
from typing import List, Optional, Union

import torch

from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)


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

SCREAMING_SNAKE_CASE							:Dict							 =				'\n    Examples:\n        ```py\n        >>> import torch\n        >>> import numpy as np\n\n        >>> from diffusers import KandinskyV22PriorPipeline, KandinskyV22ControlnetPipeline\n        >>> from transformers import pipeline\n        >>> from diffusers.utils import load_image\n\n\n        >>> def make_hint(image, depth_estimator):\n        ...     image = depth_estimator(image)["depth"]\n        ...     image = np.array(image)\n        ...     image = image[:, :, None]\n        ...     image = np.concatenate([image, image, image], axis=2)\n        ...     detected_map = torch.from_numpy(image).float() / 255.0\n        ...     hint = detected_map.permute(2, 0, 1)\n        ...     return hint\n\n\n        >>> depth_estimator = pipeline("depth-estimation")\n\n        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(\n        ...     "kandinsky-community/kandinsky-2-2-prior", torch_dtype=torch.float16\n        ... )\n        >>> pipe_prior = pipe_prior.to("cuda")\n\n        >>> pipe = KandinskyV22ControlnetPipeline.from_pretrained(\n        ...     "kandinsky-community/kandinsky-2-2-controlnet-depth", torch_dtype=torch.float16\n        ... )\n        >>> pipe = pipe.to("cuda")\n\n\n        >>> img = load_image(\n        ...     "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"\n        ...     "/kandinsky/cat.png"\n        ... ).resize((768, 768))\n\n        >>> hint = make_hint(img, depth_estimator).unsqueeze(0).half().to("cuda")\n\n        >>> prompt = "A robot, 4k photo"\n        >>> negative_prior_prompt = "lowres, text, error, cropped, worst quality, low quality, jpeg artifacts, ugly, duplicate, morbid, mutilated, out of frame, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, mutation, deformed, blurry, dehydrated, bad anatomy, bad proportions, extra limbs, cloned face, disfigured, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, fused fingers, too many fingers, long neck, username, watermark, signature"\n\n        >>> generator = torch.Generator(device="cuda").manual_seed(43)\n\n        >>> image_emb, zero_image_emb = pipe_prior(\n        ...     prompt=prompt, negative_prompt=negative_prior_prompt, generator=generator\n        ... ).to_tuple()\n\n        >>> images = pipe(\n        ...     image_embeds=image_emb,\n        ...     negative_image_embeds=zero_image_emb,\n        ...     hint=hint,\n        ...     num_inference_steps=50,\n        ...     generator=generator,\n        ...     height=768,\n        ...     width=768,\n        ... ).images\n\n        >>> images[0].save("robot_cat.png")\n        ```\n'


def 						UpperCAmelCase			(	a_		,   a_		,   a_=8						)		->	Union[str, Any]:







				"""simple docstring"""



				__A   			= height // scale_factor**2
				if height % scale_factor**2 != 0:
								new_height += 1
				__A   			= width // scale_factor**2
				if width % scale_factor**2 != 0:
								new_width += 1
				return new_height * scale_factor, new_width * scale_factor





class 							UpperCAmelCase      (  __SCREAMING_SNAKE_CASE  ):




	'''simple docstring'''





	def __init__(       self					: Optional[int]							,A					: UNetaDConditionModel							,A					: DDPMScheduler							,A					: VQModel							,):
					super().__init__()

					self.register_modules(
					    unet=A							,scheduler=A							,movq=A							,)
					__A   			= 2 ** (len(self.movq.config.block_out_channels ) - 1)






	def 				UpperCamelCase_  (       self					: Any							,A					: Any							,A					: int							,A					: Tuple							,A					: Union[str, Any]							,A					: Tuple							,A					: Optional[int] ):
					if latents is None:
									__A   			= randn_tensor(A							,generator=A							,device=A							,dtype=A )
					else:
									if latents.shape != shape:
													raise ValueError(f'''Unexpected latents shape, got {latents.shape}, expected {shape}''' )
									__A   			= latents.to(A )

