
'''simple docstring'''



import argparse

from tax import checkpoints

from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)     ->     Dict:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE      =				AutoConfig.from_pretrained(SCREAMING_SNAKE_CASE_						)
   _SCREAMING_SNAKE_CASE      =				FlaxAutoModelForSeqaSeqLM.from_config(config=SCREAMING_SNAKE_CASE_						)
   _SCREAMING_SNAKE_CASE      =				checkpoints.load_tax_checkpoint(SCREAMING_SNAKE_CASE_						)

   _SCREAMING_SNAKE_CASE      =				"""wi_0""" in tax_model["""target"""]["""encoder"""]["""layers_0"""]["""mlp"""]

   if config.model_type == "t5":
      _SCREAMING_SNAKE_CASE      =				"""SelfAttention"""
   if config.model_type == "longt5" and config.encoder_attention_type == "local":
      _SCREAMING_SNAKE_CASE      =				"""LocalSelfAttention"""
   elif config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
      _SCREAMING_SNAKE_CASE      =				"""TransientGlobalSelfAttention"""
   else:
      raise ValueError(
          """Given config is expected to have `model_type='t5'`, or `model_type='longt5` with `encoder_attention_type`"""
          """ attribute with a value from ['local', 'transient-global]."""						)

   # Encoder
   for layer_index in range(config.num_layers						):
      _SCREAMING_SNAKE_CASE      =				F"layers_{str(SCREAMING_SNAKE_CASE_						)}"

      # Self-Attention
      _SCREAMING_SNAKE_CASE      =				tax_model["""target"""]["""encoder"""][layer_name]["""attention"""]["""key"""]["""kernel"""]
      _SCREAMING_SNAKE_CASE      =				tax_model["""target"""]["""encoder"""][layer_name]["""attention"""]["""out"""]["""kernel"""]
      _SCREAMING_SNAKE_CASE      =				tax_model["""target"""]["""encoder"""][layer_name]["""attention"""]["""query"""]["""kernel"""]
      _SCREAMING_SNAKE_CASE      =				tax_model["""target"""]["""encoder"""][layer_name]["""attention"""]["""value"""]["""kernel"""]

      # Global input layer norm
      if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
         _SCREAMING_SNAKE_CASE      =				tax_model["""target"""]["""encoder"""][layer_name]["""attention"""]["""T5LayerNorm_0"""]["""scale"""]

      # Layer Normalization
      _SCREAMING_SNAKE_CASE      =				tax_model["""target"""]["""encoder"""][layer_name]["""pre_attention_layer_norm"""]["""scale"""]

      if split_mlp_wi:
         _SCREAMING_SNAKE_CASE      =				tax_model["""target"""]["""encoder"""][layer_name]["""mlp"""]["""wi_0"""]["""kernel"""]
         _SCREAMING_SNAKE_CASE      =				tax_model["""target"""]["""encoder"""][layer_name]["""mlp"""]["""wi_1"""]["""kernel"""]
      else:
         _SCREAMING_SNAKE_CASE      =				tax_model["""target"""]["""encoder"""][layer_name]["""mlp"""]["""wi"""]["""kernel"""]

      _SCREAMING_SNAKE_CASE      =				tax_model["""target"""]["""encoder"""][layer_name]["""mlp"""]["""wo"""]["""kernel"""]

      # Layer Normalization
      _SCREAMING_SNAKE_CASE      =				tax_model["""target"""]["""encoder"""][layer_name]["""pre_mlp_layer_norm"""]["""scale"""]

      # Assigning
      _SCREAMING_SNAKE_CASE      =				flax_model.params["""encoder"""]["""block"""][str(SCREAMING_SNAKE_CASE_						)]["""layer"""]
      _SCREAMING_SNAKE_CASE      =				tax_attention_key
      _SCREAMING_SNAKE_CASE      =				tax_attention_out
      _SCREAMING_SNAKE_CASE      =				tax_attention_query
      _SCREAMING_SNAKE_CASE      =				tax_attention_value

