

"""simple docstring"""




import os
import tempfile
import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch


if is_torch_available():
				import torch
				from torch import nn

				from transformers import (
				    Adafactor,
				    AdamW,
				    get_constant_schedule,
				    get_constant_schedule_with_warmup,
				    get_cosine_schedule_with_warmup,
				    get_cosine_with_hard_restarts_schedule_with_warmup,
				    get_inverse_sqrt_schedule,
				    get_linear_schedule_with_warmup,
				    get_polynomial_decay_schedule_with_warmup,
				)





def       lowercase__						(       snake_case_   :int			,		snake_case_   :Dict=10    ):
	__UpperCAmelCase      		= []
	for _ in range(lowerCAmelCase__    ):
		lrs.append(scheduler.get_lr()[0]    )
		scheduler.step()
	return lrs





def       lowercase__						(       snake_case_   :Any			,		snake_case_   :Optional[int]=10    ):
	__UpperCAmelCase      		= []
	for step in range(lowerCAmelCase__    ):
		lrs.append(scheduler.get_lr()[0]    )
		scheduler.step()
		if step == num_steps // 2:
			with tempfile.TemporaryDirectory() as tmpdirname:
				__UpperCAmelCase      		= os.path.join(lowerCAmelCase__			,		'''schedule.bin'''    )
				torch.save(scheduler.state_dict()			,		lowerCAmelCase__    )

				__UpperCAmelCase      		= torch.load(lowerCAmelCase__    )
				scheduler.load_state_dict(lowerCAmelCase__    )
	return lrs
@require_torch
class      _UpperCAmelCase   (							unittest.TestCase       ):



	def        a  (  self     :       Optional[Any]			,    _lowercase     :       Tuple			,    _lowercase     :       List[Any]			,    _lowercase     :       Dict						):
		self.assertEqual(len(__A						)			,    len(__A						)						)
		for a, b in zip(__A			,    __A						):
			self.assertAlmostEqual(__A			,    __A			,    delta=__A						)



	def        a  (  self     :       Optional[int]						):
		__UpperCAmelCase      		= torch.tensor([0.1, -0.2, -0.1]			,    requires_grad=__A						)
		__UpperCAmelCase      		= torch.tensor([0.4, 0.2, -0.5]						)
		__UpperCAmelCase      		= nn.MSELoss()
		# No warmup, constant schedule, no gradient clipping
		__UpperCAmelCase      		= AdamW(params=[w]			,    lr=2E-1			,    weight_decay=0.0						)
		for _ in range(1_00						):
			__UpperCAmelCase      		= criterion(__A			,    __A						)
			loss.backward()
			optimizer.step()
			w.grad.detach_()  # No zero_grad() function on simple tensors. we do it ourselves.
			w.grad.zero_()
		self.assertListAlmostEqual(w.tolist()			,    [0.4, 0.2, -0.5]			,    tol=1E-2						)



	def        a  (  self     :       List[str]						):
		__UpperCAmelCase      		= torch.tensor([0.1, -0.2, -0.1]			,    requires_grad=__A						)
		__UpperCAmelCase      		= torch.tensor([0.4, 0.2, -0.5]						)
		__UpperCAmelCase      		= nn.MSELoss()
		# No warmup, constant schedule, no gradient clipping
		__UpperCAmelCase      		= Adafactor(
		    params=[w]			,    lr=1E-2			,    eps=(1E-30, 1E-3)			,    clip_threshold=1.0			,    decay_rate=-0.8			,    betaa=__A			,    weight_decay=0.0			,    relative_step=__A			,    scale_parameter=__A			,    warmup_init=__A			,    )
		for _ in range(10_00						):
			__UpperCAmelCase      		= criterion(__A			,    __A						)
			loss.backward()
			optimizer.step()
			w.grad.detach_()  # No zero_grad() function on simple tensors. we do it ourselves.
			w.grad.zero_()
		self.assertListAlmostEqual(w.tolist()			,    [0.4, 0.2, -0.5]			,    tol=1E-2						)