					__A   			= latents * scheduler.init_noise_sigma
					return latents






	def 				UpperCamelCase_  (       self					: Optional[Any]							,A					: List[Any]=0 ):
					if is_accelerate_available():
									from accelerate import cpu_offload
					else:
									raise ImportError("Please install accelerate via `pip install accelerate`" )

					__A   			= torch.device(f'''cuda:{gpu_id}''' )

					__A   			= [
					    self.unet,
					    self.movq,
					]
					for cpu_offloaded_model in models:
									if cpu_offloaded_model is not None:
													cpu_offload(A							,A )






	def 				UpperCamelCase_  (       self					: str							,A					: Optional[Any]=0 ):
					if is_accelerate_available() and is_accelerate_version(">="							,"0.17.0.dev0" ):
									from accelerate import cpu_offload_with_hook
					else:
									raise ImportError("`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher." )

					__A   			= torch.device(f'''cuda:{gpu_id}''' )

					if self.device.type != "cpu":
									self.to("cpu"							,silence_dtype_warnings=A )
									torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

					__A   			= None
					for cpu_offloaded_model in [self.unet, self.movq]:
									__A			,							__A   			= cpu_offload_with_hook(A							,A							,prev_module_hook=A )

					# We'll offload the last model manually.
					__A   			= hook






	@property
	# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
	def 				UpperCamelCase_  (       self					: Union[str, Any] ):
					if not hasattr(self.unet							,"_hf_hook" ):
									return self.device
					for module in self.unet.modules():
									if (
									    hasattr(A							,"_hf_hook" )
									    and hasattr(module._hf_hook							,"execution_device" )
									    and module._hf_hook.execution_device is not None
									):
													return torch.device(module._hf_hook.execution_device )
					return self.device







	@torch.no_grad()
	@replace_example_docstring(A )
	def __call__(       self					: Union[str, Any]							,A					: Union[torch.FloatTensor, List[torch.FloatTensor]]							,A					: Union[torch.FloatTensor, List[torch.FloatTensor]]							,A					: torch.FloatTensor							,A					: int = 5_12							,A					: int = 5_12							,A					: int = 1_00							,A					: float = 4.0							,A					: int = 1							,A					: Optional[Union[torch.Generator, List[torch.Generator]]] = None							,A					: Optional[torch.FloatTensor] = None							,A					: Optional[str] = "pil"							,A					: bool = True							,):
					__A   			= self._execution_device

					__A   			= guidance_scale > 1.0

					if isinstance(A							,A ):
									__A   			= torch.cat(A							,dim=0 )
					if isinstance(A							,A ):
									__A   			= torch.cat(A							,dim=0 )
					if isinstance(A							,A ):
									__A   			= torch.cat(A							,dim=0 )

					__A   			= image_embeds.shape[0] * num_images_per_prompt

					if do_classifier_free_guidance:
									__A   			= image_embeds.repeat_interleave(A							,dim=0 )
									__A   			= negative_image_embeds.repeat_interleave(A							,dim=0 )
									__A   			= hint.repeat_interleave(A							,dim=0 )

					__A   			= torch.cat([negative_image_embeds, image_embeds]							,dim=0 ).to(dtype=self.unet.dtype							,device=A )
					__A   			= torch.cat([hint, hint]							,dim=0 ).to(dtype=self.unet.dtype							,device=A )
					self.scheduler.set_timesteps(A							,device=A )
					__A   			= self.scheduler.timesteps

					__A   			= self.movq.config.latent_channels

					__A			,							__A   			= downscale_height_and_width(A							,A							,self.movq_scale_factor )

					# create initial latent
					__A   			= self.prepare_latents(
					    (batch_size, num_channels_latents, height, width)							,image_embeds.dtype							,A							,A							,A							,self.scheduler							,)

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

									__A   			= {"image_embeds": image_embeds, "hint": hint}
									__A   			= self.unet(
									    sample=A							,timestep=A							,encoder_hidden_states=A							,added_cond_kwargs=A							,return_dict=A							,)[0]

									if do_classifier_free_guidance:
													__A			,							__A   			= noise_pred.split(latents.shape[1]							,dim=1 )
													__A			,							__A   			= noise_pred.chunk(2 )
													__A			,							__A   			= variance_pred.chunk(2 )
													__A   			= noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
													__A   			= torch.cat([noise_pred, variance_pred_text]							,dim=1 )

									if not (
									    hasattr(self.scheduler.config							,"variance_type" )
									    and self.scheduler.config.variance_type in ["learned", "learned_range"]
									):
													__A			,							__A   			= noise_pred.split(latents.shape[1]							,dim=1 )

