
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()
_lowerCamelCase        =logging.get_logger(__name__)





def    _a (						lowerCamelCase							):
	if "backbone.0.body" in orig_key:
		lowerCamelCase		:     List[Any]          =				orig_key.replace("""backbone.0.body""",     """backbone.conv_encoder.model"""							)
	if "transformer" in orig_key:
		lowerCamelCase		:     Dict          =				orig_key.replace("""transformer.""",     """"""							)
	if "norm1" in orig_key:
		if "encoder" in orig_key:
			lowerCamelCase		:     Dict          =				orig_key.replace("""norm1""",     """self_attn_layer_norm"""							)
		else:
			lowerCamelCase		:     int          =				orig_key.replace("""norm1""",     """encoder_attn_layer_norm"""							)
	if "norm2" in orig_key:
		if "encoder" in orig_key:
			lowerCamelCase		:     List[str]          =				orig_key.replace("""norm2""",     """final_layer_norm"""							)
		else:
			lowerCamelCase		:     Union[str, Any]          =				orig_key.replace("""norm2""",     """self_attn_layer_norm"""							)
	if "norm3" in orig_key:
		lowerCamelCase		:     List[str]          =				orig_key.replace("""norm3""",     """final_layer_norm"""							)
	if "linear1" in orig_key:
		lowerCamelCase		:     Any          =				orig_key.replace("""linear1""",     """fc1"""							)
	if "linear2" in orig_key:
		lowerCamelCase		:     Tuple          =				orig_key.replace("""linear2""",     """fc2"""							)
	if "query_embed" in orig_key:
		lowerCamelCase		:     int          =				orig_key.replace("""query_embed""",     """query_position_embeddings"""							)
	if "cross_attn" in orig_key:
		lowerCamelCase		:     List[str]          =				orig_key.replace("""cross_attn""",     """encoder_attn"""							)

	return orig_key





def    _a (						lowerCamelCase							):
	# transformer decoder self-attention layers
	for i in range(6							):
		# read in weights + bias of input projection layer of self-attention
		lowerCamelCase		:     Dict          =				state_dict.pop(F'''decoder.layers.{i}.self_attn.in_proj_weight'''							)
		lowerCamelCase		:     Optional[Any]          =				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
		lowerCamelCase		:     Union[str, Any]          =				in_proj_weight[:256, :]
		lowerCamelCase		:     Union[str, Any]          =				in_proj_bias[:256]
		lowerCamelCase		:     List[Any]          =				in_proj_weight[256:512, :]
		lowerCamelCase		:     Tuple          =				in_proj_bias[256:512]
		lowerCamelCase		:     Optional[Any]          =				in_proj_weight[-256:, :]
		lowerCamelCase		:     Dict          =				in_proj_bias[-256:]





def    _a (						):
	lowerCamelCase		:     Any          =				"http://images.cocodataset.org/val2017/000000039769.jpg"
	lowerCamelCase		:     Union[str, Any]          =				Image.open(requests.get(_SCREAMING_SNAKE_CASE,     stream=_SCREAMING_SNAKE_CASE							).raw							)

	return im





@torch.no_grad()
def    _a (						lowerCamelCase,     lowerCamelCase,     lowerCamelCase,     lowerCamelCase,     lowerCamelCase,     lowerCamelCase,     lowerCamelCase,     ):
	lowerCamelCase		:     Union[str, Any]          =				DeformableDetrConfig()
	# set config attributes
	if single_scale:
		lowerCamelCase		:     Union[str, Any]          =				1
	lowerCamelCase		:     str          =				dilation
	lowerCamelCase		:     Any          =				with_box_refine
	lowerCamelCase		:     Union[str, Any]          =				two_stage
	# set labels
	lowerCamelCase		:     int          =				91
	lowerCamelCase		:     str          =				"huggingface/label-files"
	lowerCamelCase		:     str          =				"coco-detection-id2label.json"
	lowerCamelCase		:     List[Any]          =				json.load(open(cached_download(hf_hub_url(_SCREAMING_SNAKE_CASE,     _SCREAMING_SNAKE_CASE,     repo_type="""dataset"""							)							),     """r"""							)							)
	lowerCamelCase		:     Optional[int]          =				{int(_SCREAMING_SNAKE_CASE							): v for k, v in idalabel.items()}
	lowerCamelCase		:     Union[str, Any]          =				idalabel
	lowerCamelCase		:     Union[str, Any]          =				{v: k for k, v in idalabel.items()}

