




import inspect
from typing import List, Optional, Union

import torch
from torch import nn
from torch.nn import functional as F
from torchvision import transforms
from transformers import CLIPImageProcessor, CLIPModel, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    DPMSolverMultistepScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import StableDiffusionPipelineOutput




class       UpperCAmelCase_   ( nn.Module							):




							'''simple docstring'''



							def __init__(       self							:						int							,  a							:						str							,  a							:						str=1.0      )      ->       int:
										super().__init__()

										SCREAMING_SNAKE_CASE     =       cut_size
										SCREAMING_SNAKE_CASE     =       cut_power







							def  _UpperCAmelCase  (       self							:						Optional[int]							,  a							:						int							,  a							:						Optional[Any]      )      ->       Optional[int]:
										SCREAMING_SNAKE_CASE  ,	SCREAMING_SNAKE_CASE     =       pixel_values.shape[2:4]
										SCREAMING_SNAKE_CASE     =       min(a							,  a      )
										SCREAMING_SNAKE_CASE     =       min(a							,  a							,  self.cut_size      )
										SCREAMING_SNAKE_CASE     =       []
										for _ in range(a      ):
													SCREAMING_SNAKE_CASE     =       int(torch.rand([]      ) ** self.cut_power * (max_size - min_size) + min_size      )
													SCREAMING_SNAKE_CASE     =       torch.randint(0							,  sideX - size + 1							,  ()      )
													SCREAMING_SNAKE_CASE     =       torch.randint(0							,  sideY - size + 1							,  ()      )
													SCREAMING_SNAKE_CASE     =       pixel_values[:, :, offsety : offsety + size, offsetx : offsetx + size]
													cutouts.append(F.adaptive_avg_poolad(a							,  self.cut_size      )      )
										return torch.cat(a      )





def        lowerCamelCase_     (     SCREAMING_SNAKE_CASE       ,						SCREAMING_SNAKE_CASE ):
			'''simple docstring'''
			SCREAMING_SNAKE_CASE     =       F.normalize(SCREAMING_SNAKE_CASE       ,						dim=-1 )
			SCREAMING_SNAKE_CASE     =       F.normalize(SCREAMING_SNAKE_CASE       ,						dim=-1 )
			return (x - y).norm(dim=-1 ).div(2 ).arcsin().pow(2 ).mul(2 )
def        lowerCamelCase_     (     SCREAMING_SNAKE_CASE       ,						SCREAMING_SNAKE_CASE ):
			'''simple docstring'''
			for param in model.parameters():
						SCREAMING_SNAKE_CASE     =       value




class       UpperCAmelCase_   ( A							):




							'''simple docstring'''



							def __init__(       self							:						Any							,  a							:						AutoencoderKL							,  a							:						CLIPTextModel							,  a							:						CLIPModel							,  a							:						CLIPTokenizer							,  a							:						UNetaDConditionModel							,  a							:						Union[PNDMScheduler, LMSDiscreteScheduler, DDIMScheduler, DPMSolverMultistepScheduler]							,  a							:						CLIPImageProcessor							,  )      ->       Any:
										super().__init__()
										self.register_modules(
										    vae=a							,  text_encoder=a							,  clip_model=a							,  tokenizer=a							,  unet=a							,  scheduler=a							,  feature_extractor=a							,  )

										SCREAMING_SNAKE_CASE     =       transforms.Normalize(mean=feature_extractor.image_mean							,  std=feature_extractor.image_std      )
										SCREAMING_SNAKE_CASE     =       (
										    feature_extractor.size
										    if isinstance(feature_extractor.size							,  a      )
										    else feature_extractor.size["""shortest_edge"""]
										)
										SCREAMING_SNAKE_CASE     =       MakeCutouts(self.cut_out_size      )

										set_requires_grad(self.text_encoder							,  a      )
										set_requires_grad(self.clip_model							,  a      )





							def  _UpperCAmelCase  (       self							:						int							,  a							:						Optional[Union[str, int]] = "auto"      )      ->       str:
										if slice_size == "auto":
													# half the attention head size is usually a good trade-off between
													# speed and memory
													SCREAMING_SNAKE_CASE     =       self.unet.config.attention_head_dim // 2
										self.unet.set_attention_slice(a      )





							def  _UpperCAmelCase  (       self							:						List[str]      )      ->       int:
										self.enable_attention_slicing(a      )





							def  _UpperCAmelCase  (       self							:						int      )      ->       Union[str, Any]:
										set_requires_grad(self.vae							,  a      )





							def  _UpperCAmelCase  (       self							:						Union[str, Any]      )      ->       Optional[int]:
										set_requires_grad(self.vae							,  a      )





							def  _UpperCAmelCase  (       self							:						List[str]      )      ->       Any:
										set_requires_grad(self.unet							,  a      )





							def  _UpperCAmelCase  (       self							:						Union[str, Any]      )      ->       Optional[Any]:
										set_requires_grad(self.unet							,  a      )





