
import inspect
import unittest
import warnings
from math import ceil, floor

from transformers import LevitConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
							import torch

							from transformers import (
							    MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
							    MODEL_MAPPING,
							    LevitForImageClassification,
							    LevitForImageClassificationWithTeacher,
							    LevitModel,
							)
							from transformers.models.levit.modeling_levit import LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
							from PIL import Image

							from transformers import LevitImageProcessor







class     lowercase		( SCREAMING_SNAKE_CASE__		):




							def 			A__				(    self):
												lowercase						     =						self.config_class(**self.inputs_dict)
												self.parent.assertTrue(hasattr(A__       ,'''hidden_sizes'''))
												self.parent.assertTrue(hasattr(A__       ,'''num_attention_heads'''))







class     lowercase		:




							def __init__(    self       ,A__       ,A__=1_3       ,A__=6_4       ,A__=3       ,A__=3       ,A__=2       ,A__=1       ,A__=1_6       ,A__=[1_2_8, 2_5_6, 3_8_4]       ,A__=[4, 6, 8]       ,A__=[2, 3, 4]       ,A__=[1_6, 1_6, 1_6]       ,A__=0       ,A__=[2, 2, 2]       ,A__=[2, 2, 2]       ,A__=0.02       ,A__=True       ,A__=True       ,A__=2       ,):
												lowercase						     =						parent
												lowercase						     =						batch_size
												lowercase						     =						image_size
												lowercase						     =						num_channels
												lowercase						     =						kernel_size
												lowercase						     =						stride
												lowercase						     =						padding
												lowercase						     =						hidden_sizes
												lowercase						     =						num_attention_heads
												lowercase						     =						depths
												lowercase						     =						key_dim
												lowercase						     =						drop_path_rate
												lowercase						     =						patch_size
												lowercase						     =						attention_ratio
												lowercase						     =						mlp_ratio
												lowercase						     =						initializer_range
												lowercase						     =						[
												    ['''Subsample''', key_dim[0], hidden_sizes[0] // key_dim[0], 4, 2, 2],
												    ['''Subsample''', key_dim[0], hidden_sizes[1] // key_dim[0], 4, 2, 2],
												]
												lowercase						     =						is_training
												lowercase						     =						use_labels
												lowercase						     =						num_labels
												lowercase						     =						initializer_range




							def 			A__				(    self):
												lowercase						     =						floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

												lowercase						     =						None
												if self.use_labels:
																	lowercase						     =						ids_tensor([self.batch_size]       ,self.num_labels)

												lowercase						     =						self.get_config()
												return config, pixel_values, labels




							def 			A__				(    self):
												return LevitConfig(
												    image_size=self.image_size       ,num_channels=self.num_channels       ,kernel_size=self.kernel_size       ,stride=self.stride       ,padding=self.padding       ,patch_size=self.patch_size       ,hidden_sizes=self.hidden_sizes       ,num_attention_heads=self.num_attention_heads       ,depths=self.depths       ,key_dim=self.key_dim       ,drop_path_rate=self.drop_path_rate       ,mlp_ratio=self.mlp_ratio       ,attention_ratio=self.attention_ratio       ,initializer_range=self.initializer_range       ,down_ops=self.down_ops       ,)




							def 			A__				(    self       ,A__       ,A__       ,A__):
												lowercase						     =						LevitModel(config=A__)
												model.to(A__)
												model.eval()
												lowercase						     =						model(A__)
												lowercase						     =						(self.image_size, self.image_size)
												lowercase     ,						lowercase						     =						image_size[0], image_size[1]
												for _ in range(4):
																	lowercase						     =						floor(((height + 2 * self.padding - self.kernel_size) / self.stride) + 1)
																	lowercase						     =						floor(((width + 2 * self.padding - self.kernel_size) / self.stride) + 1)
												self.parent.assertEqual(
												    result.last_hidden_state.shape       ,(self.batch_size, ceil(height / 4) * ceil(width / 4), self.hidden_sizes[-1])       ,)




							def 			A__				(    self       ,A__       ,A__       ,A__):
												lowercase						     =						self.num_labels
												lowercase						     =						LevitForImageClassification(A__)
												model.to(A__)
												model.eval()
												lowercase						     =						model(A__       ,labels=A__)
												self.parent.assertEqual(result.logits.shape       ,(self.batch_size, self.num_labels))





							def 			A__				(    self):
												lowercase						     =						self.prepare_config_and_inputs()
												lowercase     ,						lowercase     ,						lowercase						     =						config_and_inputs
												lowercase						     =						{'''pixel_values''': pixel_values}
												return config, inputs_dict







