






"""simple docstring"""


import importlib
import math
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Tuple, Union

import flax
import jax.numpy as jnp

from ..utils import BaseOutput


lowercase_				     =     """scheduler_config.json"""


class 					snake_case		(							UpperCAmelCase__						):


   '''simple docstring'''

   A_						:	List[str]						 = 1
   A_						:	List[Any]						 = 2
   A_						:	Union[str, Any]						 = 3
   A_						:	Optional[int]						 = 4
   A_						:	Optional[Any]						 = 5





@dataclass
class 					snake_case		(							UpperCAmelCase__						):


   '''simple docstring'''

   A_						:	Tuple						 = 42





class 					snake_case		:


   '''simple docstring'''

   A_						:	Any						 = SCHEDULER_CONFIG_NAME
   A_						:	List[Any]						 = ["dtype"]
   A_						:	Optional[int]						 = []
   A_						:	Tuple						 = True


   @classmethod
   def 				_SCREAMING_SNAKE_CASE				(	cls :     Optional[Any],       _lowerCamelCase :     Optional[int] = None,       _lowerCamelCase :     int = None,       _lowerCamelCase :     Optional[int]=False,       **_lowerCamelCase :     Union[str, Any],       ):

     '''simple docstring'''
     __A			,    __A									= cls.load_config(
         pretrained_model_name_or_path=_a,       subfolder=_a,       return_unused_kwargs=_a,       **_a,       )
     __A			,    __A									= cls.from_config(_a,       return_unused_kwargs=_a,       **_a  )

     if hasattr(_a,       '''create_state'''  ) and getattr(_a,       '''has_state''',       _a  ):
       __A									= scheduler.create_state()

     if return_unused_kwargs:
       return scheduler, state, unused_kwargs

     return scheduler, state


   def 				_SCREAMING_SNAKE_CASE				(	self :     Tuple,       _lowerCamelCase :     Any,       _lowerCamelCase :     int = False,       **_lowerCamelCase :     List[Any]  ):

     '''simple docstring'''
     self.save_config(save_directory=_a,       push_to_hub=_a,       **_a  )


   @property
   def 				_SCREAMING_SNAKE_CASE				(	self :     List[str]  ):

     '''simple docstring'''
     return self._get_compatibles()






   @classmethod
   def 				_SCREAMING_SNAKE_CASE				(	cls :     Optional[Any]  ):

     '''simple docstring'''
     __A									= list(set([cls.__name__] + cls._compatibles  )  )
     __A									= importlib.import_module(__name__.split('''.'''  )[0]  )
     __A									= [
         getattr(_a,       _a  ) for c in compatible_classes_str if hasattr(_a,       _a  )
     ]
     return compatible_classes





def 					lowerCAmelCase				(    __UpperCamelCase		,       __UpperCamelCase ):




  """simple docstring"""







  assert len(snake_case__ ) >= x.ndim
  return jnp.broadcast_to(x.reshape(x.shape + (1,) * (len(snake_case__ ) - x.ndim) )		,       snake_case__ )



def 					lowerCAmelCase				(    __UpperCamelCase		,       __UpperCamelCase=0.999		,       __UpperCamelCase=jnp.floataa ):




  """simple docstring"""







  def alpha_bar(__UpperCamelCase ):
    return math.cos((time_step + 0.008) / 1.008 * math.pi / 2 ) ** 2

  __A									= []
  for i in range(snake_case__ ):
    __A									= i / num_diffusion_timesteps
    __A									= (i + 1) / num_diffusion_timesteps
    betas.append(min(1 - alpha_bar(snake_case__ ) / alpha_bar(snake_case__ )		,       snake_case__ ) )
  return jnp.array(snake_case__		,       dtype=snake_case__ )


@flax.struct.dataclass
class 					snake_case		:


   '''simple docstring'''

   A_						:	Any						 = 42
   A_						:	Any						 = 42
   A_						:	Tuple						 = 42


   @classmethod
   def 				_SCREAMING_SNAKE_CASE				(	cls :     Optional[int],       _lowerCamelCase :     Optional[Any]  ):

