'''simple docstring'''




from dataclasses import dataclass
from typing import Any, Dict, Optional

import torch
import torch.nn.functional as F
from torch import nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..models.embeddings import ImagePositionalEmbeddings
from ..utils import BaseOutput, deprecate
from .attention import BasicTransformerBlock
from .embeddings import PatchEmbed
from .modeling_utils import ModelMixin

@dataclass
class 			lowerCamelCase_			(snake_case__					):
      '''simple docstring'''

      __UpperCamelCase:						torch.FloatTensor

class 			lowerCamelCase_			(snake_case__							,				snake_case__					):
      '''simple docstring'''

      @register_to_config
      def __init__( self		:       Optional[Any]		,      A		:       int = 16		,      A		:       int = 88		,      A		:       Optional[int] = None		,      A		:       Optional[int] = None		,      A		:       int = 1		,      A		:       float = 0.0		,      A		:       int = 32		,      A		:       Optional[int] = None		,      A		:       bool = False		,      A		:       Optional[int] = None		,      A		:       Optional[int] = None		,      A		:       Optional[int] = None		,      A		:       str = "geglu"		,      A		:       Optional[int] = None		,      A		:       bool = False		,      A		:       bool = False		,      A		:       bool = False		,      A		:       str = "layer_norm"		,      A		:       bool = True		,      ):
             super().__init__()
             _UpperCAmelCase  :						Union[str, Any]							   =					use_linear_projection
             _UpperCAmelCase  :						Tuple							   =					num_attention_heads
             _UpperCAmelCase  :						Union[str, Any]							   =					attention_head_dim
             _UpperCAmelCase  :						str							   =					num_attention_heads * attention_head_dim

             # 1. Transformer2DModel can process both standard continuous images of shape `(batch_size, num_channels, width, height)` as well as quantized image embeddings of shape `(batch_size, num_image_vectors)`
             # Define whether input is continuous or discrete depending on configuration
             _UpperCAmelCase  :						Optional[Any]							   =					(in_channels is not None) and (patch_size is None)
             _UpperCAmelCase  :						str							   =					num_vector_embeds is not None
             _UpperCAmelCase  :						str							   =					in_channels is not None and patch_size is not None

             if norm_type == "layer_norm" and num_embeds_ada_norm is not None:
                    _UpperCAmelCase  :						List[Any]							   =					(
                        F"""The configuration file of this model: {self.__class__} is outdated. `norm_type` is either not set or"""
                        " incorrectly set to `'layer_norm'`.Make sure to set `norm_type` to `'ada_norm'` in the config."
                        " Please make sure to update the config accordingly as leaving `norm_type` might led to incorrect"
                        " results in future versions. If you have downloaded this checkpoint from the Hugging Face Hub, it"
                        " would be very nice if you could open a Pull request for the `transformer/config.json` file"
                    )
                    deprecate("norm_type!=num_embeds_ada_norm"		,      "1.0.0"		,      A		,      standard_warn=A )
                    _UpperCAmelCase  :						Dict							   =					"ada_norm"

             if self.is_input_continuous and self.is_input_vectorized:
                    raise ValueError(
                        F"""Cannot define both `in_channels`: {in_channels} and `num_vector_embeds`: {num_vector_embeds}. Make"""
                        " sure that either `in_channels` or `num_vector_embeds` is None." )
             elif self.is_input_vectorized and self.is_input_patches:
                    raise ValueError(
                        F"""Cannot define both `num_vector_embeds`: {num_vector_embeds} and `patch_size`: {patch_size}. Make"""
                        " sure that either `num_vector_embeds` or `num_patches` is None." )
             elif not self.is_input_continuous and not self.is_input_vectorized and not self.is_input_patches:
                    raise ValueError(
                        F"""Has to define `in_channels`: {in_channels}, `num_vector_embeds`: {num_vector_embeds}, or patch_size:"""
                        F""" {patch_size}. Make sure that `in_channels`, `num_vector_embeds` or `num_patches` is not None.""" )

