





import time
import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch, torch_device

from ..test_modeling_common import ids_tensor


if is_torch_available():
				import torch

				from transformers.generation import (
				    MaxLengthCriteria,
				    MaxNewTokensCriteria,
				    MaxTimeCriteria,
				    StoppingCriteriaList,
				    validate_stopping_criteria,
				)







@require_torch
class   _UpperCamelCase  (       unittest.TestCase  ):







					'''simple docstring'''

					def        SCREAMING_SNAKE_CASE							(						self    :  str ,					__lowercase    :  Any		):






						'''simple docstring'''







						UpperCAmelCase_								=     3
						UpperCAmelCase_								=     2_50

						UpperCAmelCase_								=     ids_tensor((batch_size, length) ,					__lowercase		)
						UpperCAmelCase_								=     torch.ones((batch_size, length) ,					device=__lowercase ,					dtype=torch.float		) / length
						return input_ids, scores





					def        SCREAMING_SNAKE_CASE							(						self    :  Dict		):






						'''simple docstring'''







						UpperCAmelCase_						,	UpperCAmelCase_								=     self._get_tensors(5		)

						UpperCAmelCase_								=     StoppingCriteriaList(
						    [
						        MaxLengthCriteria(max_length=10		),
						        MaxTimeCriteria(max_time=0.1		),
						    ]		)

						self.assertFalse(criteria(__lowercase ,					__lowercase		)		)

						UpperCAmelCase_						,	UpperCAmelCase_								=     self._get_tensors(9		)
						self.assertFalse(criteria(__lowercase ,					__lowercase		)		)

						UpperCAmelCase_						,	UpperCAmelCase_								=     self._get_tensors(10		)
						self.assertTrue(criteria(__lowercase ,					__lowercase		)		)





					def        SCREAMING_SNAKE_CASE							(						self    :  List[Any]		):






						'''simple docstring'''







						UpperCAmelCase_								=     MaxLengthCriteria(max_length=10		)

						UpperCAmelCase_						,	UpperCAmelCase_								=     self._get_tensors(5		)
						self.assertFalse(criteria(__lowercase ,					__lowercase		)		)

						UpperCAmelCase_						,	UpperCAmelCase_								=     self._get_tensors(9		)
						self.assertFalse(criteria(__lowercase ,					__lowercase		)		)

						UpperCAmelCase_						,	UpperCAmelCase_								=     self._get_tensors(10		)
						self.assertTrue(criteria(__lowercase ,					__lowercase		)		)





					def        SCREAMING_SNAKE_CASE							(						self    :  str		):






						'''simple docstring'''







						UpperCAmelCase_								=     MaxNewTokensCriteria(start_length=5 ,					max_new_tokens=5		)

						UpperCAmelCase_						,	UpperCAmelCase_								=     self._get_tensors(5		)
						self.assertFalse(criteria(__lowercase ,					__lowercase		)		)

						UpperCAmelCase_						,	UpperCAmelCase_								=     self._get_tensors(9		)
						self.assertFalse(criteria(__lowercase ,					__lowercase		)		)

						UpperCAmelCase_						,	UpperCAmelCase_								=     self._get_tensors(10		)
						self.assertTrue(criteria(__lowercase ,					__lowercase		)		)

						UpperCAmelCase_								=     StoppingCriteriaList([criteria]		)
						self.assertEqual(criteria_list.max_length ,					10		)





					def        SCREAMING_SNAKE_CASE							(						self    :  Optional[Any]		):






						'''simple docstring'''







						UpperCAmelCase_						,	UpperCAmelCase_								=     self._get_tensors(5		)

						UpperCAmelCase_								=     MaxTimeCriteria(max_time=0.1		)
						self.assertFalse(criteria(__lowercase ,					__lowercase		)		)

						UpperCAmelCase_								=     MaxTimeCriteria(max_time=0.1 ,					initial_timestamp=time.time() - 0.2		)
						self.assertTrue(criteria(__lowercase ,					__lowercase		)		)





					def        SCREAMING_SNAKE_CASE							(						self    :  str		):






						'''simple docstring'''







						validate_stopping_criteria(StoppingCriteriaList([MaxLengthCriteria(10		)]		) ,					10		)

						with self.assertWarns(__lowercase		):
							validate_stopping_criteria(StoppingCriteriaList([MaxLengthCriteria(10		)]		) ,					11		)

						UpperCAmelCase_								=     validate_stopping_criteria(StoppingCriteriaList() ,					11		)

						self.assertEqual(len(__lowercase		) ,					1		)









import json
import os
import tempfile

import datasets
from utils import generate_example_dataset, get_duration


UpperCamelCase__   : Tuple					  = 5_00_00
UpperCamelCase__   : Any					  = 50_00

UpperCamelCase__  ,			UpperCamelCase__   : Tuple					  = os.path.split(__file__)
UpperCamelCase__   : Union[str, Any]					  = os.path.join(RESULTS_BASEPATH, """results""", RESULTS_FILENAME.replace(""".py""", """.json"""))




@get_duration
def    A_(			A				,      A   ):
	for i in range(A   ):
		UpperCAmelCase_								=     dataset[i]




@get_duration
def    A_(			A				,      A				,      A   ):
	for i in range(0				,      len(A   )				,      A   ):
		UpperCAmelCase_								=     dataset[i : i + batch_size]




@get_duration
def    A_(			A				,      A				,      A   ):
	with dataset.formatted_as(type=A   ):
		for i in range(A   ):
			UpperCAmelCase_								=     dataset[i]




@get_duration
def    A_(			A				,      A				,      A				,      A   ):
	with dataset.formatted_as(type=A   ):
		for i in range(0				,      A				,      A   ):
			UpperCAmelCase_								=     dataset[i : i + batch_size]




def    A_(			):
	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(A				,      """dataset.arrow"""   )				,      A				,      num_examples=A				,      seq_shapes={"""list""": (100,)}				,      )
		print("""first set of iterations"""   )
		for func, kwargs in functions:
			print(func.__name__				,      str(A   )   )
			UpperCAmelCase_								=     func(A				,      **A   )

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

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


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


