

import math
import unittest

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

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
	import torch

	from transformers import (
	    BioGptForCausalLM,
	    BioGptForSequenceClassification,
	    BioGptForTokenClassification,
	    BioGptModel,
	    BioGptTokenizer,
	)
	from transformers.models.biogpt.modeling_biogpt import BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST



class   lowercase_   :



						def __init__(      self				:    str      , A__				:    Union[str, Any]      , A__				:    Dict=13      , A__				:    Optional[int]=7      , A__				:    Tuple=True      , A__				:    Optional[Any]=True      , A__				:    Optional[Any]=False      , A__				:    List[str]=True      , A__				:    Union[str, Any]=99      , A__				:    str=32      , A__				:    int=5      , A__				:    Union[str, Any]=4      , A__				:    Optional[int]=37      , A__				:    List[str]="gelu"      , A__				:    List[Any]=0.1      , A__				:    Any=0.1      , A__				:    Tuple=512      , A__				:    Any=16      , A__				:    int=2      , A__				:    Tuple=0.02      , A__				:    Optional[int]=3      , A__				:    Union[str, Any]=4      , A__				:    Union[str, Any]=None      , )   ->					Tuple:
								_snake_case						=							parent
								_snake_case						=							batch_size
								_snake_case						=							seq_length
								_snake_case						=							is_training
								_snake_case						=							use_input_mask
								_snake_case						=							use_token_type_ids
								_snake_case						=							use_labels
								_snake_case						=							vocab_size
								_snake_case						=							hidden_size
								_snake_case						=							num_hidden_layers
								_snake_case						=							num_attention_heads
								_snake_case						=							intermediate_size
								_snake_case						=							hidden_act
								_snake_case						=							hidden_dropout_prob
								_snake_case						=							attention_probs_dropout_prob
								_snake_case						=							max_position_embeddings
								_snake_case						=							type_vocab_size
								_snake_case						=							type_sequence_label_size
								_snake_case						=							initializer_range
								_snake_case						=							num_labels
								_snake_case						=							num_choices
								_snake_case						=							scope



						def 						UpperCamelCase_		(      self				:    Optional[Any]		)   ->					Tuple:
								_snake_case						=							ids_tensor([self.batch_size, self.seq_length]      , self.vocab_size		)

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

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

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

								_snake_case						=							self.get_config()

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



						def 						UpperCamelCase_		(      self				:    List[str]		)   ->					int:
								return BioGptConfig(
								    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=snake_case__      , initializer_range=self.initializer_range      , )



						def 						UpperCamelCase_		(      self				:    List[Any]      , A__				:    Any      , A__				:    str      , A__				:    Any      , A__				:    int      , A__				:    int      , A__				:    Tuple      , A__				:    List[str]		)   ->					Optional[int]:
								_snake_case						=							BioGptModel(config=snake_case__		)
								model.to(snake_case__		)
								model.eval()
								_snake_case						=							model(snake_case__      , attention_mask=snake_case__		)
								_snake_case						=							model(snake_case__		)
								self.parent.assertEqual(result.last_hidden_state.shape      , (self.batch_size, self.seq_length, self.hidden_size)		)



						def 						UpperCamelCase_		(      self				:    Dict      , A__				:    Optional[int]      , A__				:    Optional[int]      , A__				:    List[str]      , A__				:    Optional[Any]      , A__				:    Union[str, Any]      , A__				:    Tuple      , A__				:    Optional[Any]      , A__				:    int      , A__				:    Dict      , )   ->					Optional[Any]:
								_snake_case						=							BioGptForCausalLM(config=snake_case__		)
								model.to(snake_case__		)
								model.eval()
								_snake_case						=							model(snake_case__      , attention_mask=snake_case__      , token_type_ids=snake_case__      , labels=snake_case__		)
								self.parent.assertEqual(result.logits.shape      , (self.batch_size, self.seq_length, self.vocab_size)		)



						def 						UpperCamelCase_		(      self				:    Optional[int]      , A__				:    Union[str, Any]      , A__				:    int      , A__				:    Any      , A__				:    Tuple      , A__				:    Any      , *A__				:    List[Any]		)   ->					str:
								_snake_case						=							BioGptModel(config=snake_case__		)
								model.to(snake_case__		)
								model.eval()

