
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


lowercase						:      Union[str, Any]							=							logging.get_logger(__name__)

# General docstring
lowercase						:      Union[str, Any]							=							"""ResNetConfig"""

# Base docstring
lowercase						:      Optional[int]							=							"""microsoft/resnet-50"""
lowercase						:      str							=							[1, 2048, 7, 7]

# Image classification docstring
lowercase						:      Optional[Any]							=							"""microsoft/resnet-50"""
lowercase						:      Tuple							=							"""tiger cat"""

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

class __snake_case	(   tf.keras.layers.Layer  ):
    def __init__(						self	,snake_case	,snake_case = 3	,snake_case = 1	,snake_case = "relu"	,**snake_case   ):



           '''simple docstring'''







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



           '''simple docstring'''







           lowercase							:  Dict 			=   (self.pad_value, self.pad_value)
           lowercase							:  Tuple 			=   tf.pad(snake_case	,[(0, 0), height_pad, width_pad, (0, 0)]   )
           lowercase							:  Union[str, Any] 			=   self.conv(snake_case   )
           return hidden_state
    def    _SCREAMING_SNAKE_CASE (						self	,snake_case	,snake_case = False   ):



           '''simple docstring'''







           lowercase							:  List[str] 			=   self.convolution(snake_case   )
           lowercase							:  Optional[Any] 			=   self.normalization(snake_case	,training=snake_case   )
           lowercase							:  List[Any] 			=   self.activation(snake_case   )
           return hidden_state






class __snake_case	(   tf.keras.layers.Layer  ):
    def __init__(						self	,snake_case	,**snake_case   ):



           '''simple docstring'''







           super().__init__(**snake_case   )
           lowercase							:  Any 			=   TFResNetConvLayer(
               config.embedding_size	,kernel_size=7	,stride=2	,activation=config.hidden_act	,name="""embedder"""	,)
           lowercase							:  int 			=   tf.keras.layers.MaxPoolaD(pool_size=3	,strides=2	,padding="""valid"""	,name="""pooler"""   )
           lowercase							:  str 			=   config.num_channels
    def    _SCREAMING_SNAKE_CASE (						self	,snake_case	,snake_case = False   ):



           '''simple docstring'''







           lowercase			,    lowercase			,    lowercase			,    lowercase							:  Dict 			=   shape_list(snake_case   )
           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."""   )
           lowercase							:  Optional[Any] 			=   pixel_values
           lowercase							:  Any 			=   self.embedder(snake_case   )
           lowercase							:  str 			=   tf.pad(snake_case	,[[0, 0], [1, 1], [1, 1], [0, 0]]   )
           lowercase							:  Dict 			=   self.pooler(snake_case   )
           return hidden_state






class __snake_case	(   tf.keras.layers.Layer  ):
    def __init__(						self	,snake_case	,snake_case = 2	,**snake_case   ):



           '''simple docstring'''







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



           '''simple docstring'''







           lowercase							:  List[str] 			=   x
           lowercase							:  List[Any] 			=   self.convolution(snake_case   )
           lowercase							:  Optional[Any] 			=   self.normalization(snake_case	,training=snake_case   )
           return hidden_state






class __snake_case	(   tf.keras.layers.Layer  ):
    def __init__(						self	,snake_case	,snake_case	,snake_case = 1	,snake_case = "relu"	,**snake_case   ):



           '''simple docstring'''







           super().__init__(**snake_case   )
           lowercase							:  Tuple 			=   in_channels != out_channels or stride != 1
           lowercase							:  str 			=   TFResNetConvLayer(snake_case	,stride=snake_case	,name="""layer.0"""   )
           lowercase							:  int 			=   TFResNetConvLayer(snake_case	,activation=snake_case	,name="""layer.1"""   )
           lowercase							:  Union[str, Any] 			=   (
               TFResNetShortCut(snake_case	,stride=snake_case	,name="""shortcut"""   )
               if should_apply_shortcut
               else tf.keras.layers.Activation("""linear"""	,name="""shortcut"""   )
           )
           lowercase							:  List[str] 			=   ACTaFN[activation]
    def    _SCREAMING_SNAKE_CASE (						self	,snake_case	,snake_case = False   ):



