
import unittest

from transformers import MobileBertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
 import torch

 from transformers import (
     MODEL_FOR_PRETRAINING_MAPPING,
     MobileBertForMaskedLM,
     MobileBertForMultipleChoice,
     MobileBertForNextSentencePrediction,
     MobileBertForPreTraining,
     MobileBertForQuestionAnswering,
     MobileBertForSequenceClassification,
     MobileBertForTokenClassification,
     MobileBertModel,
 )


class   __lowerCAmelCase		:



      """simple docstring"""







      def __init__(					self						:				Tuple ,				_snake_case						:				Union[str, Any] ,				_snake_case						:				Any=13 ,				_snake_case						:				Tuple=7 ,				_snake_case						:				List[Any]=True ,				_snake_case						:				str=True ,				_snake_case						:				Dict=True ,				_snake_case						:				Optional[int]=True ,				_snake_case						:				List[Any]=99 ,				_snake_case						:				Optional[int]=64 ,				_snake_case						:				List[Any]=32 ,				_snake_case						:				Optional[Any]=5 ,				_snake_case						:				List[str]=4 ,				_snake_case						:				Union[str, Any]=37 ,				_snake_case						:				str="gelu" ,				_snake_case						:				int=0.1 ,				_snake_case						:				Union[str, Any]=0.1 ,				_snake_case						:				Union[str, Any]=512 ,				_snake_case						:				Dict=16 ,				_snake_case						:				Tuple=2 ,				_snake_case						:				Union[str, Any]=0.02 ,				_snake_case						:				Tuple=3 ,				_snake_case						:				Union[str, Any]=4 ,				_snake_case						:				int=None ,				):
          __lowercase						:	Tuple					=				parent
          __lowercase						:	Union[str, Any]					=				batch_size
          __lowercase						:	List[str]					=				seq_length
          __lowercase						:	Optional[Any]					=				is_training
          __lowercase						:	Any					=				use_input_mask
          __lowercase						:	List[str]					=				use_token_type_ids
          __lowercase						:	str					=				use_labels
          __lowercase						:	int					=				vocab_size
          __lowercase						:	Optional[Any]					=				hidden_size
          __lowercase						:	Optional[int]					=				embedding_size
          __lowercase						:	Union[str, Any]					=				num_hidden_layers
          __lowercase						:	List[Any]					=				num_attention_heads
          __lowercase						:	str					=				intermediate_size
          __lowercase						:	List[Any]					=				hidden_act
          __lowercase						:	List[Any]					=				hidden_dropout_prob
          __lowercase						:	str					=				attention_probs_dropout_prob
          __lowercase						:	List[str]					=				max_position_embeddings
          __lowercase						:	List[str]					=				type_vocab_size
          __lowercase						:	Optional[int]					=				type_sequence_label_size
          __lowercase						:	Tuple					=				initializer_range
          __lowercase						:	Tuple					=				num_labels
          __lowercase						:	str					=				num_choices
          __lowercase						:	List[str]					=				scope




      def 						snake_case_	(					self						:				List[str] ):
          __lowercase						:	List[Any]					=				ids_tensor([self.batch_size, self.seq_length] ,				self.vocab_size )

          __lowercase						:	List[str]					=				None
          if self.use_input_mask:
              __lowercase						:	List[str]					=				random_attention_mask([self.batch_size, self.seq_length] )

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

          __lowercase						:	str					=				None
          __lowercase						:	Dict					=				None
          __lowercase						:	Union[str, Any]					=				None
          if self.use_labels:
              __lowercase						:	Union[str, Any]					=				ids_tensor([self.batch_size] ,				self.type_sequence_label_size )
              __lowercase						:	Optional[int]					=				ids_tensor([self.batch_size, self.seq_length] ,				self.num_labels )
              __lowercase						:	int					=				ids_tensor([self.batch_size] ,				self.num_choices )