							@torch.enable_grad()
							def  _UpperCAmelCase  (       self							:						str							,  a							:						int							,  a							:						int							,  a							:						List[str]							,  a							:						Optional[Any]							,  a							:						Tuple							,  a							:						int							,  a							:						Dict							,  a							:						str							,  a							:						int=True							,  )      ->       Dict:
										SCREAMING_SNAKE_CASE     =       latents.detach().requires_grad_()

										SCREAMING_SNAKE_CASE     =       self.scheduler.scale_model_input(a							,  a      )

										# predict the noise residual
										SCREAMING_SNAKE_CASE     =       self.unet(a							,  a							,  encoder_hidden_states=a      ).sample

										if isinstance(self.scheduler							,  (PNDMScheduler, DDIMScheduler, DPMSolverMultistepScheduler)      ):
													SCREAMING_SNAKE_CASE     =       self.scheduler.alphas_cumprod[timestep]
													SCREAMING_SNAKE_CASE     =       1 - alpha_prod_t
													# compute predicted original sample from predicted noise also called
													# "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
													SCREAMING_SNAKE_CASE     =       (latents - beta_prod_t ** 0.5 * noise_pred) / alpha_prod_t ** 0.5

													SCREAMING_SNAKE_CASE     =       torch.sqrt(a      )
													SCREAMING_SNAKE_CASE     =       pred_original_sample * (fac) + latents * (1 - fac)
										elif isinstance(self.scheduler							,  a      ):
													SCREAMING_SNAKE_CASE     =       self.scheduler.sigmas[index]
													SCREAMING_SNAKE_CASE     =       latents - sigma * noise_pred
										else:
													raise ValueError(f"""scheduler type {type(self.scheduler      )} not supported"""      )

										SCREAMING_SNAKE_CASE     =       1 / self.vae.config.scaling_factor * sample
										SCREAMING_SNAKE_CASE     =       self.vae.decode(a      ).sample
										SCREAMING_SNAKE_CASE     =       (image / 2 + 0.5).clamp(0							,  1      )

										if use_cutouts:
													SCREAMING_SNAKE_CASE     =       self.make_cutouts(a							,  a      )
										else:
													SCREAMING_SNAKE_CASE     =       transforms.Resize(self.cut_out_size      )(a      )
										SCREAMING_SNAKE_CASE     =       self.normalize(a      ).to(latents.dtype      )

										SCREAMING_SNAKE_CASE     =       self.clip_model.get_image_features(a      )
										SCREAMING_SNAKE_CASE     =       image_embeddings_clip / image_embeddings_clip.norm(p=2							,  dim=-1							,  keepdim=a      )

										if use_cutouts:
													SCREAMING_SNAKE_CASE     =       spherical_dist_loss(a							,  a      )
													SCREAMING_SNAKE_CASE     =       dists.view([num_cutouts, sample.shape[0], -1]      )
													SCREAMING_SNAKE_CASE     =       dists.sum(2      ).mean(0      ).sum() * clip_guidance_scale
										else:
													SCREAMING_SNAKE_CASE     =       spherical_dist_loss(a							,  a      ).mean() * clip_guidance_scale

										SCREAMING_SNAKE_CASE     =       -torch.autograd.grad(a							,  a      )[0]

										if isinstance(self.scheduler							,  a      ):
													SCREAMING_SNAKE_CASE     =       latents.detach() + grads * (sigma**2)
													SCREAMING_SNAKE_CASE     =       noise_pred_original
										else:
													SCREAMING_SNAKE_CASE     =       noise_pred_original - torch.sqrt(a      ) * grads
										return noise_pred, latents







							@torch.no_grad()
							def __call__(       self							:						List[Any]							,  a							:						Union[str, List[str]]							,  a							:						Optional[int] = 512							,  a							:						Optional[int] = 512							,  a							:						Optional[int] = 50							,  a							:						Optional[float] = 7.5							,  a							:						Optional[int] = 1							,  a							:						float = 0.0							,  a							:						Optional[float] = 100							,  a							:						Optional[Union[str, List[str]]] = None							,  a							:						Optional[int] = 4							,  a							:						Optional[bool] = True							,  a							:						Optional[torch.Generator] = None							,  a							:						Optional[torch.FloatTensor] = None							,  a							:						Optional[str] = "pil"							,  a							:						bool = True							,  )      ->       int:
										if isinstance(a							,  a      ):
													SCREAMING_SNAKE_CASE     =       1
										elif isinstance(a							,  a      ):
													SCREAMING_SNAKE_CASE     =       len(a      )
										else:
													raise ValueError(f"""`prompt` has to be of type `str` or `list` but is {type(a      )}"""      )

										if height % 8 != 0 or width % 8 != 0:
													raise ValueError(f"""`height` and `width` have to be divisible by 8 but are {height} and {width}."""      )

