
'''simple docstring'''

import inspect
import unittest

from transformers import MobileNetVaConfig
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 transformers import MobileNetVaForImageClassification, MobileNetVaModel
				from transformers.models.mobilenet_va.modeling_mobilenet_va import MOBILENET_V1_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
				from PIL import Image

				from transformers import MobileNetVaImageProcessor


class  _UpperCAmelCase ( lowerCAmelCase__    ):

					"""simple docstring"""

					def 							_lowerCAmelCase  (      self ):

							'''simple docstring'''

							a_      :	Any      =							self.config_class(**self.inputs_dict )
							self.parent.assertTrue(hasattr(lowerCAmelCase_							,		"""tf_padding""" ) )
							self.parent.assertTrue(hasattr(lowerCAmelCase_							,		"""depth_multiplier""" ) )







class  _UpperCAmelCase :

					"""simple docstring"""

					def __init__(      self							,		lowerCAmelCase_							,		lowerCAmelCase_=13							,		lowerCAmelCase_=3							,		lowerCAmelCase_=32							,		lowerCAmelCase_=0.25							,		lowerCAmelCase_=8							,		lowerCAmelCase_=True							,		lowerCAmelCase_=10_24							,		lowerCAmelCase_=32							,		lowerCAmelCase_="relu6"							,		lowerCAmelCase_=0.1							,		lowerCAmelCase_=0.02							,		lowerCAmelCase_=True							,		lowerCAmelCase_=True							,		lowerCAmelCase_=10							,		lowerCAmelCase_=None							,		):

							'''simple docstring'''

							a_      :	List[Any]      =							parent
							a_      :	Optional[int]      =							batch_size
							a_      :	Tuple      =							num_channels
							a_      :	List[str]      =							image_size
							a_      :	Any      =							depth_multiplier
							a_      :	Tuple      =							min_depth
							a_      :	Dict      =							tf_padding
							a_      :	Optional[int]      =							int(last_hidden_size * depth_multiplier )
							a_      :	Tuple      =							output_stride
							a_      :	Union[str, Any]      =							hidden_act
							a_      :	List[str]      =							classifier_dropout_prob
							a_      :	str      =							use_labels
							a_      :	Tuple      =							is_training
							a_      :	Optional[int]      =							num_labels
							a_      :	Dict      =							initializer_range
							a_      :	Union[str, Any]      =							scope


					def 							_lowerCAmelCase  (      self ):

							'''simple docstring'''

							a_      :	str      =							floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )

							a_      :	Dict      =							None
							a_      :	str      =							None
							if self.use_labels:
									a_      :	List[Any]      =							ids_tensor([self.batch_size]							,		self.num_labels )
									a_      :	Optional[int]      =							ids_tensor([self.batch_size, self.image_size, self.image_size]							,		self.num_labels )

							a_      :	Union[str, Any]      =							self.get_config()

							return config, pixel_values, labels, pixel_labels


					def 							_lowerCAmelCase  (      self ):

							'''simple docstring'''

							return MobileNetVaConfig(
							    num_channels=self.num_channels							,		image_size=self.image_size							,		depth_multiplier=self.depth_multiplier							,		min_depth=self.min_depth							,		tf_padding=self.tf_padding							,		hidden_act=self.hidden_act							,		classifier_dropout_prob=self.classifier_dropout_prob							,		initializer_range=self.initializer_range							,		)


					def 							_lowerCAmelCase  (      self							,		lowerCAmelCase_							,		lowerCAmelCase_							,		lowerCAmelCase_							,		lowerCAmelCase_ ):

							'''simple docstring'''

							a_      :	Dict      =							MobileNetVaModel(config=lowerCAmelCase_ )
							model.to(lowerCAmelCase_ )
							model.eval()
							a_      :	Union[str, Any]      =							model(lowerCAmelCase_ )
							self.parent.assertEqual(
							    result.last_hidden_state.shape							,		(
							        self.batch_size,
							        self.last_hidden_size,
							        self.image_size // self.output_stride,
							        self.image_size // self.output_stride,
							    )							,		)


					def 							_lowerCAmelCase  (      self							,		lowerCAmelCase_							,		lowerCAmelCase_							,		lowerCAmelCase_							,		lowerCAmelCase_ ):

