



"""simple docstring"""

import argparse

import torch

from transformers import YosoConfig, YosoForMaskedLM




def __lowercase    (							_a					):
    if "model" in orig_key:
        snake_case_				:  int       =			orig_key.replace('''model.'''	,    ''''''					)
    if "norm1" in orig_key:
        snake_case_				:  Union[str, Any]       =			orig_key.replace('''norm1'''	,    '''attention.output.LayerNorm'''					)
    if "norm2" in orig_key:
        snake_case_				:  Tuple       =			orig_key.replace('''norm2'''	,    '''output.LayerNorm'''					)
    if "norm" in orig_key:
        snake_case_				:  Any       =			orig_key.replace('''norm'''	,    '''LayerNorm'''					)
    if "transformer" in orig_key:
        snake_case_				:  Optional[Any]       =			orig_key.split('''.'''					)[0].split('''_'''					)[-1]
        snake_case_				:  int       =			orig_key.replace(f"transformer_{layer_num}"	,    f"encoder.layer.{layer_num}"					)
    if "mha.attn" in orig_key:
        snake_case_				:  int       =			orig_key.replace('''mha.attn'''	,    '''attention.self'''					)
    if "mha" in orig_key:
        snake_case_				:  Tuple       =			orig_key.replace('''mha'''	,    '''attention'''					)
    if "W_q" in orig_key:
        snake_case_				:  str       =			orig_key.replace('''W_q'''	,    '''self.query'''					)
    if "W_k" in orig_key:
        snake_case_				:  int       =			orig_key.replace('''W_k'''	,    '''self.key'''					)
    if "W_v" in orig_key:
        snake_case_				:  Any       =			orig_key.replace('''W_v'''	,    '''self.value'''					)
    if "ff1" in orig_key:
        snake_case_				:  Union[str, Any]       =			orig_key.replace('''ff1'''	,    '''intermediate.dense'''					)
    if "ff2" in orig_key:
        snake_case_				:  List[str]       =			orig_key.replace('''ff2'''	,    '''output.dense'''					)
    if "ff" in orig_key:
        snake_case_				:  Any       =			orig_key.replace('''ff'''	,    '''output.dense'''					)
    if "mlm_class" in orig_key:
        snake_case_				:  Union[str, Any]       =			orig_key.replace('''mlm.mlm_class'''	,    '''cls.predictions.decoder'''					)
    if "mlm" in orig_key:
        snake_case_				:  List[Any]       =			orig_key.replace('''mlm'''	,    '''cls.predictions.transform'''					)
    if "cls" not in orig_key:
        snake_case_				:  List[str]       =			'''yoso.''' + orig_key

    return orig_key




def __lowercase    (							_a	,    _a					):
    for key in orig_state_dict.copy().keys():
        snake_case_				:  str       =			orig_state_dict.pop(_a					)

        if ("pooler" in key) or ("sen_class" in key):
            continue
        else:
            snake_case_				:  Any       =			val

    snake_case_				:  Optional[Any]       =			orig_state_dict['''cls.predictions.decoder.bias''']
    snake_case_				:  int       =			torch.arange(_a					).expand((1, -1)					) + 2

    return orig_state_dict




def __lowercase    (							_a	,    _a	,    _a					):
    snake_case_				:  List[Any]       =			torch.load(_a	,    map_location='''cpu'''					)['''model_state_dict''']
    snake_case_				:  Optional[Any]       =			YosoConfig.from_json_file(_a					)
    snake_case_				:  Union[str, Any]       =			YosoForMaskedLM(_a					)

    snake_case_				:  Union[str, Any]       =			convert_checkpoint_helper(config.max_position_embeddings	,    _a					)

    print(model.load_state_dict(_a					)					)
    model.eval()
    model.save_pretrained(_a					)

    print(f"Checkpoint successfuly converted. Model saved at {pytorch_dump_path}"					)


if __name__ == "__main__":
     lowercase__					:						int												=			argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         '''--pytorch_model_path''', default=None, type=str, required=True, help='''Path to YOSO pytorch checkpoint.'''
     )
     parser.add_argument(
         '''--config_file''',
         default=None,
         type=str,
         required=True,
         help='''The json file for YOSO model config.''',
     )
     parser.add_argument(
         '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
     )
     lowercase__					:						List[str]												=			parser.parse_args()
     convert_yoso_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)