										# get prompt text embeddings
										SCREAMING_SNAKE_CASE     =       self.tokenizer(
										    a							,  padding="""max_length"""							,  max_length=self.tokenizer.model_max_length							,  truncation=a							,  return_tensors="""pt"""							,  )
										SCREAMING_SNAKE_CASE     =       self.text_encoder(text_input.input_ids.to(self.device      )      )[0]
										# duplicate text embeddings for each generation per prompt
										SCREAMING_SNAKE_CASE     =       text_embeddings.repeat_interleave(a							,  dim=0      )

										if clip_guidance_scale > 0:
													if clip_prompt is not None:
																SCREAMING_SNAKE_CASE     =       self.tokenizer(
																    a							,  padding="""max_length"""							,  max_length=self.tokenizer.model_max_length							,  truncation=a							,  return_tensors="""pt"""							,  ).input_ids.to(self.device      )
													else:
																SCREAMING_SNAKE_CASE     =       text_input.input_ids.to(self.device      )
													SCREAMING_SNAKE_CASE     =       self.clip_model.get_text_features(a      )
													SCREAMING_SNAKE_CASE     =       text_embeddings_clip / text_embeddings_clip.norm(p=2							,  dim=-1							,  keepdim=a      )
													# duplicate text embeddings clip for each generation per prompt
													SCREAMING_SNAKE_CASE     =       text_embeddings_clip.repeat_interleave(a							,  dim=0      )

										# 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.
										SCREAMING_SNAKE_CASE     =       guidance_scale > 1.0
										# get unconditional embeddings for classifier free guidance
										if do_classifier_free_guidance:
													SCREAMING_SNAKE_CASE     =       text_input.input_ids.shape[-1]
													SCREAMING_SNAKE_CASE     =       self.tokenizer([""""""]							,  padding="""max_length"""							,  max_length=a							,  return_tensors="""pt"""      )
													SCREAMING_SNAKE_CASE     =       self.text_encoder(uncond_input.input_ids.to(self.device      )      )[0]
													# duplicate unconditional embeddings for each generation per prompt
													SCREAMING_SNAKE_CASE     =       uncond_embeddings.repeat_interleave(a							,  dim=0      )

													# 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
													SCREAMING_SNAKE_CASE     =       torch.cat([uncond_embeddings, text_embeddings]      )

										# get the initial random noise unless the user supplied it

										# Unlike in other pipelines, latents need to be generated in the target device
										# for 1-to-1 results reproducibility with the CompVis implementation.
										# However this currently doesn't work in `mps`.
										SCREAMING_SNAKE_CASE     =       (batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
										SCREAMING_SNAKE_CASE     =       text_embeddings.dtype
										if latents is None:
													if self.device.type == "mps":
																# randn does not work reproducibly on mps
																SCREAMING_SNAKE_CASE     =       torch.randn(a							,  generator=a							,  device="""cpu"""							,  dtype=a      ).to(
																    self.device      )
													else:
																SCREAMING_SNAKE_CASE     =       torch.randn(a							,  generator=a							,  device=self.device							,  dtype=a      )
										else:
													if latents.shape != latents_shape:
																raise ValueError(f"""Unexpected latents shape, got {latents.shape}, expected {latents_shape}"""      )
													SCREAMING_SNAKE_CASE     =       latents.to(self.device      )

										# set timesteps
										SCREAMING_SNAKE_CASE     =       """offset""" in set(inspect.signature(self.scheduler.set_timesteps      ).parameters.keys()      )
										SCREAMING_SNAKE_CASE     =       {}
										if accepts_offset:
													SCREAMING_SNAKE_CASE     =       1

										self.scheduler.set_timesteps(a							,  **a      )

										# Some schedulers like PNDM have timesteps as arrays
										# It's more optimized to move all timesteps to correct device beforehand
										SCREAMING_SNAKE_CASE     =       self.scheduler.timesteps.to(self.device      )

										# scale the initial noise by the standard deviation required by the scheduler
										SCREAMING_SNAKE_CASE     =       latents * self.scheduler.init_noise_sigma

										# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
										# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
										# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
										# and should be between [0, 1]
										SCREAMING_SNAKE_CASE     =       """eta""" in set(inspect.signature(self.scheduler.step      ).parameters.keys()      )
										SCREAMING_SNAKE_CASE     =       {}
										if accepts_eta:
													SCREAMING_SNAKE_CASE     =       eta