             # 2. Define input layers
             if self.is_input_continuous:
                    _UpperCAmelCase  :						str							   =					in_channels

                    _UpperCAmelCase  :						List[Any]							   =					torch.nn.GroupNorm(num_groups=A		,      num_channels=A		,      eps=1E-6		,      affine=A )
                    if use_linear_projection:
                           _UpperCAmelCase  :						Union[str, Any]							   =					nn.Linear(A		,      A )
                    else:
                           _UpperCAmelCase  :						Optional[Any]							   =					nn.Convad(A		,      A		,      kernel_size=1		,      stride=1		,      padding=0 )
             elif self.is_input_vectorized:
                    assert sample_size is not None, "Transformer2DModel over discrete input must provide sample_size"
                    assert num_vector_embeds is not None, "Transformer2DModel over discrete input must provide num_embed"

                    _UpperCAmelCase  :						List[str]							   =					sample_size
                    _UpperCAmelCase  :						Optional[int]							   =					sample_size
                    _UpperCAmelCase  :						Optional[Any]							   =					num_vector_embeds
                    _UpperCAmelCase  :						Optional[int]							   =					self.height * self.width

                    _UpperCAmelCase  :						Optional[int]							   =					ImagePositionalEmbeddings(
                        num_embed=A		,      embed_dim=A		,      height=self.height		,      width=self.width )
             elif self.is_input_patches:
                    assert sample_size is not None, "Transformer2DModel over patched input must provide sample_size"

                    _UpperCAmelCase  :						str							   =					sample_size
                    _UpperCAmelCase  :						Optional[Any]							   =					sample_size

                    _UpperCAmelCase  :						Dict							   =					patch_size
                    _UpperCAmelCase  :						int							   =					PatchEmbed(
                        height=A		,      width=A		,      patch_size=A		,      in_channels=A		,      embed_dim=A		,      )

             # 3. Define transformers blocks
             _UpperCAmelCase  :						Optional[int]							   =					nn.ModuleList(
                 [
                     BasicTransformerBlock(
                         A		,      A		,      A		,      dropout=A		,      cross_attention_dim=A		,      activation_fn=A		,      num_embeds_ada_norm=A		,      attention_bias=A		,      only_cross_attention=A		,      upcast_attention=A		,      norm_type=A		,      norm_elementwise_affine=A		,      )
                     for d in range(A )
                 ] )

             # 4. Define output layers
             _UpperCAmelCase  :						List[Any]							   =					in_channels if out_channels is None else out_channels
             if self.is_input_continuous:
                    # TODO: should use out_channels for continuous projections
                    if use_linear_projection:
                           _UpperCAmelCase  :						Any							   =					nn.Linear(A		,      A )
                    else:
                           _UpperCAmelCase  :						Any							   =					nn.Convad(A		,      A		,      kernel_size=1		,      stride=1		,      padding=0 )
             elif self.is_input_vectorized:
                    _UpperCAmelCase  :						Tuple							   =					nn.LayerNorm(A )
                    _UpperCAmelCase  :						Optional[Any]							   =					nn.Linear(A		,      self.num_vector_embeds - 1 )
             elif self.is_input_patches:
                    _UpperCAmelCase  :						Tuple							   =					nn.LayerNorm(A		,      elementwise_affine=A		,      eps=1E-6 )
                    _UpperCAmelCase  :						Optional[Any]							   =					nn.Linear(A		,      2 * inner_dim )
                    _UpperCAmelCase  :						Optional[Any]							   =					nn.Linear(A		,      patch_size * patch_size * self.out_channels )





