

"""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 						_a				(      _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE=10				)    ->				Optional[int]:
							snake_case_           =		[]
							for _ in range(_SCREAMING_SNAKE_CASE				):
														lrs.append(scheduler.get_lr()[0]				)
														scheduler.step()
							return lrs







def 						_a				(      _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE=10				)    ->				int:
							snake_case_           =		[]
							for step in range(_SCREAMING_SNAKE_CASE				):
														lrs.append(scheduler.get_lr()[0]				)
														scheduler.step()
														if step == num_steps // 2:
																					with tempfile.TemporaryDirectory() as tmpdirname:
																												snake_case_           =		os.path.join(_SCREAMING_SNAKE_CASE				, """schedule.bin"""				)
																												torch.save(scheduler.state_dict()				, _SCREAMING_SNAKE_CASE				)

																												snake_case_           =		torch.load(_SCREAMING_SNAKE_CASE				)
																												scheduler.load_state_dict(_SCREAMING_SNAKE_CASE				)
							return lrs





@require_torch
class      __A     (unittest.TestCase):





							'''simple docstring'''

							def 		lowerCAmelCase       (				self							:      Optional[Any]		,       UpperCAmelCase_							:      str		,       UpperCAmelCase_							:      List[str]		,       UpperCAmelCase_							:      str       )					->Optional[Any]:



														"""simple docstring"""



														self.assertEqual(len(UpperCAmelCase_       )		,       len(UpperCAmelCase_       )       )
														for a, b in zip(UpperCAmelCase_		,       UpperCAmelCase_       ):
																					self.assertAlmostEqual(UpperCAmelCase_		,       UpperCAmelCase_		,       delta=UpperCAmelCase_       )



							def 		lowerCAmelCase       (				self							:      Optional[Any]       )					->Dict:



														"""simple docstring"""



														snake_case_           =		torch.tensor([0.1, -0.2, -0.1]		,       requires_grad=UpperCAmelCase_       )
														snake_case_           =		torch.tensor([0.4, 0.2, -0.5]       )
														snake_case_           =		nn.MSELoss()
														# No warmup, constant schedule, no gradient clipping
														snake_case_           =		AdamW(params=[w]		,       lr=2E-1		,       weight_decay=0.0       )
														for _ in range(100       ):
																					snake_case_           =		criterion(UpperCAmelCase_		,       UpperCAmelCase_       )
																					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 		lowerCAmelCase       (				self							:      Dict       )					->Optional[int]:



														"""simple docstring"""



														snake_case_           =		torch.tensor([0.1, -0.2, -0.1]		,       requires_grad=UpperCAmelCase_       )
														snake_case_           =		torch.tensor([0.4, 0.2, -0.5]       )
														snake_case_           =		nn.MSELoss()
														# No warmup, constant schedule, no gradient clipping
														snake_case_           =		Adafactor(
														    params=[w]		,       lr=1E-2		,       eps=(1E-30, 1E-3)		,       clip_threshold=1.0		,       decay_rate=-0.8		,       betaa=UpperCAmelCase_		,       weight_decay=0.0		,       relative_step=UpperCAmelCase_		,       scale_parameter=UpperCAmelCase_		,       warmup_init=UpperCAmelCase_		,       )
														for _ in range(1_000       ):
																					snake_case_           =		criterion(UpperCAmelCase_		,       UpperCAmelCase_       )
																					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      __A     (unittest.TestCase):





							'''simple docstring'''

							__lowercase:		int							      =    nn.Linear(50						,		50) if is_torch_available() else None
							__lowercase:		Tuple							      =    AdamW(m.parameters()						,		lr=10.0) if is_torch_available() else None
							__lowercase:		int							      =    10



							def 		lowerCAmelCase       (				self							:      int		,       UpperCAmelCase_							:      Dict		,       UpperCAmelCase_							:      Dict		,       UpperCAmelCase_							:      List[Any]		,       UpperCAmelCase_							:      Any=None       )					->Union[str, Any]:



														"""simple docstring"""



														self.assertEqual(len(UpperCAmelCase_       )		,       len(UpperCAmelCase_       )       )
														for a, b in zip(UpperCAmelCase_		,       UpperCAmelCase_       ):
																					self.assertAlmostEqual(UpperCAmelCase_		,       UpperCAmelCase_		,       delta=UpperCAmelCase_		,       msg=UpperCAmelCase_       )



							def 		lowerCAmelCase       (				self							:      List[Any]       )					->Any:



														"""simple docstring"""



														snake_case_           =		{"""num_warmup_steps""": 2, """num_training_steps""": 10}
														# schedulers doct format
														# function: (sched_args_dict, expected_learning_rates)
														snake_case_           =		{
														    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():
																					snake_case_       ,						snake_case_           =		data

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

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





class      __A     :





							'''simple docstring'''

							def __init__(				self							:      str		,       UpperCAmelCase_							:      List[str]       )					->List[str]:



														"""simple docstring"""



														snake_case_           =		fn



							def __call__(				self							:      Optional[Any]		,       *UpperCAmelCase_							:      Dict		,       **UpperCAmelCase_							:      int       )					->str:



														"""simple docstring"""



														return self.fn(*UpperCAmelCase_		,       **UpperCAmelCase_       )



							@classmethod
							def 		lowerCAmelCase       (				self							:      Optional[Any]		,       UpperCAmelCase_							:      Dict       )					->List[Any]:



														"""simple docstring"""



														snake_case_           =		list(map(self		,       scheduler.lr_lambdas       )       )







"""simple docstring"""
import json
import os
import tempfile
import unittest

import numpy as np
from datasets import load_dataset

from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin


if is_torch_available():
				import torch

if is_vision_available():
				from PIL import Image

				from transformers import ImageGPTImageProcessor





class      __A     (unittest.TestCase):





							'''simple docstring'''

							def __init__(				self							:      int		,       UpperCAmelCase_							:      List[str]		,       UpperCAmelCase_							:      Tuple=7		,       UpperCAmelCase_							:      Dict=3		,       UpperCAmelCase_							:      Tuple=18		,       UpperCAmelCase_							:      Optional[Any]=30		,       UpperCAmelCase_							:      str=400		,       UpperCAmelCase_							:      int=True		,       UpperCAmelCase_							:      Optional[int]=None		,       UpperCAmelCase_							:      Optional[Any]=True		,       )					->Optional[Any]:



														"""simple docstring"""



														snake_case_           =		size if size is not None else {"""height""": 18, """width""": 18}
														snake_case_           =		parent
														snake_case_           =		batch_size
														snake_case_           =		num_channels
														snake_case_           =		image_size
														snake_case_           =		min_resolution
														snake_case_           =		max_resolution
														snake_case_           =		do_resize
														snake_case_           =		size
														snake_case_           =		do_normalize



							def 		lowerCAmelCase       (				self							:      List[str]       )					->Optional[Any]:



														"""simple docstring"""



														return {
														    # here we create 2 clusters for the sake of simplicity
														    "clusters": np.asarray(
														        [
														            [0.8_866_443_634_033_203, 0.6_618_829_369_544_983, 0.3_891_746_401_786_804],
														            [-0.6_042_559_146_881_104, -0.02_295_008_860_528_469, 0.5_423_797_369_003_296],
														        ]       ),
														    "do_resize": self.do_resize,
														    "size": self.size,
														    "do_normalize": self.do_normalize,
														}





@require_torch
@require_vision
class      __A     (snake_case__						,		unittest.TestCase):





							'''simple docstring'''

							__lowercase:		List[Any]							      =    ImageGPTImageProcessor if is_vision_available() else None



							def 		lowerCAmelCase       (				self							:      Optional[int]       )					->Optional[int]:



														"""simple docstring"""



														snake_case_           =		ImageGPTImageProcessingTester(self       )



							@property
							def 		lowerCAmelCase       (				self							:      Tuple       )					->List[Any]:



														"""simple docstring"""



														return self.image_processor_tester.prepare_image_processor_dict()



							def 		lowerCAmelCase       (				self							:      Optional[Any]       )					->List[str]:



														"""simple docstring"""



														snake_case_           =		self.image_processing_class(**self.image_processor_dict       )
														self.assertTrue(hasattr(UpperCAmelCase_		,       """clusters"""       )       )
														self.assertTrue(hasattr(UpperCAmelCase_		,       """do_resize"""       )       )
														self.assertTrue(hasattr(UpperCAmelCase_		,       """size"""       )       )
														self.assertTrue(hasattr(UpperCAmelCase_		,       """do_normalize"""       )       )



							def 		lowerCAmelCase       (				self							:      Optional[int]       )					->Optional[Any]:



														"""simple docstring"""



														snake_case_           =		self.image_processing_class.from_dict(self.image_processor_dict       )
														self.assertEqual(image_processor.size		,       {"""height""": 18, """width""": 18}       )

														snake_case_           =		self.image_processing_class.from_dict(self.image_processor_dict		,       size=42       )
														self.assertEqual(image_processor.size		,       {"""height""": 42, """width""": 42}       )



							def 		lowerCAmelCase       (				self							:      Any       )					->List[Any]:



														"""simple docstring"""



														snake_case_           =		self.image_processing_class(**self.image_processor_dict       )
														snake_case_           =		json.loads(image_processor.to_json_string()       )
														for key, value in self.image_processor_dict.items():
																					if key == "clusters":
																												self.assertTrue(np.array_equal(UpperCAmelCase_		,       obj[key]       )       )
																					else:
																												self.assertEqual(obj[key]		,       UpperCAmelCase_       )



							def 		lowerCAmelCase       (				self							:      Optional[int]       )					->Dict:



														"""simple docstring"""



														snake_case_           =		self.image_processing_class(**self.image_processor_dict       )

														with tempfile.TemporaryDirectory() as tmpdirname:
																					snake_case_           =		os.path.join(UpperCAmelCase_		,       """image_processor.json"""       )
																					image_processor_first.to_json_file(UpperCAmelCase_       )
																					snake_case_           =		self.image_processing_class.from_json_file(UpperCAmelCase_       ).to_dict()

														snake_case_           =		image_processor_first.to_dict()
														for key, value in image_processor_first.items():
																					if key == "clusters":
																												self.assertTrue(np.array_equal(UpperCAmelCase_		,       image_processor_second[key]       )       )
																					else:
																												self.assertEqual(image_processor_first[key]		,       UpperCAmelCase_       )



							def 		lowerCAmelCase       (				self							:      Optional[Any]       )					->List[Any]:



														"""simple docstring"""



														snake_case_           =		self.image_processing_class(**self.image_processor_dict       )

														with tempfile.TemporaryDirectory() as tmpdirname:
																					image_processor_first.save_pretrained(UpperCAmelCase_       )
																					snake_case_           =		self.image_processing_class.from_pretrained(UpperCAmelCase_       ).to_dict()

														snake_case_           =		image_processor_first.to_dict()
														for key, value in image_processor_first.items():
																					if key == "clusters":
																												self.assertTrue(np.array_equal(UpperCAmelCase_		,       image_processor_second[key]       )       )
																					else:
																												self.assertEqual(image_processor_first[key]		,       UpperCAmelCase_       )



							@unittest.skip("""ImageGPT requires clusters at initialization"""       )
							def 		lowerCAmelCase       (				self							:      List[Any]       )					->Tuple:



														"""simple docstring"""



														pass







def 						_a				(      )    ->				str:
							snake_case_           =		load_dataset("""hf-internal-testing/fixtures_image_utils"""				, split="""test"""				)

							snake_case_           =		Image.open(dataset[4]["""file"""]				)
							snake_case_           =		Image.open(dataset[5]["""file"""]				)

							snake_case_           =		[imagea, imagea]

							return images





@require_vision
@require_torch
class      __A     (unittest.TestCase):





							'''simple docstring'''

							@slow
							def 		lowerCAmelCase       (				self							:      Tuple       )					->List[str]:



														"""simple docstring"""



														snake_case_           =		ImageGPTImageProcessor.from_pretrained("""openai/imagegpt-small"""       )

														snake_case_           =		prepare_images()

														# test non-batched
														snake_case_           =		image_processing(images[0]		,       return_tensors="""pt"""       )

														self.assertIsInstance(encoding.input_ids		,       torch.LongTensor       )
														self.assertEqual(encoding.input_ids.shape		,       (1, 1_024)       )

														snake_case_           =		[306, 191, 191]
														self.assertEqual(encoding.input_ids[0, :3].tolist()		,       UpperCAmelCase_       )

														# test batched
														snake_case_           =		image_processing(UpperCAmelCase_		,       return_tensors="""pt"""       )

														self.assertIsInstance(encoding.input_ids		,       torch.LongTensor       )
														self.assertEqual(encoding.input_ids.shape		,       (2, 1_024)       )

														snake_case_           =		[303, 13, 13]
														self.assertEqual(encoding.input_ids[1, -3:].tolist()		,       UpperCAmelCase_       )



