






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


__UpperCAmelCase      				=							logging.get_logger(__name__)

# General docstring
__UpperCAmelCase      				=							"ResNetConfig"

# Base docstring
__UpperCAmelCase      				=							"microsoft/resnet-50"
__UpperCAmelCase      				=							[1, 2_0_4_8, 7, 7]

# Image classification docstring
__UpperCAmelCase      				=							"microsoft/resnet-50"
__UpperCAmelCase      				=							"tiger cat"

__UpperCAmelCase      				=							[
    "microsoft/resnet-50",
    # See all resnet models at https://huggingface.co/models?filter=resnet
]
class 					a_(						tf.keras.layers.Layer					):



     """simple docstring"""



     def __init__(						self			:					int       ,       lowerCAmelCase__			:					int       ,       lowerCAmelCase__			:					int = 3       ,       lowerCAmelCase__			:					int = 1       ,       lowerCAmelCase__			:					str = "relu"       ,       **lowerCAmelCase__			:					Optional[int])    ->					None:

      """simple docstring"""
      super().__init__(**lowerCAmelCase__)
      SCREAMING_SNAKE_CASE							  =						kernel_size // 2
      SCREAMING_SNAKE_CASE							  =						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
      SCREAMING_SNAKE_CASE							  =						tf.keras.layers.BatchNormalization(epsilon=1e-5       ,       momentum=0.9       ,       name='normalization')
      SCREAMING_SNAKE_CASE							  =						ACTaFN[activation] if activation is not None else tf.keras.layers.Activation('linear')





     def   __UpperCamelCase		(						self			:					List[Any]       ,       lowerCAmelCase__			:					tf.Tensor)    ->					tf.Tensor:

      """simple docstring"""
      # Pad to match that done in the PyTorch Conv2D model
      SCREAMING_SNAKE_CASE							  =						SCREAMING_SNAKE_CASE							  =						(self.pad_value, self.pad_value)
      SCREAMING_SNAKE_CASE							  =						tf.pad(lowerCAmelCase__       ,       [(0, 0), height_pad, width_pad, (0, 0)])
      SCREAMING_SNAKE_CASE							  =						self.conv(lowerCAmelCase__)
      return hidden_state





     def   __UpperCamelCase		(						self			:					Optional[int]       ,       lowerCAmelCase__			:					tf.Tensor       ,       lowerCAmelCase__			:					bool = False)    ->					tf.Tensor:

      """simple docstring"""
      SCREAMING_SNAKE_CASE							  =						self.convolution(lowerCAmelCase__)
      SCREAMING_SNAKE_CASE							  =						self.normalization(lowerCAmelCase__       ,       training=lowerCAmelCase__)
      SCREAMING_SNAKE_CASE							  =						self.activation(lowerCAmelCase__)
      return hidden_state

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



     """simple docstring"""



     def __init__(						self			:					Union[str, Any]       ,       lowerCAmelCase__			:					ResNetConfig       ,       **lowerCAmelCase__			:					Optional[int])    ->					None:

      """simple docstring"""
      super().__init__(**lowerCAmelCase__)
      SCREAMING_SNAKE_CASE							  =						TFResNetConvLayer(
          config.embedding_size       ,       kernel_size=7       ,       stride=2       ,       activation=config.hidden_act       ,       name='embedder'       ,       )
      SCREAMING_SNAKE_CASE							  =						tf.keras.layers.MaxPoolaD(pool_size=3       ,       strides=2       ,       padding='valid'       ,       name='pooler')
      SCREAMING_SNAKE_CASE							  =						config.num_channels





     def   __UpperCamelCase		(						self			:					Union[str, Any]       ,       lowerCAmelCase__			:					tf.Tensor       ,       lowerCAmelCase__			:					bool = False)    ->					tf.Tensor:

      """simple docstring"""
      SCREAMING_SNAKE_CASE  , SCREAMING_SNAKE_CASE  , SCREAMING_SNAKE_CASE  , SCREAMING_SNAKE_CASE							  =						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.')
      SCREAMING_SNAKE_CASE							  =						pixel_values
      SCREAMING_SNAKE_CASE							  =						self.embedder(lowerCAmelCase__)
      SCREAMING_SNAKE_CASE							  =						tf.pad(lowerCAmelCase__       ,       [[0, 0], [1, 1], [1, 1], [0, 0]])
      SCREAMING_SNAKE_CASE							  =						self.pooler(lowerCAmelCase__)
      return hidden_state

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



     """simple docstring"""



     def __init__(						self			:					Optional[int]       ,       lowerCAmelCase__			:					int       ,       lowerCAmelCase__			:					int = 2       ,       **lowerCAmelCase__			:					Dict)    ->					None:

      """simple docstring"""
      super().__init__(**lowerCAmelCase__)
      SCREAMING_SNAKE_CASE							  =						tf.keras.layers.ConvaD(
          lowerCAmelCase__       ,       kernel_size=1       ,       strides=lowerCAmelCase__       ,       use_bias=lowerCAmelCase__       ,       name='convolution')
      # Use same default momentum and epsilon as PyTorch equivalent
      SCREAMING_SNAKE_CASE							  =						tf.keras.layers.BatchNormalization(epsilon=1e-5       ,       momentum=0.9       ,       name='normalization')





     def   __UpperCamelCase		(						self			:					str       ,       lowerCAmelCase__			:					tf.Tensor       ,       lowerCAmelCase__			:					bool = False)    ->					tf.Tensor:

      """simple docstring"""
      SCREAMING_SNAKE_CASE							  =						x
      SCREAMING_SNAKE_CASE							  =						self.convolution(lowerCAmelCase__)
      SCREAMING_SNAKE_CASE							  =						self.normalization(lowerCAmelCase__       ,       training=lowerCAmelCase__)
      return hidden_state

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



     """simple docstring"""



     def __init__(						self			:					List[Any]       ,       lowerCAmelCase__			:					int       ,       lowerCAmelCase__			:					int       ,       lowerCAmelCase__			:					int = 1       ,       lowerCAmelCase__			:					str = "relu"       ,       **lowerCAmelCase__			:					List[Any])    ->					None:

      """simple docstring"""
      super().__init__(**lowerCAmelCase__)
      SCREAMING_SNAKE_CASE							  =						in_channels != out_channels or stride != 1
      SCREAMING_SNAKE_CASE							  =						TFResNetConvLayer(lowerCAmelCase__       ,       stride=lowerCAmelCase__       ,       name='layer.0')
      SCREAMING_SNAKE_CASE							  =						TFResNetConvLayer(lowerCAmelCase__       ,       activation=lowerCAmelCase__       ,       name='layer.1')
      SCREAMING_SNAKE_CASE							  =						(
          TFResNetShortCut(lowerCAmelCase__       ,       stride=lowerCAmelCase__       ,       name='shortcut')
          if should_apply_shortcut
          else tf.keras.layers.Activation('linear'       ,       name='shortcut')
      )
      SCREAMING_SNAKE_CASE							  =						ACTaFN[activation]





     def   __UpperCamelCase		(						self			:					Optional[int]       ,       lowerCAmelCase__			:					tf.Tensor       ,       lowerCAmelCase__			:					bool = False)    ->					tf.Tensor:

      """simple docstring"""
      SCREAMING_SNAKE_CASE							  =						hidden_state
      SCREAMING_SNAKE_CASE							  =						self.conva(lowerCAmelCase__       ,       training=lowerCAmelCase__)
      SCREAMING_SNAKE_CASE							  =						self.conva(lowerCAmelCase__       ,       training=lowerCAmelCase__)
      SCREAMING_SNAKE_CASE							  =						self.shortcut(lowerCAmelCase__       ,       training=lowerCAmelCase__)
      hidden_state += residual
      SCREAMING_SNAKE_CASE							  =						self.activation(lowerCAmelCase__)
      return hidden_state