      def   _A			( self		:       Union[str, Any]		,      A		:       torch.Tensor		,      A		:       Optional[torch.Tensor] = None		,      A		:       Optional[torch.LongTensor] = None		,      A		:       Optional[torch.LongTensor] = None		,      A		:       Dict[str, Any] = None		,      A		:       Optional[torch.Tensor] = None		,      A		:       Optional[torch.Tensor] = None		,      A		:       bool = True		,      ):
             # ensure attention_mask is a bias, and give it a singleton query_tokens dimension.
             #   we may have done this conversion already, e.g. if we came here via UNet2DConditionModel#forward.
             #   we can tell by counting dims; if ndim == 2: it's a mask rather than a bias.
             # expects mask of shape:
             #   [batch, key_tokens]
             # adds singleton query_tokens dimension:
             #   [batch,                    1, key_tokens]
             # this helps to broadcast it as a bias over attention scores, which will be in one of the following shapes:
             #   [batch,  heads, query_tokens, key_tokens] (e.g. torch sdp attn)
             #   [batch * heads, query_tokens, key_tokens] (e.g. xformers or classic attn)
             if attention_mask is not None and attention_mask.ndim == 2:
                    # assume that mask is expressed as:
                    #   (1 = keep,      0 = discard)
                    # convert mask into a bias that can be added to attention scores:
                    #       (keep = +0,     discard = -10000.0)
                    _UpperCAmelCase  :						List[str]							   =					(1 - attention_mask.to(hidden_states.dtype )) * -10_000.0
                    _UpperCAmelCase  :						Dict							   =					attention_mask.unsqueeze(1 )

             # convert encoder_attention_mask to a bias the same way we do for attention_mask
             if encoder_attention_mask is not None and encoder_attention_mask.ndim == 2:
                    _UpperCAmelCase  :						Union[str, Any]							   =					(1 - encoder_attention_mask.to(hidden_states.dtype )) * -10_000.0
                    _UpperCAmelCase  :						Dict							   =					encoder_attention_mask.unsqueeze(1 )

             # 1. Input
             if self.is_input_continuous:
                    _UpperCAmelCase   ,						_UpperCAmelCase   ,						_UpperCAmelCase   ,						_UpperCAmelCase  :						List[str]							   =					hidden_states.shape
                    _UpperCAmelCase  :						Dict							   =					hidden_states

                    _UpperCAmelCase  :						Union[str, Any]							   =					self.norm(A )
                    if not self.use_linear_projection:
                           _UpperCAmelCase  :						Optional[int]							   =					self.proj_in(A )
                           _UpperCAmelCase  :						Optional[Any]							   =					hidden_states.shape[1]
                           _UpperCAmelCase  :						Optional[int]							   =					hidden_states.permute(0		,      2		,      3		,      1 ).reshape(A		,      height * width		,      A )
                    else:
                           _UpperCAmelCase  :						List[str]							   =					hidden_states.shape[1]
                           _UpperCAmelCase  :						List[Any]							   =					hidden_states.permute(0		,      2		,      3		,      1 ).reshape(A		,      height * width		,      A )
                           _UpperCAmelCase  :						Optional[int]							   =					self.proj_in(A )
             elif self.is_input_vectorized:
                    _UpperCAmelCase  :						int							   =					self.latent_image_embedding(A )
             elif self.is_input_patches:
                    _UpperCAmelCase  :						List[Any]							   =					self.pos_embed(A )

             # 2. Blocks
             for block in self.transformer_blocks:
                    _UpperCAmelCase  :						str							   =					block(
                        A		,      attention_mask=A		,      encoder_hidden_states=A		,      encoder_attention_mask=A		,      timestep=A		,      cross_attention_kwargs=A		,      class_labels=A		,      )

             # 3. Output
             if self.is_input_continuous:
                    if not self.use_linear_projection:
                           _UpperCAmelCase  :						List[Any]							   =					hidden_states.reshape(A		,      A		,      A		,      A ).permute(0		,      3		,      1		,      2 ).contiguous()
                           _UpperCAmelCase  :						List[str]							   =					self.proj_out(A )
                    else:
                           _UpperCAmelCase  :						Dict							   =					self.proj_out(A )
                           _UpperCAmelCase  :						List[Any]							   =					hidden_states.reshape(A		,      A		,      A		,      A ).permute(0		,      3		,      1		,      2 ).contiguous()