							'''simple docstring'''

							a_      :	List[Any]      =							self.num_labels
							a_      :	Any      =							MobileNetVaForImageClassification(lowerCAmelCase_ )
							model.to(lowerCAmelCase_ )
							model.eval()
							a_      :	int      =							model(lowerCAmelCase_							,		labels=lowerCAmelCase_ )
							self.parent.assertEqual(result.logits.shape							,		(self.batch_size, self.num_labels) )






					def 							_lowerCAmelCase  (      self ):

							'''simple docstring'''

							a_      :	List[Any]      =							self.prepare_config_and_inputs()
							a_      :	Dict      =							config_and_inputs
							a_      :	int      =							{"""pixel_values""": pixel_values}
							return config, inputs_dict







@require_torch
class  _UpperCAmelCase ( lowerCAmelCase__	,lowerCAmelCase__	,unittest.TestCase    ):

					"""simple docstring"""

					a_    	=					(MobileNetVaModel, MobileNetVaForImageClassification) if is_torch_available() else ()
					a_    	=					(
					    {"feature-extraction": MobileNetVaModel, "image-classification": MobileNetVaForImageClassification}
					    if is_torch_available()
					    else {}
					)

					a_    	=					False
					a_    	=					False
					a_    	=					False
					a_    	=					False


					def 							_lowerCAmelCase  (      self ):

							'''simple docstring'''

							a_      :	Any      =							MobileNetVaModelTester(self )
							a_      :	Optional[Any]      =							MobileNetVaConfigTester(self							,		config_class=lowerCAmelCase_							,		has_text_modality=lowerCAmelCase_ )


					def 							_lowerCAmelCase  (      self ):

							'''simple docstring'''

							self.config_tester.run_common_tests()


					@unittest.skip(reason="""MobileNetV1 does not use inputs_embeds""" )
					def 							_lowerCAmelCase  (      self ):

							'''simple docstring'''

							pass


					@unittest.skip(reason="""MobileNetV1 does not support input and output embeddings""" )
					def 							_lowerCAmelCase  (      self ):

							'''simple docstring'''

							pass


					@unittest.skip(reason="""MobileNetV1 does not output attentions""" )
					def 							_lowerCAmelCase  (      self ):

							'''simple docstring'''

							pass


					def 							_lowerCAmelCase  (      self ):

							'''simple docstring'''

							a_      :	Tuple      =							self.model_tester.prepare_config_and_inputs_for_common()

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

									a_      :	List[Any]      =							["""pixel_values"""]
									self.assertListEqual(arg_names[:1]							,		lowerCAmelCase_ )


					def 							_lowerCAmelCase  (      self ):

							'''simple docstring'''

							a_      :	Any      =							self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*lowerCAmelCase_ )


					def 							_lowerCAmelCase  (      self ):

							'''simple docstring'''

							def check_hidden_states_output(lowerCAmelCase_							,		lowerCAmelCase_							,		lowerCAmelCase_ ):
									a_      :	int      =							model_class(lowerCAmelCase_ )
									model.to(lowerCAmelCase_ )
									model.eval()

									with torch.no_grad():
											a_      :	Dict      =							model(**self._prepare_for_class(lowerCAmelCase_							,		lowerCAmelCase_ ) )

									a_      :	List[Any]      =							outputs.hidden_states

									a_      :	List[str]      =							26
									self.assertEqual(len(lowerCAmelCase_ )							,		lowerCAmelCase_ )

							a_      :	Optional[Any]      =							self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
									a_      :	str      =							True
									check_hidden_states_output(lowerCAmelCase_							,		lowerCAmelCase_							,		lowerCAmelCase_ )