          __lowercase						:	int					=				self.get_config()

          return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels




      def 						snake_case_	(					self						:				List[str] ):
          return MobileBertConfig(
              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 ,				embedding_size=self.embedding_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=lowercase_ ,				initializer_range=self.initializer_range ,				)




      def 						snake_case_	(					self						:				Any ,				_snake_case						:				int ,				_snake_case						:				Optional[Any] ,				_snake_case						:				Optional[int] ,				_snake_case						:				Tuple ,				_snake_case						:				Union[str, Any] ,				_snake_case						:				Union[str, Any] ,				_snake_case						:				int ):
          __lowercase						:	Optional[int]					=				MobileBertModel(config=lowercase_ )
          model.to(lowercase_ )
          model.eval()
          __lowercase						:	Optional[int]					=				model(lowercase_ ,				attention_mask=lowercase_ ,				token_type_ids=lowercase_ )
          __lowercase						:	Dict					=				model(lowercase_ ,				token_type_ids=lowercase_ )
          __lowercase						:	Union[str, Any]					=				model(lowercase_ )

          self.parent.assertEqual(result.last_hidden_state.shape ,				(self.batch_size, self.seq_length, self.hidden_size) )
          self.parent.assertEqual(result.pooler_output.shape ,				(self.batch_size, self.hidden_size) )




      def 						snake_case_	(					self						:				List[Any] ,				_snake_case						:				Optional[int] ,				_snake_case						:				int ,				_snake_case						:				Dict ,				_snake_case						:				Optional[int] ,				_snake_case						:				Tuple ,				_snake_case						:				str ,				_snake_case						:				str ):
          __lowercase						:	Optional[Any]					=				MobileBertForMaskedLM(config=lowercase_ )
          model.to(lowercase_ )
          model.eval()
          __lowercase						:	str					=				model(lowercase_ ,				attention_mask=lowercase_ ,				token_type_ids=lowercase_ ,				labels=lowercase_ )
          self.parent.assertEqual(result.logits.shape ,				(self.batch_size, self.seq_length, self.vocab_size) )




      def 						snake_case_	(					self						:				List[str] ,				_snake_case						:				Dict ,				_snake_case						:				Tuple ,				_snake_case						:				Tuple ,				_snake_case						:				List[str] ,				_snake_case						:				List[str] ,				_snake_case						:				str ,				_snake_case						:				List[str] ):
          __lowercase						:	Any					=				MobileBertForNextSentencePrediction(config=lowercase_ )
          model.to(lowercase_ )
          model.eval()
          __lowercase						:	Optional[Any]					=				model(
              lowercase_ ,				attention_mask=lowercase_ ,				token_type_ids=lowercase_ ,				labels=lowercase_ ,				)
          self.parent.assertEqual(result.logits.shape ,				(self.batch_size, 2) )




      def 						snake_case_	(					self						:				Any ,				_snake_case						:				str ,				_snake_case						:				Union[str, Any] ,				_snake_case						:				List[str] ,				_snake_case						:				Tuple ,				_snake_case						:				Any ,				_snake_case						:				str ,				_snake_case						:				List[Any] ):
          __lowercase						:	Optional[int]					=				MobileBertForPreTraining(config=lowercase_ )
          model.to(lowercase_ )
          model.eval()
          __lowercase						:	Tuple					=				model(
              lowercase_ ,				attention_mask=lowercase_ ,				token_type_ids=lowercase_ ,				labels=lowercase_ ,				next_sentence_label=lowercase_ ,				)
          self.parent.assertEqual(result.prediction_logits.shape ,				(self.batch_size, self.seq_length, self.vocab_size) )
          self.parent.assertEqual(result.seq_relationship_logits.shape ,				(self.batch_size, 2) )




