import argparse
import json
import logging
import os
import sys
from unittest.mock import patch

from transformers.testing_utils import TestCasePlus, get_gpu_count, slow


lowerCAmelCase__     						=			[
    os.path.join(os.path.dirname(__file__), dirname)
    for dirname in [
        "text-classification",
        "language-modeling",
        "summarization",
        "token-classification",
        "question-answering",
    ]
]
sys.path.extend(SRC_DIRS)


if SRC_DIRS is not None:
    import run_clm_flax
    import run_flax_glue
    import run_flax_ner
    import run_mlm_flax
    import run_qa
    import run_summarization_flax
    import run_ta_mlm_flax


logging.basicConfig(level=logging.DEBUG)

lowerCAmelCase__     						=			logging.getLogger()






def 				__lowerCamelCase		(						):
       lowerCAmelCase__					     = argparse.ArgumentParser()
       parser.add_argument('-f'     )
       lowerCAmelCase__					     = parser.parse_args()
       return args.f






def 				__lowerCamelCase		(						lowerCAmelCase__     ,  lowerCAmelCase__="eval"     ):
       lowerCAmelCase__					     = os.path.join(lowerCAmelCase__     ,  F"""{split}_results.json"""     )
       if os.path.exists(lowerCAmelCase__     ):
              with open(lowerCAmelCase__     ,  'r'     ) as f:
                     return json.load(lowerCAmelCase__     )
       raise ValueError(F"""can\'t find {path}"""     )


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



class     a_			(						lowercase_						):






 '''simple docstring'''






 def 		__snake_case						(      self :  Optional[int]):







        '''simple docstring'''



        lowerCAmelCase__					     = self.get_auto_remove_tmp_dir()
        lowerCAmelCase__					     = F"""\n            run_glue.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            --eval_steps=2\n            --warmup_steps=2\n            --seed=42\n            --max_seq_length=128\n            """.split()

        with patch.object(a__      ,   'argv'      ,   a__):
               run_flax_glue.main()
               lowerCAmelCase__					     = get_results(a__)
               self.assertGreaterEqual(result['eval_accuracy']      ,   0.75)




 @slow
 def 		__snake_case						(      self :  int):







        '''simple docstring'''



        lowerCAmelCase__					     = self.get_auto_remove_tmp_dir()
        lowerCAmelCase__					     = F"""\n            run_clm_flax.py\n            --model_name_or_path distilgpt2\n            --train_file ./tests/fixtures/sample_text.txt\n            --validation_file ./tests/fixtures/sample_text.txt\n            --do_train\n            --do_eval\n            --block_size 128\n            --per_device_train_batch_size 4\n            --per_device_eval_batch_size 4\n            --num_train_epochs 2\n            --logging_steps 2 --eval_steps 2\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            """.split()

        with patch.object(a__      ,   'argv'      ,   a__):
               run_clm_flax.main()
               lowerCAmelCase__					     = get_results(a__)
               self.assertLess(result['eval_perplexity']      ,   100)




 @slow
 def 		__snake_case						(      self :  Optional[int]):







        '''simple docstring'''



        lowerCAmelCase__					     = self.get_auto_remove_tmp_dir()
        lowerCAmelCase__					     = F"""\n            run_summarization.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            --test_file tests/fixtures/tests_samples/xsum/sample.json\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            --num_train_epochs=3\n            --warmup_steps=8\n            --do_train\n            --do_eval\n            --do_predict\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n            --predict_with_generate\n        """.split()

        with patch.object(a__      ,   'argv'      ,   a__):
               run_summarization_flax.main()
               lowerCAmelCase__					     = get_results(a__      ,   split='test')
               self.assertGreaterEqual(result['test_rouge1']      ,   10)
               self.assertGreaterEqual(result['test_rouge2']      ,   2)
               self.assertGreaterEqual(result['test_rougeL']      ,   7)
               self.assertGreaterEqual(result['test_rougeLsum']      ,   7)




 @slow
 def 		__snake_case						(      self :  List[Any]):







