

"""simple docstring"""





import argparse
import json
from pathlib import Path

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

from transformers import YolosConfig, YolosForObjectDetection, YolosImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
_a							=							logging.get_logger(__name__)




def   _A  (  UpperCamelCase_					:	str)     ->						YolosConfig:
   '''simple docstring'''


   __lowercase								=						YolosConfig()

   # size of the architecture
   if "yolos_ti" in yolos_name:
      __lowercase								=						192
      __lowercase								=						768
      __lowercase								=						12
      __lowercase								=						3
      __lowercase								=						[800, 1333]
      __lowercase								=						False
   elif yolos_name == "yolos_s_dWr":
      __lowercase								=						330
      __lowercase								=						14
      __lowercase								=						6
      __lowercase								=						1320
   elif "yolos_s" in yolos_name:
      __lowercase								=						384
      __lowercase								=						1536
      __lowercase								=						12
      __lowercase								=						6
   elif "yolos_b" in yolos_name:
      __lowercase								=						[800, 1344]

   __lowercase								=						91
   __lowercase								=						"huggingface/label-files"
   __lowercase								=						"coco-detection-id2label.json"
   __lowercase								=						json.load(open(hf_hub_download(UpperCamelCase_,      UpperCamelCase_,      repo_type="dataset"),      "r"))
   __lowercase								=						{int(UpperCamelCase_): v for k, v in idalabel.items()}
   __lowercase								=						idalabel
   __lowercase								=						{v: k for k, v in idalabel.items()}

   return config




def   _A  (  UpperCamelCase_					:	dict,      UpperCamelCase_					:	YolosConfig,      UpperCamelCase_					:	bool = False)     ->						str:
   '''simple docstring'''


   for i in range(config.num_hidden_layers):
      # read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
      __lowercase								=						state_dict.pop(F"""blocks.{i}.attn.qkv.weight""")
      __lowercase								=						state_dict.pop(F"""blocks.{i}.attn.qkv.bias""")
      # next, add query, keys and values (in that order) to the state dict
      __lowercase								=						in_proj_weight[: config.hidden_size, :]
      __lowercase								=						in_proj_bias[: config.hidden_size]
      __lowercase								=						in_proj_weight[
          config.hidden_size : config.hidden_size * 2, :
      ]
      __lowercase								=						in_proj_bias[
          config.hidden_size : config.hidden_size * 2
      ]
      __lowercase								=						in_proj_weight[-config.hidden_size :, :]
      __lowercase								=						in_proj_bias[-config.hidden_size :]




def   _A  (  UpperCamelCase_					:	str)     ->						str:
   '''simple docstring'''


   if "backbone" in name:
      __lowercase								=						name.replace("backbone",      "vit")
   if "cls_token" in name:
      __lowercase								=						name.replace("cls_token",      "embeddings.cls_token")
   if "det_token" in name:
      __lowercase								=						name.replace("det_token",      "embeddings.detection_tokens")
   if "mid_pos_embed" in name:
      __lowercase								=						name.replace("mid_pos_embed",      "encoder.mid_position_embeddings")
   if "pos_embed" in name:
      __lowercase								=						name.replace("pos_embed",      "embeddings.position_embeddings")
   if "patch_embed.proj" in name:
      __lowercase								=						name.replace("patch_embed.proj",      "embeddings.patch_embeddings.projection")
   if "blocks" in name:
      __lowercase								=						name.replace("blocks",      "encoder.layer")
   if "attn.proj" in name:
      __lowercase								=						name.replace("attn.proj",      "attention.output.dense")
   if "attn" in name:
      __lowercase								=						name.replace("attn",      "attention.self")
   if "norm1" in name:
      __lowercase								=						name.replace("norm1",      "layernorm_before")
   if "norm2" in name:
      __lowercase								=						name.replace("norm2",      "layernorm_after")
   if "mlp.fc1" in name:
      __lowercase								=						name.replace("mlp.fc1",      "intermediate.dense")
   if "mlp.fc2" in name:
      __lowercase								=						name.replace("mlp.fc2",      "output.dense")
   if "class_embed" in name:
      __lowercase								=						name.replace("class_embed",      "class_labels_classifier")
   if "bbox_embed" in name:
      __lowercase								=						name.replace("bbox_embed",      "bbox_predictor")
   if "vit.norm" in name:
      __lowercase								=						name.replace("vit.norm",      "vit.layernorm")

