,  snake_case__=32	,  snake_case__=2	,  snake_case__=3	,  snake_case__=16	,  snake_case__=[32, 64, 128]	,  snake_case__=[1, 2, 1]	,  snake_case__=[2, 2, 4]	,  snake_case__=2	,  snake_case__=2.0	,  snake_case__=True	,  snake_case__=0.0	,  snake_case__=0.0	,  snake_case__=0.1	,  snake_case__="gelu"	,  snake_case__=False	,  snake_case__=True	,  snake_case__=0.02	,  snake_case__=1e-5	,  snake_case__=True	,  snake_case__=None	,  snake_case__=True	,  snake_case__=10	,  snake_case__=8	,  snake_case__=["stage1", "stage2"]	,  snake_case__=[1, 2]	,  ):
												'''simple docstring'''
												lowercase__    :				Optional[Any]=	parent
												lowercase__    :				Any=	batch_size
												lowercase__    :				int=	image_size
												lowercase__    :				List[str]=	patch_size
												lowercase__    :				List[Any]=	num_channels
												lowercase__    :				List[Any]=	embed_dim
												lowercase__    :				List[Any]=	hidden_sizes
												lowercase__    :				List[Any]=	depths
												lowercase__    :				List[str]=	num_heads
												lowercase__    :				Union[str, Any]=	window_size
												lowercase__    :				Tuple=	mlp_ratio
												lowercase__    :				str=	qkv_bias
												lowercase__    :				Tuple=	hidden_dropout_prob
												lowercase__    :				str=	attention_probs_dropout_prob
												lowercase__    :				Dict=	drop_path_rate
												lowercase__    :				Optional[Any]=	hidden_act
												lowercase__    :				Dict=	use_absolute_embeddings
												lowercase__    :				int=	patch_norm
												lowercase__    :				Dict=	layer_norm_eps
												lowercase__    :				List[str]=	initializer_range
												lowercase__    :				str=	is_training
												lowercase__    :				Tuple=	scope
												lowercase__    :				List[Any]=	use_labels
												lowercase__    :				Optional[int]=	type_sequence_label_size
												lowercase__    :				Optional[Any]=	encoder_stride
												lowercase__    :				Any=	out_features
												lowercase__    :				int=	out_indices




						def 		UpperCAmelCase_	(       self      ):
												'''simple docstring'''
												lowercase__    :				Optional[int]=	floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]      )

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

												lowercase__    :				Optional[int]=	self.get_config()

												return config, pixel_values, labels




						def 		UpperCAmelCase_	(       self      ):
												'''simple docstring'''
												return FocalNetConfig(
												    image_size=self.image_size	,  patch_size=self.patch_size	,  num_channels=self.num_channels	,  embed_dim=self.embed_dim	,  hidden_sizes=self.hidden_sizes	,  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	,  out_features=self.out_features	,  out_indices=self.out_indices	,  )




						def 		UpperCAmelCase_	(       self	,  snake_case__	,  snake_case__	,  snake_case__      ):
												'''simple docstring'''
												lowercase__    :				Any=	FocalNetModel(config=snake_case__      )
												model.to(snake_case__      )
												model.eval()
												lowercase__    :				Optional[int]=	model(snake_case__      )

												lowercase__    :				str=	((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths      ) - 1))
												lowercase__    :				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 		UpperCAmelCase_	(       self	,  snake_case__	,  snake_case__	,  snake_case__      ):
												'''simple docstring'''
												lowercase__    :				int=	FocalNetBackbone(config=snake_case__      )
												model.to(snake_case__      )
												model.eval()
												lowercase__    :				Tuple=	model(snake_case__      )

												# verify feature maps
												self.parent.assertEqual(len(result.feature_maps      )	,  len(config.out_features      )      )
												self.parent.assertListEqual(list(result.feature_maps[0].shape      )	,  [self.batch_size, self.image_size, 8, 8]      )

												# verify channels
												self.parent.assertEqual(len(model.channels      )	,  len(config.out_features      )      )
												self.parent.assertListEqual(model.channels	,  config.hidden_sizes[:-1]      )

