

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()
_a	 =     logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
_a	 =     []
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 _a				( SCREAMING_SNAKE_CASE	:    Dict					,      SCREAMING_SNAKE_CASE	:    Tuple					,      SCREAMING_SNAKE_CASE	:    Optional[Any]	)					->					Optional[Any]:

 """simple docstring"""






 __lowerCAmelCase:     Tuple     			=   state_dict.pop(SCREAMING_SNAKE_CASE	)
 __lowerCAmelCase:     Optional[int]     			=   val







def _a				( SCREAMING_SNAKE_CASE	:    str	)					->					Tuple:

 """simple docstring"""






 __lowerCAmelCase:     List[str]     			=   OrderedDict()
 for key, value in state_dict.items():
  if "backbone.0.body" in key:
   __lowerCAmelCase:     int     			=   key.replace('backbone.0.body'					,      'backbone.conv_encoder.model'	)
   __lowerCAmelCase:     Any     			=   value
  else:
   __lowerCAmelCase:     Tuple     			=   value

 return new_state_dict







def _a				( SCREAMING_SNAKE_CASE	:    List[Any]					,      SCREAMING_SNAKE_CASE	:    int=False	)					->					str:

 """simple docstring"""






 __lowerCAmelCase:     Optional[Any]     			=   ''
 if is_panoptic:
  __lowerCAmelCase:     Optional[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)
  __lowerCAmelCase:     Optional[Any]     			=   state_dict.pop(f'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight'''	)
  __lowerCAmelCase:     List[str]     			=   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
  __lowerCAmelCase:     Dict     			=   in_proj_weight[:2_56, :]
  __lowerCAmelCase:     Any     			=   in_proj_bias[:2_56]
  __lowerCAmelCase:     List[Any]     			=   in_proj_weight[2_56:5_12, :]
  __lowerCAmelCase:     Tuple     			=   in_proj_bias[2_56:5_12]
  __lowerCAmelCase:     int     			=   in_proj_weight[-2_56:, :]
  __lowerCAmelCase:     Optional[int]     			=   in_proj_bias[-2_56:]
 # 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
  __lowerCAmelCase:     Optional[Any]     			=   state_dict.pop(f'''{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight'''	)
  __lowerCAmelCase:     int     			=   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
  __lowerCAmelCase:     List[Any]     			=   in_proj_weight[:2_56, :]
  __lowerCAmelCase:     Optional[int]     			=   in_proj_bias[:2_56]
  __lowerCAmelCase:     Tuple     			=   in_proj_weight[2_56:5_12, :]
  __lowerCAmelCase:     Optional[Any]     			=   in_proj_bias[2_56:5_12]
  __lowerCAmelCase:     Optional[int]     			=   in_proj_weight[-2_56:, :]
  __lowerCAmelCase:     str     			=   in_proj_bias[-2_56:]
  # read in weights + bias of input projection layer of cross-attention
  __lowerCAmelCase:     int     			=   state_dict.pop(
      f'''{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight'''	)
  __lowerCAmelCase:     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
  __lowerCAmelCase:     Any     			=   in_proj_weight_cross_attn[:2_56, :]
  __lowerCAmelCase:     str     			=   in_proj_bias_cross_attn[:2_56]
  __lowerCAmelCase:     Optional[Any]     			=   in_proj_weight_cross_attn[2_56:5_12, :]
  __lowerCAmelCase:     Optional[Any]     			=   in_proj_bias_cross_attn[2_56:5_12]
  __lowerCAmelCase:     List[str]     			=   in_proj_weight_cross_attn[-2_56:, :]
  __lowerCAmelCase:     Tuple     			=   in_proj_bias_cross_attn[-2_56:]







def _a				( )					->					str:

 """simple docstring"""






 __lowerCAmelCase:     Any     			=   'http://images.cocodataset.org/val2017/000000039769.jpg'
 __lowerCAmelCase:     List[str]     			=   Image.open(requests.get(SCREAMING_SNAKE_CASE					,      stream=SCREAMING_SNAKE_CASE	).raw	)

 return im







@torch.no_grad()
def _a				( SCREAMING_SNAKE_CASE	:    Optional[int]					,      SCREAMING_SNAKE_CASE	:    int	)					->					Tuple:

 """simple docstring"""






 __lowerCAmelCase:     List[Any]     			=   DetrConfig()
 # set backbone and dilation attributes
 if "resnet101" in model_name:
  __lowerCAmelCase:     str     			=   'resnet101'
 if "dc5" in model_name:
  __lowerCAmelCase:     Tuple     			=   True
 __lowerCAmelCase:     List[Any]     			=   'panoptic' in model_name
 if is_panoptic:
  __lowerCAmelCase:     List[Any]     			=   2_50
 else:
  __lowerCAmelCase:     Dict     			=   91
  __lowerCAmelCase:     List[str]     			=   'huggingface/label-files'
  __lowerCAmelCase:     Optional[Any]     			=   'coco-detection-id2label.json'
  __lowerCAmelCase:     Optional[Any]     			=   json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE					,      SCREAMING_SNAKE_CASE					,      repo_type='dataset'	)					,      'r'	)	)
  __lowerCAmelCase:     int     			=   {int(SCREAMING_SNAKE_CASE	): v for k, v in idalabel.items()}
  __lowerCAmelCase:     Optional[int]     			=   idalabel
  __lowerCAmelCase:     List[str]     			=   {v: k for k, v in idalabel.items()}