									# compute the previous noisy sample x_t -> x_t-1
									__A   			= self.scheduler.step(
									    A							,A							,A							,generator=A							,)[0]
					# post-processing
					__A   			= self.movq.decode(A							,force_not_quantize=A )["sample"]

					if output_type not in ["pt", "np", "pil"]:
									raise ValueError(f'''Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}''' )

					if output_type in ["np", "pil"]:
									__A   			= image * 0.5 + 0.5
									__A   			= image.clamp(0							,1 )
									__A   			= image.cpu().permute(0							,2							,3							,1 ).float().numpy()

					if output_type == "pil":
									__A   			= self.numpy_to_pil(A )

					if not return_dict:
									return (image,)

					return ImagePipelineOutput(images=A )


from typing import Optional, Tuple, Union

import tensorflow as tf

from ...activations_tf import ACTaFN
from ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward
from ...modeling_tf_outputs import (
    TFBaseModelOutputWithNoAttention,
    TFBaseModelOutputWithPoolingAndNoAttention,
    TFSequenceClassifierOutput,
)
from ...modeling_tf_utils import TFPreTrainedModel, TFSequenceClassificationLoss, keras_serializable, unpack_inputs
from ...tf_utils import shape_list
from ...utils import logging
from .configuration_regnet import RegNetConfig


SCREAMING_SNAKE_CASE							:str							 =				logging.get_logger(__name__)

# General docstring
SCREAMING_SNAKE_CASE							:str							 =				'RegNetConfig'

# Base docstring
SCREAMING_SNAKE_CASE							:List[str]							 =				'facebook/regnet-y-040'
SCREAMING_SNAKE_CASE							:Union[str, Any]							 =				[1, 1088, 7, 7]

# Image classification docstring
SCREAMING_SNAKE_CASE							:Optional[int]							 =				'facebook/regnet-y-040'
SCREAMING_SNAKE_CASE							:Any							 =				'tabby, tabby cat'

SCREAMING_SNAKE_CASE							:Optional[int]							 =				[
    'facebook/regnet-y-040',
    # See all regnet models at https://huggingface.co/models?filter=regnet
]





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




	'''simple docstring'''





	def __init__(       self					: Tuple							,A					: int							,A					: int = 3							,A					: int = 1							,A					: int = 1							,A					: Optional[str] = "relu"							,**A					: Dict							,):
					super().__init__(**A )
					# The padding and conv has been verified in
					# https://colab.research.google.com/gist/sayakpaul/854bc10eeaf21c9ee2119e0b9f3841a7/scratchpad.ipynb
					__A   			= tf.keras.layers.ZeroPaddingaD(padding=kernel_size // 2 )
					__A   			= tf.keras.layers.ConvaD(
					    filters=A							,kernel_size=A							,strides=A							,padding="VALID"							,groups=A							,use_bias=A							,name="convolution"							,)
					__A   			= tf.keras.layers.BatchNormalization(epsilon=1E-5							,momentum=0.9							,name="normalization" )
					__A   			= ACTaFN[activation] if activation is not None else tf.identity







	def 				UpperCamelCase_  (       self					: List[Any]							,A					: Any ):
					__A   			= self.convolution(self.padding(A ) )
					__A   			= self.normalization(A )
					__A   			= self.activation(A )
					return hidden_state





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




	'''simple docstring'''





	def __init__(       self					: Tuple							,A					: RegNetConfig							,**A					: str ):
					super().__init__(**A )
					__A   			= config.num_channels
					__A   			= TFRegNetConvLayer(
					    out_channels=config.embedding_size							,kernel_size=3							,stride=2							,activation=config.hidden_act							,name="embedder"							,)







	def 				UpperCamelCase_  (       self					: Tuple							,A					: Optional[Any] ):
					__A   			= shape_list(A )[1]
					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." )

