

"""simple docstring"""





import os
import sys
import unittest


__UpperCamelCase    : Dict				 =						os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, '''utils'''))

import check_dummies  # noqa: E402
from check_dummies import create_dummy_files, create_dummy_object, find_backend, read_init  # noqa: E402


# Align TRANSFORMERS_PATH in check_dummies with the current path
__UpperCamelCase    : Tuple				 =						os.path.join(git_repo_path, '''src''', '''diffusers''')







class 							SCREAMING_SNAKE_CASE   (						unittest.TestCase			):






					"""simple docstring"""



					def 			__lowerCAmelCase					(			self     :							Any						):
						lowerCAmelCase__      :		Any    							=      find_backend('''    if not is_torch_available():'''						)
						self.assertEqual(lowercase_  ,'''torch'''						)

						# backend_with_underscore = find_backend("    if not is_tensorflow_text_available():")
						# self.assertEqual(backend_with_underscore, "tensorflow_text")

						lowerCAmelCase__      :		Optional[Any]    							=      find_backend('''    if not (is_torch_available() and is_transformers_available()):'''						)
						self.assertEqual(lowercase_  ,'''torch_and_transformers'''						)

						# double_backend_with_underscore = find_backend(
						#    "    if not (is_sentencepiece_available() and is_tensorflow_text_available()):"
						# )
						# self.assertEqual(double_backend_with_underscore, "sentencepiece_and_tensorflow_text")

						lowerCAmelCase__      :		List[Any]    							=      find_backend(
						    '''    if not (is_torch_available() and is_transformers_available() and is_onnx_available()):'''						)
						self.assertEqual(lowercase_  ,'''torch_and_transformers_and_onnx'''						)







					def 			__lowerCAmelCase					(			self     :							Optional[Any]						):
						lowerCAmelCase__      :		List[str]    							=      read_init()
						# We don't assert on the exact list of keys to allow for smooth grow of backend-specific objects
						self.assertIn('''torch'''  ,lowercase_						)
						self.assertIn('''torch_and_transformers'''  ,lowercase_						)
						self.assertIn('''flax_and_transformers'''  ,lowercase_						)
						self.assertIn('''torch_and_transformers_and_onnx'''  ,lowercase_						)

						# Likewise, we can't assert on the exact content of a key
						self.assertIn('''UNet2DModel'''  ,objects['''torch''']						)
						self.assertIn('''FlaxUNet2DConditionModel'''  ,objects['''flax''']						)
						self.assertIn('''StableDiffusionPipeline'''  ,objects['''torch_and_transformers''']						)
						self.assertIn('''FlaxStableDiffusionPipeline'''  ,objects['''flax_and_transformers''']						)
						self.assertIn('''LMSDiscreteScheduler'''  ,objects['''torch_and_scipy''']						)
						self.assertIn('''OnnxStableDiffusionPipeline'''  ,objects['''torch_and_transformers_and_onnx''']						)







					def 			__lowerCAmelCase					(			self     :							int						):
						lowerCAmelCase__      :		List[Any]    							=      create_dummy_object('''CONSTANT'''  ,'''\'torch\''''						)
						self.assertEqual(lowercase_  ,'''\nCONSTANT = None\n'''						)

						lowerCAmelCase__      :		Optional[Any]    							=      create_dummy_object('''function'''  ,'''\'torch\''''						)
						self.assertEqual(
						    lowercase_  ,'''\ndef function(*args, **kwargs):\n    requires_backends(function, \'torch\')\n'''						)

						lowerCAmelCase__      :		Tuple    							=      '''
class FakeClass(metaclass=DummyObject):
    _backends = \'torch\'

    def __init__(self, *args, **kwargs):
        requires_backends(self, \'torch\')

    @classmethod
    def from_config(cls, *args, **kwargs):
        requires_backends(cls, \'torch\')

    @classmethod
    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, \'torch\')
'''
						lowerCAmelCase__      :		Union[str, Any]    							=      create_dummy_object('''FakeClass'''  ,'''\'torch\''''						)
						self.assertEqual(lowercase_  ,lowercase_						)







					def 			__lowerCAmelCase					(			self     :							Optional[Any]						):
						lowerCAmelCase__      :		Any    							=      '''# This file is autogenerated by the command `make fix-copies`, do not edit.
from ..utils import DummyObject, requires_backends


CONSTANT = None


def function(*args, **kwargs):
    requires_backends(function, ["torch"])


class FakeClass(metaclass=DummyObject):
    _backends = ["torch"]

    def __init__(self, *args, **kwargs):
        requires_backends(self, ["torch"])

    @classmethod
    def from_config(cls, *args, **kwargs):
        requires_backends(cls, ["torch"])

    @classmethod
    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, ["torch"])
'''
						lowerCAmelCase__      :		Optional[Any]    							=      create_dummy_files({'''torch''': ['''CONSTANT''', '''function''', '''FakeClass''']}						)
						self.assertEqual(dummy_files['''torch''']  ,lowercase_						)







"""simple docstring"""





import os
from argparse import ArgumentParser
from typing import List

import torch.utils.data

from datasets import Dataset, IterableDataset
from datasets.distributed import split_dataset_by_node


__UpperCamelCase    : str				 =						4
__UpperCamelCase    : List[str]				 =						3







class 							SCREAMING_SNAKE_CASE   (						a_			):






					"""simple docstring"""



					pass

def     __SCREAMING_SNAKE_CASE    ( A_					):
	for shard in shards:
		for i in range(A_					):
			yield {"i": i, "shard": shard}

def     __SCREAMING_SNAKE_CASE    ( ):
	lowerCAmelCase__      :		List[str]    							=      int(os.environ['''RANK''']					)
	lowerCAmelCase__      :		Union[str, Any]    							=      int(os.environ['''WORLD_SIZE''']					)

	lowerCAmelCase__      :		Tuple    							=      ArgumentParser()
	parser.add_argument('''--streaming'''		,	type=A_					)
	parser.add_argument('''--local_rank'''		,	type=A_					)
	parser.add_argument('''--num_workers'''		,	type=A_		,	default=0					)
	lowerCAmelCase__      :		Tuple    							=      parser.parse_args()
	lowerCAmelCase__      :		Tuple    							=      args.streaming
	lowerCAmelCase__      :		Any    							=      args.num_workers

	lowerCAmelCase__      :		Dict    							=      {'''shards''': [f'shard_{shard_idx}' for shard_idx in range(A_					)]}
	lowerCAmelCase__      :		Optional[int]    							=      IterableDataset.from_generator(A_		,	gen_kwargs=A_					)
	if not streaming:
		lowerCAmelCase__      :		Dict    							=      Dataset.from_list(list(A_					)					)

	lowerCAmelCase__      :		Optional[Any]    							=      split_dataset_by_node(A_		,	rank=A_		,	world_size=A_					)
	lowerCAmelCase__      :		List[Any]    							=      torch.utils.data.DataLoader(A_		,	num_workers=A_					)

	lowerCAmelCase__      :		Optional[int]    							=      NUM_SHARDS * NUM_ITEMS_PER_SHARD
	lowerCAmelCase__      :		List[str]    							=      full_size // world_size
	expected_local_size += int(rank < (full_size % world_size)					)

	lowerCAmelCase__      :		int    							=      sum(1 for _ in dataloader					)
	if local_size != expected_local_size:
		raise FailedTestError(f'local_size {local_size} != expected_local_size {expected_local_size}'					)


if __name__ == "__main__":
							main()




