'''simple docstring'''




import unittest

from transformers import PegasusTokenizer, PegasusTokenizerFast
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


__SCREAMING_SNAKE_CASE       :     Any     =  get_tests_dir("""fixtures/test_sentencepiece_no_bos.model""")

@require_sentencepiece
@require_tokenizers
class 	lowerCamelCase_	(snake_case__							, unittest.TestCase    ):
      '''simple docstring'''

      __UpperCamelCase:  Optional[Any]						=						PegasusTokenizer
      __UpperCamelCase:  Optional[Any]						=						PegasusTokenizerFast
      __UpperCamelCase:  Union[str, Any]						=						True
      __UpperCamelCase:  Any						=						True




      def      _A	(       self						:				int       ):
             super().setUp()

             # We have a SentencePiece fixture for testing
             _UpperCAmelCase     :  Optional[int]							      =    PegasusTokenizer(A       )
             tokenizer.save_pretrained(self.tmpdirname       )




      @cached_property
      def      _A	(       self						:				Optional[Any]       ):
             return PegasusTokenizer.from_pretrained("google/pegasus-large"       )




      def      _A	(       self						:				Optional[int]						,					**A						:				int       ):
             return PegasusTokenizer.from_pretrained(self.tmpdirname						,					**A       )




      def      _A	(       self						:				int						,					A						:				List[str]       ):
             return ("This is a test", "This is a test")




      def      _A	(       self						:				Any       ):
             _UpperCAmelCase     :  List[str]							      =    "</s>"
             _UpperCAmelCase     :  List[Any]							      =    1

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




      def      _A	(       self						:				Any       ):
             _UpperCAmelCase     :  Optional[int]							      =    list(self.get_tokenizer().get_vocab().keys()       )

             self.assertEqual(vocab_keys[0]						,					"<pad>"       )
             self.assertEqual(vocab_keys[1]						,					"</s>"       )
             self.assertEqual(vocab_keys[-1]						,					"v"       )
             self.assertEqual(len(A       )						,					1103       )




      def      _A	(       self						:				List[str]       ):
             self.assertEqual(self.get_tokenizer().vocab_size						,					1103       )




      def      _A	(       self						:				Union[str, Any]       ):
             _UpperCAmelCase     :  List[str]							      =    self.rust_tokenizer_class.from_pretrained(self.tmpdirname       )
             _UpperCAmelCase     :  Optional[Any]							      =    self.tokenizer_class.from_pretrained(self.tmpdirname       )
             _UpperCAmelCase     :  List[Any]							      =    (
                 "Let's see which <unk> is the better <unk_token_11> one <mask_1> It seems like this <mask_2> was important"
                 " </s> <pad> <pad> <pad>"
             )
             _UpperCAmelCase     :  Optional[Any]							      =    rust_tokenizer([raw_input_str]						,					return_tensors=A						,					add_special_tokens=A       ).input_ids[0]
             _UpperCAmelCase     :  Dict							      =    py_tokenizer([raw_input_str]						,					return_tensors=A						,					add_special_tokens=A       ).input_ids[0]
             self.assertListEqual(A						,					A       )




      def      _A	(       self						:				Dict       ):
             _UpperCAmelCase     :  str							      =    self._large_tokenizer
             # <mask_1> masks whole sentence while <mask_2> masks single word
             _UpperCAmelCase     :  Tuple							      =    "<mask_1> To ensure a <mask_2> flow of bank resolutions."
             _UpperCAmelCase     :  Optional[Any]							      =    [2, 413, 615, 114, 3, 1971, 113, 1679, 10710, 107, 1]
             _UpperCAmelCase     :  Optional[Any]							      =    tokenizer([raw_input_str]						,					return_tensors=A       ).input_ids[0]
             self.assertListEqual(A						,					A       )




