



"""simple docstring"""






import inspect
import os
import unittest

import torch

import accelerate
from accelerate import Accelerator
from accelerate.test_utils import execute_subprocess_async, require_multi_gpu
from accelerate.utils import patch_environment

class  __SCREAMING_SNAKE_CASE			(   unittest.TestCase     ):


     '''simple docstring'''





     def 	snake_case ( self							:						Optional[Any]  )->      str:
        lowerCamelCase__	:	int				=inspect.getfile(accelerate.test_utils  )
        lowerCamelCase__	:	Union[str, Any]				=os.path.sep.join(mod_file.split(os.path.sep  )[:-1] + ['''scripts''', '''test_script.py''']  )
        lowerCamelCase__	:	List[Any]				=os.path.sep.join(
            mod_file.split(os.path.sep  )[:-1] + ['''scripts''', '''test_distributed_data_loop.py''']  )
        lowerCamelCase__	:	Any				=os.path.sep.join(mod_file.split(os.path.sep  )[:-1] + ['''scripts''', '''test_ops.py''']  )



     @require_multi_gpu
     def 	snake_case ( self							:						Any  )->      List[str]:
        print(F'''Found {torch.cuda.device_count()} devices.'''  )
        lowerCamelCase__	:	Optional[int]				=['''torchrun''', F'''--nproc_per_node={torch.cuda.device_count()}''', self.test_file_path]
        with patch_environment(omp_num_threads=1  ):
           execute_subprocess_async(_A,						env=os.environ.copy()  )



     @require_multi_gpu
     def 	snake_case ( self							:						str  )->      Optional[Any]:
        print(F'''Found {torch.cuda.device_count()} devices.'''  )
        lowerCamelCase__	:	Optional[int]				=['''torchrun''', F'''--nproc_per_node={torch.cuda.device_count()}''', self.operation_file_path]
        print(F'''Command: {cmd}'''  )
        with patch_environment(omp_num_threads=1  ):
           execute_subprocess_async(_A,						env=os.environ.copy()  )



     @require_multi_gpu
     def 	snake_case ( self							:						int  )->      List[Any]:
        lowerCamelCase__	:	Dict				=['''torchrun''', F'''--nproc_per_node={torch.cuda.device_count()}''', inspect.getfile(self.__class__  )]
        with patch_environment(omp_num_threads=1  ):
           execute_subprocess_async(_A,						env=os.environ.copy()  )




     @require_multi_gpu
     def 	snake_case ( self							:						Union[str, Any]  )->      Dict:
        print(F'''Found {torch.cuda.device_count()} devices, using 2 devices only'''  )
        lowerCamelCase__	:	Union[str, Any]				=['''torchrun''', F'''--nproc_per_node={torch.cuda.device_count()}''', self.data_loop_file_path]
        with patch_environment(omp_num_threads=1,						cuda_visible_devices='''0,1'''  ):
           execute_subprocess_async(_A,						env=os.environ.copy()  )


if __name__ == "__main__":
  _lowercase				:				Tuple   		=		Accelerator()
  _lowercase				:				List[Any]   		=		(accelerator.state.process_index + 2, 1_0)
  _lowercase				:				Any   		=		torch.randint(0, 1_0, shape).to(accelerator.device)

  _lowercase				:				int   		=		""

  _lowercase				:				Dict   		=		accelerator.pad_across_processes(tensor)
  if tensora.shape[0] != accelerator.state.num_processes + 1:
    error_msg += f"Found shape {tensora.shape} but should have {accelerator.state.num_processes + 1} at dim 0."
  if not torch.equal(tensora[: accelerator.state.process_index + 2], tensor):
    error_msg += "Tensors have different values."
  if not torch.all(tensora[accelerator.state.process_index + 2 :] == 0):
    error_msg += "Padding was not done with the right value (0)."

  _lowercase				:				int   		=		accelerator.pad_across_processes(tensor, pad_first=True)
  if tensora.shape[0] != accelerator.state.num_processes + 1:
    error_msg += f"Found shape {tensora.shape} but should have {accelerator.state.num_processes + 1} at dim 0."
  _lowercase				:				Optional[Any]   		=		accelerator.state.num_processes - accelerator.state.process_index - 1
  if not torch.equal(tensora[index:], tensor):
    error_msg += "Tensors have different values."
  if not torch.all(tensora[:index] == 0):
    error_msg += "Padding was not done with the right value (0)."

  # Raise error at the end to make sure we don't stop at the first failure.
  if len(error_msg) > 0:
    raise ValueError(error_msg)









"""simple docstring"""






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


_lowercase				:				Optional[Any]   		=		{
    "configuration_clip": [
        "CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "CLIPConfig",
        "CLIPOnnxConfig",
        "CLIPTextConfig",
        "CLIPVisionConfig",
    ],
    "processing_clip": ["CLIPProcessor"],
    "tokenization_clip": ["CLIPTokenizer"],
}

try:
		if not is_tokenizers_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_lowercase				:				str   		=		["CLIPTokenizerFast"]

try:
		if not is_vision_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_lowercase				:				Any   		=		["CLIPFeatureExtractor"]
		_lowercase				:				int   		=		["CLIPImageProcessor"]

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_lowercase				:				Optional[Any]   		=		[
		    "CLIP_PRETRAINED_MODEL_ARCHIVE_LIST",
		    "CLIPModel",
		    "CLIPPreTrainedModel",
		    "CLIPTextModel",
		    "CLIPTextModelWithProjection",
		    "CLIPVisionModel",
		    "CLIPVisionModelWithProjection",
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_lowercase				:				Dict   		=		[
		    "TF_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST",
		    "TFCLIPModel",
		    "TFCLIPPreTrainedModel",
		    "TFCLIPTextModel",
		    "TFCLIPVisionModel",
		]

try:
		if not is_flax_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_lowercase				:				Union[str, Any]   		=		[
		    "FlaxCLIPModel",
		    "FlaxCLIPPreTrainedModel",
		    "FlaxCLIPTextModel",
		    "FlaxCLIPTextPreTrainedModel",
		    "FlaxCLIPVisionModel",
		    "FlaxCLIPVisionPreTrainedModel",
		]


if TYPE_CHECKING:
		from .configuration_clip import (
		    CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    CLIPConfig,
		    CLIPOnnxConfig,
		    CLIPTextConfig,
		    CLIPVisionConfig,
		)
		from .processing_clip import CLIPProcessor
		from .tokenization_clip import CLIPTokenizer

		try:
				if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_clip_fast import CLIPTokenizerFast

		try:
				if not is_vision_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .feature_extraction_clip import CLIPFeatureExtractor
				from .image_processing_clip import CLIPImageProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_clip import (
				    CLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
				    CLIPModel,
				    CLIPPreTrainedModel,
				    CLIPTextModel,
				    CLIPTextModelWithProjection,
				    CLIPVisionModel,
				    CLIPVisionModelWithProjection,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_clip import (
				    TF_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFCLIPModel,
				    TFCLIPPreTrainedModel,
				    TFCLIPTextModel,
				    TFCLIPVisionModel,
				)

		try:
				if not is_flax_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_flax_clip import (
				    FlaxCLIPModel,
				    FlaxCLIPPreTrainedModel,
				    FlaxCLIPTextModel,
				    FlaxCLIPTextPreTrainedModel,
				    FlaxCLIPVisionModel,
				    FlaxCLIPVisionPreTrainedModel,
				)


else:
		import sys

		_lowercase				:				Union[str, Any]   		=		_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)




