






from __future__ import annotations

import inspect
import unittest
from math import floor

import numpy as np

from transformers import CvtConfig
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 TFCvtForImageClassification, TFCvtModel
							from transformers.models.cvt.modeling_tf_cvt import TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
							from PIL import Image

							from transformers import AutoImageProcessor
class    _UpperCAmelCase      ( A__			):

				"""simple docstring"""




				def        lowercase__     (  self		:					Optional[int]						):

								'''simple docstring'''
								lowercase__     				=    self.config_class(**self.inputs_dict						)
								self.parent.assertTrue(hasattr(lowerCamelCase,						'''embed_dim'''						)						)
								self.parent.assertTrue(hasattr(lowerCamelCase,						'''num_heads'''						)						)



class    _UpperCAmelCase      :

				"""simple docstring"""




				def __init__(  self		:					List[str],						lowerCamelCase		:					List[str],						lowerCamelCase		:					Dict=13,						lowerCamelCase		:					Dict=64,						lowerCamelCase		:					Any=3,						lowerCamelCase		:					List[Any]=[16, 48, 96],						lowerCamelCase		:					List[Any]=[1, 3, 6],						lowerCamelCase		:					List[str]=[1, 2, 10],						lowerCamelCase		:					Optional[Any]=[7, 3, 3],						lowerCamelCase		:					Tuple=[4, 2, 2],						lowerCamelCase		:					Union[str, Any]=[2, 1, 1],						lowerCamelCase		:					Tuple=[2, 2, 2],						lowerCamelCase		:					Optional[int]=[False, False, True],						lowerCamelCase		:					Optional[int]=[0.0, 0.0, 0.0],						lowerCamelCase		:					Tuple=0.02,						lowerCamelCase		:					Any=1E-12,						lowerCamelCase		:					int=True,						lowerCamelCase		:					str=True,						lowerCamelCase		:					List[str]=2,						):

								'''simple docstring'''
								lowercase__     				=    parent
								lowercase__     				=    batch_size
								lowercase__     				=    image_size
								lowercase__     				=    patch_sizes
								lowercase__     				=    patch_stride
								lowercase__     				=    patch_padding
								lowercase__     				=    is_training
								lowercase__     				=    use_labels
								lowercase__     				=    num_labels
								lowercase__     				=    num_channels
								lowercase__     				=    embed_dim
								lowercase__     				=    num_heads
								lowercase__     				=    stride_kv
								lowercase__     				=    depth
								lowercase__     				=    cls_token
								lowercase__     				=    attention_drop_rate
								lowercase__     				=    initializer_range
								lowercase__     				=    layer_norm_eps




				def        lowercase__     (  self		:					str						):

								'''simple docstring'''
								lowercase__     				=    floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]						)

								lowercase__     				=    None
								if self.use_labels:
												# create a random int32 tensor of given shape
												lowercase__     				=    ids_tensor([self.batch_size],						self.num_labels						)

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




				def        lowercase__     (  self		:					Optional[int]						):

								'''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        lowercase__     (  self		:					Union[str, Any],						lowerCamelCase		:					List[Any],						lowerCamelCase		:					Tuple,						lowerCamelCase		:					Any						):

								'''simple docstring'''
								lowercase__     				=    TFCvtModel(config=lowerCamelCase						)
								lowercase__     				=    model(lowerCamelCase,						training=lowerCamelCase						)
								lowercase__     				=    (self.image_size, self.image_size)
								lowercase__				,		lowercase__     				=    image_size[0], image_size[1]
								for i in range(len(self.depth						)						):
												lowercase__     				=    floor(((height + 2 * self.patch_padding[i] - self.patch_sizes[i]) / self.patch_stride[i]) + 1						)
												lowercase__     				=    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        lowercase__     (  self		:					Union[str, Any],						lowerCamelCase		:					List[str],						lowerCamelCase		:					Dict,						lowerCamelCase		:					Union[str, Any]						):

								'''simple docstring'''
								lowercase__     				=    self.num_labels
								lowercase__     				=    TFCvtForImageClassification(lowerCamelCase						)
								lowercase__     				=    model(lowerCamelCase,						labels=lowerCamelCase,						training=lowerCamelCase						)
								self.parent.assertEqual(result.logits.shape,						(self.batch_size, self.num_labels)						)




				def        lowercase__     (  self		:					List[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_tf
class    _UpperCAmelCase      ( A__	,A__	,unittest.TestCase			):