      _SCREAMING_SNAKE_CASE      =				tax_attention_layer_norm

      # Global input layer norm
      if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
         _SCREAMING_SNAKE_CASE      =				tax_global_layer_norm

      if split_mlp_wi:
         _SCREAMING_SNAKE_CASE      =				tax_mlp_wi_a
         _SCREAMING_SNAKE_CASE      =				tax_mlp_wi_a
      else:
         _SCREAMING_SNAKE_CASE      =				tax_mlp_wi

      _SCREAMING_SNAKE_CASE      =				tax_mlp_wo
      _SCREAMING_SNAKE_CASE      =				tax_mlp_layer_norm

      _SCREAMING_SNAKE_CASE      =				flax_model_encoder_layer_block

   # Only for layer 0:
   _SCREAMING_SNAKE_CASE      =				tax_model["""target"""]["""encoder"""]["""relpos_bias"""]["""rel_embedding"""].T
   _SCREAMING_SNAKE_CASE      =				tax_encoder_rel_embedding

   # Side/global relative position_bias + layer norm
   if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
      _SCREAMING_SNAKE_CASE      =				tax_model["""target"""]["""encoder"""]["""side_relpos_bias"""]["""rel_embedding"""].T
      _SCREAMING_SNAKE_CASE      =				tax_encoder_global_rel_embedding

   # Assigning
   _SCREAMING_SNAKE_CASE      =				tax_model["""target"""]["""encoder"""]["""encoder_norm"""]["""scale"""]
   _SCREAMING_SNAKE_CASE      =				tax_encoder_norm

   # Decoder
   for layer_index in range(config.num_layers						):
      _SCREAMING_SNAKE_CASE      =				F"layers_{str(SCREAMING_SNAKE_CASE_						)}"

      # Self-Attention
      _SCREAMING_SNAKE_CASE      =				tax_model["""target"""]["""decoder"""][layer_name]["""self_attention"""]["""key"""]["""kernel"""]
      _SCREAMING_SNAKE_CASE      =				tax_model["""target"""]["""decoder"""][layer_name]["""self_attention"""]["""out"""]["""kernel"""]
      _SCREAMING_SNAKE_CASE      =				tax_model["""target"""]["""decoder"""][layer_name]["""self_attention"""]["""query"""]["""kernel"""]
      _SCREAMING_SNAKE_CASE      =				tax_model["""target"""]["""decoder"""][layer_name]["""self_attention"""]["""value"""]["""kernel"""]

      # Layer Normalization
      _SCREAMING_SNAKE_CASE      =				tax_model["""target"""]["""decoder"""][layer_name]["""pre_self_attention_layer_norm"""][
          """scale"""
      ]

      # Encoder-Decoder-Attention
      _SCREAMING_SNAKE_CASE      =				tax_model["""target"""]["""decoder"""][layer_name]["""encoder_decoder_attention"""]
      _SCREAMING_SNAKE_CASE      =				tax_enc_dec_attention_module["""key"""]["""kernel"""]
      _SCREAMING_SNAKE_CASE      =				tax_enc_dec_attention_module["""out"""]["""kernel"""]
      _SCREAMING_SNAKE_CASE      =				tax_enc_dec_attention_module["""query"""]["""kernel"""]
      _SCREAMING_SNAKE_CASE      =				tax_enc_dec_attention_module["""value"""]["""kernel"""]

      # Layer Normalization
      _SCREAMING_SNAKE_CASE      =				tax_model["""target"""]["""decoder"""][layer_name]["""pre_cross_attention_layer_norm"""]["""scale"""]

