


import unittest

import numpy as np

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

from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
			import jax.numpy as jnp

			from transformers.models.roberta_prelayernorm.modeling_flax_roberta_prelayernorm import (
			    FlaxRobertaPreLayerNormForCausalLM,
			    FlaxRobertaPreLayerNormForMaskedLM,
			    FlaxRobertaPreLayerNormForMultipleChoice,
			    FlaxRobertaPreLayerNormForQuestionAnswering,
			    FlaxRobertaPreLayerNormForSequenceClassification,
			    FlaxRobertaPreLayerNormForTokenClassification,
			    FlaxRobertaPreLayerNormModel,
			)

class     a		(unittest.TestCase				):




							"""simple docstring"""


							def __init__(							self	:  Tuple   ,     lowerCamelCase	:  List[str]   ,     lowerCamelCase	:  Union[str, Any]=13   ,     lowerCamelCase	:  Dict=7   ,     lowerCamelCase	:  Union[str, Any]=True   ,     lowerCamelCase	:  Dict=True   ,     lowerCamelCase	:  Optional[int]=True   ,     lowerCamelCase	:  Union[str, Any]=True   ,     lowerCamelCase	:  str=99   ,     lowerCamelCase	:  Optional[int]=32   ,     lowerCamelCase	:  Union[str, Any]=5   ,     lowerCamelCase	:  Optional[int]=4   ,     lowerCamelCase	:  Optional[int]=37   ,     lowerCamelCase	:  Union[str, Any]="gelu"   ,     lowerCamelCase	:  int=0.1   ,     lowerCamelCase	:  Union[str, Any]=0.1   ,     lowerCamelCase	:  Optional[int]=512   ,     lowerCamelCase	:  List[str]=16   ,     lowerCamelCase	:  Dict=2   ,     lowerCamelCase	:  str=0.02   ,     lowerCamelCase	:  Dict=4   ,     )     ->   Optional[Any]:
														__snake_case		:      Tuple										=       parent
														__snake_case		:      Any										=       batch_size
														__snake_case		:      List[Any]										=       seq_length
														__snake_case		:      Any										=       is_training
														__snake_case		:      Optional[int]										=       use_attention_mask
														__snake_case		:      Any										=       use_token_type_ids
														__snake_case		:      Tuple										=       use_labels
														__snake_case		:      List[str]										=       vocab_size
														__snake_case		:      Tuple										=       hidden_size
														__snake_case		:      Tuple										=       num_hidden_layers
														__snake_case		:      str										=       num_attention_heads
														__snake_case		:      Union[str, Any]										=       intermediate_size
														__snake_case		:      Optional[int]										=       hidden_act
														__snake_case		:      List[str]										=       hidden_dropout_prob
														__snake_case		:      Any										=       attention_probs_dropout_prob
														__snake_case		:      Dict										=       max_position_embeddings
														__snake_case		:      Any										=       type_vocab_size
														__snake_case		:      int										=       type_sequence_label_size
														__snake_case		:      str										=       initializer_range
														__snake_case		:      str										=       num_choices

							def    __snake_case (							self	:  Tuple    )     ->   Union[str, Any]:
														__snake_case		:      List[str]										=       ids_tensor([self.batch_size, self.seq_length]   ,     self.vocab_size    )

														__snake_case		:      Optional[int]										=       None
														if self.use_attention_mask:
																					__snake_case		:      Dict										=       random_attention_mask([self.batch_size, self.seq_length]    )

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

														__snake_case		:      Union[str, Any]										=       RobertaPreLayerNormConfig(
														    vocab_size=self.vocab_size   ,     hidden_size=self.hidden_size   ,     num_hidden_layers=self.num_hidden_layers   ,     num_attention_heads=self.num_attention_heads   ,     intermediate_size=self.intermediate_size   ,     hidden_act=self.hidden_act   ,     hidden_dropout_prob=self.hidden_dropout_prob   ,     attention_probs_dropout_prob=self.attention_probs_dropout_prob   ,     max_position_embeddings=self.max_position_embeddings   ,     type_vocab_size=self.type_vocab_size   ,     is_decoder=lowerCamelCase   ,     initializer_range=self.initializer_range   ,     )

														return config, input_ids, token_type_ids, attention_mask

							def    __snake_case (							self	:  List[Any]    )     ->   Dict:
														__snake_case		:      Dict										=       self.prepare_config_and_inputs()
														__snake_case			,      __snake_case			,      __snake_case			,      __snake_case		:      List[Any]										=       config_and_inputs
														__snake_case		:      Dict										=       {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": attention_mask}
														return config, inputs_dict






							def    __snake_case (							self	:  List[str]    )     ->   Optional[int]:
														__snake_case		:      Union[str, Any]										=       self.prepare_config_and_inputs()
														__snake_case			,      __snake_case			,      __snake_case			,      __snake_case		:      List[Any]										=       config_and_inputs