										# check if the scheduler accepts generator
										SCREAMING_SNAKE_CASE     =       """generator""" in set(inspect.signature(self.scheduler.step      ).parameters.keys()      )
										if accepts_generator:
													SCREAMING_SNAKE_CASE     =       generator

										for i, t in enumerate(self.progress_bar(a      )      ):
													# expand the latents if we are doing classifier free guidance
													SCREAMING_SNAKE_CASE     =       torch.cat([latents] * 2      ) if do_classifier_free_guidance else latents
													SCREAMING_SNAKE_CASE     =       self.scheduler.scale_model_input(a							,  a      )

													# predict the noise residual
													SCREAMING_SNAKE_CASE     =       self.unet(a							,  a							,  encoder_hidden_states=a      ).sample

													# perform classifier free guidance
													if do_classifier_free_guidance:
																SCREAMING_SNAKE_CASE  ,	SCREAMING_SNAKE_CASE     =       noise_pred.chunk(2      )
																SCREAMING_SNAKE_CASE     =       noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

													# perform clip guidance
													if clip_guidance_scale > 0:
																SCREAMING_SNAKE_CASE     =       (
																    text_embeddings.chunk(2      )[1] if do_classifier_free_guidance else text_embeddings
																)
																SCREAMING_SNAKE_CASE  ,	SCREAMING_SNAKE_CASE     =       self.cond_fn(
																    a							,  a							,  a							,  a							,  a							,  a							,  a							,  a							,  a							,  )

													# compute the previous noisy sample x_t -> x_t-1
													SCREAMING_SNAKE_CASE     =       self.scheduler.step(a							,  a							,  a							,  **a      ).prev_sample

										# scale and decode the image latents with vae
										SCREAMING_SNAKE_CASE     =       1 / self.vae.config.scaling_factor * latents
										SCREAMING_SNAKE_CASE     =       self.vae.decode(a      ).sample

										SCREAMING_SNAKE_CASE     =       (image / 2 + 0.5).clamp(0							,  1      )
										SCREAMING_SNAKE_CASE     =       image.cpu().permute(0							,  2							,  3							,  1      ).numpy()

										if output_type == "pil":
													SCREAMING_SNAKE_CASE     =       self.numpy_to_pil(a      )

										if not return_dict:
													return (image, None)

										return StableDiffusionPipelineOutput(images=a							,  nsfw_content_detected=a      )












from typing import Optional, Tuple, Union

import tensorflow as tf

from ...activations_tf import ACTaFN
from ...modeling_tf_outputs import (
    TFBaseModelOutputWithNoAttention,
    TFBaseModelOutputWithPoolingAndNoAttention,
    TFImageClassifierOutputWithNoAttention,
)
from ...modeling_tf_utils import TFPreTrainedModel, TFSequenceClassificationLoss, keras_serializable, unpack_inputs
from ...tf_utils import shape_list
from ...utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging
from .configuration_resnet import ResNetConfig


__A    :				int    						=	logging.get_logger(__name__)

# General docstring
__A    :				str    						=	"""ResNetConfig"""

# Base docstring
__A    :				Optional[Any]    						=	"""microsoft/resnet-50"""
__A    :				Tuple    						=	[1, 2_0_4_8, 7, 7]

# Image classification docstring
__A    :				Tuple    						=	"""microsoft/resnet-50"""
__A    :				List[str]    						=	"""tiger cat"""

__A    :				Dict    						=	[
    """microsoft/resnet-50""",
    # See all resnet models at https://huggingface.co/models?filter=resnet
]




class       UpperCAmelCase_   ( tf.keras.layers.Layer							):




							'''simple docstring'''



							def __init__(       self							:						List[str]							,  a							:						int							,  a							:						int = 3							,  a							:						int = 1							,  a							:						str = "relu"							,  **a							:						Dict      )      ->       None:
										super().__init__(**a      )
										SCREAMING_SNAKE_CASE     =       kernel_size // 2
										SCREAMING_SNAKE_CASE     =       tf.keras.layers.ConvaD(
										    a							,  kernel_size=a							,  strides=a							,  padding="""valid"""							,  use_bias=a							,  name="""convolution"""      )
										# Use same default momentum and epsilon as PyTorch equivalent
										SCREAMING_SNAKE_CASE     =       tf.keras.layers.BatchNormalization(epsilon=1E-5							,  momentum=0.9							,  name="""normalization"""      )
										SCREAMING_SNAKE_CASE     =       ACTaFN[activation] if activation is not None else tf.keras.layers.Activation("""linear"""      )





