




import unittest
from functools import partial

import torch

from accelerate import Accelerator, debug_launcher
from accelerate.state import AcceleratorState, GradientState
from accelerate.test_utils import require_cpu, require_huggingface_suite
from accelerate.utils import GradientAccumulationPlugin




def        __SCREAMING_SNAKE_CASE (	lowercase_=2       ,  lowercase_=True       ,  lowercase_=False   )							->						Tuple:

    """simple docstring"""


    __UpperCamelCase			=    Accelerator(step_scheduler_with_optimizer=lowercase_       ,  split_batches=lowercase_   )
    __UpperCamelCase			=    torch.nn.Linear(2       ,  4   )
    __UpperCamelCase			=    torch.optim.AdamW(model.parameters()       ,  lr=1.0   )
    __UpperCamelCase			=    torch.optim.lr_scheduler.OneCycleLR(lowercase_       ,  max_lr=0.01       ,  steps_per_epoch=2       ,  epochs=1   )
    __UpperCamelCase		,   __UpperCamelCase		,   __UpperCamelCase			=    accelerator.prepare(lowercase_       ,  lowercase_       ,  lowercase_   )

    # Optimizer has stepped
    scheduler.step()
    if step_scheduler_with_optimizer or (num_processes == 1):
        assert (
            scheduler.scheduler.last_epoch == num_processes
        ), F"Last Epoch ({scheduler.scheduler.last_epoch}) != Num Processes ({num_processes})"
    else:
        assert (
            scheduler.scheduler.last_epoch != num_processes
        ), F"Last Epoch ({scheduler.scheduler.last_epoch}) == Num Processes ({num_processes})"




def        __SCREAMING_SNAKE_CASE (	lowercase_=2       ,  lowercase_=True       ,  lowercase_=False   )							->						Optional[int]:

    """simple docstring"""


    __UpperCamelCase			=    Accelerator(step_scheduler_with_optimizer=lowercase_       ,  split_batches=lowercase_   )
    __UpperCamelCase			=    torch.nn.Linear(2       ,  4   )
    __UpperCamelCase			=    torch.optim.AdamW(model.parameters()       ,  lr=1.0   )
    __UpperCamelCase			=    torch.optim.lr_scheduler.LambdaLR(lowercase_       ,  lr_lambda=lambda lowercase_   : 1 - n / 10   )
    __UpperCamelCase		,   __UpperCamelCase		,   __UpperCamelCase			=    accelerator.prepare(lowercase_       ,  lowercase_       ,  lowercase_   )

    # Optimizer has stepped
    __UpperCamelCase			=    False
    scheduler.step()
    __UpperCamelCase			=    1 - (num_processes if (step_scheduler_with_optimizer and not split_batches) else 1) / 10
    assert (
        scheduler.get_last_lr()[0] == expected_lr
    ), F"Wrong lr found at first step, expected {expected_lr}, got {scheduler.get_last_lr()[0]}"

    # Optimizer has not stepped
    __UpperCamelCase			=    True
    scheduler.step()
    if not step_scheduler_with_optimizer:
        __UpperCamelCase			=    1 - 2 / 10
    assert (
        scheduler.get_last_lr()[0] == expected_lr
    ), F"Wrong lr found at second step, expected {expected_lr}, got {scheduler.get_last_lr()[0]}"




def        __SCREAMING_SNAKE_CASE (	lowercase_ = 2   )							->						Union[str, Any]:

    """simple docstring"""


    from transformers import get_linear_schedule_with_warmup

    __UpperCamelCase			=    [1, 2, 4]
    for num_steps in steps:
        __UpperCamelCase			=    GradientAccumulationPlugin(num_steps=lowercase_       ,  adjust_scheduler=num_steps > 1   )
        __UpperCamelCase			=    Accelerator(gradient_accumulation_plugin=lowercase_   )
        __UpperCamelCase			=    torch.nn.Linear(2       ,  4   )
        __UpperCamelCase			=    torch.optim.AdamW(model.parameters()       ,  lr=10.0   )
        __UpperCamelCase			=    get_linear_schedule_with_warmup(optimizer=lowercase_       ,  num_warmup_steps=0       ,  num_training_steps=20   )