									# check that output_hidden_states also work using config
									del inputs_dict["output_hidden_states"]
									a_      :	Tuple      =							True

									check_hidden_states_output(lowerCAmelCase_							,		lowerCAmelCase_							,		lowerCAmelCase_ )


					def 							_lowerCAmelCase  (      self ):

							'''simple docstring'''

							a_      :	Tuple      =							self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_image_classification(*lowerCAmelCase_ )






					@slow
					def 							_lowerCAmelCase  (      self ):

							'''simple docstring'''

							for model_name in MOBILENET_V1_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									a_      :	List[str]      =							MobileNetVaModel.from_pretrained(lowerCAmelCase_ )
									self.assertIsNotNone(lowerCAmelCase_ )







def 	_snake_case						(      ):
		"""simple docstring"""







		a_      :	str      =							Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""   )
		return image




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

					"""simple docstring"""

					@cached_property
					def 							_lowerCAmelCase  (      self ):

							'''simple docstring'''

							return (
							    MobileNetVaImageProcessor.from_pretrained("""google/mobilenet_v1_1.0_224""" ) if is_vision_available() else None
							)






					@slow
					def 							_lowerCAmelCase  (      self ):

							'''simple docstring'''

							a_      :	int      =							MobileNetVaForImageClassification.from_pretrained("""google/mobilenet_v1_1.0_224""" ).to(lowerCAmelCase_ )

							a_      :	Any      =							self.default_image_processor
							a_      :	Optional[Any]      =							prepare_img()
							a_      :	List[Any]      =							image_processor(images=lowerCAmelCase_							,		return_tensors="""pt""" ).to(lowerCAmelCase_ )

							# forward pass
							with torch.no_grad():
									a_      :	Optional[int]      =							model(**lowerCAmelCase_ )

							# verify the logits
							a_      :	Any      =							torch.Size((1, 10_01) )
							self.assertEqual(outputs.logits.shape							,		lowerCAmelCase_ )

							a_      :	str      =							torch.tensor([-4.1739, -1.1233, 3.1205] ).to(lowerCAmelCase_ )

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




'''simple docstring'''

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

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin


@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->UnCLIP
class  _UpperCAmelCase ( lowerCAmelCase__    ):

     """simple docstring"""

     a_    	=					42
     a_    	=					None







def 	_snake_case						(      A_       :    int     ,		A_       :    List[str]=0.999     ,		A_       :    List[Any]="cosine"     ,		):
  """simple docstring"""







  if alpha_transform_type == "cosine":

    def alpha_bar_fn(A_       :    List[Any]   ):
      return math.cos((t + 0.008) / 1.008 * math.pi / 2   ) ** 2

  elif alpha_transform_type == "exp":

    def alpha_bar_fn(A_       :    List[str]   ):
      return math.exp(t * -12.0   )

  else:
    raise ValueError(f'''Unsupported alpha_tranform_type: {alpha_transform_type}'''   )

  a_      :	int      =							[]
  for i in range(A_   ):
    a_      :	Optional[Any]      =							i / num_diffusion_timesteps
    a_      :	List[Any]      =							(i + 1) / num_diffusion_timesteps
    betas.append(min(1 - alpha_bar_fn(A_   ) / alpha_bar_fn(A_   )     ,		A_   )   )
  return torch.tensor(A_     ,		dtype=torch.floataa   )




class  _UpperCAmelCase ( lowerCAmelCase__	,lowerCAmelCase__    ):

     """simple docstring"""

     @register_to_config
     def __init__(      self							,		lowerCAmelCase_ = 10_00							,		lowerCAmelCase_ = "fixed_small_log"							,		lowerCAmelCase_ = True							,		lowerCAmelCase_ = 1.0							,		lowerCAmelCase_ = "epsilon"							,		lowerCAmelCase_ = "squaredcos_cap_v2"							,		):