        '''simple docstring'''



        lowerCAmelCase__					     = self.get_auto_remove_tmp_dir()
        lowerCAmelCase__					     = F"""\n            run_mlm.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            --overwrite_output_dir\n            --max_seq_length 128\n            --per_device_train_batch_size 4\n            --per_device_eval_batch_size 4\n            --logging_steps 2 --eval_steps 2\n            --do_train\n            --do_eval\n            --num_train_epochs=1\n        """.split()

        with patch.object(a__      ,   'argv'      ,   a__):
               run_mlm_flax.main()
               lowerCAmelCase__					     = get_results(a__)
               self.assertLess(result['eval_perplexity']      ,   42)




 @slow
 def 		__snake_case						(      self :  Dict):







        '''simple docstring'''



        lowerCAmelCase__					     = self.get_auto_remove_tmp_dir()
        lowerCAmelCase__					     = F"""\n            run_t5_mlm_flax.py\n            --model_name_or_path t5-small\n            --train_file ./tests/fixtures/sample_text.txt\n            --validation_file ./tests/fixtures/sample_text.txt\n            --do_train\n            --do_eval\n            --max_seq_length 128\n            --per_device_train_batch_size 4\n            --per_device_eval_batch_size 4\n            --num_train_epochs 2\n            --logging_steps 2 --eval_steps 2\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            """.split()

        with patch.object(a__      ,   'argv'      ,   a__):
               run_ta_mlm_flax.main()
               lowerCAmelCase__					     = get_results(a__)
               self.assertGreaterEqual(result['eval_accuracy']      ,   0.42)




 @slow
 def 		__snake_case						(      self :  Union[str, Any]):







        '''simple docstring'''



        lowerCAmelCase__					     = 7 if get_gpu_count() > 1 else 2

        lowerCAmelCase__					     = self.get_auto_remove_tmp_dir()
        lowerCAmelCase__					     = F"""\n            run_flax_ner.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            --overwrite_output_dir\n            --do_train\n            --do_eval\n            --warmup_steps=2\n            --learning_rate=2e-4\n            --logging_steps 2 --eval_steps 2\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=2\n            --num_train_epochs={epochs}\n            --seed 7\n        """.split()

        with patch.object(a__      ,   'argv'      ,   a__):
               run_flax_ner.main()
               lowerCAmelCase__					     = get_results(a__)
               self.assertGreaterEqual(result['eval_accuracy']      ,   0.75)
               self.assertGreaterEqual(result['eval_f1']      ,   0.3)




 @slow
 def 		__snake_case						(      self :  Dict):







        '''simple docstring'''



        lowerCAmelCase__					     = self.get_auto_remove_tmp_dir()
        lowerCAmelCase__					     = F"""\n            run_qa.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            --overwrite_output_dir\n            --num_train_epochs=3\n            --warmup_steps=2\n            --do_train\n            --do_eval\n            --logging_steps 2 --eval_steps 2\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n        """.split()

        with patch.object(a__      ,   'argv'      ,   a__):
               run_qa.main()
               lowerCAmelCase__					     = get_results(a__)
               self.assertGreaterEqual(result['eval_f1']      ,   30)
               self.assertGreaterEqual(result['eval_exact']      ,   30)


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


lowerCAmelCase__     						=			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_			(						SCREAMING_SNAKE_CASE						):






 '''simple docstring'''






 def __init__(      self :  int      ,   *lowercase__ :  Dict      ,   lowercase__ :  List[Any]=None      ,   lowercase__ :  Optional[Any]=None      ,   lowercase__ :  Dict=None      ,   **lowercase__ :  Optional[int]):







        '''simple docstring'''



        super().__init__(*lowercase__      ,   **lowercase__)
        lowerCAmelCase__					     = eval_examples
        lowerCAmelCase__					     = post_process_function
        lowerCAmelCase__					     = quant_trainer_args
        lowerCAmelCase__					     = 128  # default number of calibration samples




 def 		__snake_case						(      self :  Tuple      ,   lowercase__ :  Any=None):







