


import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import randn_tensor
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput
def        __lowerCamelCase		(				UpperCAmelCase_		:	str				,						UpperCAmelCase_		:	List[str]=0.999				,						UpperCAmelCase_		:	Dict="cosine"				,						):




      """simple docstring"""





      if alpha_transform_type == "cosine":

            def alpha_bar_fn(UpperCAmelCase_		:	Union[str, Any]		):
                  return math.cos((t + 0.008) / 1.008 * math.pi / 2		) ** 2

      elif alpha_transform_type == "exp":

            def alpha_bar_fn(UpperCAmelCase_		:	str		):
                  return math.exp(t * -12.0		)

      else:
            raise ValueError(F'''Unsupported alpha_tranform_type: {alpha_transform_type}'''		)

      a		:Dict											= []
      for i in range(UpperCAmelCase_		):
            a		:Dict											= i / num_diffusion_timesteps
            a		:Any											= (i + 1) / num_diffusion_timesteps
            betas.append(min(1 - alpha_bar_fn(UpperCAmelCase_		) / alpha_bar_fn(UpperCAmelCase_		)				,						UpperCAmelCase_		)		)
      return torch.tensor(UpperCAmelCase_				,						dtype=torch.floataa		)







class 	_snake_case						(   _snake_case		,					_snake_case							):
 SCREAMING_SNAKE_CASE__   = [e.name for e in KarrasDiffusionSchedulers]
 SCREAMING_SNAKE_CASE__   = 2




 @register_to_config
 def __init__(	self      ,						_lowerCamelCase = 1000      ,						_lowerCamelCase = 0.0_0085      ,						_lowerCamelCase = 0.012      ,						_lowerCamelCase = "linear"      ,						_lowerCamelCase = None      ,						_lowerCamelCase = "epsilon"      ,						_lowerCamelCase = "linspace"      ,						_lowerCamelCase = 0      ,						):
       if trained_betas is not None:
             a		:Tuple											= torch.tensor(_lowerCamelCase      ,						dtype=torch.floataa					)
       elif beta_schedule == "linear":
             a		:Any											= torch.linspace(_lowerCamelCase      ,						_lowerCamelCase      ,						_lowerCamelCase      ,						dtype=torch.floataa					)
       elif beta_schedule == "scaled_linear":
             # this schedule is very specific to the latent diffusion model.
             a		:Dict											= (
                 torch.linspace(beta_start**0.5      ,						beta_end**0.5      ,						_lowerCamelCase      ,						dtype=torch.floataa					) ** 2
             )
       elif beta_schedule == "squaredcos_cap_v2":
             # Glide cosine schedule
             a		:Any											= betas_for_alpha_bar(_lowerCamelCase					)
       else:
             raise NotImplementedError(F'''{beta_schedule} does is not implemented for {self.__class__}'''					)

       a		:List[Any]											= 1.0 - self.betas
       a		:Dict											= torch.cumprod(self.alphas      ,						dim=0					)

       #  set all values
       self.set_timesteps(_lowerCamelCase      ,						_lowerCamelCase      ,						_lowerCamelCase					)




 def  SCREAMING_SNAKE_CASE__       (	self      ,						_lowerCamelCase      ,						_lowerCamelCase=None					):
       if schedule_timesteps is None:
             a		:List[Any]											= self.timesteps

       a		:int											= (schedule_timesteps == timestep).nonzero()

       # The sigma index that is taken for the **very** first `step`
       # is always the second index (or the last index if there is only 1)
       # This way we can ensure we don't accidentally skip a sigma in
       # case we start in the middle of the denoising schedule (e.g. for image-to-image)
       if len(self._index_counter					) == 0:
             a		:Any											= 1 if len(_lowerCamelCase					) > 1 else 0
       else:
             a		:Tuple											= timestep.cpu().item() if torch.is_tensor(_lowerCamelCase					) else timestep
             a		:List[Any]											= self._index_counter[timestep_int]

       return indices[pos].item()




