


"""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 				UpperCamelCase__					(  SCREAMING_SNAKE_CASE__						):
 __lowerCamelCase		:							int		 =						checkpoints.load_tax_checkpoint(SCREAMING_SNAKE_CASE__						)
 __lowerCamelCase		:							int		 =						flatten_dict(SCREAMING_SNAKE_CASE__						)
 return flax_params
def 				UpperCamelCase__					(  SCREAMING_SNAKE_CASE__						):
 __lowerCamelCase		:							Dict		 =						{}

 __lowerCamelCase		:							Optional[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		:							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
   __lowerCamelCase		:							Any		 =						'.'.join(key[1:]						)

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

   if "decoder" in new_key:
    for old, new in DECODER_CONVERSION_MAPPING.items():
     __lowerCamelCase		:							Any		 =						new_key.replace(SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__						)

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

   __lowerCamelCase		:							Optional[Any]		 =						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		:							int		 =						torch.from_numpy(converted_dict[key].T						)
  else:
   __lowerCamelCase		:							Optional[Any]		 =						torch.from_numpy(converted_dict[key]						)

 return converted_torch_dict
def 				UpperCamelCase__					(  SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__=False      ,	SCREAMING_SNAKE_CASE__=False						):
 __lowerCamelCase		:							Optional[int]		 =						get_flax_param(SCREAMING_SNAKE_CASE__						)

 if not use_large:
  __lowerCamelCase		:							List[Any]		 =						PixaStructVisionConfig()
  __lowerCamelCase		:							List[str]		 =						PixaStructTextConfig()
 else:
  __lowerCamelCase		:							str		 =						PixaStructVisionConfig(
      hidden_size=1_536      ,	d_ff=3_968      ,	num_attention_heads=24      ,	num_hidden_layers=18						)
  __lowerCamelCase		:							List[str]		 =						PixaStructTextConfig(hidden_size=1_536      ,	d_ff=3_968      ,	num_heads=24      ,	num_layers=18						)
 __lowerCamelCase		:							Dict		 =						PixaStructConfig(
     vision_config=encoder_config.to_dict()      ,	text_config=decoder_config.to_dict()      ,	is_vqa=SCREAMING_SNAKE_CASE__						)

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

 __lowerCamelCase		:							List[str]		 =						rename_and_convert_flax_params(SCREAMING_SNAKE_CASE__						)
 model.load_state_dict(SCREAMING_SNAKE_CASE__						)

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

 if use_large:
  __lowerCamelCase		:							List[Any]		 =						4_096

 __lowerCamelCase		:							Tuple		 =						True

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

 model.save_pretrained(SCREAMING_SNAKE_CASE__						)
 processor.save_pretrained(SCREAMING_SNAKE_CASE__						)

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


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

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





import datasets


lowercase_	   =     '\\n@InProceedings{conneau2018xnli,\n  author = "Conneau, Alexis\n                 and Rinott, Ruty\n                 and Lample, Guillaume\n                 and Williams, Adina\n                 and Bowman, Samuel R.\n                 and Schwenk, Holger\n                 and Stoyanov, Veselin",\n  title = "XNLI: Evaluating Cross-lingual Sentence Representations",\n  booktitle = "Proceedings of the 2018 Conference on Empirical Methods\n               in Natural Language Processing",\n  year = "2018",\n  publisher = "Association for Computational Linguistics",\n  location = "Brussels, Belgium",\n}\n'

lowercase_	   =     '\\nXNLI is a subset of a few thousand examples from MNLI which has been translated\ninto a 14 different languages (some low-ish resource). As with MNLI, the goal is\nto predict textual entailment (does sentence A imply/contradict/neither sentence\nB) and is a classification task (given two sentences, predict one of three\nlabels).\n'

lowercase_	   =     '\nComputes XNLI score which is just simple accuracy.\nArgs:\n    predictions: Predicted labels.\n    references: Ground truth labels.\nReturns:\n    \'accuracy\': accuracy\nExamples:\n\n    >>> predictions = [0, 1]\n    >>> references = [0, 1]\n    >>> xnli_metric = datasets.load_metric("xnli")\n    >>> results = xnli_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {\'accuracy\': 1.0}\n'
def 				UpperCamelCase__					(  SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__						):
 return (preds == labels).mean()



@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION ,				_KWARGS_DESCRIPTION   )
class 	A_ (	datasets.Metric   ):




      '''simple docstring'''


      def 					_snake_case     (    self:	Union[str, Any]    ):
       return datasets.MetricInfo(
           description=_DESCRIPTION						,				citation=_CITATION						,				inputs_description=_KWARGS_DESCRIPTION						,				features=datasets.Features(
               {
                   'predictions': datasets.Value('int64' if self.config_name != 'sts-b' else 'float32'    ),
                   'references': datasets.Value('int64' if self.config_name != 'sts-b' else 'float32'    ),
               }    )						,				codebase_urls=[]						,				reference_urls=[]						,				format='numpy'						,				)




      def 					_snake_case     (    self:	int						,				a:	Optional[Any]						,				a:	Optional[Any]    ):
       return {"accuracy": simple_accuracy(a						,				a    )}