       '''simple docstring'''

       if beta_schedule != "squaredcos_cap_v2":
         raise ValueError("""UnCLIPScheduler only supports `beta_schedule`: 'squaredcos_cap_v2'""" )

       a_      :	Optional[Any]      =							betas_for_alpha_bar(lowerCAmelCase_ )

       a_      :	Optional[Any]      =							1.0 - self.betas
       a_      :	List[str]      =							torch.cumprod(self.alphas							,		dim=0 )
       a_      :	Any      =							torch.tensor(1.0 )

       # standard deviation of the initial noise distribution
       a_      :	Any      =							1.0

       # setable values
       a_      :	List[Any]      =							None
       a_      :	Any      =							torch.from_numpy(np.arange(0							,		lowerCAmelCase_ )[::-1].copy() )

       a_      :	Any      =							variance_type


     def 							_lowerCAmelCase  (      self							,		lowerCAmelCase_							,		lowerCAmelCase_ = None ):

       '''simple docstring'''

       return sample


     def 							_lowerCAmelCase  (      self							,		lowerCAmelCase_							,		lowerCAmelCase_ = None ):

       '''simple docstring'''

       a_      :	List[str]      =							num_inference_steps
       a_      :	Dict      =							(self.config.num_train_timesteps - 1) / (self.num_inference_steps - 1)
       a_      :	Optional[Any]      =							(np.arange(0							,		lowerCAmelCase_ ) * step_ratio).round()[::-1].copy().astype(np.intaa )
       a_      :	List[Any]      =							torch.from_numpy(lowerCAmelCase_ ).to(lowerCAmelCase_ )


     def 							_lowerCAmelCase  (      self							,		lowerCAmelCase_							,		lowerCAmelCase_=None							,		lowerCAmelCase_=None							,		lowerCAmelCase_=None ):

       '''simple docstring'''

       if prev_timestep is None:
         a_      :	Tuple      =							t - 1

       a_      :	Optional[Any]      =							self.alphas_cumprod[t]
       a_      :	Union[str, Any]      =							self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
       a_      :	Optional[Any]      =							1 - alpha_prod_t
       a_      :	str      =							1 - alpha_prod_t_prev

       if prev_timestep == t - 1:
         a_      :	int      =							self.betas[t]
       else:
         a_      :	Tuple      =							1 - alpha_prod_t / alpha_prod_t_prev

       # For t > 0, compute predicted variance βt (see formula (6) and (7) from https://arxiv.org/pdf/2006.11239.pdf)
       # and sample from it to get previous sample
       # x_{t-1} ~ N(pred_prev_sample, variance) == add variance to pred_sample
       a_      :	str      =							beta_prod_t_prev / beta_prod_t * beta

       if variance_type is None:
         a_      :	int      =							self.config.variance_type

       # hacks - were probably added for training stability
       if variance_type == "fixed_small_log":
         a_      :	List[Any]      =							torch.log(torch.clamp(lowerCAmelCase_							,		min=1E-2_0 ) )
         a_      :	List[Any]      =							torch.exp(0.5 * variance )
       elif variance_type == "learned_range":
         # NOTE difference with DDPM scheduler
         a_      :	Tuple      =							variance.log()
         a_      :	int      =							beta.log()

         a_      :	List[Any]      =							(predicted_variance + 1) / 2
         a_      :	Dict      =							frac * max_log + (1 - frac) * min_log

       return variance


     def 							_lowerCAmelCase  (      self							,		lowerCAmelCase_							,		lowerCAmelCase_							,		lowerCAmelCase_							,		lowerCAmelCase_ = None							,		lowerCAmelCase_=None							,		lowerCAmelCase_ = True							,		):

       '''simple docstring'''

       a_      :	Tuple      =							timestep

       if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type == "learned_range":
         a_      , a_      :	Optional[Any]      =							torch.split(lowerCAmelCase_							,		sample.shape[1]							,		dim=1 )
       else:
         a_      :	Optional[int]      =							None