 @property
 def  SCREAMING_SNAKE_CASE__       (	self					):
       # standard deviation of the initial noise distribution
       if self.config.timestep_spacing in ["linspace", "trailing"]:
             return self.sigmas.max()

       return (self.sigmas.max() ** 2 + 1) ** 0.5




 def  SCREAMING_SNAKE_CASE__       (	self      ,						_lowerCamelCase      ,						_lowerCamelCase      ,						):
       a		:Union[str, Any]											= self.index_for_timestep(_lowerCamelCase					)

       if self.state_in_first_order:
             a		:int											= self.sigmas[step_index]
       else:
             a		:Any											= self.sigmas_interpol[step_index - 1]

       a		:Union[str, Any]											= sample / ((sigma**2 + 1) ** 0.5)
       return sample




 def  SCREAMING_SNAKE_CASE__       (	self      ,						_lowerCamelCase      ,						_lowerCamelCase = None      ,						_lowerCamelCase = None      ,						):
       a		:Optional[Any]											= num_inference_steps

       a		:int											= num_train_timesteps or self.config.num_train_timesteps

       # "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
       if self.config.timestep_spacing == "linspace":
             a		:Union[str, Any]											= np.linspace(0      ,						num_train_timesteps - 1      ,						_lowerCamelCase      ,						dtype=_lowerCamelCase					)[::-1].copy()
       elif self.config.timestep_spacing == "leading":
             a		:Optional[Any]											= num_train_timesteps // self.num_inference_steps
             # creates integer timesteps by multiplying by ratio
             # casting to int to avoid issues when num_inference_step is power of 3
             a		:Dict											= (np.arange(0      ,						_lowerCamelCase					) * step_ratio).round()[::-1].copy().astype(_lowerCamelCase					)
             timesteps += self.config.steps_offset
       elif self.config.timestep_spacing == "trailing":
             a		:Optional[Any]											= num_train_timesteps / self.num_inference_steps
             # creates integer timesteps by multiplying by ratio
             # casting to int to avoid issues when num_inference_step is power of 3
             a		:Any											= (np.arange(_lowerCamelCase      ,						0      ,						-step_ratio					)).round().copy().astype(_lowerCamelCase					)
             timesteps -= 1
       else:
             raise ValueError(
                 F'''{self.config.timestep_spacing} is not supported. Please make sure to choose one of \'linspace\', \'leading\' or \'trailing\'.'''					)

       a		:Optional[int]											= np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5					)
       a		:Optional[int]											= torch.from_numpy(np.log(_lowerCamelCase					)					).to(_lowerCamelCase					)

       a		:List[Any]											= np.interp(_lowerCamelCase      ,						np.arange(0      ,						len(_lowerCamelCase					)					)      ,						_lowerCamelCase					)
       a		:Union[str, Any]											= np.concatenate([sigmas, [0.0]]					).astype(np.floataa					)
       a		:List[Any]											= torch.from_numpy(_lowerCamelCase					).to(device=_lowerCamelCase					)

       # compute up and down sigmas
       a		:Union[str, Any]											= sigmas.roll(-1					)
       a		:Tuple											= 0.0
       a		:Union[str, Any]											= (sigmas_next**2 * (sigmas**2 - sigmas_next**2) / sigmas**2) ** 0.5
       a		:Tuple											= (sigmas_next**2 - sigmas_up**2) ** 0.5
       a		:Dict											= 0.0

       # compute interpolated sigmas
       a		:List[Any]											= sigmas.log().lerp(sigmas_down.log()      ,						0.5					).exp()
       a		:Tuple											= 0.0

       # set sigmas
       a		:Union[str, Any]											= torch.cat([sigmas[:1], sigmas[1:].repeat_interleave(2					), sigmas[-1:]]					)
       a		:Optional[Any]											= torch.cat(
           [sigmas_interpol[:1], sigmas_interpol[1:].repeat_interleave(2					), sigmas_interpol[-1:]]					)
       a		:int											= torch.cat([sigmas_up[:1], sigmas_up[1:].repeat_interleave(2					), sigmas_up[-1:]]					)
       a		:str											= torch.cat([sigmas_down[:1], sigmas_down[1:].repeat_interleave(2					), sigmas_down[-1:]]					)

       if str(_lowerCamelCase					).startswith('''mps'''					):
             # mps does not support float64
             a		:Any											= torch.from_numpy(_lowerCamelCase					).to(_lowerCamelCase      ,						dtype=torch.floataa					)
       else:
             a		:Tuple											= torch.from_numpy(_lowerCamelCase					).to(_lowerCamelCase					)

