





"""simple docstring"""


import argparse
import logging
import sys
import time

import tensorflow as tf
from datasets import load_dataset

from transformers import AutoTokenizer, TFAutoModelForSequenceClassification


if __name__ == "__main__":
   a__    :   Dict    						= argparse.ArgumentParser()

   # Hyperparameters sent by the client are passed as command-line arguments to the script.
   parser.add_argument("""--epochs""", type=int, default=1)
   parser.add_argument("""--per_device_train_batch_size""", type=int, default=1_6)
   parser.add_argument("""--per_device_eval_batch_size""", type=int, default=8)
   parser.add_argument("""--model_name_or_path""", type=str)
   parser.add_argument("""--learning_rate""", type=str, default=5e-5)
   parser.add_argument("""--do_train""", type=bool, default=True)
   parser.add_argument("""--do_eval""", type=bool, default=True)
   parser.add_argument("""--output_dir""", type=str)

   a__						,       a__    :   List[str]    						= parser.parse_known_args()

   # overwrite batch size until we have tf_glue.py
   a__    :   Optional[Any]    						= 1_6
   a__    :   List[Any]    						= 1_6

   # Set up logging
   a__    :   Tuple    						= logging.getLogger(__name__)

   logging.basicConfig(
       level=logging.getLevelName("""INFO"""),
       handlers=[logging.StreamHandler(sys.stdout)],
       format="""%(asctime)s - %(name)s - %(levelname)s - %(message)s""",
   )

   # Load model and tokenizer
   a__    :   int    						= TFAutoModelForSequenceClassification.from_pretrained(args.model_name_or_path)
   a__    :   List[str]    						= AutoTokenizer.from_pretrained(args.model_name_or_path)

   # Load dataset
   a__						,       a__    :   Any    						= load_dataset("""imdb""", split=["""train""", """test"""])
   a__    :   Optional[Any]    						= train_dataset.shuffle().select(range(5_0_0_0))  # smaller the size for train dataset to 5k
   a__    :   Dict    						= test_dataset.shuffle().select(range(5_0_0))  # smaller the size for test dataset to 500

   # Preprocess train dataset
   a__    :   Any    						= train_dataset.map(
       lambda e: tokenizer(e["""text"""], truncation=True, padding="""max_length"""), batched=True
   )
   train_dataset.set_format(type="""tensorflow""", columns=["""input_ids""", """attention_mask""", """label"""])

   a__    :   int    						= {
       x: train_dataset[x].to_tensor(default_value=0, shape=[None, tokenizer.model_max_length])
       for x in ["""input_ids""", """attention_mask"""]
   }
   a__    :   str    						= tf.data.Dataset.from_tensor_slices((train_features, train_dataset["""label"""])).batch(
       args.per_device_train_batch_size
   )

   # Preprocess test dataset
   a__    :   Union[str, Any]    						= test_dataset.map(
       lambda e: tokenizer(e["""text"""], truncation=True, padding="""max_length"""), batched=True
   )
   test_dataset.set_format(type="""tensorflow""", columns=["""input_ids""", """attention_mask""", """label"""])

   a__    :   Optional[Any]    						= {
       x: test_dataset[x].to_tensor(default_value=0, shape=[None, tokenizer.model_max_length])
       for x in ["""input_ids""", """attention_mask"""]
   }
   a__    :   Optional[int]    						= tf.data.Dataset.from_tensor_slices((test_features, test_dataset["""label"""])).batch(
       args.per_device_eval_batch_size
   )

   # fine optimizer and loss
   a__    :   List[Any]    						= tf.keras.optimizers.Adam(learning_rate=args.learning_rate)
   a__    :   List[Any]    						= tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
   a__    :   Union[str, Any]    						= [tf.keras.metrics.SparseCategoricalAccuracy()]
   model.compile(optimizer=optimizer, loss=loss, metrics=metrics)

   a__    :   Union[str, Any]    						= time.time()
   a__    :   Dict    						= model.fit(tf_train_dataset, epochs=args.epochs, batch_size=args.per_device_train_batch_size)
   a__    :   str    						= time.time() - start_train_time

   logger.info("""*** Train ***""")
   logger.info(f'''train_runtime = {end_train_time}''')
   for key, value in train_results.history.items():
      logger.info(f'''  {key} = {value}''')








"""simple docstring"""


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__    :   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)
a__    :   List[str]    						= []
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__	(     _UpperCAmelCase		,   _UpperCAmelCase		,   _UpperCAmelCase		)			->     Any:

 '''simple docstring'''






 snake_case__										= state_dict.pop(_UpperCAmelCase		)
 snake_case__										= val





def 							a__	(     _UpperCAmelCase		)			->     Any:

 '''simple docstring'''






 snake_case__										= OrderedDict()
 for key, value in state_dict.items():
  if "backbone.0.body" in key:
   snake_case__										= key.replace("backbone.0.body"		,   "backbone.conv_encoder.model"		)
   snake_case__										= value
  else:
   snake_case__										= value

 return new_state_dict





def 							a__	(     _UpperCAmelCase		,   _UpperCAmelCase=False		)			->     List[Any]:

 '''simple docstring'''






 snake_case__										= ""
 if is_panoptic:
  snake_case__										= "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)
  snake_case__										= state_dict.pop(f"{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight"		)
  snake_case__										= 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
  snake_case__										= in_proj_weight[:2_5_6, :]
  snake_case__										= in_proj_bias[:2_5_6]
  snake_case__										= in_proj_weight[2_5_6:5_1_2, :]
  snake_case__										= in_proj_bias[2_5_6:5_1_2]
  snake_case__										= in_proj_weight[-2_5_6:, :]
  snake_case__										= in_proj_bias[-2_5_6:]
 # 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
  snake_case__										= state_dict.pop(f"{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight"		)
  snake_case__										= 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
  snake_case__										= in_proj_weight[:2_5_6, :]
  snake_case__										= in_proj_bias[:2_5_6]
  snake_case__										= in_proj_weight[2_5_6:5_1_2, :]
  snake_case__										= in_proj_bias[2_5_6:5_1_2]
  snake_case__										= in_proj_weight[-2_5_6:, :]
  snake_case__										= in_proj_bias[-2_5_6:]
  # read in weights + bias of input projection layer of cross-attention
  snake_case__										= state_dict.pop(
      f"{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight"		)
  snake_case__										= 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
  snake_case__										= in_proj_weight_cross_attn[:2_5_6, :]
  snake_case__										= in_proj_bias_cross_attn[:2_5_6]
  snake_case__										= in_proj_weight_cross_attn[2_5_6:5_1_2, :]
  snake_case__										= in_proj_bias_cross_attn[2_5_6:5_1_2]
  snake_case__										= in_proj_weight_cross_attn[-2_5_6:, :]
  snake_case__										= in_proj_bias_cross_attn[-2_5_6:]





def 							a__	(     )			->     Optional[int]:

 '''simple docstring'''






 snake_case__										= "http://images.cocodataset.org/val2017/000000039769.jpg"
 snake_case__										= Image.open(requests.get(_UpperCAmelCase		,   stream=_UpperCAmelCase		).raw		)

 return im





@torch.no_grad()
def 							a__	(     _UpperCAmelCase		,   _UpperCAmelCase		)			->     Any:

 '''simple docstring'''






 snake_case__										= DetrConfig()
 # set backbone and dilation attributes
 if "resnet101" in model_name:
  snake_case__										= "resnet101"
 if "dc5" in model_name:
  snake_case__										= True
 snake_case__										= "panoptic" in model_name
 if is_panoptic:
  snake_case__										= 2_5_0
 else:
  snake_case__										= 9_1
  snake_case__										= "huggingface/label-files"
  snake_case__										= "coco-detection-id2label.json"
  snake_case__										= json.load(open(hf_hub_download(_UpperCAmelCase		,   _UpperCAmelCase		,   repo_type="dataset"		)		,   "r"		)		)
  snake_case__										= {int(_UpperCAmelCase		): v for k, v in idalabel.items()}
  snake_case__										= idalabel
  snake_case__										= {v: k for k, v in idalabel.items()}

 # load image processor
 snake_case__										= "coco_panoptic" if is_panoptic else "coco_detection"
 snake_case__										= DetrImageProcessor(format=_UpperCAmelCase		)

 # prepare image
 snake_case__										= prepare_img()
 snake_case__										= image_processor(images=_UpperCAmelCase		,   return_tensors="pt"		)
 snake_case__										= encoding["pixel_values"]

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

 # load original model from torch hub
 snake_case__										= torch.hub.load("facebookresearch/detr"		,   _UpperCAmelCase		,   pretrained=_UpperCAmelCase		).eval()
 snake_case__										= detr.state_dict()
 # rename keys
 for src, dest in rename_keys:
  if is_panoptic:
   snake_case__										= "detr." + src
  rename_key(_UpperCAmelCase		,   _UpperCAmelCase		,   _UpperCAmelCase		)
 snake_case__										= rename_backbone_keys(_UpperCAmelCase		)
 # query, key and value matrices need special treatment
 read_in_q_k_v(_UpperCAmelCase		,   is_panoptic=_UpperCAmelCase		)
 # important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
 snake_case__										= "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"		)
   ):
    snake_case__										= state_dict.pop(_UpperCAmelCase		)
    snake_case__										= val
   elif "class_labels_classifier" in key or "bbox_predictor" in key:
    snake_case__										= state_dict.pop(_UpperCAmelCase		)
    snake_case__										= val
   elif key.startswith("bbox_attention"		) or key.startswith("mask_head"		):
    continue
   else:
    snake_case__										= state_dict.pop(_UpperCAmelCase		)
    snake_case__										= val
  else:
   if not key.startswith("class_labels_classifier"		) and not key.startswith("bbox_predictor"		):
    snake_case__										= state_dict.pop(_UpperCAmelCase		)
    snake_case__										= val
    # finally, create HuggingFace model and load state dict
 snake_case__										= DetrForSegmentation(_UpperCAmelCase		) if is_panoptic else DetrForObjectDetection(_UpperCAmelCase		)
 model.load_state_dict(_UpperCAmelCase		)
 model.eval()
 # verify our conversion
 snake_case__										= detr(_UpperCAmelCase		)
 snake_case__										= model(_UpperCAmelCase		)
 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(_UpperCAmelCase		).mkdir(exist_ok=_UpperCAmelCase		)
 model.save_pretrained(_UpperCAmelCase		)
 image_processor.save_pretrained(_UpperCAmelCase		)


if __name__ == "__main__":
   a__    :   List[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."""
   )
   a__    :   Optional[int]    						= parser.parse_args()
   convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)