       # 1. compute alphas, betas
       if prev_timestep is None:
         a_      :	List[Any]      =							t - 1

       a_      :	int      =							self.alphas_cumprod[t]
       a_      :	List[str]      =							self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
       a_      :	Any      =							1 - alpha_prod_t
       a_      :	Any      =							1 - alpha_prod_t_prev

       if prev_timestep == t - 1:
         a_      :	Optional[int]      =							self.betas[t]
         a_      :	Any      =							self.alphas[t]
       else:
         a_      :	List[Any]      =							1 - alpha_prod_t / alpha_prod_t_prev
         a_      :	List[str]      =							1 - beta

       # 2. compute predicted original sample from predicted noise also called
       # "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
       if self.config.prediction_type == "epsilon":
         a_      :	List[str]      =							(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
       elif self.config.prediction_type == "sample":
         a_      :	Optional[int]      =							model_output
       else:
         raise ValueError(
             f'''prediction_type given as {self.config.prediction_type} must be one of `epsilon` or `sample`'''
             """ for the UnCLIPScheduler.""" )

       # 3. Clip "predicted x_0"
       if self.config.clip_sample:
         a_      :	Any      =							torch.clamp(
             lowerCAmelCase_							,		-self.config.clip_sample_range							,		self.config.clip_sample_range )

       # 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
       # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
       a_      :	str      =							(alpha_prod_t_prev ** 0.5 * beta) / beta_prod_t
       a_      :	Dict      =							alpha ** 0.5 * beta_prod_t_prev / beta_prod_t

       # 5. Compute predicted previous sample µ_t
       # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
       a_      :	Any      =							pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

       # 6. Add noise
       a_      :	List[Any]      =							0
       if t > 0:
         a_      :	List[Any]      =							randn_tensor(
             model_output.shape							,		dtype=model_output.dtype							,		generator=lowerCAmelCase_							,		device=model_output.device )

         a_      :	List[Any]      =							self._get_variance(
             lowerCAmelCase_							,		predicted_variance=lowerCAmelCase_							,		prev_timestep=lowerCAmelCase_							,		)

         if self.variance_type == "fixed_small_log":
           a_      :	Dict      =							variance
         elif self.variance_type == "learned_range":
           a_      :	Any      =							(0.5 * variance).exp()
         else:
           raise ValueError(
               f'''variance_type given as {self.variance_type} must be one of `fixed_small_log` or `learned_range`'''
               """ for the UnCLIPScheduler.""" )

         a_      :	List[str]      =							variance * variance_noise

       a_      :	Optional[Any]      =							pred_prev_sample + variance

       if not return_dict:
         return (pred_prev_sample,)

       return UnCLIPSchedulerOutput(prev_sample=lowerCAmelCase_							,		pred_original_sample=lowerCAmelCase_ )






     def 							_lowerCAmelCase  (      self							,		lowerCAmelCase_							,		lowerCAmelCase_							,		lowerCAmelCase_							,		):

       '''simple docstring'''

       a_      :	List[str]      =							self.alphas_cumprod.to(device=original_samples.device							,		dtype=original_samples.dtype )
       a_      :	Union[str, Any]      =							timesteps.to(original_samples.device )

       a_      :	List[Any]      =							alphas_cumprod[timesteps] ** 0.5
       a_      :	Optional[Any]      =							sqrt_alpha_prod.flatten()
       while len(sqrt_alpha_prod.shape ) < len(original_samples.shape ):
         a_      :	int      =							sqrt_alpha_prod.unsqueeze(-1 )

       a_      :	Optional[Any]      =							(1 - alphas_cumprod[timesteps]) ** 0.5
       a_      :	str      =							sqrt_one_minus_alpha_prod.flatten()
       while len(sqrt_one_minus_alpha_prod.shape ) < len(original_samples.shape ):
         a_      :	str      =							sqrt_one_minus_alpha_prod.unsqueeze(-1 )

       a_      :	int      =							sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
       return noisy_samples