   return name




def   _A  (  UpperCamelCase_					:	dict,      UpperCamelCase_					:	YolosForObjectDetection)     ->						dict:
   '''simple docstring'''


   for key in orig_state_dict.copy().keys():
      __lowercase								=						orig_state_dict.pop(UpperCamelCase_)

      if "qkv" in key:
         __lowercase								=						key.split(".")
         __lowercase								=						int(key_split[2])
         __lowercase								=						model.vit.encoder.layer[layer_num].attention.attention.all_head_size
         if "weight" in key:
            __lowercase								=						val[:dim, :]
            __lowercase								=						val[
                dim : dim * 2, :
            ]
            __lowercase								=						val[-dim:, :]
         else:
            __lowercase								=						val[:dim]
            __lowercase								=						val[dim : dim * 2]
            __lowercase								=						val[-dim:]
      else:
         __lowercase								=						val

   return orig_state_dict




def   _A  (  )     ->						torch.Tensor:
   '''simple docstring'''


   __lowercase								=						"http://images.cocodataset.org/val2017/000000039769.jpg"
   __lowercase								=						Image.open(requests.get(UpperCamelCase_,      stream=UpperCamelCase_).raw)
   return im




@torch.no_grad()
def   _A  (  UpperCamelCase_					:	str,      UpperCamelCase_					:	str,      UpperCamelCase_					:	str,      UpperCamelCase_					:	bool = False)     ->						List[str]:
   '''simple docstring'''


   __lowercase								=						get_yolos_config(UpperCamelCase_)

   # load original state_dict
   __lowercase								=						torch.load(UpperCamelCase_,      map_location="cpu")["model"]

   # load 🤗 model
   __lowercase								=						YolosForObjectDetection(UpperCamelCase_)
   model.eval()
   __lowercase								=						convert_state_dict(UpperCamelCase_,      UpperCamelCase_)
   model.load_state_dict(UpperCamelCase_)

   # Check outputs on an image, prepared by YolosImageProcessor
   __lowercase								=						800 if yolos_name != "yolos_ti" else 512
   __lowercase								=						YolosImageProcessor(format="coco_detection",      size=UpperCamelCase_)
   __lowercase								=						image_processor(images=prepare_img(),      return_tensors="pt")
   __lowercase								=						model(**UpperCamelCase_)
   __lowercase		,__lowercase								=						outputs.logits, outputs.pred_boxes

   __lowercase		,__lowercase								=						None, None
   if yolos_name == "yolos_ti":
      __lowercase								=						torch.tensor(
          [[-39.5_022, -11.9_820, -17.6_888], [-29.9_574, -9.9_769, -17.7_691], [-42.3_281, -20.7_200, -30.6_294]])
      __lowercase								=						torch.tensor(
          [[0.4_021, 0.0_836, 0.7_979], [0.0_184, 0.2_609, 0.0_364], [0.1_781, 0.2_004, 0.2_095]])
   elif yolos_name == "yolos_s_200_pre":
      __lowercase								=						torch.tensor(
          [[-24.0_248, -10.3_024, -14.8_290], [-42.0_392, -16.8_200, -27.4_334], [-27.2_743, -11.8_154, -18.7_148]])
      __lowercase								=						torch.tensor(
          [[0.2_559, 0.5_455, 0.4_706], [0.2_989, 0.7_279, 0.1_875], [0.7_732, 0.4_017, 0.4_462]])
   elif yolos_name == "yolos_s_300_pre":
      __lowercase								=						torch.tensor(
          [[-36.2_220, -14.4_385, -23.5_457], [-35.6_970, -14.7_583, -21.3_935], [-31.5_939, -13.6_042, -16.8_049]])
      __lowercase								=						torch.tensor(
          [[0.7_614, 0.2_316, 0.4_728], [0.7_168, 0.4_495, 0.3_855], [0.4_996, 0.1_466, 0.9_996]])
   elif yolos_name == "yolos_s_dWr":
      __lowercase								=						torch.tensor(
          [[-42.8_668, -24.1_049, -41.1_690], [-34.7_456, -14.1_274, -24.9_194], [-33.7_898, -12.1_946, -25.6_495]])
      __lowercase								=						torch.tensor(
          [[0.5_587, 0.2_773, 0.0_605], [0.5_004, 0.3_014, 0.9_994], [0.4_999, 0.1_548, 0.9_994]])
   elif yolos_name == "yolos_base":
      __lowercase								=						torch.tensor(
          [[-40.6_064, -24.3_084, -32.6_447], [-55.1_990, -30.7_719, -35.5_877], [-51.4_311, -33.3_507, -35.6_462]])
      __lowercase								=						torch.tensor(
          [[0.5_555, 0.2_794, 0.0_655], [0.9_049, 0.2_664, 0.1_894], [0.9_183, 0.1_984, 0.1_635]])
   else:
      raise ValueError(F"""Unknown yolos_name: {yolos_name}""")

