

from logging import getLogger
from typing import Any, Callable, List, Optional, Union

import numpy as np
import PIL
import torch

from ...schedulers import DDPMScheduler
from ..onnx_utils import ORT_TO_NP_TYPE, OnnxRuntimeModel
from ..pipeline_utils import ImagePipelineOutput
from . import StableDiffusionUpscalePipeline


SCREAMING_SNAKE_CASE						:Optional[Any]      =			getLogger(__name__)


SCREAMING_SNAKE_CASE						:List[str]      =			4
SCREAMING_SNAKE_CASE						:List[str]      =			7

SCREAMING_SNAKE_CASE						:Union[str, Any]      =			{
    """float16""": torch.floataa,
    """float32""": torch.floataa,
}




def        lowerCAmelCase(	SCREAMING_SNAKE_CASE_       )->		Tuple:



    """simple docstring"""






    if isinstance(SCREAMING_SNAKE_CASE_       ,		torch.Tensor       ):
        return image
    elif isinstance(SCREAMING_SNAKE_CASE_       ,		PIL.Image.Image       ):
        UpperCamelCase_          =		[image]

    if isinstance(image[0]       ,		PIL.Image.Image       ):
        UpperCamelCase_   ,    UpperCamelCase_          =		image[0].size
        UpperCamelCase_   ,    UpperCamelCase_          =		(x - x % 6_4 for x in (w, h))  # resize to integer multiple of 32

        UpperCamelCase_          =		[np.array(i.resize((w, h)       )       )[None, :] for i in image]
        UpperCamelCase_          =		np.concatenate(SCREAMING_SNAKE_CASE_       ,		axis=0       )
        UpperCamelCase_          =		np.array(SCREAMING_SNAKE_CASE_       ).astype(np.floataa       ) / 255.0
        UpperCamelCase_          =		image.transpose(0       ,		3       ,		1       ,		2       )
        UpperCamelCase_          =		2.0 * image - 1.0
        UpperCamelCase_          =		torch.from_numpy(SCREAMING_SNAKE_CASE_       )
    elif isinstance(image[0]       ,		torch.Tensor       ):
        UpperCamelCase_          =		torch.cat(SCREAMING_SNAKE_CASE_       ,		dim=0       )

    return image






class 		__magic_name__ (				snake_case							):






     def __init__(							self						,		_lowercase						,		_lowercase						,		_lowercase						,		_lowercase						,		_lowercase						,		_lowercase						,		_lowercase = 350						,		)->			List[Any]:
         super().__init__(
             vae=_lowercase						,		text_encoder=_lowercase						,		tokenizer=_lowercase						,		unet=_lowercase						,		low_res_scheduler=_lowercase						,		scheduler=_lowercase						,		safety_checker=_lowercase						,		feature_extractor=_lowercase						,		watermarker=_lowercase						,		max_noise_level=_lowercase						,		)






     def __call__(							self						,		_lowercase						,		_lowercase						,		_lowercase = 75						,		_lowercase = 9.0						,		_lowercase = 20						,		_lowercase = None						,		_lowercase = 1						,		_lowercase = 0.0						,		_lowercase = None						,		_lowercase = None						,		_lowercase = None						,		_lowercase = None						,		_lowercase = "pil"						,		_lowercase = True						,		_lowercase = None						,		_lowercase = 1						,		)->			Union[str, Any]:
         self.check_inputs(_lowercase						,		_lowercase						,		_lowercase						,		_lowercase     )

         # 2. Define call parameters
         if prompt is not None and isinstance(_lowercase						,		_lowercase     ):
             UpperCamelCase_          =		1
         elif prompt is not None and isinstance(_lowercase						,		_lowercase     ):
             UpperCamelCase_          =		len(_lowercase     )
         else:
             UpperCamelCase_          =		prompt_embeds.shape[0]

         UpperCamelCase_          =		self._execution_device
         # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
         # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
         # corresponds to doing no classifier free guidance.
         UpperCamelCase_          =		guidance_scale > 1.0

         # 3. Encode input prompt
         UpperCamelCase_          =		self._encode_prompt(
             _lowercase						,		_lowercase						,		_lowercase						,		_lowercase						,		_lowercase						,		prompt_embeds=_lowercase						,		negative_prompt_embeds=_lowercase						,		)

         UpperCamelCase_          =		ORT_TO_PT_TYPE[str(text_embeddings.dtype     )]