     '''simple docstring'''
     __A									= scheduler.config

     if config.trained_betas is not None:
       __A									= jnp.asarray(config.trained_betas,       dtype=scheduler.dtype  )
     elif config.beta_schedule == "linear":
       __A									= jnp.linspace(config.beta_start,       config.beta_end,       config.num_train_timesteps,       dtype=scheduler.dtype  )
     elif config.beta_schedule == "scaled_linear":
       # this schedule is very specific to the latent diffusion model.
       __A									= (
           jnp.linspace(
               config.beta_start**0.5,       config.beta_end**0.5,       config.num_train_timesteps,       dtype=scheduler.dtype  )
           ** 2
       )
     elif config.beta_schedule == "squaredcos_cap_v2":
       # Glide cosine schedule
       __A									= betas_for_alpha_bar(config.num_train_timesteps,       dtype=scheduler.dtype  )
     else:
       raise NotImplementedError(
           f'beta_schedule {config.beta_schedule} is not implemented for scheduler {scheduler.__class__.__name__}'  )

     __A									= 1.0 - betas

     __A									= jnp.cumprod(_a,       axis=0  )

     return cls(
         alphas=_a,       betas=_a,       alphas_cumprod=_a,       )





def 					lowerCAmelCase				(    __UpperCamelCase		,       __UpperCamelCase		,       __UpperCamelCase		,       __UpperCamelCase ):




  """simple docstring"""







  __A									= state.alphas_cumprod

  __A									= alphas_cumprod[timesteps] ** 0.5
  __A									= sqrt_alpha_prod.flatten()
  __A									= broadcast_to_shape_from_left(snake_case__		,       original_samples.shape )

  __A									= (1 - alphas_cumprod[timesteps]) ** 0.5
  __A									= sqrt_one_minus_alpha_prod.flatten()
  __A									= broadcast_to_shape_from_left(snake_case__		,       original_samples.shape )

  return sqrt_alpha_prod, sqrt_one_minus_alpha_prod



def 					lowerCAmelCase				(    __UpperCamelCase		,       __UpperCamelCase		,       __UpperCamelCase		,       __UpperCamelCase ):




  """simple docstring"""







  __A			,    __A									= get_sqrt_alpha_prod(snake_case__		,       snake_case__		,       snake_case__		,       snake_case__ )
  __A									= sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
  return noisy_samples





def 					lowerCAmelCase				(    __UpperCamelCase		,       __UpperCamelCase		,       __UpperCamelCase		,       __UpperCamelCase ):




  """simple docstring"""







  __A			,    __A									= get_sqrt_alpha_prod(snake_case__		,       snake_case__		,       snake_case__		,       snake_case__ )
  __A									= sqrt_alpha_prod * noise - sqrt_one_minus_alpha_prod * sample
  return velocity










"""simple docstring"""




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     __magic_name__							:






				'''simple docstring'''





				def __init__(       self		, _a		, _a=13		, _a=30		, _a=2		, _a=3		, _a=True		, _a=True		, _a=32		, _a=5		, _a=4		, _a=37		, _a="gelu"		, _a=0.1		, _a=0.1		, _a=10		, _a=0.02		, _a=None		, ):





							"""simple docstring"""
							lowerCamelCase			      =					parent
							lowerCamelCase			      =					batch_size
							lowerCamelCase			      =					image_size
							lowerCamelCase			      =					patch_size
							lowerCamelCase			      =					num_channels
							lowerCamelCase			      =					is_training
							lowerCamelCase			      =					use_labels
							lowerCamelCase			      =					hidden_size
							lowerCamelCase			      =					num_hidden_layers
							lowerCamelCase			      =					num_attention_heads
							lowerCamelCase			      =					intermediate_size
							lowerCamelCase			      =					hidden_act
							lowerCamelCase			      =					hidden_dropout_prob
							lowerCamelCase			      =					attention_probs_dropout_prob
							lowerCamelCase			      =					type_sequence_label_size
							lowerCamelCase			      =					initializer_range
							lowerCamelCase			      =					scope