"""simple docstring"""

import math
import time
from typing import Dict, List, Optional

from torch.utils.data import Dataset

from transformers import SeqaSeqTrainer, 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  _UpperCAmelCase (    lowerCAmelCase__):

 def __init__(  self	: List[Any]						,  *lowercase_	: List[Any]						,  lowercase_	: Optional[Any]=None						,  lowercase_	: Tuple=None						,  **lowercase_	: Union[str, Any]       ):
     super().__init__(*lowercase_						,  **lowercase_       )
     snake_case_				:  str       =			eval_examples
     snake_case_				:  Optional[Any]       =			post_process_function

 def 		_snake_case      (  self	: Union[str, Any]						,  lowercase_	: Optional[Dataset] = None						,  lowercase_	: str=None						,  lowercase_	: Optional[List[str]] = None						,  lowercase_	: str = "eval"						,  **lowercase_	: Dict						,  ):
     snake_case_				:  int       =			gen_kwargs.copy()
     snake_case_				:  Dict       =			(
         gen_kwargs['''max_length'''] if gen_kwargs.get('''max_length'''       ) is not None else self.args.generation_max_length
     )
     snake_case_				:  Tuple       =			(
         gen_kwargs['''num_beams'''] if gen_kwargs.get('''num_beams'''       ) is not None else self.args.generation_num_beams
     )
     snake_case_				:  Optional[Any]       =			gen_kwargs

     snake_case_				:  Optional[int]       =			self.eval_dataset if eval_dataset is None else eval_dataset
     snake_case_				:  Optional[Any]       =			self.get_eval_dataloader(lowercase_       )
     snake_case_				:  Dict       =			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_				:  Optional[int]       =			self.compute_metrics
     snake_case_				:  str       =			None
     snake_case_				:  int       =			time.time()
     snake_case_				:  Any       =			self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
     try:
         snake_case_				:  str       =			eval_loop(
             lowercase_						,  description='''Evaluation'''						,  prediction_loss_only=True if compute_metrics is None else None						,  ignore_keys=lowercase_						,  metric_key_prefix=lowercase_						,  )
     finally:
         snake_case_				:  Optional[int]       =			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(
             lowercase_						,  lowercase_						,  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_				:  Tuple       =			self.post_process_function(lowercase_						,  lowercase_						,  lowercase_       )
         snake_case_				:  Optional[int]       =			self.compute_metrics(lowercase_       )

         # Prefix all keys with metric_key_prefix + '_'
         for key in list(metrics.keys()       ):
             if not key.startswith(f"{metric_key_prefix}_"       ):
                 snake_case_				:  int       =			metrics.pop(lowercase_       )

         metrics.update(output.metrics       )
     else:
         snake_case_				:  Tuple       =			output.metrics

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

     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						,  lowercase_       )
     return metrics






 def 		_snake_case      (  self	: Any						,  lowercase_	: int						,  lowercase_	: List[str]						,  lowercase_	: str=None						,  lowercase_	: str = "test"						,  **lowercase_	: Dict       ):
     snake_case_				:  Dict       =			gen_kwargs.copy()

     snake_case_				:  Dict       =			self.get_test_dataloader(lowercase_       )

     # Temporarily disable metric computation, we will do it in the loop here.
     snake_case_				:  Tuple       =			self.compute_metrics
     snake_case_				:  int       =			None
     snake_case_				:  List[Any]       =			time.time()
     snake_case_				:  int       =			self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
     try:
         snake_case_				:  List[str]       =			eval_loop(
             lowercase_						,  description='''Prediction'''						,  prediction_loss_only=True if compute_metrics is None else None						,  ignore_keys=lowercase_						,  metric_key_prefix=lowercase_						,  )
     finally:
         snake_case_				:  str       =			compute_metrics

     snake_case_				:  int       =			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(
             lowercase_						,  lowercase_						,  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_				:  Tuple       =			self.post_process_function(lowercase_						,  lowercase_						,  lowercase_						,  '''predict'''       )
     snake_case_				:  Dict       =			self.compute_metrics(lowercase_       )

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





