


"""simple docstring"""







import unittest

from transformers import BigBirdTokenizer, BigBirdTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


lowerCAmelCase__      				=   '''▁'''

lowerCAmelCase__      				=   get_tests_dir('''fixtures/test_sentencepiece.model''')


@require_sentencepiece
@require_tokenizers
class       SCREAMING_SNAKE_CASE__				(		lowercase   ,    unittest.TestCase				):
       """simple docstring"""


       a  :				str   =BigBirdTokenizer
       a  :				Union[str, Any]   =BigBirdTokenizerFast
       a  :				Tuple   =True
       a  :				Any   =True

       def        lowercase__							(						self						):






         """simple docstring"""





         super().setUp()

         lowerCAmelCase     :     str								=							self.tokenizer_class(snake_case__		,     keep_accents=snake_case__						)
         tokenizer.save_pretrained(self.tmpdirname						)

       def        lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase     :     str								=							"<s>"
         lowerCAmelCase     :     Optional[int]								=							1

         self.assertEqual(self.get_tokenizer()._convert_token_to_id(snake_case__						)		,     snake_case__						)
         self.assertEqual(self.get_tokenizer()._convert_id_to_token(snake_case__						)		,     snake_case__						)

       def        lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase     :     Dict								=							list(self.get_tokenizer().get_vocab().keys()						)

         self.assertEqual(vocab_keys[0]		,     "<unk>"						)
         self.assertEqual(vocab_keys[1]		,     "<s>"						)
         self.assertEqual(vocab_keys[-1]		,     "[MASK]"						)
         self.assertEqual(len(snake_case__						)		,     1_004						)

       def        lowercase__							(						self						):






         """simple docstring"""





         self.assertEqual(self.get_tokenizer().vocab_size		,     1_000						)

       def        lowercase__							(						self						):






         """simple docstring"""





         if not self.test_rust_tokenizer:
           return

         lowerCAmelCase     :     Tuple								=							self.get_tokenizer()
         lowerCAmelCase     :     Optional[int]								=							self.get_rust_tokenizer()

         lowerCAmelCase     :     Tuple								=							"I was born in 92000, and this is falsé."

         lowerCAmelCase     :     Optional[int]								=							tokenizer.tokenize(snake_case__						)
         lowerCAmelCase     :     int								=							rust_tokenizer.tokenize(snake_case__						)
         self.assertListEqual(snake_case__		,     snake_case__						)

         lowerCAmelCase     :     Tuple								=							tokenizer.encode(snake_case__		,     add_special_tokens=snake_case__						)
         lowerCAmelCase     :     int								=							rust_tokenizer.encode(snake_case__		,     add_special_tokens=snake_case__						)
         self.assertListEqual(snake_case__		,     snake_case__						)

         lowerCAmelCase     :     List[Any]								=							self.get_rust_tokenizer()
         lowerCAmelCase     :     Tuple								=							tokenizer.encode(snake_case__						)
         lowerCAmelCase     :     List[Any]								=							rust_tokenizer.encode(snake_case__						)
         self.assertListEqual(snake_case__		,     snake_case__						)

       def        lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase     :     Any								=							BigBirdTokenizer(snake_case__		,     keep_accents=snake_case__						)

         lowerCAmelCase     :     Union[str, Any]								=							tokenizer.tokenize("This is a test"						)
         self.assertListEqual(snake_case__		,     ["▁This", "▁is", "▁a", "▁t", "est"]						)

         self.assertListEqual(
             tokenizer.convert_tokens_to_ids(snake_case__						)		,     [285, 46, 10, 170, 382]		,     )