				"""simple docstring"""




				lowercase__       							= (TFCvtModel, TFCvtForImageClassification) if is_tf_available() else ()
				lowercase__       							= (
				    {"""feature-extraction""": TFCvtModel, """image-classification""": TFCvtForImageClassification}
				    if is_tf_available()
				    else {}
				)
				lowercase__       							= False
				lowercase__       							= False
				lowercase__       							= False
				lowercase__       							= False
				lowercase__       							= False




				def        lowercase__     (  self		:					str						):

								'''simple docstring'''
								lowercase__     				=    TFCvtModelTester(self						)
								lowercase__     				=    TFCvtConfigTester(self,						config_class=lowerCamelCase,						has_text_modality=lowerCamelCase,						hidden_size=37						)




				def        lowercase__     (  self		:					List[Any]						):

								'''simple docstring'''
								self.config_tester.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()




				@unittest.skip(reason='''Cvt does not output attentions'''						)
				def        lowercase__     (  self		:					List[Any]						):

								'''simple docstring'''
								pass




				@unittest.skip(reason='''Cvt does not use inputs_embeds'''						)
				def        lowercase__     (  self		:					Dict						):

								'''simple docstring'''
								pass




				@unittest.skip(reason='''Cvt does not support input and output embeddings'''						)
				def        lowercase__     (  self		:					Dict						):

								'''simple docstring'''
								pass




				@unittest.skipIf(
				    not is_tf_available() or len(tf.config.list_physical_devices('''GPU'''						)						) == 0,						reason='''TF does not support backprop for grouped convolutions on CPU.''',						)
				def        lowercase__     (  self		:					List[Any]						):

								'''simple docstring'''
								super().test_dataset_conversion()




				@unittest.skipIf(
				    not is_tf_available() or len(tf.config.list_physical_devices('''GPU'''						)						) == 0,						reason='''TF does not support backprop for grouped convolutions on CPU.''',						)
				@slow
				def        lowercase__     (  self		:					Optional[int]						):

								'''simple docstring'''
								super().test_keras_fit()




				@unittest.skip(reason='''Get `Failed to determine best cudnn convolution algo.` error after using TF 2.12+cuda 11.8'''						)
				def        lowercase__     (  self		:					List[Any]						):

								'''simple docstring'''
								lowercase__     				=    tf.keras.mixed_precision.Policy('''mixed_float16'''						)
								tf.keras.mixed_precision.set_global_policy(lowerCamelCase						)
								super().test_keras_fit()
								tf.keras.mixed_precision.set_global_policy('''float32'''						)




				def        lowercase__     (  self		:					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.call						)
												# 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        lowercase__     (  self		:					List[str]						):

								'''simple docstring'''
								def check_hidden_states_output(lowerCamelCase		:					List[Any],						lowerCamelCase		:					int,						lowerCamelCase		:					int						):
												lowercase__     				=    model_class(lowerCamelCase						)

												lowercase__     				=    model(**self._prepare_for_class(lowerCamelCase,						lowerCamelCase						)						)
												lowercase__     				=    outputs.hidden_states

												lowercase__     				=    len(self.model_tester.depth						)
												self.assertEqual(len(lowerCamelCase						),						lowerCamelCase						)

												# 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,
												    ],						)

								lowercase__				,		lowercase__     				=    self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
												lowercase__     				=    True
												check_hidden_states_output(lowerCamelCase,						lowerCamelCase,						lowerCamelCase						)

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

												check_hidden_states_output(lowerCamelCase,						lowerCamelCase,						lowerCamelCase						)




				def        lowercase__     (  self		:					List[str]						):

								'''simple docstring'''
								lowercase__     				=    self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*lowerCamelCase						)




				def        lowercase__     (  self		:					int						):

								'''simple docstring'''
								lowercase__     				=    self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_image_classification(*lowerCamelCase						)




				@slow
				def        lowercase__     (  self		:					Any						):

								'''simple docstring'''
								for model_name in TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
												lowercase__     				=    TFCvtModel.from_pretrained(lowerCamelCase						)
												self.assertIsNotNone(lowerCamelCase						)






def 						a    (		):






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




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

				"""simple docstring"""




				@cached_property
				def        lowercase__     (  self		:					Union[str, Any]						):

								'''simple docstring'''
								return AutoImageProcessor.from_pretrained(TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST[0]						)




				@slow
				def        lowercase__     (  self		:					Any						):