					# When running on CPU, `tf.keras.layers.Conv2D` doesn't support `NCHW` format.
					# So change the input format from `NCHW` to `NHWC`.
					# shape = (batch_size, in_height, in_width, in_channels=num_channels)
					__A   			= tf.transpose(A							,perm=(0, 2, 3, 1) )
					__A   			= self.embedder(A )
					return hidden_state





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




	'''simple docstring'''





	def __init__(       self					: Optional[int]							,A					: int							,A					: int = 2							,**A					: Tuple ):
					super().__init__(**A )
					__A   			= tf.keras.layers.ConvaD(
					    filters=A							,kernel_size=1							,strides=A							,use_bias=A							,name="convolution" )
					__A   			= tf.keras.layers.BatchNormalization(epsilon=1E-5							,momentum=0.9							,name="normalization" )







	def 				UpperCamelCase_  (       self					: Union[str, Any]							,A					: tf.Tensor							,A					: bool = False ):
					return self.normalization(self.convolution(A )							,training=A )





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




	'''simple docstring'''





	def __init__(       self					: Dict							,A					: int							,A					: int							,**A					: str ):
					super().__init__(**A )
					__A   			= tf.keras.layers.GlobalAveragePoolingaD(keepdims=A							,name="pooler" )
					__A   			= [
					    tf.keras.layers.ConvaD(filters=A							,kernel_size=1							,activation="relu"							,name="attention.0" ),
					    tf.keras.layers.ConvaD(filters=A							,kernel_size=1							,activation="sigmoid"							,name="attention.2" ),
					]







	def 				UpperCamelCase_  (       self					: Dict							,A					: List[Any] ):
					# [batch_size, h, w, num_channels] -> [batch_size, 1, 1, num_channels]
					__A   			= self.pooler(A )
					for layer_module in self.attention:
									__A   			= layer_module(A )
					__A   			= hidden_state * pooled
					return hidden_state





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




	'''simple docstring'''





	def __init__(       self					: List[str]							,A					: RegNetConfig							,A					: int							,A					: int							,A					: int = 1							,**A					: Optional[int] ):
					super().__init__(**A )
					__A   			= in_channels != out_channels or stride != 1
					__A   			= max(1							,out_channels // config.groups_width )
					__A   			= (
					    TFRegNetShortCut(A							,stride=A							,name="shortcut" )
					    if should_apply_shortcut
					    else tf.keras.layers.Activation("linear"							,name="shortcut" )
					)
					# `self.layers` instead of `self.layer` because that is a reserved argument.
					__A   			= [
					    TFRegNetConvLayer(A							,kernel_size=1							,activation=config.hidden_act							,name="layer.0" ),
					    TFRegNetConvLayer(
					        A							,stride=A							,groups=A							,activation=config.hidden_act							,name="layer.1" ),
					    TFRegNetConvLayer(A							,kernel_size=1							,activation=A							,name="layer.2" ),
					]
					__A   			= ACTaFN[config.hidden_act]







	def 				UpperCamelCase_  (       self					: int							,A					: Optional[int] ):
					__A   			= hidden_state
					for layer_module in self.layers:
									__A   			= layer_module(A )
					__A   			= self.shortcut(A )
					hidden_state += residual
					__A   			= self.activation(A )
					return hidden_state





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




	'''simple docstring'''





	def __init__(       self					: List[Any]							,A					: RegNetConfig							,A					: int							,A					: int							,A					: int = 1							,**A					: str ):
					super().__init__(**A )
					__A   			= in_channels != out_channels or stride != 1
					__A   			= max(1							,out_channels // config.groups_width )
					__A   			= (
					    TFRegNetShortCut(A							,stride=A							,name="shortcut" )
					    if should_apply_shortcut
					    else tf.keras.layers.Activation("linear"							,name="shortcut" )
					)
					__A   			= [
					    TFRegNetConvLayer(A							,kernel_size=1							,activation=config.hidden_act							,name="layer.0" ),
					    TFRegNetConvLayer(
					        A							,stride=A							,groups=A							,activation=config.hidden_act							,name="layer.1" ),
					    TFRegNetSELayer(A							,reduced_channels=int(round(in_channels / 4 ) )							,name="layer.2" ),
					    TFRegNetConvLayer(A							,kernel_size=1							,activation=A							,name="layer.3" ),
					]
					__A   			= ACTaFN[config.hidden_act]







	def 				UpperCamelCase_  (       self					: Dict							,A					: Any ):
					__A   			= hidden_state
					for layer_module in self.layers:
									__A   			= layer_module(A )
					__A   			= self.shortcut(A )
					hidden_state += residual
					__A   			= self.activation(A )
					return hidden_state