      def      _A	(       self						:				int       ):
             _UpperCAmelCase     :  Tuple							      =    self._large_tokenizer
             # The tracebacks for the following asserts are **better** without messages or self.assertEqual
             assert tokenizer.vocab_size == 96103
             assert tokenizer.pad_token_id == 0
             assert tokenizer.eos_token_id == 1
             assert tokenizer.offset == 103
             assert tokenizer.unk_token_id == tokenizer.offset + 2 == 105
             assert tokenizer.unk_token == "<unk>"
             assert tokenizer.model_max_length == 1024
             _UpperCAmelCase     :  str							      =    "To ensure a smooth flow of bank resolutions."
             _UpperCAmelCase     :  Dict							      =    [413, 615, 114, 2291, 1971, 113, 1679, 10710, 107, 1]
             _UpperCAmelCase     :  Dict							      =    tokenizer([raw_input_str]						,					return_tensors=A       ).input_ids[0]
             self.assertListEqual(A						,					A       )
             assert tokenizer.convert_ids_to_tokens([0, 1, 2, 3]       ) == ["<pad>", "</s>", "<mask_1>", "<mask_2>"]




      @require_torch
      def      _A	(       self						:				List[Any]       ):
             _UpperCAmelCase     :  str							      =    ["This is going to be way too long." * 150, "short example"]
             _UpperCAmelCase     :  int							      =    ["not super long but more than 5 tokens", "tiny"]
             _UpperCAmelCase     :  Tuple							      =    self._large_tokenizer(A						,					padding=A						,					truncation=A						,					return_tensors="pt"       )
             _UpperCAmelCase     :  Dict							      =    self._large_tokenizer(
                 text_target=A						,					max_length=5						,					padding=A						,					truncation=A						,					return_tensors="pt"       )

             assert batch.input_ids.shape == (2, 1024)
             assert batch.attention_mask.shape == (2, 1024)
             assert targets["input_ids"].shape == (2, 5)
             assert len(A       ) == 2  # input_ids, attention_mask.





      @slow
      def      _A	(       self						:				Union[str, Any]       ):
             # fmt: off
             _UpperCAmelCase     :  Optional[int]							      =    {"input_ids": [[38979, 143, 18485, 606, 130, 26669, 87686, 121, 54189, 1129, 111, 26669, 87686, 121, 9114, 14787, 121, 13249, 158, 592, 956, 121, 14621, 31576, 143, 62613, 108, 9688, 930, 43430, 11562, 62613, 304, 108, 11443, 897, 108, 9314, 17415, 63399, 108, 11443, 7614, 18316, 118, 4284, 7148, 12430, 143, 1400, 25703, 158, 111, 4284, 7148, 11772, 143, 21297, 1064, 158, 122, 204, 3506, 1754, 1133, 14787, 1581, 115, 33224, 4482, 111, 1355, 110, 29173, 317, 50833, 108, 20147, 94665, 111, 77198, 107, 1], [110, 62613, 117, 638, 112, 1133, 121, 20098, 1355, 79050, 13872, 135, 1596, 53541, 1352, 141, 13039, 5542, 124, 302, 518, 111, 268, 2956, 115, 149, 4427, 107, 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], [139, 1235, 2799, 18289, 17780, 204, 109, 9474, 1296, 107, 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]], "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, 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, 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=A						,					model_name="google/bigbird-pegasus-large-arxiv"						,					revision="ba85d0851d708441f91440d509690f1ab6353415"						,					)

@require_sentencepiece
@require_tokenizers
class 	lowerCamelCase_	(snake_case__							, unittest.TestCase    ):
      '''simple docstring'''

      __UpperCamelCase:  Dict						=						PegasusTokenizer
      __UpperCamelCase:  List[str]						=						PegasusTokenizerFast
      __UpperCamelCase:  Optional[int]						=						True
      __UpperCamelCase:  Optional[int]						=						True




      def      _A	(       self						:				List[str]       ):
             super().setUp()

             # We have a SentencePiece fixture for testing
             _UpperCAmelCase     :  Optional[Any]							      =    PegasusTokenizer(A						,					offset=0						,					mask_token_sent=A						,					mask_token="[MASK]"       )
             tokenizer.save_pretrained(self.tmpdirname       )