      # MLP
      if split_mlp_wi:
         _SCREAMING_SNAKE_CASE      =				tax_model["""target"""]["""decoder"""][layer_name]["""mlp"""]["""wi_0"""]["""kernel"""]
         _SCREAMING_SNAKE_CASE      =				tax_model["""target"""]["""decoder"""][layer_name]["""mlp"""]["""wi_1"""]["""kernel"""]
      else:
         _SCREAMING_SNAKE_CASE      =				tax_model["""target"""]["""decoder"""][layer_name]["""mlp"""]["""wi"""]["""kernel"""]

      _SCREAMING_SNAKE_CASE      =				tax_model["""target"""]["""decoder"""][layer_name]["""mlp"""]["""wo"""]["""kernel"""]

      # Layer Normalization
      _SCREAMING_SNAKE_CASE      =				tax_model["""target"""]["""decoder"""][layer_name]["""pre_mlp_layer_norm"""]["""scale"""]

      # Assigning
      _SCREAMING_SNAKE_CASE      =				flax_model.params["""decoder"""]["""block"""][str(SCREAMING_SNAKE_CASE_						)]["""layer"""]
      _SCREAMING_SNAKE_CASE      =				tax_attention_key
      _SCREAMING_SNAKE_CASE      =				tax_attention_out
      _SCREAMING_SNAKE_CASE      =				tax_attention_query
      _SCREAMING_SNAKE_CASE      =				tax_attention_value

      _SCREAMING_SNAKE_CASE      =				tax_pre_attention_layer_norm

      _SCREAMING_SNAKE_CASE      =				tax_enc_dec_attention_key
      _SCREAMING_SNAKE_CASE      =				tax_enc_dec_attention_out
      _SCREAMING_SNAKE_CASE      =				tax_enc_dec_attention_query
      _SCREAMING_SNAKE_CASE      =				tax_enc_dec_attention_value

      _SCREAMING_SNAKE_CASE      =				tax_cross_layer_norm

      if split_mlp_wi:
         _SCREAMING_SNAKE_CASE      =				tax_mlp_wi_a
         _SCREAMING_SNAKE_CASE      =				tax_mlp_wi_a
      else:
         _SCREAMING_SNAKE_CASE      =				tax_mlp_wi

      _SCREAMING_SNAKE_CASE      =				tax_mlp_wo

      _SCREAMING_SNAKE_CASE      =				txa_mlp_layer_norm

      _SCREAMING_SNAKE_CASE      =				flax_model_decoder_layer_block

   # Decoder Normalization
   _SCREAMING_SNAKE_CASE      =				tax_model["""target"""]["""decoder"""]["""decoder_norm"""]["""scale"""]
   _SCREAMING_SNAKE_CASE      =				txa_decoder_norm

   # Only for layer 0:
   _SCREAMING_SNAKE_CASE      =				tax_model["""target"""]["""decoder"""]["""relpos_bias"""]["""rel_embedding"""].T
   _SCREAMING_SNAKE_CASE      =				tax_decoder_rel_embedding

   # Token Embeddings
   _SCREAMING_SNAKE_CASE      =				tax_model["""target"""]["""token_embedder"""]["""embedding"""]
   _SCREAMING_SNAKE_CASE      =				txa_token_embeddings

   # LM Head (only in v1.1 and LongT5 checkpoints)
   if "logits_dense" in tax_model["target"]["decoder"]:
      _SCREAMING_SNAKE_CASE      =				tax_model["""target"""]["""decoder"""]["""logits_dense"""]["""kernel"""]

   flax_model.save_pretrained(SCREAMING_SNAKE_CASE_						)
   print("""T5X Model was sucessfully converted!"""						)


if __name__ == "__main__":
     UpperCamelCase__      :		Union[str, Any]       							=  argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         "--t5x_checkpoint_path", default=None, type=str, required=True, help="Path the T5X checkpoint."
     )
     parser.add_argument("--config_name", default=None, type=str, required=True, help="Config name of LongT5/T5 model.")
     parser.add_argument(
         "--flax_dump_folder_path", default=None, type=str, required=True, help="Path to the output FLAX model."
     )
     UpperCamelCase__      :		int       							=  parser.parse_args()
     convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)