								# create attention mask
								_snake_case						=							torch.ones(input_ids.shape      , dtype=torch.long      , device=snake_case__		)
								_snake_case						=							self.seq_length // 2
								_snake_case						=							0

								# first forward pass
								_snake_case						=							model(snake_case__      , attention_mask=snake_case__		).to_tuple()

								# create hypothetical next token and extent to next_input_ids
								_snake_case						=							ids_tensor((self.batch_size, 1)      , config.vocab_size		)

								# change a random masked slice from input_ids
								_snake_case						=							ids_tensor((1,)      , snake_case__		).item() + 1
								_snake_case						=							ids_tensor((self.batch_size, 1)      , config.vocab_size		).squeeze(-1		)
								_snake_case						=							random_other_next_tokens

								# append to next input_ids and attn_mask
								_snake_case						=							torch.cat([input_ids, next_tokens]      , dim=-1		)
								_snake_case						=							torch.cat(
								    [attn_mask, torch.ones((attn_mask.shape[0], 1)      , dtype=torch.long      , device=snake_case__		)]      , dim=1      , )

								# get two different outputs
								_snake_case						=							model(snake_case__      , attention_mask=snake_case__		)['''last_hidden_state''']
								_snake_case						=							model(snake_case__      , past_key_values=snake_case__      , attention_mask=snake_case__		)['''last_hidden_state''']

								# select random slice
								_snake_case						=							ids_tensor((1,)      , output_from_past.shape[-1]		).item()
								_snake_case						=							output_from_no_past[:, -1, random_slice_idx].detach()
								_snake_case						=							output_from_past[:, 0, random_slice_idx].detach()

								# test that outputs are equal for slice
								self.parent.assertTrue(torch.allclose(snake_case__      , snake_case__      , atol=1e-3		)		)



						def 						UpperCamelCase_		(      self				:    List[Any]      , A__				:    Optional[Any]      , A__				:    str      , A__				:    List[Any]      , A__				:    Any      , A__				:    List[str]      , *A__				:    List[Any]		)   ->					int:
								_snake_case						=							BioGptModel(config=snake_case__		).to(snake_case__		).eval()

								_snake_case						=							torch.ones(input_ids.shape      , dtype=torch.long      , device=snake_case__		)

								# first forward pass
								_snake_case						=							model(snake_case__      , attention_mask=snake_case__      , use_cache=snake_case__		)

								_snake_case						=							outputs.to_tuple()

								# create hypothetical multiple next token and extent to next_input_ids
								_snake_case						=							ids_tensor((self.batch_size, 3)      , config.vocab_size		)
								_snake_case						=							ids_tensor((self.batch_size, 3)      , 2		)

								# append to next input_ids and
								_snake_case						=							torch.cat([input_ids, next_tokens]      , dim=-1		)
								_snake_case						=							torch.cat([attention_mask, next_attn_mask]      , dim=-1		)

								_snake_case						=							model(snake_case__      , attention_mask=snake_case__		)['''last_hidden_state''']
								_snake_case						=							model(snake_case__      , attention_mask=snake_case__      , past_key_values=snake_case__		)[
								    '''last_hidden_state'''
								]

								# select random slice
								_snake_case						=							ids_tensor((1,)      , output_from_past.shape[-1]		).item()
								_snake_case						=							output_from_no_past[:, -3:, random_slice_idx].detach()
								_snake_case						=							output_from_past[:, :, random_slice_idx].detach()

								self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]		)

								# test that outputs are equal for slice
								self.parent.assertTrue(torch.allclose(snake_case__      , snake_case__      , atol=1e-3		)		)



						def 						UpperCamelCase_		(      self				:    Tuple      , A__				:    Optional[int]      , A__				:    Tuple      , A__				:    List[Any]      , A__				:    Union[str, Any]      , A__				:    Union[str, Any]      , *A__				:    Any      , A__				:    Dict=False		)   ->					Dict:
								_snake_case						=							BioGptForCausalLM(snake_case__		)
								model.to(snake_case__		)
								if gradient_checkpointing:
										model.gradient_checkpointing_enable()