       a		:Tuple											= self.sigma_to_t(_lowerCamelCase					).to(_lowerCamelCase      ,						dtype=timesteps.dtype					)
       a		:Any											= torch.stack((timesteps_interpol[:-2, None], timesteps[1:, None])      ,						dim=-1					).flatten()

       a		:Any											= torch.cat([timesteps[:1], interleaved_timesteps]					)

       a		:List[str]											= None

       # for exp beta schedules, such as the one for `pipeline_shap_e.py`
       # we need an index counter
       a		:int											= defaultdict(_lowerCamelCase					)




 def  SCREAMING_SNAKE_CASE__       (	self      ,						_lowerCamelCase					):
       # get log sigma
       a		:str											= sigma.log()

       # get distribution
       a		:Optional[int]											= log_sigma - self.log_sigmas[:, None]

       # get sigmas range
       a		:Union[str, Any]											= dists.ge(0					).cumsum(dim=0					).argmax(dim=0					).clamp(max=self.log_sigmas.shape[0] - 2					)
       a		:Optional[Any]											= low_idx + 1

       a		:int											= self.log_sigmas[low_idx]
       a		:Tuple											= self.log_sigmas[high_idx]

       # interpolate sigmas
       a		:Any											= (low - log_sigma) / (low - high)
       a		:str											= w.clamp(0      ,						1					)

       # transform interpolation to time range
       a		:List[Any]											= (1 - w) * low_idx + w * high_idx
       a		:Dict											= t.view(sigma.shape					)
       return t




 @property
 def  SCREAMING_SNAKE_CASE__       (	self					):
       return self.sample is None




 def  SCREAMING_SNAKE_CASE__       (	self      ,						_lowerCamelCase      ,						_lowerCamelCase      ,						_lowerCamelCase      ,						_lowerCamelCase = None      ,						_lowerCamelCase = True      ,						):
       a		:Union[str, Any]											= self.index_for_timestep(_lowerCamelCase					)

       # advance index counter by 1
       a		:Union[str, Any]											= timestep.cpu().item() if torch.is_tensor(_lowerCamelCase					) else timestep
       self._index_counter[timestep_int] += 1

       if self.state_in_first_order:
             a		:Optional[int]											= self.sigmas[step_index]
             a		:Any											= self.sigmas_interpol[step_index]
             a		:int											= self.sigmas_up[step_index]
             a		:int											= self.sigmas_down[step_index - 1]
       else:
             # 2nd order / KPDM2's method
             a		:str											= self.sigmas[step_index - 1]
             a		:Optional[Any]											= self.sigmas_interpol[step_index - 1]
             a		:Optional[Any]											= self.sigmas_up[step_index - 1]
             a		:Optional[int]											= self.sigmas_down[step_index - 1]

       # currently only gamma=0 is supported. This usually works best anyways.
       # We can support gamma in the future but then need to scale the timestep before
       # passing it to the model which requires a change in API
       a		:List[str]											= 0
       a		:Any											= sigma * (gamma + 1)  # Note: sigma_hat == sigma for now

       a		:List[str]											= model_output.device
       a		:List[Any]											= randn_tensor(model_output.shape      ,						dtype=model_output.dtype      ,						device=_lowerCamelCase      ,						generator=_lowerCamelCase					)

       # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
       if self.config.prediction_type == "epsilon":
             a		:Dict											= sigma_hat if self.state_in_first_order else sigma_interpol
             a		:int											= sample - sigma_input * model_output
       elif self.config.prediction_type == "v_prediction":
             a		:Any											= sigma_hat if self.state_in_first_order else sigma_interpol
             a		:Union[str, Any]											= model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
                 sample / (sigma_input**2 + 1)
             )
       elif self.config.prediction_type == "sample":
             raise NotImplementedError('''prediction_type not implemented yet: sample'''					)
       else:
             raise ValueError(
                 F'''prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`'''					)

       if self.state_in_first_order:
             # 2. Convert to an ODE derivative for 1st order
             a		:List[Any]											= (sample - pred_original_sample) / sigma_hat
             # 3. delta timestep
             a		:List[Any]											= sigma_interpol - sigma_hat

