

'''simple docstring'''

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

import numpy as np
import torch

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


lowerCAmelCase_     =      logging.get_logger(__name__)  # pylint: disable=invalid-name







@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->EulerAncestralDiscrete
class lowerCamelCase   (    __lowerCAmelCase				):
				snake_case_							     = 42
				snake_case_							     = None

def      __magic_name__							(			A       , A=0.999       , A="cosine"       , )      ->		Optional[int]:
	if alpha_transform_type == "cosine":

		def alpha_bar_fn(A				):
			return math.cos((t + 0.008) / 1.008 * math.pi / 2				) ** 2

	elif alpha_transform_type == "exp":

		def alpha_bar_fn(A				):
			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 lowerCamelCase   (    __lowerCAmelCase				,				__lowerCAmelCase				):
				snake_case_							     = [e.name for e in KarrasDiffusionSchedulers]
				snake_case_							     = 1

				@register_to_config
				def __init__(       self,						lowercase_ = 1000,						lowercase_ = 0.0_001,						lowercase_ = 0.02,						lowercase_ = "linear",						lowercase_ = None,						lowercase_ = "epsilon",						lowercase_ = "linspace",						lowercase_ = 0,						)    ->							List[str]:
					if trained_betas is not None:
						snake_case							=					torch.tensor(lowercase_,						dtype=torch.floataa )
					elif beta_schedule == "linear":
						snake_case							=					torch.linspace(lowercase_,						lowercase_,						lowercase_,						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,						lowercase_,						dtype=torch.floataa ) ** 2
						)
					elif beta_schedule == "squaredcos_cap_v2":
						# Glide cosine schedule
						snake_case							=					betas_for_alpha_bar(lowercase_ )
					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 )

					snake_case							=					np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5 )
					snake_case							=					np.concatenate([sigmas[::-1], [0.0]] ).astype(np.floataa )
					snake_case							=					torch.from_numpy(lowercase_ )

					# setable values
					snake_case							=					None
					snake_case							=					np.linspace(0,						num_train_timesteps - 1,						lowercase_,						dtype=lowercase_ )[::-1].copy()
					snake_case							=					torch.from_numpy(lowercase_ )
					snake_case							=					False

				@property
				def 					_lowerCamelCase    (       self )    ->							Any:
					# 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 					_lowerCamelCase    (       self,						lowercase_,						lowercase_ )    ->							torch.FloatTensor:
					if isinstance(lowercase_,						torch.Tensor ):
						snake_case							=					timestep.to(self.timesteps.device )
					snake_case							=					(self.timesteps == timestep).nonzero().item()
					snake_case							=					self.sigmas[step_index]
					snake_case							=					sample / ((sigma**2 + 1) ** 0.5)
					snake_case							=					True
					return sample

				def 					_lowerCamelCase    (       self,						lowercase_,						lowercase_ = None )    ->							List[Any]:
					snake_case							=					num_inference_steps

					# "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,						self.config.num_train_timesteps - 1,						lowercase_,						dtype=lowercase_ )[
						    ::-1
						].copy()
					elif self.config.timestep_spacing == "leading":
						snake_case							=					self.config.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,						lowercase_ ) * step_ratio).round()[::-1].copy().astype(lowercase_ )
						timesteps += self.config.steps_offset
					elif self.config.timestep_spacing == "trailing":
						snake_case							=					self.config.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(self.config.num_train_timesteps,						0,						-step_ratio )).round().copy().astype(lowercase_ )
						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							=					np.interp(lowercase_,						np.arange(0,						len(lowercase_ ) ),						lowercase_ )
					snake_case							=					np.concatenate([sigmas, [0.0]] ).astype(np.floataa )
					snake_case							=					torch.from_numpy(lowercase_ ).to(device=lowercase_ )
					if str(lowercase_ ).startswith('mps' ):
						# mps does not support float64
						snake_case							=					torch.from_numpy(lowercase_ ).to(lowercase_,						dtype=torch.floataa )
					else:
						snake_case							=					torch.from_numpy(lowercase_ ).to(device=lowercase_ )