         lowerCAmelCase     :     Any								=							tokenizer.tokenize("I was born in 92000, and this is falsé."						)
         self.assertListEqual(
             snake_case__		,     [
                 SPIECE_UNDERLINE + "I",
                 SPIECE_UNDERLINE + "was",
                 SPIECE_UNDERLINE + "b",
                 "or",
                 "n",
                 SPIECE_UNDERLINE + "in",
                 SPIECE_UNDERLINE + "",
                 "9",
                 "2",
                 "0",
                 "0",
                 "0",
                 ",",
                 SPIECE_UNDERLINE + "and",
                 SPIECE_UNDERLINE + "this",
                 SPIECE_UNDERLINE + "is",
                 SPIECE_UNDERLINE + "f",
                 "al",
                 "s",
                 "é",
                 ".",
             ]		,     )
         lowerCAmelCase     :     str								=							tokenizer.convert_tokens_to_ids(snake_case__						)
         self.assertListEqual(
             snake_case__		,     [8, 21, 84, 55, 24, 19, 7, 0, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 0, 4]		,     )

         lowerCAmelCase     :     List[str]								=							tokenizer.convert_ids_to_tokens(snake_case__						)
         self.assertListEqual(
             snake_case__		,     [
                 SPIECE_UNDERLINE + "I",
                 SPIECE_UNDERLINE + "was",
                 SPIECE_UNDERLINE + "b",
                 "or",
                 "n",
                 SPIECE_UNDERLINE + "in",
                 SPIECE_UNDERLINE + "",
                 "<unk>",
                 "2",
                 "0",
                 "0",
                 "0",
                 ",",
                 SPIECE_UNDERLINE + "and",
                 SPIECE_UNDERLINE + "this",
                 SPIECE_UNDERLINE + "is",
                 SPIECE_UNDERLINE + "f",
                 "al",
                 "s",
                 "<unk>",
                 ".",
             ]		,     )

       @cached_property
       def        lowercase__							(						self						):






         """simple docstring"""





         return BigBirdTokenizer.from_pretrained("google/bigbird-roberta-base"						)

       @slow
       def        lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase     :     List[Any]								=							"Hello World!"
         lowerCAmelCase     :     Any								=							[65, 18_536, 2_260, 101, 66]

         self.assertListEqual(snake_case__		,     self.big_tokenizer.encode(snake_case__						)						)

       @slow
       def        lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase     :     Union[str, Any]								=							(
             "This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) \" [ ] ! : - . Also we will"
             " add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth"
         )
         # fmt: off
         lowerCAmelCase     :     List[str]								=							[65, 871, 419, 358, 946, 991, 2_521, 452, 358, 1_357, 387, 7_751, 3_536, 112, 985, 456, 126, 865, 938, 5_400, 5_734, 458, 1_368, 467, 786, 2_462, 5_246, 1_159, 633, 865, 4_519, 457, 582, 852, 2_557, 427, 916, 508, 405, 34_324, 497, 391, 408, 11_342, 1_244, 385, 100, 938, 985, 456, 574, 362, 12_597, 3_200, 3_129, 1_172, 66]  # noqa: E231
         # fmt: on
         self.assertListEqual(snake_case__		,     self.big_tokenizer.encode(snake_case__						)						)

       @require_torch
       @slow
       def        lowercase__							(						self						):






         """simple docstring"""





         import torch

         from transformers import BigBirdConfig, BigBirdModel

         # Build sequence
         lowerCAmelCase     :     Dict								=							list(self.big_tokenizer.get_vocab().keys()						)[:10]
         lowerCAmelCase     :     int								=							" ".join(snake_case__						)
         lowerCAmelCase     :     Dict								=							self.big_tokenizer.encode_plus(snake_case__		,     return_tensors="pt"		,     return_token_type_ids=snake_case__						)
         lowerCAmelCase     :     Any								=							self.big_tokenizer.batch_encode_plus(
             [sequence + " " + sequence]		,     return_tensors="pt"		,     return_token_type_ids=snake_case__						)

         lowerCAmelCase     :     str								=							BigBirdConfig(attention_type="original_full"						)
         lowerCAmelCase     :     Any								=							BigBirdModel(snake_case__						)

         assert model.get_input_embeddings().weight.shape[0] >= self.big_tokenizer.vocab_size

         with torch.no_grad():
           model(**snake_case__						)
           model(**snake_case__						)

