




import numpy

# List of input, output pairs
UpperCAmelCase_									=	(
    ((5, 2, 3), 15),
    ((6, 5, 9), 25),
    ((11, 12, 13), 41),
    ((1, 1, 1), 8),
    ((11, 12, 13), 41),
)
UpperCAmelCase_									=	(((515, 22, 13), 555), ((61, 35, 49), 150))
UpperCAmelCase_									=	[2, 4, 1, 5]
UpperCAmelCase_									=	len(train_data)
UpperCAmelCase_									=	0.009




def 					lowerCamelCase__       (						A__       :					Dict  ,			A__       :					Optional[int]="train"	):





       '''simple docstring'''



       return calculate_hypothesis_value(A__  ,			A__	) - output(
           A__  ,			A__	)




def 					lowerCamelCase__       (						A__       :					List[Any]	):





       '''simple docstring'''



       __lowerCamelCase   =						0
       for i in range(len(A__	) - 1	):
              hyp_val += data_input_tuple[i] * parameter_vector[i + 1]
       hyp_val += parameter_vector[0]
       return hyp_val




def 					lowerCamelCase__       (						A__       :					Union[str, Any]  ,			A__       :					Dict	):





       '''simple docstring'''



       if data_set == "train":
              return train_data[example_no][1]
       elif data_set == "test":
              return test_data[example_no][1]
       return None




def 					lowerCamelCase__       (						A__       :					Dict  ,			A__       :					int	):





       '''simple docstring'''



       if data_set == "train":
              return _hypothesis_value(train_data[example_no][0]	)
       elif data_set == "test":
              return _hypothesis_value(test_data[example_no][0]	)
       return None




def 					lowerCamelCase__       (						A__       :					str  ,			A__       :					List[Any]=m	):





       '''simple docstring'''



       __lowerCamelCase   =						0
       for i in range(A__	):
              if index == -1:
                     summation_value += _error(A__	)
              else:
                     summation_value += _error(A__	) * train_data[i][0][index]
       return summation_value




def 					lowerCamelCase__       (						A__       :					str	):





       '''simple docstring'''



       __lowerCamelCase   =						summation_of_cost_derivative(A__  ,			A__	) / m
       return cost_derivative_value




def 					lowerCamelCase__       (						):





       '''simple docstring'''



       global parameter_vector
       # Tune these values to set a tolerance value for predicted output
       __lowerCamelCase   =						0.000_002
       __lowerCamelCase   =						0
       __lowerCamelCase   =						0
       while True:
              j += 1
              __lowerCamelCase   =						[0, 0, 0, 0]
              for i in range(0  ,			len(A__	)	):
                     __lowerCamelCase   =						get_cost_derivative(i - 1	)
                     __lowerCamelCase   =						(
                         parameter_vector[i] - LEARNING_RATE * cost_derivative
                     )
              if numpy.allclose(
                  A__  ,			A__  ,			atol=A__  ,			rtol=A__  ,			):
                     break
              __lowerCamelCase   =						temp_parameter_vector
       print(("""Number of iterations:""", j)	)




def 					lowerCamelCase__       (						):





       '''simple docstring'''



       for i in range(len(A__	)	):
              print(("""Actual output value:""", output(A__  ,			"""test"""	))	)
              print(("""Hypothesis output:""", calculate_hypothesis_value(A__  ,			"""test"""	))	)


if __name__ == "__main__":
       run_gradient_descent()
       print('\nTesting gradient descent for a linear hypothesis function.\n')
       test_gradient_descent()







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 __UpperCamelCase			(       _A  ):
						lowerCAmelCase_      			=  checkpoints.load_tax_checkpoint(_A  )
						lowerCAmelCase_      			=  flatten_dict(_A  )
						return flax_params





def __UpperCamelCase			(       _A  ):
						lowerCAmelCase_      			=  {}

						lowerCAmelCase_      			=  {
						    '''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''',
						}

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

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

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

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

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

																		lowerCAmelCase_      			=  flax_dict[key]

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

						return converted_torch_dict





def __UpperCamelCase			(       _A ,  _A ,  _A=False ,  _A=False  ):
						lowerCAmelCase_      			=  get_flax_param(_A  )

						if not use_large:
												lowerCAmelCase_      			=  PixaStructVisionConfig()
												lowerCAmelCase_      			=  PixaStructTextConfig()
						else:
												lowerCAmelCase_      			=  PixaStructVisionConfig(
												    hidden_size=1536 ,  d_ff=3968 ,  num_attention_heads=24 ,  num_hidden_layers=18  )
												lowerCAmelCase_      			=  PixaStructTextConfig(hidden_size=1536 ,  d_ff=3968 ,  num_heads=24 ,  num_layers=18  )
						lowerCAmelCase_      			=  PixaStructConfig(
						    vision_config=encoder_config.to_dict() ,  text_config=decoder_config.to_dict() ,  is_vqa=_A  )

						lowerCAmelCase_      			=  PixaStructForConditionalGeneration(_A  )

						lowerCAmelCase_      			=  rename_and_convert_flax_params(_A  )
						model.load_state_dict(_A  )

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

						if use_large:
												lowerCAmelCase_      			=  4096

						lowerCAmelCase_      			=  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     =			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
						)




