


"""simple docstring"""




import unittest

from transformers import MraConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_torch_available():
			import torch

			from transformers import (
			    MraForMaskedLM,
			    MraForMultipleChoice,
			    MraForQuestionAnswering,
			    MraForSequenceClassification,
			    MraForTokenClassification,
			    MraModel,
			)
			from transformers.models.mra.modeling_mra import MRA_PRETRAINED_MODEL_ARCHIVE_LIST


class 	_a							:

							def __init__(							self:     str   ,						UpperCamelCase_:     Optional[int]   ,						UpperCamelCase_:     List[Any]=2   ,						UpperCamelCase_:     List[str]=8   ,						UpperCamelCase_:     Optional[Any]=True   ,						UpperCamelCase_:     List[str]=True   ,						UpperCamelCase_:     Dict=True   ,						UpperCamelCase_:     Tuple=True   ,						UpperCamelCase_:     Tuple=99   ,						UpperCamelCase_:     Any=16   ,						UpperCamelCase_:     Union[str, Any]=5   ,						UpperCamelCase_:     int=2   ,						UpperCamelCase_:     Any=36   ,						UpperCamelCase_:     List[Any]="gelu"   ,						UpperCamelCase_:     int=0.0   ,						UpperCamelCase_:     Optional[Any]=0.0   ,						UpperCamelCase_:     Dict=512   ,						UpperCamelCase_:     Dict=16   ,						UpperCamelCase_:     Dict=2   ,						UpperCamelCase_:     int=0.02   ,						UpperCamelCase_:     Union[str, Any]=3   ,						UpperCamelCase_:     List[Any]=4   ,						UpperCamelCase_:     Dict=None   ,						)      ->       Any:





									"""simple docstring"""







									lowercase__						=				parent
									lowercase__						=				batch_size
									lowercase__						=				seq_length
									lowercase__						=				is_training
									lowercase__						=				use_input_mask
									lowercase__						=				use_token_type_ids
									lowercase__						=				use_labels
									lowercase__						=				vocab_size
									lowercase__						=				hidden_size
									lowercase__						=				num_hidden_layers
									lowercase__						=				num_attention_heads
									lowercase__						=				intermediate_size
									lowercase__						=				hidden_act
									lowercase__						=				hidden_dropout_prob
									lowercase__						=				attention_probs_dropout_prob
									lowercase__						=				max_position_embeddings
									lowercase__						=				type_vocab_size
									lowercase__						=				type_sequence_label_size
									lowercase__						=				initializer_range
									lowercase__						=				num_labels
									lowercase__						=				num_choices
									lowercase__						=				scope

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





									"""simple docstring"""







									lowercase__						=				ids_tensor([self.batch_size, self.seq_length]   ,						self.vocab_size							)

									lowercase__						=				None
									if self.use_input_mask:
											lowercase__						=				random_attention_mask([self.batch_size, self.seq_length]							)

									lowercase__						=				None
									if self.use_token_type_ids:
											lowercase__						=				ids_tensor([self.batch_size, self.seq_length]   ,						self.type_vocab_size							)

									lowercase__						=				None
									lowercase__						=				None
									lowercase__						=				None
									if self.use_labels:
											lowercase__						=				ids_tensor([self.batch_size]   ,						self.type_sequence_label_size							)
											lowercase__						=				ids_tensor([self.batch_size, self.seq_length]   ,						self.num_labels							)
											lowercase__						=				ids_tensor([self.batch_size]   ,						self.num_choices							)

									lowercase__						=				self.get_config()

									return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels

							def 				lowerCamelCase_			(							self:     Optional[int]							)      ->       str:





									"""simple docstring"""







									return MraConfig(
									    vocab_size=self.vocab_size   ,						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   ,						max_position_embeddings=self.max_position_embeddings   ,						type_vocab_size=self.type_vocab_size   ,						is_decoder=SCREAMING_SNAKE_CASE_   ,						initializer_range=self.initializer_range   ,						)

							def 				lowerCamelCase_			(							self:     List[str]							)      ->       Union[str, Any]:





									"""simple docstring"""







									lowercase__						=				self.get_config()
									lowercase__						=				300
									return config

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





									"""simple docstring"""