                    _UpperCAmelCase  :						str							   =					hidden_states + residual
             elif self.is_input_vectorized:
                    _UpperCAmelCase  :						List[Any]							   =					self.norm_out(A )
                    _UpperCAmelCase  :						Dict							   =					self.out(A )
                    # (batch, self.num_vector_embeds - 1, self.num_latent_pixels)
                    _UpperCAmelCase  :						Any							   =					logits.permute(0		,      2		,      1 )

                    # log(p(x_0))
                    _UpperCAmelCase  :						List[str]							   =					F.log_softmax(logits.double()		,      dim=1 ).float()
             elif self.is_input_patches:
                    # TODO: cleanup!
                    _UpperCAmelCase  :						Any							   =					self.transformer_blocks[0].norma.emb(
                        A		,      A		,      hidden_dtype=hidden_states.dtype )
                    _UpperCAmelCase   ,						_UpperCAmelCase  :						Optional[Any]							   =					self.proj_out_a(F.silu(A ) ).chunk(2		,      dim=1 )
                    _UpperCAmelCase  :						Union[str, Any]							   =					self.norm_out(A ) * (1 + scale[:, None]) + shift[:, None]
                    _UpperCAmelCase  :						str							   =					self.proj_out_a(A )

                    # unpatchify
                    _UpperCAmelCase  :						Any							   =					int(hidden_states.shape[1] ** 0.5 )
                    _UpperCAmelCase  :						str							   =					hidden_states.reshape(
                        shape=(-1, height, width, self.patch_size, self.patch_size, self.out_channels) )
                    _UpperCAmelCase  :						Optional[int]							   =					torch.einsum("nhwpqc->nchpwq"		,      A )
                    _UpperCAmelCase  :						str							   =					hidden_states.reshape(
                        shape=(-1, self.out_channels, height * self.patch_size, width * self.patch_size) )

             if not return_dict:
                    return (output,)

             return TransformeraDModelOutput(sample=A )


'''simple docstring'''




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


__SCREAMING_SNAKE_CASE :  Tuple  =						logging.get_logger(__name__)

# General docstring
__SCREAMING_SNAKE_CASE :  List[str]  =						"""ResNetConfig"""

# Base docstring
__SCREAMING_SNAKE_CASE :  List[str]  =						"""microsoft/resnet-50"""
__SCREAMING_SNAKE_CASE :  Tuple  =						[1, 2_048, 7, 7]

# Image classification docstring
__SCREAMING_SNAKE_CASE :  Optional[Any]  =						"""microsoft/resnet-50"""
__SCREAMING_SNAKE_CASE :  Union[str, Any]  =						"""tiger cat"""

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

class 			lowerCamelCase_			(tf.keras.layers.Layer					):
      '''simple docstring'''

      def __init__( self		:       List[Any]		,      A		:       int		,      A		:       int = 3		,      A		:       int = 1		,      A		:       str = "relu"		,      **A		:       List[Any] ):
             super().__init__(**A )
             _UpperCAmelCase  :						Dict							   =					kernel_size // 2
             _UpperCAmelCase  :						Dict							   =					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
             _UpperCAmelCase  :						Optional[Any]							   =					tf.keras.layers.BatchNormalization(epsilon=1E-5		,      momentum=0.9		,      name="normalization" )
             _UpperCAmelCase  :						Tuple							   =					ACTaFN[activation] if activation is not None else tf.keras.layers.Activation("linear" )




      def   _A			( self		:       Optional[Any]		,      A		:       tf.Tensor ):
             # Pad to match that done in the PyTorch Conv2D model
             _UpperCAmelCase  :						Optional[Any]							   =					(self.pad_value, self.pad_value)
             _UpperCAmelCase  :						Any							   =					tf.pad(A		,      [(0, 0), height_pad, width_pad, (0, 0)] )
             _UpperCAmelCase  :						Dict							   =					self.conv(A )
             return hidden_state





