import json
import os
import tempfile

import datasets
from utils import generate_example_dataset, get_duration


__lowerCamelCase      :			List[Any]							       =		5_0000
__lowerCamelCase      :			Union[str, Any]							       =		5000

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



@get_duration
def      __SCREAMING_SNAKE_CASE  ( __UpperCamelCase   :	datasets.Dataset			,					__UpperCamelCase   :	int     )							-> Any:







  """simple docstring"""





  for i in range(__UpperCamelCase     ):
    SCREAMING_SNAKE_CASE__      							=				dataset[i]



@get_duration
def      __SCREAMING_SNAKE_CASE  ( __UpperCamelCase   :	datasets.Dataset			,					__UpperCamelCase   :	Any			,					__UpperCamelCase   :	Tuple     )							-> Dict:







  """simple docstring"""





  for i in range(0			,					len(__UpperCamelCase     )			,					__UpperCamelCase     ):
    SCREAMING_SNAKE_CASE__      							=				dataset[i : i + batch_size]



@get_duration
def      __SCREAMING_SNAKE_CASE  ( __UpperCamelCase   :	datasets.Dataset			,					__UpperCamelCase   :	Union[str, Any]			,					__UpperCamelCase   :	Optional[int]     )							-> Union[str, Any]:







  """simple docstring"""





  with dataset.formatted_as(type=__UpperCamelCase     ):
    for i in range(__UpperCamelCase     ):
      SCREAMING_SNAKE_CASE__      							=				dataset[i]



@get_duration
def      __SCREAMING_SNAKE_CASE  ( __UpperCamelCase   :	datasets.Dataset			,					__UpperCamelCase   :	Any			,					__UpperCamelCase   :	Dict			,					__UpperCamelCase   :	Optional[int]     )							-> int:







  """simple docstring"""





  with dataset.formatted_as(type=__UpperCamelCase     ):
    for i in range(0			,					__UpperCamelCase			,					__UpperCamelCase     ):
      SCREAMING_SNAKE_CASE__      							=				dataset[i : i + batch_size]



def      __SCREAMING_SNAKE_CASE  ( )							-> Optional[int]:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__      							=				{"""num examples""": SPEED_TEST_N_EXAMPLES}
  SCREAMING_SNAKE_CASE__      							=				[
      (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}),
  ]

  SCREAMING_SNAKE_CASE__      							=				[
      (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"""     )
    SCREAMING_SNAKE_CASE__      							=				datasets.Features(
        {"""list""": datasets.Sequence(datasets.Value("""float32"""     )     ), """numbers""": datasets.Value("""float32"""     )}     )
    SCREAMING_SNAKE_CASE__      							=				generate_example_dataset(
        os.path.join(__UpperCamelCase			,					"""dataset.arrow"""     )			,					__UpperCamelCase			,					num_examples=__UpperCamelCase			,					seq_shapes={"""list""": (1_00,)}			,					)
    print("""first set of iterations"""     )
    for func, kwargs in functions:
      print(func.__name__			,					str(__UpperCamelCase     )     )
      SCREAMING_SNAKE_CASE__      							=				func(__UpperCamelCase			,					**__UpperCamelCase     )

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

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


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

import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device


if is_torch_available():
   from transformers import AutoModelForSeqaSeqLM, AutoTokenizer







@require_torch
@require_sentencepiece
@require_tokenizers
class __snake_case    (						unittest.TestCase		):




   @slow
   def      __a						(   self      :    List[str]				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				AutoModelForSeqaSeqLM.from_pretrained("""google/mt5-small"""	,     return_dict=_lowercase				).to(_lowercase				)
     SCREAMING_SNAKE_CASE__      							=				AutoTokenizer.from_pretrained("""google/mt5-small"""				)

     SCREAMING_SNAKE_CASE__      							=				tokenizer("""Hello there"""	,     return_tensors="""pt"""				).input_ids
     SCREAMING_SNAKE_CASE__      							=				tokenizer("""Hi I am"""	,     return_tensors="""pt"""				).input_ids

     SCREAMING_SNAKE_CASE__      							=				model(input_ids.to(_lowercase				)	,     labels=labels.to(_lowercase				)				).loss
     SCREAMING_SNAKE_CASE__      							=				-(labels.shape[-1] * loss.item())

     SCREAMING_SNAKE_CASE__      							=				-84.91_27
     self.assertTrue(abs(mtf_score - EXPECTED_SCORE				) < 1E-4				)
