

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()
__UpperCAmelCase								=			logging.get_logger(__name__)





def      snake_case_				(__A					:						Tuple					)      ->     List[str]:
   __lowerCAmelCase  :      Tuple    =						YolosConfig()

   # size of the architecture
   if "yolos_ti" in yolos_name:
      __lowerCAmelCase  :      Dict    =						1_9_2
      __lowerCAmelCase  :      Optional[Any]    =						7_6_8
      __lowerCAmelCase  :      Optional[int]    =						1_2
      __lowerCAmelCase  :      Any    =						3
      __lowerCAmelCase  :      List[Any]    =						[8_0_0, 1_3_3_3]
      __lowerCAmelCase  :      Tuple    =						False
   elif yolos_name == "yolos_s_dWr":
      __lowerCAmelCase  :      Dict    =						3_3_0
      __lowerCAmelCase  :      Any    =						1_4
      __lowerCAmelCase  :      Tuple    =						6
      __lowerCAmelCase  :      Any    =						1_3_2_0
   elif "yolos_s" in yolos_name:
      __lowerCAmelCase  :      Any    =						3_8_4
      __lowerCAmelCase  :      int    =						1_5_3_6
      __lowerCAmelCase  :      List[Any]    =						1_2
      __lowerCAmelCase  :      Tuple    =						6
   elif "yolos_b" in yolos_name:
      __lowerCAmelCase  :      Tuple    =						[8_0_0, 1_3_4_4]

   __lowerCAmelCase  :      Dict    =						9_1
   __lowerCAmelCase  :      Any    =						'huggingface/label-files'
   __lowerCAmelCase  :      List[str]    =						'coco-detection-id2label.json'
   __lowerCAmelCase  :      Union[str, Any]    =						json.load(open(hf_hub_download(_A ,       _A ,       repo_type="""dataset"""					) ,       """r"""					)					)
   __lowerCAmelCase  :      List[str]    =						{int(_A					): v for k, v in idalabel.items()}
   __lowerCAmelCase  :      Tuple    =						idalabel
   __lowerCAmelCase  :      Optional[Any]    =						{v: k for k, v in idalabel.items()}

   return config





def      snake_case_				(__A					:						Optional[int] ,       __A					:						List[str] ,       __A					:						int = False					)      ->     Optional[int]:
   for i in range(config.num_hidden_layers					):
      # read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
      __lowerCAmelCase  :      List[str]    =						state_dict.pop(f'''blocks.{i}.attn.qkv.weight'''					)
      __lowerCAmelCase  :      int    =						state_dict.pop(f'''blocks.{i}.attn.qkv.bias'''					)
      # next, add query, keys and values (in that order) to the state dict
      __lowerCAmelCase  :      List[Any]    =						in_proj_weight[: config.hidden_size, :]
      __lowerCAmelCase  :      Dict    =						in_proj_bias[: config.hidden_size]
      __lowerCAmelCase  :      Any    =						in_proj_weight[
          config.hidden_size : config.hidden_size * 2, :
      ]
      __lowerCAmelCase  :      Any    =						in_proj_bias[
          config.hidden_size : config.hidden_size * 2
      ]
      __lowerCAmelCase  :      Optional[int]    =						in_proj_weight[-config.hidden_size :, :]
      __lowerCAmelCase  :      str    =						in_proj_bias[-config.hidden_size :]





