


'''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 			_lowercase       (				UpperCamelCase__       :					Optional[int]			):
 __A  :		Tuple       =			checkpoints.load_tax_checkpoint(UpperCamelCase__			)
 __A  :		Dict       =			flatten_dict(UpperCamelCase__			)
 return flax_params


def 			_lowercase       (				UpperCamelCase__       :					Any			):
 __A  :		Optional[Any]       =			{}

 __A  :		int       =			{
     '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',
 }

 __A  :		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
   __A  :		Optional[int]       =			'.'.join(key[1:]			)

   # rename the key
   for old, new in CONVERSION_MAPPING.items():
    __A  :		str       =			new_key.replace(UpperCamelCase__,				UpperCamelCase__			)

   if "decoder" in new_key:
    for old, new in DECODER_CONVERSION_MAPPING.items():
     __A  :		List[Any]       =			new_key.replace(UpperCamelCase__,				UpperCamelCase__			)

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

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

   __A  :		Tuple       =			flax_dict[key]

 __A  :		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):
   __A  :		Optional[Any]       =			torch.from_numpy(converted_dict[key].T			)
  else:
   __A  :		List[str]       =			torch.from_numpy(converted_dict[key]			)

 return converted_torch_dict


def 			_lowercase       (				UpperCamelCase__       :					Dict,				UpperCamelCase__       :					Optional[int],				UpperCamelCase__       :					int=False,				UpperCamelCase__       :					List[str]=False			):
 __A  :		Dict       =			get_flax_param(UpperCamelCase__			)

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

 __A  :		Tuple       =			PixaStructForConditionalGeneration(UpperCamelCase__			)

 __A  :		str       =			rename_and_convert_flax_params(UpperCamelCase__			)
 model.load_state_dict(UpperCamelCase__			)

 __A  :		List[Any]       =			AutoTokenizer.from_pretrained('ybelkada/test-pix2struct-tokenizer'			)
 __A  :		Union[str, Any]       =			PixaStructImageProcessor()
 __A  :		List[str]       =			PixaStructProcessor(image_processor=UpperCamelCase__,				tokenizer=UpperCamelCase__			)

 if use_large:
  __A  :		List[str]       =			4096

 __A  :		List[Any]       =			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__":
 UpperCAmelCase_ :     Union[str, Any]								=      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_ :     Dict								=      parser.parse_args()

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










'''simple docstring'''
import argparse

from transformers import (
    TapasConfig,
    TapasForMaskedLM,
    TapasForQuestionAnswering,
    TapasForSequenceClassification,
    TapasModel,
    TapasTokenizer,
    load_tf_weights_in_tapas,
)
from transformers.utils import logging


logging.set_verbosity_info()


def 			_lowercase       (				UpperCamelCase__       :					Optional[int],				UpperCamelCase__       :					Tuple,				UpperCamelCase__       :					Tuple,				UpperCamelCase__       :					List[Any],				UpperCamelCase__       :					Optional[int]			):
 # Initialise PyTorch model.
 # If you want to convert a checkpoint that uses absolute position embeddings, make sure to set reset_position_index_per_cell of
 # TapasConfig to False.

 # initialize configuration from json file
 __A  :		Optional[int]       =			TapasConfig.from_json_file(UpperCamelCase__			)
 # set absolute/relative position embeddings parameter
 __A  :		List[str]       =			reset_position_index_per_cell

 # set remaining parameters of TapasConfig as well as the model based on the task
 if task == "SQA":
  __A  :		Optional[int]       =			TapasForQuestionAnswering(config=UpperCamelCase__			)
 elif task == "WTQ":
  # run_task_main.py hparams
  __A  :		List[str]       =			4
  __A  :		Any       =			True
  # hparam_utils.py hparams
  __A  :		Any       =			0.664694
  __A  :		Dict       =			0.207951
  __A  :		Optional[Any]       =			0.121194
  __A  :		Any       =			True
  __A  :		Any       =			True
  __A  :		int       =			False
  __A  :		Optional[int]       =			0.0352513

  __A  :		List[Any]       =			TapasForQuestionAnswering(config=UpperCamelCase__			)
 elif task == "WIKISQL_SUPERVISED":
  # run_task_main.py hparams
  __A  :		Tuple       =			4
  __A  :		Optional[int]       =			False
  # hparam_utils.py hparams
  __A  :		List[str]       =			36.4519
  __A  :		Union[str, Any]       =			0.903421
  __A  :		List[str]       =			222.088
  __A  :		Optional[Any]       =			True
  __A  :		Optional[int]       =			True
  __A  :		Union[str, Any]       =			True
  __A  :		str       =			0.763141

  __A  :		Union[str, Any]       =			TapasForQuestionAnswering(config=UpperCamelCase__			)
 elif task == "TABFACT":
  __A  :		Union[str, Any]       =			TapasForSequenceClassification(config=UpperCamelCase__			)
 elif task == "MLM":
  __A  :		Any       =			TapasForMaskedLM(config=UpperCamelCase__			)
 elif task == "INTERMEDIATE_PRETRAINING":
  __A  :		List[Any]       =			TapasModel(config=UpperCamelCase__			)
 else:
  raise ValueError(f"""Task {task} not supported."""			)

 print(f"""Building PyTorch model from configuration: {config}"""			)
 # Load weights from tf checkpoint
 load_tf_weights_in_tapas(UpperCamelCase__,				UpperCamelCase__,				UpperCamelCase__			)

 # Save pytorch-model (weights and configuration)
 print(f"""Save PyTorch model to {pytorch_dump_path}"""			)
 model.save_pretrained(UpperCamelCase__			)

 # Save tokenizer files
 print(f"""Save tokenizer files to {pytorch_dump_path}"""			)
 __A  :		int       =			TapasTokenizer(vocab_file=tf_checkpoint_path[:-10] + 'vocab.txt',				model_max_length=512			)
 tokenizer.save_pretrained(UpperCamelCase__			)

 print('Used relative position embeddings:',				model.config.reset_position_index_per_cell			)


if __name__ == "__main__":
 UpperCAmelCase_ :     Dict								=      argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     '--task', default='SQA', type=str, help='Model task for which to convert a checkpoint. Defaults to SQA.'
 )
 parser.add_argument(
     '--reset_position_index_per_cell',
     default=False,
     action='store_true',
     help='Whether to use relative position embeddings or not. Defaults to True.',
 )
 parser.add_argument(
     '--tf_checkpoint_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
 )
 parser.add_argument(
     '--tapas_config_file',
     default=None,
     type=str,
     required=True,
     help=(
         'The config json file corresponding to the pre-trained TAPAS model. \n'
         'This specifies the model architecture.'
     ),
 )
 parser.add_argument(
     '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
 )
 UpperCAmelCase_ :     Any								=      parser.parse_args()
 convert_tf_checkpoint_to_pytorch(
     args.task,
     args.reset_position_index_per_cell,
     args.tf_checkpoint_path,
     args.tapas_config_file,
     args.pytorch_dump_path,
 )






