
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 				lowercase					(		_SCREAMING_SNAKE_CASE   ):
		__lowercase       :       torch.FloatTensor
		__lowercase       :       Optional[torch.FloatTensor]	     =				None






def       A       (					lowercase    ,       lowercase=0.9_9_9    ,       lowercase="cosine"    ,       )   ->  Optional[int]:
		'''simple docstring'''

		if alpha_transform_type == "cosine":

				def alpha_bar_fn(lowercase ):
						return math.cos((t + 0.0_0_8) / 1.0_0_8 * math.pi / 2 ) ** 2

		elif alpha_transform_type == "exp":

				def alpha_bar_fn(lowercase ):
						return math.exp(t * -1_2.0 )

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

		UpperCamelCase		=     []
		for i in range(lowercase ):
				UpperCamelCase		=     i / num_diffusion_timesteps
				UpperCamelCase		=     (i + 1) / num_diffusion_timesteps
				betas.append(min(1 - alpha_bar_fn(lowercase ) / alpha_bar_fn(lowercase )    ,       lowercase ) )
		return torch.tensor(lowercase    ,       dtype=torch.floataa )


class 				lowercase					(		_SCREAMING_SNAKE_CASE  ,   _SCREAMING_SNAKE_CASE   ):



		@register_to_config
		def __init__( self				,      A_ = 1_000				,      A_ = "fixed_small_log"				,      A_ = True				,      A_ = 1.0				,      A_ = "epsilon"				,      A_ = "squaredcos_cap_v2"				,      )  ->					Optional[Any]:
				"""simple docstring"""
				if beta_schedule != "squaredcos_cap_v2":
						raise ValueError('UnCLIPScheduler only supports `beta_schedule`: \'squaredcos_cap_v2\''       )

				UpperCamelCase		=     betas_for_alpha_bar(A_       )

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

				# standard deviation of the initial noise distribution
				UpperCamelCase		=     1.0

				# setable values
				UpperCamelCase		=     None
				UpperCamelCase		=     torch.from_numpy(np.arange(0				,      A_       )[::-1].copy()       )

				UpperCamelCase		=     variance_type



		def __UpperCamelCase ( self				,      A_				,      A_ = None       )  ->					torch.FloatTensor:
				"""simple docstring"""
				return sample



		def __UpperCamelCase ( self				,      A_				,      A_ = None       )  ->					List[Any]:
				"""simple docstring"""
				UpperCamelCase		=     num_inference_steps
				UpperCamelCase		=     (self.config.num_train_timesteps - 1) / (self.num_inference_steps - 1)
				UpperCamelCase		=     (np.arange(0				,      A_       ) * step_ratio).round()[::-1].copy().astype(np.intaa       )
				UpperCamelCase		=     torch.from_numpy(A_       ).to(A_       )



		def __UpperCamelCase ( self				,      A_				,      A_=None				,      A_=None				,      A_=None       )  ->					Optional[Any]:
				"""simple docstring"""
				if prev_timestep is None:
						UpperCamelCase		=     t - 1

				UpperCamelCase		=     self.alphas_cumprod[t]
				UpperCamelCase		=     self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
				UpperCamelCase		=     1 - alpha_prod_t
				UpperCamelCase		=     1 - alpha_prod_t_prev

				if prev_timestep == t - 1:
						UpperCamelCase		=     self.betas[t]
				else:
						UpperCamelCase		=     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
				UpperCamelCase		=     beta_prod_t_prev / beta_prod_t * beta

				if variance_type is None:
						UpperCamelCase		=     self.config.variance_type

				# hacks - were probably added for training stability
				if variance_type == "fixed_small_log":
						UpperCamelCase		=     torch.log(torch.clamp(A_				,      min=1e-20       )       )
						UpperCamelCase		=     torch.exp(0.5 * variance       )
				elif variance_type == "learned_range":
						# NOTE difference with DDPM scheduler
						UpperCamelCase		=     variance.log()
						UpperCamelCase		=     beta.log()