								_snake_case						=							model(snake_case__      , labels=snake_case__		)
								self.parent.assertEqual(result.loss.shape      , ()		)
								self.parent.assertEqual(result.logits.shape      , (self.batch_size, self.seq_length, self.vocab_size)		)
								result.loss.backward()



						def 						UpperCamelCase_		(      self				:    Optional[Any]      , A__				:    int      , *A__				:    str		)   ->					Union[str, Any]:
								_snake_case						=							BioGptModel(snake_case__		)
								_snake_case						=							model.config.initializer_range / math.sqrt(2 * model.config.num_hidden_layers		)
								for key in model.state_dict().keys():
										if "c_proj" in key and "weight" in key:
												self.parent.assertLessEqual(abs(torch.std(model.state_dict()[key]		) - model_std		)      , 0.001		)
												self.parent.assertLessEqual(abs(torch.mean(model.state_dict()[key]		) - 0.0		)      , 0.01		)



						def 						UpperCamelCase_		(      self				:    int      , A__				:    Dict      , A__				:    List[Any]      , A__				:    Dict      , A__				:    Tuple      , A__				:    Tuple      , *A__				:    Optional[Any]		)   ->					Optional[Any]:
								_snake_case						=							self.num_labels
								_snake_case						=							BioGptForTokenClassification(snake_case__		)
								model.to(snake_case__		)
								model.eval()
								_snake_case						=							model(snake_case__      , attention_mask=snake_case__      , token_type_ids=snake_case__		)
								self.parent.assertEqual(result.logits.shape      , (self.batch_size, self.seq_length, self.num_labels)		)







						def 						UpperCamelCase_		(      self				:    Tuple		)   ->					Optional[Any]:
								_snake_case						=							self.prepare_config_and_inputs()
								(
								    _snake_case  
								)						=							config_and_inputs
								_snake_case						=							{'''input_ids''': input_ids, '''attention_mask''': input_mask}
								return config, inputs_dict



@require_torch
class   lowercase_   (  lowerCamelCase__       ,							lowerCamelCase__       ,							lowerCamelCase__       ,							unittest.TestCase					):
						UpperCamelCase_      :		Union[str, Any]										=    (
						    (BioGptModel, BioGptForCausalLM, BioGptForSequenceClassification, BioGptForTokenClassification)
						    if is_torch_available()
						    else ()
						)
						UpperCamelCase_      :		Optional[Any]										=    (BioGptForCausalLM,) if is_torch_available() else ()
						UpperCamelCase_      :		int										=    (
						    {
						        """feature-extraction""": BioGptModel,
						        """text-classification""": BioGptForSequenceClassification,
						        """text-generation""": BioGptForCausalLM,
						        """token-classification""": BioGptForTokenClassification,
						        """zero-shot""": BioGptForSequenceClassification,
						    }
						    if is_torch_available()
						    else {}
						)
						UpperCamelCase_      :		Optional[int]										=    False



						def 						UpperCamelCase_		(      self				:    int		)   ->					Union[str, Any]:
								_snake_case						=							BioGptModelTester(self		)
								_snake_case						=							ConfigTester(self      , config_class=snake_case__      , hidden_size=37		)



						def 						UpperCamelCase_		(      self				:    str		)   ->					Union[str, Any]:
								self.config_tester.run_common_tests()



						def 						UpperCamelCase_		(      self				:    Any		)   ->					Dict:
								_snake_case						=							self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*snake_case__		)



						def 						UpperCamelCase_		(      self				:    Dict		)   ->					int:
								_snake_case						=							self.model_tester.prepare_config_and_inputs()
								for type in ["absolute", "relative_key", "relative_key_query"]:
										_snake_case						=							type
										self.model_tester.create_and_check_model(*snake_case__		)



						def 						UpperCamelCase_		(      self				:    Optional[Any]		)   ->					List[str]:
								_snake_case						=							self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_biogpt_model_attention_mask_past(*snake_case__		)



						def 						UpperCamelCase_		(      self				:    Tuple		)   ->					str:
								_snake_case						=							self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_forward_and_backwards(*snake_case__      , gradient_checkpointing=snake_case__		)



						def 						UpperCamelCase_		(      self				:    int		)   ->					str:
								_snake_case						=							self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_biogpt_model_past_large_inputs(*snake_case__		)



						def 						UpperCamelCase_		(      self				:    Any		)   ->					int:
								_snake_case						=							self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_biogpt_weight_initialization(*snake_case__		)



						def 						UpperCamelCase_		(      self				:    Any		)   ->					Dict:
								_snake_case						=							self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_biogpt_for_token_classification(*snake_case__		)



