



import tempfile
import unittest

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from transformers.testing_utils import (
    is_torch_available,
    require_optimum,
    require_torch,
    slow,
)


if is_torch_available():
    import torch



@require_torch
@require_optimum
@slow
class 							lowerCAmelCase_						(    unittest.TestCase				):
       def    snake_case_			(     self			)	->						Optional[Any]:
          UpperCamelCase       :    Optional[int]		      =      'hf-internal-testing/tiny-random-t5'
          UpperCamelCase       :    str		      =      AutoTokenizer.from_pretrained(lowercase_			)
          UpperCamelCase       :    List[str]		      =      AutoModelForSeqaSeqLM.from_pretrained(lowercase_			)

          UpperCamelCase       :    List[Any]		      =      tokenizer('This is me',     return_tensors='pt'			)

          UpperCamelCase       :    Any		      =      model.to_bettertransformer()

          self.assertTrue(any('BetterTransformer' in mod.__class__.__name__ for _, mod in model.named_modules()			)			)

          UpperCamelCase       :    int		      =      model.generate(**lowercase_			)

          UpperCamelCase       :    List[Any]		      =      model.reverse_bettertransformer()

          self.assertFalse(any('BetterTransformer' in mod.__class__.__name__ for _, mod in model.named_modules()			)			)

          with tempfile.TemporaryDirectory() as tmpdirname:
             model.save_pretrained(lowercase_			)

             UpperCamelCase       :    Dict		      =      AutoModelForSeqaSeqLM.from_pretrained(lowercase_			)

             self.assertFalse(
                 any('BetterTransformer' in mod.__class__.__name__ for _, mod in model_reloaded.named_modules()			)			)

             UpperCamelCase       :    Optional[Any]		      =      model_reloaded.generate(**lowercase_			)
             self.assertTrue(torch.allclose(lowercase_,     lowercase_			)			)






       def    snake_case_			(     self			)	->						Any:
          UpperCamelCase       :    Any		      =      'hf-internal-testing/tiny-random-t5'
          UpperCamelCase       :    int		      =      AutoModelForSeqaSeqLM.from_pretrained(lowercase_			)

          UpperCamelCase       :    List[str]		      =      model.to_bettertransformer()

          with tempfile.TemporaryDirectory() as tmpdirname:
             with self.assertRaises(lowercase_			):
                model.save_pretrained(lowercase_			)

             UpperCamelCase       :    List[str]		      =      model.reverse_bettertransformer()
             model.save_pretrained(lowercase_			)










'''simple docstring'''

import json
import os
import tempfile

import datasets
from utils import generate_example_dataset, get_duration


__lowerCAmelCase				:      int						      =5_0000
__lowerCAmelCase				:      Union[str, Any]						      =5000

__lowerCAmelCase      ,	__lowerCAmelCase				:      Tuple						      =os.path.split(__file__)
__lowerCAmelCase				:      int						      =os.path.join(RESULTS_BASEPATH, "results", RESULTS_FILENAME.replace(".py", ".json"))





@get_duration
def 		UpperCamelCase							(				_lowerCamelCase   :   datasets.Dataset		,			_lowerCamelCase   :   int			):
    for i in range(_lowerCamelCase			):
        A__							=     dataset[i]





@get_duration
def 		UpperCamelCase							(				_lowerCamelCase   :   datasets.Dataset		,			_lowerCamelCase   :   Optional[int]		,			_lowerCamelCase   :   Any			):
    for i in range(0		,			len(_lowerCamelCase			)		,			_lowerCamelCase			):
        A__							=     dataset[i : i + batch_size]





@get_duration
def 		UpperCamelCase							(				_lowerCamelCase   :   datasets.Dataset		,			_lowerCamelCase   :   Optional[Any]		,			_lowerCamelCase   :   List[str]			):
    with dataset.formatted_as(type=_lowerCamelCase			):
        for i in range(_lowerCamelCase			):
            A__							=     dataset[i]





@get_duration
def 		UpperCamelCase							(				_lowerCamelCase   :   datasets.Dataset		,			_lowerCamelCase   :   Optional[int]		,			_lowerCamelCase   :   int		,			_lowerCamelCase   :   Dict			):
    with dataset.formatted_as(type=_lowerCamelCase			):
        for i in range(0		,			_lowerCamelCase		,			_lowerCamelCase			):
            A__							=     dataset[i : i + batch_size]





def 		UpperCamelCase							(				):
    A__							=     {"num examples": SPEED_TEST_N_EXAMPLES}
    A__							=     [
        (read, {"length": SMALL_TEST}),
        (read, {"length": SPEED_TEST_N_EXAMPLES}),
        (read_batch, {"length": SPEED_TEST_N_EXAMPLES, "batch_size": 10}),
        (read_batch, {"length": SPEED_TEST_N_EXAMPLES, "batch_size": 1_00}),
        (read_batch, {"length": SPEED_TEST_N_EXAMPLES, "batch_size": 10_00}),
        (read_formatted, {"type": "numpy", "length": SMALL_TEST}),
        (read_formatted, {"type": "pandas", "length": SMALL_TEST}),
        (read_formatted, {"type": "torch", "length": SMALL_TEST}),
        (read_formatted, {"type": "tensorflow", "length": SMALL_TEST}),
        (read_formatted_batch, {"type": "numpy", "length": SMALL_TEST, "batch_size": 10}),
        (read_formatted_batch, {"type": "numpy", "length": SMALL_TEST, "batch_size": 10_00}),
    ]

    A__							=     [
        (read, {"length": SMALL_TEST}),
        (read, {"length": SPEED_TEST_N_EXAMPLES}),
        (read_batch, {"length": SPEED_TEST_N_EXAMPLES, "batch_size": 10}),
        (read_batch, {"length": SPEED_TEST_N_EXAMPLES, "batch_size": 1_00}),
        (read_batch, {"length": SPEED_TEST_N_EXAMPLES, "batch_size": 10_00}),
        (read_formatted, {"type": "numpy", "length": SMALL_TEST}),
        (read_formatted_batch, {"type": "numpy", "length": SMALL_TEST, "batch_size": 10}),
        (read_formatted_batch, {"type": "numpy", "length": SMALL_TEST, "batch_size": 10_00}),
    ]
    with tempfile.TemporaryDirectory() as tmp_dir:
        print("generating dataset"			)
        A__							=     datasets.Features(
            {"list": datasets.Sequence(datasets.Value("float32"			)			), "numbers": datasets.Value("float32"			)}			)
        A__							=     generate_example_dataset(
            os.path.join(_lowerCamelCase		,			"dataset.arrow"			)		,			_lowerCamelCase		,			num_examples=_lowerCamelCase		,			seq_shapes={"list": (1_00,)}		,			)
        print("first set of iterations"			)
        for func, kwargs in functions:
            print(func.__name__		,			str(_lowerCamelCase			)			)
            A__							=     func(_lowerCamelCase		,			**_lowerCamelCase			)

        print("shuffling dataset"			)
        A__							=     dataset.shuffle()
        print("Second set of iterations (after shuffling"			)
        for func, kwargs in functions_shuffled:
            print("shuffled "		,			func.__name__		,			str(_lowerCamelCase			)			)
            A__							=     func(
                _lowerCamelCase		,			**_lowerCamelCase			)

    with open(_lowerCamelCase		,			"wb"			) as f:
        f.write(json.dumps(_lowerCamelCase			).encode("utf-8"			)			)


if __name__ == "__main__":  # useful to run the profiler
  benchmark_iterating()