             # store for 2nd order step
             a		:Optional[Any]											= sample
             a		:int											= dt
             a		:List[str]											= sample + derivative * dt
       else:
             # DPM-Solver-2
             # 2. Convert to an ODE derivative for 2nd order
             a		:int											= (sample - pred_original_sample) / sigma_interpol
             # 3. delta timestep
             a		:Optional[int]											= sigma_down - sigma_hat

             a		:Dict											= self.sample
             a		:List[str]											= None

             a		:Tuple											= sample + derivative * dt
             a		:Dict											= prev_sample + noise * sigma_up

       if not return_dict:
             return (prev_sample,)

       return SchedulerOutput(prev_sample=_lowerCamelCase					)




 def  SCREAMING_SNAKE_CASE__       (	self      ,						_lowerCamelCase      ,						_lowerCamelCase      ,						_lowerCamelCase      ,						):
       # Make sure sigmas and timesteps have the same device and dtype as original_samples
       a		:Optional[int]											= self.sigmas.to(device=original_samples.device      ,						dtype=original_samples.dtype					)
       if original_samples.device.type == "mps" and torch.is_floating_point(_lowerCamelCase					):
             # mps does not support float64
             a		:Tuple											= self.timesteps.to(original_samples.device      ,						dtype=torch.floataa					)
             a		:List[str]											= timesteps.to(original_samples.device      ,						dtype=torch.floataa					)
       else:
             a		:str											= self.timesteps.to(original_samples.device					)
             a		:Union[str, Any]											= timesteps.to(original_samples.device					)

       a		:List[Any]											= [self.index_for_timestep(_lowerCamelCase      ,						_lowerCamelCase					) for t in timesteps]

       a		:int											= sigmas[step_indices].flatten()
       while len(sigma.shape					) < len(original_samples.shape					):
             a		:str											= sigma.unsqueeze(-1					)

       a		:Tuple											= original_samples + noise * sigma
       return noisy_samples




 def __len__(	self					):
       return self.config.num_train_timesteps









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


snake_case       :					int        =  logging.get_logger(__name__)

# General docstring
snake_case       :					Optional[int]        =  '''ResNetConfig'''

# Base docstring
snake_case       :					Dict        =  '''microsoft/resnet-50'''
snake_case       :					List[str]        =  [1, 20_48, 7, 7]

# Image classification docstring
snake_case       :					int        =  '''microsoft/resnet-50'''
snake_case       :					Tuple        =  '''tiger cat'''

snake_case       :					Union[str, Any]        =  [
    '''microsoft/resnet-50''',
    # See all resnet models at https://huggingface.co/models?filter=resnet
]







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




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




 def  SCREAMING_SNAKE_CASE__       (	self      ,						_lowerCamelCase					):
       # Pad to match that done in the PyTorch Conv2D model
       a		:Optional[Any]											= (self.pad_value, self.pad_value)
       a		:List[str]											= tf.pad(_lowerCamelCase      ,						[(0, 0), height_pad, width_pad, (0, 0)]					)
       a		:Optional[Any]											= self.conv(_lowerCamelCase					)
       return hidden_state




 def  SCREAMING_SNAKE_CASE__       (	self      ,						_lowerCamelCase      ,						_lowerCamelCase = False					):
       a		:Tuple											= self.convolution(_lowerCamelCase					)
       a		:str											= self.normalization(_lowerCamelCase      ,						training=_lowerCamelCase					)
       a		:int											= self.activation(_lowerCamelCase					)
       return hidden_state







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




 def __init__(	self      ,						_lowerCamelCase      ,						**_lowerCamelCase					):
       super().__init__(**_lowerCamelCase					)
       a		:Optional[int]											= TFResNetConvLayer(
           config.embedding_size      ,						kernel_size=7      ,						stride=2      ,						activation=config.hidden_act      ,						name='''embedder'''      ,						)
       a		:List[Any]											= tf.keras.layers.MaxPoolaD(pool_size=3      ,						strides=2      ,						padding='''valid'''      ,						name='''pooler'''					)
       a		:Any											= config.num_channels




 def  SCREAMING_SNAKE_CASE__       (	self      ,						_lowerCamelCase      ,						_lowerCamelCase = False					):
       a							,       a							,       a							,       a		:int											= shape_list(_lowerCamelCase					)
       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.'''					)
       a		:Dict											= pixel_values
       a		:int											= self.embedder(_lowerCamelCase					)
       a		:str											= tf.pad(_lowerCamelCase      ,						[[0, 0], [1, 1], [1, 1], [0, 0]]					)
       a		:int											= self.pooler(_lowerCamelCase					)
       return hidden_state