				def 					_lowerCamelCase    (       self,						lowercase_,						lowercase_,						lowercase_,						lowercase_ = None,						lowercase_ = True,						)    ->							Union[EulerAncestralDiscreteSchedulerOutput, Tuple]:

					if (
					    isinstance(lowercase_,						lowercase_ )
					    or isinstance(lowercase_,						torch.IntTensor )
					    or isinstance(lowercase_,						torch.LongTensor )
					):
						raise ValueError(
						    (
						        'Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to'
						        ' `EulerDiscreteScheduler.step()` is not supported. Make sure to pass'
						        ' one of the `scheduler.timesteps` as a timestep.'
						    ),						)

					if not self.is_scale_input_called:
						logger.warning(
						    'The `scale_model_input` function should be called before `step` to ensure correct denoising. '
						    'See `StableDiffusionPipeline` for a usage example.' )

					if isinstance(lowercase_,						torch.Tensor ):
						snake_case							=					timestep.to(self.timesteps.device )

					snake_case							=					(self.timesteps == timestep).nonzero().item()
					snake_case							=					self.sigmas[step_index]

					# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
					if self.config.prediction_type == "epsilon":
						snake_case							=					sample - sigma * model_output
					elif self.config.prediction_type == "v_prediction":
						# * c_out + input * c_skip
						snake_case							=					model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**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`''' )

					snake_case							=					self.sigmas[step_index]
					snake_case							=					self.sigmas[step_index + 1]
					snake_case							=					(sigma_to**2 * (sigma_from**2 - sigma_to**2) / sigma_from**2) ** 0.5
					snake_case							=					(sigma_to**2 - sigma_up**2) ** 0.5

					# 2. Convert to an ODE derivative
					snake_case							=					(sample - pred_original_sample) / sigma

					snake_case							=					sigma_down - sigma

					snake_case							=					sample + derivative * dt

					snake_case							=					model_output.device
					snake_case							=					randn_tensor(model_output.shape,						dtype=model_output.dtype,						device=lowercase_,						generator=lowercase_ )

					snake_case							=					prev_sample + noise * sigma_up

					if not return_dict:
						return (prev_sample,)

					return EulerAncestralDiscreteSchedulerOutput(
					    prev_sample=lowercase_,						pred_original_sample=lowercase_ )

				def 					_lowerCamelCase    (       self,						lowercase_,						lowercase_,						lowercase_,						)    ->							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(lowercase_ ):
						# 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							=					[(schedule_timesteps == t).nonzero().item() 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 )    ->							List[str]:
					return self.config.num_train_timesteps








'''simple docstring'''

import unittest

import requests

from transformers import MODEL_FOR_VISION_2_SEQ_MAPPING, TF_MODEL_FOR_VISION_2_SEQ_MAPPING, is_vision_available
from transformers.pipelines import pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    is_torch_available,
    require_tf,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_torch_available():
						from transformers.pytorch_utils import is_torch_greater_or_equal_than_1_11
else:
						lowerCAmelCase_     =      False


if is_vision_available():
						from PIL import Image
else:







						class lowerCamelCase   :

										@staticmethod
										def 					_lowerCamelCase    (       *lowercase_,						**lowercase_ )    ->							List[Any]:
											pass







@is_pipeline_test
@require_vision
class lowerCamelCase   (    unittest.TestCase				):
				snake_case_							     = MODEL_FOR_VISION_2_SEQ_MAPPING
				snake_case_							     = TF_MODEL_FOR_VISION_2_SEQ_MAPPING

				def 					_lowerCamelCase    (       self,						lowercase_,						lowercase_,						lowercase_ )    ->							Optional[Any]:
					snake_case							=					pipeline('image-to-text',						model=lowercase_,						tokenizer=lowercase_,						image_processor=lowercase_ )
					snake_case							=					[
					    Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' ),
					    './tests/fixtures/tests_samples/COCO/000000039769.png',
					]
					return pipe, examples

				def 					_lowerCamelCase    (       self,						lowercase_,						lowercase_ )    ->							Tuple:
					snake_case							=					pipe(lowercase_ )
					self.assertEqual(
					    lowercase_,						[
					        [{'generated_text': ANY(lowercase_ )}],
					        [{'generated_text': ANY(lowercase_ )}],
					    ],						)