								'''simple docstring'''
								lowercase__     				=    TFCvtForImageClassification.from_pretrained(TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST[0]						)

								lowercase__     				=    self.default_image_processor
								lowercase__     				=    prepare_img()
								lowercase__     				=    image_processor(images=lowerCamelCase,						return_tensors='''tf'''						)

								# forward pass
								lowercase__     				=    model(**lowerCamelCase						)

								# verify the logits
								lowercase__     				=    tf.TensorShape((1, 1_000)						)
								self.assertEqual(outputs.logits.shape,						lowerCamelCase						)

								lowercase__     				=    tf.constant([0.9285, 0.9015, -0.3150]						)
								self.assertTrue(np.allclose(outputs.logits[0, :3].numpy(),						lowerCamelCase,						atol=1E-4						)						)












# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch

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

import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin, SchedulerOutput
@dataclass
class    _UpperCAmelCase      ( A__			):

				"""simple docstring"""




				lowercase__       							= 42
				lowercase__       							= 42



class    _UpperCAmelCase      ( A__	,A__			):

				"""simple docstring"""




				lowercase__       							= 1




				@register_to_config
				def __init__(  self		:					Union[str, Any],						lowerCamelCase		:					int = 2_000,						lowerCamelCase		:					float = 0.15,						lowerCamelCase		:					float = 0.01,						lowerCamelCase		:					float = 1348.0,						lowerCamelCase		:					float = 1E-5,						lowerCamelCase		:					int = 1,						):

								'''simple docstring'''
								# standard deviation of the initial noise distribution
								lowercase__     				=    sigma_max

								# setable values
								lowercase__     				=    None

								self.set_sigmas(lowerCamelCase,						lowerCamelCase,						lowerCamelCase,						lowerCamelCase						)




				def        lowercase__     (  self		:					List[str],						lowerCamelCase		:					torch.FloatTensor,						lowerCamelCase		:					Optional[int] = None						):

								'''simple docstring'''
								return sample




				def        lowercase__     (  self		:					Dict,						lowerCamelCase		:					int,						lowerCamelCase		:					float = None,						lowerCamelCase		:					Union[str, torch.device] = None						):

								'''simple docstring'''
								lowercase__     				=    sampling_eps if sampling_eps is not None else self.config.sampling_eps

								lowercase__     				=    torch.linspace(1,						lowerCamelCase,						lowerCamelCase,						device=lowerCamelCase						)




				def        lowercase__     (  self		:					str,						lowerCamelCase		:					int,						lowerCamelCase		:					float = None,						lowerCamelCase		:					float = None,						lowerCamelCase		:					float = None						):

								'''simple docstring'''
								lowercase__     				=    sigma_min if sigma_min is not None else self.config.sigma_min
								lowercase__     				=    sigma_max if sigma_max is not None else self.config.sigma_max
								lowercase__     				=    sampling_eps if sampling_eps is not None else self.config.sampling_eps
								if self.timesteps is None:
												self.set_timesteps(lowerCamelCase,						lowerCamelCase						)

								lowercase__     				=    sigma_min * (sigma_max / sigma_min) ** (self.timesteps / sampling_eps)
								lowercase__     				=    torch.exp(torch.linspace(math.log(lowerCamelCase						),						math.log(lowerCamelCase						),						lowerCamelCase						)						)
								lowercase__     				=    torch.tensor([sigma_min * (sigma_max / sigma_min) ** t for t in self.timesteps]						)




				def        lowercase__     (  self		:					Optional[int],						lowerCamelCase		:					str,						lowerCamelCase		:					str						):

								'''simple docstring'''
								return torch.where(
								    timesteps == 0,						torch.zeros_like(t.to(timesteps.device						)						),						self.discrete_sigmas[timesteps - 1].to(timesteps.device						),						)




				def        lowercase__     (  self		:					Tuple,						lowerCamelCase		:					torch.FloatTensor,						lowerCamelCase		:					int,						lowerCamelCase		:					torch.FloatTensor,						lowerCamelCase		:					Optional[torch.Generator] = None,						lowerCamelCase		:					bool = True,						):

								'''simple docstring'''
								if self.timesteps is None:
												raise ValueError(
												    '''`self.timesteps` is not set, you need to run \'set_timesteps\' after creating the scheduler'''						)

								lowercase__     				=    timestep * torch.ones(
								    sample.shape[0],						device=sample.device						)  # torch.repeat_interleave(timestep, sample.shape[0])
								lowercase__     				=    (timestep * (len(self.timesteps						) - 1)).long()