	# load image processor
	lowerCamelCase		:     Union[str, Any]          =				DeformableDetrImageProcessor(format="""coco_detection"""							)

	# prepare image
	lowerCamelCase		:     Optional[int]          =				prepare_img()
	lowerCamelCase		:     Dict          =				image_processor(images=_SCREAMING_SNAKE_CASE,     return_tensors="""pt"""							)
	lowerCamelCase		:     List[str]          =				encoding["pixel_values"]

	logger.info("""Converting model..."""							)

	# load original state dict
	lowerCamelCase		:     Union[str, Any]          =				torch.load(_SCREAMING_SNAKE_CASE,     map_location="""cpu"""							)["model"]
	# rename keys
	for key in state_dict.copy().keys():
		lowerCamelCase		:     List[str]          =				state_dict.pop(_SCREAMING_SNAKE_CASE							)
		lowerCamelCase		:     Dict          =				val
	# query, key and value matrices need special treatment
	read_in_q_k_v(_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		:     List[str]          =				"model."
	for key in state_dict.copy().keys():
		if not key.startswith("""class_embed"""							) and not key.startswith("""bbox_embed"""							):
			lowerCamelCase		:     Optional[int]          =				state_dict.pop(_SCREAMING_SNAKE_CASE							)
			lowerCamelCase		:     str          =				val
    # finally, create HuggingFace model and load state dict
	lowerCamelCase		:     Tuple          =				DeformableDetrForObjectDetection(_SCREAMING_SNAKE_CASE							)
	model.load_state_dict(_SCREAMING_SNAKE_CASE							)
	model.eval()

	lowerCamelCase		:     Optional[Any]          =				"cuda" if torch.cuda.is_available() else "cpu"
	model.to(_SCREAMING_SNAKE_CASE							)
	# verify our conversion
	lowerCamelCase		:     int          =				model(pixel_values.to(_SCREAMING_SNAKE_CASE							)							)

	lowerCamelCase		:     Optional[int]          =				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]]							)
	lowerCamelCase		:     Tuple          =				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:
		lowerCamelCase		:     Any          =				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]]							)
		lowerCamelCase		:     Union[str, Any]          =				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:
		lowerCamelCase		:     int          =				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]]							)
		lowerCamelCase		:     Union[str, Any]          =				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:
		lowerCamelCase		:     List[str]          =				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]]							)
		lowerCamelCase		:     int          =				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:
		lowerCamelCase		:     Optional[int]          =				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]]							)
		lowerCamelCase		:     List[Any]          =				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(_SCREAMING_SNAKE_CASE							),     atol=1e-4							)
	assert torch.allclose(outputs.pred_boxes[0, :3, :3],     expected_boxes.to(_SCREAMING_SNAKE_CASE							),     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(_SCREAMING_SNAKE_CASE							).mkdir(exist_ok=_SCREAMING_SNAKE_CASE							)
	model.save_pretrained(_SCREAMING_SNAKE_CASE							)
	image_processor.save_pretrained(_SCREAMING_SNAKE_CASE							)