@require_torch
class     lowercase		( SCREAMING_SNAKE_CASE__	,  SCREAMING_SNAKE_CASE__	,  unittest.TestCase		):
							lowercase_	:	str	 =(
							    (LevitModel, LevitForImageClassification, LevitForImageClassificationWithTeacher)
							    if is_torch_available()
							    else ()
							)
							lowercase_	:	Optional[Any]	 =(
							    {
							        '''feature-extraction''': LevitModel,
							        '''image-classification''': (LevitForImageClassification, LevitForImageClassificationWithTeacher),
							    }
							    if is_torch_available()
							    else {}
							)

							lowercase_	:	Tuple	 =False
							lowercase_	:	Any	 =False
							lowercase_	:	Any	 =False
							lowercase_	:	Dict	 =False
							lowercase_	:	List[str]	 =False




							def 			A__				(    self):
												lowercase						     =						LevitModelTester(self)
												lowercase						     =						ConfigTester(self       ,config_class=A__       ,has_text_modality=A__       ,hidden_size=3_7)




							def 			A__				(    self):
												self.create_and_test_config_common_properties()
												self.config_tester.create_and_test_config_to_json_string()
												self.config_tester.create_and_test_config_to_json_file()
												self.config_tester.create_and_test_config_from_and_save_pretrained()
												self.config_tester.create_and_test_config_with_num_labels()
												self.config_tester.check_config_can_be_init_without_params()
												self.config_tester.check_config_arguments_init()




							def 			A__				(    self):
												return




							@unittest.skip(reason='''Levit does not use inputs_embeds''')
							def 			A__				(    self):
												pass




							@unittest.skip(reason='''Levit does not support input and output embeddings''')
							def 			A__				(    self):
												pass




							@unittest.skip(reason='''Levit does not output attentions''')
							def 			A__				(    self):
												pass




							def 			A__				(    self):
												lowercase     ,						lowercase						     =						self.model_tester.prepare_config_and_inputs_for_common()

												for model_class in self.all_model_classes:
																	lowercase						     =						model_class(A__)
																	lowercase						     =						inspect.signature(model.forward)
																	# signature.parameters is an OrderedDict => so arg_names order is deterministic
																	lowercase						     =						[*signature.parameters.keys()]

																	lowercase						     =						['''pixel_values''']
																	self.assertListEqual(arg_names[:1]       ,A__)




							def 			A__				(    self):
												def check_hidden_states_output(A__       ,A__       ,A__):
																	lowercase						     =						model_class(A__)
																	model.to(A__)
																	model.eval()

																	with torch.no_grad():
																						lowercase						     =						model(**self._prepare_for_class(A__       ,A__))

																	lowercase						     =						outputs.hidden_states

																	lowercase						     =						len(self.model_tester.depths) + 1
																	self.assertEqual(len(A__)       ,A__)

																	lowercase						     =						(self.model_tester.image_size, self.model_tester.image_size)
																	lowercase     ,						lowercase						     =						image_size[0], image_size[1]
																	for _ in range(4):
																						lowercase						     =						floor(
																						    (
																						        (height + 2 * self.model_tester.padding - self.model_tester.kernel_size)
																						        / self.model_tester.stride
																						    )
																						    + 1)
																						lowercase						     =						floor(
																						    (
																						        (width + 2 * self.model_tester.padding - self.model_tester.kernel_size)
																						        / self.model_tester.stride
																						    )
																						    + 1)
																	# verify the first hidden states (first block)
																	self.assertListEqual(
																	    list(hidden_states[0].shape[-2:])       ,[
																	        height * width,
																	        self.model_tester.hidden_sizes[0],
																	    ]       ,)

												lowercase     ,						lowercase						     =						self.model_tester.prepare_config_and_inputs_for_common()

												for model_class in self.all_model_classes:
																	lowercase						     =						True
																	check_hidden_states_output(A__       ,A__       ,A__)