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




 def __init__(	self      ,						_lowerCamelCase      ,						_lowerCamelCase = 2      ,						**_lowerCamelCase					):
       super().__init__(**_lowerCamelCase					)
       a		:int											= tf.keras.layers.ConvaD(
           _lowerCamelCase      ,						kernel_size=1      ,						strides=_lowerCamelCase      ,						use_bias=_lowerCamelCase      ,						name='''convolution'''					)
       # Use same default momentum and epsilon as PyTorch equivalent
       a		:Optional[Any]											= tf.keras.layers.BatchNormalization(epsilon=1e-5      ,						momentum=0.9      ,						name='''normalization'''					)




 def  SCREAMING_SNAKE_CASE__       (	self      ,						_lowerCamelCase      ,						_lowerCamelCase = False					):
       a		:Optional[Any]											= x
       a		:Union[str, Any]											= self.convolution(_lowerCamelCase					)
       a		:str											= self.normalization(_lowerCamelCase      ,						training=_lowerCamelCase					)
       return hidden_state







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




 def __init__(	self      ,						_lowerCamelCase      ,						_lowerCamelCase      ,						_lowerCamelCase = 1      ,						_lowerCamelCase = "relu"      ,						**_lowerCamelCase					):
       super().__init__(**_lowerCamelCase					)
       a		:Union[str, Any]											= in_channels != out_channels or stride != 1
       a		:str											= TFResNetConvLayer(_lowerCamelCase      ,						stride=_lowerCamelCase      ,						name='''layer.0'''					)
       a		:Any											= TFResNetConvLayer(_lowerCamelCase      ,						activation=_lowerCamelCase      ,						name='''layer.1'''					)
       a		:Any											= (
           TFResNetShortCut(_lowerCamelCase      ,						stride=_lowerCamelCase      ,						name='''shortcut'''					)
           if should_apply_shortcut
           else tf.keras.layers.Activation('''linear'''      ,						name='''shortcut'''					)
       )
       a		:Union[str, Any]											= ACTaFN[activation]




 def  SCREAMING_SNAKE_CASE__       (	self      ,						_lowerCamelCase      ,						_lowerCamelCase = False					):
       a		:List[str]											= hidden_state
       a		:Optional[int]											= self.conva(_lowerCamelCase      ,						training=_lowerCamelCase					)
       a		:Any											= self.conva(_lowerCamelCase      ,						training=_lowerCamelCase					)
       a		:Tuple											= self.shortcut(_lowerCamelCase      ,						training=_lowerCamelCase					)
       hidden_state += residual
       a		:Dict											= self.activation(_lowerCamelCase					)
       return hidden_state







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




 def __init__(	self      ,						_lowerCamelCase      ,						_lowerCamelCase      ,						_lowerCamelCase = 1      ,						_lowerCamelCase = "relu"      ,						_lowerCamelCase = 4      ,						**_lowerCamelCase      ,						):
       super().__init__(**_lowerCamelCase					)
       a		:str											= in_channels != out_channels or stride != 1
       a		:Optional[int]											= out_channels // reduction
       a		:List[Any]											= TFResNetConvLayer(_lowerCamelCase      ,						kernel_size=1      ,						name='''layer.0'''					)
       a		:Tuple											= TFResNetConvLayer(_lowerCamelCase      ,						stride=_lowerCamelCase      ,						name='''layer.1'''					)
       a		:Union[str, Any]											= TFResNetConvLayer(_lowerCamelCase      ,						kernel_size=1      ,						activation=_lowerCamelCase      ,						name='''layer.2'''					)
       a		:Optional[int]											= (
           TFResNetShortCut(_lowerCamelCase      ,						stride=_lowerCamelCase      ,						name='''shortcut'''					)
           if should_apply_shortcut
           else tf.keras.layers.Activation('''linear'''      ,						name='''shortcut'''					)
       )
       a		:str											= ACTaFN[activation]