'''simple docstring'''



import logging
import os

import quant_trainer
import torch
from torch.utils.data import DataLoader

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


UpperCamelCase__      :		Tuple       							=  logging.getLogger(__name__)

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

class    _a				(_lowerCamelCase):





       """simple docstring"""




       def __init__(							self       ,							*A__       ,							A__=None       ,							A__=None       ,							A__=None       ,							**A__      )    ->			Optional[int]:
          super().__init__(*A__       ,							**A__      )
          _SCREAMING_SNAKE_CASE      =				eval_examples
          _SCREAMING_SNAKE_CASE      =				post_process_function
          _SCREAMING_SNAKE_CASE      =				quant_trainer_args
          _SCREAMING_SNAKE_CASE      =				1_28  # default number of calibration samples






       def        UpperCamelCase				(							self       ,							A__=None      )    ->			Union[str, Any]:
          if calib_dataset is None and self.calib_dataset is None:
             raise ValueError("""Trainer: calibration requires an calib_dataset."""      )
          _SCREAMING_SNAKE_CASE      =				calib_dataset if calib_dataset is not None else self.calib_dataset

          _SCREAMING_SNAKE_CASE      =				self._remove_unused_columns(A__       ,							description="""Calibration"""      )

          return DataLoader(
              A__       ,							batch_size=self.args.eval_batch_size       ,							collate_fn=self.data_collator       ,							drop_last=self.args.dataloader_drop_last       ,							num_workers=self.args.dataloader_num_workers       ,							pin_memory=self.args.dataloader_pin_memory       ,							shuffle=A__       ,							)






       def        UpperCamelCase				(							self       ,							A__=None      )    ->			str:
          _SCREAMING_SNAKE_CASE      =				self.train_dataset if calib_dataset is None else calib_dataset
          _SCREAMING_SNAKE_CASE      =				self.get_calib_dataloader(A__      )

          _SCREAMING_SNAKE_CASE      =				self.model
          quant_trainer.configure_model(A__       ,							self.quant_trainer_args       ,							calib=A__      )
          model.eval()
          quant_trainer.enable_calibration(A__      )

          logger.info("""***** Running calibration *****"""      )
          logger.info(F"  Num examples = {self.calib_num}"      )
          logger.info(F"  Batch size = {calib_dataloader.batch_size}"      )

          for step, inputs in enumerate(A__      ):
             # Prediction step
             _SCREAMING_SNAKE_CASE ,							_SCREAMING_SNAKE_CASE ,							_SCREAMING_SNAKE_CASE      =				self.prediction_step(A__       ,							A__       ,							prediction_loss_only=A__      )
             if (step + 1) * calib_dataloader.batch_size >= self.calib_num:
                break

          quant_trainer.finish_calibration(A__       ,							self.quant_trainer_args      )
          _SCREAMING_SNAKE_CASE      =				model






       def        UpperCamelCase				(							self       ,							A__=None       ,							A__=None       ,							A__=None       ,							A__ = "eval"      )    ->			List[Any]:
          _SCREAMING_SNAKE_CASE      =				self.eval_dataset if eval_dataset is None else eval_dataset
          _SCREAMING_SNAKE_CASE      =				self.get_eval_dataloader(A__      )
          _SCREAMING_SNAKE_CASE      =				self.eval_examples if eval_examples is None else eval_examples

          # Temporarily disable metric computation, we will do it in the loop here.
          _SCREAMING_SNAKE_CASE      =				self.compute_metrics
          _SCREAMING_SNAKE_CASE      =				None
          _SCREAMING_SNAKE_CASE      =				self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
          try:
             _SCREAMING_SNAKE_CASE      =				eval_loop(
                 A__       ,							description="""Evaluation"""       ,							prediction_loss_only=True if compute_metrics is None else None       ,							ignore_keys=A__       ,							)
          finally:
             _SCREAMING_SNAKE_CASE      =				compute_metrics

          if self.post_process_function is not None and self.compute_metrics is not None:
             _SCREAMING_SNAKE_CASE      =				self.post_process_function(A__       ,							A__       ,							output.predictions      )
             _SCREAMING_SNAKE_CASE      =				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}_"      ):
                   _SCREAMING_SNAKE_CASE      =				metrics.pop(A__      )

             self.log(A__      )
          else:
             _SCREAMING_SNAKE_CASE      =				{}