								# mps requires indices to be in the same device, so we use cpu as is the default with cuda
								lowercase__     				=    timesteps.to(self.discrete_sigmas.device						)

								lowercase__     				=    self.discrete_sigmas[timesteps].to(sample.device						)
								lowercase__     				=    self.get_adjacent_sigma(lowerCamelCase,						lowerCamelCase						).to(sample.device						)
								lowercase__     				=    torch.zeros_like(lowerCamelCase						)
								lowercase__     				=    (sigma**2 - adjacent_sigma**2) ** 0.5

								# equation 6 in the paper: the model_output modeled by the network is grad_x log pt(x)
								# also equation 47 shows the analog from SDE models to ancestral sampling methods
								lowercase__     				=    diffusion.flatten()
								while len(diffusion.shape						) < len(sample.shape						):
												lowercase__     				=    diffusion.unsqueeze(-1						)
								lowercase__     				=    drift - diffusion**2 * model_output

								#  equation 6: sample noise for the diffusion term of
								lowercase__     				=    randn_tensor(
								    sample.shape,						layout=sample.layout,						generator=lowerCamelCase,						device=sample.device,						dtype=sample.dtype						)
								lowercase__     				=    sample - drift  # subtract because `dt` is a small negative timestep
								# TODO is the variable diffusion the correct scaling term for the noise?
								lowercase__     				=    prev_sample_mean + diffusion * noise  # add impact of diffusion field g

								if not return_dict:
												return (prev_sample, prev_sample_mean)

								return SdeVeOutput(prev_sample=lowerCamelCase,						prev_sample_mean=lowerCamelCase						)




				def        lowercase__     (  self		:					int,						lowerCamelCase		:					torch.FloatTensor,						lowerCamelCase		:					torch.FloatTensor,						lowerCamelCase		:					Optional[torch.Generator] = None,						lowerCamelCase		:					bool = True,						):

								'''simple docstring'''
								if self.timesteps is None:
												raise ValueError(
												    '''`self.timesteps` is not set, you need to run \'set_timesteps\' after creating the scheduler'''						)

								# For small batch sizes, the paper "suggest replacing norm(z) with sqrt(d), where d is the dim. of z"
								# sample noise for correction
								lowercase__     				=    randn_tensor(sample.shape,						layout=sample.layout,						generator=lowerCamelCase						).to(sample.device						)

								# compute step size from the model_output, the noise, and the snr
								lowercase__     				=    torch.norm(model_output.reshape(model_output.shape[0],						-1						),						dim=-1						).mean()
								lowercase__     				=    torch.norm(noise.reshape(noise.shape[0],						-1						),						dim=-1						).mean()
								lowercase__     				=    (self.config.snr * noise_norm / grad_norm) ** 2 * 2
								lowercase__     				=    step_size * torch.ones(sample.shape[0]						).to(sample.device						)
								# self.repeat_scalar(step_size, sample.shape[0])

								# compute corrected sample: model_output term and noise term
								lowercase__     				=    step_size.flatten()
								while len(step_size.shape						) < len(sample.shape						):
												lowercase__     				=    step_size.unsqueeze(-1						)
								lowercase__     				=    sample + step_size * model_output
								lowercase__     				=    prev_sample_mean + ((step_size * 2) ** 0.5) * noise

								if not return_dict:
												return (prev_sample,)

								return SchedulerOutput(prev_sample=lowerCamelCase						)




				def        lowercase__     (  self		:					List[str],						lowerCamelCase		:					torch.FloatTensor,						lowerCamelCase		:					torch.FloatTensor,						lowerCamelCase		:					torch.FloatTensor,						):

								'''simple docstring'''
								# Make sure sigmas and timesteps have the same device and dtype as original_samples
								lowercase__     				=    timesteps.to(original_samples.device						)
								lowercase__     				=    self.discrete_sigmas.to(original_samples.device						)[timesteps]
								lowercase__     				=    (
								    noise * sigmas[:, None, None, None]
								    if noise is not None
								    else torch.randn_like(lowerCamelCase						) * sigmas[:, None, None, None]
								)
								lowercase__     				=    noise + original_samples
								return noisy_samples




				def __len__(  self		:					Union[str, Any]						):

								'''simple docstring'''
								return self.config.num_train_timesteps