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




	'''simple docstring'''





	def __init__(       self					: List[str]							,A					: RegNetConfig							,A					: int							,A					: int							,A					: int = 2							,A					: int = 2							,**A					: Optional[int] ):
					super().__init__(**A )

					__A   			= TFRegNetXLayer if config.layer_type == "x" else TFRegNetYLayer
					__A   			= [
					    # downsampling is done in the first layer with stride of 2
					    layer(A							,A							,A							,stride=A							,name="layers.0" ),
					    *[layer(A							,A							,A							,name=f'''layers.{i+1}''' ) for i in range(depth - 1 )],
					]







	def 				UpperCamelCase_  (       self					: Any							,A					: List[str] ):
					for layer_module in self.layers:
									__A   			= layer_module(A )
					return hidden_state





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




	'''simple docstring'''





	def __init__(       self					: Any							,A					: RegNetConfig							,**A					: List[str] ):
					super().__init__(**A )
					__A   			= []
					# based on `downsample_in_first_stage`, the first layer of the first stage may or may not downsample the input
					self.stages.append(
					    TFRegNetStage(
					        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"							,) )
					__A   			= zip(config.hidden_sizes							,config.hidden_sizes[1:] )
					for i, ((in_channels, out_channels), depth) in enumerate(zip(A							,config.depths[1:] ) ):
									self.stages.append(TFRegNetStage(A							,A							,A							,depth=A							,name=f'''stages.{i+1}''' ) )







	def 				UpperCamelCase_  (       self					: List[str]							,A					: tf.Tensor							,A					: bool = False							,A					: bool = True ):
					__A   			= () if output_hidden_states else None

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

									__A   			= stage_module(A )

					if output_hidden_states:
									__A   			= 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 )





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




	'''simple docstring'''





	snake_case_					    =     RegNetConfig






	def __init__(       self					: int							,A					: Optional[int]							,**A					: Dict ):
					super().__init__(**A )
					__A   			= config
					__A   			= TFRegNetEmbeddings(A							,name="embedder" )
					__A   			= TFRegNetEncoder(A							,name="encoder" )
					__A   			= tf.keras.layers.GlobalAveragePoolingaD(keepdims=A							,name="pooler" )







	@unpack_inputs
	def 				UpperCamelCase_  (       self					: Tuple							,A					: tf.Tensor							,A					: Optional[bool] = None							,A					: Optional[bool] = None							,A					: bool = False							,):
					__A   			= (
					    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
					)
					__A   			= return_dict if return_dict is not None else self.config.use_return_dict

					__A   			= self.embedder(A							,training=A )

					__A   			= self.encoder(
					    A							,output_hidden_states=A							,return_dict=A							,training=A )

					__A   			= encoder_outputs[0]
					__A   			= self.pooler(A )

					# Change to NCHW output format have uniformity in the modules
					__A   			= tf.transpose(A							,perm=(0, 3, 1, 2) )
					__A   			= tf.transpose(A							,perm=(0, 3, 1, 2) )

					# Change the other hidden state outputs to NCHW as well
					if output_hidden_states:
									__A   			= tuple([tf.transpose(A							,perm=(0, 3, 1, 2) ) for h in encoder_outputs[1]] )

					if not return_dict:
									return (last_hidden_state, pooled_output) + encoder_outputs[1:]

					return TFBaseModelOutputWithPoolingAndNoAttention(
					    last_hidden_state=A							,pooler_output=A							,hidden_states=hidden_states if output_hidden_states else encoder_outputs.hidden_states							,)





class 							UpperCAmelCase      (  __SCREAMING_SNAKE_CASE  ):




	'''simple docstring'''





	snake_case_					    =     RegNetConfig
	snake_case_					    =     "regnet"
	snake_case_					    =     "pixel_values"