						@slow
						def 						UpperCamelCase_		(      self				:    Any		)   ->					Union[str, Any]:
								_snake_case						=							BioGptForCausalLM.from_pretrained('''microsoft/biogpt'''		)
								model.to(snake_case__		)
								_snake_case						=							BioGptTokenizer.from_pretrained('''microsoft/biogpt'''		)

								_snake_case						=							'''left'''

								# Define PAD Token = EOS Token = 50256
								_snake_case						=							tokenizer.eos_token
								_snake_case						=							model.config.eos_token_id

								# use different length sentences to test batching
								_snake_case						=							[
								    '''Hello, my dog is a little''',
								    '''Today, I''',
								]

								_snake_case						=							tokenizer(snake_case__      , return_tensors='''pt'''      , padding=snake_case__		)
								_snake_case						=							inputs['''input_ids'''].to(snake_case__		)

								_snake_case						=							model.generate(
								    input_ids=snake_case__      , attention_mask=inputs['''attention_mask'''].to(snake_case__		)      , )

								_snake_case						=							tokenizer(sentences[0]      , return_tensors='''pt'''		).input_ids.to(snake_case__		)
								_snake_case						=							model.generate(input_ids=snake_case__		)

								_snake_case						=							inputs_non_padded.shape[-1] - inputs['''attention_mask'''][-1].long().sum().cpu().item()
								_snake_case						=							tokenizer(sentences[1]      , return_tensors='''pt'''		).input_ids.to(snake_case__		)
								_snake_case						=							model.generate(input_ids=snake_case__      , max_length=model.config.max_length - num_paddings		)

								_snake_case						=							tokenizer.batch_decode(snake_case__      , skip_special_tokens=snake_case__		)
								_snake_case						=							tokenizer.decode(output_non_padded[0]      , skip_special_tokens=snake_case__		)
								_snake_case						=							tokenizer.decode(output_padded[0]      , skip_special_tokens=snake_case__		)

								_snake_case						=							[
								    '''Hello, my dog is a little bit bigger than a little bit.''',
								    '''Today, I have a good idea of how to use the information''',
								]
								self.assertListEqual(snake_case__      , snake_case__		)
								self.assertListEqual(snake_case__      , [non_padded_sentence, padded_sentence]		)



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





								for model_name in BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
										_snake_case						=							BioGptModel.from_pretrained(snake_case__		)
										self.assertIsNotNone(snake_case__		)



						def 						UpperCamelCase_		(      self				:    Union[str, Any]		)   ->					List[Any]:
								_snake_case						=							self.model_tester.prepare_config_and_inputs_for_common()
								_snake_case						=							3
								_snake_case						=							input_dict['''input_ids''']
								_snake_case						=							input_ids.ne(1		).to(snake_case__		)
								_snake_case						=							ids_tensor([self.model_tester.batch_size]      , self.model_tester.type_sequence_label_size		)
								_snake_case						=							BioGptForSequenceClassification(snake_case__		)
								model.to(snake_case__		)
								model.eval()
								_snake_case						=							model(snake_case__      , attention_mask=snake_case__      , labels=snake_case__		)
								self.assertEqual(result.logits.shape      , (self.model_tester.batch_size, self.model_tester.num_labels)		)







						def 						UpperCamelCase_		(      self				:    Optional[int]		)   ->					int:
								_snake_case						=							self.model_tester.prepare_config_and_inputs_for_common()
								_snake_case						=							3
								_snake_case						=							'''multi_label_classification'''
								_snake_case						=							input_dict['''input_ids''']
								_snake_case						=							input_ids.ne(1		).to(snake_case__		)
								_snake_case						=							ids_tensor(
								    [self.model_tester.batch_size, config.num_labels]      , self.model_tester.type_sequence_label_size		).to(torch.float		)
								_snake_case						=							BioGptForSequenceClassification(snake_case__		)
								model.to(snake_case__		)
								model.eval()
								_snake_case						=							model(snake_case__      , attention_mask=snake_case__      , labels=snake_case__		)
								self.assertEqual(result.logits.shape      , (self.model_tester.batch_size, self.model_tester.num_labels)		)



