





'''simple docstring'''







import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput

def    __UpperCamelCase	(	a    : Any					,   a    : Any=0.999					,   a    : Any="cosine"					,   ) ->List[str]:
 if alpha_transform_type == "cosine":

  def alpha_bar_fn(a    : Union[str, Any]     ):
   return math.cos((t + 0.008) / 1.008 * math.pi / 2     ) ** 2

 elif alpha_transform_type == "exp":

  def alpha_bar_fn(a    : Tuple     ):
   return math.exp(t * -12.0     )

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

 snake_case				=			[]
 for i in range(a     ):
  snake_case				=			i / num_diffusion_timesteps
  snake_case				=			(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 			_lowercase						(       __a				,		__a					):
     _UpperCAmelCase										=					[e.name for e in KarrasDiffusionSchedulers]
     _UpperCAmelCase										=					2







     @register_to_config
     def __init__( self    ,   A__ = 10_00    ,   A__ = 0.0_0_0_8_5    ,   A__ = 0.0_1_2    ,   A__ = "linear"    ,   A__ = None    ,   A__ = "epsilon"    ,   A__ = "linspace"    ,   A__ = 0    ,   ) ->	List[Any]:
      if trained_betas is not None:
       snake_case				=			torch.tensor(A__    ,   dtype=torch.floataa    )
      elif beta_schedule == "linear":
       snake_case				=			torch.linspace(A__    ,   A__    ,   A__    ,   dtype=torch.floataa    )
      elif beta_schedule == "scaled_linear":
       # this schedule is very specific to the latent diffusion model.
       snake_case				=			(
           torch.linspace(beta_start**0.5    ,   beta_end**0.5    ,   A__    ,   dtype=torch.floataa    ) ** 2
       )
      elif beta_schedule == "squaredcos_cap_v2":
       # Glide cosine schedule
       snake_case				=			betas_for_alpha_bar(A__    )
      else:
       raise NotImplementedError(F"""{beta_schedule} does is not implemented for {self.__class__}"""    )

      snake_case				=			1.0 - self.betas
      snake_case				=			torch.cumprod(self.alphas    ,   dim=0    )

      #  set all values
      self.set_timesteps(A__    ,   A__    ,   A__    )







     def     UpperCamelCase			( self    ,   A__    ,   A__=None    ) ->	List[str]:
      if schedule_timesteps is None:
       snake_case				=			self.timesteps

      snake_case				=			(schedule_timesteps == timestep).nonzero()

      # The sigma index that is taken for the **very** first `step`
      # is always the second index (or the last index if there is only 1)
      # This way we can ensure we don't accidentally skip a sigma in
      # case we start in the middle of the denoising schedule (e.g. for image-to-image)
      if len(self._index_counter    ) == 0:
       snake_case				=			1 if len(A__    ) > 1 else 0
      else:
       snake_case				=			timestep.cpu().item() if torch.is_tensor(A__    ) else timestep
       snake_case				=			self._index_counter[timestep_int]

      return indices[pos].item()







     @property
     def     UpperCamelCase			( self    ) ->	List[str]:
      # standard deviation of the initial noise distribution
      if self.config.timestep_spacing in ["linspace", "trailing"]:
       return self.sigmas.max()

      return (self.sigmas.max() ** 2 + 1) ** 0.5







     def     UpperCamelCase			( self    ,   A__    ,   A__    ,   ) ->	torch.FloatTensor:
      snake_case				=			self.index_for_timestep(A__    )

      if self.state_in_first_order:
       snake_case				=			self.sigmas[step_index]
      else:
       snake_case				=			self.sigmas_interpol[step_index]

      snake_case				=			sample / ((sigma**2 + 1) ** 0.5)
      return sample







     def     UpperCamelCase			( self    ,   A__    ,   A__ = None    ,   A__ = None    ,   ) ->	Any:
      snake_case				=			num_inference_steps

      snake_case				=			num_train_timesteps or self.config.num_train_timesteps

      # "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
      if self.config.timestep_spacing == "linspace":
       snake_case				=			np.linspace(0    ,   num_train_timesteps - 1    ,   A__    ,   dtype=A__    )[::-1].copy()
      elif self.config.timestep_spacing == "leading":
       snake_case				=			num_train_timesteps // self.num_inference_steps
       # creates integer timesteps by multiplying by ratio
       # casting to int to avoid issues when num_inference_step is power of 3
       snake_case				=			(np.arange(0    ,   A__    ) * step_ratio).round()[::-1].copy().astype(A__    )
       timesteps += self.config.steps_offset
      elif self.config.timestep_spacing == "trailing":
       snake_case				=			num_train_timesteps / self.num_inference_steps
       # creates integer timesteps by multiplying by ratio
       # casting to int to avoid issues when num_inference_step is power of 3
       snake_case				=			(np.arange(A__    ,   0    ,   -step_ratio    )).round().copy().astype(A__    )
       timesteps -= 1
      else:
       raise ValueError(
           F"""{self.config.timestep_spacing} is not supported. Please make sure to choose one of 'linspace', 'leading' or 'trailing'."""    )