														__snake_case		:      Tuple										=       True
														__snake_case		:      Optional[int]										=       floats_tensor([self.batch_size, self.seq_length, self.hidden_size]    )
														__snake_case		:      Optional[Any]										=       ids_tensor([self.batch_size, self.seq_length]   ,     vocab_size=2    )

														return (
														    config,
														    input_ids,
														    token_type_ids,
														    encoder_hidden_states,
														    encoder_attention_mask,
														)

@require_flax
# Copied from tests.models.roberta.test_modelling_flax_roberta.FlaxRobertaPreLayerNormModelTest with ROBERTA->ROBERTA_PRELAYERNORM,Roberta->RobertaPreLayerNorm,roberta-base->andreasmadsen/efficient_mlm_m0.40
class     a		(_lowerCAmelCase					,    unittest.TestCase				):




							"""simple docstring"""


							__UpperCAmelCase   :							str					  =  True

							__UpperCAmelCase   :							List[Any]					  =  (
							    (
							        FlaxRobertaPreLayerNormModel,
							        FlaxRobertaPreLayerNormForCausalLM,
							        FlaxRobertaPreLayerNormForMaskedLM,
							        FlaxRobertaPreLayerNormForSequenceClassification,
							        FlaxRobertaPreLayerNormForTokenClassification,
							        FlaxRobertaPreLayerNormForMultipleChoice,
							        FlaxRobertaPreLayerNormForQuestionAnswering,
							    )
							    if is_flax_available()
							    else ()
							)

							def    __snake_case (							self	:  Tuple    )     ->   Tuple:
														__snake_case		:      Tuple										=       FlaxRobertaPreLayerNormModelTester(self    )






							@slow
							def    __snake_case (							self	:  Optional[Any]    )     ->   str:
														for model_class_name in self.all_model_classes:
																					__snake_case		:      str										=       model_class_name.from_pretrained("andreasmadsen/efficient_mlm_m0.40"   ,     from_pt=lowerCamelCase    )
																					__snake_case		:      Tuple										=       model(np.ones((1, 1)    )    )
																					self.assertIsNotNone(lowerCamelCase    )

@require_flax
class     a		(unittest.TestCase				):




							"""simple docstring"""


							@slow
							def    __snake_case (							self	:  List[Any]    )     ->   Optional[Any]:
														__snake_case		:      Dict										=       FlaxRobertaPreLayerNormForMaskedLM.from_pretrained("andreasmadsen/efficient_mlm_m0.40"   ,     from_pt=lowerCamelCase    )

														__snake_case		:      List[Any]										=       np.array([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]]   ,     dtype=jnp.intaa    )
														__snake_case		:      Optional[Any]										=       model(lowerCamelCase    )[0]
														__snake_case		:      List[str]										=       [1, 11, 50265]
														self.assertEqual(list(output.shape    )   ,     lowerCamelCase    )
														# compare the actual values for a slice.
														__snake_case		:      List[Any]										=       np.array(
														    [[[40.48_80, 18.01_99, -5.23_67], [-1.88_77, -4.08_85, 10.70_85], [-2.26_13, -5.61_10, 7.26_65]]]   ,     dtype=np.floataa    )
														self.assertTrue(np.allclose(output[:, :3, :3]   ,     lowerCamelCase   ,     atol=1E-4    )    )






							@slow
							def    __snake_case (							self	:  List[str]    )     ->   Optional[Any]:
														__snake_case		:      Union[str, Any]										=       FlaxRobertaPreLayerNormModel.from_pretrained("andreasmadsen/efficient_mlm_m0.40"   ,     from_pt=lowerCamelCase    )

														__snake_case		:      str										=       np.array([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]]   ,     dtype=jnp.intaa    )
														__snake_case		:      List[Any]										=       model(lowerCamelCase    )[0]
														# compare the actual values for a slice.
														__snake_case		:      List[str]										=       np.array(
														    [[[0.02_08, -0.03_56, 0.02_37], [-0.15_69, -0.04_11, -0.26_26], [0.18_79, 0.01_25, -0.00_89]]]   ,     dtype=np.floataa    )
														self.assertTrue(np.allclose(output[:, :3, :3]   ,     lowerCamelCase   ,     atol=1E-4    )    )