          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()      )

          _SCREAMING_SNAKE_CASE      =				self.callback_handler.on_evaluate(self.args       ,							self.state       ,							self.control       ,							A__      )
          return metrics






       def        UpperCamelCase				(							self       ,							A__       ,							A__       ,							A__=None       ,							A__ = "test"      )    ->			List[str]:
          _SCREAMING_SNAKE_CASE      =				self.get_test_dataloader(A__      )

          # Temporarily disable metric computation, we will do it in the loop here.
          _SCREAMING_SNAKE_CASE      =				self.compute_metrics
          _SCREAMING_SNAKE_CASE      =				None
          _SCREAMING_SNAKE_CASE      =				self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
          try:
             _SCREAMING_SNAKE_CASE      =				eval_loop(
                 A__       ,							description="""Prediction"""       ,							prediction_loss_only=True if compute_metrics is None else None       ,							ignore_keys=A__       ,							)
          finally:
             _SCREAMING_SNAKE_CASE      =				compute_metrics

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

          _SCREAMING_SNAKE_CASE      =				self.post_process_function(A__       ,							A__       ,							output.predictions       ,							"""predict"""      )
          _SCREAMING_SNAKE_CASE      =				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}_"      ):
                _SCREAMING_SNAKE_CASE      =				metrics.pop(A__      )

          return PredictionOutput(predictions=predictions.predictions       ,							label_ids=predictions.label_ids       ,							metrics=A__      )






       def        UpperCamelCase				(							self       ,							A__="./"      )    ->			Tuple:
          _SCREAMING_SNAKE_CASE      =				self.eval_dataset
          _SCREAMING_SNAKE_CASE      =				self.get_eval_dataloader(A__      )

          _SCREAMING_SNAKE_CASE      =				next(iter(A__      )      )

          # saving device - to make it consistent
          _SCREAMING_SNAKE_CASE      =				torch.device("""cuda""" if torch.cuda.is_available() else """cpu"""      )

          # convert to tuple
          _SCREAMING_SNAKE_CASE      =				tuple(v.to(A__      ) for k, v in batch.items()      )

          logger.info("""Converting model to be onnx compatible"""      )
          from pytorch_quantization.nn import TensorQuantizer

          _SCREAMING_SNAKE_CASE      =				True

          _SCREAMING_SNAKE_CASE      =				self.model.to(A__      )

          model.eval()
          model.float()

          _SCREAMING_SNAKE_CASE      =				model.module if hasattr(A__       ,							"""module"""      ) else model
          quant_trainer.configure_model(A__       ,							self.quant_trainer_args      )

          _SCREAMING_SNAKE_CASE      =				os.path.join(A__       ,							"""model.onnx"""      )
          logger.info(F"exporting model to {output_model_file}"      )

          _SCREAMING_SNAKE_CASE      =				{0: """batch_size""", 1: """seq_len"""}

          torch.onnx.export(
              A__       ,							A__       ,							A__       ,							export_params=A__       ,							opset_version=13       ,							do_constant_folding=A__       ,							input_names=["""input_ids""", """attention_mask""", """token_type_ids"""]       ,							output_names=["""output_start_logits""", """output_end_logits"""]       ,							dynamic_axes={
                  """input_ids""": axes,
                  """attention_mask""": axes,
                  """token_type_ids""": axes,
                  """output_start_logits""": axes,
                  """output_end_logits""": axes,
              }       ,							verbose=A__       ,							)
          logger.info("""onnx export finished"""      )