 def  SCREAMING_SNAKE_CASE__       (	self      ,						_lowerCamelCase      ,						_lowerCamelCase = False					):
       a		:Union[str, Any]											= hidden_state
       a		:Tuple											= self.conva(_lowerCamelCase      ,						training=_lowerCamelCase					)
       a		:int											= self.conva(_lowerCamelCase      ,						training=_lowerCamelCase					)
       a		:Any											= self.conva(_lowerCamelCase      ,						training=_lowerCamelCase					)
       a		:int											= self.shortcut(_lowerCamelCase      ,						training=_lowerCamelCase					)
       hidden_state += residual
       a		:List[Any]											= self.activation(_lowerCamelCase					)
       return hidden_state







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




 def __init__(	self      ,						_lowerCamelCase      ,						_lowerCamelCase      ,						_lowerCamelCase      ,						_lowerCamelCase = 2      ,						_lowerCamelCase = 2      ,						**_lowerCamelCase					):
       super().__init__(**_lowerCamelCase					)

       a		:Dict											= TFResNetBottleNeckLayer if config.layer_type == '''bottleneck''' else TFResNetBasicLayer

       a		:Dict											= [layer(_lowerCamelCase      ,						_lowerCamelCase      ,						stride=_lowerCamelCase      ,						activation=config.hidden_act      ,						name='''layers.0'''					)]
       layers += [
           layer(_lowerCamelCase      ,						_lowerCamelCase      ,						activation=config.hidden_act      ,						name=F'''layers.{i + 1}'''					)
           for i in range(depth - 1					)
       ]
       a		:str											= layers




 def  SCREAMING_SNAKE_CASE__       (	self      ,						_lowerCamelCase      ,						_lowerCamelCase = False					):
       for layer in self.stage_layers:
             a		:List[Any]											= layer(_lowerCamelCase      ,						training=_lowerCamelCase					)
       return hidden_state







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




 def __init__(	self      ,						_lowerCamelCase      ,						**_lowerCamelCase					):
       super().__init__(**_lowerCamelCase					)
       # based on `downsample_in_first_stage` the first layer of the first stage may or may not downsample the input
       a		:List[Any]											= [
           TFResNetStage(
               _lowerCamelCase      ,						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(_lowerCamelCase      ,						_lowerCamelCase      ,						_lowerCamelCase      ,						depth=_lowerCamelCase      ,						name=F'''stages.{i + 1}'''					)					)




 def  SCREAMING_SNAKE_CASE__       (	self      ,						_lowerCamelCase      ,						_lowerCamelCase = False      ,						_lowerCamelCase = True      ,						_lowerCamelCase = False      ,						):
       a		:str											= () if output_hidden_states else None

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

             a		:Optional[int]											= stage_module(_lowerCamelCase      ,						training=_lowerCamelCase					)

       if output_hidden_states:
             a		:Union[str, Any]											= 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=_lowerCamelCase      ,						hidden_states=_lowerCamelCase					)







class 	_snake_case						(   _snake_case							):
 SCREAMING_SNAKE_CASE__   = ResNetConfig
 SCREAMING_SNAKE_CASE__   = 'resnet'
 SCREAMING_SNAKE_CASE__   = 'pixel_values'




 @property
 def  SCREAMING_SNAKE_CASE__       (	self					):
       return {"pixel_values": tf.TensorSpec(shape=(None, self.config.num_channels, 224, 224)      ,						dtype=tf.floataa					)}







snake_case       :					List[str]        =  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.
'''


snake_case       :					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 	_snake_case						(   tf.keras.layers.Layer							):
 SCREAMING_SNAKE_CASE__   = ResNetConfig




 def __init__(	self      ,						_lowerCamelCase      ,						**_lowerCamelCase					):
       super().__init__(**_lowerCamelCase					)
       a		:str											= config
       a		:Union[str, Any]											= TFResNetEmbeddings(_lowerCamelCase      ,						name='''embedder'''					)
       a		:Tuple											= TFResNetEncoder(_lowerCamelCase      ,						name='''encoder'''					)
       a		:List[str]											= tf.keras.layers.GlobalAveragePoolingaD(keepdims=_lowerCamelCase					)