         # 4. Preprocess image
         UpperCamelCase_          =		preprocess(_lowercase     )
         UpperCamelCase_          =		image.cpu()

         # 5. set timesteps
         self.scheduler.set_timesteps(_lowercase						,		device=_lowercase     )
         UpperCamelCase_          =		self.scheduler.timesteps

         # 5. Add noise to image
         UpperCamelCase_          =		torch.tensor([noise_level]						,		dtype=torch.long						,		device=_lowercase     )
         UpperCamelCase_          =		torch.randn(image.shape						,		generator=_lowercase						,		device=_lowercase						,		dtype=_lowercase     )
         UpperCamelCase_          =		self.low_res_scheduler.add_noise(_lowercase						,		_lowercase						,		_lowercase     )

         UpperCamelCase_          =		2 if do_classifier_free_guidance else 1
         UpperCamelCase_          =		np.concatenate([image] * batch_multiplier * num_images_per_prompt     )
         UpperCamelCase_          =		np.concatenate([noise_level] * image.shape[0]     )

         # 6. Prepare latent variables
         UpperCamelCase_   ,    UpperCamelCase_          =		image.shape[2:]
         UpperCamelCase_          =		self.prepare_latents(
             batch_size * num_images_per_prompt						,		_lowercase						,		_lowercase						,		_lowercase						,		_lowercase						,		_lowercase						,		_lowercase						,		_lowercase						,		)

         # 7. Check that sizes of image and latents match
         UpperCamelCase_          =		image.shape[1]
         if NUM_LATENT_CHANNELS + num_channels_image != NUM_UNET_INPUT_CHANNELS:
             raise ValueError(
                 "Incorrect configuration settings! The config of `pipeline.unet` expects"
                 F" {NUM_UNET_INPUT_CHANNELS} but received `num_channels_latents`: {NUM_LATENT_CHANNELS} +"
                 F" `num_channels_image`: {num_channels_image} "
                 F" = {NUM_LATENT_CHANNELS+num_channels_image}. Please verify the config of"
                 " `pipeline.unet` or your `image` input."     )

         # 8. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
         UpperCamelCase_          =		self.prepare_extra_step_kwargs(_lowercase						,		_lowercase     )

         UpperCamelCase_          =		next(
             (input.type for input in self.unet.model.get_inputs() if input.name == "timestep")						,		"tensor(float)"     )
         UpperCamelCase_          =		ORT_TO_NP_TYPE[timestep_dtype]

         # 9. Denoising loop
         UpperCamelCase_          =		len(_lowercase     ) - num_inference_steps * self.scheduler.order
         with self.progress_bar(total=_lowercase     ) as progress_bar:
             for i, t in enumerate(_lowercase     ):
                 # expand the latents if we are doing classifier free guidance
                 UpperCamelCase_          =		np.concatenate([latents] * 2     ) if do_classifier_free_guidance else latents

                 # concat latents, mask, masked_image_latents in the channel dimension
                 UpperCamelCase_          =		self.scheduler.scale_model_input(_lowercase						,		_lowercase     )
                 UpperCamelCase_          =		np.concatenate([latent_model_input, image]						,		axis=1     )

                 # timestep to tensor
                 UpperCamelCase_          =		np.array([t]						,		dtype=_lowercase     )

                 # predict the noise residual
                 UpperCamelCase_          =		self.unet(
                     sample=_lowercase						,		timestep=_lowercase						,		encoder_hidden_states=_lowercase						,		class_labels=noise_level.astype(np.intaa     )						,		)[0]

                 # perform guidance
                 if do_classifier_free_guidance:
                     UpperCamelCase_   ,    UpperCamelCase_          =		np.split(_lowercase						,		2     )
                 UpperCamelCase_          =		noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

                 # compute the previous noisy sample x_t -> x_t-1
                 UpperCamelCase_          =		self.scheduler.step(
                     torch.from_numpy(_lowercase     )						,		_lowercase						,		_lowercase						,		**_lowercase     ).prev_sample

                 # call the callback, if provided
                 if i == len(_lowercase     ) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
                     progress_bar.update()
                     if callback is not None and i % callback_steps == 0:
                         callback(_lowercase						,		_lowercase						,		_lowercase     )