      def 						snake_case_	(					self						:				str ,				_snake_case						:				Union[str, Any] ,				_snake_case						:				Optional[Any] ,				_snake_case						:				str ,				_snake_case						:				Any ,				_snake_case						:				Optional[int] ,				_snake_case						:				str ,				_snake_case						:				Union[str, Any] ):
          __lowercase						:	Optional[int]					=				MobileBertForQuestionAnswering(config=lowercase_ )
          model.to(lowercase_ )
          model.eval()
          __lowercase						:	List[str]					=				model(
              lowercase_ ,				attention_mask=lowercase_ ,				token_type_ids=lowercase_ ,				start_positions=lowercase_ ,				end_positions=lowercase_ ,				)
          self.parent.assertEqual(result.start_logits.shape ,				(self.batch_size, self.seq_length) )
          self.parent.assertEqual(result.end_logits.shape ,				(self.batch_size, self.seq_length) )




      def 						snake_case_	(					self						:				List[Any] ,				_snake_case						:				List[Any] ,				_snake_case						:				Tuple ,				_snake_case						:				str ,				_snake_case						:				List[str] ,				_snake_case						:				List[Any] ,				_snake_case						:				Tuple ,				_snake_case						:				Tuple ):
          __lowercase						:	int					=				self.num_labels
          __lowercase						:	List[str]					=				MobileBertForSequenceClassification(lowercase_ )
          model.to(lowercase_ )
          model.eval()
          __lowercase						:	Dict					=				model(lowercase_ ,				attention_mask=lowercase_ ,				token_type_ids=lowercase_ ,				labels=lowercase_ )
          self.parent.assertEqual(result.logits.shape ,				(self.batch_size, self.num_labels) )




      def 						snake_case_	(					self						:				str ,				_snake_case						:				List[str] ,				_snake_case						:				Optional[int] ,				_snake_case						:				Dict ,				_snake_case						:				List[str] ,				_snake_case						:				int ,				_snake_case						:				Dict ,				_snake_case						:				Union[str, Any] ):
          __lowercase						:	str					=				self.num_labels
          __lowercase						:	Tuple					=				MobileBertForTokenClassification(config=lowercase_ )
          model.to(lowercase_ )
          model.eval()
          __lowercase						:	Union[str, Any]					=				model(lowercase_ ,				attention_mask=lowercase_ ,				token_type_ids=lowercase_ ,				labels=lowercase_ )
          self.parent.assertEqual(result.logits.shape ,				(self.batch_size, self.seq_length, self.num_labels) )




      def 						snake_case_	(					self						:				Tuple ,				_snake_case						:				Any ,				_snake_case						:				Dict ,				_snake_case						:				Union[str, Any] ,				_snake_case						:				Optional[Any] ,				_snake_case						:				Union[str, Any] ,				_snake_case						:				Optional[Any] ,				_snake_case						:				Optional[int] ):
          __lowercase						:	str					=				self.num_choices
          __lowercase						:	Dict					=				MobileBertForMultipleChoice(config=lowercase_ )
          model.to(lowercase_ )
          model.eval()
          __lowercase						:	Dict					=				input_ids.unsqueeze(1 ).expand(-1 ,				self.num_choices ,				-1 ).contiguous()
          __lowercase						:	Dict					=				token_type_ids.unsqueeze(1 ).expand(-1 ,				self.num_choices ,				-1 ).contiguous()
          __lowercase						:	int					=				input_mask.unsqueeze(1 ).expand(-1 ,				self.num_choices ,				-1 ).contiguous()
          __lowercase						:	str					=				model(
              lowercase_ ,				attention_mask=lowercase_ ,				token_type_ids=lowercase_ ,				labels=lowercase_ ,				)
          self.parent.assertEqual(result.logits.shape ,				(self.batch_size, self.num_choices) )




      def 						snake_case_	(					self						:				Tuple ):
          __lowercase						:	int					=				self.prepare_config_and_inputs()
          (
              __lowercase   
          )						:	List[Any]					=				config_and_inputs
          __lowercase						:	Dict					=				{"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
          return config, inputs_dict






@require_torch
class   __lowerCAmelCase		(		lowercase__       ,       lowercase__       ,       unittest.TestCase					):



      """simple docstring"""







