import inspect
import unittest

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

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 torch import nn

						from transformers import ViTMSNForImageClassification, ViTMSNModel
						from transformers.models.vit_msn.modeling_vit_msn import VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
						from PIL import Image

						from transformers import ViTImageProcessor






class      __lowercase					:

						def __init__(	self				:      int							,							__lowerCamelCase				:      List[str]							,							__lowerCamelCase				:      Optional[Any]=13							,							__lowerCamelCase				:      Optional[Any]=30							,							__lowerCamelCase				:      Any=2							,							__lowerCamelCase				:      List[Any]=3							,							__lowerCamelCase				:      Optional[int]=True							,							__lowerCamelCase				:      str=True							,							__lowerCamelCase				:      int=32							,							__lowerCamelCase				:      List[Any]=5							,							__lowerCamelCase				:      Dict=4							,							__lowerCamelCase				:      Dict=37							,							__lowerCamelCase				:      str="gelu"							,							__lowerCamelCase				:      List[str]=0.1							,							__lowerCamelCase				:      Any=0.1							,							__lowerCamelCase				:      Dict=10							,							__lowerCamelCase				:      Union[str, Any]=0.02							,							__lowerCamelCase				:      Union[str, Any]=None							,							)							->		Dict:


									'''simple docstring'''

									lowercase							=		parent
									lowercase							=		batch_size
									lowercase							=		image_size
									lowercase							=		patch_size
									lowercase							=		num_channels
									lowercase							=		is_training
									lowercase							=		use_labels
									lowercase							=		hidden_size
									lowercase							=		num_hidden_layers
									lowercase							=		num_attention_heads
									lowercase							=		intermediate_size
									lowercase							=		hidden_act
									lowercase							=		hidden_dropout_prob
									lowercase							=		attention_probs_dropout_prob
									lowercase							=		type_sequence_label_size
									lowercase							=		initializer_range
									lowercase							=		scope

									# in ViT MSN, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
									lowercase							=		(image_size // patch_size) ** 2
									lowercase							=		num_patches + 1

						def        __a		(	self				:      Any   )							->		Optional[Any]:


									'''simple docstring'''

									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.type_sequence_label_size   )

									lowercase							=		self.get_config()

									return config, pixel_values, labels

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


									'''simple docstring'''

									return ViTMSNConfig(
									    image_size=self.image_size							,							patch_size=self.patch_size							,							num_channels=self.num_channels							,							hidden_size=self.hidden_size							,							num_hidden_layers=self.num_hidden_layers							,							num_attention_heads=self.num_attention_heads							,							intermediate_size=self.intermediate_size							,							hidden_act=self.hidden_act							,							hidden_dropout_prob=self.hidden_dropout_prob							,							attention_probs_dropout_prob=self.attention_probs_dropout_prob							,							initializer_range=self.initializer_range							,							)

						def        __a		(	self				:      str							,							__lowerCamelCase				:      List[Any]							,							__lowerCamelCase				:      Any							,							__lowerCamelCase				:      Optional[int]   )							->		List[Any]:


									'''simple docstring'''

									lowercase							=		ViTMSNModel(config=__lowerCamelCase   )
									model.to(__lowerCamelCase   )
									model.eval()
									lowercase							=		model(__lowerCamelCase   )
									self.parent.assertEqual(result.last_hidden_state.shape							,							(self.batch_size, self.seq_length, self.hidden_size)   )

						def        __a		(	self				:      str							,							__lowerCamelCase				:      Optional[Any]							,							__lowerCamelCase				:      Any							,							__lowerCamelCase				:      int   )							->		Union[str, Any]:


									'''simple docstring'''

									lowercase							=		self.type_sequence_label_size
									lowercase							=		ViTMSNForImageClassification(__lowerCamelCase   )
									model.to(__lowerCamelCase   )
									model.eval()
									lowercase							=		model(__lowerCamelCase							,							labels=__lowerCamelCase   )
									print('''Pixel and labels shape: {pixel_values.shape}, {labels.shape}'''   )
									print('''Labels: {labels}'''   )
									self.parent.assertEqual(result.logits.shape							,							(self.batch_size, self.type_sequence_label_size)   )

									# test greyscale images
									lowercase							=		1
									lowercase							=		ViTMSNForImageClassification(__lowerCamelCase   )
									model.to(__lowerCamelCase   )
									model.eval()

									lowercase							=		floats_tensor([self.batch_size, 1, self.image_size, self.image_size]   )
									lowercase							=		model(__lowerCamelCase   )
									self.parent.assertEqual(result.logits.shape							,							(self.batch_size, self.type_sequence_label_size)   )