       @slow
       def        lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase     :     List[str]								=							BigBirdTokenizer.from_pretrained("google/bigbird-roberta-base"						)
         lowerCAmelCase     :     Union[str, Any]								=							tokenizer.decode(tokenizer("Paris is the [MASK]."						).input_ids						)

         self.assertTrue(decoded_text == "[CLS] Paris is the[MASK].[SEP]"						)







       @slow
       def        lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase     :     Any								=							{"input_ids": [[65, 39_286, 458, 36_335, 2_001, 456, 13_073, 13_266, 455, 113, 7_746, 1_741, 11_157, 391, 13_073, 13_266, 455, 113, 3_967, 35_412, 113, 4_936, 109, 3_870, 2_377, 113, 30_084, 45_720, 458, 134, 17_496, 112, 503, 11_672, 113, 118, 112, 5_665, 13_347, 38_687, 112, 1_496, 31_389, 112, 3_268, 47_264, 134, 962, 112, 16_377, 8_035, 23_130, 430, 12_169, 15_518, 28_592, 458, 146, 41_697, 109, 391, 12_169, 15_518, 16_689, 458, 146, 41_358, 109, 452, 726, 4_034, 111, 763, 35_412, 5_082, 388, 1_903, 111, 9_051, 391, 2_870, 48_918, 1_900, 1_123, 550, 998, 112, 9_586, 15_985, 455, 391, 410, 22_955, 37_636, 114, 66], [65, 448, 17_496, 419, 3_663, 385, 763, 113, 27_533, 2_870, 3_283, 13_043, 1_639, 24_713, 523, 656, 24_013, 18_550, 2_521, 517, 27_014, 21_244, 420, 1_212, 1_465, 391, 927, 4_833, 388, 578, 11_786, 114, 66, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [65, 484, 2_169, 7_687, 21_932, 18_146, 726, 363, 17_032, 3_391, 114, 66, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], "attention_mask": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
         # fmt: on

         self.tokenizer_integration_test_util(
             expected_encoding=snake_case__		,     model_name="google/bigbird-roberta-base"		,     revision="215c99f1600e06f83acce68422f2035b2b5c3510"		,     )










import argparse
import json
import pickle
from pathlib import Path

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

from transformers import MaskFormerConfig, MaskFormerForInstanceSegmentation, MaskFormerImageProcessor, SwinConfig
from transformers.utils import logging


logging.set_verbosity_info()
a_   :str        =      logging.get_logger(__name__)
def        lowercase_							(A : str      ):
       snake_case__							:	Tuple   	=	SwinConfig.from_pretrained(
           'microsoft/swin-tiny-patch4-window7-224'		,					out_features=['stage1', 'stage2', 'stage3', 'stage4']      )
       snake_case__							:	List[Any]   	=	MaskFormerConfig(backbone_config=A      )

       snake_case__							:	Union[str, Any]   	=	'huggingface/label-files'
       if "ade20k-full" in model_name:
              # this should be ok
              snake_case__							:	Dict   	=	8_4_7
              snake_case__							:	List[str]   	=	'maskformer-ade20k-full-id2label.json'
       elif "ade" in model_name:
              # this should be ok
              snake_case__							:	Union[str, Any]   	=	1_5_0
              snake_case__							:	Any   	=	'ade20k-id2label.json'
       elif "coco-stuff" in model_name:
              # this should be ok
              snake_case__							:	List[str]   	=	1_7_1
              snake_case__							:	Union[str, Any]   	=	'maskformer-coco-stuff-id2label.json'
       elif "coco" in model_name:
              # TODO
              snake_case__							:	Dict   	=	1_3_3
              snake_case__							:	str   	=	'coco-panoptic-id2label.json'
       elif "cityscapes" in model_name:
              # this should be ok
              snake_case__							:	List[str]   	=	1_9
              snake_case__							:	Union[str, Any]   	=	'cityscapes-id2label.json'
       elif "vistas" in model_name:
              # this should be ok
              snake_case__							:	Tuple   	=	6_5
              snake_case__							:	List[str]   	=	'mapillary-vistas-id2label.json'