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



     """simple docstring"""



     def __init__(						self			:					List[str]       ,       lowerCAmelCase__			:					int       ,       lowerCAmelCase__			:					int       ,       lowerCAmelCase__			:					int = 1       ,       lowerCAmelCase__			:					str = "relu"       ,       lowerCAmelCase__			:					int = 4       ,       **lowerCAmelCase__			:					Tuple       ,       )    ->					None:

      """simple docstring"""
      super().__init__(**lowerCAmelCase__)
      SCREAMING_SNAKE_CASE							  =						in_channels != out_channels or stride != 1
      SCREAMING_SNAKE_CASE							  =						out_channels // reduction
      SCREAMING_SNAKE_CASE							  =						TFResNetConvLayer(lowerCAmelCase__       ,       kernel_size=1       ,       name='layer.0')
      SCREAMING_SNAKE_CASE							  =						TFResNetConvLayer(lowerCAmelCase__       ,       stride=lowerCAmelCase__       ,       name='layer.1')
      SCREAMING_SNAKE_CASE							  =						TFResNetConvLayer(lowerCAmelCase__       ,       kernel_size=1       ,       activation=lowerCAmelCase__       ,       name='layer.2')
      SCREAMING_SNAKE_CASE							  =						(
          TFResNetShortCut(lowerCAmelCase__       ,       stride=lowerCAmelCase__       ,       name='shortcut')
          if should_apply_shortcut
          else tf.keras.layers.Activation('linear'       ,       name='shortcut')
      )
      SCREAMING_SNAKE_CASE							  =						ACTaFN[activation]





     def   __UpperCamelCase		(						self			:					Any       ,       lowerCAmelCase__			:					tf.Tensor       ,       lowerCAmelCase__			:					bool = False)    ->					tf.Tensor:

      """simple docstring"""
      SCREAMING_SNAKE_CASE							  =						hidden_state
      SCREAMING_SNAKE_CASE							  =						self.conva(lowerCAmelCase__       ,       training=lowerCAmelCase__)
      SCREAMING_SNAKE_CASE							  =						self.conva(lowerCAmelCase__       ,       training=lowerCAmelCase__)
      SCREAMING_SNAKE_CASE							  =						self.conva(lowerCAmelCase__       ,       training=lowerCAmelCase__)
      SCREAMING_SNAKE_CASE							  =						self.shortcut(lowerCAmelCase__       ,       training=lowerCAmelCase__)
      hidden_state += residual
      SCREAMING_SNAKE_CASE							  =						self.activation(lowerCAmelCase__)
      return hidden_state

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



     """simple docstring"""



     def __init__(						self			:					Optional[Any]       ,       lowerCAmelCase__			:					ResNetConfig       ,       lowerCAmelCase__			:					int       ,       lowerCAmelCase__			:					int       ,       lowerCAmelCase__			:					int = 2       ,       lowerCAmelCase__			:					int = 2       ,       **lowerCAmelCase__			:					List[str])    ->					None:

      """simple docstring"""
      super().__init__(**lowerCAmelCase__)

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

      SCREAMING_SNAKE_CASE							  =						[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)
      ]
      SCREAMING_SNAKE_CASE							  =						layers





     def   __UpperCamelCase		(						self			:					Tuple       ,       lowerCAmelCase__			:					tf.Tensor       ,       lowerCAmelCase__			:					bool = False)    ->					tf.Tensor:

      """simple docstring"""
      for layer in self.stage_layers:
       SCREAMING_SNAKE_CASE							  =						layer(lowerCAmelCase__       ,       training=lowerCAmelCase__)
      return hidden_state

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



     """simple docstring"""



     def __init__(						self			:					Any       ,       lowerCAmelCase__			:					ResNetConfig       ,       **lowerCAmelCase__			:					int)    ->					None:

      """simple docstring"""
      super().__init__(**lowerCAmelCase__)
      # based on `downsample_in_first_stage` the first layer of the first stage may or may not downsample the input
      SCREAMING_SNAKE_CASE							  =						[
          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   __UpperCamelCase		(						self			:					Tuple       ,       lowerCAmelCase__			:					tf.Tensor       ,       lowerCAmelCase__			:					bool = False       ,       lowerCAmelCase__			:					bool = True       ,       lowerCAmelCase__			:					bool = False       ,       )    ->					TFBaseModelOutputWithNoAttention:

      """simple docstring"""
      SCREAMING_SNAKE_CASE							  =						() if output_hidden_states else None

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

       SCREAMING_SNAKE_CASE							  =						stage_module(lowerCAmelCase__       ,       training=lowerCAmelCase__)

      if output_hidden_states:
       SCREAMING_SNAKE_CASE							  =						hidden_states + (hidden_state,)

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

      return TFBaseModelOutputWithNoAttention(last_hidden_state=lowerCAmelCase__       ,       hidden_states=lowerCAmelCase__)

class 					a_(						lowercase__					):



     """simple docstring"""



     __snake_case						:							int       			=ResNetConfig
     __snake_case						:							List[Any]       			='''resnet'''
     __snake_case						:							Any       			='''pixel_values'''





     @property
     def   __UpperCamelCase		(						self			:					Dict)    ->					int:

      """simple docstring"""
      return {"pixel_values": tf.TensorSpec(shape=(None, self.config.num_channels, 2_2_4, 2_2_4)       ,       dtype=tf.floataa)}

__UpperCAmelCase      				=							R"\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\n    Parameters:\n        config ([`ResNetConfig`]): 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"


__UpperCAmelCase      				=							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            [`ConvNextImageProcessor.__call__`] for details.\n\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"
@keras_serializable
class 					a_(						tf.keras.layers.Layer					):



     """simple docstring"""



     __snake_case						:							Tuple       			=ResNetConfig





     def __init__(						self			:					Any       ,       lowerCAmelCase__			:					ResNetConfig       ,       **lowerCAmelCase__			:					List[str])    ->					None:

      """simple docstring"""
      super().__init__(**lowerCAmelCase__)
      SCREAMING_SNAKE_CASE							  =						config
      SCREAMING_SNAKE_CASE							  =						TFResNetEmbeddings(lowerCAmelCase__       ,       name='embedder')
      SCREAMING_SNAKE_CASE							  =						TFResNetEncoder(lowerCAmelCase__       ,       name='encoder')
      SCREAMING_SNAKE_CASE							  =						tf.keras.layers.GlobalAveragePoolingaD(keepdims=lowerCAmelCase__)





     @unpack_inputs
     def   __UpperCamelCase		(						self			:					Dict       ,       lowerCAmelCase__			:					tf.Tensor       ,       lowerCAmelCase__			:					Optional[bool] = None       ,       lowerCAmelCase__			:					Optional[bool] = None       ,       lowerCAmelCase__			:					bool = False       ,       )    ->					Union[Tuple[tf.Tensor], TFBaseModelOutputWithPoolingAndNoAttention]:

      """simple docstring"""
      SCREAMING_SNAKE_CASE							  =						(
          output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
      )
      SCREAMING_SNAKE_CASE							  =						return_dict if return_dict is not None else self.config.use_return_dict

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

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

      SCREAMING_SNAKE_CASE							  =						encoder_outputs[0]

      SCREAMING_SNAKE_CASE							  =						self.pooler(lowerCAmelCase__)

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

      SCREAMING_SNAKE_CASE							  =						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.'''	,    lowercase__	,    )
class 					a_(						lowercase__					):



