


"""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				=						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 	lowercase__				(					A_				):





   def __init__(							self      ,   *SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE=None      ,   SCREAMING_SNAKE_CASE=None      ,   SCREAMING_SNAKE_CASE=None      ,   **SCREAMING_SNAKE_CASE)  ->  str:
      super().__init__(*SCREAMING_SNAKE_CASE      ,   **SCREAMING_SNAKE_CASE)
      _lowerCamelCase	:					Union[str, Any]   =		eval_examples
      _lowerCamelCase	:					Union[str, Any]   =		post_process_function
      _lowerCamelCase	:					List[str]   =		quant_trainer_args
      _lowerCamelCase	:					Optional[int]   =		128  # default number of calibration samples





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

      _lowerCamelCase	:					List[Any]   =		self._remove_unused_columns(SCREAMING_SNAKE_CASE      ,   description="""Calibration""")

      return DataLoader(
          SCREAMING_SNAKE_CASE      ,   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=SCREAMING_SNAKE_CASE      ,   )





   def 		UpperCamelCase_  (							self      ,   SCREAMING_SNAKE_CASE=None)  ->  Optional[int]:
      _lowerCamelCase	:					List[str]   =		self.train_dataset if calib_dataset is None else calib_dataset
      _lowerCamelCase	:					Optional[Any]   =		self.get_calib_dataloader(SCREAMING_SNAKE_CASE)

      _lowerCamelCase	:					Any   =		self.model
      quant_trainer.configure_model(SCREAMING_SNAKE_CASE      ,   self.quant_trainer_args      ,   calib=SCREAMING_SNAKE_CASE)
      model.eval()
      quant_trainer.enable_calibration(SCREAMING_SNAKE_CASE)

      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(SCREAMING_SNAKE_CASE):
         # Prediction step
         _lowerCamelCase   ,    _lowerCamelCase   ,    _lowerCamelCase	:					Union[str, Any]   =		self.prediction_step(SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE      ,   prediction_loss_only=SCREAMING_SNAKE_CASE)
         if (step + 1) * calib_dataloader.batch_size >= self.calib_num:
            break

      quant_trainer.finish_calibration(SCREAMING_SNAKE_CASE      ,   self.quant_trainer_args)
      _lowerCamelCase	:					int   =		model





   def 		UpperCamelCase_  (							self      ,   SCREAMING_SNAKE_CASE=None      ,   SCREAMING_SNAKE_CASE=None      ,   SCREAMING_SNAKE_CASE=None      ,   SCREAMING_SNAKE_CASE = "eval")  ->  Optional[Any]:
      _lowerCamelCase	:					Tuple   =		self.eval_dataset if eval_dataset is None else eval_dataset
      _lowerCamelCase	:					Optional[int]   =		self.get_eval_dataloader(SCREAMING_SNAKE_CASE)
      _lowerCamelCase	:					Tuple   =		self.eval_examples if eval_examples is None else eval_examples

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

      if self.post_process_function is not None and self.compute_metrics is not None:
         _lowerCamelCase	:					Union[str, Any]   =		self.post_process_function(SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE      ,   output.predictions)
         _lowerCamelCase	:					Union[str, Any]   =		self.compute_metrics(SCREAMING_SNAKE_CASE)

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

         self.log(SCREAMING_SNAKE_CASE)
      else:
         _lowerCamelCase	:					str   =		{}

      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())

      _lowerCamelCase	:					Dict   =		self.callback_handler.on_evaluate(self.args      ,   self.state      ,   self.control      ,   SCREAMING_SNAKE_CASE)
      return metrics





   def 		UpperCamelCase_  (							self      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE=None      ,   SCREAMING_SNAKE_CASE = "test")  ->  Union[str, Any]:
      _lowerCamelCase	:					Union[str, Any]   =		self.get_test_dataloader(SCREAMING_SNAKE_CASE)

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

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

      _lowerCamelCase	:					List[str]   =		self.post_process_function(SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE      ,   output.predictions      ,   """predict""")
      _lowerCamelCase	:					Any   =		self.compute_metrics(SCREAMING_SNAKE_CASE)