							def  _UpperCAmelCase  (       self							:						str							,  a							:						tf.Tensor      )      ->       tf.Tensor:
										# Pad to match that done in the PyTorch Conv2D model
										SCREAMING_SNAKE_CASE     =       SCREAMING_SNAKE_CASE     =       (self.pad_value, self.pad_value)
										SCREAMING_SNAKE_CASE     =       tf.pad(a							,  [(0, 0), height_pad, width_pad, (0, 0)]      )
										SCREAMING_SNAKE_CASE     =       self.conv(a      )
										return hidden_state







							def  _UpperCAmelCase  (       self							:						int							,  a							:						tf.Tensor							,  a							:						bool = False      )      ->       tf.Tensor:
										SCREAMING_SNAKE_CASE     =       self.convolution(a      )
										SCREAMING_SNAKE_CASE     =       self.normalization(a							,  training=a      )
										SCREAMING_SNAKE_CASE     =       self.activation(a      )
										return hidden_state





class       UpperCAmelCase_   ( tf.keras.layers.Layer							):




							'''simple docstring'''



							def __init__(       self							:						Optional[Any]							,  a							:						ResNetConfig							,  **a							:						Dict      )      ->       None:
										super().__init__(**a      )
										SCREAMING_SNAKE_CASE     =       TFResNetConvLayer(
										    config.embedding_size							,  kernel_size=7							,  stride=2							,  activation=config.hidden_act							,  name="""embedder"""							,  )
										SCREAMING_SNAKE_CASE     =       tf.keras.layers.MaxPoolaD(pool_size=3							,  strides=2							,  padding="""valid"""							,  name="""pooler"""      )
										SCREAMING_SNAKE_CASE     =       config.num_channels







							def  _UpperCAmelCase  (       self							:						Union[str, Any]							,  a							:						tf.Tensor							,  a							:						bool = False      )      ->       tf.Tensor:
										SCREAMING_SNAKE_CASE  ,	SCREAMING_SNAKE_CASE  ,	SCREAMING_SNAKE_CASE  ,	SCREAMING_SNAKE_CASE     =       shape_list(a      )
										if tf.executing_eagerly() and num_channels != self.num_channels:
													raise ValueError(
													    """Make sure that the channel dimension of the pixel values match with the one set in the configuration."""      )
										SCREAMING_SNAKE_CASE     =       pixel_values
										SCREAMING_SNAKE_CASE     =       self.embedder(a      )
										SCREAMING_SNAKE_CASE     =       tf.pad(a							,  [[0, 0], [1, 1], [1, 1], [0, 0]]      )
										SCREAMING_SNAKE_CASE     =       self.pooler(a      )
										return hidden_state





class       UpperCAmelCase_   ( tf.keras.layers.Layer							):




							'''simple docstring'''



							def __init__(       self							:						Dict							,  a							:						int							,  a							:						int = 2							,  **a							:						Optional[int]      )      ->       None:
										super().__init__(**a      )
										SCREAMING_SNAKE_CASE     =       tf.keras.layers.ConvaD(
										    a							,  kernel_size=1							,  strides=a							,  use_bias=a							,  name="""convolution"""      )
										# Use same default momentum and epsilon as PyTorch equivalent
										SCREAMING_SNAKE_CASE     =       tf.keras.layers.BatchNormalization(epsilon=1E-5							,  momentum=0.9							,  name="""normalization"""      )







							def  _UpperCAmelCase  (       self							:						str							,  a							:						tf.Tensor							,  a							:						bool = False      )      ->       tf.Tensor:
										SCREAMING_SNAKE_CASE     =       x
										SCREAMING_SNAKE_CASE     =       self.convolution(a      )
										SCREAMING_SNAKE_CASE     =       self.normalization(a							,  training=a      )
										return hidden_state





class       UpperCAmelCase_   ( tf.keras.layers.Layer							):




							'''simple docstring'''



							def __init__(       self							:						Optional[Any]							,  a							:						int							,  a							:						int							,  a							:						int = 1							,  a							:						str = "relu"							,  **a							:						List[Any]      )      ->       None:
										super().__init__(**a      )
										SCREAMING_SNAKE_CASE     =       in_channels != out_channels or stride != 1
										SCREAMING_SNAKE_CASE     =       TFResNetConvLayer(a							,  stride=a							,  name="""layer.0"""      )
										SCREAMING_SNAKE_CASE     =       TFResNetConvLayer(a							,  activation=a							,  name="""layer.1"""      )
										SCREAMING_SNAKE_CASE     =       (
										    TFResNetShortCut(a							,  stride=a							,  name="""shortcut"""      )
										    if should_apply_shortcut
										    else tf.keras.layers.Activation("""linear"""							,  name="""shortcut"""      )
										)
										SCREAMING_SNAKE_CASE     =       ACTaFN[activation]







							def  _UpperCAmelCase  (       self							:						int							,  a							:						tf.Tensor							,  a							:						bool = False      )      ->       tf.Tensor:
										SCREAMING_SNAKE_CASE     =       hidden_state
										SCREAMING_SNAKE_CASE     =       self.conva(a							,  training=a      )
										SCREAMING_SNAKE_CASE     =       self.conva(a							,  training=a      )
										SCREAMING_SNAKE_CASE     =       self.shortcut(a							,  training=a      )
										hidden_state += residual
										SCREAMING_SNAKE_CASE     =       self.activation(a      )
										return hidden_state





class       UpperCAmelCase_   ( tf.keras.layers.Layer							):