     """simple docstring"""



     def __init__(						self			:					Optional[Any]       ,       lowerCAmelCase__			:					ResNetConfig       ,       **lowerCAmelCase__			:					str)    ->					None:

      """simple docstring"""
      super().__init__(lowerCAmelCase__       ,       **lowerCAmelCase__)
      SCREAMING_SNAKE_CASE							  =						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   __UpperCamelCase		(						self			:					Tuple       ,       lowerCAmelCase__			:					tf.Tensor       ,       lowerCAmelCase__			:					Optional[bool] = None       ,       lowerCAmelCase__			:					Optional[bool] = None       ,       lowerCAmelCase__			:					bool = False       ,       )    ->					Union[Tuple[tf.Tensor], TFBaseModelOutputWithPoolingAndNoAttention]:

      """simple docstring"""
      SCREAMING_SNAKE_CASE							  =						(
          output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
      )
      SCREAMING_SNAKE_CASE							  =						return_dict if return_dict is not None else self.config.use_return_dict

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

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



     """simple docstring"""



     def __init__(						self			:					List[Any]       ,       lowerCAmelCase__			:					ResNetConfig       ,       **lowerCAmelCase__			:					str)    ->					None:

      """simple docstring"""
      super().__init__(lowerCAmelCase__       ,       **lowerCAmelCase__)
      SCREAMING_SNAKE_CASE							  =						config.num_labels
      SCREAMING_SNAKE_CASE							  =						TFResNetMainLayer(lowerCAmelCase__       ,       name='resnet')
      # classification head
      SCREAMING_SNAKE_CASE							  =						(
          tf.keras.layers.Dense(config.num_labels       ,       name='classifier.1')
          if config.num_labels > 0
          else tf.keras.layers.Activation('linear'       ,       name='classifier.1')
      )





     def   __UpperCamelCase		(						self			:					List[Any]       ,       lowerCAmelCase__			:					tf.Tensor)    ->					tf.Tensor:

      """simple docstring"""
      SCREAMING_SNAKE_CASE							  =						tf.keras.layers.Flatten()(lowerCAmelCase__)
      SCREAMING_SNAKE_CASE							  =						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   __UpperCamelCase		(						self			:					str       ,       lowerCAmelCase__			:					tf.Tensor = None       ,       lowerCAmelCase__			:					tf.Tensor = None       ,       lowerCAmelCase__			:					bool = None       ,       lowerCAmelCase__			:					bool = None       ,       lowerCAmelCase__			:					bool = False       ,       )    ->					Union[Tuple[tf.Tensor], TFImageClassifierOutputWithNoAttention]:

      """simple docstring"""
      SCREAMING_SNAKE_CASE							  =						return_dict if return_dict is not None else self.config.use_return_dict

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

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

      SCREAMING_SNAKE_CASE							  =						self.classifier(lowerCAmelCase__)

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

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

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












import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import cached_download, hf_hub_url
from PIL import Image

from transformers import DeformableDetrConfig, DeformableDetrForObjectDetection, DeformableDetrImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
__UpperCAmelCase      				=							logging.get_logger(__name__)





def 		A_			(		lowercase_	)							->Tuple:







 """simple docstring"""