 # load image processor
 __lowerCAmelCase:     Optional[Any]     			=   'coco_panoptic' if is_panoptic else 'coco_detection'
 __lowerCAmelCase:     List[Any]     			=   DetrImageProcessor(format=SCREAMING_SNAKE_CASE	)

 # prepare image
 __lowerCAmelCase:     Tuple     			=   prepare_img()
 __lowerCAmelCase:     Optional[Any]     			=   image_processor(images=SCREAMING_SNAKE_CASE					,      return_tensors='pt'	)
 __lowerCAmelCase:     List[str]     			=   encoding['pixel_values']

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

 # load original model from torch hub
 __lowerCAmelCase:     List[Any]     			=   torch.hub.load('facebookresearch/detr'					,      SCREAMING_SNAKE_CASE					,      pretrained=SCREAMING_SNAKE_CASE	).eval()
 __lowerCAmelCase:     Optional[int]     			=   detr.state_dict()
 # rename keys
 for src, dest in rename_keys:
  if is_panoptic:
   __lowerCAmelCase:     Dict     			=   'detr.' + src
  rename_key(SCREAMING_SNAKE_CASE					,      SCREAMING_SNAKE_CASE					,      SCREAMING_SNAKE_CASE	)
 __lowerCAmelCase:     List[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
 __lowerCAmelCase:     int     			=   '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'	)
   ):
    __lowerCAmelCase:     int     			=   state_dict.pop(SCREAMING_SNAKE_CASE	)
    __lowerCAmelCase:     Union[str, Any]     			=   val
   elif "class_labels_classifier" in key or "bbox_predictor" in key:
    __lowerCAmelCase:     Optional[int]     			=   state_dict.pop(SCREAMING_SNAKE_CASE	)
    __lowerCAmelCase:     int     			=   val
   elif key.startswith('bbox_attention'	) or key.startswith('mask_head'	):
    continue
   else:
    __lowerCAmelCase:     Optional[Any]     			=   state_dict.pop(SCREAMING_SNAKE_CASE	)
    __lowerCAmelCase:     Optional[Any]     			=   val
  else:
   if not key.startswith('class_labels_classifier'	) and not key.startswith('bbox_predictor'	):
    __lowerCAmelCase:     Optional[Any]     			=   state_dict.pop(SCREAMING_SNAKE_CASE	)
    __lowerCAmelCase:     List[str]     			=   val
    # finally, create HuggingFace model and load state dict
 __lowerCAmelCase:     Any     			=   DetrForSegmentation(SCREAMING_SNAKE_CASE	) if is_panoptic else DetrForObjectDetection(SCREAMING_SNAKE_CASE	)
 model.load_state_dict(SCREAMING_SNAKE_CASE	)
 model.eval()
 # verify our conversion
 __lowerCAmelCase:     Dict     			=   detr(SCREAMING_SNAKE_CASE	)
 __lowerCAmelCase:     Union[str, Any]     			=   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__":
  _a	 =     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.'''
  )
  _a	 =     parser.parse_args()
  convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)








from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_keras_nlp_available,
    is_tensorflow_text_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


_a	 =     {
    '''configuration_gpt2''': ['''GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GPT2Config''', '''GPT2OnnxConfig'''],
    '''tokenization_gpt2''': ['''GPT2Tokenizer'''],
}

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _a	 =     ['''GPT2TokenizerFast''']

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _a	 =     [
      '''GPT2_PRETRAINED_MODEL_ARCHIVE_LIST''',
      '''GPT2DoubleHeadsModel''',
      '''GPT2ForQuestionAnswering''',
      '''GPT2ForSequenceClassification''',
      '''GPT2ForTokenClassification''',
      '''GPT2LMHeadModel''',
      '''GPT2Model''',
      '''GPT2PreTrainedModel''',
      '''load_tf_weights_in_gpt2''',
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _a	 =     [
      '''TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST''',
      '''TFGPT2DoubleHeadsModel''',
      '''TFGPT2ForSequenceClassification''',
      '''TFGPT2LMHeadModel''',
      '''TFGPT2MainLayer''',
      '''TFGPT2Model''',
      '''TFGPT2PreTrainedModel''',
  ]

try:
  if not is_keras_nlp_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _a	 =     ['''TFGPT2Tokenizer''']

try:
  if not is_flax_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _a	 =     ['''FlaxGPT2LMHeadModel''', '''FlaxGPT2Model''', '''FlaxGPT2PreTrainedModel''']

if TYPE_CHECKING:
  from .configuration_gpta import GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTaConfig, GPTaOnnxConfig
  from .tokenization_gpta import GPTaTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_gpta_fast import GPTaTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_gpta import (
        GPT2_PRETRAINED_MODEL_ARCHIVE_LIST,
        GPTaDoubleHeadsModel,
        GPTaForQuestionAnswering,
        GPTaForSequenceClassification,
        GPTaForTokenClassification,
        GPTaLMHeadModel,
        GPTaModel,
        GPTaPreTrainedModel,
        load_tf_weights_in_gpta,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_gpta import (
        TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFGPTaDoubleHeadsModel,
        TFGPTaForSequenceClassification,
        TFGPTaLMHeadModel,
        TFGPTaMainLayer,
        TFGPTaModel,
        TFGPTaPreTrainedModel,
    )

  try:
    if not is_keras_nlp_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_gpta_tf import TFGPTaTokenizer

  try:
    if not is_flax_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_flax_gpta import FlaxGPTaLMHeadModel, FlaxGPTaModel, FlaxGPTaPreTrainedModel

else:
  import sys

  _a	 =     _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)





