
'''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


__snake_case:  Dict								=			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  _UpperCAmelCase ( lowerCAmelCase__    ):

     """simple docstring"""

     def __init__(      self							,		*lowerCAmelCase_							,		lowerCAmelCase_=None							,		lowerCAmelCase_=None							,		lowerCAmelCase_=None							,		**lowerCAmelCase_ ):

       '''simple docstring'''

       super().__init__(*lowerCAmelCase_							,		**lowerCAmelCase_ )
       a_      :	Tuple      =							eval_examples
       a_      :	Optional[Any]      =							post_process_function
       a_      :	List[str]      =							quant_trainer_args
       a_      :	List[str]      =							1_28  # default number of calibration samples


     def 							_lowerCAmelCase  (      self							,		lowerCAmelCase_=None ):

       '''simple docstring'''

       if calib_dataset is None and self.calib_dataset is None:
         raise ValueError("""Trainer: calibration requires an calib_dataset.""" )
       a_      :	str      =							calib_dataset if calib_dataset is not None else self.calib_dataset

       a_      :	Dict      =							self._remove_unused_columns(lowerCAmelCase_							,		description="""Calibration""" )

       return DataLoader(
           lowerCAmelCase_							,		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=lowerCAmelCase_							,		)


     def 							_lowerCAmelCase  (      self							,		lowerCAmelCase_=None ):

       '''simple docstring'''

       a_      :	Union[str, Any]      =							self.train_dataset if calib_dataset is None else calib_dataset
       a_      :	int      =							self.get_calib_dataloader(lowerCAmelCase_ )

       a_      :	Dict      =							self.model
       quant_trainer.configure_model(lowerCAmelCase_							,		self.quant_trainer_args							,		calib=lowerCAmelCase_ )
       model.eval()
       quant_trainer.enable_calibration(lowerCAmelCase_ )

       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(lowerCAmelCase_ ):
         # Prediction step
         a_      , a_      , a_      :	Optional[int]      =							self.prediction_step(lowerCAmelCase_							,		lowerCAmelCase_							,		prediction_loss_only=lowerCAmelCase_ )
         if (step + 1) * calib_dataloader.batch_size >= self.calib_num:
           break

       quant_trainer.finish_calibration(lowerCAmelCase_							,		self.quant_trainer_args )
       a_      :	Optional[int]      =							model


     def 							_lowerCAmelCase  (      self							,		lowerCAmelCase_=None							,		lowerCAmelCase_=None							,		lowerCAmelCase_=None							,		lowerCAmelCase_ = "eval" ):

       '''simple docstring'''

       a_      :	str      =							self.eval_dataset if eval_dataset is None else eval_dataset
       a_      :	List[str]      =							self.get_eval_dataloader(lowerCAmelCase_ )
       a_      :	str      =							self.eval_examples if eval_examples is None else eval_examples

       # Temporarily disable metric computation, we will do it in the loop here.
       a_      :	List[str]      =							self.compute_metrics
       a_      :	Tuple      =							None
       a_      :	Optional[Any]      =							self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
       try:
         a_      :	List[str]      =							eval_loop(
             lowerCAmelCase_							,		description="""Evaluation"""							,		prediction_loss_only=True if compute_metrics is None else None							,		ignore_keys=lowerCAmelCase_							,		)
       finally:
         a_      :	int      =							compute_metrics

       if self.post_process_function is not None and self.compute_metrics is not None:
         a_      :	Optional[Any]      =							self.post_process_function(lowerCAmelCase_							,		lowerCAmelCase_							,		output.predictions )
         a_      :	Tuple      =							self.compute_metrics(lowerCAmelCase_ )

         # Prefix all keys with metric_key_prefix + '_'
         for key in list(metrics.keys() ):
           if not key.startswith(f'''{metric_key_prefix}_''' ):
             a_      :	Optional[int]      =							metrics.pop(lowerCAmelCase_ )

         self.log(lowerCAmelCase_ )
       else:
         a_      :	Any      =							{}

       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() )

       a_      :	Optional[Any]      =							self.callback_handler.on_evaluate(self.args							,		self.state							,		self.control							,		lowerCAmelCase_ )
       return metrics


     def 							_lowerCAmelCase  (      self							,		lowerCAmelCase_							,		lowerCAmelCase_							,		lowerCAmelCase_=None							,		lowerCAmelCase_ = "test" ):

       '''simple docstring'''

       a_      :	List[Any]      =							self.get_test_dataloader(lowerCAmelCase_ )

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

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

       a_      :	Tuple      =							self.post_process_function(lowerCAmelCase_							,		lowerCAmelCase_							,		output.predictions							,		"""predict""" )
       a_      :	List[Any]      =							self.compute_metrics(lowerCAmelCase_ )

       # Prefix all keys with metric_key_prefix + '_'
       for key in list(metrics.keys() ):
         if not key.startswith(f'''{metric_key_prefix}_''' ):
           a_      :	Tuple      =							metrics.pop(lowerCAmelCase_ )

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






     def 							_lowerCAmelCase  (      self							,		lowerCAmelCase_="./" ):

       '''simple docstring'''

       a_      :	Any      =							self.eval_dataset
       a_      :	Tuple      =							self.get_eval_dataloader(lowerCAmelCase_ )

       a_      :	List[str]      =							next(iter(lowerCAmelCase_ ) )