      A__		:	Optional[Any]         =			(
          (
              MobileBertModel,
              MobileBertForMaskedLM,
              MobileBertForMultipleChoice,
              MobileBertForNextSentencePrediction,
              MobileBertForPreTraining,
              MobileBertForQuestionAnswering,
              MobileBertForSequenceClassification,
              MobileBertForTokenClassification,
          )
          if is_torch_available()
          else ()
      )
      A__		:	Optional[Any]         =			(
          {
              """feature-extraction""": MobileBertModel,
              """fill-mask""": MobileBertForMaskedLM,
              """question-answering""": MobileBertForQuestionAnswering,
              """text-classification""": MobileBertForSequenceClassification,
              """token-classification""": MobileBertForTokenClassification,
              """zero-shot""": MobileBertForSequenceClassification,
          }
          if is_torch_available()
          else {}
      )
      A__		:	Any         =			True




      def 						snake_case_	(					self						:				Tuple ,				_snake_case						:				Union[str, Any] ,				_snake_case						:				Tuple ,				_snake_case						:				Any=False ):
          __lowercase						:	str					=				super()._prepare_for_class(lowercase_ ,				lowercase_ ,				return_labels=lowercase_ )

          if return_labels:
              if model_class in get_values(lowercase_ ):
                  __lowercase						:	Optional[Any]					=				torch.zeros(
                      (self.model_tester.batch_size, self.model_tester.seq_length) ,				dtype=torch.long ,				device=lowercase_ )
                  __lowercase						:	Dict					=				torch.zeros(
                      self.model_tester.batch_size ,				dtype=torch.long ,				device=lowercase_ )
          return inputs_dict




      def 						snake_case_	(					self						:				List[str] ):
          __lowercase						:	str					=				MobileBertModelTester(self )
          __lowercase						:	Tuple					=				ConfigTester(self ,				config_class=lowercase_ ,				hidden_size=37 )




      def 						snake_case_	(					self						:				int ):
          self.config_tester.run_common_tests()




      def 						snake_case_	(					self						:				List[str] ):
          __lowercase						:	Dict					=				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_mobilebert_model(*lowercase_ )




      def 						snake_case_	(					self						:				Optional[int] ):
          __lowercase						:	List[Any]					=				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_mobilebert_for_masked_lm(*lowercase_ )




      def 						snake_case_	(					self						:				List[str] ):
          __lowercase						:	Optional[Any]					=				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_mobilebert_for_multiple_choice(*lowercase_ )




      def 						snake_case_	(					self						:				Dict ):
          __lowercase						:	Dict					=				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_mobilebert_for_next_sequence_prediction(*lowercase_ )




      def 						snake_case_	(					self						:				List[Any] ):
          __lowercase						:	str					=				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_mobilebert_for_pretraining(*lowercase_ )




      def 						snake_case_	(					self						:				Optional[int] ):
          __lowercase						:	Union[str, Any]					=				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_mobilebert_for_question_answering(*lowercase_ )




      def 						snake_case_	(					self						:				int ):
          __lowercase						:	Optional[int]					=				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_mobilebert_for_sequence_classification(*lowercase_ )




      def 						snake_case_	(					self						:				Dict ):
          __lowercase						:	List[Any]					=				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_mobilebert_for_token_classification(*lowercase_ )






def 			UpperCAmelCase_     (  __lowerCAmelCase		)       ->  Tuple:
    return torch.tensor(
        __lowerCamelCase		,    dtype=torch.long		,    device=__lowerCamelCase		,    )


__lowerCAmelCase    :	int						=						1e-3





@require_torch
@require_sentencepiece
@require_tokenizers
class   __lowerCAmelCase		(		unittest.TestCase					):



      """simple docstring"""