												# verify backbone works with out_features=None
												lowercase__    :				Union[str, Any]=	None
												lowercase__    :				Dict=	FocalNetBackbone(config=snake_case__      )
												model.to(snake_case__      )
												model.eval()
												lowercase__    :				Optional[Any]=	model(snake_case__      )

												# verify feature maps
												self.parent.assertEqual(len(result.feature_maps      )	,  1      )
												self.parent.assertListEqual(list(result.feature_maps[0].shape      )	,  [self.batch_size, self.image_size * 2, 4, 4]      )

												# verify channels
												self.parent.assertEqual(len(model.channels      )	,  1      )
												self.parent.assertListEqual(model.channels	,  [config.hidden_sizes[-1]]      )




						def 		UpperCAmelCase_	(       self	,  snake_case__	,  snake_case__	,  snake_case__      ):
												'''simple docstring'''
												lowercase__    :				int=	FocalNetForMaskedImageModeling(config=snake_case__      )
												model.to(snake_case__      )
												model.eval()
												lowercase__    :				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
												lowercase__    :				Optional[Any]=	1
												lowercase__    :				Optional[int]=	FocalNetForMaskedImageModeling(snake_case__      )
												model.to(snake_case__      )
												model.eval()

												lowercase__    :				List[Any]=	floats_tensor([self.batch_size, 1, self.image_size, self.image_size]      )
												lowercase__    :				Any=	model(snake_case__      )
												self.parent.assertEqual(result.reconstruction.shape	,  (self.batch_size, 1, self.image_size, self.image_size)      )




						def 		UpperCAmelCase_	(       self	,  snake_case__	,  snake_case__	,  snake_case__      ):
												'''simple docstring'''
												lowercase__    :				str=	self.type_sequence_label_size
												lowercase__    :				Any=	FocalNetForImageClassification(snake_case__      )
												model.to(snake_case__      )
												model.eval()
												lowercase__    :				Tuple=	model(snake_case__	,  labels=snake_case__      )
												self.parent.assertEqual(result.logits.shape	,  (self.batch_size, self.type_sequence_label_size)      )

												# test greyscale images
												lowercase__    :				Optional[Any]=	1
												lowercase__    :				List[Any]=	FocalNetForImageClassification(snake_case__      )
												model.to(snake_case__      )
												model.eval()

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




						def 		UpperCAmelCase_	(       self      ):
												'''simple docstring'''
												lowercase__    :				Union[str, Any]=	self.prepare_config_and_inputs()

												lowercase__,     lowercase__,     lowercase__    :				List[Any]=	config_and_inputs
												lowercase__    :				int=	{"pixel_values": pixel_values}
												return config, inputs_dict


@require_torch
class    __UpperCAmelCase(       SCREAMING_SNAKE_CASE__  ,  SCREAMING_SNAKE_CASE__  ,  unittest.TestCase							):




						"""simple docstring"""




						__lowerCamelCase    	= (
						    (
						        FocalNetModel,
						        FocalNetForImageClassification,
						        FocalNetForMaskedImageModeling,
						        FocalNetBackbone,
						    )
						    if is_torch_available()
						    else ()
						)
						__lowerCamelCase    	= (
						    {"feature-extraction": FocalNetModel, "image-classification": FocalNetForImageClassification}
						    if is_torch_available()
						    else {}
						)
						__lowerCamelCase    	= False

						__lowerCamelCase    	= False
						__lowerCamelCase    	= False
						__lowerCamelCase    	= False
						__lowerCamelCase    	= False




						def 		UpperCAmelCase_	(       self      ):
												'''simple docstring'''
												lowercase__    :				Union[str, Any]=	FocalNetModelTester(self      )
												lowercase__    :				Dict=	ConfigTester(self	,  config_class=snake_case__	,  embed_dim=37	,  has_text_modality=snake_case__      )




						def 		UpperCAmelCase_	(       self      ):
												'''simple docstring'''
												self.create_and_test_config_common_properties()
												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 		UpperCAmelCase_	(       self      ):
												'''simple docstring'''
												return




						def 		UpperCAmelCase_	(       self      ):
												'''simple docstring'''
												lowercase__    :				int=	self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_model(*snake_case__      )




						def 		UpperCAmelCase_	(       self      ):
												'''simple docstring'''
												lowercase__    :				List[Any]=	self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_backbone(*snake_case__      )




						def 		UpperCAmelCase_	(       self      ):
												'''simple docstring'''
												lowercase__    :				Dict=	self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_masked_image_modeling(*snake_case__      )




						def 		UpperCAmelCase_	(       self      ):
												'''simple docstring'''
												lowercase__    :				str=	self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_image_classification(*snake_case__      )




