
"""simple docstring"""


import torch

from accelerate import PartialState
from accelerate.utils.operations import broadcast, gather, gather_object, pad_across_processes, reduce






def    lowerCAmelCase_   (		snake_case_		:			int			)   ->Dict:
			return (torch.arange(state.num_processes			) + 1.0 + (state.num_processes * state.process_index)).to(state.device			)






def    lowerCAmelCase_   (		snake_case_		:			str			)   ->List[Any]:
			lowerCamelCase__	:     Dict              =create_tensor(__lowerCamelCase			)
			lowerCamelCase__	:     int              =gather(__lowerCamelCase			)
			assert gathered_tensor.tolist() == list(range(1							,    state.num_processes**2 + 1			)			)






def    lowerCAmelCase_   (		snake_case_		:			List[str]			)   ->List[str]:
			lowerCamelCase__	:     List[str]              =[state.process_index]
			lowerCamelCase__	:     Dict              =gather_object(__lowerCamelCase			)
			assert len(__lowerCamelCase			) == state.num_processes, f"""{gathered_obj}, {len(__lowerCamelCase			)} != {state.num_processes}"""
			assert gathered_obj == list(range(state.num_processes			)			), f"""{gathered_obj} != {list(range(state.num_processes			)			)}"""






def    lowerCAmelCase_   (		snake_case_		:			str			)   ->List[Any]:
			lowerCamelCase__	:     str              =create_tensor(__lowerCamelCase			)
			lowerCamelCase__	:     Optional[Any]              =broadcast(__lowerCamelCase			)
			assert broadcasted_tensor.shape == torch.Size([state.num_processes]			)
			assert broadcasted_tensor.tolist() == list(range(1							,    state.num_processes + 1			)			)






def    lowerCAmelCase_   (		snake_case_		:			Dict			)   ->List[str]:
			# We need to pad the tensor with one more element if we are the main process
			# to ensure that we can pad
			if state.is_main_process:
						lowerCamelCase__	:     Any              =torch.arange(state.num_processes + 1			).to(state.device			)
			else:
						lowerCamelCase__	:     Tuple              =torch.arange(state.num_processes			).to(state.device			)
			lowerCamelCase__	:     List[str]              =pad_across_processes(__lowerCamelCase			)
			assert padded_tensor.shape == torch.Size([state.num_processes + 1]			)
			if not state.is_main_process:
						assert padded_tensor.tolist() == list(range(0							,    state.num_processes			)			) + [0]






def    lowerCAmelCase_   (		snake_case_		:			int			)   ->Optional[int]:
			# For now runs on only two processes
			if state.num_processes != 2:
						return
			lowerCamelCase__	:     Tuple              =create_tensor(__lowerCamelCase			)
			lowerCamelCase__	:     Dict              =reduce(__lowerCamelCase							,    'sum'			)
			lowerCamelCase__	:     Optional[int]              =torch.tensor([4.0, 6]			).to(state.device			)
			assert torch.allclose(__lowerCamelCase							,    __lowerCamelCase			), f"""{reduced_tensor} != {truth_tensor}"""






def    lowerCAmelCase_   (		snake_case_		:			List[Any]			)   ->int:
			# For now runs on only two processes
			if state.num_processes != 2:
						return
			lowerCamelCase__	:     List[str]              =create_tensor(__lowerCamelCase			)
			lowerCamelCase__	:     int              =reduce(__lowerCamelCase							,    'mean'			)
			lowerCamelCase__	:     List[str]              =torch.tensor([2.0, 3]			).to(state.device			)
			assert torch.allclose(__lowerCamelCase							,    __lowerCamelCase			), f"""{reduced_tensor} != {truth_tensor}"""






def    lowerCAmelCase_   (		snake_case_		:			Dict			)   ->Optional[int]:
			# For xla_spawn (TPUs)
			main()






def    lowerCAmelCase_   (		)   ->List[str]:
			lowerCamelCase__	:     List[str]              =PartialState()
			state.print(f"""State: {state}"""			)
			state.print('testing gather'			)
			test_gather(__lowerCamelCase			)
			state.print('testing gather_object'			)
			test_gather_object(__lowerCamelCase			)
			state.print('testing broadcast'			)
			test_broadcast(__lowerCamelCase			)
			state.print('testing pad_across_processes'			)
			test_pad_across_processes(__lowerCamelCase			)
			state.print('testing reduce_sum'			)
			test_reduce_sum(__lowerCamelCase			)
			state.print('testing reduce_mean'			)
			test_reduce_mean(__lowerCamelCase			)


if __name__ == "__main__":
							main()




"""simple docstring"""







from typing import TYPE_CHECKING

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


lowerCAmelCase_   						=   {'configuration_xglm': ['XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP', 'XGLMConfig']}

try:
							if not is_sentencepiece_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCAmelCase_   						=   ['XGLMTokenizer']

try:
							if not is_tokenizers_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCAmelCase_   						=   ['XGLMTokenizerFast']

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCAmelCase_   						=   [
							    'XGLM_PRETRAINED_MODEL_ARCHIVE_LIST',
							    'XGLMForCausalLM',
							    'XGLMModel',
							    'XGLMPreTrainedModel',
							]


try:
							if not is_flax_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCAmelCase_   						=   [
							    'FlaxXGLMForCausalLM',
							    'FlaxXGLMModel',
							    'FlaxXGLMPreTrainedModel',
							]


try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCAmelCase_   						=   [
							    'TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST',
							    'TFXGLMForCausalLM',
							    'TFXGLMModel',
							    'TFXGLMPreTrainedModel',
							]


if TYPE_CHECKING:
							from .configuration_xglm import XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XGLMConfig

							try:
														if not is_sentencepiece_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_xglm import XGLMTokenizer

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_xglm_fast import XGLMTokenizerFast

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_xglm import XGLM_PRETRAINED_MODEL_ARCHIVE_LIST, XGLMForCausalLM, XGLMModel, XGLMPreTrainedModel

							try:
														if not is_flax_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_flax_xglm import FlaxXGLMForCausalLM, FlaxXGLMModel, FlaxXGLMPreTrainedModel

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_xglm import (
														    TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TFXGLMForCausalLM,
														    TFXGLMModel,
														    TFXGLMPreTrainedModel,
														)


else:
							import sys

							lowerCAmelCase_   						=   _LazyModule(__name__, globals()['__file__'], _import_structure)