	@property
	def 				UpperCamelCase_  (       self					: Optional[Any] ):
					return {"pixel_values": tf.TensorSpec(shape=(None, self.config.num_channels, 2_24, 2_24)							,dtype=tf.floataa )}





SCREAMING_SNAKE_CASE							:Dict							 =				R'\n    Parameters:\n    This model is a Tensorflow\n    [tf.keras.layers.Layer](https://www.tensorflow.org/api_docs/python/tf/keras/layers/Layer) sub-class. Use it as a\n    regular Tensorflow Module and refer to the Tensorflow documentation for all matter related to general usage and\n    behavior.\n        config ([`RegNetConfig`]): Model configuration class with all the parameters of the model.\n            Initializing with a config file does not load the weights associated with the model, only the\n            configuration. Check out the [`~TFPreTrainedModel.from_pretrained`] method to load the model weights.\n'

SCREAMING_SNAKE_CASE							:Dict							 =				R'\n    Args:\n        pixel_values (`tf.Tensor` of shape `(batch_size, num_channels, height, width)`):\n            Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See\n            [`ConveNextImageProcessor.__call__`] for details.\n        output_hidden_states (`bool`, *optional*):\n            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for\n            more detail.\n        return_dict (`bool`, *optional*):\n            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.\n'





@add_start_docstrings(
    "The bare RegNet model outputting raw features without any specific head on top."			,			__SCREAMING_SNAKE_CASE			,			)
class 							UpperCAmelCase      (  __SCREAMING_SNAKE_CASE  ):




	'''simple docstring'''





	def __init__(       self					: List[Any]							,A					: RegNetConfig							,*A					: List[Any]							,**A					: str ):
					super().__init__(A							,*A							,**A )
					__A   			= TFRegNetMainLayer(A							,name="regnet" )







	@unpack_inputs
	@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							,)
	def 				UpperCamelCase_  (       self					: Tuple							,A					: tf.Tensor							,A					: Optional[bool] = None							,A					: Optional[bool] = None							,A					: int=False							,):
					__A   			= (
					    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
					)
					__A   			= return_dict if return_dict is not None else self.config.use_return_dict

					__A   			= self.regnet(
					    pixel_values=A							,output_hidden_states=A							,return_dict=A							,training=A							,)
					if not return_dict:
									return (outputs[0],) + outputs[1:]

					return TFBaseModelOutputWithPoolingAndNoAttention(
					    last_hidden_state=outputs.last_hidden_state							,pooler_output=outputs.pooler_output							,hidden_states=outputs.hidden_states							,)





@add_start_docstrings(
    "\n    RegNet Model with an image classification head on top (a linear layer on top of the pooled features), e.g. for\n    ImageNet.\n    "			,			__SCREAMING_SNAKE_CASE			,			)
class 							UpperCAmelCase      (  __SCREAMING_SNAKE_CASE			,			__SCREAMING_SNAKE_CASE  ):




	'''simple docstring'''





	def __init__(       self					: Optional[int]							,A					: RegNetConfig							,*A					: str							,**A					: Tuple ):
					super().__init__(A							,*A							,**A )
					__A   			= config.num_labels
					__A   			= TFRegNetMainLayer(A							,name="regnet" )
					# classification head
					__A   			= [
					    tf.keras.layers.Flatten(),
					    tf.keras.layers.Dense(config.num_labels							,name="classifier.1" ) if config.num_labels > 0 else tf.identity,
					]







	@unpack_inputs
	@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							,)
	def 				UpperCamelCase_  (       self					: List[str]							,A					: tf.Tensor = None							,A					: tf.Tensor = None							,A					: bool = None							,A					: bool = None							,A					: Union[str, Any]=False							,):
					__A   			= (
					    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
					)
					__A   			= return_dict if return_dict is not None else self.config.use_return_dict

					__A   			= self.regnet(
					    A							,output_hidden_states=A							,return_dict=A							,training=A )

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

					__A   			= self.classifier[0](A )
					__A   			= self.classifier[1](A )

					__A   			= None if labels is None else self.hf_compute_loss(labels=A							,logits=A )

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

					return TFSequenceClassifierOutput(loss=A							,logits=A							,hidden_states=outputs.hidden_states )