      @cached_property
      def      _A	(       self						:				Optional[Any]       ):
             return PegasusTokenizer.from_pretrained("google/bigbird-pegasus-large-arxiv"       )




      def      _A	(       self						:				Optional[Any]						,					**A						:				Dict       ):
             return PegasusTokenizer.from_pretrained(self.tmpdirname						,					**A       )




      def      _A	(       self						:				Dict						,					A						:				Dict       ):
             return ("This is a test", "This is a test")




      def      _A	(       self						:				Dict       ):
             _UpperCAmelCase     :  Tuple							      =    self.rust_tokenizer_class.from_pretrained(self.tmpdirname       )
             _UpperCAmelCase     :  Any							      =    self.tokenizer_class.from_pretrained(self.tmpdirname       )
             _UpperCAmelCase     :  List[Any]							      =    (
                 "Let's see which <unk> is the better <unk_token> one [MASK] It seems like this [MASK] was important </s>"
                 " <pad> <pad> <pad>"
             )
             _UpperCAmelCase     :  List[str]							      =    rust_tokenizer([raw_input_str]						,					return_tensors=A						,					add_special_tokens=A       ).input_ids[0]
             _UpperCAmelCase     :  Union[str, Any]							      =    py_tokenizer([raw_input_str]						,					return_tensors=A						,					add_special_tokens=A       ).input_ids[0]
             self.assertListEqual(A						,					A       )




      @require_torch
      def      _A	(       self						:				int       ):
             _UpperCAmelCase     :  Optional[Any]							      =    ["This is going to be way too long." * 1000, "short example"]
             _UpperCAmelCase     :  Any							      =    ["not super long but more than 5 tokens", "tiny"]
             _UpperCAmelCase     :  List[str]							      =    self._large_tokenizer(A						,					padding=A						,					truncation=A						,					return_tensors="pt"       )
             _UpperCAmelCase     :  Any							      =    self._large_tokenizer(
                 text_target=A						,					max_length=5						,					padding=A						,					truncation=A						,					return_tensors="pt"       )

             assert batch.input_ids.shape == (2, 4096)
             assert batch.attention_mask.shape == (2, 4096)
             assert targets["input_ids"].shape == (2, 5)
             assert len(A       ) == 2  # input_ids, attention_mask.





      def      _A	(       self						:				Tuple       ):
             _UpperCAmelCase     :  List[Any]							      =    (
                 "This is an example string that is used to test the original TF implementation against the HF"
                 " implementation"
             )

             _UpperCAmelCase     :  Optional[Any]							      =    self._large_tokenizer(A       ).input_ids

             self.assertListEqual(
                 A						,					[182, 117, 142, 587, 4211, 120, 117, 263, 112, 804, 109, 856, 25016, 3137, 464, 109, 26955, 3137, 1]						,					)


'''simple docstring'''




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()
__SCREAMING_SNAKE_CASE       :     Union[str, Any]     =  logging.get_logger(__name__)

def 	UpperCamelCase_							(					_UpperCAmelCase  :      str    )    ->    Optional[Any]:


       """simple docstring"""







       _UpperCAmelCase     :  int							      =    SwinConfig.from_pretrained(
           "microsoft/swin-tiny-patch4-window7-224"			,	out_features=["stage1", "stage2", "stage3", "stage4"]    )
       _UpperCAmelCase     :  List[Any]							      =    MaskFormerConfig(backbone_config=_UpperCAmelCase    )

       _UpperCAmelCase     :  Tuple							      =    "huggingface/label-files"
       if "ade20k-full" in model_name:
              # this should be ok
              _UpperCAmelCase     :  Dict							      =    847
              _UpperCAmelCase     :  Any							      =    "maskformer-ade20k-full-id2label.json"
       elif "ade" in model_name:
              # this should be ok
              _UpperCAmelCase     :  Any							      =    150
              _UpperCAmelCase     :  Any							      =    "ade20k-id2label.json"
       elif "coco-stuff" in model_name:
              # this should be ok
              _UpperCAmelCase     :  Tuple							      =    171
              _UpperCAmelCase     :  Union[str, Any]							      =    "maskformer-coco-stuff-id2label.json"
       elif "coco" in model_name:
              # TODO
              _UpperCAmelCase     :  Any							      =    133
              _UpperCAmelCase     :  int							      =    "coco-panoptic-id2label.json"
       elif "cityscapes" in model_name:
              # this should be ok
              _UpperCAmelCase     :  Optional[int]							      =    19
              _UpperCAmelCase     :  str							      =    "cityscapes-id2label.json"
       elif "vistas" in model_name:
              # this should be ok
              _UpperCAmelCase     :  Optional[int]							      =    65
              _UpperCAmelCase     :  Tuple							      =    "mapillary-vistas-id2label.json"

