


import unittest
from pathlib import Path
from tempfile import NamedTemporaryFile, TemporaryDirectory

from transformers import BertConfig, BertTokenizerFast, FeatureExtractionPipeline
from transformers.convert_graph_to_onnx import (
    convert,
    ensure_valid_input,
    generate_identified_filename,
    infer_shapes,
    quantize,
)
from transformers.testing_utils import require_tf, require_tokenizers, require_torch, slow






class      UpperCamelCase					:


   '''simple docstring'''







   def 		UpperCamelCase  (     self   ,							UpperCamelCase_   ,							UpperCamelCase_   ,							UpperCamelCase_		):
        return None






class      UpperCamelCase					:


   '''simple docstring'''







   def 		UpperCamelCase  (     self   ,							UpperCamelCase_   ,							UpperCamelCase_   ,							UpperCamelCase_   ,							UpperCamelCase_		):
        return None






class      UpperCamelCase					(		unittest.TestCase     ):


   '''simple docstring'''







   lowercase					:	Tuple									=[
       # (model_name, model_kwargs)
       ("""bert-base-cased""", {}),
       ("""gpt2""", {"""use_cache""": False}),  # We don't support exporting GPT2 past keys anymore
   ]
   @require_tf
   @slow
   def 		UpperCamelCase  (     self		):
        for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
             self._test_export(UpperCamelCase_   ,							'''tf'''   ,							12   ,							**UpperCamelCase_		)
   @require_torch
   @slow
   def 		UpperCamelCase  (     self		):
        for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
             self._test_export(UpperCamelCase_   ,							'''pt'''   ,							12   ,							**UpperCamelCase_		)
   @require_torch
   @slow
   def 		UpperCamelCase  (     self		):
        from transformers import BertModel

        lowercase_       :List[str]     =       ['''[UNK]''', '''[SEP]''', '''[CLS]''', '''[PAD]''', '''[MASK]''', '''some''', '''other''', '''words''']
        with NamedTemporaryFile(mode='''w+t'''		) as vocab_file:
             vocab_file.write('''\n'''.join(UpperCamelCase_		)		)
             vocab_file.flush()
             lowercase_       :Optional[Any]     =       BertTokenizerFast(vocab_file.name		)

        with TemporaryDirectory() as bert_save_dir:
             lowercase_       :List[Any]     =       BertModel(BertConfig(vocab_size=len(UpperCamelCase_		)		)		)
             model.save_pretrained(UpperCamelCase_		)
             self._test_export(UpperCamelCase_   ,							'''pt'''   ,							12   ,							UpperCamelCase_		)
   @require_tf
   @slow
   def 		UpperCamelCase  (     self		):
        for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
             lowercase_       :str     =       self._test_export(UpperCamelCase_   ,							'''tf'''   ,							12   ,							**UpperCamelCase_		)
             lowercase_       :int     =       quantize(Path(UpperCamelCase_		)		)

             # Ensure the actual quantized model is not bigger than the original one
             if quantized_path.stat().st_size >= Path(UpperCamelCase_		).stat().st_size:
                  self.fail('''Quantized model is bigger than initial ONNX model'''		)
   @require_torch
   @slow
   def 		UpperCamelCase  (     self		):
        for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
             lowercase_       :Dict     =       self._test_export(UpperCamelCase_   ,							'''pt'''   ,							12   ,							**UpperCamelCase_		)
             lowercase_       :int     =       quantize(UpperCamelCase_		)

             # Ensure the actual quantized model is not bigger than the original one
             if quantized_path.stat().st_size >= Path(UpperCamelCase_		).stat().st_size:
                  self.fail('''Quantized model is bigger than initial ONNX model'''		)
   def 		UpperCamelCase  (     self   ,							UpperCamelCase_   ,							UpperCamelCase_   ,							UpperCamelCase_   ,							UpperCamelCase_=None   ,							**UpperCamelCase_		):
        try:
             # Compute path
             with TemporaryDirectory() as tempdir:
                  lowercase_       :Optional[Any]     =       Path(UpperCamelCase_		).joinpath('''model.onnx'''		)

             # Remove folder if exists
             if path.parent.exists():
                  path.parent.rmdir()

             # Export
             convert(UpperCamelCase_   ,							UpperCamelCase_   ,							UpperCamelCase_   ,							UpperCamelCase_   ,							UpperCamelCase_   ,							**UpperCamelCase_		)

             return path
        except Exception as e:
             self.fail(UpperCamelCase_		)
   @require_torch
   @require_tokenizers
   @slow
   def 		UpperCamelCase  (     self		):
        from transformers import BertModel

        lowercase_       :int     =       BertModel(BertConfig.from_pretrained('''lysandre/tiny-bert-random'''		)		)
        lowercase_       :Optional[int]     =       BertTokenizerFast.from_pretrained('''lysandre/tiny-bert-random'''		)
        self._test_infer_dynamic_axis(UpperCamelCase_   ,							UpperCamelCase_   ,							'''pt'''		)
   @require_tf
   @require_tokenizers
   @slow
   def 		UpperCamelCase  (     self		):
        from transformers import TFBertModel