        # 10. Post-processing
         UpperCamelCase_          =		self.decode_latents(latents.float()     )

         # 11. Convert to PIL
         if output_type == "pil":
             UpperCamelCase_          =		self.numpy_to_pil(_lowercase     )

         if not return_dict:
             return (image,)

         return ImagePipelineOutput(images=_lowercase     )






     def      UpperCAmelCase_							(							self						,		_lowercase     )->			Optional[int]:
         UpperCamelCase_          =		1 / 0.08_333 * latents
         UpperCamelCase_          =		self.vae(latent_sample=_lowercase     )[0]
         UpperCamelCase_          =		np.clip(image / 2 + 0.5						,		0						,		1     )
         UpperCamelCase_          =		image.transpose((0, 2, 3, 1)     )
         return image






     def      UpperCAmelCase_							(							self						,		_lowercase						,		_lowercase						,		_lowercase						,		_lowercase						,		_lowercase						,		_lowercase = None						,		_lowercase = None						,		)->			Tuple:
         if prompt is not None and isinstance(_lowercase						,		_lowercase     ):
             UpperCamelCase_          =		1
         elif prompt is not None and isinstance(_lowercase						,		_lowercase     ):
             UpperCamelCase_          =		len(_lowercase     )
         else:
             UpperCamelCase_          =		prompt_embeds.shape[0]

         if prompt_embeds is None:
             UpperCamelCase_          =		self.tokenizer(
                 _lowercase						,		padding="max_length"						,		max_length=self.tokenizer.model_max_length						,		truncation=_lowercase						,		return_tensors="pt"						,		)
             UpperCamelCase_          =		text_inputs.input_ids
             UpperCamelCase_          =		self.tokenizer(_lowercase						,		padding="longest"						,		return_tensors="pt"     ).input_ids

             if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(
                 _lowercase						,		_lowercase     ):
                 UpperCamelCase_          =		self.tokenizer.batch_decode(
                     untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]     )
                 logger.warning(
                     "The following part of your input was truncated because CLIP can only handle sequences up to"
                     F" {self.tokenizer.model_max_length} tokens: {removed_text}"     )

             # no positional arguments to text_encoder
             UpperCamelCase_          =		self.text_encoder(
                 input_ids=text_input_ids.int().to(_lowercase     )						,		)
             UpperCamelCase_          =		prompt_embeds[0]

         UpperCamelCase_   ,    UpperCamelCase_   ,    UpperCamelCase_          =		prompt_embeds.shape
         # duplicate text embeddings for each generation per prompt, using mps friendly method
         UpperCamelCase_          =		prompt_embeds.repeat(1						,		_lowercase     )
         UpperCamelCase_          =		prompt_embeds.reshape(bs_embed * num_images_per_prompt						,		_lowercase						,		-1     )

         # get unconditional embeddings for classifier free guidance
         if do_classifier_free_guidance and negative_prompt_embeds is None:
             UpperCamelCase_          =		42
             if negative_prompt is None:
                 UpperCamelCase_          =		[""] * batch_size
             elif type(_lowercase     ) is not type(_lowercase     ):
                 raise TypeError(
                     F"`negative_prompt` should be the same type to `prompt`, but got {type(_lowercase     )} !="
                     F" {type(_lowercase     )}."     )
             elif isinstance(_lowercase						,		_lowercase     ):
                 UpperCamelCase_          =		[negative_prompt]
             elif batch_size != len(_lowercase     ):
                 raise ValueError(
                     F"`negative_prompt`: {negative_prompt} has batch size {len(_lowercase     )}, but `prompt`:"
                     F" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
                     " the batch size of `prompt`."     )
             else:
                 UpperCamelCase_          =		negative_prompt

             UpperCamelCase_          =		text_input_ids.shape[-1]
             UpperCamelCase_          =		self.tokenizer(
                 _lowercase						,		padding="max_length"						,		max_length=_lowercase						,		truncation=_lowercase						,		return_tensors="pt"						,		)

             # if hasattr(uncond_input, "attention_mask"):
             #     attention_mask = uncond_input.attention_mask.to(device)
             # else:
             #     attention_mask = None

             UpperCamelCase_          =		self.text_encoder(
                 input_ids=uncond_input.input_ids.int().to(_lowercase     )						,		)
             UpperCamelCase_          =		uncond_embeddings[0]

         if do_classifier_free_guidance:
             UpperCamelCase_          =		uncond_embeddings.shape[1]
             # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
             UpperCamelCase_          =		uncond_embeddings.repeat(1						,		_lowercase     )
             UpperCamelCase_          =		uncond_embeddings.reshape(batch_size * num_images_per_prompt						,		_lowercase						,		-1     )