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







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

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

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

							return config







def     lowerCAmelCase_					( __lowerCamelCase     ):
							__snake_case		:      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     lowerCAmelCase_					( __lowerCamelCase							,     __lowerCamelCase							,     __lowerCamelCase     ):
							__snake_case		:      Dict										=       dct.pop(__lowerCamelCase     )
							__snake_case		:      Any										=       val







def     lowerCAmelCase_					( __lowerCamelCase							,     __lowerCamelCase     ):
							__snake_case		:      List[Any]										=       [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		:      Tuple										=       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		:      Tuple										=       in_proj_weight[:dim, :]
																					__snake_case		:      Tuple										=       in_proj_bias[: dim]
																					__snake_case		:      Union[str, Any]										=       in_proj_weight[
																					    dim : dim * 2, :
																					]
																					__snake_case		:      Tuple										=       in_proj_bias[
																					    dim : dim * 2
																					]
																					__snake_case		:      str										=       in_proj_weight[
																					    -dim :, :
																					]
																					__snake_case		:      Any										=       in_proj_bias[-dim :]
																					# fmt: on







def     lowerCAmelCase_					( __lowerCamelCase							,     __lowerCamelCase     ):
							# fmt: off
							__snake_case		:      Optional[int]										=       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[str]										=       state_dict.pop(F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_weight'     )
														__snake_case		:      Union[str, Any]										=       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		:      Optional[int]										=       in_proj_bias[:config.hidden_size]
														__snake_case		:      Any										=       in_proj_weight[hidden_size : hidden_size * 2, :]
														__snake_case		:      Any										=       in_proj_bias[hidden_size : hidden_size * 2]
														__snake_case		:      Tuple										=       in_proj_weight[-hidden_size :, :]
														__snake_case		:      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)
														__snake_case		:      Optional[Any]										=       state_dict.pop(F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_weight'     )
														__snake_case		:      Union[str, Any]										=       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		:      int										=       in_proj_weight[: hidden_size, :]
														__snake_case		:      Tuple										=       in_proj_bias[:config.hidden_size]
														__snake_case		:      str										=       in_proj_weight[hidden_size : hidden_size * 2, :]
														__snake_case		:      Optional[Any]										=       in_proj_bias[hidden_size : hidden_size * 2]
														__snake_case		:      Optional[Any]										=       in_proj_weight[-hidden_size :, :]
														__snake_case		:      Tuple										=       in_proj_bias[-hidden_size :]
							# fmt: on







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







@torch.no_grad()
def     lowerCAmelCase_					( __lowerCamelCase							,     __lowerCamelCase							,     __lowerCamelCase							,     __lowerCamelCase = False     ):
							__snake_case		:      Optional[int]										=       get_maskformer_config(__lowerCamelCase     )

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

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

							# rename keys
							__snake_case		:      Tuple										=       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():
														__snake_case		:      int										=       torch.from_numpy(__lowerCamelCase     )

							# load 🤗 model
							__snake_case		:      List[str]										=       MaskFormerForInstanceSegmentation(__lowerCamelCase     )
							model.eval()

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

							__snake_case			,      __snake_case		:      List[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
							__snake_case		:      Union[str, Any]										=       prepare_img()
							if "vistas" in model_name:
														__snake_case		:      Optional[int]										=       6_5
							elif "cityscapes" in model_name:
														__snake_case		:      Optional[int]										=       6_5_5_3_5
							else:
														__snake_case		:      Union[str, Any]										=       2_5_5
							__snake_case		:      Union[str, Any]										=       True if "ade" in model_name else False
							__snake_case		:      str										=       MaskFormerImageProcessor(ignore_index=__lowerCamelCase							,     reduce_labels=__lowerCamelCase     )

							__snake_case		:      List[str]										=       image_processor(__lowerCamelCase							,     return_tensors="pt"     )

							__snake_case		:      Tuple										=       model(**__lowerCamelCase     )

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

							if model_name == "maskformer-swin-tiny-ade":
														__snake_case		:      Optional[Any]										=       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]							,     __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__":
			_snake_case					:		List[str]      =    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."
			)

			_snake_case					:		List[str]      =    parser.parse_args()
			convert_maskformer_checkpoint(
			    args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
			)