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





				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							lowerCamelCase			      =					floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )

							lowerCamelCase			      =					None
							if self.use_labels:
										lowerCamelCase			      =					ids_tensor([self.batch_size]		, self.type_sequence_label_size )

							lowerCamelCase			      =					self.get_config()

							return config, pixel_values, labels





				def    _lowerCAmelCase				(       self ):





							"""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    _lowerCAmelCase				(       self		, _a		, _a		, _a ):





							"""simple docstring"""
							lowerCamelCase			      =					ViTMSNModel(config=_a )
							model.to(_a )
							model.eval()
							lowerCamelCase			      =					model(_a )
							self.parent.assertEqual(result.last_hidden_state.shape		, (self.batch_size, self.seq_length, self.hidden_size) )





				def    _lowerCAmelCase				(       self		, _a		, _a		, _a ):





							"""simple docstring"""
							lowerCamelCase			      =					self.type_sequence_label_size
							lowerCamelCase			      =					ViTMSNForImageClassification(_a )
							model.to(_a )
							model.eval()
							lowerCamelCase			      =					model(_a		, labels=_a )
							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
							lowerCamelCase			      =					1
							lowerCamelCase			      =					ViTMSNForImageClassification(_a )
							model.to(_a )
							model.eval()

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





				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							lowerCamelCase			      =					self.prepare_config_and_inputs()
							lowerCamelCase      ,   lowerCamelCase      ,   lowerCamelCase			      =					config_and_inputs
							lowerCamelCase			      =					{"""pixel_values""": pixel_values}
							return config, inputs_dict






@require_torch
class     __magic_name__							(       UpperCAmelCase__						,					UpperCAmelCase__						,					unittest.TestCase				):






				'''simple docstring'''
				__UpperCamelCase			  =						(ViTMSNModel, ViTMSNForImageClassification) if is_torch_available() else ()
				__UpperCamelCase			  =						(
				    {"feature-extraction": ViTMSNModel, "image-classification": ViTMSNForImageClassification}
				    if is_torch_available()
				    else {}
				)

				__UpperCamelCase			  =						False
				__UpperCamelCase			  =						False
				__UpperCamelCase			  =						False
				__UpperCamelCase			  =						False





				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							lowerCamelCase			      =					ViTMSNModelTester(self )
							lowerCamelCase			      =					ConfigTester(self		, config_class=_a		, has_text_modality=_a		, hidden_size=37 )





				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							self.config_tester.run_common_tests()





				@unittest.skip(reason="""ViTMSN does not use inputs_embeds""" )
				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							pass





				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							lowerCamelCase      ,   lowerCamelCase			      =					self.model_tester.prepare_config_and_inputs_for_common()

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





				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							lowerCamelCase      ,   lowerCamelCase			      =					self.model_tester.prepare_config_and_inputs_for_common()

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

										lowerCamelCase			      =					["""pixel_values"""]
										self.assertListEqual(arg_names[:1]		, _a )





				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							lowerCamelCase			      =					self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*_a )





				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							lowerCamelCase			      =					self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_image_classification(*_a )





				@slow
				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							for model_name in VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
										lowerCamelCase			      =					ViTMSNModel.from_pretrained(_a )
										self.assertIsNotNone(_a )




def     a__ (				)						->					Any:
			lowerCamelCase			      =					Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""				)
			return image






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






				'''simple docstring'''





				@cached_property
				def    _lowerCAmelCase				(       self ):





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





				@slow
				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							torch.manual_seed(2 )
							lowerCamelCase			      =					ViTMSNForImageClassification.from_pretrained("""facebook/vit-msn-small""" ).to(_a )

							lowerCamelCase			      =					self.default_image_processor
							lowerCamelCase			      =					prepare_img()
							lowerCamelCase			      =					image_processor(images=_a		, return_tensors="""pt""" ).to(_a )

							# forward pass
							with torch.no_grad():
										lowerCamelCase			      =					model(**_a )

							# verify the logits
							lowerCamelCase			      =					torch.Size((1, 1_000) )
							self.assertEqual(outputs.logits.shape		, _a )

							lowerCamelCase			      =					torch.tensor([-0.0_803, -0.4_454, -0.2_375] ).to(_a )

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