           '''simple docstring'''







           lowercase							:  int 			=   hidden_state
           lowercase							:  Tuple 			=   self.conva(snake_case	,training=snake_case   )
           lowercase							:  str 			=   self.conva(snake_case	,training=snake_case   )
           lowercase							:  int 			=   self.shortcut(snake_case	,training=snake_case   )
           hidden_state += residual
           lowercase							:  str 			=   self.activation(snake_case   )
           return hidden_state






class __snake_case	(   tf.keras.layers.Layer  ):
    def __init__(						self	,snake_case	,snake_case	,snake_case = 1	,snake_case = "relu"	,snake_case = 4	,**snake_case	,):



           '''simple docstring'''







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



           '''simple docstring'''







           lowercase							:  Optional[int] 			=   hidden_state
           lowercase							:  List[Any] 			=   self.conva(snake_case	,training=snake_case   )
           lowercase							:  Optional[int] 			=   self.conva(snake_case	,training=snake_case   )
           lowercase							:  Optional[int] 			=   self.conva(snake_case	,training=snake_case   )
           lowercase							:  Optional[Any] 			=   self.shortcut(snake_case	,training=snake_case   )
           hidden_state += residual
           lowercase							:  Tuple 			=   self.activation(snake_case   )
           return hidden_state






class __snake_case	(   tf.keras.layers.Layer  ):
    def __init__(						self	,snake_case	,snake_case	,snake_case	,snake_case = 2	,snake_case = 2	,**snake_case   ):



           '''simple docstring'''







           super().__init__(**snake_case   )

           lowercase							:  List[str] 			=   TFResNetBottleNeckLayer if config.layer_type == """bottleneck""" else TFResNetBasicLayer

           lowercase							:  Optional[Any] 			=   [layer(snake_case	,snake_case	,stride=snake_case	,activation=config.hidden_act	,name="""layers.0"""   )]
           layers += [
               layer(snake_case	,snake_case	,activation=config.hidden_act	,name=f"layers.{i + 1}"   )
               for i in range(depth - 1   )
           ]
           lowercase							:  Union[str, Any] 			=   layers
    def    _SCREAMING_SNAKE_CASE (						self	,snake_case	,snake_case = False   ):



           '''simple docstring'''







           for layer in self.stage_layers:
                  lowercase							:  List[str] 			=   layer(snake_case	,training=snake_case   )
           return hidden_state






class __snake_case	(   tf.keras.layers.Layer  ):
    def __init__(						self	,snake_case	,**snake_case   ):



           '''simple docstring'''







           super().__init__(**snake_case   )
           # based on `downsample_in_first_stage` the first layer of the first stage may or may not downsample the input
           lowercase							:  Dict 			=   [
               TFResNetStage(
                   snake_case	,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(snake_case	,snake_case	,snake_case	,depth=snake_case	,name=f"stages.{i + 1}"   )   )
    def    _SCREAMING_SNAKE_CASE (						self	,snake_case	,snake_case = False	,snake_case = True	,snake_case = False	,):



           '''simple docstring'''







           lowercase							:  int 			=   () if output_hidden_states else None

           for stage_module in self.stages:
                  if output_hidden_states:
                         lowercase							:  Optional[Any] 			=   hidden_states + (hidden_state,)

                  lowercase							:  int 			=   stage_module(snake_case	,training=snake_case   )

           if output_hidden_states:
                  lowercase							:  str 			=   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=snake_case	,hidden_states=snake_case   )






class __snake_case	(   lowerCAmelCase  ):
    _a	:		List[Any]=       ResNetConfig
    _a	:		Optional[int]=       "resnet"
    _a	:		Tuple=       "pixel_values"
    @property
    def    _SCREAMING_SNAKE_CASE (						self   ):



           '''simple docstring'''