				@require_tf
				def 					_lowerCamelCase    (       self )    ->							int:
					snake_case							=					pipeline('image-to-text',						model='hf-internal-testing/tiny-random-vit-gpt2',						framework='tf' )
					snake_case							=					'./tests/fixtures/tests_samples/COCO/000000039769.png'

					snake_case							=					pipe(lowercase_ )
					self.assertEqual(
					    lowercase_,						[
					        {
					            'generated_text': 'growthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthGOGO'
					        },
					    ],						)

					snake_case							=					pipe([image, image] )
					self.assertEqual(
					    lowercase_,						[
					        [
					            {
					                'generated_text': 'growthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthGOGO'
					            }
					        ],
					        [
					            {
					                'generated_text': 'growthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthGOGO'
					            }
					        ],
					    ],						)

					snake_case							=					pipe(lowercase_,						max_new_tokens=1 )
					self.assertEqual(
					    lowercase_,						[{'generated_text': 'growth'}],						)

				@require_torch
				def 					_lowerCamelCase    (       self )    ->							Tuple:
					snake_case							=					pipeline('image-to-text',						model='hf-internal-testing/tiny-random-vit-gpt2' )
					snake_case							=					'./tests/fixtures/tests_samples/COCO/000000039769.png'

					snake_case							=					pipe(lowercase_ )
					self.assertEqual(
					    lowercase_,						[
					        {
					            'generated_text': 'growthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthGOGO'
					        },
					    ],						)

					snake_case							=					pipe([image, image] )
					self.assertEqual(
					    lowercase_,						[
					        [
					            {
					                'generated_text': 'growthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthGOGO'
					            }
					        ],
					        [
					            {
					                'generated_text': 'growthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthGOGO'
					            }
					        ],
					    ],						)

				@require_torch
				def 					_lowerCamelCase    (       self )    ->							Union[str, Any]:
					snake_case							=					pipeline('image-to-text',						model='hf-internal-testing/tiny-random-BlipForConditionalGeneration' )
					snake_case							=					'./tests/fixtures/tests_samples/COCO/000000039769.png'
					snake_case							=					'a photo of'

					snake_case							=					pipe(lowercase_,						prompt=lowercase_ )
					self.assertTrue(outputs[0]['generated_text'].startswith(lowercase_ ) )

				@slow
				@require_torch
				def 					_lowerCamelCase    (       self )    ->							Optional[Any]:
					snake_case							=					pipeline('image-to-text',						model='ydshieh/vit-gpt2-coco-en' )
					snake_case							=					'./tests/fixtures/tests_samples/COCO/000000039769.png'

					snake_case							=					pipe(lowercase_ )
					self.assertEqual(lowercase_,						[{'generated_text': 'a cat laying on a blanket next to a cat laying on a bed '}] )

					snake_case							=					pipe([image, image] )
					self.assertEqual(
					    lowercase_,						[
					        [{'generated_text': 'a cat laying on a blanket next to a cat laying on a bed '}],
					        [{'generated_text': 'a cat laying on a blanket next to a cat laying on a bed '}],
					    ],						)

				@slow
				@require_torch
				def 					_lowerCamelCase    (       self )    ->							List[Any]:
					snake_case							=					pipeline('image-to-text',						model='Salesforce/blip-image-captioning-base' )
					snake_case							=					'https://huggingface.co/datasets/sayakpaul/sample-datasets/resolve/main/pokemon.png'
					snake_case							=					Image.open(requests.get(lowercase_,						stream=lowercase_ ).raw )

					snake_case							=					pipe(lowercase_ )
					self.assertEqual(lowercase_,						[{'generated_text': 'a pink pokemon pokemon with a blue shirt and a blue shirt'}] )