 if "backbone.0.body" in orig_key:
  SCREAMING_SNAKE_CASE							  =						orig_key.replace('backbone.0.body'					,       'backbone.conv_encoder.model'	)
 if "transformer" in orig_key:
  SCREAMING_SNAKE_CASE							  =						orig_key.replace('transformer.'					,       ''	)
 if "norm1" in orig_key:
  if "encoder" in orig_key:
   SCREAMING_SNAKE_CASE							  =						orig_key.replace('norm1'					,       'self_attn_layer_norm'	)
  else:
   SCREAMING_SNAKE_CASE							  =						orig_key.replace('norm1'					,       'encoder_attn_layer_norm'	)
 if "norm2" in orig_key:
  if "encoder" in orig_key:
   SCREAMING_SNAKE_CASE							  =						orig_key.replace('norm2'					,       'final_layer_norm'	)
  else:
   SCREAMING_SNAKE_CASE							  =						orig_key.replace('norm2'					,       'self_attn_layer_norm'	)
 if "norm3" in orig_key:
  SCREAMING_SNAKE_CASE							  =						orig_key.replace('norm3'					,       'final_layer_norm'	)
 if "linear1" in orig_key:
  SCREAMING_SNAKE_CASE							  =						orig_key.replace('linear1'					,       'fc1'	)
 if "linear2" in orig_key:
  SCREAMING_SNAKE_CASE							  =						orig_key.replace('linear2'					,       'fc2'	)
 if "query_embed" in orig_key:
  SCREAMING_SNAKE_CASE							  =						orig_key.replace('query_embed'					,       'query_position_embeddings'	)
 if "cross_attn" in orig_key:
  SCREAMING_SNAKE_CASE							  =						orig_key.replace('cross_attn'					,       'encoder_attn'	)

 return orig_key





def 		A_			(		lowercase_	)							->Dict:







 """simple docstring"""

 for i in range(6	):
  # read in weights + bias of input projection layer of self-attention
  SCREAMING_SNAKE_CASE							  =						state_dict.pop(f'''decoder.layers.{i}.self_attn.in_proj_weight'''	)
  SCREAMING_SNAKE_CASE							  =						state_dict.pop(f'''decoder.layers.{i}.self_attn.in_proj_bias'''	)
  # next, add query, keys and values (in that order) to the state dict
  SCREAMING_SNAKE_CASE							  =						in_proj_weight[:2_5_6, :]
  SCREAMING_SNAKE_CASE							  =						in_proj_bias[:2_5_6]
  SCREAMING_SNAKE_CASE							  =						in_proj_weight[2_5_6:5_1_2, :]
  SCREAMING_SNAKE_CASE							  =						in_proj_bias[2_5_6:5_1_2]
  SCREAMING_SNAKE_CASE							  =						in_proj_weight[-2_5_6:, :]
  SCREAMING_SNAKE_CASE							  =						in_proj_bias[-2_5_6:]





def 		A_			(		)							->Union[str, Any]:







 """simple docstring"""

 SCREAMING_SNAKE_CASE							  =						'http://images.cocodataset.org/val2017/000000039769.jpg'
 SCREAMING_SNAKE_CASE							  =						Image.open(requests.get(lowercase_					,       stream=lowercase_	).raw	)

 return im





@torch.no_grad()
def 		A_			(		lowercase_					,       lowercase_					,       lowercase_					,       lowercase_					,       lowercase_					,       lowercase_					,       lowercase_					,       )							->int:







 """simple docstring"""

 SCREAMING_SNAKE_CASE							  =						DeformableDetrConfig()
 # set config attributes
 if single_scale:
  SCREAMING_SNAKE_CASE							  =						1
 SCREAMING_SNAKE_CASE							  =						dilation
 SCREAMING_SNAKE_CASE							  =						with_box_refine
 SCREAMING_SNAKE_CASE							  =						two_stage
 # set labels
 SCREAMING_SNAKE_CASE							  =						9_1
 SCREAMING_SNAKE_CASE							  =						'huggingface/label-files'
 SCREAMING_SNAKE_CASE							  =						'coco-detection-id2label.json'
 SCREAMING_SNAKE_CASE							  =						json.load(open(cached_download(hf_hub_url(lowercase_					,       lowercase_					,       repo_type='dataset'	)	)					,       'r'	)	)
 SCREAMING_SNAKE_CASE							  =						{int(lowercase_	): v for k, v in idalabel.items()}
 SCREAMING_SNAKE_CASE							  =						idalabel
 SCREAMING_SNAKE_CASE							  =						{v: k for k, v in idalabel.items()}