       snake_case__							:	Dict   	=	json.load(open(hf_hub_download(A		,					A		,					repo_type='dataset'      )		,					'r'      )      )
       snake_case__							:	List[str]   	=	{int(A      ): v for k, v in idalabel.items()}

       return config
def        lowercase_							(A : Any      ):
       snake_case__							:	Optional[int]   	=	[]
       # stem
       # fmt: off
       rename_keys.append(('backbone.patch_embed.proj.weight', 'model.pixel_level_module.encoder.model.embeddings.patch_embeddings.projection.weight')      )
       rename_keys.append(('backbone.patch_embed.proj.bias', 'model.pixel_level_module.encoder.model.embeddings.patch_embeddings.projection.bias')      )
       rename_keys.append(('backbone.patch_embed.norm.weight', 'model.pixel_level_module.encoder.model.embeddings.norm.weight')      )
       rename_keys.append(('backbone.patch_embed.norm.bias', 'model.pixel_level_module.encoder.model.embeddings.norm.bias')      )
       # stages
       for i in range(len(config.backbone_config.depths      )      ):
              for j in range(config.backbone_config.depths[i]      ):
                     rename_keys.append((F'''backbone.layers.{i}.blocks.{j}.norm1.weight''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_before.weight''')      )
                     rename_keys.append((F'''backbone.layers.{i}.blocks.{j}.norm1.bias''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_before.bias''')      )
                     rename_keys.append((F'''backbone.layers.{i}.blocks.{j}.attn.relative_position_bias_table''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_bias_table''')      )
                     rename_keys.append((F'''backbone.layers.{i}.blocks.{j}.attn.relative_position_index''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_index''')      )
                     rename_keys.append((F'''backbone.layers.{i}.blocks.{j}.attn.proj.weight''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight''')      )
                     rename_keys.append((F'''backbone.layers.{i}.blocks.{j}.attn.proj.bias''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias''')      )
                     rename_keys.append((F'''backbone.layers.{i}.blocks.{j}.norm2.weight''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_after.weight''')      )
                     rename_keys.append((F'''backbone.layers.{i}.blocks.{j}.norm2.bias''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_after.bias''')      )
                     rename_keys.append((F'''backbone.layers.{i}.blocks.{j}.mlp.fc1.weight''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight''')      )
                     rename_keys.append((F'''backbone.layers.{i}.blocks.{j}.mlp.fc1.bias''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias''')      )
                     rename_keys.append((F'''backbone.layers.{i}.blocks.{j}.mlp.fc2.weight''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.output.dense.weight''')      )
                     rename_keys.append((F'''backbone.layers.{i}.blocks.{j}.mlp.fc2.bias''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.output.dense.bias''')      )

              if i < 3:
                     rename_keys.append((F'''backbone.layers.{i}.downsample.reduction.weight''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.reduction.weight''')      )
                     rename_keys.append((F'''backbone.layers.{i}.downsample.norm.weight''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.norm.weight''')      )
                     rename_keys.append((F'''backbone.layers.{i}.downsample.norm.bias''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.norm.bias''')      )
              rename_keys.append((F'''backbone.norm{i}.weight''', F'''model.pixel_level_module.encoder.hidden_states_norms.{i}.weight''')      )
              rename_keys.append((F'''backbone.norm{i}.bias''', F'''model.pixel_level_module.encoder.hidden_states_norms.{i}.bias''')      )