						UpperCamelCase		=     (predicted_variance + 1) / 2
						UpperCamelCase		=     frac * max_log + (1 - frac) * min_log

				return variance



		def __UpperCamelCase ( self				,      A_				,      A_				,      A_				,      A_ = None				,      A_=None				,      A_ = True				,      )  ->					Union[UnCLIPSchedulerOutput, Tuple]:
				"""simple docstring"""
				UpperCamelCase		=     timestep

				if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type == "learned_range":
						UpperCamelCase	,  UpperCamelCase		=     torch.split(A_				,      sample.shape[1]				,      dim=1       )
				else:
						UpperCamelCase		=     None

				# 1. compute alphas, betas
				if prev_timestep is None:
						UpperCamelCase		=     t - 1

				UpperCamelCase		=     self.alphas_cumprod[t]
				UpperCamelCase		=     self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
				UpperCamelCase		=     1 - alpha_prod_t
				UpperCamelCase		=     1 - alpha_prod_t_prev

				if prev_timestep == t - 1:
						UpperCamelCase		=     self.betas[t]
						UpperCamelCase		=     self.alphas[t]
				else:
						UpperCamelCase		=     1 - alpha_prod_t / alpha_prod_t_prev
						UpperCamelCase		=     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":
						UpperCamelCase		=     (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
				elif self.config.prediction_type == "sample":
						UpperCamelCase		=     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:
						UpperCamelCase		=     torch.clamp(
						    A_				,      -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
				UpperCamelCase		=     (alpha_prod_t_prev ** 0.5 * beta) / beta_prod_t
				UpperCamelCase		=     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
				UpperCamelCase		=     pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

				# 6. Add noise
				UpperCamelCase		=     0
				if t > 0:
						UpperCamelCase		=     randn_tensor(
						    model_output.shape				,      dtype=model_output.dtype				,      generator=A_				,      device=model_output.device       )

						UpperCamelCase		=     self._get_variance(
						    A_				,      predicted_variance=A_				,      prev_timestep=A_				,      )

						if self.variance_type == "fixed_small_log":
								UpperCamelCase		=     variance
						elif self.variance_type == "learned_range":
								UpperCamelCase		=     (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.'       )

						UpperCamelCase		=     variance * variance_noise

				UpperCamelCase		=     pred_prev_sample + variance

				if not return_dict:
						return (pred_prev_sample,)

				return UnCLIPSchedulerOutput(prev_sample=A_				,      pred_original_sample=A_       )



		def __UpperCamelCase ( self				,      A_				,      A_				,      A_				,      )  ->					torch.FloatTensor:
				"""simple docstring"""
				# Make sure alphas_cumprod and timestep have same device and dtype as original_samples
				UpperCamelCase		=     self.alphas_cumprod.to(device=original_samples.device				,      dtype=original_samples.dtype       )
				UpperCamelCase		=     timesteps.to(original_samples.device       )

				UpperCamelCase		=     alphas_cumprod[timesteps] ** 0.5
				UpperCamelCase		=     sqrt_alpha_prod.flatten()
				while len(sqrt_alpha_prod.shape       ) < len(original_samples.shape       ):
						UpperCamelCase		=     sqrt_alpha_prod.unsqueeze(-1       )

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

				UpperCamelCase		=     sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
				return noisy_samples


import inspect
import unittest
from math import floor

from transformers import CvtConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
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 CvtForImageClassification, CvtModel
					from transformers.models.cvt.modeling_cvt import CVT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
					from PIL import Image

					from transformers import AutoImageProcessor


class 				lowercase					(		_SCREAMING_SNAKE_CASE   ):



		def __UpperCamelCase ( self       )  ->					int:
				"""simple docstring"""
				UpperCamelCase		=     self.config_class(**self.inputs_dict       )
				self.parent.assertTrue(hasattr(A_				,      'embed_dim'       )       )
				self.parent.assertTrue(hasattr(A_				,      'num_heads'       )       )






class 				lowercase					:



		def __init__( self				,      A_				,      A_=13				,      A_=64				,      A_=3				,      A_=[16, 48, 96]				,      A_=[1, 3, 6]				,      A_=[1, 2, 10]				,      A_=[7, 3, 3]				,      A_=[4, 2, 2]				,      A_=[2, 1, 1]				,      A_=[2, 2, 2]				,      A_=[False, False, True]				,      A_=[0.0, 0.0, 0.0]				,      A_=0.02				,      A_=1e-12				,      A_=True				,      A_=True				,      A_=2				,      )  ->					int:
				"""simple docstring"""
				UpperCamelCase		=     parent
				UpperCamelCase		=     batch_size
				UpperCamelCase		=     image_size
				UpperCamelCase		=     patch_sizes
				UpperCamelCase		=     patch_stride
				UpperCamelCase		=     patch_padding
				UpperCamelCase		=     is_training
				UpperCamelCase		=     use_labels
				UpperCamelCase		=     num_labels
				UpperCamelCase		=     num_channels
				UpperCamelCase		=     embed_dim
				UpperCamelCase		=     num_heads
				UpperCamelCase		=     stride_kv
				UpperCamelCase		=     depth
				UpperCamelCase		=     cls_token
				UpperCamelCase		=     attention_drop_rate
				UpperCamelCase		=     initializer_range
				UpperCamelCase		=     layer_norm_eps



		def __UpperCamelCase ( self       )  ->					List[str]:
				"""simple docstring"""
				UpperCamelCase		=     floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]       )

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

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



		def __UpperCamelCase ( self       )  ->					List[Any]:
				"""simple docstring"""
				return CvtConfig(
				    image_size=self.image_size				,      num_labels=self.num_labels				,      num_channels=self.num_channels				,      embed_dim=self.embed_dim				,      num_heads=self.num_heads				,      patch_sizes=self.patch_sizes				,      patch_padding=self.patch_padding				,      patch_stride=self.patch_stride				,      stride_kv=self.stride_kv				,      depth=self.depth				,      cls_token=self.cls_token				,      attention_drop_rate=self.attention_drop_rate				,      initializer_range=self.initializer_range				,      )



		def __UpperCamelCase ( self				,      A_				,      A_				,      A_       )  ->					Any:
				"""simple docstring"""
				UpperCamelCase		=     CvtModel(config=A_       )
				model.to(A_       )
				model.eval()
				UpperCamelCase		=     model(A_       )
				UpperCamelCase		=     (self.image_size, self.image_size)
				UpperCamelCase	,  UpperCamelCase		=     image_size[0], image_size[1]
				for i in range(len(self.depth       )       ):
						UpperCamelCase		=     floor(((height + 2 * self.patch_padding[i] - self.patch_sizes[i]) / self.patch_stride[i]) + 1       )
						UpperCamelCase		=     floor(((width + 2 * self.patch_padding[i] - self.patch_sizes[i]) / self.patch_stride[i]) + 1       )
				self.parent.assertEqual(result.last_hidden_state.shape				,      (self.batch_size, self.embed_dim[-1], height, width)       )



		def __UpperCamelCase ( self				,      A_				,      A_				,      A_       )  ->					Tuple:
				"""simple docstring"""
				UpperCamelCase		=     self.num_labels
				UpperCamelCase		=     CvtForImageClassification(A_       )
				model.to(A_       )
				model.eval()
				UpperCamelCase		=     model(A_				,      labels=A_       )
				self.parent.assertEqual(result.logits.shape				,      (self.batch_size, self.num_labels)       )



		def __UpperCamelCase ( self       )  ->					Tuple:
				"""simple docstring"""
				UpperCamelCase		=     self.prepare_config_and_inputs()
				UpperCamelCase	,  UpperCamelCase	,  UpperCamelCase		=     config_and_inputs
				UpperCamelCase		=     {'pixel_values': pixel_values}
				return config, inputs_dict






@require_torch
class 				lowercase					(		_SCREAMING_SNAKE_CASE  ,   _SCREAMING_SNAKE_CASE  ,   unittest.TestCase   ):
		__lowercase       :       List[str]	     =				(CvtModel, CvtForImageClassification) if is_torch_available() else ()
		__lowercase       :       Tuple	     =				(
		    {"feature-extraction": CvtModel, "image-classification": CvtForImageClassification}
		    if is_torch_available()
		    else {}
		)