      snake_case				=			np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5    )
      snake_case				=			torch.from_numpy(np.log(A__    )    ).to(A__    )

      snake_case				=			np.interp(A__    ,   np.arange(0    ,   len(A__    )    )    ,   A__    )
      snake_case				=			np.concatenate([sigmas, [0.0]]    ).astype(np.floataa    )
      snake_case				=			torch.from_numpy(A__    ).to(device=A__    )

      # interpolate sigmas
      snake_case				=			sigmas.log().lerp(sigmas.roll(1    ).log()    ,   0.5    ).exp()

      snake_case				=			torch.cat([sigmas[:1], sigmas[1:].repeat_interleave(2    ), sigmas[-1:]]    )
      snake_case				=			torch.cat(
          [sigmas_interpol[:1], sigmas_interpol[1:].repeat_interleave(2    ), sigmas_interpol[-1:]]    )

      if str(A__    ).startswith('''mps'''    ):
       # mps does not support float64
       snake_case				=			torch.from_numpy(A__    ).to(A__    ,   dtype=torch.floataa    )
      else:
       snake_case				=			torch.from_numpy(A__    ).to(A__    )

      # interpolate timesteps
      snake_case				=			self.sigma_to_t(A__    ).to(A__    ,   dtype=timesteps.dtype    )
      snake_case				=			torch.stack((timesteps_interpol[1:-1, None], timesteps[1:, None])    ,   dim=-1    ).flatten()

      snake_case				=			torch.cat([timesteps[:1], interleaved_timesteps]    )

      snake_case				=			None

      # for exp beta schedules, such as the one for `pipeline_shap_e.py`
      # we need an index counter
      snake_case				=			defaultdict(A__    )







     def     UpperCamelCase			( self    ,   A__    ) ->	int:
      # get log sigma
      snake_case				=			sigma.log()

      # get distribution
      snake_case				=			log_sigma - self.log_sigmas[:, None]

      # get sigmas range
      snake_case				=			dists.ge(0    ).cumsum(dim=0    ).argmax(dim=0    ).clamp(max=self.log_sigmas.shape[0] - 2    )
      snake_case				=			low_idx + 1

      snake_case				=			self.log_sigmas[low_idx]
      snake_case				=			self.log_sigmas[high_idx]

      # interpolate sigmas
      snake_case				=			(low - log_sigma) / (low - high)
      snake_case				=			w.clamp(0    ,   1    )

      # transform interpolation to time range
      snake_case				=			(1 - w) * low_idx + w * high_idx
      snake_case				=			t.view(sigma.shape    )
      return t







     @property
     def     UpperCamelCase			( self    ) ->	List[str]:
      return self.sample is None







     def     UpperCamelCase			( self    ,   A__    ,   A__    ,   A__    ,   A__ = True    ,   ) ->	Union[SchedulerOutput, Tuple]:
      snake_case				=			self.index_for_timestep(A__    )

      # advance index counter by 1
      snake_case				=			timestep.cpu().item() if torch.is_tensor(A__    ) else timestep
      self._index_counter[timestep_int] += 1

      if self.state_in_first_order:
       snake_case				=			self.sigmas[step_index]
       snake_case				=			self.sigmas_interpol[step_index + 1]
       snake_case				=			self.sigmas[step_index + 1]
      else:
       # 2nd order / KDPM2's method
       snake_case				=			self.sigmas[step_index - 1]
       snake_case				=			self.sigmas_interpol[step_index]
       snake_case				=			self.sigmas[step_index]

      # currently only gamma=0 is supported. This usually works best anyways.
      # We can support gamma in the future but then need to scale the timestep before
      # passing it to the model which requires a change in API
      snake_case				=			0
      snake_case				=			sigma * (gamma + 1)  # Note: sigma_hat == sigma for now

      # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
      if self.config.prediction_type == "epsilon":
       snake_case				=			sigma_hat if self.state_in_first_order else sigma_interpol
       snake_case				=			sample - sigma_input * model_output
      elif self.config.prediction_type == "v_prediction":
       snake_case				=			sigma_hat if self.state_in_first_order else sigma_interpol
       snake_case				=			model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
           sample / (sigma_input**2 + 1)
       )
      elif self.config.prediction_type == "sample":
       raise NotImplementedError('''prediction_type not implemented yet: sample'''    )
      else:
       raise ValueError(
           F"""prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`"""    )

      if self.state_in_first_order:
       # 2. Convert to an ODE derivative for 1st order
       snake_case				=			(sample - pred_original_sample) / sigma_hat
       # 3. delta timestep
       snake_case				=			sigma_interpol - sigma_hat