																	# check that output_hidden_states also work using config
																	del inputs_dict["output_hidden_states"]
																	lowercase						     =						True

																	check_hidden_states_output(A__       ,A__       ,A__)




							@unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.''')
							def 			A__				(    self):
												pass




							def 			A__				(    self       ,A__       ,A__       ,A__=False):
												lowercase						     =						super()._prepare_for_class(A__       ,A__       ,return_labels=A__)

												if return_labels:
																	if model_class.__name__ == "LevitForImageClassificationWithTeacher":
																						del inputs_dict["labels"]

												return inputs_dict




							def 			A__				(    self):
												lowercase						     =						self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_model(*A__)




							def 			A__				(    self):
												lowercase						     =						self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_image_classification(*A__)




							def 			A__				(    self):
												if not self.model_tester.is_training:
																	return

												lowercase     ,						lowercase						     =						self.model_tester.prepare_config_and_inputs_for_common()
												lowercase						     =						True

												for model_class in self.all_model_classes:
																	# LevitForImageClassificationWithTeacher supports inference-only
																	if (
																	    model_class in get_values(A__)
																	    or model_class.__name__ == "LevitForImageClassificationWithTeacher"
																	):
																						continue
																	lowercase						     =						model_class(A__)
																	model.to(A__)
																	model.train()
																	lowercase						     =						self._prepare_for_class(A__       ,A__       ,return_labels=A__)
																	lowercase						     =						model(**A__).loss
																	loss.backward()




							def 			A__				(    self):
												lowercase     ,						lowercase						     =						self.model_tester.prepare_config_and_inputs_for_common()
												if not self.model_tester.is_training:
																	return

												lowercase						     =						False
												lowercase						     =						True

												for model_class in self.all_model_classes:
																	if model_class in get_values(A__) or not model_class.supports_gradient_checkpointing:
																						continue
																	# LevitForImageClassificationWithTeacher supports inference-only
																	if model_class.__name__ == "LevitForImageClassificationWithTeacher":
																						continue
																	lowercase						     =						model_class(A__)
																	model.gradient_checkpointing_enable()
																	model.to(A__)
																	model.train()
																	lowercase						     =						self._prepare_for_class(A__       ,A__       ,return_labels=A__)
																	lowercase						     =						model(**A__).loss
																	loss.backward()




							def 			A__				(    self):
												lowercase     ,						lowercase						     =						self.model_tester.prepare_config_and_inputs_for_common()

												lowercase						     =						[
												    {'''title''': '''multi_label_classification''', '''num_labels''': 2, '''dtype''': torch.float},
												    {'''title''': '''single_label_classification''', '''num_labels''': 1, '''dtype''': torch.long},
												    {'''title''': '''regression''', '''num_labels''': 1, '''dtype''': torch.float},
												]

												for model_class in self.all_model_classes:
																	if (
																	    model_class
																	    not in [
																	        *get_values(A__),
																	    ]
																	    or model_class.__name__ == "LevitForImageClassificationWithTeacher"
																	):
																						continue

																	for problem_type in problem_types:
																						with self.subTest(msg=f'Testing {model_class} with {problem_type["title"]}'):
																											lowercase						     =						problem_type['''title''']
																											lowercase						     =						problem_type['''num_labels''']

																											lowercase						     =						model_class(A__)
																											model.to(A__)
																											model.train()

																											lowercase						     =						self._prepare_for_class(A__       ,A__       ,return_labels=A__)

																											if problem_type["num_labels"] > 1:
																																lowercase						     =						inputs['''labels'''].unsqueeze(1).repeat(1       ,problem_type['''num_labels'''])

																											lowercase						     =						inputs['''labels'''].to(problem_type['''dtype'''])