      def   _A			( self		:       List[str]		,      A		:       tf.Tensor		,      A		:       bool = False ):
             _UpperCAmelCase  :						int							   =					self.convolution(A )
             _UpperCAmelCase  :						Optional[int]							   =					self.normalization(A		,      training=A )
             _UpperCAmelCase  :						List[str]							   =					self.activation(A )
             return hidden_state

class 			lowerCamelCase_			(tf.keras.layers.Layer					):
      '''simple docstring'''

      def __init__( self		:       List[str]		,      A		:       ResNetConfig		,      **A		:       Tuple ):
             super().__init__(**A )
             _UpperCAmelCase  :						str							   =					TFResNetConvLayer(
                 config.embedding_size		,      kernel_size=7		,      stride=2		,      activation=config.hidden_act		,      name="embedder"		,      )
             _UpperCAmelCase  :						Optional[int]							   =					tf.keras.layers.MaxPoolaD(pool_size=3		,      strides=2		,      padding="valid"		,      name="pooler" )
             _UpperCAmelCase  :						List[str]							   =					config.num_channels





      def   _A			( self		:       List[str]		,      A		:       tf.Tensor		,      A		:       bool = False ):
             _UpperCAmelCase   ,						_UpperCAmelCase   ,						_UpperCAmelCase   ,						_UpperCAmelCase  :						Any							   =					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." )
             _UpperCAmelCase  :						Any							   =					pixel_values
             _UpperCAmelCase  :						Union[str, Any]							   =					self.embedder(A )
             _UpperCAmelCase  :						int							   =					tf.pad(A		,      [[0, 0], [1, 1], [1, 1], [0, 0]] )
             _UpperCAmelCase  :						Tuple							   =					self.pooler(A )
             return hidden_state

class 			lowerCamelCase_			(tf.keras.layers.Layer					):
      '''simple docstring'''

      def __init__( self		:       str		,      A		:       int		,      A		:       int = 2		,      **A		:       Union[str, Any] ):
             super().__init__(**A )
             _UpperCAmelCase  :						Dict							   =					tf.keras.layers.ConvaD(
                 A		,      kernel_size=1		,      strides=A		,      use_bias=A		,      name="convolution" )
             # Use same default momentum and epsilon as PyTorch equivalent
             _UpperCAmelCase  :						Tuple							   =					tf.keras.layers.BatchNormalization(epsilon=1E-5		,      momentum=0.9		,      name="normalization" )





      def   _A			( self		:       List[Any]		,      A		:       tf.Tensor		,      A		:       bool = False ):
             _UpperCAmelCase  :						List[Any]							   =					x
             _UpperCAmelCase  :						Tuple							   =					self.convolution(A )
             _UpperCAmelCase  :						List[str]							   =					self.normalization(A		,      training=A )
             return hidden_state

class 			lowerCamelCase_			(tf.keras.layers.Layer					):
      '''simple docstring'''

      def __init__( self		:       Optional[Any]		,      A		:       int		,      A		:       int		,      A		:       int = 1		,      A		:       str = "relu"		,      **A		:       Dict ):
             super().__init__(**A )
             _UpperCAmelCase  :						List[Any]							   =					in_channels != out_channels or stride != 1
             _UpperCAmelCase  :						Dict							   =					TFResNetConvLayer(A		,      stride=A		,      name="layer.0" )
             _UpperCAmelCase  :						int							   =					TFResNetConvLayer(A		,      activation=A		,      name="layer.1" )
             _UpperCAmelCase  :						List[Any]							   =					(
                 TFResNetShortCut(A		,      stride=A		,      name="shortcut" )
                 if should_apply_shortcut
                 else tf.keras.layers.Activation("linear"		,      name="shortcut" )
             )
             _UpperCAmelCase  :						Optional[int]							   =					ACTaFN[activation]





      def   _A			( self		:       Any		,      A		:       tf.Tensor		,      A		:       bool = False ):
             _UpperCAmelCase  :						Dict							   =					hidden_state
             _UpperCAmelCase  :						Optional[Any]							   =					self.conva(A		,      training=A )
             _UpperCAmelCase  :						List[str]							   =					self.conva(A		,      training=A )
             _UpperCAmelCase  :						Dict							   =					self.shortcut(A		,      training=A )
             hidden_state += residual
             _UpperCAmelCase  :						List[str]							   =					self.activation(A )
             return hidden_state

class 			lowerCamelCase_			(tf.keras.layers.Layer					):
      '''simple docstring'''

