





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 					lowercase__ (			unittest.TestCase       ):

   '''simple docstring'''


   def __init__(					self,	__magic_name__,	__magic_name__=2,	__magic_name__=56,	__magic_name__=True,	__magic_name__=True,	__magic_name__=True,	__magic_name__=True,	__magic_name__=99,	__magic_name__=32,	__magic_name__=2,	__magic_name__=2,	__magic_name__=7,	__magic_name__="gelu_new",	__magic_name__=0.1,	__magic_name__=0.1,	__magic_name__=512,	__magic_name__=16,	__magic_name__=2,	__magic_name__=0.02,	__magic_name__=4,	__magic_name__="block_sparse",	__magic_name__=True,	__magic_name__=False,	__magic_name__=2,	__magic_name__=3,	)				->   Optional[int]:






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

       UpperCamelCase__							:       Dict      =   rescale_embeddings
       UpperCamelCase__							:       Tuple      =   attention_type
       UpperCamelCase__							:       Union[str, Any]      =   use_bias
       UpperCamelCase__							:       Union[str, Any]      =   block_size
       UpperCamelCase__							:       Optional[int]      =   num_random_blocks




   def    UpperCamelCase__						(					self							)				->   int:






       """simple docstring"""
       UpperCamelCase__							:       int      =   ids_tensor([self.batch_size, self.seq_length],	self.vocab_size							)

       UpperCamelCase__							:       int      =   None
       if self.use_attention_mask:
           UpperCamelCase__							:       List[str]      =   random_attention_mask([self.batch_size, self.seq_length]							)

       UpperCamelCase__							:       Optional[int]      =   None
       if self.use_token_type_ids:
           UpperCamelCase__							:       List[str]      =   ids_tensor([self.batch_size, self.seq_length],	self.type_vocab_size							)

       UpperCamelCase__							:       str      =   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=__magic_name__,	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    UpperCamelCase__						(					self							)				->   str:






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






@require_flax
class 					lowercase__ (			__lowerCamelCase    ,       unittest.TestCase       ):

   '''simple docstring'''


   a		:							Any     	=	(
       (
           FlaxBigBirdForCausalLM,
           FlaxBigBirdModel,
           FlaxBigBirdForPreTraining,
           FlaxBigBirdForMaskedLM,
           FlaxBigBirdForMultipleChoice,
           FlaxBigBirdForQuestionAnswering,
           FlaxBigBirdForSequenceClassification,
           FlaxBigBirdForTokenClassification,
       )
       if is_flax_available()
       else ()
   )

   a		:							List[str]     	=	False
   a		:							str     	=	False




   def    UpperCamelCase__						(					self							)				->   Any:






       """simple docstring"""
       UpperCamelCase__							:       Union[str, Any]      =   FlaxBigBirdModelTester(self							)




   @slow
   # copied from `test_modeling_flax_common` because it takes much longer than other models
   def    UpperCamelCase__						(					self							)				->   Dict:






       """simple docstring"""
       super().test_from_pretrained_save_pretrained()




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






       """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    UpperCamelCase__						(					self							)				->   Dict:






       """simple docstring"""
       super().test_no_automatic_init()




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






       """simple docstring"""
       super().test_hidden_states_output()




   @slow
   def    UpperCamelCase__						(					self							)				->   List[str]:






       """simple docstring"""
       for model_class_name in self.all_model_classes:
           UpperCamelCase__							:       List[str]      =   model_class_name.from_pretrained('''google/bigbird-roberta-base'''							)
           self.assertIsNotNone(__magic_name__							)




   def    UpperCamelCase__						(					self							)				->   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    UpperCamelCase__						(					self							)				->   Any:






       """simple docstring"""
       UpperCamelCase__     ,UpperCamelCase__							:       Dict      =   self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
           with self.subTest(model_class.__name__							):
               UpperCamelCase__							:       Union[str, Any]      =   self._prepare_for_class(__magic_name__,	__magic_name__							)
               UpperCamelCase__							:       Tuple      =   model_class(__magic_name__							)

               @jax.jit
               def model_jitted(__magic_name__,	__magic_name__=None,	**__magic_name__							):
                   return model(input_ids=__magic_name__,	attention_mask=__magic_name__,	**__magic_name__							)

               with self.subTest('''JIT Enabled'''							):
                   UpperCamelCase__							:       str      =   model_jitted(**__magic_name__							).to_tuple()

               with self.subTest('''JIT Disabled'''							):
                   with jax.disable_jit():
                       UpperCamelCase__							:       Dict      =   model_jitted(**__magic_name__							).to_tuple()

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




   def    UpperCamelCase__						(					self,	__magic_name__,	__magic_name__,	__magic_name__,	__magic_name__=1E-5,	__magic_name__="outputs",	__magic_name__=None							)				->   List[Any]:






       """simple docstring"""
       # `bigbird_block_sparse_attention` in `FlaxBigBird` returns `attention_probs = None`, while in PyTorch version,
       # an effort was done to return `attention_probs` (yet to be verified).
       if name.startswith('''outputs.attentions'''							):
           return
       else:
           super().check_pt_flax_outputs(__magic_name__,	__magic_name__,	__magic_name__,	__magic_name__,	__magic_name__,	__magic_name__							)








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 	lowerCAmelCase_					(						__UpperCAmelCase:    str   )   ->			YolosConfig:
    UpperCamelCase__							:       str      =   YolosConfig()