       _UpperCAmelCase     :  List[Any]							      =    json.load(open(hf_hub_download(_UpperCAmelCase			,	_UpperCAmelCase			,	repo_type="dataset"    )			,	"r"    )    )
       _UpperCAmelCase     :  Tuple							      =    {int(_UpperCAmelCase    ): v for k, v in idalabel.items()}

       return config

def 	UpperCamelCase_							(					_UpperCAmelCase  :      Optional[int]    )    ->    List[Any]:


       """simple docstring"""







       _UpperCAmelCase     :  Dict							      =    []
       # 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 	UpperCamelCase_							(					_UpperCAmelCase  :      int			,	_UpperCAmelCase  :      Tuple			,	_UpperCAmelCase  :      List[Any]    )    ->    List[str]:


       """simple docstring"""







       _UpperCAmelCase     :  Optional[int]							      =    dct.pop(_UpperCAmelCase    )
       _UpperCAmelCase     :  List[str]							      =    val

def 	UpperCamelCase_							(					_UpperCAmelCase  :      List[Any]			,	_UpperCAmelCase  :      Tuple    )    ->    Optional[int]:


       """simple docstring"""







       _UpperCAmelCase     :  List[str]							      =    [int(backbone_config.embed_dim * 2**i    ) for i in range(len(backbone_config.depths    )    )]
       for i in range(len(backbone_config.depths    )    ):
              _UpperCAmelCase     :  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)
                     _UpperCAmelCase     :  Any							      =    state_dict.pop(F"""backbone.layers.{i}.blocks.{j}.attn.qkv.weight"""    )
                     _UpperCAmelCase     :  Optional[int]							      =    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
                     _UpperCAmelCase     :  List[str]							      =    in_proj_weight[:dim, :]
                     _UpperCAmelCase     :  Tuple							      =    in_proj_bias[: dim]
                     _UpperCAmelCase     :  List[Any]							      =    in_proj_weight[
                         dim : dim * 2, :
                     ]
                     _UpperCAmelCase     :  List[str]							      =    in_proj_bias[
                         dim : dim * 2
                     ]
                     _UpperCAmelCase     :  Optional[Any]							      =    in_proj_weight[
                         -dim :, :
                     ]
                     _UpperCAmelCase     :  Dict							      =    in_proj_bias[-dim :]
                     # fmt: on

def 	UpperCamelCase_							(					_UpperCAmelCase  :      Dict			,	_UpperCAmelCase  :      str    )    ->    Dict:


       """simple docstring"""







       _UpperCAmelCase     :  Union[str, Any]							      =    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)
              _UpperCAmelCase     :  Dict							      =    state_dict.pop(F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_weight"""    )
              _UpperCAmelCase     :  Dict							      =    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
              _UpperCAmelCase     :  int							      =    in_proj_weight[: hidden_size, :]
              _UpperCAmelCase     :  Union[str, Any]							      =    in_proj_bias[:config.hidden_size]
              _UpperCAmelCase     :  List[str]							      =    in_proj_weight[hidden_size : hidden_size * 2, :]
              _UpperCAmelCase     :  List[str]							      =    in_proj_bias[hidden_size : hidden_size * 2]
              _UpperCAmelCase     :  int							      =    in_proj_weight[-hidden_size :, :]
              _UpperCAmelCase     :  Optional[Any]							      =    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)
              _UpperCAmelCase     :  Optional[Any]							      =    state_dict.pop(F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_weight"""    )
              _UpperCAmelCase     :  Tuple							      =    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
              _UpperCAmelCase     :  Any							      =    in_proj_weight[: hidden_size, :]
              _UpperCAmelCase     :  Tuple							      =    in_proj_bias[:config.hidden_size]
              _UpperCAmelCase     :  Dict							      =    in_proj_weight[hidden_size : hidden_size * 2, :]
              _UpperCAmelCase     :  Dict							      =    in_proj_bias[hidden_size : hidden_size * 2]
              _UpperCAmelCase     :  Optional[int]							      =    in_proj_weight[-hidden_size :, :]
              _UpperCAmelCase     :  Union[str, Any]							      =    in_proj_bias[-hidden_size :]
       # fmt: on