									(
									    lowercase__							
									)						=				self.prepare_config_and_inputs()

									lowercase__						=				True
									lowercase__						=				floats_tensor([self.batch_size, self.seq_length, self.hidden_size]							)
									lowercase__						=				ids_tensor([self.batch_size, self.seq_length]   ,						vocab_size=2							)

									return (
									    config,
									    input_ids,
									    token_type_ids,
									    input_mask,
									    sequence_labels,
									    token_labels,
									    choice_labels,
									    encoder_hidden_states,
									    encoder_attention_mask,
									)

							def 				lowerCamelCase_			(							self:     Any   ,						UpperCamelCase_:     List[Any]   ,						UpperCamelCase_:     Optional[Any]   ,						UpperCamelCase_:     Optional[int]   ,						UpperCamelCase_:     str   ,						UpperCamelCase_:     Dict   ,						UpperCamelCase_:     Dict   ,						UpperCamelCase_:     Any							)      ->       List[str]:





									"""simple docstring"""







									lowercase__						=				MraModel(config=SCREAMING_SNAKE_CASE_							)
									model.to(SCREAMING_SNAKE_CASE_							)
									model.eval()
									lowercase__						=				model(SCREAMING_SNAKE_CASE_   ,						attention_mask=SCREAMING_SNAKE_CASE_   ,						token_type_ids=SCREAMING_SNAKE_CASE_							)
									lowercase__						=				model(SCREAMING_SNAKE_CASE_   ,						token_type_ids=SCREAMING_SNAKE_CASE_							)
									lowercase__						=				model(SCREAMING_SNAKE_CASE_							)
									self.parent.assertEqual(result.last_hidden_state.shape   ,						(self.batch_size, self.seq_length, self.hidden_size)							)

							def 				lowerCamelCase_			(							self:     Dict   ,						UpperCamelCase_:     str   ,						UpperCamelCase_:     Union[str, Any]   ,						UpperCamelCase_:     List[str]   ,						UpperCamelCase_:     List[str]   ,						UpperCamelCase_:     List[str]   ,						UpperCamelCase_:     Optional[int]   ,						UpperCamelCase_:     int   ,						UpperCamelCase_:     Dict   ,						UpperCamelCase_:     Tuple   ,						)      ->       Tuple:





									"""simple docstring"""







									lowercase__						=				True
									lowercase__						=				MraModel(SCREAMING_SNAKE_CASE_							)
									model.to(SCREAMING_SNAKE_CASE_							)
									model.eval()
									lowercase__						=				model(
									    SCREAMING_SNAKE_CASE_   ,						attention_mask=SCREAMING_SNAKE_CASE_   ,						token_type_ids=SCREAMING_SNAKE_CASE_   ,						encoder_hidden_states=SCREAMING_SNAKE_CASE_   ,						encoder_attention_mask=SCREAMING_SNAKE_CASE_   ,						)
									lowercase__						=				model(
									    SCREAMING_SNAKE_CASE_   ,						attention_mask=SCREAMING_SNAKE_CASE_   ,						token_type_ids=SCREAMING_SNAKE_CASE_   ,						encoder_hidden_states=SCREAMING_SNAKE_CASE_   ,						)
									lowercase__						=				model(SCREAMING_SNAKE_CASE_   ,						attention_mask=SCREAMING_SNAKE_CASE_   ,						token_type_ids=SCREAMING_SNAKE_CASE_							)
									self.parent.assertEqual(result.last_hidden_state.shape   ,						(self.batch_size, self.seq_length, self.hidden_size)							)

							def 				lowerCamelCase_			(							self:     Any   ,						UpperCamelCase_:     List[Any]   ,						UpperCamelCase_:     Any   ,						UpperCamelCase_:     str   ,						UpperCamelCase_:     Dict   ,						UpperCamelCase_:     int   ,						UpperCamelCase_:     Any   ,						UpperCamelCase_:     Any							)      ->       List[Any]:





									"""simple docstring"""







									lowercase__						=				MraForMaskedLM(config=SCREAMING_SNAKE_CASE_							)
									model.to(SCREAMING_SNAKE_CASE_							)
									model.eval()
									lowercase__						=				model(SCREAMING_SNAKE_CASE_   ,						attention_mask=SCREAMING_SNAKE_CASE_   ,						token_type_ids=SCREAMING_SNAKE_CASE_   ,						labels=SCREAMING_SNAKE_CASE_							)
									self.parent.assertEqual(result.logits.shape   ,						(self.batch_size, self.seq_length, self.vocab_size)							)

							def 				lowerCamelCase_			(							self:     int   ,						UpperCamelCase_:     List[str]   ,						UpperCamelCase_:     List[Any]   ,						UpperCamelCase_:     List[Any]   ,						UpperCamelCase_:     Tuple   ,						UpperCamelCase_:     Optional[Any]   ,						UpperCamelCase_:     Optional[Any]   ,						UpperCamelCase_:     Optional[int]							)      ->       Any:





									"""simple docstring"""







									lowercase__						=				MraForQuestionAnswering(config=SCREAMING_SNAKE_CASE_							)
									model.to(SCREAMING_SNAKE_CASE_							)
									model.eval()
									lowercase__						=				model(
									    SCREAMING_SNAKE_CASE_   ,						attention_mask=SCREAMING_SNAKE_CASE_   ,						token_type_ids=SCREAMING_SNAKE_CASE_   ,						start_positions=SCREAMING_SNAKE_CASE_   ,						end_positions=SCREAMING_SNAKE_CASE_   ,						)
									self.parent.assertEqual(result.start_logits.shape   ,						(self.batch_size, self.seq_length)							)
									self.parent.assertEqual(result.end_logits.shape   ,						(self.batch_size, self.seq_length)							)

							def 				lowerCamelCase_			(							self:     Any   ,						UpperCamelCase_:     Optional[int]   ,						UpperCamelCase_:     List[str]   ,						UpperCamelCase_:     List[str]   ,						UpperCamelCase_:     Tuple   ,						UpperCamelCase_:     Optional[Any]   ,						UpperCamelCase_:     Union[str, Any]   ,						UpperCamelCase_:     Optional[int]							)      ->       List[Any]:





									"""simple docstring"""







									lowercase__						=				self.num_labels
									lowercase__						=				MraForSequenceClassification(SCREAMING_SNAKE_CASE_							)
									model.to(SCREAMING_SNAKE_CASE_							)
									model.eval()
									lowercase__						=				model(SCREAMING_SNAKE_CASE_   ,						attention_mask=SCREAMING_SNAKE_CASE_   ,						token_type_ids=SCREAMING_SNAKE_CASE_   ,						labels=SCREAMING_SNAKE_CASE_							)
									self.parent.assertEqual(result.logits.shape   ,						(self.batch_size, self.num_labels)							)

							def 				lowerCamelCase_			(							self:     List[Any]   ,						UpperCamelCase_:     str   ,						UpperCamelCase_:     Dict   ,						UpperCamelCase_:     Tuple   ,						UpperCamelCase_:     Dict   ,						UpperCamelCase_:     Dict   ,						UpperCamelCase_:     List[str]   ,						UpperCamelCase_:     List[str]							)      ->       Tuple:





									"""simple docstring"""







									lowercase__						=				self.num_labels
									lowercase__						=				MraForTokenClassification(config=SCREAMING_SNAKE_CASE_							)
									model.to(SCREAMING_SNAKE_CASE_							)
									model.eval()
									lowercase__						=				model(SCREAMING_SNAKE_CASE_   ,						attention_mask=SCREAMING_SNAKE_CASE_   ,						token_type_ids=SCREAMING_SNAKE_CASE_   ,						labels=SCREAMING_SNAKE_CASE_							)
									self.parent.assertEqual(result.logits.shape   ,						(self.batch_size, self.seq_length, self.num_labels)							)

							def 				lowerCamelCase_			(							self:     Dict   ,						UpperCamelCase_:     int   ,						UpperCamelCase_:     Dict   ,						UpperCamelCase_:     Any   ,						UpperCamelCase_:     Union[str, Any]   ,						UpperCamelCase_:     List[Any]   ,						UpperCamelCase_:     List[str]   ,						UpperCamelCase_:     Optional[int]							)      ->       Any:





									"""simple docstring"""







									lowercase__						=				self.num_choices
									lowercase__						=				MraForMultipleChoice(config=SCREAMING_SNAKE_CASE_							)
									model.to(SCREAMING_SNAKE_CASE_							)
									model.eval()
									lowercase__						=				input_ids.unsqueeze(1							).expand(-1   ,						self.num_choices   ,						-1							).contiguous()
									lowercase__						=				token_type_ids.unsqueeze(1							).expand(-1   ,						self.num_choices   ,						-1							).contiguous()
									lowercase__						=				input_mask.unsqueeze(1							).expand(-1   ,						self.num_choices   ,						-1							).contiguous()
									lowercase__						=				model(
									    SCREAMING_SNAKE_CASE_   ,						attention_mask=SCREAMING_SNAKE_CASE_   ,						token_type_ids=SCREAMING_SNAKE_CASE_   ,						labels=SCREAMING_SNAKE_CASE_   ,						)
									self.parent.assertEqual(result.logits.shape   ,						(self.batch_size, self.num_choices)							)


							def 				lowerCamelCase_			(							self:     List[Any]							)      ->       Dict:





									"""simple docstring"""







									lowercase__						=				self.prepare_config_and_inputs()
									(
									    lowercase__							
									)						=				config_and_inputs
									lowercase__						=				{"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": input_mask}
									return config, inputs_dict


@require_torch
class 	_a							(      _UpperCAmelCase					,     unittest.TestCase      ):
							_lowercase     :      Tuple  				=			(
							    (
							        MraModel,
							        MraForMaskedLM,
							        MraForMultipleChoice,
							        MraForQuestionAnswering,
							        MraForSequenceClassification,
							        MraForTokenClassification,
							    )
							    if is_torch_available()
							    else ()
							)
							_lowercase     :      List[str]  				=			False
							_lowercase     :      List[Any]  				=			False
							_lowercase     :      List[Any]  				=			False
							_lowercase     :      Optional[int]  				=			False

							_lowercase     :      List[Any]  				=			()

							def 				lowerCamelCase_			(							self:     str							)      ->       Tuple:





									"""simple docstring"""







									lowercase__						=				MraModelTester(self							)
									lowercase__						=				ConfigTester(self   ,						config_class=SCREAMING_SNAKE_CASE_   ,						hidden_size=37							)

							def 				lowerCamelCase_			(							self:     Optional[Any]							)      ->       Optional[Any]:





									"""simple docstring"""







									self.config_tester.run_common_tests()

							def 				lowerCamelCase_			(							self:     Any							)      ->       str:





									"""simple docstring"""







									lowercase__						=				self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE_							)

							def 				lowerCamelCase_			(							self:     List[str]							)      ->       Optional[int]:





									"""simple docstring"""







									lowercase__						=				self.model_tester.prepare_config_and_inputs()
									for type in ["absolute", "relative_key", "relative_key_query"]:
											lowercase__						=				type
											self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE_							)

							def 				lowerCamelCase_			(							self:     Dict							)      ->       int:





									"""simple docstring"""







									lowercase__						=				self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_masked_lm(*SCREAMING_SNAKE_CASE_							)

							def 				lowerCamelCase_			(							self:     Optional[int]							)      ->       Dict:





									"""simple docstring"""







									lowercase__						=				self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_multiple_choice(*SCREAMING_SNAKE_CASE_							)

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





									"""simple docstring"""







									lowercase__						=				self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_question_answering(*SCREAMING_SNAKE_CASE_							)

							def 				lowerCamelCase_			(							self:     List[Any]							)      ->       int:





									"""simple docstring"""







									lowercase__						=				self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_sequence_classification(*SCREAMING_SNAKE_CASE_							)

							def 				lowerCamelCase_			(							self:     Optional[int]							)      ->       str:





									"""simple docstring"""







									lowercase__						=				self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_token_classification(*SCREAMING_SNAKE_CASE_							)

							@slow
							def 				lowerCamelCase_			(							self:     Tuple							)      ->       Optional[Any]:





									"""simple docstring"""







									for model_name in MRA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
											lowercase__						=				MraModel.from_pretrained(SCREAMING_SNAKE_CASE_							)
											self.assertIsNotNone(SCREAMING_SNAKE_CASE_							)


							@unittest.skip(reason='''MRA does not output attentions'''							)
							def 				lowerCamelCase_			(							self:     Any							)      ->       Tuple:





									"""simple docstring"""







									return


@require_torch
class 	_a							(      unittest.TestCase      ):

							@slow
							def 				lowerCamelCase_			(							self:     List[Any]							)      ->       Optional[int]:





									"""simple docstring"""







									lowercase__						=				MraModel.from_pretrained('''uw-madison/mra-base-512-4'''							)
									lowercase__						=				torch.arange(256							).unsqueeze(0							)

									with torch.no_grad():
											lowercase__						=				model(SCREAMING_SNAKE_CASE_							)[0]

									lowercase__						=				torch.Size((1, 256, 768)							)
									self.assertEqual(output.shape   ,						SCREAMING_SNAKE_CASE_							)

									lowercase__						=				torch.tensor(
									    [[[-0.0140, 0.0830, -0.0381], [0.1546, 0.1402, 0.0220], [0.1162, 0.0851, 0.0165]]]							)

									self.assertTrue(torch.allclose(output[:, :3, :3]   ,						SCREAMING_SNAKE_CASE_   ,						atol=1E-4							)							)

							@slow
							def 				lowerCamelCase_			(							self:     Any							)      ->       Union[str, Any]:





									"""simple docstring"""







									lowercase__						=				MraForMaskedLM.from_pretrained('''uw-madison/mra-base-512-4'''							)
									lowercase__						=				torch.arange(256							).unsqueeze(0							)

									with torch.no_grad():
											lowercase__						=				model(SCREAMING_SNAKE_CASE_							)[0]

									lowercase__						=				50_265

									lowercase__						=				torch.Size((1, 256, vocab_size)							)
									self.assertEqual(output.shape   ,						SCREAMING_SNAKE_CASE_							)

									lowercase__						=				torch.tensor(
									    [[[9.2595, -3.6038, 11.8819], [9.3869, -3.2693, 11.0956], [11.8524, -3.4938, 13.1210]]]							)

									self.assertTrue(torch.allclose(output[:, :3, :3]   ,						SCREAMING_SNAKE_CASE_   ,						atol=1E-4							)							)


							@slow
							def 				lowerCamelCase_			(							self:     int							)      ->       Dict:





									"""simple docstring"""







									lowercase__						=				MraForMaskedLM.from_pretrained('''uw-madison/mra-base-4096-8-d3'''							)
									lowercase__						=				torch.arange(4_096							).unsqueeze(0							)

									with torch.no_grad():
											lowercase__						=				model(SCREAMING_SNAKE_CASE_							)[0]

									lowercase__						=				50_265

									lowercase__						=				torch.Size((1, 4_096, vocab_size)							)
									self.assertEqual(output.shape   ,						SCREAMING_SNAKE_CASE_							)

									lowercase__						=				torch.tensor(
									    [[[5.4789, -2.3564, 7.5064], [7.9067, -1.3369, 9.9668], [9.0712, -1.8106, 7.0380]]]							)

									self.assertTrue(torch.allclose(output[:, :3, :3]   ,						SCREAMING_SNAKE_CASE_   ,						atol=1E-4							)							)






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       _a			(				SCREAMING_SNAKE_CASE					):


		"""simple docstring"""




		lowercase__						=				checkpoints.load_tax_checkpoint(SCREAMING_SNAKE_CASE					)
		lowercase__						=				flatten_dict(SCREAMING_SNAKE_CASE					)
		return flax_params
def       _a			(				SCREAMING_SNAKE_CASE					):


		"""simple docstring"""




		lowercase__						=				{}

		lowercase__						=				{
		    '''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__						=				{
		    '''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__						=				'''.'''.join(key[1:]					)

						# rename the key
						for old, new in CONVERSION_MAPPING.items():
								lowercase__						=				new_key.replace(SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE					)

						if "decoder" in new_key:
								for old, new in DECODER_CONVERSION_MAPPING.items():
										lowercase__						=				new_key.replace(SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE					)

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

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

						lowercase__						=				flax_dict[key]

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

		return converted_torch_dict
def       _a			(				SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE=False						,    SCREAMING_SNAKE_CASE=False					):


		"""simple docstring"""




		lowercase__						=				get_flax_param(SCREAMING_SNAKE_CASE					)

		if not use_large:
				lowercase__						=				PixaStructVisionConfig()
				lowercase__						=				PixaStructTextConfig()
		else:
				lowercase__						=				PixaStructVisionConfig(
				    hidden_size=15_36						,    d_ff=39_68						,    num_attention_heads=24						,    num_hidden_layers=18					)
				lowercase__						=				PixaStructTextConfig(hidden_size=15_36						,    d_ff=39_68						,    num_heads=24						,    num_layers=18					)
		lowercase__						=				PixaStructConfig(
		    vision_config=encoder_config.to_dict()						,    text_config=decoder_config.to_dict()						,    is_vqa=SCREAMING_SNAKE_CASE					)

		lowercase__						=				PixaStructForConditionalGeneration(SCREAMING_SNAKE_CASE					)

		lowercase__						=				rename_and_convert_flax_params(SCREAMING_SNAKE_CASE					)
		model.load_state_dict(SCREAMING_SNAKE_CASE					)

		lowercase__						=				AutoTokenizer.from_pretrained('''ybelkada/test-pix2struct-tokenizer'''					)
		lowercase__						=				PixaStructImageProcessor()
		lowercase__						=				PixaStructProcessor(image_processor=SCREAMING_SNAKE_CASE						,    tokenizer=SCREAMING_SNAKE_CASE					)

		if use_large:
				lowercase__						=				40_96

		lowercase__						=				True

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

		model.save_pretrained(SCREAMING_SNAKE_CASE					)
		processor.save_pretrained(SCREAMING_SNAKE_CASE					)

		print('''Model saved in {}'''.format(SCREAMING_SNAKE_CASE					)					)


if __name__ == "__main__":
			lowerCAmelCase	=							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.')
			lowerCAmelCase	=							parser.parse_args()

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


