




import collections
import inspect
import unittest

from transformers import DinatConfig
from transformers.testing_utils import require_natten, require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
 import torch
 from torch import nn

 from transformers import DinatBackbone, DinatForImageClassification, DinatModel
 from transformers.models.dinat.modeling_dinat import DINAT_PRETRAINED_MODEL_ARCHIVE_LIST

if is_vision_available():
 from PIL import Image

 from transformers import AutoImageProcessor







class 		__UpperCAmelCase:





     """simple docstring"""



     def __init__(      self :       Tuple						,				A :       Optional[Any]						,				A :       int=13						,				A :       Optional[Any]=64						,				A :       List[Any]=4						,				A :       Optional[Any]=3						,				A :       Optional[int]=16						,				A :       Any=[1, 2, 1]						,				A :       Optional[int]=[2, 4, 8]						,				A :       Union[str, Any]=3						,				A :       Dict=[[3], [1, 2], [1]]						,				A :       str=2.0						,				A :       List[str]=True						,				A :       Optional[int]=0.0						,				A :       Dict=0.0						,				A :       List[Any]=0.1						,				A :       Tuple="gelu"						,				A :       Any=True						,				A :       str=0.0_2						,				A :       List[str]=1E-5						,				A :       Any=True						,				A :       Tuple=None						,				A :       List[Any]=True						,				A :       Any=10						,				A :       Optional[int]=["stage1", "stage2"]						,				A :       Dict=[1, 2]						,				)			->			Any:






         """simple docstring"""
         a__      =       parent
         a__      =       batch_size
         a__      =       image_size
         a__      =       patch_size
         a__      =       num_channels
         a__      =       embed_dim
         a__      =       depths
         a__      =       num_heads
         a__      =       kernel_size
         a__      =       dilations
         a__      =       mlp_ratio
         a__      =       qkv_bias
         a__      =       hidden_dropout_prob
         a__      =       attention_probs_dropout_prob
         a__      =       drop_path_rate
         a__      =       hidden_act
         a__      =       patch_norm
         a__      =       layer_norm_eps
         a__      =       initializer_range
         a__      =       is_training
         a__      =       scope
         a__      =       use_labels
         a__      =       num_labels
         a__      =       out_features
         a__      =       out_indices

     def 				__SCREAMING_SNAKE_CASE		(      self :       Optional[int]    )			->			Optional[int]:






         """simple docstring"""
         a__      =       floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]    )

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

         a__      =       self.get_config()

         return config, pixel_values, labels

     def 				__SCREAMING_SNAKE_CASE		(      self :       int    )			->			Optional[int]:






         """simple docstring"""
         return DinatConfig(
             num_labels=self.num_labels						,				image_size=self.image_size						,				patch_size=self.patch_size						,				num_channels=self.num_channels						,				embed_dim=self.embed_dim						,				depths=self.depths						,				num_heads=self.num_heads						,				kernel_size=self.kernel_size						,				dilations=self.dilations						,				mlp_ratio=self.mlp_ratio						,				qkv_bias=self.qkv_bias						,				hidden_dropout_prob=self.hidden_dropout_prob						,				attention_probs_dropout_prob=self.attention_probs_dropout_prob						,				drop_path_rate=self.drop_path_rate						,				hidden_act=self.hidden_act						,				patch_norm=self.patch_norm						,				layer_norm_eps=self.layer_norm_eps						,				initializer_range=self.initializer_range						,				out_features=self.out_features						,				out_indices=self.out_indices						,				)

     def 				__SCREAMING_SNAKE_CASE		(      self :       Union[str, Any]						,				A :       Any						,				A :       int						,				A :       int    )			->			Optional[int]:






         """simple docstring"""
         a__      =       DinatModel(config=A    )
         model.to(A    )
         model.eval()
         a__      =       model(A    )

         a__      =       a__      =       (config.image_size // config.patch_size) // (2 ** (len(config.depths    ) - 1))
         a__      =       int(config.embed_dim * 2 ** (len(config.depths    ) - 1)    )

         self.parent.assertEqual(
             result.last_hidden_state.shape						,				(self.batch_size, expected_height, expected_width, expected_dim)    )

     def 				__SCREAMING_SNAKE_CASE		(      self :       Any						,				A :       Any						,				A :       str						,				A :       Optional[int]    )			->			int:






         """simple docstring"""
         a__      =       DinatForImageClassification(A    )
         model.to(A    )
         model.eval()
         a__      =       model(A						,				labels=A    )
         self.parent.assertEqual(result.logits.shape						,				(self.batch_size, self.num_labels)    )

         # test greyscale images
         a__      =       1
         a__      =       DinatForImageClassification(A    )
         model.to(A    )
         model.eval()

         a__      =       floats_tensor([self.batch_size, 1, self.image_size, self.image_size]    )
         a__      =       model(A    )
         self.parent.assertEqual(result.logits.shape						,				(self.batch_size, self.num_labels)    )

     def 				__SCREAMING_SNAKE_CASE		(      self :       Tuple						,				A :       List[str]						,				A :       Union[str, Any]						,				A :       Tuple    )			->			Optional[Any]:






         """simple docstring"""
         a__      =       DinatBackbone(config=A    )
         model.to(A    )
         model.eval()
         a__      =       model(A    )

         # verify hidden states
         self.parent.assertEqual(len(result.feature_maps    )						,				len(config.out_features    )    )
         self.parent.assertListEqual(list(result.feature_maps[0].shape    )						,				[self.batch_size, model.channels[0], 16, 16]    )

         # verify channels
         self.parent.assertEqual(len(model.channels    )						,				len(config.out_features    )    )

         # verify backbone works with out_features=None
         a__      =       None
         a__      =       DinatBackbone(config=A    )
         model.to(A    )
         model.eval()
         a__      =       model(A    )

         # verify feature maps
         self.parent.assertEqual(len(result.feature_maps    )						,				1    )
         self.parent.assertListEqual(list(result.feature_maps[0].shape    )						,				[self.batch_size, model.channels[-1], 4, 4]    )

         # verify channels
         self.parent.assertEqual(len(model.channels    )						,				1    )






     def 				__SCREAMING_SNAKE_CASE		(      self :       Dict    )			->			Optional[Any]:






         """simple docstring"""
         a__      =       self.prepare_config_and_inputs()
         a__ ,		a__ ,		a__      =       config_and_inputs
         a__      =       {"""pixel_values""": pixel_values}
         return config, inputs_dict







@require_natten
@require_torch
class 		__UpperCAmelCase(		lowercase__       ,	lowercase__       ,	unittest.TestCase ):





     """simple docstring"""



     __UpperCAmelCase	: int						=    (
         (
             DinatModel,
             DinatForImageClassification,
             DinatBackbone,
         )
         if is_torch_available()
         else ()
     )
     __UpperCAmelCase	: Optional[int]						=    (
         {"""feature-extraction""": DinatModel, """image-classification""": DinatForImageClassification}
         if is_torch_available()
         else {}
     )
     __UpperCAmelCase	: List[str]						=    False

     __UpperCAmelCase	: Dict						=    False
     __UpperCAmelCase	: Optional[int]						=    False
     __UpperCAmelCase	: Optional[int]						=    False
     __UpperCAmelCase	: int						=    False

     def 				__SCREAMING_SNAKE_CASE		(      self :       str    )			->			Dict:






         """simple docstring"""
         a__      =       DinatModelTester(self    )
         a__      =       ConfigTester(self						,				config_class=A						,				embed_dim=37    )

     def 				__SCREAMING_SNAKE_CASE		(      self :       Optional[int]    )			->			Any:






         """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 				__SCREAMING_SNAKE_CASE		(      self :       str    )			->			int:






         """simple docstring"""
         return

     def 				__SCREAMING_SNAKE_CASE		(      self :       Optional[int]    )			->			List[Any]:






         """simple docstring"""
         a__      =       self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*A    )

     def 				__SCREAMING_SNAKE_CASE		(      self :       Optional[int]    )			->			int:






         """simple docstring"""
         a__      =       self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_image_classification(*A    )

     def 				__SCREAMING_SNAKE_CASE		(      self :       Optional[Any]    )			->			Optional[Any]:






         """simple docstring"""
         a__      =       self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_backbone(*A    )

     @unittest.skip(reason="""Dinat does not use inputs_embeds"""    )
     def 				__SCREAMING_SNAKE_CASE		(      self :       str    )			->			List[Any]:






         """simple docstring"""
         pass

     @unittest.skip(reason="""Dinat does not use feedforward chunking"""    )
     def 				__SCREAMING_SNAKE_CASE		(      self :       int    )			->			Optional[Any]:






         """simple docstring"""
         pass

     def 				__SCREAMING_SNAKE_CASE		(      self :       Optional[int]    )			->			List[Any]:






         """simple docstring"""
         a__ ,		a__      =       self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
             a__      =       model_class(A    )
             self.assertIsInstance(model.get_input_embeddings()						,				(nn.Module)    )
             a__      =       model.get_output_embeddings()
             self.assertTrue(x is None or isinstance(A						,				nn.Linear    )    )

     def 				__SCREAMING_SNAKE_CASE		(      self :       str    )			->			Optional[int]:






         """simple docstring"""
         a__ ,		a__      =       self.model_tester.prepare_config_and_inputs_for_common()

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

             a__      =       ["""pixel_values"""]
             self.assertListEqual(arg_names[:1]						,				A    )

     def 				__SCREAMING_SNAKE_CASE		(      self :       Union[str, Any]    )			->			List[str]:






         """simple docstring"""
         self.skipTest("""Dinat's attention operation is handled entirely by NATTEN."""    )

     def 				__SCREAMING_SNAKE_CASE		(      self :       Optional[int]						,				A :       List[str]						,				A :       List[str]						,				A :       int						,				A :       List[Any]    )			->			List[str]:






         """simple docstring"""
         a__      =       model_class(A    )
         model.to(A    )
         model.eval()

         with torch.no_grad():
             a__      =       model(**self._prepare_for_class(A						,				A    )    )

         a__      =       outputs.hidden_states

         a__      =       getattr(
             self.model_tester						,				"""expected_num_hidden_layers"""						,				len(self.model_tester.depths    ) + 1    )
         self.assertEqual(len(A    )						,				A    )

         # Dinat has a different seq_length
         a__      =       (
             config.patch_size
             if isinstance(config.patch_size						,				collections.abc.Iterable    )
             else (config.patch_size, config.patch_size)
         )

         a__      =       image_size[0] // patch_size[0]
         a__      =       image_size[1] // patch_size[1]

         self.assertListEqual(
             list(hidden_states[0].shape[-3:]    )						,				[height, width, self.model_tester.embed_dim]						,				)

         if model_class.__name__ != "DinatBackbone":
             a__      =       outputs.reshaped_hidden_states
             self.assertEqual(len(A    )						,				A    )

             a__ ,		a__ ,		a__ ,		a__      =       reshaped_hidden_states[0].shape
             a__      =       (
                 reshaped_hidden_states[0].view(A						,				A						,				A						,				A    ).permute(0						,				2						,				3						,				1    )
             )
             self.assertListEqual(
                 list(reshaped_hidden_states.shape[-3:]    )						,				[height, width, self.model_tester.embed_dim]						,				)

     def 				__SCREAMING_SNAKE_CASE		(      self :       Tuple    )			->			List[Any]:






         """simple docstring"""
         a__ ,		a__      =       self.model_tester.prepare_config_and_inputs_for_common()

         a__      =       (
             self.model_tester.image_size
             if isinstance(self.model_tester.image_size						,				collections.abc.Iterable    )
             else (self.model_tester.image_size, self.model_tester.image_size)
         )

         for model_class in self.all_model_classes:
             a__      =       True
             self.check_hidden_states_output(A						,				A						,				A						,				A    )

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

             self.check_hidden_states_output(A						,				A						,				A						,				A    )

     @slow
     def 				__SCREAMING_SNAKE_CASE		(      self :       Any    )			->			Union[str, Any]:






         """simple docstring"""
         for model_name in DINAT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             a__      =       DinatModel.from_pretrained(A    )
             self.assertIsNotNone(A    )






     def 				__SCREAMING_SNAKE_CASE		(      self :       Optional[Any]    )			->			Optional[Any]:






         """simple docstring"""
         a__ ,		a__      =       self.model_tester.prepare_config_and_inputs_for_common()

         a__      =       _config_zero_init(A    )
         for model_class in self.all_model_classes:
             a__      =       model_class(config=A    )
             for name, param in model.named_parameters():
                 if "embeddings" not in name and param.requires_grad:
                     self.assertIn(
                         ((param.data.mean() * 1E9).round() / 1E9).item()						,				[0.0, 1.0]						,				msg=f"""Parameter {name} of model {model_class} seems not properly initialized"""						,				)







@require_natten
@require_vision
@require_torch
class 		__UpperCAmelCase(		unittest.TestCase ):





     """simple docstring"""



     @cached_property
     def 				__SCREAMING_SNAKE_CASE		(      self :       List[str]    )			->			Optional[int]:






         """simple docstring"""
         return AutoImageProcessor.from_pretrained("""shi-labs/dinat-mini-in1k-224"""    ) if is_vision_available() else None






     @slow
     def 				__SCREAMING_SNAKE_CASE		(      self :       Tuple    )			->			List[str]:






         """simple docstring"""
         a__      =       DinatForImageClassification.from_pretrained("""shi-labs/dinat-mini-in1k-224"""    ).to(A    )
         a__      =       self.default_image_processor

         a__      =       Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""    )
         a__      =       image_processor(images=A						,				return_tensors="""pt"""    ).to(A    )

         # forward pass
         with torch.no_grad():
             a__      =       model(**A    )

         # verify the logits
         a__      =       torch.Size((1, 10_00)    )
         self.assertEqual(outputs.logits.shape						,				A    )
         a__      =       torch.tensor([-0.1_5_4_5, -0.7_6_6_7, 0.4_6_4_2]    ).to(A    )
         self.assertTrue(torch.allclose(outputs.logits[0, :3]						,				A						,				atol=1E-4    )    )







@require_torch
@require_natten
class 		__UpperCAmelCase(		unittest.TestCase       ,	lowercase__ ):





     """simple docstring"""



     __UpperCAmelCase	: Union[str, Any]						=    (DinatBackbone,) if is_torch_available() else ()
     __UpperCAmelCase	: Dict						=    DinatConfig

     def 				__SCREAMING_SNAKE_CASE		(      self :       Any    )			->			int:






         """simple docstring"""
         a__      =       DinatModelTester(self    )












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[int]			   =       getLogger(__name__)


SCREAMING_SNAKE_CASE_						:					Union[str, Any]			   =       4
SCREAMING_SNAKE_CASE_						:					List[str]			   =       7

SCREAMING_SNAKE_CASE_						:					List[str]			   =       {
    '''float16''': torch.floataa,
    '''float32''': torch.floataa,
}
def        __lowerCAmelCase (UpperCAmelCase_		):
    if isinstance(UpperCAmelCase_   ,    torch.Tensor		):
        return image
    elif isinstance(UpperCAmelCase_   ,    PIL.Image.Image		):
        a__      =       [image]

    if isinstance(image[0]   ,    PIL.Image.Image		):
        a__ ,		a__      =       image[0].size
        a__ ,		a__      =       (x - x % 64 for x in (w, h))  # resize to integer multiple of 32

        a__      =       [np.array(i.resize((w, h)		)		)[None, :] for i in image]
        a__      =       np.concatenate(UpperCAmelCase_   ,    axis=0		)
        a__      =       np.array(UpperCAmelCase_		).astype(np.floataa		) / 2_5_5.0
        a__      =       image.transpose(0   ,    3   ,    1   ,    2		)
        a__      =       2.0 * image - 1.0
        a__      =       torch.from_numpy(UpperCAmelCase_		)
    elif isinstance(image[0]   ,    torch.Tensor		):
        a__      =       torch.cat(UpperCAmelCase_   ,    dim=0		)

    return image







class 		__UpperCAmelCase(		lowercase__ ):