def 	UpperCamelCase_							(					)    ->    torch.Tensor:


       """simple docstring"""







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

@torch.no_grad()
def 	UpperCamelCase_							(					_UpperCAmelCase  :      str			,	_UpperCAmelCase  :      str			,	_UpperCAmelCase  :      str			,	_UpperCAmelCase  :      bool = False    )    ->    List[str]:


       """simple docstring"""







       _UpperCAmelCase     :  Optional[int]							      =    get_maskformer_config(_UpperCAmelCase    )

       # load original state_dict
       with open(_UpperCAmelCase			,	"rb"    ) as f:
              _UpperCAmelCase     :  Optional[int]							      =    pickle.load(_UpperCAmelCase    )
       _UpperCAmelCase     :  Optional[int]							      =    data["model"]

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

       # rename keys
       _UpperCAmelCase     :  Any							      =    create_rename_keys(_UpperCAmelCase    )
       for src, dest in rename_keys:
              rename_key(_UpperCAmelCase			,	_UpperCAmelCase			,	_UpperCAmelCase    )
       read_in_swin_q_k_v(_UpperCAmelCase			,	config.backbone_config    )
       read_in_decoder_q_k_v(_UpperCAmelCase			,	_UpperCAmelCase    )

       # update to torch tensors
       for key, value in state_dict.items():
              _UpperCAmelCase     :  Tuple							      =    torch.from_numpy(_UpperCAmelCase    )

       # load 🤗 model
       _UpperCAmelCase     :  Union[str, Any]							      =    MaskFormerForInstanceSegmentation(_UpperCAmelCase    )
       model.eval()

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

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

       # verify results
       _UpperCAmelCase     :  Optional[int]							      =    prepare_img()
       if "vistas" in model_name:
              _UpperCAmelCase     :  int							      =    65
       elif "cityscapes" in model_name:
              _UpperCAmelCase     :  Tuple							      =    65_535
       else:
              _UpperCAmelCase     :  Any							      =    255
       _UpperCAmelCase     :  Optional[Any]							      =    True if "ade" in model_name else False
       _UpperCAmelCase     :  Optional[int]							      =    MaskFormerImageProcessor(ignore_index=_UpperCAmelCase			,	reduce_labels=_UpperCAmelCase    )

       _UpperCAmelCase     :  Optional[int]							      =    image_processor(_UpperCAmelCase			,	return_tensors="pt"    )

       _UpperCAmelCase     :  List[Any]							      =    model(**_UpperCAmelCase    )

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

       if model_name == "maskformer-swin-tiny-ade":
              _UpperCAmelCase     :  Tuple							      =    torch.tensor(
                  [[3.6_3_5_3, -4.4_7_7_0, -2.6_0_6_5], [0.5_0_8_1, -4.2_3_9_4, -3.5_3_4_3], [2.1_9_0_9, -5.0_3_5_3, -1.9_3_2_3]]    )
       assert torch.allclose(outputs.class_queries_logits[0, :3, :3]			,	_UpperCAmelCase			,	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(_UpperCAmelCase    ).mkdir(exist_ok=_UpperCAmelCase    )
              model.save_pretrained(_UpperCAmelCase    )
              image_processor.save_pretrained(_UpperCAmelCase    )

       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__":
  __SCREAMING_SNAKE_CASE       :     Optional[Any]     =  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."""
  )

  __SCREAMING_SNAKE_CASE       :     int     =  parser.parse_args()
  convert_maskformer_checkpoint(
      args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
  )

