





'''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


A_		:  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 					__snake_case  (					__SCREAMING_SNAKE_CASE							):






   '''simple docstring'''





   def __init__(							self	,		*__SCREAMING_SNAKE_CASE	,		__SCREAMING_SNAKE_CASE=None	,		__SCREAMING_SNAKE_CASE=None	,		__SCREAMING_SNAKE_CASE=None	,		**__SCREAMING_SNAKE_CASE       ):
    super().__init__(*__SCREAMING_SNAKE_CASE	,		**__SCREAMING_SNAKE_CASE       )
    snake_case__     :					List[str]    =  eval_examples
    snake_case__     :					List[str]    =  post_process_function
    snake_case__     :					Optional[Any]    =  quant_trainer_args
    snake_case__     :					List[str]    =  1_2_8  # default number of calibration samples

   def 		__UpperCamelCase						(							self	,		__SCREAMING_SNAKE_CASE=None       ):
    if calib_dataset is None and self.calib_dataset is None:
     raise ValueError("""Trainer: calibration requires an calib_dataset."""       )
    snake_case__     :					List[str]    =  calib_dataset if calib_dataset is not None else self.calib_dataset

    snake_case__     :					Dict    =  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       ):
    snake_case__     :					List[Any]    =  self.train_dataset if calib_dataset is None else calib_dataset
    snake_case__     :					Dict    =  self.get_calib_dataloader(__SCREAMING_SNAKE_CASE       )

    snake_case__     :					Optional[int]    =  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
     snake_case__  ,							snake_case__  ,							snake_case__     :					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       )
    snake_case__     :					Optional[Any]    =  model

   def 		__UpperCamelCase						(							self	,		__SCREAMING_SNAKE_CASE=None	,		__SCREAMING_SNAKE_CASE=None	,		__SCREAMING_SNAKE_CASE=None	,		__SCREAMING_SNAKE_CASE = "eval"       ):
    snake_case__     :					Dict    =  self.eval_dataset if eval_dataset is None else eval_dataset
    snake_case__     :					Dict    =  self.get_eval_dataloader(__SCREAMING_SNAKE_CASE       )
    snake_case__     :					str    =  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__     :					Tuple    =  self.compute_metrics
    snake_case__     :					Tuple    =  None
    snake_case__     :					Optional[int]    =  self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
    try:
     snake_case__     :					Optional[Any]    =  eval_loop(
         __SCREAMING_SNAKE_CASE	,		description="""Evaluation"""	,		prediction_loss_only=True if compute_metrics is None else None	,		ignore_keys=__SCREAMING_SNAKE_CASE	,		)
    finally:
     snake_case__     :					List[str]    =  compute_metrics

    if self.post_process_function is not None and self.compute_metrics is not None:
     snake_case__     :					Any    =  self.post_process_function(__SCREAMING_SNAKE_CASE	,		__SCREAMING_SNAKE_CASE	,		output.predictions       )
     snake_case__     :					str    =  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}_"       ):
       snake_case__     :					str    =  metrics.pop(__SCREAMING_SNAKE_CASE       )

     self.log(__SCREAMING_SNAKE_CASE       )
    else:
     snake_case__     :					Union[str, 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()       )

    snake_case__     :					int    =  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"       ):
    snake_case__     :					int    =  self.get_test_dataloader(__SCREAMING_SNAKE_CASE       )

    # Temporarily disable metric computation, we will do it in the loop here.
    snake_case__     :					Dict    =  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
    try:
     snake_case__     :					str    =  eval_loop(
         __SCREAMING_SNAKE_CASE	,		description="""Prediction"""	,		prediction_loss_only=True if compute_metrics is None else None	,		ignore_keys=__SCREAMING_SNAKE_CASE	,		)
    finally:
     snake_case__     :					Union[str, Any]    =  compute_metrics

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

    snake_case__     :					List[str]    =  self.post_process_function(__SCREAMING_SNAKE_CASE	,		__SCREAMING_SNAKE_CASE	,		output.predictions	,		"""predict"""       )
    snake_case__     :					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}_"       ):
      snake_case__     :					Optional[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="./"       ):
    snake_case__     :					List[Any]    =  self.eval_dataset
    snake_case__     :					Union[str, Any]    =  self.get_eval_dataloader(__SCREAMING_SNAKE_CASE       )

    snake_case__     :					List[str]    =  next(iter(__SCREAMING_SNAKE_CASE       )       )