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






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

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


lowercase						:      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  ):
    _a	:		Optional[int]=       ResNetConfig
    def __init__(						self	,snake_case	,**snake_case   ):



           '''simple docstring'''







           super().__init__(**snake_case   )
           lowercase							:  Union[str, Any] 			=   config
           lowercase							:  Any 			=   TFResNetEmbeddings(snake_case	,name="""embedder"""   )
           lowercase							:  Optional[Any] 			=   TFResNetEncoder(snake_case	,name="""encoder"""   )
           lowercase							:  Optional[Any] 			=   tf.keras.layers.GlobalAveragePoolingaD(keepdims=snake_case   )
    @unpack_inputs
    def    _SCREAMING_SNAKE_CASE (						self	,snake_case	,snake_case = None	,snake_case = None	,snake_case = False	,):



           '''simple docstring'''







           lowercase							:  List[Any] 			=   (
               output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
           )
           lowercase							:  str 			=   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)
           lowercase							:  int 			=   tf.transpose(snake_case	,perm=[0, 2, 3, 1]   )
           lowercase							:  Union[str, Any] 			=   self.embedder(snake_case	,training=snake_case   )

           lowercase							:  List[Any] 			=   self.encoder(
               snake_case	,output_hidden_states=snake_case	,return_dict=snake_case	,training=snake_case   )

           lowercase							:  Any 			=   encoder_outputs[0]

           lowercase							:  str 			=   self.pooler(snake_case   )

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

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

           lowercase							:  List[str] 			=   hidden_states if output_hidden_states else None

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






@add_start_docstrings(
    "The bare ResNet model outputting raw features without any specific head on top."      ,			lowerCAmelCase      ,			)
class __snake_case	(   lowerCAmelCase  ):
    def __init__(						self	,snake_case	,**snake_case   ):



           '''simple docstring'''







           super().__init__(snake_case	,**snake_case   )
           lowercase							:  int 			=   TFResNetMainLayer(config=snake_case	,name="""resnet"""   )
    @add_start_docstrings_to_model_forward(snake_case   )
    @add_code_sample_docstrings(
        checkpoint=_CHECKPOINT_FOR_DOC	,output_type=snake_case	,config_class=_CONFIG_FOR_DOC	,modality="""vision"""	,expected_output=_EXPECTED_OUTPUT_SHAPE	,)
    @unpack_inputs
    def    _SCREAMING_SNAKE_CASE (						self	,snake_case	,snake_case = None	,snake_case = None	,snake_case = False	,):



           '''simple docstring'''







           lowercase							:  Tuple 			=   (
               output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
           )
           lowercase							:  List[str] 			=   return_dict if return_dict is not None else self.config.use_return_dict

           lowercase							:  Tuple 			=   self.resnet(
               pixel_values=snake_case	,output_hidden_states=snake_case	,return_dict=snake_case	,training=snake_case	,)
           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    "      ,			lowerCAmelCase      ,			)
class __snake_case	(   lowerCAmelCase      ,			lowerCAmelCase  ):
    def __init__(						self	,snake_case	,**snake_case   ):



           '''simple docstring'''







           super().__init__(snake_case	,**snake_case   )
           lowercase							:  int 			=   config.num_labels
           lowercase							:  str 			=   TFResNetMainLayer(snake_case	,name="""resnet"""   )
           # classification head
           lowercase							:  Dict 			=   (
               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	,snake_case   ):



           '''simple docstring'''







           lowercase							:  Dict 			=   tf.keras.layers.Flatten()(snake_case   )
           lowercase							:  Any 			=   self.classifier_layer(snake_case   )
           return logits
    @add_start_docstrings_to_model_forward(snake_case   )
    @add_code_sample_docstrings(
        checkpoint=_IMAGE_CLASS_CHECKPOINT	,output_type=snake_case	,config_class=_CONFIG_FOR_DOC	,expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT	,)
    @unpack_inputs
    def    _SCREAMING_SNAKE_CASE (						self	,snake_case = None	,snake_case = None	,snake_case = None	,snake_case = None	,snake_case = False	,):