       # store for 2nd order step
       snake_case				=			sample
      else:
       # DPM-Solver-2
       # 2. Convert to an ODE derivative for 2nd order
       snake_case				=			(sample - pred_original_sample) / sigma_interpol

       # 3. delta timestep
       snake_case				=			sigma_next - sigma_hat

       snake_case				=			self.sample
       snake_case				=			None

      snake_case				=			sample + derivative * dt

      if not return_dict:
       return (prev_sample,)

      return SchedulerOutput(prev_sample=A__    )







     def     UpperCamelCase			( self    ,   A__    ,   A__    ,   A__    ,   ) ->	torch.FloatTensor:
      # Make sure sigmas and timesteps have the same device and dtype as original_samples
      snake_case				=			self.sigmas.to(device=original_samples.device    ,   dtype=original_samples.dtype    )
      if original_samples.device.type == "mps" and torch.is_floating_point(A__    ):
       # mps does not support float64
       snake_case				=			self.timesteps.to(original_samples.device    ,   dtype=torch.floataa    )
       snake_case				=			timesteps.to(original_samples.device    ,   dtype=torch.floataa    )
      else:
       snake_case				=			self.timesteps.to(original_samples.device    )
       snake_case				=			timesteps.to(original_samples.device    )

      snake_case				=			[self.index_for_timestep(A__    ,   A__    ) for t in timesteps]

      snake_case				=			sigmas[step_indices].flatten()
      while len(sigma.shape    ) < len(original_samples.shape    ):
       snake_case				=			sigma.unsqueeze(-1    )

      snake_case				=			original_samples + noise * sigma
      return noisy_samples







     def __len__( self    ) ->	Tuple:
      return self.config.num_train_timesteps








'''simple docstring'''







from __future__ import annotations

import inspect
import unittest

from transformers import ViTConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
   import tensorflow as tf

   from transformers import TFViTForImageClassification, TFViTModel


if is_vision_available():
   from PIL import Image

   from transformers import ViTImageProcessor
class 			_lowercase						:







     def __init__( self    ,   A__    ,   A__=13    ,   A__=30    ,   A__=2    ,   A__=3    ,   A__=True    ,   A__=True    ,   A__=32    ,   A__=2    ,   A__=4    ,   A__=37    ,   A__="gelu"    ,   A__=0.1    ,   A__=0.1    ,   A__=10    ,   A__=0.0_2    ,   A__=3    ,   A__=None    ,   ) ->	List[Any]:
      snake_case				=			parent
      snake_case				=			batch_size
      snake_case				=			image_size
      snake_case				=			patch_size
      snake_case				=			num_channels
      snake_case				=			is_training
      snake_case				=			use_labels
      snake_case				=			hidden_size
      snake_case				=			num_hidden_layers
      snake_case				=			num_attention_heads
      snake_case				=			intermediate_size
      snake_case				=			hidden_act
      snake_case				=			hidden_dropout_prob
      snake_case				=			attention_probs_dropout_prob
      snake_case				=			type_sequence_label_size
      snake_case				=			initializer_range
      snake_case				=			scope

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







     def     UpperCamelCase			( self    ) ->	int:
      snake_case				=			floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]    )

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

      snake_case				=			self.get_config()

      return config, pixel_values, labels







     def     UpperCamelCase			( self    ) ->	int:
      return ViTConfig(
          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    ,   is_decoder=A__    ,   initializer_range=self.initializer_range    ,   )







     def     UpperCamelCase			( self    ,   A__    ,   A__    ,   A__    ) ->	Union[str, Any]:
      snake_case				=			TFViTModel(config=A__    )
      snake_case				=			model(A__    ,   training=A__    )
      self.parent.assertEqual(result.last_hidden_state.shape    ,   (self.batch_size, self.seq_length, self.hidden_size)    )

      # Test with an image with different size than the one specified in config.
      snake_case				=			self.image_size // 2
      snake_case				=			pixel_values[:, :, :image_size, :image_size]
      snake_case				=			model(A__    ,   interpolate_pos_encoding=A__    ,   training=A__    )
      snake_case				=			(image_size // self.patch_size) ** 2 + 1
      self.parent.assertEqual(result.last_hidden_state.shape    ,   (self.batch_size, seq_length, self.hidden_size)    )







     def     UpperCamelCase			( self    ,   A__    ,   A__    ,   A__    ) ->	Optional[int]:
      snake_case				=			self.type_sequence_label_size
      snake_case				=			TFViTForImageClassification(A__    )
      snake_case				=			model(A__    ,   labels=A__    ,   training=A__    )
      self.parent.assertEqual(result.logits.shape    ,   (self.batch_size, self.type_sequence_label_size)    )