    # saving device - to make it consistent
    snake_case__     :					Union[str, Any]    =  torch.device("""cuda""" if torch.cuda.is_available() else """cpu"""       )

    # convert to tuple
    snake_case__     :					Tuple    =  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

    snake_case__     :					Optional[Any]    =  True

    snake_case__     :					Optional[Any]    =  self.model.to(__SCREAMING_SNAKE_CASE       )

    model.eval()
    model.float()

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

    snake_case__     :					Tuple    =  os.path.join(__SCREAMING_SNAKE_CASE	,		"""model.onnx"""       )
    logger.info(f"exporting model to {output_model_file}"       )

    snake_case__     :					List[str]    =  {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=1_3	,		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)

lowerCamelCase__						=				logging.getLogger()


def 				__A()		->					str:

		"""simple docstring"""






		_UpperCamelCase         =						argparse.ArgumentParser()
		parser.add_argument("""-f"""			)
		_UpperCamelCase         =						parser.parse_args()
		return args.f


def 				__A(lowerCAmelCase			)		->					Any:

		"""simple docstring"""






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


def 				__A()		->					Tuple:

		"""simple docstring"""






		_UpperCamelCase         =						torch.cuda.is_available() and torch_device == """cuda"""
		return is_using_cuda and is_apex_available()


lowerCamelCase__						=				logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)







class 							lowerCAmelCase__   ( __lowercase						):

						@classmethod
						def 			A_					(      cls					)       ->   Tuple:


								'''simple docstring'''

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

						@classmethod
						def 			A_					(      cls					)       ->   str:


								'''simple docstring'''

								shutil.rmtree(cls.tmpdir					)

						@mock.patch.dict(os.environ							,				{"""WANDB_MODE""": """offline"""}					)
						def 			A_					(      self					)       ->   Any:


								'''simple docstring'''

								_UpperCamelCase         =						self.get_auto_remove_tmp_dir()
								_UpperCamelCase         =						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					)
								_UpperCamelCase         =						get_results(a					)
								self.assertGreaterEqual(result["""eval_accuracy"""]							,				0.75					)
								self.assertTrue(os.path.exists(os.path.join(a							,				"""epoch_0"""					)					)					)
								self.assertTrue(os.path.exists(os.path.join(a							,				"""glue_no_trainer"""					)					)					)

						@mock.patch.dict(os.environ							,				{"""WANDB_MODE""": """offline"""}					)
						def 			A_					(      self					)       ->   List[Any]:


								'''simple docstring'''

								_UpperCamelCase         =						self.get_auto_remove_tmp_dir()
								_UpperCamelCase         =						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					)
								_UpperCamelCase         =						get_results(a					)
								self.assertLess(result["""perplexity"""]							,				1_00					)
								self.assertTrue(os.path.exists(os.path.join(a							,				"""epoch_0"""					)					)					)
								self.assertTrue(os.path.exists(os.path.join(a							,				"""clm_no_trainer"""					)					)					)

						@mock.patch.dict(os.environ							,				{"""WANDB_MODE""": """offline"""}					)
						def 			A_					(      self					)       ->   Tuple:


								'''simple docstring'''

								_UpperCamelCase         =						self.get_auto_remove_tmp_dir()
								_UpperCamelCase         =						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					)
								_UpperCamelCase         =						get_results(a					)
								self.assertLess(result["""perplexity"""]							,				42					)
								self.assertTrue(os.path.exists(os.path.join(a							,				"""epoch_0"""					)					)					)
								self.assertTrue(os.path.exists(os.path.join(a							,				"""mlm_no_trainer"""					)					)					)

						@mock.patch.dict(os.environ							,				{"""WANDB_MODE""": """offline"""}					)
						def 			A_					(      self					)       ->   Dict:


								'''simple docstring'''