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


									'''simple docstring'''

									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					(    _A       ,     _A       ,     unittest.TestCase    ):
						lowercase					 =     (ViTMSNModel, ViTMSNForImageClassification) if is_torch_available() else ()
						lowercase					 =     (
						    {'feature-extraction': ViTMSNModel, 'image-classification': ViTMSNForImageClassification}
						    if is_torch_available()
						    else {}
						)

						lowercase					 =     False
						lowercase					 =     False
						lowercase					 =     False
						lowercase					 =     False

						def        __a		(	self				:      Union[str, Any]   )							->		Optional[int]:


									'''simple docstring'''

									lowercase							=		ViTMSNModelTester(self   )
									lowercase							=		ConfigTester(self							,							config_class=__lowerCamelCase							,							has_text_modality=__lowerCamelCase							,							hidden_size=37   )

						def        __a		(	self				:      str   )							->		Tuple:


									'''simple docstring'''

									self.config_tester.run_common_tests()

						@unittest.skip(reason='''ViTMSN does not use inputs_embeds'''   )
						def        __a		(	self				:      List[Any]   )							->		str:


									'''simple docstring'''

									pass

						def        __a		(	self				:      Union[str, Any]   )							->		Dict:


									'''simple docstring'''

									lowercase		,lowercase							=		self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
												lowercase							=		model_class(__lowerCamelCase   )
												self.assertIsInstance(model.get_input_embeddings()							,							(nn.Module)   )
												lowercase							=		model.get_output_embeddings()
												self.assertTrue(x is None or isinstance(__lowerCamelCase							,							nn.Linear   )   )

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


									'''simple docstring'''

									lowercase		,lowercase							=		self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
												lowercase							=		model_class(__lowerCamelCase   )
												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]							,							__lowerCamelCase   )

						def        __a		(	self				:      Any   )							->		Tuple:


									'''simple docstring'''

									lowercase							=		self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*__lowerCamelCase   )

						def        __a		(	self				:      Tuple   )							->		List[Any]:


									'''simple docstring'''

									lowercase							=		self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_image_classification(*__lowerCamelCase   )



						@slow
						def        __a		(	self				:      int   )							->		Dict:


									'''simple docstring'''

									for model_name in VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
												lowercase							=		ViTMSNModel.from_pretrained(__lowerCamelCase   )
												self.assertIsNotNone(__lowerCamelCase   )






def        __UpperCAmelCase    (       )->       int:



			"""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				:      List[Any]   )							->		str:


									'''simple docstring'''

									return ViTImageProcessor.from_pretrained('''facebook/vit-msn-small'''   ) if is_vision_available() else None



						@slow
						def        __a		(	self				:      List[Any]   )							->		List[str]:


									'''simple docstring'''

									torch.manual_seed(2   )
									lowercase							=		ViTMSNForImageClassification.from_pretrained('''facebook/vit-msn-small'''   ).to(__lowerCamelCase   )

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

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

									# verify the logits
									lowercase							=		torch.Size((1, 10_00)   )
									self.assertEqual(outputs.logits.shape							,							__lowerCamelCase   )

									lowercase							=		torch.tensor([-0.0803, -0.4454, -0.2375]   ).to(__lowerCamelCase   )

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






from dataclasses import dataclass
from typing import Optional, Tuple, Union

import flax
import jax.numpy as jnp
from jax import random

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .scheduling_utils_flax import FlaxSchedulerMixin






@flax.struct.dataclass
class      __lowercase					:
						# setable values
						lowercase					 =     None
						lowercase					 =     None
						lowercase					 =     None  # sigma(t_i)

						@classmethod
						def        __a		(	cls				:      List[str]   )							->		Dict:


									'''simple docstring'''

									return cls()






@dataclass
class      __lowercase					(    _A    ):
						lowercase					 =     42
						lowercase					 =     42
						lowercase					 =     42






class      __lowercase					(    _A       ,     _A    ):

						@property
						def        __a		(	self				:      Union[str, Any]   )							->		Optional[Any]:


									'''simple docstring'''

									return True

						@register_to_config
						def __init__(	self				:      int							,							__lowerCamelCase				:      float = 0.02							,							__lowerCamelCase				:      float = 1_00							,							__lowerCamelCase				:      float = 1.007							,							__lowerCamelCase				:      float = 80							,							__lowerCamelCase				:      float = 0.05							,							__lowerCamelCase				:      float = 50							,							)							->		List[Any]:


									'''simple docstring'''

									pass

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


									'''simple docstring'''

									return KarrasVeSchedulerState.create()

						def        __a		(	self				:      str							,							__lowerCamelCase				:      KarrasVeSchedulerState							,							__lowerCamelCase				:      int							,							__lowerCamelCase				:      Tuple = ()   )							->		KarrasVeSchedulerState:


									'''simple docstring'''

									lowercase							=		jnp.arange(0							,							__lowerCamelCase   )[::-1].copy()
									lowercase							=		[
									    (
									        self.config.sigma_max**2
									        * (self.config.sigma_min**2 / self.config.sigma_max**2) ** (i / (num_inference_steps - 1))
									    )
									    for i in timesteps
									]

									return state.replace(
									    num_inference_steps=__lowerCamelCase							,							schedule=jnp.array(__lowerCamelCase							,							dtype=jnp.floataa   )							,							timesteps=__lowerCamelCase							,							)

						def        __a		(	self				:      Optional[Any]							,							__lowerCamelCase				:      KarrasVeSchedulerState							,							__lowerCamelCase				:      jnp.ndarray							,							__lowerCamelCase				:      float							,							__lowerCamelCase				:      random.KeyArray							,							)							->		Tuple[jnp.ndarray, float]:


									'''simple docstring'''

									if self.config.s_min <= sigma <= self.config.s_max:
												lowercase							=		min(self.config.s_churn / state.num_inference_steps							,							2**0.5 - 1   )
									else:
												lowercase							=		0

									# sample eps ~ N(0, S_noise^2 * I)
									lowercase							=		random.split(__lowerCamelCase							,							num=1   )
									lowercase							=		self.config.s_noise * random.normal(key=__lowerCamelCase							,							shape=sample.shape   )
									lowercase							=		sigma + gamma * sigma
									lowercase							=		sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)

									return sample_hat, sigma_hat

						def        __a		(	self				:      str							,							__lowerCamelCase				:      KarrasVeSchedulerState							,							__lowerCamelCase				:      jnp.ndarray							,							__lowerCamelCase				:      float							,							__lowerCamelCase				:      float							,							__lowerCamelCase				:      jnp.ndarray							,							__lowerCamelCase				:      bool = True							,							)							->		Union[FlaxKarrasVeOutput, Tuple]:


									'''simple docstring'''

									lowercase							=		sample_hat + sigma_hat * model_output
									lowercase							=		(sample_hat - pred_original_sample) / sigma_hat
									lowercase							=		sample_hat + (sigma_prev - sigma_hat) * derivative

									if not return_dict:
												return (sample_prev, derivative, state)

									return FlaxKarrasVeOutput(prev_sample=__lowerCamelCase							,							derivative=__lowerCamelCase							,							state=__lowerCamelCase   )

						def        __a		(	self				:      str							,							__lowerCamelCase				:      KarrasVeSchedulerState							,							__lowerCamelCase				:      jnp.ndarray							,							__lowerCamelCase				:      float							,							__lowerCamelCase				:      float							,							__lowerCamelCase				:      jnp.ndarray							,							__lowerCamelCase				:      jnp.ndarray							,							__lowerCamelCase				:      jnp.ndarray							,							__lowerCamelCase				:      bool = True							,							)							->		Union[FlaxKarrasVeOutput, Tuple]:


									'''simple docstring'''

									lowercase							=		sample_prev + sigma_prev * model_output
									lowercase							=		(sample_prev - pred_original_sample) / sigma_prev
									lowercase							=		sample_hat + (sigma_prev - sigma_hat) * (0.5 * derivative + 0.5 * derivative_corr)

									if not return_dict:
												return (sample_prev, derivative, state)

									return FlaxKarrasVeOutput(prev_sample=__lowerCamelCase							,							derivative=__lowerCamelCase							,							state=__lowerCamelCase   )



						def        __a		(	self				:      int							,							__lowerCamelCase				:      KarrasVeSchedulerState							,							__lowerCamelCase				:      str							,							__lowerCamelCase				:      str							,							__lowerCamelCase				:      Dict   )							->		Tuple:


									'''simple docstring'''

									raise NotImplementedError()