						@unittest.skip(reason="FocalNet does not use inputs_embeds"      )
						def 		UpperCAmelCase_	(       self      ):
												'''simple docstring'''
												pass




						@unittest.skip(reason="FocalNet does not use feedforward chunking"      )
						def 		UpperCAmelCase_	(       self      ):
												'''simple docstring'''
												pass




						def 		UpperCAmelCase_	(       self      ):
												'''simple docstring'''
												lowercase__,     lowercase__    :				int=	self.model_tester.prepare_config_and_inputs_for_common()

												for model_class in self.all_model_classes[:-1]:
																		lowercase__    :				Tuple=	model_class(snake_case__      )
																		self.assertIsInstance(model.get_input_embeddings()	,  (nn.Module)      )
																		lowercase__    :				Any=	model.get_output_embeddings()
																		self.assertTrue(x is None or isinstance(snake_case__	,  nn.Linear      )      )




						def 		UpperCAmelCase_	(       self      ):
												'''simple docstring'''
												lowercase__,     lowercase__    :				Tuple=	self.model_tester.prepare_config_and_inputs_for_common()

												for model_class in self.all_model_classes[:-1]:
																		lowercase__    :				int=	model_class(snake_case__      )
																		lowercase__    :				Union[str, Any]=	inspect.signature(model.forward      )
																		# signature.parameters is an OrderedDict => so arg_names order is deterministic
																		lowercase__    :				Union[str, Any]=	[*signature.parameters.keys()]

																		lowercase__    :				int=	["pixel_values"]
																		self.assertListEqual(arg_names[:1]	,  snake_case__      )




						def 		UpperCAmelCase_	(       self	,  snake_case__	,  snake_case__	,  snake_case__	,  snake_case__      ):
												'''simple docstring'''
												lowercase__    :				Tuple=	model_class(snake_case__      )
												model.to(snake_case__      )
												model.eval()

												with torch.no_grad():
																		lowercase__    :				Any=	model(**self._prepare_for_class(snake_case__	,  snake_case__      )      )

												lowercase__    :				Dict=	outputs.hidden_states

												lowercase__    :				Dict=	getattr(
												    self.model_tester	,  "expected_num_hidden_layers"	,  len(self.model_tester.depths      ) + 1      )
												self.assertEqual(len(snake_case__      )	,  snake_case__      )

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

												lowercase__    :				List[Any]=	(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]	,  )

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

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




						def 		UpperCAmelCase_	(       self      ):
												'''simple docstring'''
												lowercase__,     lowercase__    :				Tuple=	self.model_tester.prepare_config_and_inputs_for_common()

												lowercase__    :				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[:-1]:
																		lowercase__    :				Any=	True
																		self.check_hidden_states_output(snake_case__	,  snake_case__	,  snake_case__	,  snake_case__      )

																		# check that output_hidden_states also work using config
																		del inputs_dict["output_hidden_states"]
																		lowercase__    :				Optional[int]=	True

																		self.check_hidden_states_output(snake_case__	,  snake_case__	,  snake_case__	,  snake_case__      )




						def 		UpperCAmelCase_	(       self      ):
												'''simple docstring'''
												lowercase__,     lowercase__    :				str=	self.model_tester.prepare_config_and_inputs_for_common()
												lowercase__    :				List[str]=	3

												lowercase__    :				Any=	(
												    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)
												)
												lowercase__    :				Any=	(
												    config.patch_size
												    if isinstance(config.patch_size	,  collections.abc.Iterable      )
												    else (config.patch_size, config.patch_size)
												)

												lowercase__    :				List[str]=	image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
												lowercase__    :				Dict=	image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

												for model_class in self.all_model_classes[:-1]:
																		lowercase__    :				List[str]=	True
																		self.check_hidden_states_output(snake_case__	,  snake_case__	,  snake_case__	,  (padded_height, padded_width)      )