   assert torch.allclose(logits[0, :3, :3],      UpperCamelCase_,      atol=1E-4)
   assert torch.allclose(pred_boxes[0, :3, :3],      UpperCamelCase_,      atol=1E-4)

   Path(UpperCamelCase_).mkdir(exist_ok=UpperCamelCase_)
   print(F"""Saving model {yolos_name} to {pytorch_dump_folder_path}""")
   model.save_pretrained(UpperCamelCase_)
   print(F"""Saving image processor to {pytorch_dump_folder_path}""")
   image_processor.save_pretrained(UpperCamelCase_)

   if push_to_hub:
      __lowercase								=						{
          "yolos_ti": "yolos-tiny",
          "yolos_s_200_pre": "yolos-small",
          "yolos_s_300_pre": "yolos-small-300",
          "yolos_s_dWr": "yolos-small-dwr",
          "yolos_base": "yolos-base",
      }

      print("Pushing to the hub...")
      __lowercase								=						model_mapping[yolos_name]
      image_processor.push_to_hub(UpperCamelCase_,      organization="hustvl")
      model.push_to_hub(UpperCamelCase_,      organization="hustvl")


if __name__ == "__main__":
   _a							=							argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       '--yolos_name',
       default='yolos_s_200_pre',
       type=str,
       help=(
           'Name of the YOLOS model you\'d like to convert. Should be one of \'yolos_ti\', \'yolos_s_200_pre\','
           ' \'yolos_s_300_pre\', \'yolos_s_dWr\', \'yolos_base\'.'
       ),
   )
   parser.add_argument(
       '--checkpoint_path', default=None, type=str, help='Path to the original state dict (.pth file).'
   )
   parser.add_argument(
       '--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
   )
   parser.add_argument(
       '--push_to_hub', action='store_true', help='Whether or not to push the converted model to the 🤗 hub.'
   )

   _a							=							parser.parse_args()
   convert_yolos_checkpoint(args.yolos_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)







"""simple docstring"""





import unittest

from transformers import (
    MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
    TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
    TextClassificationPipeline,
    pipeline,
)
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_tf, require_torch, slow

from .test_pipelines_common import ANY


# These 2 model types require different inputs than those of the usual text models.
_a							=							{'LayoutLMv2Config', 'LayoutLMv3Config'}




@is_pipeline_test
class  _lowerCAmelCase  (     unittest.TestCase       ):







 """simple docstring"""







 __UpperCAmelCase     : str       		=     MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
 __UpperCAmelCase     : str       		=     TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING

 if model_mapping is not None:
  __UpperCAmelCase     : Optional[Any]       		=     {config: model for config, model in model_mapping.items() if config.__name__ not in _TO_SKIP}
 if tf_model_mapping is not None:
  __UpperCAmelCase     : Tuple       		=     {
      config: model for config, model in tf_model_mapping.items() if config.__name__ not in _TO_SKIP
  }


 @require_torch
 def  _lowercase			(      self							:      Optional[int]     ):
    __lowercase								=						pipeline(
        task="text-classification", model="hf-internal-testing/tiny-random-distilbert", framework="pt"     )