		__lowercase       :       Tuple	     =				False
		__lowercase       :       Union[str, Any]	     =				False
		__lowercase       :       Optional[Any]	     =				False
		__lowercase       :       List[str]	     =				False
		__lowercase       :       Dict	     =				False



		def __UpperCamelCase ( self       )  ->					List[Any]:
				"""simple docstring"""
				UpperCamelCase		=     CvtModelTester(self       )
				UpperCamelCase		=     ConfigTester(self				,      config_class=A_				,      has_text_modality=A_				,      hidden_size=37       )



		def __UpperCamelCase ( self       )  ->					str:
				"""simple docstring"""
				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 __UpperCamelCase ( self       )  ->					Any:
				"""simple docstring"""
				return



		@unittest.skip(reason='Cvt does not output attentions'       )
		def __UpperCamelCase ( self       )  ->					Any:
				"""simple docstring"""
				pass



		@unittest.skip(reason='Cvt does not use inputs_embeds'       )
		def __UpperCamelCase ( self       )  ->					List[str]:
				"""simple docstring"""
				pass



		@unittest.skip(reason='Cvt does not support input and output embeddings'       )
		def __UpperCamelCase ( self       )  ->					Optional[Any]:
				"""simple docstring"""
				pass



		def __UpperCamelCase ( self       )  ->					Union[str, Any]:
				"""simple docstring"""
				UpperCamelCase	,  UpperCamelCase		=     self.model_tester.prepare_config_and_inputs_for_common()

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

						UpperCamelCase		=     ['pixel_values']
						self.assertListEqual(arg_names[:1]				,      A_       )



		def __UpperCamelCase ( self       )  ->					Any:
				"""simple docstring"""
				UpperCamelCase		=     self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_model(*A_       )



		def __UpperCamelCase ( self       )  ->					Tuple:
				"""simple docstring"""
				def check_hidden_states_output(A_				,      A_				,      A_       ):
						UpperCamelCase		=     model_class(A_       )
						model.to(A_       )
						model.eval()

						with torch.no_grad():
								UpperCamelCase		=     model(**self._prepare_for_class(A_				,      A_       )       )

						UpperCamelCase		=     outputs.hidden_states

						UpperCamelCase		=     len(self.model_tester.depth       )
						self.assertEqual(len(A_       )				,      A_       )

						# verify the first hidden states (first block)
						self.assertListEqual(
						    list(hidden_states[0].shape[-3:]       )				,      [
						        self.model_tester.embed_dim[0],
						        self.model_tester.image_size // 4,
						        self.model_tester.image_size // 4,
						    ]				,      )

				UpperCamelCase	,  UpperCamelCase		=     self.model_tester.prepare_config_and_inputs_for_common()

				for model_class in self.all_model_classes:
						UpperCamelCase		=     True
						check_hidden_states_output(A_				,      A_				,      A_       )

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

						check_hidden_states_output(A_				,      A_				,      A_       )



		def __UpperCamelCase ( self       )  ->					Union[str, Any]:
				"""simple docstring"""
				UpperCamelCase		=     self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_image_classification(*A_       )



		@unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.'       )
		def __UpperCamelCase ( self       )  ->					Dict:
				"""simple docstring"""
				pass



		@slow
		def __UpperCamelCase ( self       )  ->					Dict:
				"""simple docstring"""
				for model_name in CVT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
						UpperCamelCase		=     CvtModel.from_pretrained(A_       )
						self.assertIsNotNone(A_       )






def       A       (					)   ->  Tuple:
		'''simple docstring'''

		UpperCamelCase		=     Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
		return image


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



		@cached_property
		def __UpperCamelCase ( self       )  ->					List[str]:
				"""simple docstring"""
				return AutoImageProcessor.from_pretrained(CVT_PRETRAINED_MODEL_ARCHIVE_LIST[0]       )



		@slow
		def __UpperCamelCase ( self       )  ->					Tuple:
				"""simple docstring"""
				UpperCamelCase		=     CvtForImageClassification.from_pretrained(CVT_PRETRAINED_MODEL_ARCHIVE_LIST[0]       ).to(A_       )

				UpperCamelCase		=     self.default_image_processor
				UpperCamelCase		=     prepare_img()
				UpperCamelCase		=     image_processor(images=A_				,      return_tensors='pt'       ).to(A_       )

				# forward pass
				with torch.no_grad():
						UpperCamelCase		=     model(**A_       )

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

				UpperCamelCase		=     torch.tensor([0.9285, 0.9015, -0.3150]       ).to(A_       )

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