 @unpack_inputs
 def  SCREAMING_SNAKE_CASE__       (	self      ,						_lowerCamelCase      ,						_lowerCamelCase = None      ,						_lowerCamelCase = None      ,						_lowerCamelCase = False      ,						):
       a		:Union[str, Any]											= (
           output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
       )
       a		:List[Any]											= 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)
       a		:Dict											= tf.transpose(_lowerCamelCase      ,						perm=[0, 2, 3, 1]					)
       a		:List[Any]											= self.embedder(_lowerCamelCase      ,						training=_lowerCamelCase					)

       a		:Union[str, Any]											= self.encoder(
           _lowerCamelCase      ,						output_hidden_states=_lowerCamelCase      ,						return_dict=_lowerCamelCase      ,						training=_lowerCamelCase					)

       a		:List[str]											= encoder_outputs[0]

       a		:int											= self.pooler(_lowerCamelCase					)

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

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

       a		:str											= hidden_states if output_hidden_states else None

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







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




 def __init__(	self      ,						_lowerCamelCase      ,						**_lowerCamelCase					):
       super().__init__(_lowerCamelCase      ,						**_lowerCamelCase					)
       a		:str											= TFResNetMainLayer(config=_lowerCamelCase      ,						name='''resnet'''					)




 @add_start_docstrings_to_model_forward(_lowerCamelCase					)
 @add_code_sample_docstrings(
     checkpoint=_CHECKPOINT_FOR_DOC      ,						output_type=_lowerCamelCase      ,						config_class=_CONFIG_FOR_DOC      ,						modality='''vision'''      ,						expected_output=_EXPECTED_OUTPUT_SHAPE      ,						)
 @unpack_inputs
 def  SCREAMING_SNAKE_CASE__       (	self      ,						_lowerCamelCase      ,						_lowerCamelCase = None      ,						_lowerCamelCase = None      ,						_lowerCamelCase = False      ,						):
       a		:Tuple											= (
           output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
       )
       a		:List[str]											= return_dict if return_dict is not None else self.config.use_return_dict

       a		:Optional[Any]											= self.resnet(
           pixel_values=_lowerCamelCase      ,						output_hidden_states=_lowerCamelCase      ,						return_dict=_lowerCamelCase      ,						training=_lowerCamelCase      ,						)
       return resnet_outputs







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




 def __init__(	self      ,						_lowerCamelCase      ,						**_lowerCamelCase					):
       super().__init__(_lowerCamelCase      ,						**_lowerCamelCase					)
       a		:Optional[int]											= config.num_labels
       a		:List[str]											= TFResNetMainLayer(_lowerCamelCase      ,						name='''resnet'''					)
       # classification head
       a		:Optional[Any]											= (
           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  SCREAMING_SNAKE_CASE__       (	self      ,						_lowerCamelCase					):
       a		:Optional[int]											= tf.keras.layers.Flatten()(_lowerCamelCase					)
       a		:Tuple											= self.classifier_layer(_lowerCamelCase					)
       return logits




 @add_start_docstrings_to_model_forward(_lowerCamelCase					)
 @add_code_sample_docstrings(
     checkpoint=_IMAGE_CLASS_CHECKPOINT      ,						output_type=_lowerCamelCase      ,						config_class=_CONFIG_FOR_DOC      ,						expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT      ,						)
 @unpack_inputs
 def  SCREAMING_SNAKE_CASE__       (	self      ,						_lowerCamelCase = None      ,						_lowerCamelCase = None      ,						_lowerCamelCase = None      ,						_lowerCamelCase = None      ,						_lowerCamelCase = False      ,						):
       a		:Optional[Any]											= return_dict if return_dict is not None else self.config.use_return_dict

       a		:Any											= self.resnet(
           _lowerCamelCase      ,						output_hidden_states=_lowerCamelCase      ,						return_dict=_lowerCamelCase      ,						training=_lowerCamelCase					)

       a		:Tuple											= outputs.pooler_output if return_dict else outputs[1]

       a		:Optional[int]											= self.classifier(_lowerCamelCase					)

       a		:List[Any]											= None if labels is None else self.hf_compute_loss(_lowerCamelCase      ,						_lowerCamelCase					)

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

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