    __lowercase								=						text_classifier("This is great !"     )
    self.assertEqual(nested_simplify(UpperCAmelCase__     ), [{"label": "LABEL_0", "score": 0.504}]     )

    __lowercase								=						text_classifier("This is great !", top_k=2     )
    self.assertEqual(
        nested_simplify(UpperCAmelCase__     ), [{"label": "LABEL_0", "score": 0.504}, {"label": "LABEL_1", "score": 0.496}]     )

    __lowercase								=						text_classifier(["This is great !", "This is bad"], top_k=2     )
    self.assertEqual(
        nested_simplify(UpperCAmelCase__     ), [
            [{"label": "LABEL_0", "score": 0.504}, {"label": "LABEL_1", "score": 0.496}],
            [{"label": "LABEL_0", "score": 0.504}, {"label": "LABEL_1", "score": 0.496}],
        ], )

    __lowercase								=						text_classifier("This is great !", top_k=1     )
    self.assertEqual(nested_simplify(UpperCAmelCase__     ), [{"label": "LABEL_0", "score": 0.504}]     )

    # Legacy behavior
    __lowercase								=						text_classifier("This is great !", return_all_scores=UpperCAmelCase__     )
    self.assertEqual(nested_simplify(UpperCAmelCase__     ), [{"label": "LABEL_0", "score": 0.504}]     )

    __lowercase								=						text_classifier("This is great !", return_all_scores=UpperCAmelCase__     )
    self.assertEqual(
        nested_simplify(UpperCAmelCase__     ), [[{"label": "LABEL_0", "score": 0.504}, {"label": "LABEL_1", "score": 0.496}]]     )

    __lowercase								=						text_classifier(["This is great !", "Something else"], return_all_scores=UpperCAmelCase__     )
    self.assertEqual(
        nested_simplify(UpperCAmelCase__     ), [
            [{"label": "LABEL_0", "score": 0.504}, {"label": "LABEL_1", "score": 0.496}],
            [{"label": "LABEL_0", "score": 0.504}, {"label": "LABEL_1", "score": 0.496}],
        ], )

    __lowercase								=						text_classifier(["This is great !", "Something else"], return_all_scores=UpperCAmelCase__     )
    self.assertEqual(
        nested_simplify(UpperCAmelCase__     ), [
            {"label": "LABEL_0", "score": 0.504},
            {"label": "LABEL_0", "score": 0.504},
        ], )


 @require_torch
 def  _lowercase			(      self							:      Dict     ):
    import torch

    __lowercase								=						pipeline(
        task="text-classification", model="hf-internal-testing/tiny-random-distilbert", framework="pt", device=torch.device("cpu"     ), )

    __lowercase								=						text_classifier("This is great !"     )
    self.assertEqual(nested_simplify(UpperCAmelCase__     ), [{"label": "LABEL_0", "score": 0.504}]     )


 @require_tf
 def  _lowercase			(      self							:      Union[str, Any]     ):
    __lowercase								=						pipeline(
        task="text-classification", model="hf-internal-testing/tiny-random-distilbert", framework="tf"     )

    __lowercase								=						text_classifier("This is great !"     )
    self.assertEqual(nested_simplify(UpperCAmelCase__     ), [{"label": "LABEL_0", "score": 0.504}]     )


 @slow
 @require_torch
 def  _lowercase			(      self							:      Dict     ):
    __lowercase								=						pipeline("text-classification"     )

    __lowercase								=						text_classifier("This is great !"     )
    self.assertEqual(nested_simplify(UpperCAmelCase__     ), [{"label": "POSITIVE", "score": 1.0}]     )
    __lowercase								=						text_classifier("This is bad !"     )
    self.assertEqual(nested_simplify(UpperCAmelCase__     ), [{"label": "NEGATIVE", "score": 1.0}]     )
    __lowercase								=						text_classifier("Birds are a type of animal"     )
    self.assertEqual(nested_simplify(UpperCAmelCase__     ), [{"label": "POSITIVE", "score": 0.988}]     )


 @slow
 @require_tf
 def  _lowercase			(      self							:      Tuple     ):
    __lowercase								=						pipeline("text-classification", framework="tf"     )