       # FPN
       rename_keys.append(('sem_seg_head.layer_4.weight', 'model.pixel_level_module.decoder.fpn.stem.0.weight')      )
       rename_keys.append(('sem_seg_head.layer_4.norm.weight', 'model.pixel_level_module.decoder.fpn.stem.1.weight')      )
       rename_keys.append(('sem_seg_head.layer_4.norm.bias', 'model.pixel_level_module.decoder.fpn.stem.1.bias')      )
       for source_index, target_index in zip(range(3		,					0		,					-1      )		,					range(0		,					3      )      ):
              rename_keys.append((F'''sem_seg_head.adapter_{source_index}.weight''', F'''model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.0.weight''')      )
              rename_keys.append((F'''sem_seg_head.adapter_{source_index}.norm.weight''', F'''model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.1.weight''')      )
              rename_keys.append((F'''sem_seg_head.adapter_{source_index}.norm.bias''', F'''model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.1.bias''')      )
              rename_keys.append((F'''sem_seg_head.layer_{source_index}.weight''', F'''model.pixel_level_module.decoder.fpn.layers.{target_index}.block.0.weight''')      )
              rename_keys.append((F'''sem_seg_head.layer_{source_index}.norm.weight''', F'''model.pixel_level_module.decoder.fpn.layers.{target_index}.block.1.weight''')      )
              rename_keys.append((F'''sem_seg_head.layer_{source_index}.norm.bias''', F'''model.pixel_level_module.decoder.fpn.layers.{target_index}.block.1.bias''')      )
       rename_keys.append(('sem_seg_head.mask_features.weight', 'model.pixel_level_module.decoder.mask_projection.weight')      )
       rename_keys.append(('sem_seg_head.mask_features.bias', 'model.pixel_level_module.decoder.mask_projection.bias')      )

       # Transformer decoder
       for idx in range(config.decoder_config.decoder_layers      ):
              # self-attention out projection
              rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.out_proj.weight''', F'''model.transformer_module.decoder.layers.{idx}.self_attn.out_proj.weight''')      )
              rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.out_proj.bias''', F'''model.transformer_module.decoder.layers.{idx}.self_attn.out_proj.bias''')      )
              # cross-attention out projection
              rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.out_proj.weight''', F'''model.transformer_module.decoder.layers.{idx}.encoder_attn.out_proj.weight''')      )
              rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.out_proj.bias''', F'''model.transformer_module.decoder.layers.{idx}.encoder_attn.out_proj.bias''')      )
              # MLP 1
              rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear1.weight''', F'''model.transformer_module.decoder.layers.{idx}.fc1.weight''')      )
              rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear1.bias''', F'''model.transformer_module.decoder.layers.{idx}.fc1.bias''')      )
              # MLP 2
              rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear2.weight''', F'''model.transformer_module.decoder.layers.{idx}.fc2.weight''')      )
              rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear2.bias''', F'''model.transformer_module.decoder.layers.{idx}.fc2.bias''')      )
              # layernorm 1 (self-attention layernorm)
              rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm1.weight''', F'''model.transformer_module.decoder.layers.{idx}.self_attn_layer_norm.weight''')      )
              rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm1.bias''', F'''model.transformer_module.decoder.layers.{idx}.self_attn_layer_norm.bias''')      )
              # layernorm 2 (cross-attention layernorm)
              rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm2.weight''', F'''model.transformer_module.decoder.layers.{idx}.encoder_attn_layer_norm.weight''')      )
              rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm2.bias''', F'''model.transformer_module.decoder.layers.{idx}.encoder_attn_layer_norm.bias''')      )
              # layernorm 3 (final layernorm)
              rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm3.weight''', F'''model.transformer_module.decoder.layers.{idx}.final_layer_norm.weight''')      )
              rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm3.bias''', F'''model.transformer_module.decoder.layers.{idx}.final_layer_norm.bias''')      )

       rename_keys.append(('sem_seg_head.predictor.transformer.decoder.norm.weight', 'model.transformer_module.decoder.layernorm.weight')      )
       rename_keys.append(('sem_seg_head.predictor.transformer.decoder.norm.bias', 'model.transformer_module.decoder.layernorm.bias')      )

