

# Copyright (c) 2021-, NVIDIA CORPORATION.  All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

####################################################################################################

#
# Note: If when running this conversion script you're getting an exception:
#     ModuleNotFoundError: No module named 'megatron.model.enums'
# you need to tell python where to find the clone of Megatron-LM, e.g.:
#
# cd /tmp
# git clone https://github.com/NVIDIA/Megatron-LM
# PYTHONPATH=/tmp/Megatron-LM python src/transformers/models/megatron_gpt2/convert_megatron_gpt2_checkpoint.py ...
#
# if you already have it cloned elsewhere, simply adjust the path to the existing path
#
# If the training was done using a Megatron-LM fork, e.g.,
# https://github.com/microsoft/Megatron-DeepSpeed/ then chances are that you need to have that one
# in your path, i.e., /path/to/Megatron-DeepSpeed/
#

import argparse
import os
import re
import zipfile

import torch

from transformers import AutoTokenizer, GPTaConfig

def       __snake_case						( __UpperCamelCase	: Tuple				,__UpperCamelCase	: Dict				,__UpperCamelCase	: Tuple=0    ):



      """simple docstring"""

      if name is None:
            A_						  =				None
      else:
            A_						  =				"." * max(0				,spaces - 2    ) + "# {:" + str(50 - spaces    ) + "s}"
            A_						  =				fmt.format(__UpperCamelCase    )

      # Print and recurse (if needed).
      if isinstance(__UpperCamelCase				,__UpperCamelCase    ):
            if msg is not None:
                  print(__UpperCamelCase    )
            for k in val.keys():
                  recursive_print(__UpperCamelCase				,val[k]				,spaces + 2    )
      elif isinstance(__UpperCamelCase				,torch.Tensor    ):
            print(__UpperCamelCase				,":"				,val.size()    )
      else:
            print(__UpperCamelCase				,":"				,__UpperCamelCase    )

def       __snake_case						( __UpperCamelCase	: List[str]				,__UpperCamelCase	: Tuple				,__UpperCamelCase	: Union[str, Any]				,__UpperCamelCase	: Any				,__UpperCamelCase	: Any    ):



      """simple docstring"""

      A_						  =				param.size()
      if checkpoint_version == 1.0:
            # version 1.0 stores [num_heads * hidden_size * num_splits, :]
            A_						  =				(num_heads, hidden_size, num_splits) + input_shape[1:]
            A_						  =				param.view(*__UpperCamelCase    )
            A_						  =				param.transpose(0				,2    )
            A_						  =				param.transpose(1				,2    ).contiguous()
      elif checkpoint_version >= 2.0:
            # other versions store [num_heads * num_splits * hidden_size, :]
            A_						  =				(num_heads, num_splits, hidden_size) + input_shape[1:]
            A_						  =				param.view(*__UpperCamelCase    )
            A_						  =				param.transpose(0				,1    ).contiguous()
      A_						  =				param.view(*__UpperCamelCase    )
      return param

def       __snake_case						( __UpperCamelCase	: str				,__UpperCamelCase	: Any				,__UpperCamelCase	: Any    ):



      """simple docstring"""

      A_						  =				{}

      # old versions did not store training args
      A_						  =				input_state_dict.get("args"				,__UpperCamelCase    )
      if ds_args is not None:
            # do not make the user write a config file when the exact dimensions/sizes are already in the checkpoint
            # from pprint import pprint
            # pprint(vars(ds_args))

            A_						  =				ds_args.padded_vocab_size
            A_						  =				ds_args.max_position_embeddings
            A_						  =				ds_args.hidden_size
            A_						  =				ds_args.num_layers
            A_						  =				ds_args.num_attention_heads
            A_						  =				ds_args.ffn_hidden_size
            # pprint(config)

      # The number of heads.
      A_						  =				config.n_head
      # The hidden_size per head.
      A_						  =				config.n_embd // config.n_head
      # Megatron-LM checkpoint version
      if "checkpoint_version" in input_state_dict.keys():
            A_						  =				input_state_dict["checkpoint_version"]
      else:
            A_						  =				0.0