      # Test with an image with different size than the one specified in config.
      snake_case				=			self.image_size // 2
      snake_case				=			pixel_values[:, :, :image_size, :image_size]
      snake_case				=			model(A__    ,   interpolate_pos_encoding=A__    ,   training=A__    )
      self.parent.assertEqual(result.logits.shape    ,   (self.batch_size, self.type_sequence_label_size)    )

      # test greyscale images
      snake_case				=			1
      snake_case				=			TFViTForImageClassification(A__    )
      snake_case				=			floats_tensor([self.batch_size, 1, self.image_size, self.image_size]    )
      snake_case				=			model(A__    )
      self.parent.assertEqual(result.logits.shape    ,   (self.batch_size, self.type_sequence_label_size)    )







     def     UpperCamelCase			( self    ) ->	Union[str, Any]:
      snake_case				=			self.prepare_config_and_inputs()
      snake_case				,		snake_case				,		snake_case				=			config_and_inputs
      snake_case				=			{'''pixel_values''': pixel_values}
      return config, inputs_dict




@require_tf
class 			_lowercase						(       __a				,		__a				,		unittest.TestCase					):
     _UpperCAmelCase										=					(TFViTModel, TFViTForImageClassification) if is_tf_available() else ()
     _UpperCAmelCase										=					(
         {'''feature-extraction''': TFViTModel, '''image-classification''': TFViTForImageClassification}
         if is_tf_available()
         else {}
     )

     _UpperCAmelCase										=					False
     _UpperCAmelCase										=					False
     _UpperCAmelCase										=					False







     def     UpperCamelCase			( self    ) ->	List[Any]:
      snake_case				=			TFViTModelTester(self    )
      snake_case				=			ConfigTester(self    ,   config_class=A__    ,   has_text_modality=A__    ,   hidden_size=37    )







     def     UpperCamelCase			( self    ) ->	int:
      self.config_tester.run_common_tests()







     @unittest.skip(reason='''ViT does not use inputs_embeds'''    )
     def     UpperCamelCase			( self    ) ->	int:
      pass







     @unittest.skip(reason='''ViT does not use inputs_embeds'''    )
     def     UpperCamelCase			( self    ) ->	str:
      pass







     def     UpperCamelCase			( self    ) ->	Union[str, Any]:
      snake_case				,		snake_case				=			self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
       snake_case				=			model_class(A__    )
       self.assertIsInstance(model.get_input_embeddings()    ,   (tf.keras.layers.Layer)    )
       snake_case				=			model.get_output_embeddings()
       self.assertTrue(x is None or isinstance(A__    ,   tf.keras.layers.Layer    )    )







     def     UpperCamelCase			( self    ) ->	List[Any]:
      snake_case				,		snake_case				=			self.model_tester.prepare_config_and_inputs_for_common()

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

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







     def     UpperCamelCase			( self    ) ->	Union[str, Any]:
      snake_case				=			self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*A__    )







     def     UpperCamelCase			( self    ) ->	Optional[Any]:
      snake_case				=			self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_image_classification(*A__    )







     @slow
     def     UpperCamelCase			( self    ) ->	Any:
      snake_case				=			TFViTModel.from_pretrained('''google/vit-base-patch16-224'''    )
      self.assertIsNotNone(A__    )




def    __UpperCamelCase	(	) ->Any:
 snake_case				=			Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''     )
 return image






@require_tf
@require_vision
class 			_lowercase						(       unittest.TestCase					):







     @cached_property
     def     UpperCamelCase			( self    ) ->	Optional[int]:
      return ViTImageProcessor.from_pretrained('''google/vit-base-patch16-224'''    ) if is_vision_available() else None







     @slow
     def     UpperCamelCase			( self    ) ->	Dict:
      snake_case				=			TFViTForImageClassification.from_pretrained('''google/vit-base-patch16-224'''    )

      snake_case				=			self.default_image_processor
      snake_case				=			prepare_img()
      snake_case				=			image_processor(images=A__    ,   return_tensors='''tf'''    )

      # forward pass
      snake_case				=			model(**A__    )

      # verify the logits
      snake_case				=			tf.TensorShape((1, 10_00)    )
      self.assertEqual(outputs.logits.shape    ,   A__    )

      snake_case				=			tf.constant([-0.2_7_4_4, 0.8_2_1_5, -0.0_8_3_6]    )

      tf.debugging.assert_near(outputs.logits[0, :3]    ,   A__    ,   atol=1e-4    )