             # For classifier free guidance, we need to do two forward passes.
             # Here we concatenate the unconditional and text embeddings into a single batch
             # to avoid doing two forward passes
             UpperCamelCase_          =		np.concatenate([uncond_embeddings, prompt_embeds]     )

         return prompt_embeds



import unittest

from transformers import (
    MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
    TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
    PreTrainedTokenizer,
    is_vision_available,
)
from transformers.pipelines import ImageClassificationPipeline, pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_torch,
    require_torch_or_tf,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_vision_available():
 from PIL import Image
else:






 class 		__magic_name__ :






      @staticmethod
      def      UpperCAmelCase_							(							*_lowercase						,		**_lowercase     )->			Tuple:
          pass






@is_pipeline_test
@require_torch_or_tf
@require_vision
class 		__magic_name__ (				unittest.TestCase							):
     UpperCamelCase_     :Union[str, Any]									=	MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
     UpperCamelCase_     :Any									=	TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING






     def      UpperCAmelCase_							(							self						,		_lowercase						,		_lowercase						,		_lowercase     )->			List[Any]:
         UpperCamelCase_          =		ImageClassificationPipeline(model=_lowercase						,		image_processor=_lowercase						,		top_k=2     )
         UpperCamelCase_          =		[
             Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"     ),
             "http://images.cocodataset.org/val2017/000000039769.jpg",
         ]
         return image_classifier, examples






     def      UpperCAmelCase_							(							self						,		_lowercase						,		_lowercase     )->			List[str]:
         UpperCamelCase_          =		image_classifier("./tests/fixtures/tests_samples/COCO/000000039769.png"     )

         self.assertEqual(
             _lowercase						,		[
                 {"score": ANY(_lowercase     ), "label": ANY(_lowercase     )},
                 {"score": ANY(_lowercase     ), "label": ANY(_lowercase     )},
             ]						,		)

         import datasets

         UpperCamelCase_          =		datasets.load_dataset("hf-internal-testing/fixtures_image_utils"						,		"image"						,		split="test"     )

         # Accepts URL + PIL.Image + lists
         UpperCamelCase_          =		image_classifier(
             [
                 Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"     ),
                 "http://images.cocodataset.org/val2017/000000039769.jpg",
                 # RGBA
                 dataset[0]["file"],
                 # LA
                 dataset[1]["file"],
                 # L
                 dataset[2]["file"],
             ]     )
         self.assertEqual(
             _lowercase						,		[
                 [
                     {"score": ANY(_lowercase     ), "label": ANY(_lowercase     )},
                     {"score": ANY(_lowercase     ), "label": ANY(_lowercase     )},
                 ],
                 [
                     {"score": ANY(_lowercase     ), "label": ANY(_lowercase     )},
                     {"score": ANY(_lowercase     ), "label": ANY(_lowercase     )},
                 ],
                 [
                     {"score": ANY(_lowercase     ), "label": ANY(_lowercase     )},
                     {"score": ANY(_lowercase     ), "label": ANY(_lowercase     )},
                 ],
                 [
                     {"score": ANY(_lowercase     ), "label": ANY(_lowercase     )},
                     {"score": ANY(_lowercase     ), "label": ANY(_lowercase     )},
                 ],
                 [
                     {"score": ANY(_lowercase     ), "label": ANY(_lowercase     )},
                     {"score": ANY(_lowercase     ), "label": ANY(_lowercase     )},
                 ],
             ]						,		)






     @require_torch
     def      UpperCAmelCase_							(							self     )->			str:
         UpperCamelCase_          =		"hf-internal-testing/tiny-random-vit"
         UpperCamelCase_          =		pipeline("image-classification"						,		model=_lowercase     )

         UpperCamelCase_          =		image_classifier("http://images.cocodataset.org/val2017/000000039769.jpg"     )
         self.assertEqual(
             nested_simplify(_lowercase						,		decimals=4     )						,		[{"label": "LABEL_1", "score": 0.574}, {"label": "LABEL_0", "score": 0.426}]						,		)

