

"""simple docstring"""




import baseaa
import io
import json
import os
from copy import deepcopy

from ..optimizer import AcceleratedOptimizer
from ..scheduler import AcceleratedScheduler
class      _UpperCAmelCase   :



	def __init__(  self     :       Tuple			,    _lowercase     :       List[Any]						):
		if isinstance(__A			,    __A						):
			# Don't modify user's data should they want to reuse it (e.g. in tests), because once we
			# modified it, it will not be accepted here again, since `auto` values would have been overridden
			__UpperCAmelCase      		= deepcopy(__A						)
		elif os.path.exists(__A						):
			with io.open(__A			,    '''r'''			,    encoding='''utf-8'''						) as f:
				__UpperCAmelCase      		= json.load(__A						)
		else:
			try:
				__UpperCAmelCase      		= baseaa.urlsafe_baadecode(__A						).decode('''utf-8'''						)
				__UpperCAmelCase      		= json.loads(__A						)
			except (UnicodeDecodeError, AttributeError, ValueError):
				raise ValueError(
				    F'''Expected a string path to an existing deepspeed config, or a dictionary, or a base64 encoded string. Received: {config_file_or_dict}'''						)

		__UpperCAmelCase      		= config

		self.set_stage_and_offload()



	def        a  (  self     :       Tuple						):
		# zero stage - this is done as early as possible, before model is created, to allow
		# ``is_deepspeed_zero3_enabled`` query and getting to the early deepspeed config object
		# during ``zero.Init()`` which needs to know the dtype, and some other hparams.
		__UpperCAmelCase      		= self.get_value('''zero_optimization.stage'''			,    -1						)

		# offload
		__UpperCAmelCase      		= False
		if self.is_zeroa() or self.is_zeroa():
			__UpperCAmelCase      		= set(['''cpu''', '''nvme''']						)
			__UpperCAmelCase      		= set(
			    [
			        self.get_value('''zero_optimization.offload_optimizer.device'''						),
			        self.get_value('''zero_optimization.offload_param.device'''						),
			    ]						)
			if len(offload_devices & offload_devices_valid						) > 0:
				__UpperCAmelCase      		= True



	def        a  (  self     :       Any			,    _lowercase     :       List[str]						):
		__UpperCAmelCase      		= self.config

		# find the config node of interest if it exists
		__UpperCAmelCase      		= ds_key_long.split('''.'''						)
		__UpperCAmelCase      		= nodes.pop()
		for node in nodes:
			__UpperCAmelCase      		= config.get(__A						)
			if config is None:
				return None, ds_key

		return config, ds_key



	def        a  (  self     :       List[Any]			,    _lowercase     :       Optional[Any]			,    _lowercase     :       Dict=None						):
		__UpperCAmelCase		,							__UpperCAmelCase      		= self.find_config_node(__A						)
		if config is None:
			return default
		return config.get(__A			,    __A						)



	def        a  (  self     :       str			,    _lowercase     :       Dict			,    _lowercase     :       Tuple=False						):
		__UpperCAmelCase      		= self.config

		# find the config node of interest if it exists
		__UpperCAmelCase      		= ds_key_long.split('''.'''						)
		for node in nodes:
			__UpperCAmelCase      		= config
			__UpperCAmelCase      		= config.get(__A						)
			if config is None:
				if must_exist:
					raise ValueError(F'''Can\'t find {ds_key_long} entry in the config: {self.config}'''						)
				else:
					return

        # if found remove it
		if parent_config is not None:
			parent_config.pop(__A						)



	def        a  (  self     :       Optional[Any]			,    _lowercase     :       Union[str, Any]						):
		__UpperCAmelCase      		= self.get_value(__A						)
		return False if value is None else bool(__A						)



	def        a  (  self     :       Tuple			,    _lowercase     :       Optional[int]						):
		__UpperCAmelCase      		= self.get_value(__A						)
		return False if value is None else not bool(__A						)



	def        a  (  self     :       Any						):
		return self._stage == 2



	def        a  (  self     :       Any						):
		return self._stage == 3



	def        a  (  self     :       List[str]						):
		return self._offload






class      _UpperCAmelCase   :



	def __init__(  self     :       Optional[Any]			,    _lowercase     :       List[str]						):
		__UpperCAmelCase      		= engine



	def        a  (  self     :       List[str]			,    _lowercase     :       Union[str, Any]			,    **_lowercase     :       List[str]						):
		# runs backpropagation and handles mixed precision
		self.engine.backward(__A			,    **__A						)