      # The model.
      A_						  =				input_state_dict["model"]
      # The language model.
      A_						  =				model["language_model"]
      # The embeddings.
      A_						  =				lm["embedding"]

      # The word embeddings.
      A_						  =				embeddings["word_embeddings"]["weight"]
      # Truncate the embedding table to vocab_size rows.
      A_						  =				word_embeddings[: config.vocab_size, :]
      A_						  =				word_embeddings

      # The position embeddings.
      A_						  =				embeddings["position_embeddings"]["weight"]
      # Read the causal mask dimension (seqlen). [max_sequence_length, hidden_size]
      A_						  =				pos_embeddings.size(0    )
      if n_positions != config.n_positions:
            raise ValueError(
                f'''pos_embeddings.max_sequence_length={n_positions} and config.n_positions={config.n_positions} don\'t match'''    )
      # Store the position embeddings.
      A_						  =				pos_embeddings

      # The transformer.
      A_						  =				lm["transformer"] if "transformer" in lm.keys() else lm["encoder"]

      # The regex to extract layer names.
      A_						  =				re.compile(R"layers\.(\d+)\.([a-z0-9_.]+)\.([a-z]+)"    )

      # The simple map of names for "automated" rules.
      A_						  =				{
          "attention.dense": ".attn.c_proj.",
          "self_attention.dense": ".attn.c_proj.",
          "mlp.dense_h_to_4h": ".mlp.c_fc.",
          "mlp.dense_4h_to_h": ".mlp.c_proj.",
      }

      # Extract the layers.
      for key, val in transformer.items():
            # Match the name.
            A_						  =				layer_re.match(__UpperCamelCase    )

            # Stop if that's not a layer
            if m is None:
                  break

            # The index of the layer.
            A_						  =				int(m.group(1    )    )
            # The name of the operation.
            A_						  =				m.group(2    )
            # Is it a weight or a bias?
            A_						  =				m.group(3    )

            # The name of the layer.
            A_						  =				f'''transformer.h.{layer_idx}'''

            # For layernorm(s), simply store the layer norm.
            if op_name.endswith("layernorm"    ):
                  A_						  =				"ln_1" if op_name.startswith("input"    ) else "ln_2"
                  A_						  =				val

            # Transpose the QKV matrix.
            elif (
                op_name == "attention.query_key_value" or op_name == "self_attention.query_key_value"
            ) and weight_or_bias == "weight":
                  # Insert a tensor of 1x1xDxD bias.
                  A_						  =				torch.tril(torch.ones((n_positions, n_positions)				,dtype=torch.floataa    )    ).view(
                      1				,1				,__UpperCamelCase				,__UpperCamelCase    )
                  A_						  =				causal_mask

                  # Insert a "dummy" tensor for masked_bias.
                  A_						  =				torch.tensor(-1E4				,dtype=torch.floataa    )
                  A_						  =				masked_bias

                  A_						  =				fix_query_key_value_ordering(__UpperCamelCase				,__UpperCamelCase				,3				,__UpperCamelCase				,__UpperCamelCase    )
                  # Megatron stores (3*D) x D but transformers-GPT2 expects D x 3*D.
                  A_						  =				out_val.transpose(0				,1    ).contiguous()
                  # Store.
                  A_						  =				out_val

            # Transpose the bias.
            elif (
                op_name == "attention.query_key_value" or op_name == "self_attention.query_key_value"
            ) and weight_or_bias == "bias":
                  A_						  =				fix_query_key_value_ordering(__UpperCamelCase				,__UpperCamelCase				,3				,__UpperCamelCase				,__UpperCamelCase    )
                  # Store. No change of shape.
                  A_						  =				out_val

            # Transpose the weights.
            elif weight_or_bias == "weight":
                  A_						  =				megatron_to_transformers[op_name]
                  A_						  =				val.transpose(0				,1    )

            # Copy the bias.
            elif weight_or_bias == "bias":
                  A_						  =				megatron_to_transformers[op_name]
                  A_						  =				val

    # DEBUG.
      assert config.n_layer == layer_idx + 1

      # The final layernorm.
      A_						  =				transformer["final_layernorm.weight"]
      A_						  =				transformer["final_layernorm.bias"]