           '''simple docstring'''







           lowercase							:  Union[str, Any] 			=   return_dict if return_dict is not None else self.config.use_return_dict

           lowercase							:  int 			=   self.resnet(
               snake_case	,output_hidden_states=snake_case	,return_dict=snake_case	,training=snake_case   )

           lowercase							:  int 			=   outputs.pooler_output if return_dict else outputs[1]

           lowercase							:  int 			=   self.classifier(snake_case   )

           lowercase							:  str 			=   None if labels is None else self.hf_compute_loss(snake_case	,snake_case   )

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

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


import argparse
import json
from collections import OrderedDict
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import DetrConfig, DetrForObjectDetection, DetrForSegmentation, DetrImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
lowercase						:      Union[str, Any]							=							logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
lowercase						:      Tuple							=							[]
for i in range(6):
  # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
  rename_keys.append(
      (F'''transformer.encoder.layers.{i}.self_attn.out_proj.weight''', F'''encoder.layers.{i}.self_attn.out_proj.weight''')
  )
  rename_keys.append(
      (F'''transformer.encoder.layers.{i}.self_attn.out_proj.bias''', F'''encoder.layers.{i}.self_attn.out_proj.bias''')
  )
  rename_keys.append((F'''transformer.encoder.layers.{i}.linear1.weight''', F'''encoder.layers.{i}.fc1.weight'''))
  rename_keys.append((F'''transformer.encoder.layers.{i}.linear1.bias''', F'''encoder.layers.{i}.fc1.bias'''))
  rename_keys.append((F'''transformer.encoder.layers.{i}.linear2.weight''', F'''encoder.layers.{i}.fc2.weight'''))
  rename_keys.append((F'''transformer.encoder.layers.{i}.linear2.bias''', F'''encoder.layers.{i}.fc2.bias'''))
  rename_keys.append(
      (F'''transformer.encoder.layers.{i}.norm1.weight''', F'''encoder.layers.{i}.self_attn_layer_norm.weight''')
  )
  rename_keys.append((F'''transformer.encoder.layers.{i}.norm1.bias''', F'''encoder.layers.{i}.self_attn_layer_norm.bias'''))
  rename_keys.append((F'''transformer.encoder.layers.{i}.norm2.weight''', F'''encoder.layers.{i}.final_layer_norm.weight'''))
  rename_keys.append((F'''transformer.encoder.layers.{i}.norm2.bias''', F'''encoder.layers.{i}.final_layer_norm.bias'''))
  # decoder layers: 2 times output projection, 2 feedforward neural networks and 3 layernorms
  rename_keys.append(
      (F'''transformer.decoder.layers.{i}.self_attn.out_proj.weight''', F'''decoder.layers.{i}.self_attn.out_proj.weight''')
  )
  rename_keys.append(
      (F'''transformer.decoder.layers.{i}.self_attn.out_proj.bias''', F'''decoder.layers.{i}.self_attn.out_proj.bias''')
  )
  rename_keys.append(
      (
          F'''transformer.decoder.layers.{i}.multihead_attn.out_proj.weight''',
          F'''decoder.layers.{i}.encoder_attn.out_proj.weight''',
      )
  )
  rename_keys.append(
      (
          F'''transformer.decoder.layers.{i}.multihead_attn.out_proj.bias''',
          F'''decoder.layers.{i}.encoder_attn.out_proj.bias''',
      )
  )
  rename_keys.append((F'''transformer.decoder.layers.{i}.linear1.weight''', F'''decoder.layers.{i}.fc1.weight'''))
  rename_keys.append((F'''transformer.decoder.layers.{i}.linear1.bias''', F'''decoder.layers.{i}.fc1.bias'''))
  rename_keys.append((F'''transformer.decoder.layers.{i}.linear2.weight''', F'''decoder.layers.{i}.fc2.weight'''))
  rename_keys.append((F'''transformer.decoder.layers.{i}.linear2.bias''', F'''decoder.layers.{i}.fc2.bias'''))
  rename_keys.append(
      (F'''transformer.decoder.layers.{i}.norm1.weight''', F'''decoder.layers.{i}.self_attn_layer_norm.weight''')
  )
  rename_keys.append((F'''transformer.decoder.layers.{i}.norm1.bias''', F'''decoder.layers.{i}.self_attn_layer_norm.bias'''))
  rename_keys.append(
      (F'''transformer.decoder.layers.{i}.norm2.weight''', F'''decoder.layers.{i}.encoder_attn_layer_norm.weight''')
  )
  rename_keys.append(
      (F'''transformer.decoder.layers.{i}.norm2.bias''', F'''decoder.layers.{i}.encoder_attn_layer_norm.bias''')
  )
  rename_keys.append((F'''transformer.decoder.layers.{i}.norm3.weight''', F'''decoder.layers.{i}.final_layer_norm.weight'''))
  rename_keys.append((F'''transformer.decoder.layers.{i}.norm3.bias''', F'''decoder.layers.{i}.final_layer_norm.bias'''))