		# Deepspeed's `engine.step` performs the following operations:
		# - gradient accumulation check
		# - gradient clipping
		# - optimizer step
		# - zero grad
		# - checking overflow
		# - lr_scheduler step (only if engine.lr_scheduler is not None)
		self.engine.step()
		# and this plugin overrides the above calls with no-ops when Accelerate runs under
		# Deepspeed, but allows normal functionality for non-Deepspeed cases thus enabling a simple
		# training loop that works transparently under many training regimes.






class      _UpperCAmelCase   (							__A       ):



	def __init__(  self     :       Optional[int]			,    _lowercase     :       Any						):
		super().__init__(__A			,    device_placement=__A			,    scaler=__A						)
		__UpperCAmelCase      		= hasattr(self.optimizer			,    '''overflow'''						)



	def        a  (  self     :       List[Any]			,    _lowercase     :       Any=None						):
		pass  # `accelerator.backward(loss)` is doing that automatically. Therefore, its implementation is not needed



	def        a  (  self     :       Optional[int]						):
		pass  # `accelerator.backward(loss)` is doing that automatically. Therefore, its implementation is not needed



	@property
	def        a  (  self     :       Union[str, Any]						):
		if self.__has_overflow__:
			return self.optimizer.overflow
		return False






class      _UpperCAmelCase   (							__A       ):



	def __init__(  self     :       Dict			,    _lowercase     :       int			,    _lowercase     :       Dict						):
		super().__init__(__A			,    __A						)



	def        a  (  self     :       Optional[int]						):
		pass  # `accelerator.backward(loss)` is doing that automatically. Therefore, its implementation is not needed






class      _UpperCAmelCase   :



	def __init__(  self     :       Dict			,    _lowercase     :       Tuple			,    _lowercase     :       Union[str, Any]=0.001			,    _lowercase     :       Union[str, Any]=0			,    **_lowercase     :       Dict						):
		__UpperCAmelCase      		= params
		__UpperCAmelCase      		= lr
		__UpperCAmelCase      		= weight_decay
		__UpperCAmelCase      		= kwargs






class      _UpperCAmelCase   :



	def __init__(  self     :       List[Any]			,    _lowercase     :       List[Any]			,    _lowercase     :       List[str]=None			,    _lowercase     :       Optional[Any]=0			,    **_lowercase     :       Optional[Any]						):
		__UpperCAmelCase      		= optimizer
		__UpperCAmelCase      		= total_num_steps
		__UpperCAmelCase      		= warmup_num_steps
		__UpperCAmelCase      		= kwargs




# 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.
import numpy as np
import torch

from ..models.clipseg import CLIPSegForImageSegmentation
from ..utils import is_vision_available, requires_backends
from .base import PipelineTool


if is_vision_available():
  from PIL import Image


class     __UpperCAmelCase							(       __A							):


     """simple docstring"""
     _lowerCamelCase   =      (
         """This is a tool that creates a segmentation mask of an image according to a label. It cannot create an image."""
         """It takes two arguments named `image` which should be the original image, and `label` which should be a text """
         """describing the elements what should be identified in the segmentation mask. The tool returns the mask."""
     )
     _lowerCamelCase   =      """CIDAS/clipseg-rd64-refined"""
     _lowerCamelCase   =      """image_segmenter"""
     _lowerCamelCase   =      CLIPSegForImageSegmentation

     _lowerCamelCase   =      ["""image""", """text"""]
     _lowerCamelCase   =      ["""image"""]


     def __init__(	self  ,       *__A  ,       **__A    ):
        requires_backends(self  ,       ["""vision"""]    )
        super().__init__(*__A  ,       **__A    )


     def 	snake_case_  (	self  ,       __A  ,       __A    ):
        return self.pre_processor(text=[label]  ,       images=[image]  ,       padding=__A  ,       return_tensors="""pt"""    )


     def 	snake_case_  (	self  ,       __A    ):
        with torch.no_grad():
           __a					=  self.model(**__A    ).logits
        return logits


     def 	snake_case_  (	self  ,       __A    ):
        __a					=  outputs.cpu().detach().numpy()
        __a					=  0
        __a					=  1
        return Image.fromarray((array * 255).astype(np.uinta    )    )



