




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_			:       List[Any]						       =logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
lowercase_			:       Dict						       =[]
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 							__SCREAMING_SNAKE_CASE						(    snake_case_			,					snake_case_			,					snake_case_		):

						'''simple docstring'''


						_UpperCAmelCase									=  state_dict.pop(lowerCamelCase__		)
						_UpperCAmelCase									=  val


def 							__SCREAMING_SNAKE_CASE						(    snake_case_		):

						'''simple docstring'''


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

						return new_state_dict


def 							__SCREAMING_SNAKE_CASE						(    snake_case_			,					snake_case_=False		):

						'''simple docstring'''


						_UpperCAmelCase									=  ""
						if is_panoptic:
												_UpperCAmelCase									=  "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)
												_UpperCAmelCase									=  state_dict.pop(f"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight"""		)
												_UpperCAmelCase									=  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
												_UpperCAmelCase									=  in_proj_weight[:256, :]
												_UpperCAmelCase									=  in_proj_bias[:256]
												_UpperCAmelCase									=  in_proj_weight[256:512, :]
												_UpperCAmelCase									=  in_proj_bias[256:512]
												_UpperCAmelCase									=  in_proj_weight[-256:, :]
												_UpperCAmelCase									=  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
												_UpperCAmelCase									=  state_dict.pop(f"""{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight"""		)
												_UpperCAmelCase									=  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
												_UpperCAmelCase									=  in_proj_weight[:256, :]
												_UpperCAmelCase									=  in_proj_bias[:256]
												_UpperCAmelCase									=  in_proj_weight[256:512, :]
												_UpperCAmelCase									=  in_proj_bias[256:512]
												_UpperCAmelCase									=  in_proj_weight[-256:, :]
												_UpperCAmelCase									=  in_proj_bias[-256:]
												# read in weights + bias of input projection layer of cross-attention
												_UpperCAmelCase									=  state_dict.pop(
												    f"""{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight"""		)
												_UpperCAmelCase									=  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
												_UpperCAmelCase									=  in_proj_weight_cross_attn[:256, :]
												_UpperCAmelCase									=  in_proj_bias_cross_attn[:256]
												_UpperCAmelCase									=  in_proj_weight_cross_attn[256:512, :]
												_UpperCAmelCase									=  in_proj_bias_cross_attn[256:512]
												_UpperCAmelCase									=  in_proj_weight_cross_attn[-256:, :]
												_UpperCAmelCase									=  in_proj_bias_cross_attn[-256:]


def 							__SCREAMING_SNAKE_CASE						(    ):

						'''simple docstring'''


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

						return im


@torch.no_grad()
def 							__SCREAMING_SNAKE_CASE						(    snake_case_			,					snake_case_		):

						'''simple docstring'''


						_UpperCAmelCase									=  DetrConfig()
						# set backbone and dilation attributes
						if "resnet101" in model_name:
												_UpperCAmelCase									=  "resnet101"
						if "dc5" in model_name:
												_UpperCAmelCase									=  True
						_UpperCAmelCase									=  "panoptic" in model_name
						if is_panoptic:
												_UpperCAmelCase									=  250
						else:
												_UpperCAmelCase									=  91
												_UpperCAmelCase									=  "huggingface/label-files"
												_UpperCAmelCase									=  "coco-detection-id2label.json"
												_UpperCAmelCase									=  json.load(open(hf_hub_download(lowerCamelCase__			,					lowerCamelCase__			,					repo_type="dataset"		)			,					"r"		)		)
												_UpperCAmelCase									=  {int(lowerCamelCase__		): v for k, v in idalabel.items()}
												_UpperCAmelCase									=  idalabel
												_UpperCAmelCase									=  {v: k for k, v in idalabel.items()}

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

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

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

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


if __name__ == "__main__":
				lowercase_			:       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.'
				)
				lowercase_			:       Any						       =parser.parse_args()
				convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)










import os
import shutil
import unittest
from unittest.mock import patch

from transformers.testing_utils import CaptureStd, is_pt_tf_cross_test, require_torch






class 					__lowerCAmelCase				( unittest.TestCase				):







			@patch("sys.argv"					,					["fakeprogrampath", "env"]   )
			def UpperCamelCase    ( self					:    List[str]   ):




									"""simple docstring"""


									import transformers.commands.transformers_cli

									with CaptureStd() as cs:
															transformers.commands.transformers_cli.main()
									self.assertIn("Python version"					,					cs.out   )
									self.assertIn("Platform"					,					cs.out   )
									self.assertIn("Using distributed or parallel set-up in script?"					,					cs.out   )







			@is_pt_tf_cross_test
			@patch(
			    "sys.argv"					,					["fakeprogrampath", "pt-to-tf", "--model-name", "hf-internal-testing/tiny-random-gptj", "--no-pr"]   )
			def UpperCamelCase    ( self					:    Optional[Any]   ):




									"""simple docstring"""


									import transformers.commands.transformers_cli

									shutil.rmtree("/tmp/hf-internal-testing/tiny-random-gptj"					,					ignore_errors=snake_case__   )  # cleans potential past runs
									transformers.commands.transformers_cli.main()

									# The original repo has no TF weights -- if they exist, they were created by the CLI
									self.assertTrue(os.path.exists("/tmp/hf-internal-testing/tiny-random-gptj/tf_model.h5"   )   )







			@require_torch
			@patch("sys.argv"					,					["fakeprogrampath", "download", "hf-internal-testing/tiny-random-gptj", "--cache-dir", "/tmp"]   )
			def UpperCamelCase    ( self					:    Any   ):




									"""simple docstring"""


									import transformers.commands.transformers_cli

									# # remove any previously downloaded model to start clean
									shutil.rmtree("/tmp/models--hf-internal-testing--tiny-random-gptj"					,					ignore_errors=snake_case__   )

									# run the command
									transformers.commands.transformers_cli.main()

									# check if the model files are downloaded correctly on /tmp/models--hf-internal-testing--tiny-random-gptj
									self.assertTrue(os.path.exists("/tmp/models--hf-internal-testing--tiny-random-gptj/blobs"   )   )
									self.assertTrue(os.path.exists("/tmp/models--hf-internal-testing--tiny-random-gptj/refs"   )   )
									self.assertTrue(os.path.exists("/tmp/models--hf-internal-testing--tiny-random-gptj/snapshots"   )   )







			@require_torch
			@patch(
			    "sys.argv"					,					[
			        "fakeprogrampath",
			        "download",
			        "hf-internal-testing/test_dynamic_model_with_tokenizer",
			        "--trust-remote-code",
			        "--cache-dir",
			        "/tmp",
			    ]					,					)
			def UpperCamelCase    ( self					:    int   ):




									"""simple docstring"""


									import transformers.commands.transformers_cli

									# # remove any previously downloaded model to start clean
									shutil.rmtree("/tmp/models--hf-internal-testing--test_dynamic_model_with_tokenizer"					,					ignore_errors=snake_case__   )

									# run the command
									transformers.commands.transformers_cli.main()

									# check if the model files are downloaded correctly on /tmp/models--hf-internal-testing--test_dynamic_model_with_tokenizer
									self.assertTrue(os.path.exists("/tmp/models--hf-internal-testing--test_dynamic_model_with_tokenizer/blobs"   )   )
									self.assertTrue(os.path.exists("/tmp/models--hf-internal-testing--test_dynamic_model_with_tokenizer/refs"   )   )
									self.assertTrue(
									    os.path.exists("/tmp/models--hf-internal-testing--test_dynamic_model_with_tokenizer/snapshots"   )   )