       # heads on top
       rename_keys.append(('sem_seg_head.predictor.query_embed.weight', 'model.transformer_module.queries_embedder.weight')      )

       rename_keys.append(('sem_seg_head.predictor.input_proj.weight', 'model.transformer_module.input_projection.weight')      )
       rename_keys.append(('sem_seg_head.predictor.input_proj.bias', 'model.transformer_module.input_projection.bias')      )

       rename_keys.append(('sem_seg_head.predictor.class_embed.weight', 'class_predictor.weight')      )
       rename_keys.append(('sem_seg_head.predictor.class_embed.bias', 'class_predictor.bias')      )

       for i in range(3      ):
              rename_keys.append((F'''sem_seg_head.predictor.mask_embed.layers.{i}.weight''', F'''mask_embedder.{i}.0.weight''')      )
              rename_keys.append((F'''sem_seg_head.predictor.mask_embed.layers.{i}.bias''', F'''mask_embedder.{i}.0.bias''')      )
       # fmt: on

       return rename_keys
def        lowercase_							(A : Tuple		,					A : Tuple		,					A : Optional[Any]      ):
       snake_case__							:	Optional[int]   	=	dct.pop(A      )
       snake_case__							:	Union[str, Any]   	=	val
def        lowercase_							(A : Optional[Any]		,					A : Tuple      ):
       snake_case__							:	Optional[int]   	=	[int(backbone_config.embed_dim * 2**i      ) for i in range(len(backbone_config.depths      )      )]
       for i in range(len(backbone_config.depths      )      ):
              snake_case__							:	Optional[int]   	=	num_features[i]
              for j in range(backbone_config.depths[i]      ):
                     # fmt: off
                     # read in weights + bias of input projection layer (in original implementation, this is a single matrix + bias)
                     snake_case__							:	int   	=	state_dict.pop(F'''backbone.layers.{i}.blocks.{j}.attn.qkv.weight'''      )
                     snake_case__							:	Tuple   	=	state_dict.pop(F'''backbone.layers.{i}.blocks.{j}.attn.qkv.bias'''      )
                     # next, add query, keys and values (in that order) to the state dict
                     snake_case__							:	str   	=	in_proj_weight[:dim, :]
                     snake_case__							:	int   	=	in_proj_bias[: dim]
                     snake_case__							:	List[Any]   	=	in_proj_weight[
                         dim : dim * 2, :
                     ]
                     snake_case__							:	List[str]   	=	in_proj_bias[
                         dim : dim * 2
                     ]
                     snake_case__							:	List[Any]   	=	in_proj_weight[
                         -dim :, :
                     ]
                     snake_case__							:	Dict   	=	in_proj_bias[-dim :]
                     # fmt: on
def        lowercase_							(A : List[str]		,					A : List[Any]      ):
       # fmt: off
       snake_case__							:	str   	=	config.decoder_config.hidden_size
       for idx in range(config.decoder_config.decoder_layers      ):
              # read in weights + bias of self-attention input projection layer (in the original implementation, this is a single matrix + bias)
              snake_case__							:	List[Any]   	=	state_dict.pop(F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_weight'''      )
              snake_case__							:	int   	=	state_dict.pop(F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_bias'''      )
              # next, add query, keys and values (in that order) to the state dict
              snake_case__							:	Any   	=	in_proj_weight[: hidden_size, :]
              snake_case__							:	Tuple   	=	in_proj_bias[:config.hidden_size]
              snake_case__							:	List[str]   	=	in_proj_weight[hidden_size : hidden_size * 2, :]
              snake_case__							:	Dict   	=	in_proj_bias[hidden_size : hidden_size * 2]
              snake_case__							:	Any   	=	in_proj_weight[-hidden_size :, :]
              snake_case__							:	int   	=	in_proj_bias[-hidden_size :]
              # read in weights + bias of cross-attention input projection layer (in the original implementation, this is a single matrix + bias)
              snake_case__							:	List[Any]   	=	state_dict.pop(F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_weight'''      )
              snake_case__							:	List[str]   	=	state_dict.pop(F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_bias'''      )
              # next, add query, keys and values (in that order) to the state dict
              snake_case__							:	Optional[int]   	=	in_proj_weight[: hidden_size, :]
              snake_case__							:	Optional[Any]   	=	in_proj_bias[:config.hidden_size]
              snake_case__							:	int   	=	in_proj_weight[hidden_size : hidden_size * 2, :]
              snake_case__							:	List[str]   	=	in_proj_bias[hidden_size : hidden_size * 2]
              snake_case__							:	List[str]   	=	in_proj_weight[-hidden_size :, :]
              snake_case__							:	str   	=	in_proj_bias[-hidden_size :]
       # fmt: on
def        lowercase_							():
       snake_case__							:	Any   	=	'http://images.cocodataset.org/val2017/000000039769.jpg'
       snake_case__							:	int   	=	Image.open(requests.get(A		,					stream=A      ).raw      )
       return im
@torch.no_grad()
def        lowercase_							(A : str		,					A : str		,					A : str		,					A : bool = False      ):
       snake_case__							:	Optional[int]   	=	get_maskformer_config(A      )