         UpperCamelCase_          =		image_classifier(
             [
                 "http://images.cocodataset.org/val2017/000000039769.jpg",
                 "http://images.cocodataset.org/val2017/000000039769.jpg",
             ]						,		top_k=2						,		)
         self.assertEqual(
             nested_simplify(_lowercase						,		decimals=4     )						,		[
                 [{"label": "LABEL_1", "score": 0.574}, {"label": "LABEL_0", "score": 0.426}],
                 [{"label": "LABEL_1", "score": 0.574}, {"label": "LABEL_0", "score": 0.426}],
             ]						,		)






     @require_tf
     def      UpperCAmelCase_							(							self     )->			Tuple:
         UpperCamelCase_          =		"hf-internal-testing/tiny-random-vit"
         UpperCamelCase_          =		pipeline("image-classification"						,		model=_lowercase						,		framework="tf"     )

         UpperCamelCase_          =		image_classifier("http://images.cocodataset.org/val2017/000000039769.jpg"     )
         self.assertEqual(
             nested_simplify(_lowercase						,		decimals=4     )						,		[{"label": "LABEL_1", "score": 0.574}, {"label": "LABEL_0", "score": 0.426}]						,		)

         UpperCamelCase_          =		image_classifier(
             [
                 "http://images.cocodataset.org/val2017/000000039769.jpg",
                 "http://images.cocodataset.org/val2017/000000039769.jpg",
             ]						,		top_k=2						,		)
         self.assertEqual(
             nested_simplify(_lowercase						,		decimals=4     )						,		[
                 [{"label": "LABEL_1", "score": 0.574}, {"label": "LABEL_0", "score": 0.426}],
                 [{"label": "LABEL_1", "score": 0.574}, {"label": "LABEL_0", "score": 0.426}],
             ]						,		)






     def      UpperCAmelCase_							(							self     )->			Optional[Any]:
         UpperCamelCase_          =		PreTrainedTokenizer()

         # Assert that the pipeline can be initialized with a feature extractor that is not in any mapping
         UpperCamelCase_          =		pipeline(
             "image-classification"						,		model="hf-internal-testing/tiny-random-vit"						,		tokenizer=_lowercase     )

         self.assertIs(image_classifier.tokenizer						,		_lowercase     )






     @slow
     @require_torch
     def      UpperCAmelCase_							(							self     )->			int:
         # Perceiver is not tested by `run_pipeline_test` properly.
         # That is because the type of feature_extractor and model preprocessor need to be kept
         # in sync, which is not the case in the current design
         UpperCamelCase_          =		pipeline("image-classification"						,		model="deepmind/vision-perceiver-conv"     )
         UpperCamelCase_          =		image_classifier("http://images.cocodataset.org/val2017/000000039769.jpg"     )
         self.assertEqual(
             nested_simplify(_lowercase						,		decimals=4     )						,		[
                 {"score": 0.4_385, "label": "tabby, tabby cat"},
                 {"score": 0.321, "label": "tiger cat"},
                 {"score": 0.0_502, "label": "Egyptian cat"},
                 {"score": 0.0_137, "label": "crib, cot"},
                 {"score": 0.007, "label": "radiator"},
             ]						,		)

         UpperCamelCase_          =		pipeline("image-classification"						,		model="deepmind/vision-perceiver-fourier"     )
         UpperCamelCase_          =		image_classifier("http://images.cocodataset.org/val2017/000000039769.jpg"     )
         self.assertEqual(
             nested_simplify(_lowercase						,		decimals=4     )						,		[
                 {"score": 0.5_658, "label": "tabby, tabby cat"},
                 {"score": 0.1_309, "label": "tiger cat"},
                 {"score": 0.0_722, "label": "Egyptian cat"},
                 {"score": 0.0_707, "label": "remote control, remote"},
                 {"score": 0.0_082, "label": "computer keyboard, keypad"},
             ]						,		)

         UpperCamelCase_          =		pipeline("image-classification"						,		model="deepmind/vision-perceiver-learned"     )
         UpperCamelCase_          =		image_classifier("http://images.cocodataset.org/val2017/000000039769.jpg"     )
         self.assertEqual(
             nested_simplify(_lowercase						,		decimals=4     )						,		[
                 {"score": 0.3_022, "label": "tabby, tabby cat"},
                 {"score": 0.2_362, "label": "Egyptian cat"},
                 {"score": 0.1_856, "label": "tiger cat"},
                 {"score": 0.0_324, "label": "remote control, remote"},
                 {"score": 0.0_096, "label": "quilt, comforter, comfort, puff"},
             ]						,		)