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

       # convert to tuple
       a_      :	Optional[int]      =							tuple(v.to(lowerCAmelCase_ ) for k, v in batch.items() )

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

       a_      :	List[str]      =							True

       a_      :	str      =							self.model.to(lowerCAmelCase_ )

       model.eval()
       model.float()

       a_      :	Optional[int]      =							model.module if hasattr(lowerCAmelCase_							,		"""module""" ) else model
       quant_trainer.configure_model(lowerCAmelCase_							,		self.quant_trainer_args )

       a_      :	Union[str, Any]      =							os.path.join(lowerCAmelCase_							,		"""model.onnx""" )
       logger.info(f'''exporting model to {output_model_file}''' )

       a_      :	Dict      =							{0: """batch_size""", 1: """seq_len"""}

       torch.onnx.export(
           lowerCAmelCase_							,		lowerCAmelCase_							,		lowerCAmelCase_							,		export_params=lowerCAmelCase_							,		opset_version=13							,		do_constant_folding=lowerCAmelCase_							,		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=lowerCAmelCase_							,		)
       logger.info("""onnx export finished""" )




'''simple docstring'''

from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging


__snake_case:  Tuple								=			logging.get_logger(__name__)

__snake_case:  Tuple								=			{
    "t5-small": "https://huggingface.co/t5-small/resolve/main/config.json",
    "t5-base": "https://huggingface.co/t5-base/resolve/main/config.json",
    "t5-large": "https://huggingface.co/t5-large/resolve/main/config.json",
    "t5-3b": "https://huggingface.co/t5-3b/resolve/main/config.json",
    "t5-11b": "https://huggingface.co/t5-11b/resolve/main/config.json",
}


class  _UpperCAmelCase ( lowerCAmelCase__    ):

     """simple docstring"""

     a_    	=					"t5"
     a_    	=					["past_key_values"]
     a_    	=					{"hidden_size": "d_model", "num_attention_heads": "num_heads", "num_hidden_layers": "num_layers"}


     def __init__(      self							,		lowerCAmelCase_=3_21_28							,		lowerCAmelCase_=5_12							,		lowerCAmelCase_=64							,		lowerCAmelCase_=20_48							,		lowerCAmelCase_=6							,		lowerCAmelCase_=None							,		lowerCAmelCase_=8							,		lowerCAmelCase_=32							,		lowerCAmelCase_=1_28							,		lowerCAmelCase_=0.1							,		lowerCAmelCase_=1E-6							,		lowerCAmelCase_=1.0							,		lowerCAmelCase_="relu"							,		lowerCAmelCase_=True							,		lowerCAmelCase_=True							,		lowerCAmelCase_=0							,		lowerCAmelCase_=1							,		**lowerCAmelCase_							,		):

       '''simple docstring'''

       a_      :	List[Any]      =							vocab_size
       a_      :	List[str]      =							d_model
       a_      :	Union[str, Any]      =							d_kv
       a_      :	List[Any]      =							d_ff
       a_      :	int      =							num_layers
       a_      :	Any      =							(
           num_decoder_layers if num_decoder_layers is not None else self.num_layers
       )  # default = symmetry
       a_      :	Union[str, Any]      =							num_heads
       a_      :	int      =							relative_attention_num_buckets
       a_      :	Dict      =							relative_attention_max_distance
       a_      :	Union[str, Any]      =							dropout_rate
       a_      :	Union[str, Any]      =							layer_norm_epsilon
       a_      :	List[Any]      =							initializer_factor
       a_      :	Any      =							feed_forward_proj
       a_      :	List[Any]      =							use_cache

       a_      :	Dict      =							self.feed_forward_proj.split("""-""" )
       a_      :	Union[str, Any]      =							act_info[-1]
       a_      :	str      =							act_info[0] == """gated"""

       if len(lowerCAmelCase_ ) > 1 and act_info[0] != "gated" or len(lowerCAmelCase_ ) > 2:
         raise ValueError(
             f'''`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer.'''
             """Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. """
             """'gated-gelu' or 'relu'""" )

       # for backwards compatibility
       if feed_forward_proj == "gated-gelu":
         a_      :	List[str]      =							"""gelu_new"""

       super().__init__(
           pad_token_id=lowerCAmelCase_							,		eos_token_id=lowerCAmelCase_							,		is_encoder_decoder=lowerCAmelCase_							,		**lowerCAmelCase_							,		)







class  _UpperCAmelCase ( lowerCAmelCase__    ):

     """simple docstring"""

     @property
     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	List[Any]      =							{
           """input_ids""": {0: """batch""", 1: """encoder_sequence"""},
           """attention_mask""": {0: """batch""", 1: """encoder_sequence"""},
       }
       if self.use_past:
         a_      :	Tuple      =							"""past_encoder_sequence + sequence"""
         a_      :	str      =							{0: """batch"""}
         a_      :	Any      =							{0: """batch""", 1: """past_decoder_sequence + sequence"""}
       else:
         a_      :	List[Any]      =							{0: """batch""", 1: """decoder_sequence"""}
         a_      :	Optional[int]      =							{0: """batch""", 1: """decoder_sequence"""}

       if self.use_past:
         self.fill_with_past_key_values_(lowerCAmelCase_							,		direction="""inputs""" )

       return common_inputs






     @property
     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       return 13