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




						@slow
						def 		UpperCAmelCase_	(       self      ):
												'''simple docstring'''
												for model_name in FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																		lowercase__    :				List[str]=	FocalNetModel.from_pretrained(snake_case__      )
																		self.assertIsNotNone(snake_case__      )




						def 		UpperCAmelCase_	(       self      ):
												'''simple docstring'''
												lowercase__,     lowercase__    :				Dict=	self.model_tester.prepare_config_and_inputs_for_common()

												lowercase__    :				int=	_config_zero_init(snake_case__      )
												for model_class in self.all_model_classes:
																		lowercase__    :				Any=	model_class(config=snake_case__      )
																		for name, param in model.named_parameters():
																								if "embeddings" 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							):




						"""simple docstring"""




						@cached_property
						def 		UpperCAmelCase_	(       self      ):
												'''simple docstring'''
												# TODO update organization
												return AutoImageProcessor.from_pretrained("microsoft/focalnet-tiny"      ) if is_vision_available() else None




						@slow
						def 		UpperCAmelCase_	(       self      ):
												'''simple docstring'''
												lowercase__    :				Tuple=	FocalNetForImageClassification.from_pretrained("microsoft/focalnet-tiny"      ).to(snake_case__      )
												lowercase__    :				Tuple=	self.default_image_processor

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

												# forward pass
												with torch.no_grad():
																		lowercase__    :				int=	model(**snake_case__      )

												# verify the logits
												lowercase__    :				str=	torch.Size((1, 1000)      )
												self.assertEqual(outputs.logits.shape	,  snake_case__      )
												lowercase__    :				List[str]=	torch.tensor([0.21_66, -0.43_68, 0.21_91]      ).to(snake_case__      )
												self.assertTrue(torch.allclose(outputs.logits[0, :3]	,  snake_case__	,  atol=1e-4      )      )
												self.assertTrue(outputs.logits.argmax(dim=-1      ).item()	,  281      )


@require_torch
class    __UpperCAmelCase(       SCREAMING_SNAKE_CASE__  ,  unittest.TestCase							):




						"""simple docstring"""




						__lowerCamelCase    	= (FocalNetBackbone,) if is_torch_available() else ()
						__lowerCamelCase    	= FocalNetConfig

						__lowerCamelCase    	= False




						def 		UpperCAmelCase_	(       self      ):
												'''simple docstring'''
												lowercase__    :				Union[str, Any]=	FocalNetModelTester(self      )














"""simple docstring"""







import argparse
import os
import re

import torch
from flax.traverse_util import flatten_dict
from tax import checkpoints

from transformers import (
    AutoTokenizer,
    PixaStructConfig,
    PixaStructForConditionalGeneration,
    PixaStructImageProcessor,
    PixaStructProcessor,
    PixaStructTextConfig,
    PixaStructVisionConfig,
)





def 						lowercase__(A						)    ->Dict:




						"""simple docstring"""



						lowercase__    :				Dict=	checkpoints.load_tax_checkpoint(A						)
						lowercase__    :				List[Any]=	flatten_dict(A						)
						return flax_params





def 						lowercase__(A						)    ->Tuple:




						"""simple docstring"""



						lowercase__    :				Union[str, Any]=	{}

						lowercase__    :				Any=	{
						    "token_embedder": "embeddings",
						    "encoder_norm": "layernorm",
						    "kernel": "weight",
						    ".out": ".output",
						    "scale": "weight",
						    "embedders_0.pos_embedding": "row_embedder.weight",
						    "embedders_1.pos_embedding": "column_embedder.weight",
						}

						lowercase__    :				Tuple=	{
						    "query": "attention.query",
						    "key": "attention.key",
						    "value": "attention.value",
						    "output.dense": "output",
						    "encoder_decoder_attention.o": "encoder_decoder_attention.attention.o",
						    "pre_self_attention_layer_norm": "self_attention.layer_norm",
						    "pre_cross_attention_layer_norm": "encoder_decoder_attention.layer_norm",
						    "mlp.": "mlp.DenseReluDense.",
						    "pre_mlp_layer_norm": "mlp.layer_norm",
						    "self_attention.o": "self_attention.attention.o",
						    "decoder.embeddings.embedding": "decoder.embed_tokens.weight",
						    "decoder.relpos_bias.rel_embedding": "decoder.layer.0.self_attention.attention.relative_attention_bias.weight",
						    "decoder.decoder_norm.weight": "decoder.final_layer_norm.weight",
						    "decoder.logits_dense.weight": "decoder.lm_head.weight",
						}

						for key in flax_dict.keys():
												if "target" in key:
																		# remove the first prefix from the key
																		lowercase__    :				Any=	".".join(key[1:]						)