							'''simple docstring'''



							def __init__(       self							:						List[Any]							,  a							:						int							,  a							:						int							,  a							:						int = 1							,  a							:						str = "relu"							,  a							:						int = 4							,  **a							:						List[Any]							,  )      ->       None:
										super().__init__(**a      )
										SCREAMING_SNAKE_CASE     =       in_channels != out_channels or stride != 1
										SCREAMING_SNAKE_CASE     =       out_channels // reduction
										SCREAMING_SNAKE_CASE     =       TFResNetConvLayer(a							,  kernel_size=1							,  name="""layer.0"""      )
										SCREAMING_SNAKE_CASE     =       TFResNetConvLayer(a							,  stride=a							,  name="""layer.1"""      )
										SCREAMING_SNAKE_CASE     =       TFResNetConvLayer(a							,  kernel_size=1							,  activation=a							,  name="""layer.2"""      )
										SCREAMING_SNAKE_CASE     =       (
										    TFResNetShortCut(a							,  stride=a							,  name="""shortcut"""      )
										    if should_apply_shortcut
										    else tf.keras.layers.Activation("""linear"""							,  name="""shortcut"""      )
										)
										SCREAMING_SNAKE_CASE     =       ACTaFN[activation]







							def  _UpperCAmelCase  (       self							:						Union[str, Any]							,  a							:						tf.Tensor							,  a							:						bool = False      )      ->       tf.Tensor:
										SCREAMING_SNAKE_CASE     =       hidden_state
										SCREAMING_SNAKE_CASE     =       self.conva(a							,  training=a      )
										SCREAMING_SNAKE_CASE     =       self.conva(a							,  training=a      )
										SCREAMING_SNAKE_CASE     =       self.conva(a							,  training=a      )
										SCREAMING_SNAKE_CASE     =       self.shortcut(a							,  training=a      )
										hidden_state += residual
										SCREAMING_SNAKE_CASE     =       self.activation(a      )
										return hidden_state





class       UpperCAmelCase_   ( tf.keras.layers.Layer							):




							'''simple docstring'''



							def __init__(       self							:						Dict							,  a							:						ResNetConfig							,  a							:						int							,  a							:						int							,  a							:						int = 2							,  a							:						int = 2							,  **a							:						Union[str, Any]      )      ->       None:
										super().__init__(**a      )

										SCREAMING_SNAKE_CASE     =       TFResNetBottleNeckLayer if config.layer_type == """bottleneck""" else TFResNetBasicLayer

										SCREAMING_SNAKE_CASE     =       [layer(a							,  a							,  stride=a							,  activation=config.hidden_act							,  name="""layers.0"""      )]
										layers += [
										    layer(a							,  a							,  activation=config.hidden_act							,  name=f"""layers.{i + 1}"""      )
										    for i in range(depth - 1      )
										]
										SCREAMING_SNAKE_CASE     =       layers







							def  _UpperCAmelCase  (       self							:						Optional[int]							,  a							:						tf.Tensor							,  a							:						bool = False      )      ->       tf.Tensor:
										for layer in self.stage_layers:
													SCREAMING_SNAKE_CASE     =       layer(a							,  training=a      )
										return hidden_state





class       UpperCAmelCase_   ( tf.keras.layers.Layer							):




							'''simple docstring'''



							def __init__(       self							:						Dict							,  a							:						ResNetConfig							,  **a							:						Tuple      )      ->       None:
										super().__init__(**a      )
										# based on `downsample_in_first_stage` the first layer of the first stage may or may not downsample the input
										SCREAMING_SNAKE_CASE     =       [
										    TFResNetStage(
										        a							,  config.embedding_size							,  config.hidden_sizes[0]							,  stride=2 if config.downsample_in_first_stage else 1							,  depth=config.depths[0]							,  name="""stages.0"""							,  )
										]
										for i, (in_channels, out_channels, depth) in enumerate(
										    zip(config.hidden_sizes							,  config.hidden_sizes[1:]							,  config.depths[1:]      )      ):
													self.stages.append(TFResNetStage(a							,  a							,  a							,  depth=a							,  name=f"""stages.{i + 1}"""      )      )







							def  _UpperCAmelCase  (       self							:						Any							,  a							:						tf.Tensor							,  a							:						bool = False							,  a							:						bool = True							,  a							:						bool = False							,  )      ->       TFBaseModelOutputWithNoAttention:
										SCREAMING_SNAKE_CASE     =       () if output_hidden_states else None

										for stage_module in self.stages:
													if output_hidden_states:
																SCREAMING_SNAKE_CASE     =       hidden_states + (hidden_state,)