      # Prefix all keys with metric_key_prefix + '_'
      for key in list(metrics.keys()):
         if not key.startswith(F'{metric_key_prefix}_'):
            _lowerCamelCase	:					int   =		metrics.pop(SCREAMING_SNAKE_CASE)

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





   def 		UpperCamelCase_  (							self      ,   SCREAMING_SNAKE_CASE="./")  ->  Optional[Any]:
      _lowerCamelCase	:					Optional[int]   =		self.eval_dataset
      _lowerCamelCase	:					Any   =		self.get_eval_dataloader(SCREAMING_SNAKE_CASE)

      _lowerCamelCase	:					int   =		next(iter(SCREAMING_SNAKE_CASE))

      # saving device - to make it consistent
      _lowerCamelCase	:					Optional[int]   =		torch.device("""cuda""" if torch.cuda.is_available() else """cpu""")

      # convert to tuple
      _lowerCamelCase	:					Union[str, Any]   =		tuple(v.to(SCREAMING_SNAKE_CASE) for k, v in batch.items())

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

      _lowerCamelCase	:					Tuple   =		True

      _lowerCamelCase	:					str   =		self.model.to(SCREAMING_SNAKE_CASE)

      model.eval()
      model.float()

      _lowerCamelCase	:					List[Any]   =		model.module if hasattr(SCREAMING_SNAKE_CASE      ,   """module""") else model
      quant_trainer.configure_model(SCREAMING_SNAKE_CASE      ,   self.quant_trainer_args)

      _lowerCamelCase	:					Optional[Any]   =		os.path.join(SCREAMING_SNAKE_CASE      ,   """model.onnx""")
      logger.info(F'exporting model to {output_model_file}')

      _lowerCamelCase	:					Union[str, Any]   =		{0: """batch_size""", 1: """seq_len"""}

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








import argparse
import json
import logging
import os
import shutil
import sys
import tempfile
import unittest
from unittest import mock

import torch
from accelerate.utils import write_basic_config

from transformers.testing_utils import TestCasePlus, get_gpu_count, run_command, slow, torch_device
from transformers.utils import is_apex_available


logging.basicConfig(level=logging.DEBUG)

a__      :     Any         =		logging.getLogger()

def      UpperCAmelCase_    (    )	->				Any:
     '''simple docstring'''


     A_          =       argparse.ArgumentParser()
     parser.add_argument('''-f'''				)
     A_          =       parser.parse_args()
     return args.f

def      UpperCAmelCase_    (    _UpperCAmelCase							:Union[str, Any]				)	->				str:
     '''simple docstring'''


     A_          =       {}
     A_          =       os.path.join(_UpperCAmelCase  ,						'''all_results.json'''				)
     if os.path.exists(_UpperCAmelCase				):
          with open(_UpperCAmelCase  ,						'''r'''				) as f:
               A_          =       json.load(_UpperCAmelCase				)
     else:
          raise ValueError(f'can\'t find {path}'				)
     return results

def      UpperCAmelCase_    (    )	->				Union[str, Any]:
     '''simple docstring'''


     A_          =       torch.cuda.is_available() and torch_device == '''cuda'''
     return is_using_cuda and is_apex_available()


a__      :     List[Any]         =		logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)





class UpperCAmelCase_				( _SCREAMING_SNAKE_CASE ):


  @classmethod
  def 						__UpperCAmelCase     (							cls ):



       """simple docstring"""





       A_          =       tempfile.mkdtemp()
       A_          =       os.path.join(cls.tmpdir ,'''default_config.yml''' )
       write_basic_config(save_location=cls.configPath )
       A_          =       ['''accelerate''', '''launch''', '''--config_file''', cls.configPath]


  @classmethod
  def 						__UpperCAmelCase     (							cls ):



       """simple docstring"""





       shutil.rmtree(cls.tmpdir )