def      snake_case_				(__A					:						Any					)      ->     Optional[Any]:
   if "backbone" in name:
      __lowerCAmelCase  :      Tuple    =						name.replace("""backbone""" ,       """vit"""					)
   if "cls_token" in name:
      __lowerCAmelCase  :      List[str]    =						name.replace("""cls_token""" ,       """embeddings.cls_token"""					)
   if "det_token" in name:
      __lowerCAmelCase  :      List[str]    =						name.replace("""det_token""" ,       """embeddings.detection_tokens"""					)
   if "mid_pos_embed" in name:
      __lowerCAmelCase  :      Dict    =						name.replace("""mid_pos_embed""" ,       """encoder.mid_position_embeddings"""					)
   if "pos_embed" in name:
      __lowerCAmelCase  :      Union[str, Any]    =						name.replace("""pos_embed""" ,       """embeddings.position_embeddings"""					)
   if "patch_embed.proj" in name:
      __lowerCAmelCase  :      Optional[int]    =						name.replace("""patch_embed.proj""" ,       """embeddings.patch_embeddings.projection"""					)
   if "blocks" in name:
      __lowerCAmelCase  :      Dict    =						name.replace("""blocks""" ,       """encoder.layer"""					)
   if "attn.proj" in name:
      __lowerCAmelCase  :      Dict    =						name.replace("""attn.proj""" ,       """attention.output.dense"""					)
   if "attn" in name:
      __lowerCAmelCase  :      Any    =						name.replace("""attn""" ,       """attention.self"""					)
   if "norm1" in name:
      __lowerCAmelCase  :      Dict    =						name.replace("""norm1""" ,       """layernorm_before"""					)
   if "norm2" in name:
      __lowerCAmelCase  :      int    =						name.replace("""norm2""" ,       """layernorm_after"""					)
   if "mlp.fc1" in name:
      __lowerCAmelCase  :      List[Any]    =						name.replace("""mlp.fc1""" ,       """intermediate.dense"""					)
   if "mlp.fc2" in name:
      __lowerCAmelCase  :      Dict    =						name.replace("""mlp.fc2""" ,       """output.dense"""					)
   if "class_embed" in name:
      __lowerCAmelCase  :      List[str]    =						name.replace("""class_embed""" ,       """class_labels_classifier"""					)
   if "bbox_embed" in name:
      __lowerCAmelCase  :      List[Any]    =						name.replace("""bbox_embed""" ,       """bbox_predictor"""					)
   if "vit.norm" in name:
      __lowerCAmelCase  :      List[str]    =						name.replace("""vit.norm""" ,       """vit.layernorm"""					)

   return name





def      snake_case_				(__A					:						str ,       __A					:						Dict					)      ->     Any:
   for key in orig_state_dict.copy().keys():
      __lowerCAmelCase  :      Dict    =						orig_state_dict.pop(_A					)

      if "qkv" in key:
         __lowerCAmelCase  :      Tuple    =						key.split("""."""					)
         __lowerCAmelCase  :      Any    =						int(key_split[2]					)
         __lowerCAmelCase  :      Union[str, Any]    =						model.vit.encoder.layer[layer_num].attention.attention.all_head_size
         if "weight" in key:
            __lowerCAmelCase  :      List[Any]    =						val[:dim, :]
            __lowerCAmelCase  :      List[Any]    =						val[
                dim : dim * 2, :
            ]
            __lowerCAmelCase  :      int    =						val[-dim:, :]
         else:
            __lowerCAmelCase  :      Any    =						val[:dim]
            __lowerCAmelCase  :      Optional[int]    =						val[dim : dim * 2]
            __lowerCAmelCase  :      Dict    =						val[-dim:]
      else:
         __lowerCAmelCase  :      Any    =						val

   return orig_state_dict





def      snake_case_				()      ->     Optional[Any]:
   __lowerCAmelCase  :      Union[str, Any]    =						'http://images.cocodataset.org/val2017/000000039769.jpg'
   __lowerCAmelCase  :      List[Any]    =						Image.open(requests.get(_A ,       stream=_A					).raw					)
   return im





@torch.no_grad()
def      snake_case_				(__A					:						Optional[int] ,       __A					:						Any ,       __A					:						Any ,       __A					:						Tuple = False					)      ->     int:
   __lowerCAmelCase  :      int    =						get_yolos_config(_A					)