																											# This tests that we do not trigger the warning form PyTorch "Using a target size that is different
																											# to the input size. This will likely lead to incorrect results due to broadcasting. Please ensure
																											# they have the same size." which is a symptom something in wrong for the regression problem.
																											# See https://github.com/huggingface/transformers/issues/11780
																											with warnings.catch_warnings(record=A__) as warning_list:
																																lowercase						     =						model(**A__).loss
																											for w in warning_list:
																																if "Using a target size that is different to the input size" in str(w.message):
																																					raise ValueError(
																																					    f'Something is going wrong in the regression problem: intercepted {w.message}')

																											loss.backward()





							@slow
							def 			A__				(    self):
												for model_name in LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																	lowercase						     =						LevitModel.from_pretrained(A__)
																	self.assertIsNotNone(A__)



def 	UpperCamelCase   (							):
					'''simple docstring'''
					lowercase						     =						Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''		)
					return image







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




							@cached_property
							def 			A__				(    self):
												return LevitImageProcessor.from_pretrained(LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[0])





							@slow
							def 			A__				(    self):
												lowercase						     =						LevitForImageClassificationWithTeacher.from_pretrained(LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]).to(
												    A__)

												lowercase						     =						self.default_image_processor
												lowercase						     =						prepare_img()
												lowercase						     =						image_processor(images=A__       ,return_tensors='''pt''').to(A__)

												# forward pass
												with torch.no_grad():
																	lowercase						     =						model(**A__)

												# verify the logits
												lowercase						     =						torch.Size((1, 1_0_0_0))
												self.assertEqual(outputs.logits.shape       ,A__)

												lowercase						     =						torch.tensor([1.0448, -0.3745, -1.8317]).to(A__)

												self.assertTrue(torch.allclose(outputs.logits[0, :3]       ,A__       ,atol=1E-4))






import unittest
from dataclasses import dataclass

import pytest

from accelerate.commands.config.config_args import SageMakerConfig
from accelerate.utils import ComputeEnvironment
from accelerate.utils.launch import _convert_nargs_to_dict







@dataclass
class     lowercase		( SCREAMING_SNAKE_CASE__		):
							lowercase_	:	int	 =ComputeEnvironment.AMAZON_SAGEMAKER
							lowercase_	:	Optional[int]	 =True
							lowercase_	:	Any	 ='''ml.p3.2xlarge'''
							lowercase_	:	Any	 ='''accelerate_sagemaker_execution_role'''
							lowercase_	:	Union[str, Any]	 ='''hf-sm'''
							lowercase_	:	Any	 ='''us-east-1'''
							lowercase_	:	List[str]	 =1
							lowercase_	:	Any	 ='''accelerate-sagemaker-1'''
							lowercase_	:	Union[str, Any]	 ='''1.6'''
							lowercase_	:	Any	 ='''4.4'''
							lowercase_	:	Any	 ='''train.py'''
							lowercase_	:	int	 =[
							    '''--model_name_or_path''',
							    '''bert''',
							    '''--do_train''',
							    '''False''',
							    '''--epochs''',
							    '''3''',
							    '''--learning_rate''',
							    '''5e-5''',
							    '''--max_steps''',
							    '''50.5''',
							]
							lowercase_	:	List[Any]	 =[
							    '''--model_name_or_path''',
							    '''bert''',
							    '''--do_train''',
							    '''--do_test''',
							    '''False''',
							    '''--do_predict''',
							    '''--epochs''',
							    '''3''',
							    '''--learning_rate''',
							    '''5e-5''',
							    '''--max_steps''',
							    '''50.5''',
							]







class     lowercase		( unittest.TestCase		):




							def 			A__				(    self):
												# If no defaults are changed, `to_kwargs` returns an empty dict.
												lowercase						     =						_convert_nargs_to_dict(MockLaunchConfig.success_training_script_args)
												assert isinstance(converted_args['''model_name_or_path''']       ,A__)
												assert isinstance(converted_args['''do_train''']       ,A__)
												assert isinstance(converted_args['''epochs''']       ,A__)
												assert isinstance(converted_args['''learning_rate''']       ,A__)
												assert isinstance(converted_args['''max_steps''']       ,A__)

												with pytest.raises(A__):
																	_convert_nargs_to_dict(MockLaunchConfig.fail_training_script_args)