     """simple docstring"""



     def __init__(      self :       int						,				A :       OnnxRuntimeModel						,				A :       OnnxRuntimeModel						,				A :       Any						,				A :       OnnxRuntimeModel						,				A :       DDPMScheduler						,				A :       Any						,				A :       int = 3_50						,				)			->			List[Any]:






         """simple docstring"""
         super().__init__(
             vae=A						,				text_encoder=A						,				tokenizer=A						,				unet=A						,				low_res_scheduler=A						,				scheduler=A						,				safety_checker=A						,				feature_extractor=A						,				watermarker=A						,				max_noise_level=A						,				)

     def __call__(      self :       Optional[int]						,				A :       Union[str, List[str]]						,				A :       Union[torch.FloatTensor, PIL.Image.Image, List[PIL.Image.Image]]						,				A :       int = 75						,				A :       float = 9.0						,				A :       int = 20						,				A :       Optional[Union[str, List[str]]] = None						,				A :       Optional[int] = 1						,				A :       float = 0.0						,				A :       Optional[Union[torch.Generator, List[torch.Generator]]] = None						,				A :       Optional[torch.FloatTensor] = None						,				A :       Optional[np.ndarray] = None						,				A :       Optional[np.ndarray] = None						,				A :       Optional[str] = "pil"						,				A :       bool = True						,				A :       Optional[Callable[[int, int, torch.FloatTensor], None]] = None						,				A :       Optional[int] = 1						,				)			->			Tuple:






         """simple docstring"""
         self.check_inputs(A						,				A						,				A						,				A    )

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

         a__      =       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.
         a__      =       guidance_scale > 1.0

         # 3. Encode input prompt
         a__      =       self._encode_prompt(
             A						,				A						,				A						,				A						,				A						,				prompt_embeds=A						,				negative_prompt_embeds=A						,				)

         a__      =       ORT_TO_PT_TYPE[str(text_embeddings.dtype    )]

         # 4. Preprocess image
         a__      =       preprocess(A    )
         a__      =       image.cpu()

         # 5. set timesteps
         self.scheduler.set_timesteps(A						,				device=A    )
         a__      =       self.scheduler.timesteps

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

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

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

         # 7. Check that sizes of image and latents match
         a__      =       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
         a__      =       self.prepare_extra_step_kwargs(A						,				A    )

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

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

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

                 # timestep to tensor
                 a__      =       np.array([t]						,				dtype=A    )

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

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

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

                 # call the callback, if provided
                 if i == len(A    ) - 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(A						,				A						,				A    )

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

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

         if not return_dict:
             return (image,)

         return ImagePipelineOutput(images=A    )

     def 				__SCREAMING_SNAKE_CASE		(      self :       List[Any]						,				A :       Dict    )			->			List[str]:






         """simple docstring"""
         a__      =       1 / 0.0_8_3_3_3 * latents
         a__      =       self.vae(latent_sample=A    )[0]
         a__      =       np.clip(image / 2 + 0.5						,				0						,				1    )
         a__      =       image.transpose((0, 2, 3, 1)    )
         return image






     def 				__SCREAMING_SNAKE_CASE		(      self :       List[str]						,				A :       Union[str, List[str]]						,				A :       str						,				A :       Optional[int]						,				A :       bool						,				A :       Optional[str]						,				A :       Optional[torch.FloatTensor] = None						,				A :       Optional[torch.FloatTensor] = None						,				)			->			Tuple:






         """simple docstring"""
         if prompt is not None and isinstance(A						,				A    ):
             a__      =       1
         elif prompt is not None and isinstance(A						,				A    ):
             a__      =       len(A    )
         else:
             a__      =       prompt_embeds.shape[0]

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

             if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(
                 A						,				A    ):
                 a__      =       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
             a__      =       self.text_encoder(
                 input_ids=text_input_ids.int().to(A    )						,				)
             a__      =       prompt_embeds[0]

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

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

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

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

             a__      =       self.text_encoder(
                 input_ids=uncond_input.input_ids.int().to(A    )						,				)
             a__      =       uncond_embeddings[0]

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

         return prompt_embeds