  @mock.patch.dict(os.environ ,{'''WANDB_MODE''': '''offline'''} )
  def 						__UpperCAmelCase     (							self ):



       """simple docstring"""





       A_          =       self.get_auto_remove_tmp_dir()
       A_          =       f'\n            {self.examples_dir}/pytorch/text-classification/run_glue_no_trainer.py\n            --model_name_or_path distilbert-base-uncased\n            --output_dir {tmp_dir}\n            --train_file ./tests/fixtures/tests_samples/MRPC/train.csv\n            --validation_file ./tests/fixtures/tests_samples/MRPC/dev.csv\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n            --learning_rate=1e-4\n            --seed=42\n            --checkpointing_steps epoch\n            --with_tracking\n        '.split()

       if is_cuda_and_apex_available():
            testargs.append('''--fp16''' )

       run_command(self._launch_args + testargs )
       A_          =       get_results(__snake_case )
       self.assertGreaterEqual(result['''eval_accuracy'''] ,0.75 )
       self.assertTrue(os.path.exists(os.path.join(__snake_case ,'''epoch_0''' ) ) )
       self.assertTrue(os.path.exists(os.path.join(__snake_case ,'''glue_no_trainer''' ) ) )


  @mock.patch.dict(os.environ ,{'''WANDB_MODE''': '''offline'''} )
  def 						__UpperCAmelCase     (							self ):



       """simple docstring"""





       A_          =       self.get_auto_remove_tmp_dir()
       A_          =       f'\n            {self.examples_dir}/pytorch/language-modeling/run_clm_no_trainer.py\n            --model_name_or_path distilgpt2\n            --train_file ./tests/fixtures/sample_text.txt\n            --validation_file ./tests/fixtures/sample_text.txt\n            --block_size 128\n            --per_device_train_batch_size 5\n            --per_device_eval_batch_size 5\n            --num_train_epochs 2\n            --output_dir {tmp_dir}\n            --checkpointing_steps epoch\n            --with_tracking\n        '.split()

       if torch.cuda.device_count() > 1:
            # Skipping because there are not enough batches to train the model + would need a drop_last to work.
            return

       run_command(self._launch_args + testargs )
       A_          =       get_results(__snake_case )
       self.assertLess(result['''perplexity'''] ,1_0_0 )
       self.assertTrue(os.path.exists(os.path.join(__snake_case ,'''epoch_0''' ) ) )
       self.assertTrue(os.path.exists(os.path.join(__snake_case ,'''clm_no_trainer''' ) ) )


  @mock.patch.dict(os.environ ,{'''WANDB_MODE''': '''offline'''} )
  def 						__UpperCAmelCase     (							self ):



       """simple docstring"""





       A_          =       self.get_auto_remove_tmp_dir()
       A_          =       f'\n            {self.examples_dir}/pytorch/language-modeling/run_mlm_no_trainer.py\n            --model_name_or_path distilroberta-base\n            --train_file ./tests/fixtures/sample_text.txt\n            --validation_file ./tests/fixtures/sample_text.txt\n            --output_dir {tmp_dir}\n            --num_train_epochs=1\n            --checkpointing_steps epoch\n            --with_tracking\n        '.split()

       run_command(self._launch_args + testargs )
       A_          =       get_results(__snake_case )
       self.assertLess(result['''perplexity'''] ,4_2 )
       self.assertTrue(os.path.exists(os.path.join(__snake_case ,'''epoch_0''' ) ) )
       self.assertTrue(os.path.exists(os.path.join(__snake_case ,'''mlm_no_trainer''' ) ) )


  @mock.patch.dict(os.environ ,{'''WANDB_MODE''': '''offline'''} )
  def 						__UpperCAmelCase     (							self ):



       """simple docstring"""