      @slow
      def 						snake_case_	(					self						:				Tuple ):
          __lowercase						:	int					=				MobileBertModel.from_pretrained('''google/mobilebert-uncased''' ).to(lowercase_ )
          __lowercase						:	int					=				_long_tensor([[101, 7110, 1005, 1056, 2023, 1_1333, 1_7413, 1029, 102]] )
          with torch.no_grad():
              __lowercase						:	Union[str, Any]					=				model(lowercase_ )[0]
          __lowercase						:	int					=				torch.Size((1, 9, 512) )
          self.assertEqual(output.shape ,				lowercase_ )
          __lowercase						:	List[str]					=				torch.tensor(
              [
                  [
                      [-2.4_7_3_6_5_2_6E0_7, 8.2_6_9_1_6_5_6E0_4, 1.6_5_2_1_8_3_8E0_5],
                      [-5.7_5_4_1_7_0_4E-0_1, 3.9_0_5_6_0_2_2E0_0, 4.4_0_1_1_5_0_7E0_0],
                      [2.6_0_4_7_3_5_9E0_0, 1.5_6_7_7_6_5_2E0_0, -1.7_3_2_4_1_8_8E-0_1],
                  ]
              ] ,				device=lowercase_ ,				)

          # MobileBERT results range from 10e0 to 10e8. Even a 0.0000001% difference with a value of 10e8 results in a
          # ~1 difference, it's therefore not a good idea to measure using addition.
          # Here, we instead divide the expected result with the result in order to obtain ~1. We then check that the
          # result is held between bounds: 1 - TOLERANCE < expected_result / result < 1 + TOLERANCE
          __lowercase						:	Tuple					=				torch.all((expected_slice / output[..., :3, :3]) >= 1 - TOLERANCE )
          __lowercase						:	str					=				torch.all((expected_slice / output[..., :3, :3]) <= 1 + TOLERANCE )

          self.assertTrue(lower_bound and upper_bound )




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







def 	__a     (		__lowerCamelCase  ):
   UpperCAmelCase_					:					Optional[Any]			     =    SwinConfig.from_pretrained(
       "microsoft/swin-tiny-patch4-window7-224",		out_features=["stage1", "stage2", "stage3", "stage4"]  )
   UpperCAmelCase_					:					Dict			     =    MaskFormerConfig(backbone_config=__lowerCamelCase  )

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

   UpperCAmelCase_					:					Any			     =    json.load(open(hf_hub_download(__lowerCamelCase,		__lowerCamelCase,		repo_type="dataset"  ),		"r"  )  )
   UpperCAmelCase_					:					int			     =    {int(__lowerCamelCase  ): v for k, v in idalabel.items()}

   return config







def 	__a     (		__lowerCamelCase  ):
   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 	__a     (		__lowerCamelCase,		__lowerCamelCase,		__lowerCamelCase  ):
   UpperCAmelCase_					:					Union[str, Any]			     =    dct.pop(__lowerCamelCase  )
   UpperCAmelCase_					:					str			     =    val







def 	__a     (		__lowerCamelCase,		__lowerCamelCase  ):
   UpperCAmelCase_					:					int			     =    [int(backbone_config.embed_dim * 2**i  ) for i in range(len(backbone_config.depths  )  )]
   for i in range(len(backbone_config.depths  )  ):
      UpperCAmelCase_					:					List[Any]			     =    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_					:					Tuple			     =    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_					:					Tuple			     =    in_proj_weight[:dim, :]
         UpperCAmelCase_					:					List[Any]			     =    in_proj_bias[: dim]
         UpperCAmelCase_					:					Any			     =    in_proj_weight[
             dim : dim * 2, :
         ]
         UpperCAmelCase_					:					Optional[int]			     =    in_proj_bias[
             dim : dim * 2
         ]
         UpperCAmelCase_					:					Tuple			     =    in_proj_weight[
             -dim :, :
         ]
         UpperCAmelCase_					:					Tuple			     =    in_proj_bias[-dim :]
         # fmt: on