																		# rename the key
																		for old, new in CONVERSION_MAPPING.items():
																								lowercase__    :				List[Any]=	new_key.replace(A ,		A						)

																		if "decoder" in new_key:
																								for old, new in DECODER_CONVERSION_MAPPING.items():
																														lowercase__    :				List[str]=	new_key.replace(A ,		A						)

																		if "layers" in new_key and "decoder" not in new_key:
																								# use regex to replace the layer number
																								lowercase__    :				List[str]=	re.sub(R"layers_(\d+)" ,		R"layer.\1" ,		A						)
																								lowercase__    :				Any=	new_key.replace("encoder" ,		"encoder.encoder"						)

																		elif "layers" in new_key and "decoder" in new_key:
																								# use regex to replace the layer number
																								lowercase__    :				str=	re.sub(R"layers_(\d+)" ,		R"layer.\1" ,		A						)

																		lowercase__    :				Dict=	flax_dict[key]

						lowercase__    :				str=	{}
						# convert converted_dict into torch format
						for key in converted_dict.keys():
												if ("embed_tokens" not in key) and ("embedder" not in key):
																		lowercase__    :				Dict=	torch.from_numpy(converted_dict[key].T						)
												else:
																		lowercase__    :				Any=	torch.from_numpy(converted_dict[key]						)

						return converted_torch_dict





def 						lowercase__(A ,		A ,		A=False ,		A=False						)    ->Union[str, Any]:




						"""simple docstring"""



						lowercase__    :				Tuple=	get_flax_param(A						)

						if not use_large:
												lowercase__    :				int=	PixaStructVisionConfig()
												lowercase__    :				str=	PixaStructTextConfig()
						else:
												lowercase__    :				List[Any]=	PixaStructVisionConfig(
												    hidden_size=1_536 ,		d_ff=3_968 ,		num_attention_heads=24 ,		num_hidden_layers=18						)
												lowercase__    :				Optional[Any]=	PixaStructTextConfig(hidden_size=1_536 ,		d_ff=3_968 ,		num_heads=24 ,		num_layers=18						)
						lowercase__    :				Any=	PixaStructConfig(
						    vision_config=encoder_config.to_dict() ,		text_config=decoder_config.to_dict() ,		is_vqa=A						)

						lowercase__    :				Optional[Any]=	PixaStructForConditionalGeneration(A						)

						lowercase__    :				Tuple=	rename_and_convert_flax_params(A						)
						model.load_state_dict(A						)

						lowercase__    :				List[str]=	AutoTokenizer.from_pretrained("ybelkada/test-pix2struct-tokenizer"						)
						lowercase__    :				List[str]=	PixaStructImageProcessor()
						lowercase__    :				Dict=	PixaStructProcessor(image_processor=A ,		tokenizer=A						)

						if use_large:
												lowercase__    :				Tuple=	4_096

						lowercase__    :				List[Any]=	True

						# mkdir if needed
						os.makedirs(A ,		exist_ok=A						)

						model.save_pretrained(A						)
						processor.save_pretrained(A						)

						print("Model saved in {}".format(A						)						)


if __name__ == "__main__":
			a			:    int				 =		argparse.ArgumentParser()
			parser.add_argument("""--t5x_checkpoint_path""", default=None, type=str, help="""Path to the original T5x checkpoint.""")
			parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
			parser.add_argument("""--use_large""", action="""store_true""", help="""Use large model.""")
			parser.add_argument("""--is_vqa""", action="""store_true""", help="""Use large model.""")
			a			:    Optional[int]				 =		parser.parse_args()

			convert_pixastruct_original_pytorch_checkpoint_to_hf(
			    args.tax_checkpoint_path, args.pytorch_dump_folder_path, args.use_large
			)