        '''simple docstring'''



        if calib_dataset is None and self.calib_dataset is None:
               raise ValueError('Trainer: calibration requires an calib_dataset.')
        lowerCAmelCase__					     = calib_dataset if calib_dataset is not None else self.calib_dataset

        lowerCAmelCase__					     = self._remove_unused_columns(lowercase__      ,   description='Calibration')

        return DataLoader(
            lowercase__      ,   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=lowercase__      ,   )




 def 		__snake_case						(      self :  List[Any]      ,   lowercase__ :  Union[str, Any]=None):







        '''simple docstring'''



        lowerCAmelCase__					     = self.train_dataset if calib_dataset is None else calib_dataset
        lowerCAmelCase__					     = self.get_calib_dataloader(lowercase__)

        lowerCAmelCase__					     = self.model
        quant_trainer.configure_model(lowercase__      ,   self.quant_trainer_args      ,   calib=lowercase__)
        model.eval()
        quant_trainer.enable_calibration(lowercase__)

        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(lowercase__):
               # Prediction step
               lowerCAmelCase__     , lowerCAmelCase__     , lowerCAmelCase__					     = self.prediction_step(lowercase__      ,   lowercase__      ,   prediction_loss_only=lowercase__)
               if (step + 1) * calib_dataloader.batch_size >= self.calib_num:
                      break

        quant_trainer.finish_calibration(lowercase__      ,   self.quant_trainer_args)
        lowerCAmelCase__					     = model




 def 		__snake_case						(      self :  Optional[Any]      ,   lowercase__ :  List[Any]=None      ,   lowercase__ :  Optional[Any]=None      ,   lowercase__ :  List[Any]=None      ,   lowercase__ :  str = "eval"):







        '''simple docstring'''



        lowerCAmelCase__					     = self.eval_dataset if eval_dataset is None else eval_dataset
        lowerCAmelCase__					     = self.get_eval_dataloader(lowercase__)
        lowerCAmelCase__					     = self.eval_examples if eval_examples is None else eval_examples

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

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

               self.log(lowercase__)
        else:
               lowerCAmelCase__					     = {}

        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__					     = self.callback_handler.on_evaluate(self.args      ,   self.state      ,   self.control      ,   lowercase__)
        return metrics




 def 		__snake_case						(      self :  Optional[int]      ,   lowercase__ :  str      ,   lowercase__ :  Any      ,   lowercase__ :  List[str]=None      ,   lowercase__ :  str = "test"):







        '''simple docstring'''



        lowerCAmelCase__					     = self.get_test_dataloader(lowercase__)

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

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

        lowerCAmelCase__					     = self.post_process_function(lowercase__      ,   lowercase__      ,   output.predictions      ,   'predict')
        lowerCAmelCase__					     = 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}_"""):
                      lowerCAmelCase__					     = metrics.pop(lowercase__)

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




 def 		__snake_case						(      self :  List[str]      ,   lowercase__ :  List[str]="./"):







        '''simple docstring'''



        lowerCAmelCase__					     = self.eval_dataset
        lowerCAmelCase__					     = self.get_eval_dataloader(lowercase__)

        lowerCAmelCase__					     = next(iter(lowercase__))

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

        # convert to tuple
        lowerCAmelCase__					     = tuple(v.to(lowercase__) for k, v in batch.items())

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

        lowerCAmelCase__					     = True

        lowerCAmelCase__					     = self.model.to(lowercase__)

        model.eval()
        model.float()

        lowerCAmelCase__					     = model.module if hasattr(lowercase__      ,   'module') else model
        quant_trainer.configure_model(lowercase__      ,   self.quant_trainer_args)

        lowerCAmelCase__					     = os.path.join(lowercase__      ,   'model.onnx')
        logger.info(F"""exporting model to {output_model_file}""")

        lowerCAmelCase__					     = {0: 'batch_size', 1: 'seq_len'}

        torch.onnx.export(
            lowercase__      ,   lowercase__      ,   lowercase__      ,   export_params=lowercase__      ,   opset_version=13      ,   do_constant_folding=lowercase__      ,   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=lowercase__      ,   )
        logger.info('onnx export finished')