      # For LM head, transformers' wants the matrix to weight embeddings.
      A_						  =				word_embeddings

      # It should be done!
      return output_state_dict

def       __snake_case						( ):



      """simple docstring"""

      A_						  =				argparse.ArgumentParser()
      parser.add_argument("--print-checkpoint-structure"				,action="store_true"    )
      parser.add_argument(
          "path_to_checkpoint"				,type=__UpperCamelCase				,help="Path to the checkpoint file (.zip archive or direct .pt file)"				,)
      parser.add_argument(
          "--config_file"				,default=""				,type=__UpperCamelCase				,help="An optional config json file describing the pre-trained model."				,)
      A_						  =				parser.parse_args()

      # Extract the basename.
      A_						  =				os.path.dirname(args.path_to_checkpoint    )

      # Load the model.
      # the .zip is very optional, let's keep it for backward compatibility
      print(f'''Extracting PyTorch state dictionary from {args.path_to_checkpoint}'''    )
      if args.path_to_checkpoint.endswith(".zip"    ):
            with zipfile.ZipFile(args.path_to_checkpoint				,"r"    ) as checkpoint:
                  with checkpoint.open("release/mp_rank_00/model_optim_rng.pt"    ) as pytorch_dict:
                        A_						  =				torch.load(__UpperCamelCase				,map_location="cpu"    )
      else:
            A_						  =				torch.load(args.path_to_checkpoint				,map_location="cpu"    )

      A_						  =				input_state_dict.get("args"				,__UpperCamelCase    )

      # Read the config, or default to the model released by NVIDIA.
      if args.config_file == "":
            if ds_args is not None:
                  if ds_args.bias_gelu_fusion:
                        A_						  =				"gelu_fast"
                  elif ds_args.openai_gelu:
                        A_						  =				"gelu_new"
                  else:
                        A_						  =				"gelu"
            else:
                  # in the very early days this used to be "gelu_new"
                  A_						  =				"gelu_new"

            # Spell out all parameters in case the defaults change.
            A_						  =				GPTaConfig(
                vocab_size=5_0257				,n_positions=1024				,n_embd=1024				,n_layer=24				,n_head=16				,n_inner=4096				,activation_function=__UpperCamelCase				,resid_pdrop=0.1				,embd_pdrop=0.1				,attn_pdrop=0.1				,layer_norm_epsilon=1E-5				,initializer_range=0.02				,summary_type="cls_index"				,summary_use_proj=__UpperCamelCase				,summary_activation=__UpperCamelCase				,summary_proj_to_labels=__UpperCamelCase				,summary_first_dropout=0.1				,scale_attn_weights=__UpperCamelCase				,use_cache=__UpperCamelCase				,bos_token_id=5_0256				,eos_token_id=5_0256				,)
      else:
            A_						  =				GPTaConfig.from_json_file(args.config_file    )

      A_						  =				["GPT2LMHeadModel"]

      # Convert.
      print("Converting"    )
      A_						  =				convert_megatron_checkpoint(__UpperCamelCase				,__UpperCamelCase				,__UpperCamelCase    )

      # Print the structure of converted state dict.
      if args.print_checkpoint_structure:
            recursive_print(__UpperCamelCase				,__UpperCamelCase    )

      # Add tokenizer class info to config
      # see https://github.com/huggingface/transformers/issues/13906)
      if ds_args is not None:
            A_						  =				ds_args.tokenizer_type
            if tokenizer_type == "GPT2BPETokenizer":
                  A_						  =				"gpt2"
            elif tokenizer_type == "PretrainedFromHF":
                  A_						  =				ds_args.tokenizer_name_or_path
            else:
                  raise ValueError(f'''Unrecognized tokenizer_type {tokenizer_type}'''    )
      else:
            A_						  =				"gpt2"

      A_						  =				AutoTokenizer.from_pretrained(__UpperCamelCase    )
      A_						  =				type(__UpperCamelCase    ).__name__
      A_						  =				tokenizer_class