# convolutional projection + query embeddings + layernorm of decoder + class and bounding box heads
rename_keys.extend(
    [
        ("""input_proj.weight""", """input_projection.weight"""),
        ("""input_proj.bias""", """input_projection.bias"""),
        ("""query_embed.weight""", """query_position_embeddings.weight"""),
        ("""transformer.decoder.norm.weight""", """decoder.layernorm.weight"""),
        ("""transformer.decoder.norm.bias""", """decoder.layernorm.bias"""),
        ("""class_embed.weight""", """class_labels_classifier.weight"""),
        ("""class_embed.bias""", """class_labels_classifier.bias"""),
        ("""bbox_embed.layers.0.weight""", """bbox_predictor.layers.0.weight"""),
        ("""bbox_embed.layers.0.bias""", """bbox_predictor.layers.0.bias"""),
        ("""bbox_embed.layers.1.weight""", """bbox_predictor.layers.1.weight"""),
        ("""bbox_embed.layers.1.bias""", """bbox_predictor.layers.1.bias"""),
        ("""bbox_embed.layers.2.weight""", """bbox_predictor.layers.2.weight"""),
        ("""bbox_embed.layers.2.bias""", """bbox_predictor.layers.2.bias"""),
    ]
)







def 		_snake_case(						SCREAMING_SNAKE_CASE__      ,			SCREAMING_SNAKE_CASE__      ,			SCREAMING_SNAKE_CASE__       )				->					Optional[int]:
       lowercase							:  Optional[int] 			=   state_dict.pop(SCREAMING_SNAKE_CASE__       )
       lowercase							:  str 			=   val







def 		_snake_case(						SCREAMING_SNAKE_CASE__       )				->					Optional[Any]:
       lowercase							:  int 			=   OrderedDict()
       for key, value in state_dict.items():
              if "backbone.0.body" in key:
                     lowercase							:  Optional[Any] 			=   key.replace("""backbone.0.body"""      ,			"""backbone.conv_encoder.model"""       )
                     lowercase							:  Optional[int] 			=   value
              else:
                     lowercase							:  Dict 			=   value

       return new_state_dict







def 		_snake_case(						SCREAMING_SNAKE_CASE__      ,			SCREAMING_SNAKE_CASE__=False       )				->					Optional[Any]:
       lowercase							:  str 			=   """"""
       if is_panoptic:
              lowercase							:  Union[str, Any] 			=   """detr."""