        lowercase_       :Optional[int]     =       TFBertModel(BertConfig.from_pretrained('''lysandre/tiny-bert-random'''		)		)
        lowercase_       :Any     =       BertTokenizerFast.from_pretrained('''lysandre/tiny-bert-random'''		)
        self._test_infer_dynamic_axis(UpperCamelCase_   ,							UpperCamelCase_   ,							'''tf'''		)
   def 		UpperCamelCase  (     self   ,							UpperCamelCase_   ,							UpperCamelCase_   ,							UpperCamelCase_		):
        lowercase_       :int     =       FeatureExtractionPipeline(UpperCamelCase_   ,							UpperCamelCase_		)

        lowercase_       :Any     =       ['''input_ids''', '''token_type_ids''', '''attention_mask''', '''output_0''', '''output_1''']
        lowercase_    ,      lowercase_    ,      lowercase_    ,      lowercase_       :str     =       infer_shapes(UpperCamelCase_   ,							UpperCamelCase_		)

        # Assert all variables are present
        self.assertEqual(len(UpperCamelCase_		)   ,							len(UpperCamelCase_		)		)
        self.assertTrue(all(var_name in shapes for var_name in variable_names		)		)
        self.assertSequenceEqual(variable_names[:3]   ,							UpperCamelCase_		)
        self.assertSequenceEqual(variable_names[3:]   ,							UpperCamelCase_		)

        # Assert inputs are {0: batch, 1: sequence}
        for var_name in ["input_ids", "token_type_ids", "attention_mask"]:
             self.assertDictEqual(shapes[var_name]   ,							{0: '''batch''', 1: '''sequence'''}		)

        # Assert outputs are {0: batch, 1: sequence} and {0: batch}
        self.assertDictEqual(shapes['''output_0''']   ,							{0: '''batch''', 1: '''sequence'''}		)
        self.assertDictEqual(shapes['''output_1''']   ,							{0: '''batch'''}		)
   def 		UpperCamelCase  (     self		):
        lowercase_       :Union[str, Any]     =       ['''input_ids''', '''attention_mask''', '''token_type_ids''']
        lowercase_       :List[str]     =       {'''input_ids''': [1, 2, 3, 4], '''attention_mask''': [0, 0, 0, 0], '''token_type_ids''': [1, 1, 1, 1]}
        lowercase_    ,      lowercase_       :Any     =       ensure_valid_input(FuncContiguousArgs()   ,							UpperCamelCase_   ,							UpperCamelCase_		)

        # Should have exactly the same number of args (all are valid)
        self.assertEqual(len(UpperCamelCase_		)   ,							3		)

        # Should have exactly the same input names
        self.assertEqual(set(UpperCamelCase_		)   ,							set(UpperCamelCase_		)		)

        # Parameter should be reordered according to their respective place in the function:
        # (input_ids, token_type_ids, attention_mask)
        self.assertEqual(UpperCamelCase_   ,							(tokens['''input_ids'''], tokens['''token_type_ids'''], tokens['''attention_mask'''])		)

        # Generated args are interleaved with another args (for instance parameter "past" in GPT2)
        lowercase_    ,      lowercase_       :Tuple     =       ensure_valid_input(FuncNonContiguousArgs()   ,							UpperCamelCase_   ,							UpperCamelCase_		)

        # Should have exactly the one arg (all before the one not provided "some_other_args")
        self.assertEqual(len(UpperCamelCase_		)   ,							1		)
        self.assertEqual(len(UpperCamelCase_		)   ,							1		)

        # Should have only "input_ids"
        self.assertEqual(inputs_args[0]   ,							tokens['''input_ids''']		)
        self.assertEqual(ordered_input_names[0]   ,							'''input_ids'''		)




   def 		UpperCamelCase  (     self		):
        lowercase_       :int     =       generate_identified_filename(Path('''/home/something/my_fake_model.onnx'''		)   ,							'''-test'''		)
        self.assertEqual('''/home/something/my_fake_model-test.onnx'''   ,							generated.as_posix()		)





from typing import Dict

from transformers import EvalPrediction, HfArgumentParser, TrainingArguments, is_torch_available
from transformers.testing_utils import (
    TestCasePlus,
    execute_subprocess_async,
    get_torch_dist_unique_port,
    require_torch_multi_gpu,
    require_torch_neuroncore,
)
from transformers.training_args import ParallelMode
from transformers.utils import logging


SCREAMING_SNAKE_CASE       :		List[str]         =				logging.get_logger(__name__)


if is_torch_available():
   import torch
   from torch import nn
   from torch.utils.data import Dataset

   from transformers import Trainer






   class      UpperCamelCase					(		lowercase__     ):


      '''simple docstring'''







      def __init__(     self   ,							UpperCamelCase_ = 101		):
           lowercase_       :Tuple     =       length
      def __len__(     self		):
           return self.length




      def __getitem__(     self   ,							UpperCamelCase_		):
           return i






   class      UpperCamelCase					:


      '''simple docstring'''







      def __call__(     self   ,							UpperCamelCase_		):
           return {"input_ids": torch.tensor(UpperCamelCase_		), "labels": torch.tensor(UpperCamelCase_		)}






   class      UpperCamelCase					(		nn.Module     ):


      '''simple docstring'''







      def __init__(     self		):
           super().__init__()
           # Add some (unused) params otherwise DDP will complain.
           lowercase_       :List[Any]     =       nn.Linear(120   ,							80		)




      def 		UpperCamelCase  (     self   ,							UpperCamelCase_   ,							UpperCamelCase_=None		):
           if labels is not None:
                return torch.tensor(0.0   ,							device=input_ids.device		), input_ids
           else:
                return input_ids






class      UpperCamelCase					(		lowercase__     ):


   '''simple docstring'''







   @require_torch_neuroncore
   def 		UpperCamelCase  (     self		):
        lowercase_       :int     =       f"--nproc_per_node=2\n            --master_port={get_torch_dist_unique_port()}\n            {self.test_file_dir}/test_trainer_distributed.py\n        ".split()
        lowercase_       :List[str]     =       self.get_auto_remove_tmp_dir()
        lowercase_       :List[str]     =       f"--output_dir {output_dir}".split()
        lowercase_       :Any     =       ['''torchrun'''] + distributed_args + args
        execute_subprocess_async(UpperCamelCase_   ,							env=self.get_env()		)
        # successful return here == success - any errors would have caused an error in the sub-call






class      UpperCamelCase					(		lowercase__     ):


   '''simple docstring'''







   @require_torch_multi_gpu
   def 		UpperCamelCase  (     self		):
        lowercase_       :Any     =       f"--nproc_per_node={torch.cuda.device_count()}\n            --master_port={get_torch_dist_unique_port()}\n            {self.test_file_dir}/test_trainer_distributed.py\n        ".split()
        lowercase_       :str     =       self.get_auto_remove_tmp_dir()
        lowercase_       :List[str]     =       f"--output_dir {output_dir}".split()
        lowercase_       :Dict     =       ['''torchrun'''] + distributed_args + args
        execute_subprocess_async(UpperCamelCase_   ,							env=self.get_env()		)
        # successful return here == success - any errors would have caused an error in the sub-call


if __name__ == "__main__":
   # The script below is meant to be run under torch.distributed, on a machine with multiple GPUs:
   #
   # PYTHONPATH="src" python -m torch.distributed.run --nproc_per_node 2 --output_dir output_dir ./tests/test_trainer_distributed.py

   SCREAMING_SNAKE_CASE       :		Optional[int]         =				HfArgumentParser((TrainingArguments,))
   SCREAMING_SNAKE_CASE       :		Optional[int]         =				parser.parse_args_into_dataclasses()[0]

   logger.warning(
       f"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}, "
       f"distributed training: {training_args.parallel_mode != ParallelMode.NOT_DISTRIBUTED}"
   )

   # Essentially, what we want to verify in the distributed case is that we get all samples back,
   # in the right order. (this is crucial for prediction for instance)
   for dataset_length in [101, 40, 7]:
      SCREAMING_SNAKE_CASE       :		Dict         =				DummyDataset(dataset_length)




      def     UpperCamelCase    (      _a		)					->							Dict:
           '''simple docstring'''





           lowercase_       :Optional[Any]     =       list(range(len(_a		)		)		)
           lowercase_       :Optional[Any]     =       p.predictions.tolist() == sequential and p.label_ids.tolist() == sequential
           if not success and training_args.local_rank == 0:
                logger.warning(
                    '''Predictions and/or labels do not match expected results:\n  - predictions: '''
                    f"{p.predictions.tolist()}\n  - labels: {p.label_ids.tolist()}\n  - expected: {sequential}"		)
           return {"success": success}

      SCREAMING_SNAKE_CASE       :		Optional[Any]         =				Trainer(
          model=DummyModel(),
          args=training_args,
          data_collator=DummyDataCollator(),
          eval_dataset=dataset,
          compute_metrics=compute_metrics,
      )
      SCREAMING_SNAKE_CASE       :		Any         =				trainer.evaluate()
      logger.info(metrics)
      if metrics["eval_success"] is not True:
         logger.error(metrics)
         exit(1)

      SCREAMING_SNAKE_CASE       :		Optional[int]         =				trainer.predict(dataset)
      logger.info(p.metrics)
      if p.metrics["test_success"] is not True:
         logger.error(p.metrics)
         exit(1)

      SCREAMING_SNAKE_CASE       :		Union[str, Any]         =				2

      SCREAMING_SNAKE_CASE       :		Any         =				trainer.evaluate()
      logger.info(metrics)
      if metrics["eval_success"] is not True:
         logger.error(metrics)
         exit(1)

      SCREAMING_SNAKE_CASE       :		List[str]         =				trainer.predict(dataset)
      logger.info(p.metrics)
      if p.metrics["test_success"] is not True:
         logger.error(p.metrics)
         exit(1)
      SCREAMING_SNAKE_CASE       :		Optional[int]         =				None