@require_torch
class   lowercase_   (  unittest.TestCase					):



						@slow
						def 						UpperCamelCase_		(      self				:    str		)   ->					List[Any]:
								_snake_case						=							BioGptForCausalLM.from_pretrained('''microsoft/biogpt'''		)
								_snake_case						=							torch.tensor([[2, 4805, 9, 656, 21]]		)
								_snake_case						=							model(snake_case__		)[0]

								_snake_case						=							42384

								_snake_case						=							torch.Size((1, 5, vocab_size)		)
								self.assertEqual(output.shape      , snake_case__		)

								_snake_case						=							torch.tensor(
								    [[[-9.5236, -9.8918, 10.4557], [-11.0469, -9.6423, 8.1022], [-8.8664, -7.8826, 5.5325]]]		)

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







						@slow
						def 						UpperCamelCase_		(      self				:    Optional[Any]		)   ->					Dict:
								_snake_case						=							BioGptTokenizer.from_pretrained('''microsoft/biogpt'''		)
								_snake_case						=							BioGptForCausalLM.from_pretrained('''microsoft/biogpt'''		)
								model.to(snake_case__		)

								torch.manual_seed(0		)
								_snake_case						=							tokenizer('''COVID-19 is'''      , return_tensors='''pt'''		).to(snake_case__		)
								_snake_case						=							model.generate(
								    **snake_case__      , min_length=100      , max_length=1024      , num_beams=5      , early_stopping=snake_case__      , )
								_snake_case						=							tokenizer.decode(output_ids[0]      , skip_special_tokens=snake_case__		)

								_snake_case						=							(
								    '''COVID-19 is a global pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the'''
								    ''' causative agent of coronavirus disease 2019 (COVID-19), which has spread to more than 200 countries and'''
								    ''' territories, including the United States (US), Canada, Australia, New Zealand, the United Kingdom (UK),'''
								    ''' and the United States of America (USA), as of March 11, 2020, with more than 800,000 confirmed cases and'''
								    ''' more than 800,000 deaths.'''
								)
								self.assertEqual(snake_case__      , snake_case__		)




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 				snake_case_(_UpperCamelCase					)							->     Optional[int]:



  """simple docstring"""






  _snake_case						=							checkpoints.load_tax_checkpoint(_UpperCamelCase					)
  _snake_case						=							flatten_dict(_UpperCamelCase					)
  return flax_params



def 				snake_case_(_UpperCamelCase					)							->     List[str]:



  """simple docstring"""






  _snake_case						=							{}

  _snake_case						=							{
      '''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''',
  }

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

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

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

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

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

      _snake_case						=							flax_dict[key]

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

  return converted_torch_dict



def 				snake_case_(_UpperCamelCase			,	_UpperCamelCase			,	_UpperCamelCase=False			,	_UpperCamelCase=False					)							->     List[Any]:



  """simple docstring"""






  _snake_case						=							get_flax_param(_UpperCamelCase					)

  if not use_large:
    _snake_case						=							PixaStructVisionConfig()
    _snake_case						=							PixaStructTextConfig()
  else:
    _snake_case						=							PixaStructVisionConfig(
        hidden_size=1_536			,	d_ff=3_968			,	num_attention_heads=24			,	num_hidden_layers=18					)
    _snake_case						=							PixaStructTextConfig(hidden_size=1_536			,	d_ff=3_968			,	num_heads=24			,	num_layers=18					)
  _snake_case						=							PixaStructConfig(
      vision_config=encoder_config.to_dict()			,	text_config=decoder_config.to_dict()			,	is_vqa=_UpperCamelCase					)

  _snake_case						=							PixaStructForConditionalGeneration(_UpperCamelCase					)

  _snake_case						=							rename_and_convert_flax_params(_UpperCamelCase					)
  model.load_state_dict(_UpperCamelCase					)

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

  if use_large:
    _snake_case						=							4_096

  _snake_case						=							True

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

  model.save_pretrained(_UpperCamelCase					)
  processor.save_pretrained(_UpperCamelCase					)

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


if __name__ == "__main__":
 __A      		=     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      		=     parser.parse_args()

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