       # first: transformer encoder
       for i in range(6       ):
              # read in weights + bias of input projection layer (in PyTorch's MultiHeadAttention, this is a single matrix + bias)
              lowercase							:  Union[str, Any] 			=   state_dict.pop(f"{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight"       )
              lowercase							:  Any 			=   state_dict.pop(f"{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_bias"       )
              # next, add query, keys and values (in that order) to the state dict
              lowercase							:  Union[str, Any] 			=   in_proj_weight[:256, :]
              lowercase							:  str 			=   in_proj_bias[:256]
              lowercase							:  Union[str, Any] 			=   in_proj_weight[256:512, :]
              lowercase							:  Optional[int] 			=   in_proj_bias[256:512]
              lowercase							:  List[Any] 			=   in_proj_weight[-256:, :]
              lowercase							:  List[Any] 			=   in_proj_bias[-256:]
       # next: transformer decoder (which is a bit more complex because it also includes cross-attention)
       for i in range(6       ):
              # read in weights + bias of input projection layer of self-attention
              lowercase							:  int 			=   state_dict.pop(f"{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight"       )
              lowercase							:  List[Any] 			=   state_dict.pop(f"{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_bias"       )
              # next, add query, keys and values (in that order) to the state dict
              lowercase							:  Any 			=   in_proj_weight[:256, :]
              lowercase							:  int 			=   in_proj_bias[:256]
              lowercase							:  Tuple 			=   in_proj_weight[256:512, :]
              lowercase							:  Optional[Any] 			=   in_proj_bias[256:512]
              lowercase							:  int 			=   in_proj_weight[-256:, :]
              lowercase							:  Optional[Any] 			=   in_proj_bias[-256:]
              # read in weights + bias of input projection layer of cross-attention
              lowercase							:  int 			=   state_dict.pop(
                  f"{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight"       )
              lowercase							:  Dict 			=   state_dict.pop(f"{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_bias"       )
              # next, add query, keys and values (in that order) of cross-attention to the state dict
              lowercase							:  List[str] 			=   in_proj_weight_cross_attn[:256, :]
              lowercase							:  Optional[Any] 			=   in_proj_bias_cross_attn[:256]
              lowercase							:  int 			=   in_proj_weight_cross_attn[256:512, :]
              lowercase							:  Optional[int] 			=   in_proj_bias_cross_attn[256:512]
              lowercase							:  Union[str, Any] 			=   in_proj_weight_cross_attn[-256:, :]
              lowercase							:  int 			=   in_proj_bias_cross_attn[-256:]







def 		_snake_case(						)				->					str:
       lowercase							:  Tuple 			=   """http://images.cocodataset.org/val2017/000000039769.jpg"""
       lowercase							:  int 			=   Image.open(requests.get(SCREAMING_SNAKE_CASE__      ,			stream=SCREAMING_SNAKE_CASE__       ).raw       )

       return im







@torch.no_grad()
def 		_snake_case(						SCREAMING_SNAKE_CASE__      ,			SCREAMING_SNAKE_CASE__       )				->					Any:
       lowercase							:  List[Any] 			=   DetrConfig()
       # set backbone and dilation attributes
       if "resnet101" in model_name:
              lowercase							:  Union[str, Any] 			=   """resnet101"""
       if "dc5" in model_name:
              lowercase							:  List[str] 			=   True
       lowercase							:  Union[str, Any] 			=   """panoptic""" in model_name
       if is_panoptic:
              lowercase							:  List[str] 			=   250
       else:
              lowercase							:  List[Any] 			=   91
              lowercase							:  List[str] 			=   """huggingface/label-files"""
              lowercase							:  Union[str, Any] 			=   """coco-detection-id2label.json"""
              lowercase							:  Optional[int] 			=   json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE__      ,			SCREAMING_SNAKE_CASE__      ,			repo_type="""dataset"""       )      ,			"""r"""       )       )
              lowercase							:  int 			=   {int(SCREAMING_SNAKE_CASE__       ): v for k, v in idalabel.items()}
              lowercase							:  List[Any] 			=   idalabel
              lowercase							:  List[Any] 			=   {v: k for k, v in idalabel.items()}

       # load image processor
       lowercase							:  Any 			=   """coco_panoptic""" if is_panoptic else """coco_detection"""
       lowercase							:  Dict 			=   DetrImageProcessor(format=SCREAMING_SNAKE_CASE__       )