 # load image processor
 SCREAMING_SNAKE_CASE							  =						DeformableDetrImageProcessor(format='coco_detection'	)

 # prepare image
 SCREAMING_SNAKE_CASE							  =						prepare_img()
 SCREAMING_SNAKE_CASE							  =						image_processor(images=lowercase_					,       return_tensors='pt'	)
 SCREAMING_SNAKE_CASE							  =						encoding['pixel_values']

 logger.info('Converting model...'	)

 # load original state dict
 SCREAMING_SNAKE_CASE							  =						torch.load(lowercase_					,       map_location='cpu'	)['model']
 # rename keys
 for key in state_dict.copy().keys():
  SCREAMING_SNAKE_CASE							  =						state_dict.pop(lowercase_	)
  SCREAMING_SNAKE_CASE							  =						val
 # query, key and value matrices need special treatment
 read_in_q_k_v(lowercase_	)
 # important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
 SCREAMING_SNAKE_CASE							  =						'model.'
 for key in state_dict.copy().keys():
  if not key.startswith('class_embed'	) and not key.startswith('bbox_embed'	):
   SCREAMING_SNAKE_CASE							  =						state_dict.pop(lowercase_	)
   SCREAMING_SNAKE_CASE							  =						val
    # finally, create HuggingFace model and load state dict
 SCREAMING_SNAKE_CASE							  =						DeformableDetrForObjectDetection(lowercase_	)
 model.load_state_dict(lowercase_	)
 model.eval()

 SCREAMING_SNAKE_CASE							  =						'cuda' if torch.cuda.is_available() else 'cpu'
 model.to(lowercase_	)
 # verify our conversion
 SCREAMING_SNAKE_CASE							  =						model(pixel_values.to(lowercase_	)	)

 SCREAMING_SNAKE_CASE							  =						torch.tensor(
     [[-9.6_6_4_5, -4.3_4_4_9, -5.8_7_0_5], [-9.7_0_3_5, -3.8_5_0_4, -5.0_7_2_4], [-1_0.5_6_3_4, -5.3_3_7_9, -7.5_1_1_6]]	)
 SCREAMING_SNAKE_CASE							  =						torch.tensor([[0.8_6_9_3, 0.2_2_8_9, 0.2_4_9_2], [0.3_1_5_0, 0.5_4_8_9, 0.5_8_4_5], [0.5_5_6_3, 0.7_5_8_0, 0.8_5_1_8]]	)

 if single_scale:
  SCREAMING_SNAKE_CASE							  =						torch.tensor(
      [[-9.9_0_5_1, -4.2_5_4_1, -6.4_8_5_2], [-9.6_9_4_7, -4.0_8_5_4, -6.8_0_3_3], [-1_0.0_6_6_5, -5.8_4_7_0, -7.7_0_0_3]]	)
  SCREAMING_SNAKE_CASE							  =						torch.tensor([[0.7_2_9_2, 0.4_9_9_1, 0.5_5_3_2], [0.7_9_5_9, 0.2_4_2_6, 0.4_2_3_6], [0.7_5_8_2, 0.3_5_1_8, 0.4_4_5_1]]	)

 if single_scale and dilation:
  SCREAMING_SNAKE_CASE							  =						torch.tensor(
      [[-8.9_6_5_2, -4.1_0_7_4, -5.6_6_3_5], [-9.0_5_9_6, -4.9_4_4_7, -6.6_0_7_5], [-1_0.1_1_7_8, -4.5_2_7_5, -6.2_6_7_1]]	)
  SCREAMING_SNAKE_CASE							  =						torch.tensor([[0.7_6_6_5, 0.4_1_3_0, 0.4_7_6_9], [0.8_3_6_4, 0.1_8_4_1, 0.3_3_9_1], [0.6_2_6_1, 0.3_8_9_5, 0.7_9_7_8]]	)