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





      def   _A			( self		:       Optional[int]		,      A		:       tf.Tensor		,      A		:       bool = False ):
             _UpperCAmelCase  :						Optional[Any]							   =					hidden_state
             _UpperCAmelCase  :						int							   =					self.conva(A		,      training=A )
             _UpperCAmelCase  :						Any							   =					self.conva(A		,      training=A )
             _UpperCAmelCase  :						List[Any]							   =					self.conva(A		,      training=A )
             _UpperCAmelCase  :						List[str]							   =					self.shortcut(A		,      training=A )
             hidden_state += residual
             _UpperCAmelCase  :						Tuple							   =					self.activation(A )
             return hidden_state

class 			lowerCamelCase_			(tf.keras.layers.Layer					):
      '''simple docstring'''

      def __init__( self		:       Dict		,      A		:       ResNetConfig		,      A		:       int		,      A		:       int		,      A		:       int = 2		,      A		:       int = 2		,      **A		:       Dict ):
             super().__init__(**A )

             _UpperCAmelCase  :						Optional[int]							   =					TFResNetBottleNeckLayer if config.layer_type == "bottleneck" else TFResNetBasicLayer

             _UpperCAmelCase  :						Dict							   =					[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 )
             ]
             _UpperCAmelCase  :						int							   =					layers





      def   _A			( self		:       List[Any]		,      A		:       tf.Tensor		,      A		:       bool = False ):
             for layer in self.stage_layers:
                    _UpperCAmelCase  :						int							   =					layer(A		,      training=A )
             return hidden_state

class 			lowerCamelCase_			(tf.keras.layers.Layer					):
      '''simple docstring'''

      def __init__( self		:       Optional[int]		,      A		:       ResNetConfig		,      **A		:       Optional[Any] ):
             super().__init__(**A )
             # based on `downsample_in_first_stage` the first layer of the first stage may or may not downsample the input
             _UpperCAmelCase  :						Optional[Any]							   =					[
                 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   _A			( self		:       Tuple		,      A		:       tf.Tensor		,      A		:       bool = False		,      A		:       bool = True		,      A		:       bool = False		,      ):
             _UpperCAmelCase  :						List[str]							   =					() if output_hidden_states else None

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

                    _UpperCAmelCase  :						str							   =					stage_module(A		,      training=A )

             if output_hidden_states:
                    _UpperCAmelCase  :						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=A		,      hidden_states=A )

class 			lowerCamelCase_			(snake_case__					):
      '''simple docstring'''

      __UpperCamelCase:						str      			=		ResNetConfig
      __UpperCamelCase:						List[str]      			=		"resnet"
      __UpperCamelCase:						int      			=		"pixel_values"




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

__SCREAMING_SNAKE_CASE :  Dict  =						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.
"""


__SCREAMING_SNAKE_CASE :  int  =						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 			lowerCamelCase_			(tf.keras.layers.Layer					):
      '''simple docstring'''

      __UpperCamelCase:						Any      			=		ResNetConfig




      def __init__( self		:       int		,      A		:       ResNetConfig		,      **A		:       str ):
             super().__init__(**A )
             _UpperCAmelCase  :						int							   =					config
             _UpperCAmelCase  :						str							   =					TFResNetEmbeddings(A		,      name="embedder" )
             _UpperCAmelCase  :						List[str]							   =					TFResNetEncoder(A		,      name="encoder" )
             _UpperCAmelCase  :						List[Any]							   =					tf.keras.layers.GlobalAveragePoolingaD(keepdims=A )