       # prepare image
       lowercase							:  Optional[Any] 			=   prepare_img()
       lowercase							:  int 			=   image_processor(images=SCREAMING_SNAKE_CASE__      ,			return_tensors="""pt"""       )
       lowercase							:  int 			=   encoding["""pixel_values"""]

       logger.info(f"Converting model {model_name}..."       )

       # load original model from torch hub
       lowercase							:  Optional[int] 			=   torch.hub.load("""facebookresearch/detr"""      ,			SCREAMING_SNAKE_CASE__      ,			pretrained=SCREAMING_SNAKE_CASE__       ).eval()
       lowercase							:  Optional[int] 			=   detr.state_dict()
       # rename keys
       for src, dest in rename_keys:
              if is_panoptic:
                     lowercase							:  Optional[Any] 			=   """detr.""" + src
              rename_key(SCREAMING_SNAKE_CASE__      ,			SCREAMING_SNAKE_CASE__      ,			SCREAMING_SNAKE_CASE__       )
       lowercase							:  str 			=   rename_backbone_keys(SCREAMING_SNAKE_CASE__       )
       # query, key and value matrices need special treatment
       read_in_q_k_v(SCREAMING_SNAKE_CASE__      ,			is_panoptic=SCREAMING_SNAKE_CASE__       )
       # important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
       lowercase							:  List[Any] 			=   """detr.model.""" if is_panoptic else """model."""
       for key in state_dict.copy().keys():
              if is_panoptic:
                     if (
                         key.startswith("""detr"""       )
                         and not key.startswith("""class_labels_classifier"""       )
                         and not key.startswith("""bbox_predictor"""       )
                     ):
                            lowercase							:  Optional[Any] 			=   state_dict.pop(SCREAMING_SNAKE_CASE__       )
                            lowercase							:  Optional[Any] 			=   val
                     elif "class_labels_classifier" in key or "bbox_predictor" in key:
                            lowercase							:  Tuple 			=   state_dict.pop(SCREAMING_SNAKE_CASE__       )
                            lowercase							:  str 			=   val
                     elif key.startswith("""bbox_attention"""       ) or key.startswith("""mask_head"""       ):
                            continue
                     else:
                            lowercase							:  Any 			=   state_dict.pop(SCREAMING_SNAKE_CASE__       )
                            lowercase							:  Dict 			=   val
              else:
                     if not key.startswith("""class_labels_classifier"""       ) and not key.startswith("""bbox_predictor"""       ):
                            lowercase							:  List[str] 			=   state_dict.pop(SCREAMING_SNAKE_CASE__       )
                            lowercase							:  Union[str, Any] 			=   val
    # finally, create HuggingFace model and load state dict
       lowercase							:  int 			=   DetrForSegmentation(SCREAMING_SNAKE_CASE__       ) if is_panoptic else DetrForObjectDetection(SCREAMING_SNAKE_CASE__       )
       model.load_state_dict(SCREAMING_SNAKE_CASE__       )
       model.eval()
       # verify our conversion
       lowercase							:  Union[str, Any] 			=   detr(SCREAMING_SNAKE_CASE__       )
       lowercase							:  int 			=   model(SCREAMING_SNAKE_CASE__       )
       assert torch.allclose(outputs.logits      ,			original_outputs["""pred_logits"""]      ,			atol=1e-4       )
       assert torch.allclose(outputs.pred_boxes      ,			original_outputs["""pred_boxes"""]      ,			atol=1e-4       )
       if is_panoptic:
              assert torch.allclose(outputs.pred_masks      ,			original_outputs["""pred_masks"""]      ,			atol=1e-4       )

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


if __name__ == "__main__":
  lowercase						:      Any							=							argparse.ArgumentParser()

  parser.add_argument(
      """--model_name""", default="""detr_resnet50""", type=str, help="""Name of the DETR model you'd like to convert."""
  )
  parser.add_argument(
      """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the folder to output PyTorch model."""
  )
  lowercase						:      List[Any]							=							parser.parse_args()
  convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)