      # Store the config to file.
      print("Saving config"    )
      config.save_pretrained(__UpperCamelCase    )

      # Save tokenizer based on args
      print(f'''Adding {tokenizer_class} tokenizer files'''    )
      tokenizer.save_pretrained(__UpperCamelCase    )

      # Store the state_dict to file.
      A_						  =				os.path.join(__UpperCamelCase				,"pytorch_model.bin"    )
      print(f'''Saving checkpoint to "{output_checkpoint_file}"'''    )
      torch.save(__UpperCamelCase				,__UpperCamelCase    )


####################################################################################################

if __name__ == "__main__":
      main()

####################################################################################################






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					:				List[str]				=			logging.get_logger(__name__)


def 							__snake_case							(    lowerCAmelCase_	)				->						Optional[int]:
    SCREAMING_SNAKE_CASE__											=			SwinConfig.from_pretrained(
        '''microsoft/swin-tiny-patch4-window7-224''' ,							out_features=['''stage1''', '''stage2''', '''stage3''', '''stage4''']	)
    SCREAMING_SNAKE_CASE__											=			MaskFormerConfig(backbone_config=lowerCAmelCase_	)

    SCREAMING_SNAKE_CASE__											=			'''huggingface/label-files'''
    if "ade20k-full" in model_name:
        # this should be ok
        SCREAMING_SNAKE_CASE__											=			8_4_7
        SCREAMING_SNAKE_CASE__											=			'''maskformer-ade20k-full-id2label.json'''
    elif "ade" in model_name:
        # this should be ok
        SCREAMING_SNAKE_CASE__											=			1_5_0
        SCREAMING_SNAKE_CASE__											=			'''ade20k-id2label.json'''
    elif "coco-stuff" in model_name:
        # this should be ok
        SCREAMING_SNAKE_CASE__											=			1_7_1
        SCREAMING_SNAKE_CASE__											=			'''maskformer-coco-stuff-id2label.json'''
    elif "coco" in model_name:
        # TODO
        SCREAMING_SNAKE_CASE__											=			1_3_3
        SCREAMING_SNAKE_CASE__											=			'''coco-panoptic-id2label.json'''
    elif "cityscapes" in model_name:
        # this should be ok
        SCREAMING_SNAKE_CASE__											=			1_9
        SCREAMING_SNAKE_CASE__											=			'''cityscapes-id2label.json'''
    elif "vistas" in model_name:
        # this should be ok
        SCREAMING_SNAKE_CASE__											=			6_5
        SCREAMING_SNAKE_CASE__											=			'''mapillary-vistas-id2label.json'''

    SCREAMING_SNAKE_CASE__											=			json.load(open(hf_hub_download(lowerCAmelCase_ ,							lowerCAmelCase_ ,							repo_type='''dataset'''	) ,							'''r'''	)	)
    SCREAMING_SNAKE_CASE__											=			{int(lowerCAmelCase_	): v for k, v in idalabel.items()}

    return config


def 							__snake_case							(    lowerCAmelCase_	)				->						Union[str, Any]:
    SCREAMING_SNAKE_CASE__											=			[]
    # 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 							__snake_case							(    lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_	)				->						str:
    SCREAMING_SNAKE_CASE__											=			dct.pop(lowerCAmelCase_	)
    SCREAMING_SNAKE_CASE__											=			val


def 							__snake_case							(    lowerCAmelCase_ ,							lowerCAmelCase_	)				->						Any:
    SCREAMING_SNAKE_CASE__											=			[int(backbone_config.embed_dim * 2**i	) for i in range(len(backbone_config.depths	)	)]
    for i in range(len(backbone_config.depths	)	):
        SCREAMING_SNAKE_CASE__											=			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)
            SCREAMING_SNAKE_CASE__											=			state_dict.pop(f'''backbone.layers.{i}.blocks.{j}.attn.qkv.weight'''	)
            SCREAMING_SNAKE_CASE__											=			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
            SCREAMING_SNAKE_CASE__											=			in_proj_weight[:dim, :]
            SCREAMING_SNAKE_CASE__											=			in_proj_bias[: dim]
            SCREAMING_SNAKE_CASE__											=			in_proj_weight[
                dim : dim * 2, :
            ]
            SCREAMING_SNAKE_CASE__											=			in_proj_bias[
                dim : dim * 2
            ]
            SCREAMING_SNAKE_CASE__											=			in_proj_weight[
                -dim :, :
            ]
            SCREAMING_SNAKE_CASE__											=			in_proj_bias[-dim :]
            # fmt: on