													SCREAMING_SNAKE_CASE     =       stage_module(a							,  training=a      )

										if output_hidden_states:
													SCREAMING_SNAKE_CASE     =       hidden_states + (hidden_state,)

										if not return_dict:
													return tuple(v for v in [hidden_state, hidden_states] if v is not None      )

										return TFBaseModelOutputWithNoAttention(last_hidden_state=a							,  hidden_states=a      )





class       UpperCAmelCase_   ( A							):




							'''simple docstring'''



							a__    	=  ResNetConfig
							a__    	=  '''resnet'''
							a__    	=  '''pixel_values'''





							@property
							def  _UpperCAmelCase  (       self							:						Any      )      ->       Union[str, Any]:
										return {"pixel_values": tf.TensorSpec(shape=(None, self.config.num_channels, 224, 224)							,  dtype=tf.floataa      )}





__A    :				Any    						=	r"""
    This model is a TensorFlow
    [tf.keras.layers.Layer](https://www.tensorflow.org/api_docs/python/tf/keras/layers/Layer) sub-class. Use it as a
    regular TensorFlow Module and refer to the TensorFlow documentation for all matter related to general usage and
    behavior.

    Parameters:
        config ([`ResNetConfig`]): Model configuration class with all the parameters of the model.
            Initializing with a config file does not load the weights associated with the model, only the
            configuration. Check out the [`~TFPreTrainedModel.from_pretrained`] method to load the model weights.
"""


__A    :				Optional[Any]    						=	r"""
    Args:
        pixel_values (`tf.Tensor` of shape `(batch_size, num_channels, height, width)`):
            Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See
            [`ConvNextImageProcessor.__call__`] for details.

        output_hidden_states (`bool`, *optional*):
            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
            more detail.
        return_dict (`bool`, *optional*):
            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""




@keras_serializable
class       UpperCAmelCase_   ( tf.keras.layers.Layer							):




							'''simple docstring'''



							a__    	=  ResNetConfig





							def __init__(       self							:						Optional[Any]							,  a							:						ResNetConfig							,  **a							:						List[Any]      )      ->       None:
										super().__init__(**a      )
										SCREAMING_SNAKE_CASE     =       config
										SCREAMING_SNAKE_CASE     =       TFResNetEmbeddings(a							,  name="""embedder"""      )
										SCREAMING_SNAKE_CASE     =       TFResNetEncoder(a							,  name="""encoder"""      )
										SCREAMING_SNAKE_CASE     =       tf.keras.layers.GlobalAveragePoolingaD(keepdims=a      )







							@unpack_inputs
							def  _UpperCAmelCase  (       self							:						List[str]							,  a							:						tf.Tensor							,  a							:						Optional[bool] = None							,  a							:						Optional[bool] = None							,  a							:						bool = False							,  )      ->       Union[Tuple[tf.Tensor], TFBaseModelOutputWithPoolingAndNoAttention]:
										SCREAMING_SNAKE_CASE     =       (
										    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
										)
										SCREAMING_SNAKE_CASE     =       return_dict if return_dict is not None else self.config.use_return_dict

										# TF 2.0 image layers can't use NCHW format when running on CPU.
										# We transpose to NHWC format and then transpose back after the full forward pass.
										# (batch_size, num_channels, height, width) -> (batch_size, height, width, num_channels)
										SCREAMING_SNAKE_CASE     =       tf.transpose(a							,  perm=[0, 2, 3, 1]      )
										SCREAMING_SNAKE_CASE     =       self.embedder(a							,  training=a      )

										SCREAMING_SNAKE_CASE     =       self.encoder(
										    a							,  output_hidden_states=a							,  return_dict=a							,  training=a      )

										SCREAMING_SNAKE_CASE     =       encoder_outputs[0]

										SCREAMING_SNAKE_CASE     =       self.pooler(a      )

										# Transpose all the outputs to the NCHW format
										# (batch_size, height, width, num_channels) -> (batch_size, num_channels, height, width)
										SCREAMING_SNAKE_CASE     =       tf.transpose(a							,  (0, 3, 1, 2)      )
										SCREAMING_SNAKE_CASE     =       tf.transpose(a							,  (0, 3, 1, 2)      )
										SCREAMING_SNAKE_CASE     =       ()
										for hidden_state in encoder_outputs[1:]:
													SCREAMING_SNAKE_CASE     =       hidden_states + tuple(tf.transpose(a							,  (0, 3, 1, 2)      ) for h in hidden_state      )

										if not return_dict:
													return (last_hidden_state, pooled_output) + hidden_states