    __lowercase								=						text_classifier("This is great !"     )
    self.assertEqual(nested_simplify(UpperCAmelCase__     ), [{"label": "POSITIVE", "score": 1.0}]     )
    __lowercase								=						text_classifier("This is bad !"     )
    self.assertEqual(nested_simplify(UpperCAmelCase__     ), [{"label": "NEGATIVE", "score": 1.0}]     )
    __lowercase								=						text_classifier("Birds are a type of animal"     )
    self.assertEqual(nested_simplify(UpperCAmelCase__     ), [{"label": "POSITIVE", "score": 0.988}]     )


 def  _lowercase			(      self							:      Union[str, Any], UpperCAmelCase__							:      str, UpperCAmelCase__							:      int, UpperCAmelCase__							:      Tuple     ):
    __lowercase								=						TextClassificationPipeline(model=UpperCAmelCase__, tokenizer=UpperCAmelCase__     )
    return text_classifier, ["HuggingFace is in", "This is another test"]



 def  _lowercase			(      self							:      Any, UpperCAmelCase__							:      List[Any], UpperCAmelCase__							:      str     ):
    __lowercase								=						text_classifier.model
    # Small inputs because BartTokenizer tiny has maximum position embeddings = 22
    __lowercase								=						"HuggingFace is in"
    __lowercase								=						text_classifier(UpperCAmelCase__     )

    self.assertEqual(nested_simplify(UpperCAmelCase__     ), [{"label": ANY(UpperCAmelCase__     ), "score": ANY(UpperCAmelCase__     )}]     )
    self.assertTrue(outputs[0]["label"] in model.config.idalabel.values()     )

    __lowercase								=						["HuggingFace is in ", "Paris is in France"]
    __lowercase								=						text_classifier(UpperCAmelCase__     )
    self.assertEqual(
        nested_simplify(UpperCAmelCase__     ), [{"label": ANY(UpperCAmelCase__     ), "score": ANY(UpperCAmelCase__     )}, {"label": ANY(UpperCAmelCase__     ), "score": ANY(UpperCAmelCase__     )}], )
    self.assertTrue(outputs[0]["label"] in model.config.idalabel.values()     )
    self.assertTrue(outputs[1]["label"] in model.config.idalabel.values()     )

    # Forcing to get all results with `top_k=None`
    # This is NOT the legacy format
    __lowercase								=						text_classifier(UpperCAmelCase__, top_k=UpperCAmelCase__     )
    __lowercase								=						len(model.config.idalabel.values()     )
    self.assertEqual(
        nested_simplify(UpperCAmelCase__     ), [[{"label": ANY(UpperCAmelCase__     ), "score": ANY(UpperCAmelCase__     )}] * N, [{"label": ANY(UpperCAmelCase__     ), "score": ANY(UpperCAmelCase__     )}] * N], )

    __lowercase								=						{"text": "HuggingFace is in ", "text_pair": "Paris is in France"}
    __lowercase								=						text_classifier(UpperCAmelCase__     )
    self.assertEqual(
        nested_simplify(UpperCAmelCase__     ), {"label": ANY(UpperCAmelCase__     ), "score": ANY(UpperCAmelCase__     )}, )
    self.assertTrue(outputs["label"] in model.config.idalabel.values()     )

    # This might be used a text pair, but tokenizer + pipe interaction
    # makes it hard to understand that it's not using the pair properly
    # https://github.com/huggingface/transformers/issues/17305
    # We disabled this usage instead as it was outputting wrong outputs.
    __lowercase								=						[["HuggingFace is in ", "Paris is in France"]]
    with self.assertRaises(UpperCAmelCase__     ):
       text_classifier(UpperCAmelCase__     )

    # This used to be valid for doing text pairs
    # We're keeping it working because of backward compatibility
    __lowercase								=						text_classifier([[["HuggingFace is in ", "Paris is in France"]]]     )
    self.assertEqual(
        nested_simplify(UpperCAmelCase__     ), [{"label": ANY(UpperCAmelCase__     ), "score": ANY(UpperCAmelCase__     )}], )
    self.assertTrue(outputs[0]["label"] in model.config.idalabel.values()     )