       A_          =       7 if get_gpu_count() > 1 else 2

       A_          =       self.get_auto_remove_tmp_dir()
       A_          =       f'\n            {self.examples_dir}/pytorch/token-classification/run_ner_no_trainer.py\n            --model_name_or_path bert-base-uncased\n            --train_file tests/fixtures/tests_samples/conll/sample.json\n            --validation_file tests/fixtures/tests_samples/conll/sample.json\n            --output_dir {tmp_dir}\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=2\n            --num_train_epochs={epochs}\n            --seed 7\n            --checkpointing_steps epoch\n            --with_tracking\n        '.split()

       run_command(self._launch_args + testargs )
       A_          =       get_results(__snake_case )
       self.assertGreaterEqual(result['''eval_accuracy'''] ,0.75 )
       self.assertLess(result['''train_loss'''] ,0.5 )
       self.assertTrue(os.path.exists(os.path.join(__snake_case ,'''epoch_0''' ) ) )
       self.assertTrue(os.path.exists(os.path.join(__snake_case ,'''ner_no_trainer''' ) ) )


  @unittest.skip(reason='''Fix me @muellerzr''' )
  @mock.patch.dict(os.environ ,{'''WANDB_MODE''': '''offline'''} )
  def 						__UpperCAmelCase     (							self ):



       """simple docstring"""





       A_          =       self.get_auto_remove_tmp_dir()
       A_          =       f'\n            {self.examples_dir}/pytorch/question-answering/run_qa_no_trainer.py\n            --model_name_or_path bert-base-uncased\n            --version_2_with_negative\n            --train_file tests/fixtures/tests_samples/SQUAD/sample.json\n            --validation_file tests/fixtures/tests_samples/SQUAD/sample.json\n            --output_dir {tmp_dir}\n            --seed=42\n            --max_train_steps=10\n            --num_warmup_steps=2\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n            --checkpointing_steps epoch\n            --with_tracking\n        '.split()

       run_command(self._launch_args + testargs )
       A_          =       get_results(__snake_case )
       # Because we use --version_2_with_negative the testing script uses SQuAD v2 metrics.
       self.assertGreaterEqual(result['''eval_f1'''] ,2_8 )
       self.assertGreaterEqual(result['''eval_exact'''] ,2_8 )
       self.assertTrue(os.path.exists(os.path.join(__snake_case ,'''epoch_0''' ) ) )
       self.assertTrue(os.path.exists(os.path.join(__snake_case ,'''qa_no_trainer''' ) ) )


  @mock.patch.dict(os.environ ,{'''WANDB_MODE''': '''offline'''} )
  def 						__UpperCAmelCase     (							self ):



       """simple docstring"""





       A_          =       self.get_auto_remove_tmp_dir()
       A_          =       f'\n            {self.examples_dir}/pytorch/multiple-choice/run_swag_no_trainer.py\n            --model_name_or_path bert-base-uncased\n            --train_file tests/fixtures/tests_samples/swag/sample.json\n            --validation_file tests/fixtures/tests_samples/swag/sample.json\n            --output_dir {tmp_dir}\n            --max_train_steps=20\n            --num_warmup_steps=2\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n            --with_tracking\n        '.split()

       run_command(self._launch_args + testargs )
       A_          =       get_results(__snake_case )
       self.assertGreaterEqual(result['''eval_accuracy'''] ,0.8 )
       self.assertTrue(os.path.exists(os.path.join(__snake_case ,'''swag_no_trainer''' ) ) )


  @slow
  @mock.patch.dict(os.environ ,{'''WANDB_MODE''': '''offline'''} )
  def 						__UpperCAmelCase     (							self ):



       """simple docstring"""





       A_          =       self.get_auto_remove_tmp_dir()
       A_          =       f'\n            {self.examples_dir}/pytorch/summarization/run_summarization_no_trainer.py\n            --model_name_or_path t5-small\n            --train_file tests/fixtures/tests_samples/xsum/sample.json\n            --validation_file tests/fixtures/tests_samples/xsum/sample.json\n            --output_dir {tmp_dir}\n            --max_train_steps=50\n            --num_warmup_steps=8\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n            --checkpointing_steps epoch\n            --with_tracking\n        '.split()

       run_command(self._launch_args + testargs )
       A_          =       get_results(__snake_case )
       self.assertGreaterEqual(result['''eval_rouge1'''] ,1_0 )
       self.assertGreaterEqual(result['''eval_rouge2'''] ,2 )
       self.assertGreaterEqual(result['''eval_rougeL'''] ,7 )
       self.assertGreaterEqual(result['''eval_rougeLsum'''] ,7 )
       self.assertTrue(os.path.exists(os.path.join(__snake_case ,'''epoch_0''' ) ) )
       self.assertTrue(os.path.exists(os.path.join(__snake_case ,'''summarization_no_trainer''' ) ) )


