




'''simple docstring'''



import unittest

from transformers.testing_utils import CaptureStdout
from transformers.tools.python_interpreter import evaluate







def       _UpperCamelCase			(_lowerCamelCase				:  List[Any]						)-> List[Any]:




     '''simple docstring'''





     return x + 2







class      lowerCAmelCase (			unittest.TestCase):


       def    lowerCAmelCase   ( self	)   ->	Optional[int]:



            '''simple docstring'''




            __snake_case            =	'''x = 3'''
            __snake_case            =	{}
            __snake_case            =	evaluate(__SCREAMING_SNAKE_CASE     ,	{}     ,	state=__SCREAMING_SNAKE_CASE	)
            assert result == 3
            self.assertDictEqual(__SCREAMING_SNAKE_CASE     ,	{'''x''': 3}	)

            __snake_case            =	'''x = y'''
            __snake_case            =	{'''y''': 5}
            __snake_case            =	evaluate(__SCREAMING_SNAKE_CASE     ,	{}     ,	state=__SCREAMING_SNAKE_CASE	)
            # evaluate returns the value of the last assignment.
            assert result == 5
            self.assertDictEqual(__SCREAMING_SNAKE_CASE     ,	{'''x''': 5, '''y''': 5}	)


       def    lowerCAmelCase   ( self	)   ->	List[str]:



            '''simple docstring'''




            __snake_case            =	'''y = add_two(x)'''
            __snake_case            =	{'''x''': 3}
            __snake_case            =	evaluate(__SCREAMING_SNAKE_CASE     ,	{'''add_two''': add_two}     ,	state=__SCREAMING_SNAKE_CASE	)
            assert result == 5
            self.assertDictEqual(__SCREAMING_SNAKE_CASE     ,	{'''x''': 3, '''y''': 5}	)

            # Won't work without the tool
            with CaptureStdout() as out:
                 __snake_case            =	evaluate(__SCREAMING_SNAKE_CASE     ,	{}     ,	state=__SCREAMING_SNAKE_CASE	)
            assert result is None
            assert "tried to execute add_two" in out.out


       def    lowerCAmelCase   ( self	)   ->	Tuple:



            '''simple docstring'''




            __snake_case            =	'''x = 3'''
            __snake_case            =	{}
            __snake_case            =	evaluate(__SCREAMING_SNAKE_CASE     ,	{}     ,	state=__SCREAMING_SNAKE_CASE	)
            assert result == 3
            self.assertDictEqual(__SCREAMING_SNAKE_CASE     ,	{'''x''': 3}	)


       def    lowerCAmelCase   ( self	)   ->	List[str]:



            '''simple docstring'''




            __snake_case            =	'''test_dict = {\'x\': x, \'y\': add_two(x)}'''
            __snake_case            =	{'''x''': 3}
            __snake_case            =	evaluate(__SCREAMING_SNAKE_CASE     ,	{'''add_two''': add_two}     ,	state=__SCREAMING_SNAKE_CASE	)
            self.assertDictEqual(__SCREAMING_SNAKE_CASE     ,	{'''x''': 3, '''y''': 5}	)
            self.assertDictEqual(__SCREAMING_SNAKE_CASE     ,	{'''x''': 3, '''test_dict''': {'''x''': 3, '''y''': 5}}	)


       def    lowerCAmelCase   ( self	)   ->	List[Any]:



            '''simple docstring'''




            __snake_case            =	'''x = 3\ny = 5'''
            __snake_case            =	{}
            __snake_case            =	evaluate(__SCREAMING_SNAKE_CASE     ,	{}     ,	state=__SCREAMING_SNAKE_CASE	)
            # evaluate returns the value of the last assignment.
            assert result == 5
            self.assertDictEqual(__SCREAMING_SNAKE_CASE     ,	{'''x''': 3, '''y''': 5}	)


       def    lowerCAmelCase   ( self	)   ->	List[Any]:



            '''simple docstring'''




            __snake_case            =	'''text = f\'This is x: {x}.\''''
            __snake_case            =	{'''x''': 3}
            __snake_case            =	evaluate(__SCREAMING_SNAKE_CASE     ,	{}     ,	state=__SCREAMING_SNAKE_CASE	)
            # evaluate returns the value of the last assignment.
            assert result == "This is x: 3."
            self.assertDictEqual(__SCREAMING_SNAKE_CASE     ,	{'''x''': 3, '''text''': '''This is x: 3.'''}	)


       def    lowerCAmelCase   ( self	)   ->	Optional[int]:



            '''simple docstring'''




            __snake_case            =	'''if x <= 3:\n    y = 2\nelse:\n    y = 5'''
            __snake_case            =	{'''x''': 3}
            __snake_case            =	evaluate(__SCREAMING_SNAKE_CASE     ,	{}     ,	state=__SCREAMING_SNAKE_CASE	)
            # evaluate returns the value of the last assignment.
            assert result == 2
            self.assertDictEqual(__SCREAMING_SNAKE_CASE     ,	{'''x''': 3, '''y''': 2}	)

            __snake_case            =	{'''x''': 8}
            __snake_case            =	evaluate(__SCREAMING_SNAKE_CASE     ,	{}     ,	state=__SCREAMING_SNAKE_CASE	)
            # evaluate returns the value of the last assignment.
            assert result == 5
            self.assertDictEqual(__SCREAMING_SNAKE_CASE     ,	{'''x''': 8, '''y''': 5}	)


       def    lowerCAmelCase   ( self	)   ->	Optional[int]:



            '''simple docstring'''




            __snake_case            =	'''test_list = [x, add_two(x)]'''
            __snake_case            =	{'''x''': 3}
            __snake_case            =	evaluate(__SCREAMING_SNAKE_CASE     ,	{'''add_two''': add_two}     ,	state=__SCREAMING_SNAKE_CASE	)
            self.assertListEqual(__SCREAMING_SNAKE_CASE     ,	[3, 5]	)
            self.assertDictEqual(__SCREAMING_SNAKE_CASE     ,	{'''x''': 3, '''test_list''': [3, 5]}	)


       def    lowerCAmelCase   ( self	)   ->	str:



            '''simple docstring'''




            __snake_case            =	'''y = x'''
            __snake_case            =	{'''x''': 3}
            __snake_case            =	evaluate(__SCREAMING_SNAKE_CASE     ,	{}     ,	state=__SCREAMING_SNAKE_CASE	)
            assert result == 3
            self.assertDictEqual(__SCREAMING_SNAKE_CASE     ,	{'''x''': 3, '''y''': 3}	)


       def    lowerCAmelCase   ( self	)   ->	Optional[int]:



            '''simple docstring'''




            __snake_case            =	'''test_list = [x, add_two(x)]\ntest_list[1]'''
            __snake_case            =	{'''x''': 3}
            __snake_case            =	evaluate(__SCREAMING_SNAKE_CASE     ,	{'''add_two''': add_two}     ,	state=__SCREAMING_SNAKE_CASE	)
            assert result == 5
            self.assertDictEqual(__SCREAMING_SNAKE_CASE     ,	{'''x''': 3, '''test_list''': [3, 5]}	)

            __snake_case            =	'''test_dict = {\'x\': x, \'y\': add_two(x)}\ntest_dict[\'y\']'''
            __snake_case            =	{'''x''': 3}
            __snake_case            =	evaluate(__SCREAMING_SNAKE_CASE     ,	{'''add_two''': add_two}     ,	state=__SCREAMING_SNAKE_CASE	)
            assert result == 5
            self.assertDictEqual(__SCREAMING_SNAKE_CASE     ,	{'''x''': 3, '''test_dict''': {'''x''': 3, '''y''': 5}}	)




       def    lowerCAmelCase   ( self	)   ->	List[Any]:



            '''simple docstring'''




            __snake_case            =	'''x = 0\nfor i in range(3):\n    x = i'''
            __snake_case            =	{}
            __snake_case            =	evaluate(__SCREAMING_SNAKE_CASE     ,	{'''range''': range}     ,	state=__SCREAMING_SNAKE_CASE	)
            assert result == 2
            self.assertDictEqual(__SCREAMING_SNAKE_CASE     ,	{'''x''': 2, '''i''': 2}	)











'''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 a_     (    __UpperCAmelCase					)				->					int:





			"""simple docstring"""







			snake_case:    List[str]      							=checkpoints.load_tax_checkpoint(__UpperCAmelCase					)
			snake_case:    str      							=flatten_dict(__UpperCAmelCase					)
			return flax_params







def a_     (    __UpperCAmelCase					)				->					Optional[Any]:





			"""simple docstring"""







			snake_case:    int      							={}

			snake_case:    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',
			}

			snake_case:    Union[str, Any]      							={
			    '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:    Dict      							='.'.join(key[1:]					)

									# rename the key
									for old, new in CONVERSION_MAPPING.items():
												snake_case:    Union[str, Any]      							=new_key.replace(__UpperCAmelCase				,							__UpperCAmelCase					)

									if "decoder" in new_key:
												for old, new in DECODER_CONVERSION_MAPPING.items():
															snake_case:    Optional[Any]      							=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:    Any      							=re.sub(R'layers_(\d+)'				,							R'layer.\1'				,							__UpperCAmelCase					)
												snake_case:    Optional[int]      							=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:    Optional[Any]      							=re.sub(R'layers_(\d+)'				,							R'layer.\1'				,							__UpperCAmelCase					)

									snake_case:    Optional[int]      							=flax_dict[key]

			snake_case:    Optional[int]      							={}
			# 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:    Union[str, Any]      							=torch.from_numpy(converted_dict[key].T					)
						else:
									snake_case:    Union[str, Any]      							=torch.from_numpy(converted_dict[key]					)

			return converted_torch_dict







def a_     (    __UpperCAmelCase				,							__UpperCAmelCase				,							__UpperCAmelCase=False				,							__UpperCAmelCase=False					)				->					Tuple:





			"""simple docstring"""







			snake_case:    Any      							=get_flax_param(__UpperCAmelCase					)

			if not use_large:
						snake_case:    Optional[int]      							=PixaStructVisionConfig()
						snake_case:    int      							=PixaStructTextConfig()
			else:
						snake_case:    Tuple      							=PixaStructVisionConfig(
						    hidden_size=15_36				,							d_ff=39_68				,							num_attention_heads=24				,							num_hidden_layers=18					)
						snake_case:    List[Any]      							=PixaStructTextConfig(hidden_size=15_36				,							d_ff=39_68				,							num_heads=24				,							num_layers=18					)
			snake_case:    Any      							=PixaStructConfig(
			    vision_config=encoder_config.to_dict()				,							text_config=decoder_config.to_dict()				,							is_vqa=__UpperCAmelCase					)

			snake_case:    List[Any]      							=PixaStructForConditionalGeneration(__UpperCAmelCase					)

			snake_case:    str      							=rename_and_convert_flax_params(__UpperCAmelCase					)
			model.load_state_dict(__UpperCAmelCase					)

			snake_case:    List[Any]      							=AutoTokenizer.from_pretrained('ybelkada/test-pix2struct-tokenizer'					)
			snake_case:    Optional[Any]      							=PixaStructImageProcessor()
			snake_case:    Optional[int]      							=PixaStructProcessor(image_processor=__UpperCAmelCase				,							tokenizer=__UpperCAmelCase					)

			if use_large:
						snake_case:    Optional[Any]      							=40_96

			snake_case:    str      							=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
	)