       # load original state_dict
       with open(A		,					'rb'      ) as f:
              snake_case__							:	List[Any]   	=	pickle.load(A      )
       snake_case__							:	Optional[int]   	=	data['model']

       # for name, param in state_dict.items():
       #     print(name, param.shape)

       # rename keys
       snake_case__							:	List[str]   	=	create_rename_keys(A      )
       for src, dest in rename_keys:
              rename_key(A		,					A		,					A      )
       read_in_swin_q_k_v(A		,					config.backbone_config      )
       read_in_decoder_q_k_v(A		,					A      )

       # update to torch tensors
       for key, value in state_dict.items():
              snake_case__							:	int   	=	torch.from_numpy(A      )

       # load 🤗 model
       snake_case__							:	str   	=	MaskFormerForInstanceSegmentation(A      )
       model.eval()

       for name, param in model.named_parameters():
              print(A		,					param.shape      )

       snake_case__	,				snake_case__							:	Union[str, Any]   	=	model.load_state_dict(A		,					strict=A      )
       assert missing_keys == [
           "model.pixel_level_module.encoder.model.layernorm.weight",
           "model.pixel_level_module.encoder.model.layernorm.bias",
       ]
       assert len(A      ) == 0, F'''Unexpected keys: {unexpected_keys}'''

       # verify results
       snake_case__							:	Optional[Any]   	=	prepare_img()
       if "vistas" in model_name:
              snake_case__							:	int   	=	6_5
       elif "cityscapes" in model_name:
              snake_case__							:	Dict   	=	6_5_5_3_5
       else:
              snake_case__							:	Tuple   	=	2_5_5
       snake_case__							:	Optional[int]   	=	True if 'ade' in model_name else False
       snake_case__							:	Dict   	=	MaskFormerImageProcessor(ignore_index=A		,					reduce_labels=A      )

       snake_case__							:	Any   	=	image_processor(A		,					return_tensors='pt'      )

       snake_case__							:	Any   	=	model(**A      )

       print('Logits:'		,					outputs.class_queries_logits[0, :3, :3]      )

       if model_name == "maskformer-swin-tiny-ade":
              snake_case__							:	Tuple   	=	torch.tensor(
                  [[3.6353, -4.4770, -2.6065], [0.5081, -4.2394, -3.5343], [2.1909, -5.0353, -1.9323]]      )
       assert torch.allclose(outputs.class_queries_logits[0, :3, :3]		,					A		,					atol=1e-4      )
       print('Looks ok!'      )

       if pytorch_dump_folder_path is not None:
              print(F'''Saving model and image processor to {pytorch_dump_folder_path}'''      )
              Path(A      ).mkdir(exist_ok=A      )
              model.save_pretrained(A      )
              image_processor.save_pretrained(A      )

       if push_to_hub:
              print('Pushing model and image processor to the hub...'      )
              model.push_to_hub(F'''nielsr/{model_name}'''      )
              image_processor.push_to_hub(F'''nielsr/{model_name}'''      )


if __name__ == "__main__":
 a_   :Optional[int]        =      argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     "--model_name",
     default="maskformer-swin-tiny-ade",
     type=str,
     help=("Name of the MaskFormer model you'd like to convert",),
 )
 parser.add_argument(
     "--checkpoint_path",
     default="/Users/nielsrogge/Documents/MaskFormer_checkpoints/MaskFormer-Swin-tiny-ADE20k/model.pkl",
     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_   :Dict        =      parser.parse_args()
 convert_maskformer_checkpoint(
     args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
 )