  @slow
  @mock.patch.dict(os.environ ,{'''WANDB_MODE''': '''offline'''} )
  def 						__UpperCAmelCase     (							self ):



       """simple docstring"""





       A_          =       self.get_auto_remove_tmp_dir()
       A_          =       f'\n            {self.examples_dir}/pytorch/translation/run_translation_no_trainer.py\n            --model_name_or_path sshleifer/student_marian_en_ro_6_1\n            --source_lang en\n            --target_lang ro\n            --train_file tests/fixtures/tests_samples/wmt16/sample.json\n            --validation_file tests/fixtures/tests_samples/wmt16/sample.json\n            --output_dir {tmp_dir}\n            --max_train_steps=50\n            --num_warmup_steps=8\n            --num_beams=6\n            --learning_rate=3e-3\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n            --source_lang en_XX\n            --target_lang ro_RO\n            --checkpointing_steps epoch\n            --with_tracking\n        '.split()

       run_command(self._launch_args + testargs )
       A_          =       get_results(__snake_case )
       self.assertGreaterEqual(result['''eval_bleu'''] ,3_0 )
       self.assertTrue(os.path.exists(os.path.join(__snake_case ,'''epoch_0''' ) ) )
       self.assertTrue(os.path.exists(os.path.join(__snake_case ,'''translation_no_trainer''' ) ) )


  @slow
  def 						__UpperCAmelCase     (							self ):



       """simple docstring"""





       A_          =       logging.StreamHandler(sys.stdout )
       logger.addHandler(__snake_case )

       A_          =       self.get_auto_remove_tmp_dir()
       A_          =       f'\n            {self.examples_dir}/pytorch/semantic-segmentation/run_semantic_segmentation_no_trainer.py\n            --dataset_name huggingface/semantic-segmentation-test-sample\n            --output_dir {tmp_dir}\n            --max_train_steps=10\n            --num_warmup_steps=2\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n            --checkpointing_steps epoch\n        '.split()

       run_command(self._launch_args + testargs )
       A_          =       get_results(__snake_case )
       self.assertGreaterEqual(result['''eval_overall_accuracy'''] ,0.10 )




  @mock.patch.dict(os.environ ,{'''WANDB_MODE''': '''offline'''} )
  def 						__UpperCAmelCase     (							self ):



       """simple docstring"""





       A_          =       self.get_auto_remove_tmp_dir()
       A_          =       f'\n            {self.examples_dir}/pytorch/image-classification/run_image_classification_no_trainer.py\n            --model_name_or_path google/vit-base-patch16-224-in21k\n            --dataset_name hf-internal-testing/cats_vs_dogs_sample\n            --learning_rate 1e-4\n            --per_device_train_batch_size 2\n            --per_device_eval_batch_size 1\n            --max_train_steps 2\n            --train_val_split 0.1\n            --seed 42\n            --output_dir {tmp_dir}\n            --with_tracking\n            --checkpointing_steps 1\n        '.split()

       if is_cuda_and_apex_available():
            testargs.append('''--fp16''' )

       run_command(self._launch_args + testargs )
       A_          =       get_results(__snake_case )
       # The base model scores a 25%
       self.assertGreaterEqual(result['''eval_accuracy'''] ,0.6 )
       self.assertTrue(os.path.exists(os.path.join(__snake_case ,'''step_1''' ) ) )
       self.assertTrue(os.path.exists(os.path.join(__snake_case ,'''image_classification_no_trainer''' ) ) )