      @unpack_inputs
      def   _A			( self		:       str		,      A		:       tf.Tensor		,      A		:       Optional[bool] = None		,      A		:       Optional[bool] = None		,      A		:       bool = False		,      ):
             _UpperCAmelCase  :						Union[str, Any]							   =					(
                 output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
             )
             _UpperCAmelCase  :						int							   =					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)
             _UpperCAmelCase  :						Optional[Any]							   =					tf.transpose(A		,      perm=[0, 2, 3, 1] )
             _UpperCAmelCase  :						int							   =					self.embedder(A		,      training=A )

             _UpperCAmelCase  :						List[Any]							   =					self.encoder(
                 A		,      output_hidden_states=A		,      return_dict=A		,      training=A )

             _UpperCAmelCase  :						Any							   =					encoder_outputs[0]

             _UpperCAmelCase  :						List[Any]							   =					self.pooler(A )

             # Transpose all the outputs to the NCHW format
             # (batch_size, height, width, num_channels) -> (batch_size, num_channels, height, width)
             _UpperCAmelCase  :						str							   =					tf.transpose(A		,      (0, 3, 1, 2) )
             _UpperCAmelCase  :						Optional[int]							   =					tf.transpose(A		,      (0, 3, 1, 2) )
             _UpperCAmelCase  :						Dict							   =					()
             for hidden_state in encoder_outputs[1:]:
                    _UpperCAmelCase  :						Optional[Any]							   =					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

             _UpperCAmelCase  :						List[Any]							   =					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."							,				snake_case__							,				)
class 			lowerCamelCase_			(snake_case__					):
      '''simple docstring'''

      def __init__( self		:       str		,      A		:       ResNetConfig		,      **A		:       Optional[int] ):
             super().__init__(A		,      **A )
             _UpperCAmelCase  :						List[str]							   =					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   _A			( self		:       Optional[int]		,      A		:       tf.Tensor		,      A		:       Optional[bool] = None		,      A		:       Optional[bool] = None		,      A		:       bool = False		,      ):
             _UpperCAmelCase  :						List[str]							   =					(
                 output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
             )
             _UpperCAmelCase  :						Tuple							   =					return_dict if return_dict is not None else self.config.use_return_dict

             _UpperCAmelCase  :						str							   =					self.resnet(
                 pixel_values=A		,      output_hidden_states=A		,      return_dict=A		,      training=A		,      )
             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 			lowerCamelCase_			(snake_case__							,				snake_case__					):
      '''simple docstring'''

      def __init__( self		:       Tuple		,      A		:       ResNetConfig		,      **A		:       Any ):
             super().__init__(A		,      **A )
             _UpperCAmelCase  :						List[Any]							   =					config.num_labels
             _UpperCAmelCase  :						int							   =					TFResNetMainLayer(A		,      name="resnet" )
             # classification head
             _UpperCAmelCase  :						Optional[int]							   =					(
                 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   _A			( self		:       Optional[int]		,      A		:       tf.Tensor ):
             _UpperCAmelCase  :						List[Any]							   =					tf.keras.layers.Flatten()(A )
             _UpperCAmelCase  :						List[str]							   =					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   _A			( self		:       List[Any]		,      A		:       tf.Tensor = None		,      A		:       tf.Tensor = None		,      A		:       bool = None		,      A		:       bool = None		,      A		:       bool = False		,      ):
             _UpperCAmelCase  :						Any							   =					return_dict if return_dict is not None else self.config.use_return_dict

             _UpperCAmelCase  :						Optional[Any]							   =					self.resnet(
                 A		,      output_hidden_states=A		,      return_dict=A		,      training=A )

             _UpperCAmelCase  :						List[Any]							   =					outputs.pooler_output if return_dict else outputs[1]

             _UpperCAmelCase  :						Optional[int]							   =					self.classifier(A )

             _UpperCAmelCase  :						Dict							   =					None if labels is None else self.hf_compute_loss(A		,      A )

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

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