@require_torch
class      _UpperCAmelCase   (							unittest.TestCase       ):
	a__		: Optional[int]					=					nn.Linear(50		,    50       ) if is_torch_available() else None
	a__		: List[str]					=					AdamW(m.parameters()		,    lr=10.0       ) if is_torch_available() else None
	a__		: List[Any]					=					10



	def        a  (  self     :       Optional[Any]			,    _lowercase     :       Union[str, Any]			,    _lowercase     :       List[Any]			,    _lowercase     :       Union[str, Any]			,    _lowercase     :       List[str]=None						):
		self.assertEqual(len(__A						)			,    len(__A						)						)
		for a, b in zip(__A			,    __A						):
			self.assertAlmostEqual(__A			,    __A			,    delta=__A			,    msg=__A						)



	def        a  (  self     :       List[Any]						):
		__UpperCAmelCase      		= {'''num_warmup_steps''': 2, '''num_training_steps''': 10}
		# schedulers doct format
		# function: (sched_args_dict, expected_learning_rates)
		__UpperCAmelCase      		= {
		    get_constant_schedule: ({}, [10.0] * self.num_steps),
		    get_constant_schedule_with_warmup: (
		        {'''num_warmup_steps''': 4},
		        [0.0, 2.5, 5.0, 7.5, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0],
		    ),
		    get_linear_schedule_with_warmup: (
		        {**common_kwargs},
		        [0.0, 5.0, 10.0, 8.75, 7.5, 6.25, 5.0, 3.75, 2.5, 1.25],
		    ),
		    get_cosine_schedule_with_warmup: (
		        {**common_kwargs},
		        [0.0, 5.0, 10.0, 9.61, 8.53, 6.91, 5.0, 3.08, 1.46, 0.38],
		    ),
		    get_cosine_with_hard_restarts_schedule_with_warmup: (
		        {**common_kwargs, '''num_cycles''': 2},
		        [0.0, 5.0, 10.0, 8.53, 5.0, 1.46, 10.0, 8.53, 5.0, 1.46],
		    ),
		    get_polynomial_decay_schedule_with_warmup: (
		        {**common_kwargs, '''power''': 2.0, '''lr_end''': 1E-7},
		        [0.0, 5.0, 10.0, 7.656, 5.625, 3.906, 2.5, 1.406, 0.625, 0.156],
		    ),
		    get_inverse_sqrt_schedule: (
		        {'''num_warmup_steps''': 2},
		        [0.0, 5.0, 10.0, 8.165, 7.071, 6.325, 5.774, 5.345, 5.0, 4.714],
		    ),
		}

		for scheduler_func, data in scheds.items():
			__UpperCAmelCase		,							__UpperCAmelCase      		= data

			__UpperCAmelCase      		= scheduler_func(self.optimizer			,    **__A						)
			self.assertEqual(len([scheduler.get_lr()[0]]						)			,    1						)
			__UpperCAmelCase      		= unwrap_schedule(__A			,    self.num_steps						)
			self.assertListAlmostEqual(
			    __A			,    __A			,    tol=1E-2			,    msg=F'''failed for {scheduler_func} in normal scheduler'''			,    )

			__UpperCAmelCase      		= scheduler_func(self.optimizer			,    **__A						)
			if scheduler_func.__name__ != "get_constant_schedule":
				LambdaScheduleWrapper.wrap_scheduler(__A						)  # wrap to test picklability of the schedule
			__UpperCAmelCase      		= unwrap_and_save_reload_schedule(__A			,    self.num_steps						)
			self.assertListEqual(__A			,    __A			,    msg=F'''failed for {scheduler_func} in save and reload'''						)






class      _UpperCAmelCase   :



	def __init__(  self     :       str			,    _lowercase     :       Union[str, Any]						):
		__UpperCAmelCase      		= fn



	def __call__(  self     :       List[str]			,    *_lowercase     :       Any			,    **_lowercase     :       Dict						):
		return self.fn(*__A			,    **__A						)



	@classmethod
	def        a  (  self     :       Tuple			,    _lowercase     :       Optional[Any]						):
		__UpperCAmelCase      		= list(map(self			,    scheduler.lr_lambdas						)						)




# 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    )    )



