





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__							(   SCREAMING_SNAKE_CASE__)       ->   Any:
  __snake_case:						str			  =						checkpoints.load_tax_checkpoint(SCREAMING_SNAKE_CASE__)
  __snake_case:						int			  =						flatten_dict(SCREAMING_SNAKE_CASE__)
  return flax_params







def       A__							(   SCREAMING_SNAKE_CASE__)       ->   str:
  __snake_case:						str			  =						{}

  __snake_case:						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""",
  }

  __snake_case:						Dict			  =						{
      """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:						List[Any]			  =						""".""".join(key[1:])

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

      if "decoder" in new_key:
        for old, new in DECODER_CONVERSION_MAPPING.items():
          __snake_case:						int			  =						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
        __snake_case:						int			  =						re.sub(r"""layers_(\d+)""" , r"""layer.\1""" , SCREAMING_SNAKE_CASE__)
        __snake_case:						Union[str, Any]			  =						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:						Dict			  =						re.sub(r"""layers_(\d+)""" , r"""layer.\1""" , SCREAMING_SNAKE_CASE__)

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

  return converted_torch_dict







def       A__							(   SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__=False , SCREAMING_SNAKE_CASE__=False)       ->   int:
  __snake_case:						Dict			  =						get_flax_param(SCREAMING_SNAKE_CASE__)

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

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

  __snake_case:						Optional[Any]			  =						rename_and_convert_flax_params(SCREAMING_SNAKE_CASE__)
  model.load_state_dict(SCREAMING_SNAKE_CASE__)

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

  if use_large:
    __snake_case:						Dict			  =						4096

  __snake_case:						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__":
      __UpperCAmelCase	:       Dict    	=							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	:       List[str]    	=							parser.parse_args()

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













import math
import time

from transformers import Trainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput, speed_metrics


if is_torch_tpu_available(check_device=False):
      import torch_xla.core.xla_model as xm
      import torch_xla.debug.metrics as met







class    __snake_case	(							__lowerCamelCase      ):





       '''simple docstring'''







       def __init__(  self			:   str							,    *A			:   Dict							,    A			:   Optional[int]=None							,    A			:   Tuple=None							,    **A			:   Optional[int]		):
         super().__init__(*A							,    **A		)
         __snake_case:						List[Any]			  =						eval_examples
         __snake_case:						str			  =						post_process_function




       def 							UpperCAmelCase__			(  self			:   List[Any]							,    A			:   Dict=None							,    A			:   int=None							,    A			:   List[Any]=None							,    A			:   str = "eval"		):
         __snake_case:						int			  =						self.eval_dataset if eval_dataset is None else eval_dataset
         __snake_case:						Any			  =						self.get_eval_dataloader(A		)
         __snake_case:						Optional[Any]			  =						self.eval_examples if eval_examples is None else eval_examples

         # Temporarily disable metric computation, we will do it in the loop here.
         __snake_case:						Union[str, Any]			  =						self.compute_metrics
         __snake_case:						List[str]			  =						None
         __snake_case:						Tuple			  =						self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
         __snake_case:						Tuple			  =						time.time()
         try:
           __snake_case:						Any			  =						eval_loop(
               A							,    description="""Evaluation"""							,    prediction_loss_only=True if compute_metrics is None else None							,    ignore_keys=A							,    metric_key_prefix=A							,    )
         finally:
           __snake_case:						Optional[int]			  =						compute_metrics
         __snake_case:						Union[str, Any]			  =						self.args.eval_batch_size * self.args.world_size
         if f'''{metric_key_prefix}_jit_compilation_time''' in output.metrics:
           start_time += output.metrics[f'''{metric_key_prefix}_jit_compilation_time''']
         output.metrics.update(
             speed_metrics(
                 A							,    A							,    num_samples=output.num_samples							,    num_steps=math.ceil(output.num_samples / total_batch_size		)							,    )		)
         if self.post_process_function is not None and self.compute_metrics is not None and self.args.should_save:
           # Only the main node write the results by default
           __snake_case:						List[str]			  =						self.post_process_function(A							,    A							,    output.predictions		)
           __snake_case:						List[Any]			  =						self.compute_metrics(A		)

           # Prefix all keys with metric_key_prefix + '_'
           for key in list(metrics.keys()		):
             if not key.startswith(f'''{metric_key_prefix}_'''		):
               __snake_case:						str			  =						metrics.pop(A		)
           metrics.update(output.metrics		)
         else:
           __snake_case:						List[Any]			  =						output.metrics

         if self.args.should_log:
           # Only the main node log the results by default
           self.log(A		)

         if self.args.tpu_metrics_debug or self.args.debug:
           # tpu-comment: Logging debug metrics for PyTorch/XLA (compile, execute times, ops, etc.)
           xm.master_print(met.metrics_report()		)

         __snake_case:						str			  =						self.callback_handler.on_evaluate(self.args							,    self.state							,    self.control							,    A		)
         return metrics






       def 							UpperCAmelCase__			(  self			:   Optional[Any]							,    A			:   List[Any]							,    A			:   List[str]							,    A			:   str=None							,    A			:   str = "test"		):
         __snake_case:						Optional[Any]			  =						self.get_test_dataloader(A		)

         # Temporarily disable metric computation, we will do it in the loop here.
         __snake_case:						Optional[int]			  =						self.compute_metrics
         __snake_case:						List[Any]			  =						None
         __snake_case:						str			  =						self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
         __snake_case:						Dict			  =						time.time()
         try:
           __snake_case:						str			  =						eval_loop(
               A							,    description="""Prediction"""							,    prediction_loss_only=True if compute_metrics is None else None							,    ignore_keys=A							,    metric_key_prefix=A							,    )
         finally:
           __snake_case:						List[Any]			  =						compute_metrics
         __snake_case:						Dict			  =						self.args.eval_batch_size * self.args.world_size
         if f'''{metric_key_prefix}_jit_compilation_time''' in output.metrics:
           start_time += output.metrics[f'''{metric_key_prefix}_jit_compilation_time''']
         output.metrics.update(
             speed_metrics(
                 A							,    A							,    num_samples=output.num_samples							,    num_steps=math.ceil(output.num_samples / total_batch_size		)							,    )		)

         if self.post_process_function is None or self.compute_metrics is None:
           return output

         __snake_case:						Union[str, Any]			  =						self.post_process_function(A							,    A							,    output.predictions							,    """predict"""		)
         __snake_case:						str			  =						self.compute_metrics(A		)

         # Prefix all keys with metric_key_prefix + '_'
         for key in list(metrics.keys()		):
           if not key.startswith(f'''{metric_key_prefix}_'''		):
             __snake_case:						List[str]			  =						metrics.pop(A		)
         metrics.update(output.metrics		)
         return PredictionOutput(predictions=predictions.predictions							,    label_ids=predictions.label_ids							,    metrics=A		)