def 	__a     (		__lowerCamelCase,		__lowerCamelCase  ):
   # fmt: off
   UpperCAmelCase_					:					Dict			     =    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_					:					int			     =    state_dict.pop(f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_weight"""  )
      UpperCAmelCase_					:					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
      UpperCAmelCase_					:					Any			     =    in_proj_weight[: hidden_size, :]
      UpperCAmelCase_					:					int			     =    in_proj_bias[:config.hidden_size]
      UpperCAmelCase_					:					Any			     =    in_proj_weight[hidden_size : hidden_size * 2, :]
      UpperCAmelCase_					:					List[Any]			     =    in_proj_bias[hidden_size : hidden_size * 2]
      UpperCAmelCase_					:					Dict			     =    in_proj_weight[-hidden_size :, :]
      UpperCAmelCase_					:					List[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_					:					str			     =    state_dict.pop(f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_weight"""  )
      UpperCAmelCase_					:					Dict			     =    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_					:					str			     =    in_proj_weight[: hidden_size, :]
      UpperCAmelCase_					:					Tuple			     =    in_proj_bias[:config.hidden_size]
      UpperCAmelCase_					:					int			     =    in_proj_weight[hidden_size : hidden_size * 2, :]
      UpperCAmelCase_					:					List[str]			     =    in_proj_bias[hidden_size : hidden_size * 2]
      UpperCAmelCase_					:					List[Any]			     =    in_proj_weight[-hidden_size :, :]
      UpperCAmelCase_					:					Optional[Any]			     =    in_proj_bias[-hidden_size :]
   # fmt: on







def 	__a     (		):
   UpperCAmelCase_					:					List[Any]			     =    "http://images.cocodataset.org/val2017/000000039769.jpg"
   UpperCAmelCase_					:					Tuple			     =    Image.open(requests.get(__lowerCamelCase,		stream=__lowerCamelCase  ).raw  )
   return im







@torch.no_grad()
def 	__a     (		__lowerCamelCase,		__lowerCamelCase,		__lowerCamelCase,		__lowerCamelCase = False  ):
   UpperCAmelCase_					:					List[str]			     =    get_maskformer_config(__lowerCamelCase  )

   # load original state_dict
   with open(__lowerCamelCase,		"rb"  ) as f:
      UpperCAmelCase_					:					Union[str, Any]			     =    pickle.load(__lowerCamelCase  )
   UpperCAmelCase_					:					str			     =    data["model"]

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

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

   # update to torch tensors
   for key, value in state_dict.items():
      UpperCAmelCase_					:					Optional[int]			     =    torch.from_numpy(__lowerCamelCase  )

   # load 🤗 model
   UpperCAmelCase_					:					Dict			     =    MaskFormerForInstanceSegmentation(__lowerCamelCase  )
   model.eval()

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

   UpperCAmelCase_     ,   UpperCAmelCase_					:					str			     =    model.load_state_dict(__lowerCamelCase,		strict=__lowerCamelCase  )
   assert missing_keys == [
       "model.pixel_level_module.encoder.model.layernorm.weight",
       "model.pixel_level_module.encoder.model.layernorm.bias",
   ]
   assert len(__lowerCamelCase  ) == 0, f"""Unexpected keys: {unexpected_keys}"""

   # verify results
   UpperCAmelCase_					:					Optional[int]			     =    prepare_img()
   if "vistas" in model_name:
      UpperCAmelCase_					:					List[str]			     =    65
   elif "cityscapes" in model_name:
      UpperCAmelCase_					:					Tuple			     =    6_5535
   else:
      UpperCAmelCase_					:					Dict			     =    255
   UpperCAmelCase_					:					Optional[Any]			     =    True if "ade" in model_name else False
   UpperCAmelCase_					:					Dict			     =    MaskFormerImageProcessor(ignore_index=__lowerCamelCase,		reduce_labels=__lowerCamelCase  )

   UpperCAmelCase_					:					Union[str, Any]			     =    image_processor(__lowerCamelCase,		return_tensors="pt"  )

   UpperCAmelCase_					:					Dict			     =    model(**__lowerCamelCase  )

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

   if model_name == "maskformer-swin-tiny-ade":
      UpperCAmelCase_					:					Any			     =    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],		__lowerCamelCase,		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(__lowerCamelCase  ).mkdir(exist_ok=__lowerCamelCase  )
      model.save_pretrained(__lowerCamelCase  )
      image_processor.save_pretrained(__lowerCamelCase  )

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