								_UpperCamelCase         =						7 if get_gpu_count() > 1 else 2

								_UpperCamelCase         =						self.get_auto_remove_tmp_dir()
								_UpperCamelCase         =						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					)
								_UpperCamelCase         =						get_results(a					)
								self.assertGreaterEqual(result["""eval_accuracy"""]							,				0.75					)
								self.assertLess(result["""train_loss"""]							,				0.5					)
								self.assertTrue(os.path.exists(os.path.join(a							,				"""epoch_0"""					)					)					)
								self.assertTrue(os.path.exists(os.path.join(a							,				"""ner_no_trainer"""					)					)					)

						@unittest.skip(reason="""Fix me @muellerzr"""					)
						@mock.patch.dict(os.environ							,				{"""WANDB_MODE""": """offline"""}					)
						def 			A_					(      self					)       ->   Dict:


								'''simple docstring'''

								_UpperCamelCase         =						self.get_auto_remove_tmp_dir()
								_UpperCamelCase         =						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					)
								_UpperCamelCase         =						get_results(a					)
								# Because we use --version_2_with_negative the testing script uses SQuAD v2 metrics.
								self.assertGreaterEqual(result["""eval_f1"""]							,				28					)
								self.assertGreaterEqual(result["""eval_exact"""]							,				28					)
								self.assertTrue(os.path.exists(os.path.join(a							,				"""epoch_0"""					)					)					)
								self.assertTrue(os.path.exists(os.path.join(a							,				"""qa_no_trainer"""					)					)					)

						@mock.patch.dict(os.environ							,				{"""WANDB_MODE""": """offline"""}					)
						def 			A_					(      self					)       ->   Dict:


								'''simple docstring'''

								_UpperCamelCase         =						self.get_auto_remove_tmp_dir()
								_UpperCamelCase         =						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					)
								_UpperCamelCase         =						get_results(a					)
								self.assertGreaterEqual(result["""eval_accuracy"""]							,				0.8					)
								self.assertTrue(os.path.exists(os.path.join(a							,				"""swag_no_trainer"""					)					)					)

						@slow
						@mock.patch.dict(os.environ							,				{"""WANDB_MODE""": """offline"""}					)
						def 			A_					(      self					)       ->   Optional[Any]:


								'''simple docstring'''

								_UpperCamelCase         =						self.get_auto_remove_tmp_dir()
								_UpperCamelCase         =						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					)
								_UpperCamelCase         =						get_results(a					)
								self.assertGreaterEqual(result["""eval_rouge1"""]							,				10					)
								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(a							,				"""epoch_0"""					)					)					)
								self.assertTrue(os.path.exists(os.path.join(a							,				"""summarization_no_trainer"""					)					)					)

						@slow
						@mock.patch.dict(os.environ							,				{"""WANDB_MODE""": """offline"""}					)
						def 			A_					(      self					)       ->   Optional[Any]:


								'''simple docstring'''

								_UpperCamelCase         =						self.get_auto_remove_tmp_dir()
								_UpperCamelCase         =						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					)
								_UpperCamelCase         =						get_results(a					)
								self.assertGreaterEqual(result["""eval_bleu"""]							,				30					)
								self.assertTrue(os.path.exists(os.path.join(a							,				"""epoch_0"""					)					)					)
								self.assertTrue(os.path.exists(os.path.join(a							,				"""translation_no_trainer"""					)					)					)

						@slow
						def 			A_					(      self					)       ->   Optional[Any]:


								'''simple docstring'''

								_UpperCamelCase         =						logging.StreamHandler(sys.stdout					)
								logger.addHandler(a					)

								_UpperCamelCase         =						self.get_auto_remove_tmp_dir()
								_UpperCamelCase         =						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					)
								_UpperCamelCase         =						get_results(a					)
								self.assertGreaterEqual(result["""eval_overall_accuracy"""]							,				0.10					)







						@mock.patch.dict(os.environ							,				{"""WANDB_MODE""": """offline"""}					)
						def 			A_					(      self					)       ->   List[str]:


								'''simple docstring'''

								_UpperCamelCase         =						self.get_auto_remove_tmp_dir()
								_UpperCamelCase         =						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					)
								_UpperCamelCase         =						get_results(a					)
								# The base model scores a 25%
								self.assertGreaterEqual(result["""eval_accuracy"""]							,				0.6					)
								self.assertTrue(os.path.exists(os.path.join(a							,				"""step_1"""					)					)					)
								self.assertTrue(os.path.exists(os.path.join(a							,				"""image_classification_no_trainer"""					)					)					)



