



'''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_	(				_lowerCAmelCase			) -> Any:
  __lowerCamelCase			:			Optional[int]       			=					checkpoints.load_tax_checkpoint(_lowerCAmelCase			)
  __lowerCamelCase			:			Any       			=					flatten_dict(_lowerCAmelCase			)
  return flax_params

def 			a_	(				_lowerCAmelCase			) -> Union[str, Any]:
  __lowerCamelCase			:			List[str]       			=					{}

  __lowerCamelCase			:			List[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',
  }

  __lowerCamelCase			:			List[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
      __lowerCamelCase			:			Dict       			=					'.'.join(key[1:]			)

      # rename the key
      for old, new in CONVERSION_MAPPING.items():
        __lowerCamelCase			:			int       			=					new_key.replace(_lowerCAmelCase			,_lowerCAmelCase			)

      if "decoder" in new_key:
        for old, new in DECODER_CONVERSION_MAPPING.items():
          __lowerCamelCase			:			Optional[Any]       			=					new_key.replace(_lowerCAmelCase			,_lowerCAmelCase			)

      if "layers" in new_key and "decoder" not in new_key:
        # use regex to replace the layer number
        __lowerCamelCase			:			int       			=					re.sub(r'layers_(\d+)'			,r'layer.\1'			,_lowerCAmelCase			)
        __lowerCamelCase			:			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
        __lowerCamelCase			:			Optional[Any]       			=					re.sub(r'layers_(\d+)'			,r'layer.\1'			,_lowerCAmelCase			)

      __lowerCamelCase			:			Optional[int]       			=					flax_dict[key]

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

  return converted_torch_dict

def 			a_	(				_lowerCAmelCase			,_lowerCAmelCase			,_lowerCAmelCase=False			,_lowerCAmelCase=False			) -> Any:
  __lowerCamelCase			:			List[str]       			=					get_flax_param(_lowerCAmelCase			)

  if not use_large:
    __lowerCamelCase			:			Optional[Any]       			=					PixaStructVisionConfig()
    __lowerCamelCase			:			int       			=					PixaStructTextConfig()
  else:
    __lowerCamelCase			:			str       			=					PixaStructVisionConfig(
        hidden_size=1536			,d_ff=3968			,num_attention_heads=24			,num_hidden_layers=18			)
    __lowerCamelCase			:			Optional[int]       			=					PixaStructTextConfig(hidden_size=1536			,d_ff=3968			,num_heads=24			,num_layers=18			)
  __lowerCamelCase			:			Dict       			=					PixaStructConfig(
      vision_config=encoder_config.to_dict()			,text_config=decoder_config.to_dict()			,is_vqa=_lowerCAmelCase			)

  __lowerCamelCase			:			Union[str, Any]       			=					PixaStructForConditionalGeneration(_lowerCAmelCase			)

  __lowerCamelCase			:			Optional[Any]       			=					rename_and_convert_flax_params(_lowerCAmelCase			)
  model.load_state_dict(_lowerCAmelCase			)

  __lowerCamelCase			:			Tuple       			=					AutoTokenizer.from_pretrained('ybelkada/test-pix2struct-tokenizer'			)
  __lowerCamelCase			:			str       			=					PixaStructImageProcessor()
  __lowerCamelCase			:			Optional[int]       			=					PixaStructProcessor(image_processor=_lowerCAmelCase			,tokenizer=_lowerCAmelCase			)

  if use_large:
    __lowerCamelCase			:			Dict       			=					4096

  __lowerCamelCase			:			Dict       			=					True

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

  model.save_pretrained(_lowerCAmelCase			)
  processor.save_pretrained(_lowerCAmelCase			)

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


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

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











'''simple docstring'''





def 			a_	(				_lowerCAmelCase			,_lowerCAmelCase			) -> tuple[float, float]:

  # Check if the input is valid
  if not len(_lowerCAmelCase			) == len(_lowerCAmelCase			) == 3:
    raise ValueError('Please enter a valid equation.'			)
  if equationa[0] == equationa[1] == equationa[0] == equationa[1] == 0:
    raise ValueError('Both a & b of two equations can\'t be zero.'			)

  # Extract the coefficients
  __lowerCamelCase			,__lowerCamelCase			,__lowerCamelCase			:			Union[str, Any]       			=					equationa
  __lowerCamelCase			,__lowerCamelCase			,__lowerCamelCase			:			Any       			=					equationa

  # Calculate the determinants of the matrices
  __lowerCamelCase			:			Union[str, Any]       			=					aa * ba - aa * ba
  __lowerCamelCase			:			Dict       			=					ca * ba - ca * ba
  __lowerCamelCase			:			List[str]       			=					aa * ca - aa * ca

  # Check if the system of linear equations has a solution (using Cramer's rule)
  if determinant == 0:
    if determinant_x == determinant_y == 0:
      raise ValueError('Infinite solutions. (Consistent system)'			)
    else:
      raise ValueError('No solution. (Inconsistent system)'			)
  else:
    if determinant_x == determinant_y == 0:
      # Trivial solution (Inconsistent system)
      return (0.0, 0.0)
    else:
      __lowerCamelCase			:			Tuple       			=					determinant_x / determinant
      __lowerCamelCase			:			Union[str, Any]       			=					determinant_y / determinant
      # Non-Trivial Solution (Consistent system)
      return (x, y)