   # load original state_dict
   __lowerCAmelCase  :      Dict    =						torch.load(_A ,       map_location="""cpu"""					)['model']

   # load 🤗 model
   __lowerCAmelCase  :      str    =						YolosForObjectDetection(_A					)
   model.eval()
   __lowerCAmelCase  :      Any    =						convert_state_dict(_A ,       _A					)
   model.load_state_dict(_A					)

   # Check outputs on an image, prepared by YolosImageProcessor
   __lowerCAmelCase  :      Dict    =						8_0_0 if yolos_name != 'yolos_ti' else 5_1_2
   __lowerCAmelCase  :      str    =						YolosImageProcessor(format="""coco_detection""" ,       size=_A					)
   __lowerCAmelCase  :      List[str]    =						image_processor(images=prepare_img() ,       return_tensors="""pt"""					)
   __lowerCAmelCase  :      List[Any]    =						model(**_A					)
   __lowerCAmelCase  :      List[Any]    =						outputs.logits, outputs.pred_boxes

   __lowerCAmelCase  :      List[str]    =						None, None
   if yolos_name == "yolos_ti":
      __lowerCAmelCase  :      List[str]    =						torch.tensor(
          [[-39.5022, -11.9820, -17.6888], [-29.9574, -9.9769, -17.7691], [-42.3281, -20.7200, -30.6294]]					)
      __lowerCAmelCase  :      Optional[int]    =						torch.tensor(
          [[0.4021, 0.0836, 0.7979], [0.0184, 0.2609, 0.0364], [0.1781, 0.2004, 0.2095]]					)
   elif yolos_name == "yolos_s_200_pre":
      __lowerCAmelCase  :      List[str]    =						torch.tensor(
          [[-24.0248, -10.3024, -14.8290], [-42.0392, -16.8200, -27.4334], [-27.2743, -11.8154, -18.7148]]					)
      __lowerCAmelCase  :      Tuple    =						torch.tensor(
          [[0.2559, 0.5455, 0.4706], [0.2989, 0.7279, 0.1875], [0.7732, 0.4017, 0.4462]]					)
   elif yolos_name == "yolos_s_300_pre":
      __lowerCAmelCase  :      Tuple    =						torch.tensor(
          [[-36.2220, -14.4385, -23.5457], [-35.6970, -14.7583, -21.3935], [-31.5939, -13.6042, -16.8049]]					)
      __lowerCAmelCase  :      str    =						torch.tensor(
          [[0.7614, 0.2316, 0.4728], [0.7168, 0.4495, 0.3855], [0.4996, 0.1466, 0.9996]]					)
   elif yolos_name == "yolos_s_dWr":
      __lowerCAmelCase  :      str    =						torch.tensor(
          [[-42.8668, -24.1049, -41.1690], [-34.7456, -14.1274, -24.9194], [-33.7898, -12.1946, -25.6495]]					)
      __lowerCAmelCase  :      Dict    =						torch.tensor(
          [[0.5587, 0.2773, 0.0605], [0.5004, 0.3014, 0.9994], [0.4999, 0.1548, 0.9994]]					)
   elif yolos_name == "yolos_base":
      __lowerCAmelCase  :      int    =						torch.tensor(
          [[-40.6064, -24.3084, -32.6447], [-55.1990, -30.7719, -35.5877], [-51.4311, -33.3507, -35.6462]]					)
      __lowerCAmelCase  :      str    =						torch.tensor(
          [[0.5555, 0.2794, 0.0655], [0.9049, 0.2664, 0.1894], [0.9183, 0.1984, 0.1635]]					)
   else:
      raise ValueError(f'''Unknown yolos_name: {yolos_name}'''					)