        __UpperCamelCase		,   __UpperCamelCase		,   __UpperCamelCase			=    accelerator.prepare(lowercase_       ,  lowercase_       ,  lowercase_   )

        for i in range(10 * num_steps   ):
            with accelerator.accumulate(lowercase_   ):
                optimizer.step()
                scheduler.step()

            if i == (10 * num_steps - 2):
                assert (
                    scheduler.get_last_lr()[0] != 0
                ), F"Wrong lr found at second-to-last step, expected non-zero, got {scheduler.get_last_lr()[0]}. num_steps: {num_steps}"
        assert (
            scheduler.get_last_lr()[0] == 0
        ), F"Wrong lr found at last step, expected 0, got {scheduler.get_last_lr()[0]}"
        GradientState._reset_state()


@require_cpu
class 			_lowerCamelCase    (      unittest.TestCase ):

      """simple docstring"""

      def    snake_case						(  self       :			List[str]					):
          debug_launcher(partial(snake_case			,							num_processes=1					)			,							num_processes=1					)
          debug_launcher(partial(snake_case			,							num_processes=1			,							split_batches=snake_case					)			,							num_processes=1					)







      def    snake_case						(  self       :			Optional[int]					):
          debug_launcher(partial(snake_case			,							num_processes=1					)			,							num_processes=1					)
          debug_launcher(partial(snake_case			,							num_processes=1			,							split_batches=snake_case					)			,							num_processes=1					)







      def    snake_case						(  self       :			str					):
          debug_launcher(partial(snake_case			,							num_processes=1			,							step_scheduler_with_optimizer=snake_case					)			,							num_processes=1					)







      def    snake_case						(  self       :			int					):
          debug_launcher(partial(snake_case			,							num_processes=1			,							step_scheduler_with_optimizer=snake_case					)			,							num_processes=1					)







      def    snake_case						(  self       :			Optional[int]					):
          AcceleratorState._reset_state(snake_case					)
          debug_launcher(snake_case					)
          debug_launcher(partial(snake_case			,							num_processes=1			,							split_batches=snake_case					)			,							num_processes=1					)







      def    snake_case						(  self       :			Dict					):
          AcceleratorState._reset_state(snake_case					)
          debug_launcher(snake_case					)
          debug_launcher(partial(snake_case			,							num_processes=1			,							split_batches=snake_case					)			,							num_processes=1					)







      def    snake_case						(  self       :			int					):
          AcceleratorState._reset_state(snake_case					)
          debug_launcher(partial(snake_case			,							step_scheduler_with_optimizer=snake_case					)					)







      def    snake_case						(  self       :			List[Any]					):
          AcceleratorState._reset_state(snake_case					)
          debug_launcher(partial(snake_case			,							step_scheduler_with_optimizer=snake_case					)					)







      @require_huggingface_suite
      def    snake_case						(  self       :			Dict					):
          AcceleratorState._reset_state(snake_case					)
          debug_launcher(partial(snake_case			,							num_processes=1					)					)
          debug_launcher(snake_case					)











'''simple docstring'''




# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

from ..utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


a  :     Optional[Any]       =       {
    """agents""": ["""Agent""", """AzureOpenAiAgent""", """HfAgent""", """LocalAgent""", """OpenAiAgent"""],
    """base""": ["""PipelineTool""", """RemoteTool""", """Tool""", """launch_gradio_demo""", """load_tool"""],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							a  :     Dict       =       ["""DocumentQuestionAnsweringTool"""]
							a  :     List[str]       =       ["""ImageCaptioningTool"""]
							a  :     Tuple       =       ["""ImageQuestionAnsweringTool"""]
							a  :     Tuple       =       ["""ImageSegmentationTool"""]
							a  :     List[str]       =       ["""SpeechToTextTool"""]
							a  :     Dict       =       ["""TextClassificationTool"""]
							a  :     Union[str, Any]       =       ["""TextQuestionAnsweringTool"""]
							a  :     List[str]       =       ["""TextSummarizationTool"""]
							a  :     Optional[Any]       =       ["""TextToSpeechTool"""]
							a  :     str       =       ["""TranslationTool"""]

if TYPE_CHECKING:
							from .agents import Agent, AzureOpenAiAgent, HfAgent, LocalAgent, OpenAiAgent
							from .base import PipelineTool, RemoteTool, Tool, launch_gradio_demo, load_tool

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .document_question_answering import DocumentQuestionAnsweringTool
														from .image_captioning import ImageCaptioningTool
														from .image_question_answering import ImageQuestionAnsweringTool
														from .image_segmentation import ImageSegmentationTool
														from .speech_to_text import SpeechToTextTool
														from .text_classification import TextClassificationTool
														from .text_question_answering import TextQuestionAnsweringTool
														from .text_summarization import TextSummarizationTool
														from .text_to_speech import TextToSpeechTool
														from .translation import TranslationTool
else:
							import sys

							a  :     Dict       =       _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