    # size of the architecture
    if "yolos_ti" in yolos_name:
        UpperCamelCase__							:       Dict      =   192
        UpperCamelCase__							:       Dict      =   768
        UpperCamelCase__							:       Optional[Any]      =   12
        UpperCamelCase__							:       List[Any]      =   3
        UpperCamelCase__							:       Optional[int]      =   [800, 1333]
        UpperCamelCase__							:       Tuple      =   False
    elif yolos_name == "yolos_s_dWr":
        UpperCamelCase__							:       int      =   330
        UpperCamelCase__							:       Tuple      =   14
        UpperCamelCase__							:       str      =   6
        UpperCamelCase__							:       Optional[int]      =   1320
    elif "yolos_s" in yolos_name:
        UpperCamelCase__							:       Optional[int]      =   384
        UpperCamelCase__							:       Any      =   1536
        UpperCamelCase__							:       Union[str, Any]      =   12
        UpperCamelCase__							:       int      =   6
    elif "yolos_b" in yolos_name:
        UpperCamelCase__							:       Dict      =   [800, 1344]

    UpperCamelCase__							:       List[Any]      =   91
    UpperCamelCase__							:       str      =   '''huggingface/label-files'''
    UpperCamelCase__							:       Dict      =   '''coco-detection-id2label.json'''
    UpperCamelCase__							:       List[str]      =   json.load(open(hf_hub_download(__UpperCAmelCase	,     __UpperCAmelCase	,     repo_type='''dataset'''   )	,     '''r'''   )   )
    UpperCamelCase__							:       str      =   {int(__UpperCAmelCase   ): v for k, v in idalabel.items()}
    UpperCamelCase__							:       str      =   idalabel
    UpperCamelCase__							:       Optional[int]      =   {v: k for k, v in idalabel.items()}

    return config




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




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

    return name




def 	lowerCAmelCase_					(						__UpperCAmelCase:    dict	,     __UpperCAmelCase:    YolosForObjectDetection   )   ->			dict:
    for key in orig_state_dict.copy().keys():
        UpperCamelCase__							:       Union[str, Any]      =   orig_state_dict.pop(__UpperCAmelCase   )

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

    return orig_state_dict




def 	lowerCAmelCase_					(						)   ->			torch.Tensor:
    UpperCamelCase__							:       str      =   '''http://images.cocodataset.org/val2017/000000039769.jpg'''
    UpperCamelCase__							:       Dict      =   Image.open(requests.get(__UpperCAmelCase	,     stream=__UpperCAmelCase   ).raw   )
    return im




@torch.no_grad()
def 	lowerCAmelCase_					(						__UpperCAmelCase:    str	,     __UpperCAmelCase:    str	,     __UpperCAmelCase:    str	,     __UpperCAmelCase:    bool = False   )   ->			Optional[int]:
    UpperCamelCase__							:       List[str]      =   get_yolos_config(__UpperCAmelCase   )

    # load original state_dict
    UpperCamelCase__							:       List[Any]      =   torch.load(__UpperCAmelCase	,     map_location='''cpu'''   )['''model''']

    # load 🤗 model
    UpperCamelCase__							:       List[Any]      =   YolosForObjectDetection(__UpperCAmelCase   )
    model.eval()
    UpperCamelCase__							:       Dict      =   convert_state_dict(__UpperCAmelCase	,     __UpperCAmelCase   )
    model.load_state_dict(__UpperCAmelCase   )

    # Check outputs on an image, prepared by YolosImageProcessor
    UpperCamelCase__							:       List[Any]      =   800 if yolos_name != '''yolos_ti''' else 512
    UpperCamelCase__							:       int      =   YolosImageProcessor(format='''coco_detection'''	,     size=__UpperCAmelCase   )
    UpperCamelCase__							:       Union[str, Any]      =   image_processor(images=prepare_img()	,     return_tensors='''pt'''   )
    UpperCamelCase__							:       Dict      =   model(**__UpperCAmelCase   )
    UpperCamelCase__     ,UpperCamelCase__							:       Optional[Any]      =   outputs.logits, outputs.pred_boxes

    UpperCamelCase__     ,UpperCamelCase__							:       int      =   None, None
    if yolos_name == "yolos_ti":
        UpperCamelCase__							:       List[str]      =   torch.tensor(
            [[-39.5022, -11.9820, -17.6888], [-29.9574, -9.9769, -17.7691], [-42.3281, -20.7200, -30.6294]]   )
        UpperCamelCase__							:       Dict      =   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":
        UpperCamelCase__							:       Optional[int]      =   torch.tensor(
            [[-24.0248, -10.3024, -14.8290], [-42.0392, -16.8200, -27.4334], [-27.2743, -11.8154, -18.7148]]   )
        UpperCamelCase__							:       Union[str, Any]      =   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":
        UpperCamelCase__							:       Optional[Any]      =   torch.tensor(
            [[-36.2220, -14.4385, -23.5457], [-35.6970, -14.7583, -21.3935], [-31.5939, -13.6042, -16.8049]]   )
        UpperCamelCase__							:       List[Any]      =   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":
        UpperCamelCase__							:       Any      =   torch.tensor(
            [[-42.8668, -24.1049, -41.1690], [-34.7456, -14.1274, -24.9194], [-33.7898, -12.1946, -25.6495]]   )
        UpperCamelCase__							:       List[Any]      =   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":
        UpperCamelCase__							:       Optional[Any]      =   torch.tensor(
            [[-40.6064, -24.3084, -32.6447], [-55.1990, -30.7719, -35.5877], [-51.4311, -33.3507, -35.6462]]   )
        UpperCamelCase__							:       List[Any]      =   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]	,     __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:
        UpperCamelCase__							:       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...'''   )
        UpperCamelCase__							:       Optional[int]      =   model_mapping[yolos_name]
        image_processor.push_to_hub(__UpperCAmelCase	,     organization='''hustvl'''   )
        model.push_to_hub(__UpperCAmelCase	,     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)