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

   Path(_A					).mkdir(exist_ok=_A					)
   print(f'''Saving model {yolos_name} to {pytorch_dump_folder_path}'''					)
   model.save_pretrained(_A					)
   print(f'''Saving image processor to {pytorch_dump_folder_path}'''					)
   image_processor.save_pretrained(_A					)

   if push_to_hub:
      __lowerCAmelCase  :      List[Any]    =						{
          '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..."""					)
      __lowerCAmelCase  :      List[str]    =						model_mapping[yolos_name]
      image_processor.push_to_hub(_A ,       organization="""hustvl"""					)
      model.push_to_hub(_A ,       organization="""hustvl"""					)


if __name__ == "__main__":
   __UpperCAmelCase								=			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."""
   )

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





import unittest

from transformers import BigBirdConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
   import jax

   from transformers.models.big_bird.modeling_flax_big_bird import (
       FlaxBigBirdForCausalLM,
       FlaxBigBirdForMaskedLM,
       FlaxBigBirdForMultipleChoice,
       FlaxBigBirdForPreTraining,
       FlaxBigBirdForQuestionAnswering,
       FlaxBigBirdForSequenceClassification,
       FlaxBigBirdForTokenClassification,
       FlaxBigBirdModel,
   )



class SCREAMING_SNAKE_CASE     (  unittest.TestCase			):
  """simple docstring"""






  def __init__(      self			:   Dict	,							lowerCAmelCase			:   Tuple	,							lowerCAmelCase			:   str=2	,							lowerCAmelCase			:   Optional[Any]=56	,							lowerCAmelCase			:   Any=True	,							lowerCAmelCase			:   Optional[int]=True	,							lowerCAmelCase			:   Any=True	,							lowerCAmelCase			:   Union[str, Any]=True	,							lowerCAmelCase			:   Tuple=99	,							lowerCAmelCase			:   Optional[Any]=32	,							lowerCAmelCase			:   List[str]=2	,							lowerCAmelCase			:   int=2	,							lowerCAmelCase			:   str=7	,							lowerCAmelCase			:   List[Any]="gelu_new"	,							lowerCAmelCase			:   Optional[int]=0.1	,							lowerCAmelCase			:   Optional[Any]=0.1	,							lowerCAmelCase			:   Optional[Any]=5_12	,							lowerCAmelCase			:   Dict=16	,							lowerCAmelCase			:   int=2	,							lowerCAmelCase			:   Optional[Any]=0.02	,							lowerCAmelCase			:   Tuple=4	,							lowerCAmelCase			:   Union[str, Any]="block_sparse"	,							lowerCAmelCase			:   List[str]=True	,							lowerCAmelCase			:   Optional[int]=False	,							lowerCAmelCase			:   Optional[Any]=2	,							lowerCAmelCase			:   List[Any]=3	,							)							->     Tuple:






     """simple docstring"""







     __lowerCAmelCase  :      Tuple    =						parent
     __lowerCAmelCase  :      Union[str, Any]    =						batch_size
     __lowerCAmelCase  :      List[Any]    =						seq_length
     __lowerCAmelCase  :      int    =						is_training
     __lowerCAmelCase  :      Union[str, Any]    =						use_attention_mask
     __lowerCAmelCase  :      Tuple    =						use_token_type_ids
     __lowerCAmelCase  :      Union[str, Any]    =						use_labels
     __lowerCAmelCase  :      List[str]    =						vocab_size
     __lowerCAmelCase  :      int    =						hidden_size
     __lowerCAmelCase  :      Tuple    =						num_hidden_layers
     __lowerCAmelCase  :      List[str]    =						num_attention_heads
     __lowerCAmelCase  :      Optional[Any]    =						intermediate_size
     __lowerCAmelCase  :      List[Any]    =						hidden_act
     __lowerCAmelCase  :      Optional[Any]    =						hidden_dropout_prob
     __lowerCAmelCase  :      List[str]    =						attention_probs_dropout_prob
     __lowerCAmelCase  :      Optional[int]    =						max_position_embeddings
     __lowerCAmelCase  :      str    =						type_vocab_size
     __lowerCAmelCase  :      List[Any]    =						type_sequence_label_size
     __lowerCAmelCase  :      Tuple    =						initializer_range
     __lowerCAmelCase  :      str    =						num_choices

