




import json
import os
import tempfile

import datasets
from utils import generate_example_dataset, get_duration


_lowerCAmelCase						:   int		=   5_0_0_0_0
_lowerCAmelCase						:   List[Any]		=   5_0_0_0

_lowerCAmelCase						:   Any		=   os.path.split(__file__)
_lowerCAmelCase						:   Optional[int]		=   os.path.join(RESULTS_BASEPATH, "results", RESULTS_FILENAME.replace(".py", ".json"))



@get_duration
def 						lowerCAmelCase		(		_lowerCAmelCase							:   datasets.Dataset						,							_lowerCAmelCase							:   List[Any]	):




 """simple docstring"""







 for i in range(_lowerCAmelCase	):
  UpperCAmelCase__							 =						dataset[i]



@get_duration
def 						lowerCAmelCase		(		_lowerCAmelCase							:   datasets.Dataset						,							_lowerCAmelCase							:   List[str]						,							_lowerCAmelCase							:   List[Any]	):




 """simple docstring"""







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



@get_duration
def 						lowerCAmelCase		(		_lowerCAmelCase							:   datasets.Dataset						,							_lowerCAmelCase							:   List[str]						,							_lowerCAmelCase							:   Optional[int]	):




 """simple docstring"""







 with dataset.formatted_as(type=_lowerCAmelCase	):
  for i in range(_lowerCAmelCase	):
   UpperCAmelCase__							 =						dataset[i]



@get_duration
def 						lowerCAmelCase		(		_lowerCAmelCase							:   datasets.Dataset						,							_lowerCAmelCase							:   Tuple						,							_lowerCAmelCase							:   str						,							_lowerCAmelCase							:   Any	):




 """simple docstring"""







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



def 						lowerCAmelCase		(		):




 """simple docstring"""







 UpperCAmelCase__							 =						{"num examples": SPEED_TEST_N_EXAMPLES}
 UpperCAmelCase__							 =						[
     (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": 100}),
     (read_batch, {"length": SPEED_TEST_N_EXAMPLES, "batch_size": 1000}),
     (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": 1000}),
 ]

 UpperCAmelCase__							 =						[
     (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": 100}),
     (read_batch, {"length": SPEED_TEST_N_EXAMPLES, "batch_size": 1000}),
     (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": 1000}),
 ]
 with tempfile.TemporaryDirectory() as tmp_dir:
  print("generating dataset"	)
  UpperCAmelCase__							 =						datasets.Features(
      {"list": datasets.Sequence(datasets.Value("float32"	)	), "numbers": datasets.Value("float32"	)}	)
  UpperCAmelCase__							 =						generate_example_dataset(
      os.path.join(_lowerCAmelCase						,							"dataset.arrow"	)						,							_lowerCAmelCase						,							num_examples=_lowerCAmelCase						,							seq_shapes={"list": (100,)}						,							)
  print("first set of iterations"	)
  for func, kwargs in functions:
   print(func.__name__						,							str(_lowerCAmelCase	)	)
   UpperCAmelCase__							 =						func(_lowerCAmelCase						,							**_lowerCAmelCase	)

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











# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny model through reduction of a normal pre-trained model, but keeping the
# full vocab, merges file, and thus also resulting in a larger model due to a large vocab size.
# This gives ~3MB in total for all files.
#
# If you want a 50 times smaller than this see `fsmt-make-super-tiny-model.py`, which is slightly more complicated
#
#
# It will be used then as "stas/tiny-wmt19-en-de"

# Build
from transformers import FSMTTokenizer, FSMTConfig, FSMTForConditionalGeneration

_lowerCAmelCase						:   int		=   "facebook/wmt19-en-de"
_lowerCAmelCase						:   int		=   FSMTTokenizer.from_pretrained(mname)
# get the correct vocab sizes, etc. from the master model
_lowerCAmelCase						:   Dict		=   FSMTConfig.from_pretrained(mname)
config.update(
    dict(
        d_model=4,
        encoder_layers=1,
        decoder_layers=1,
        encoder_ffn_dim=4,
        decoder_ffn_dim=4,
        encoder_attention_heads=1,
        decoder_attention_heads=1,
    )
)

_lowerCAmelCase						:   List[Any]		=   FSMTForConditionalGeneration(config)
print(F'''num of params {tiny_model.num_parameters()}''')

# Test
_lowerCAmelCase						:   Optional[Any]		=   tokenizer(["Making tiny model"], return_tensors="pt")
_lowerCAmelCase						:   Optional[Any]		=   tiny_model(**batch)

print("test output:", len(outputs.logits[0]))

# Save
_lowerCAmelCase						:   Optional[Any]		=   "tiny-wmt19-en-de"
tiny_model.half()  # makes it smaller
tiny_model.save_pretrained(mname_tiny)
tokenizer.save_pretrained(mname_tiny)

print(F'''Generated {mname_tiny}''')

# Upload
# transformers-cli upload tiny-wmt19-en-de