def 							__snake_case							(    lowerCAmelCase_ ,							lowerCAmelCase_	)				->						Tuple:
    # fmt: off
    SCREAMING_SNAKE_CASE__											=			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)
        SCREAMING_SNAKE_CASE__											=			state_dict.pop(f'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_weight'''	)
        SCREAMING_SNAKE_CASE__											=			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
        SCREAMING_SNAKE_CASE__											=			in_proj_weight[: hidden_size, :]
        SCREAMING_SNAKE_CASE__											=			in_proj_bias[:config.hidden_size]
        SCREAMING_SNAKE_CASE__											=			in_proj_weight[hidden_size : hidden_size * 2, :]
        SCREAMING_SNAKE_CASE__											=			in_proj_bias[hidden_size : hidden_size * 2]
        SCREAMING_SNAKE_CASE__											=			in_proj_weight[-hidden_size :, :]
        SCREAMING_SNAKE_CASE__											=			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)
        SCREAMING_SNAKE_CASE__											=			state_dict.pop(f'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_weight'''	)
        SCREAMING_SNAKE_CASE__											=			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
        SCREAMING_SNAKE_CASE__											=			in_proj_weight[: hidden_size, :]
        SCREAMING_SNAKE_CASE__											=			in_proj_bias[:config.hidden_size]
        SCREAMING_SNAKE_CASE__											=			in_proj_weight[hidden_size : hidden_size * 2, :]
        SCREAMING_SNAKE_CASE__											=			in_proj_bias[hidden_size : hidden_size * 2]
        SCREAMING_SNAKE_CASE__											=			in_proj_weight[-hidden_size :, :]
        SCREAMING_SNAKE_CASE__											=			in_proj_bias[-hidden_size :]
    # fmt: on


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


@torch.no_grad()
def 							__snake_case							(    lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_ = False	)				->						Optional[Any]:
    SCREAMING_SNAKE_CASE__											=			get_maskformer_config(lowerCAmelCase_	)

    # load original state_dict
    with open(lowerCAmelCase_ ,							'''rb'''	) as f:
        SCREAMING_SNAKE_CASE__											=			pickle.load(lowerCAmelCase_	)
    SCREAMING_SNAKE_CASE__											=			data['''model''']

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

    # rename keys
    SCREAMING_SNAKE_CASE__											=			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():
        SCREAMING_SNAKE_CASE__											=			torch.from_numpy(lowerCAmelCase_	)

    # load 🤗 model
    SCREAMING_SNAKE_CASE__											=			MaskFormerForInstanceSegmentation(lowerCAmelCase_	)
    model.eval()

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

    SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__											=			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
    SCREAMING_SNAKE_CASE__											=			prepare_img()
    if "vistas" in model_name:
        SCREAMING_SNAKE_CASE__											=			6_5
    elif "cityscapes" in model_name:
        SCREAMING_SNAKE_CASE__											=			6_5_5_3_5
    else:
        SCREAMING_SNAKE_CASE__											=			2_5_5
    SCREAMING_SNAKE_CASE__											=			True if '''ade''' in model_name else False
    SCREAMING_SNAKE_CASE__											=			MaskFormerImageProcessor(ignore_index=lowerCAmelCase_ ,							reduce_labels=lowerCAmelCase_	)

    SCREAMING_SNAKE_CASE__											=			image_processor(lowerCAmelCase_ ,							return_tensors='''pt'''	)

    SCREAMING_SNAKE_CASE__											=			model(**lowerCAmelCase_	)

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

    if model_name == "maskformer-swin-tiny-ade":
        SCREAMING_SNAKE_CASE__											=			torch.tensor(
            [[3.63_53, -4.47_70, -2.60_65], [0.50_81, -4.23_94, -3.53_43], [2.19_09, -5.03_53, -1.93_23]]	)
    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					:				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."""
  )

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