										SCREAMING_SNAKE_CASE     =       hidden_states if output_hidden_states else None

										return TFBaseModelOutputWithPoolingAndNoAttention(
										    last_hidden_state=a							,  pooler_output=a							,  hidden_states=a							,  )





@add_start_docstrings(
    '''The bare ResNet model outputting raw features without any specific head on top.'''		,   A		,   )
class       UpperCAmelCase_   ( A							):




							'''simple docstring'''



							def __init__(       self							:						List[Any]							,  a							:						ResNetConfig							,  **a							:						str      )      ->       None:
										super().__init__(a							,  **a      )
										SCREAMING_SNAKE_CASE     =       TFResNetMainLayer(config=a							,  name="""resnet"""      )







							@add_start_docstrings_to_model_forward(a      )
							@add_code_sample_docstrings(
							    checkpoint=_CHECKPOINT_FOR_DOC							,  output_type=a							,  config_class=_CONFIG_FOR_DOC							,  modality="""vision"""							,  expected_output=_EXPECTED_OUTPUT_SHAPE							,  )
							@unpack_inputs
							def  _UpperCAmelCase  (       self							:						List[Any]							,  a							:						tf.Tensor							,  a							:						Optional[bool] = None							,  a							:						Optional[bool] = None							,  a							:						bool = False							,  )      ->       Union[Tuple[tf.Tensor], TFBaseModelOutputWithPoolingAndNoAttention]:
										SCREAMING_SNAKE_CASE     =       (
										    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
										)
										SCREAMING_SNAKE_CASE     =       return_dict if return_dict is not None else self.config.use_return_dict

										SCREAMING_SNAKE_CASE     =       self.resnet(
										    pixel_values=a							,  output_hidden_states=a							,  return_dict=a							,  training=a							,  )
										return resnet_outputs





@add_start_docstrings(
    '''
    ResNet Model with an image classification head on top (a linear layer on top of the pooled features), e.g. for
    ImageNet.
    '''		,   A		,   )
class       UpperCAmelCase_   ( A		,   A							):




							'''simple docstring'''



							def __init__(       self							:						Union[str, Any]							,  a							:						ResNetConfig							,  **a							:						Optional[Any]      )      ->       None:
										super().__init__(a							,  **a      )
										SCREAMING_SNAKE_CASE     =       config.num_labels
										SCREAMING_SNAKE_CASE     =       TFResNetMainLayer(a							,  name="""resnet"""      )
										# classification head
										SCREAMING_SNAKE_CASE     =       (
										    tf.keras.layers.Dense(config.num_labels							,  name="""classifier.1"""      )
										    if config.num_labels > 0
										    else tf.keras.layers.Activation("""linear"""							,  name="""classifier.1"""      )
										)





							def  _UpperCAmelCase  (       self							:						Union[str, Any]							,  a							:						tf.Tensor      )      ->       tf.Tensor:
										SCREAMING_SNAKE_CASE     =       tf.keras.layers.Flatten()(a      )
										SCREAMING_SNAKE_CASE     =       self.classifier_layer(a      )
										return logits







							@add_start_docstrings_to_model_forward(a      )
							@add_code_sample_docstrings(
							    checkpoint=_IMAGE_CLASS_CHECKPOINT							,  output_type=a							,  config_class=_CONFIG_FOR_DOC							,  expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT							,  )
							@unpack_inputs
							def  _UpperCAmelCase  (       self							:						Any							,  a							:						tf.Tensor = None							,  a							:						tf.Tensor = None							,  a							:						bool = None							,  a							:						bool = None							,  a							:						bool = False							,  )      ->       Union[Tuple[tf.Tensor], TFImageClassifierOutputWithNoAttention]:
										SCREAMING_SNAKE_CASE     =       return_dict if return_dict is not None else self.config.use_return_dict

										SCREAMING_SNAKE_CASE     =       self.resnet(
										    a							,  output_hidden_states=a							,  return_dict=a							,  training=a      )

										SCREAMING_SNAKE_CASE     =       outputs.pooler_output if return_dict else outputs[1]

										SCREAMING_SNAKE_CASE     =       self.classifier(a      )

										SCREAMING_SNAKE_CASE     =       None if labels is None else self.hf_compute_loss(a							,  a      )

										if not return_dict:
													SCREAMING_SNAKE_CASE     =       (logits,) + outputs[2:]
													return (loss,) + output if loss is not None else output

										return TFImageClassifierOutputWithNoAttention(loss=a							,  logits=a							,  hidden_states=outputs.hidden_states      )