				@slow
				@require_torch
				def 					_lowerCamelCase    (       self )    ->							Dict:
					snake_case							=					pipeline('image-to-text',						model='microsoft/git-base-coco' )
					snake_case							=					'https://huggingface.co/datasets/sayakpaul/sample-datasets/resolve/main/pokemon.png'
					snake_case							=					Image.open(requests.get(lowercase_,						stream=lowercase_ ).raw )

					snake_case							=					pipe(lowercase_ )
					self.assertEqual(lowercase_,						[{'generated_text': 'a cartoon of a purple character.'}] )

				@slow
				@require_torch
				def 					_lowerCamelCase    (       self )    ->							Optional[Any]:
					snake_case							=					pipeline('image-to-text',						model='Salesforce/blip-image-captioning-base' )
					snake_case							=					'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/ai2d-demo.jpg'
					snake_case							=					Image.open(requests.get(lowercase_,						stream=lowercase_ ).raw )

					snake_case							=					'a photography of'

					snake_case							=					pipe(lowercase_,						prompt=lowercase_ )
					self.assertEqual(lowercase_,						[{'generated_text': 'a photography of a volcano'}] )

					with self.assertRaises(lowercase_ ):
						snake_case							=					pipe([image, image],						prompt=[prompt, prompt] )

				@slow
				@require_torch
				def 					_lowerCamelCase    (       self )    ->							Dict:
					snake_case							=					pipeline('image-to-text',						model='microsoft/git-base-coco' )
					snake_case							=					'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/ai2d-demo.jpg'
					snake_case							=					Image.open(requests.get(lowercase_,						stream=lowercase_ ).raw )

					snake_case							=					'a photo of a'

					snake_case							=					pipe(lowercase_,						prompt=lowercase_ )
					self.assertEqual(lowercase_,						[{'generated_text': 'a photo of a tent with a tent and a tent in the background.'}] )

					with self.assertRaises(lowercase_ ):
						snake_case							=					pipe([image, image],						prompt=[prompt, prompt] )

				@unittest.skipIf(
				    not is_torch_greater_or_equal_than_1_11,						reason='`Pix2StructImageProcessor` requires `torch>=1.11.0`.' )
				@slow
				@require_torch
				def 					_lowerCamelCase    (       self )    ->							Any:
					snake_case							=					pipeline('image-to-text',						model='google/pix2struct-ai2d-base' )
					snake_case							=					'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/ai2d-demo.jpg'
					snake_case							=					Image.open(requests.get(lowercase_,						stream=lowercase_ ).raw )

					snake_case							=					'What does the label 15 represent? (1) lava (2) core (3) tunnel (4) ash cloud'

					snake_case							=					pipe(lowercase_,						prompt=lowercase_ )
					self.assertEqual(lowercase_,						[{'generated_text': 'ash cloud'}] )

					with self.assertRaises(lowercase_ ):
						snake_case							=					pipe([image, image],						prompt=[prompt, prompt] )






				@slow
				@require_tf
				def 					_lowerCamelCase    (       self )    ->							Optional[int]:
					snake_case							=					pipeline('image-to-text',						model='ydshieh/vit-gpt2-coco-en',						framework='tf' )
					snake_case							=					'./tests/fixtures/tests_samples/COCO/000000039769.png'

					snake_case							=					pipe(lowercase_ )
					self.assertEqual(lowercase_,						[{'generated_text': 'a cat laying on a blanket next to a cat laying on a bed '}] )

					snake_case							=					pipe([image, image] )
					self.assertEqual(
					    lowercase_,						[
					        [{'generated_text': 'a cat laying on a blanket next to a cat laying on a bed '}],
					        [{'generated_text': 'a cat laying on a blanket next to a cat laying on a bed '}],
					    ],						)