     __lowerCAmelCase  :      Any    =						rescale_embeddings
     __lowerCAmelCase  :      str    =						attention_type
     __lowerCAmelCase  :      List[Any]    =						use_bias
     __lowerCAmelCase  :      List[str]    =						block_size
     __lowerCAmelCase  :      Union[str, Any]    =						num_random_blocks







  def 	SCREAMING_SNAKE_CASE   (      self			:   List[Any]   )							->     str:






     """simple docstring"""







     __lowerCAmelCase  :      str    =						ids_tensor([self.batch_size, self.seq_length]	,							self.vocab_size   )

     __lowerCAmelCase  :      Optional[Any]    =						None
     if self.use_attention_mask:
        __lowerCAmelCase  :      Optional[Any]    =						random_attention_mask([self.batch_size, self.seq_length]   )

     __lowerCAmelCase  :      Any    =						None
     if self.use_token_type_ids:
        __lowerCAmelCase  :      Optional[int]    =						ids_tensor([self.batch_size, self.seq_length]	,							self.type_vocab_size   )

     __lowerCAmelCase  :      List[Any]    =						BigBirdConfig(
         vocab_size=self.vocab_size	,							hidden_size=self.hidden_size	,							num_hidden_layers=self.num_hidden_layers	,							num_attention_heads=self.num_attention_heads	,							intermediate_size=self.intermediate_size	,							hidden_act=self.hidden_act	,							hidden_dropout_prob=self.hidden_dropout_prob	,							attention_probs_dropout_prob=self.attention_probs_dropout_prob	,							max_position_embeddings=self.max_position_embeddings	,							type_vocab_size=self.type_vocab_size	,							is_decoder=lowerCAmelCase	,							initializer_range=self.initializer_range	,							attention_type=self.attention_type	,							block_size=self.block_size	,							num_random_blocks=self.num_random_blocks	,							use_bias=self.use_bias	,							rescale_embeddings=self.rescale_embeddings	,							)

     return config, input_ids, token_type_ids, attention_mask







  def 	SCREAMING_SNAKE_CASE   (      self			:   Tuple   )							->     int:






     """simple docstring"""







     __lowerCAmelCase  :      List[str]    =						self.prepare_config_and_inputs()
     __lowerCAmelCase   ,__lowerCAmelCase   ,__lowerCAmelCase   ,__lowerCAmelCase  :      Any    =						config_and_inputs
     __lowerCAmelCase  :      Union[str, Any]    =						{
         """input_ids""": input_ids,
         """token_type_ids""": token_type_ids,
         """attention_mask""": attention_mask,
     }
     return config, inputs_dict






@require_flax
class SCREAMING_SNAKE_CASE     (  a_       ,							unittest.TestCase			):
  """simple docstring"""






  lowerCamelCase   :      List[Any]										=(
      (
          FlaxBigBirdForCausalLM,
          FlaxBigBirdModel,
          FlaxBigBirdForPreTraining,
          FlaxBigBirdForMaskedLM,
          FlaxBigBirdForMultipleChoice,
          FlaxBigBirdForQuestionAnswering,
          FlaxBigBirdForSequenceClassification,
          FlaxBigBirdForTokenClassification,
      )
      if is_flax_available()
      else ()
  )

  lowerCamelCase   :      List[str]										=False
  lowerCamelCase   :      Union[str, Any]										=False







  def 	SCREAMING_SNAKE_CASE   (      self			:   Tuple   )							->     Any:






     """simple docstring"""







     __lowerCAmelCase  :      str    =						FlaxBigBirdModelTester(self   )







  @slow
  # copied from `test_modeling_flax_common` because it takes much longer than other models
  def 	SCREAMING_SNAKE_CASE   (      self			:   Dict   )							->     Optional[Any]:






     """simple docstring"""







     super().test_from_pretrained_save_pretrained()







  @slow
  # copied from `test_modeling_flax_common` because it takes much longer than other models
  def 	SCREAMING_SNAKE_CASE   (      self			:   Dict   )							->     List[str]:






     """simple docstring"""







     super().test_from_pretrained_with_no_automatic_init()







  @slow
  # copied from `test_modeling_flax_common` because it takes much longer than other models
  def 	SCREAMING_SNAKE_CASE   (      self			:   List[str]   )							->     Optional[int]:






     """simple docstring"""







     super().test_no_automatic_init()







  @slow
  # copied from `test_modeling_flax_common` because it takes much longer than other models
  def 	SCREAMING_SNAKE_CASE   (      self			:   Tuple   )							->     Optional[int]:






     """simple docstring"""







     super().test_hidden_states_output()







  @slow
  def 	SCREAMING_SNAKE_CASE   (      self			:   Tuple   )							->     Optional[int]:






     """simple docstring"""







     for model_class_name in self.all_model_classes:
        __lowerCAmelCase  :      List[str]    =						model_class_name.from_pretrained("""google/bigbird-roberta-base"""   )
        self.assertIsNotNone(lowerCAmelCase   )







  def 	SCREAMING_SNAKE_CASE   (      self			:   List[Any]   )							->     List[str]:






     """simple docstring"""







     if self.test_attn_probs:
        super().test_attention_outputs()







  @slow
  # copied from `test_modeling_flax_common` because it takes much longer than other models
  def 	SCREAMING_SNAKE_CASE   (      self			:   List[Any]   )							->     List[Any]:






     """simple docstring"""







     __lowerCAmelCase   ,__lowerCAmelCase  :      Tuple    =						self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
        with self.subTest(model_class.__name__   ):
           __lowerCAmelCase  :      List[str]    =						self._prepare_for_class(lowerCAmelCase	,							lowerCAmelCase   )
           __lowerCAmelCase  :      Dict    =						model_class(lowerCAmelCase   )

           @jax.jit
           def model_jitted(lowerCAmelCase			:   Tuple	,							lowerCAmelCase			:   Optional[Any]=None	,							**lowerCAmelCase			:   Union[str, Any]   ):
              return model(input_ids=lowerCAmelCase	,							attention_mask=lowerCAmelCase	,							**lowerCAmelCase   )

           with self.subTest("""JIT Enabled"""   ):
              __lowerCAmelCase  :      str    =						model_jitted(**lowerCAmelCase   ).to_tuple()

           with self.subTest("""JIT Disabled"""   ):
              with jax.disable_jit():
                 __lowerCAmelCase  :      List[Any]    =						model_jitted(**lowerCAmelCase   ).to_tuple()

           self.assertEqual(len(lowerCAmelCase   )	,							len(lowerCAmelCase   )   )
           for jitted_output, output in zip(lowerCAmelCase	,							lowerCAmelCase   ):
              self.assertEqual(jitted_output.shape	,							output.shape   )







  def 	SCREAMING_SNAKE_CASE   (      self			:   Optional[Any]	,							lowerCAmelCase			:   int	,							lowerCAmelCase			:   int	,							lowerCAmelCase			:   Optional[int]	,							lowerCAmelCase			:   Union[str, Any]=1e-5	,							lowerCAmelCase			:   Union[str, Any]="outputs"	,							lowerCAmelCase			:   Union[str, Any]=None   )							->     Optional[int]:






     """simple docstring"""







     if name.startswith("""outputs.attentions"""   ):
        return
     else:
        super().check_pt_flax_outputs(lowerCAmelCase	,							lowerCAmelCase	,							lowerCAmelCase	,							lowerCAmelCase	,							lowerCAmelCase	,							lowerCAmelCase   )