	# Push to hub
	if push_to_hub:
		lowerCamelCase		:     Tuple          =				"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=_SCREAMING_SNAKE_CASE,     organization="""nielsr""",     commit_message="""Add model"""							)


if __name__ == "__main__":
						_lowerCamelCase        =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."""
						)
						_lowerCamelCase        =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,
						)






import unittest

import pytest

from transformers import (
    MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    MBartaaTokenizerFast,
    MBartConfig,
    MBartForConditionalGeneration,
    TranslationPipeline,
    pipeline,
)
from transformers.testing_utils import is_pipeline_test, require_tf, require_torch, slow

from .test_pipelines_common import ANY





@is_pipeline_test
class 							A__		(     unittest.TestCase):
      _UpperCAmelCase    :   Tuple   	=      MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
      _UpperCAmelCase    :   Optional[Any]   	=      TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
      def 						UpperCamelCase__							(				self   ,  __magic_name__   ,  __magic_name__   ,  __magic_name__				):
       if isinstance(model.config   ,  __magic_name__				):
        lowerCamelCase      ,   lowerCamelCase		:     Union[str, Any]          =				list(tokenizer.lang_code_to_id.keys()				)[:2]
        lowerCamelCase		:     Tuple          =				TranslationPipeline(model=__magic_name__   ,  tokenizer=__magic_name__   ,  src_lang=__magic_name__   ,  tgt_lang=__magic_name__				)
       else:
        lowerCamelCase		:     Tuple          =				TranslationPipeline(model=__magic_name__   ,  tokenizer=__magic_name__				)
       return translator, ["Some string", "Some other text"]
      def 						UpperCamelCase__							(				self   ,  __magic_name__   ,  __magic_name__				):
       lowerCamelCase		:     Optional[Any]          =				translator("""Some string"""				)
       self.assertEqual(__magic_name__   ,  [{"""translation_text""": ANY(__magic_name__				)}]				)

       lowerCamelCase		:     Dict          =				translator(["""Some string"""]				)
       self.assertEqual(__magic_name__   ,  [{"""translation_text""": ANY(__magic_name__				)}]				)

       lowerCamelCase		:     Any          =				translator(["""Some string""", """other string"""]				)
       self.assertEqual(__magic_name__   ,  [{"""translation_text""": ANY(__magic_name__				)}, {"""translation_text""": ANY(__magic_name__				)}]				)
      @require_torch
      def 						UpperCamelCase__							(				self				):
       lowerCamelCase		:     Any          =				pipeline("""translation_en_to_ro"""   ,  model="""patrickvonplaten/t5-tiny-random"""   ,  framework="""pt"""				)
       lowerCamelCase		:     Union[str, Any]          =				translator("""This is a test string"""   ,  max_length=2_0				)
       self.assertEqual(
           __magic_name__   ,  [
               {
                   """translation_text""": (
                       """Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide"""
                       """ Beide Beide"""
                   )
               }
           ]   ,  )
      @require_tf
      def 						UpperCamelCase__							(				self				):
       lowerCamelCase		:     str          =				pipeline("""translation_en_to_ro"""   ,  model="""patrickvonplaten/t5-tiny-random"""   ,  framework="""tf"""				)
       lowerCamelCase		:     Tuple          =				translator("""This is a test string"""   ,  max_length=2_0				)
       self.assertEqual(
           __magic_name__   ,  [
               {
                   """translation_text""": (
                       """Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide"""
                       """ Beide Beide"""
                   )
               }
           ]   ,  )
      @require_torch
      def 						UpperCamelCase__							(				self				):
       lowerCamelCase		:     int          =				pipeline("""translation_en_to_de"""   ,  model="""patrickvonplaten/t5-tiny-random"""   ,  framework="""pt"""				)
       lowerCamelCase		:     List[Any]          =				translator("""This is a test string"""   ,  max_length=2_0				)
       self.assertEqual(
           __magic_name__   ,  [
               {
                   """translation_text""": (
                       """monoton monoton monoton monoton monoton monoton monoton monoton monoton monoton urine urine"""
                       """ urine urine urine urine urine urine urine"""
                   )
               }
           ]   ,  )