 if with_box_refine:
  SCREAMING_SNAKE_CASE							  =						torch.tensor(
      [[-8.8_8_9_5, -5.4_1_8_7, -6.8_1_5_3], [-8.4_7_0_6, -6.1_6_6_8, -7.6_1_8_4], [-9.0_0_4_2, -5.5_3_5_9, -6.9_1_4_1]]	)
  SCREAMING_SNAKE_CASE							  =						torch.tensor([[0.7_8_2_8, 0.2_2_0_8, 0.4_3_2_3], [0.0_8_9_2, 0.5_9_9_6, 0.1_3_1_9], [0.5_5_2_4, 0.6_3_8_9, 0.8_9_1_4]]	)

 if with_box_refine and two_stage:
  SCREAMING_SNAKE_CASE							  =						torch.tensor(
      [[-6.7_1_0_8, -4.3_2_1_3, -6.3_7_7_7], [-8.9_0_1_4, -6.1_7_9_9, -6.7_2_4_0], [-6.9_3_1_5, -4.4_7_3_5, -6.2_2_9_8]]	)
  SCREAMING_SNAKE_CASE							  =						torch.tensor([[0.2_5_8_3, 0.5_4_9_9, 0.4_6_8_3], [0.7_6_5_2, 0.9_0_6_8, 0.4_8_8_2], [0.5_4_9_0, 0.2_7_6_3, 0.0_5_6_4]]	)

 print('Logits:'					,       outputs.logits[0, :3, :3]	)

 assert torch.allclose(outputs.logits[0, :3, :3]					,       expected_logits.to(lowercase_	)					,       atol=1e-4	)
 assert torch.allclose(outputs.pred_boxes[0, :3, :3]					,       expected_boxes.to(lowercase_	)					,       atol=1e-4	)

 print('Everything ok!'	)

 # Save model and image processor
 logger.info(f'''Saving PyTorch model and image processor to {pytorch_dump_folder_path}...'''	)
 Path(lowercase_	).mkdir(exist_ok=lowercase_	)
 model.save_pretrained(lowercase_	)
 image_processor.save_pretrained(lowercase_	)

 # Push to hub
 if push_to_hub:
  SCREAMING_SNAKE_CASE							  =						'deformable-detr'
  model_name += "-single-scale" if single_scale else ""
  model_name += "-dc5" if dilation else ""
  model_name += "-with-box-refine" if with_box_refine else ""
  model_name += "-two-stage" if two_stage else ""
  print('Pushing model to hub...'	)
  model.push_to_hub(repo_path_or_name=lowercase_					,       organization='nielsr'					,       commit_message='Add model'	)


if __name__ == "__main__":
      __UpperCAmelCase      				=							argparse.ArgumentParser()

      parser.add_argument(
          "--checkpoint_path",
          type=str,
          default="/home/niels/checkpoints/deformable_detr/r50_deformable_detr-checkpoint.pth",
          help="Path to Pytorch checkpoint (.pth file) you'd like to convert.",
      )
      parser.add_argument("--single_scale", action="store_true", help="Whether to set config.num_features_levels = 1.")
      parser.add_argument("--dilation", action="store_true", help="Whether to set config.dilation=True.")
      parser.add_argument("--with_box_refine", action="store_true", help="Whether to set config.with_box_refine=True.")
      parser.add_argument("--two_stage", action="store_true", help="Whether to set config.two_stage=True.")
      parser.add_argument(
          "--pytorch_dump_folder_path",
          default=None,
          type=str,
          required=True,
          help="Path to the folder to output PyTorch model.",
      )
      parser.add_argument(
          "--push_to_hub", action="store_true", help="Whether or not to push the converted model to the 🤗 hub."
      )
      __UpperCAmelCase      				=							parser.parse_args()
      convert_deformable_detr_checkpoint(
          args.checkpoint_path,
          args.single_scale,
          args.dilation,
          args.with_box_refine,
          args.two_stage,
          args.pytorch_dump_folder_path,
          args.push_to_hub,
      )