      @require_tf
      def 						UpperCamelCase__							(				self				):
       lowerCamelCase		:     Union[str, Any]          =				pipeline("""translation_en_to_de"""   ,  model="""patrickvonplaten/t5-tiny-random"""   ,  framework="""tf"""				)
       lowerCamelCase		:     Union[str, Any]          =				translator("""This is a test string"""   ,  max_length=2_0				)
       self.assertEqual(
           __magic_name__   ,  [
               {
                   """translation_text""": (
                       """monoton monoton monoton monoton monoton monoton monoton monoton monoton monoton urine urine"""
                       """ urine urine urine urine urine urine urine"""
                   )
               }
           ]   ,  )





class 							A__		(     unittest.TestCase):
      @require_torch
      @slow
      def 						UpperCamelCase__							(				self				):
       # We don't provide a default for this pair
       with self.assertRaises(__magic_name__				):
        pipeline(task="""translation_cn_to_ar"""				)

       # but we do for this one
       lowerCamelCase		:     Optional[Any]          =				pipeline(task="""translation_en_to_de"""				)
       self.assertEqual(translator._preprocess_params["""src_lang"""]   ,  """en"""				)
       self.assertEqual(translator._preprocess_params["""tgt_lang"""]   ,  """de"""				)
      @require_torch
      @slow
      def 						UpperCamelCase__							(				self				):
       lowerCamelCase		:     Optional[int]          =				MBartForConditionalGeneration.from_pretrained("""facebook/mbart-large-50-many-to-many-mmt"""				)
       lowerCamelCase		:     List[Any]          =				MBartaaTokenizerFast.from_pretrained("""facebook/mbart-large-50-many-to-many-mmt"""				)

       lowerCamelCase		:     Any          =				pipeline(task="""translation"""   ,  model=__magic_name__   ,  tokenizer=__magic_name__				)
       # Missing src_lang, tgt_lang
       with self.assertRaises(__magic_name__				):
        translator("""This is a test"""				)

       lowerCamelCase		:     Optional[int]          =				translator("""This is a test"""   ,  src_lang="""en_XX"""   ,  tgt_lang="""ar_AR"""				)
       self.assertEqual(__magic_name__   ,  [{"""translation_text""": """هذا إختبار"""}]				)

       lowerCamelCase		:     Optional[Any]          =				translator("""This is a test"""   ,  src_lang="""en_XX"""   ,  tgt_lang="""hi_IN"""				)
       self.assertEqual(__magic_name__   ,  [{"""translation_text""": """यह एक परीक्षण है"""}]				)

       # src_lang, tgt_lang can be defined at pipeline call time
       lowerCamelCase		:     str          =				pipeline(task="""translation"""   ,  model=__magic_name__   ,  tokenizer=__magic_name__   ,  src_lang="""en_XX"""   ,  tgt_lang="""ar_AR"""				)
       lowerCamelCase		:     Union[str, Any]          =				translator("""This is a test"""				)
       self.assertEqual(__magic_name__   ,  [{"""translation_text""": """هذا إختبار"""}]				)
      @require_torch
      def 						UpperCamelCase__							(				self				):
       lowerCamelCase		:     List[str]          =				"""patrickvonplaten/t5-tiny-random"""
       lowerCamelCase		:     Tuple          =				pipeline(task="""translation_cn_to_ar"""   ,  model=__magic_name__				)
       self.assertEqual(translator._preprocess_params["""src_lang"""]   ,  """cn"""				)
       self.assertEqual(translator._preprocess_params["""tgt_lang"""]   ,  """ar"""				)
      @require_torch
      def 						UpperCamelCase__							(				self				):
       lowerCamelCase		:     Dict          =				"""patrickvonplaten/t5-tiny-random"""
       with pytest.warns(__magic_name__   ,  match=r""".*translation_en_to_de.*"""				):
        lowerCamelCase		:     Optional[int]          =				pipeline(task="""translation"""   ,  model=__magic_name__				)
       self.assertEqual(translator.task   ,  """translation_en_to_de"""				)
       self.assertEqual(translator._preprocess_params["""src_lang"""]   ,  """en"""				)
       self.assertEqual(translator._preprocess_params["""tgt_lang"""]   ,  """de"""				)






      @require_torch
      def 						UpperCamelCase__							(				self				):
       with self.assertRaises(__magic_name__				):
        pipeline(task="""translation"""				)




