

# 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 	__a     (      A__			:  Optional[Any]			,  A__			:  List[str]			,  A__			:  Optional[int]=0       ):
    # Format the message.
    if name is None:
        SCREAMING_SNAKE_CASE										=   None
    else:
        SCREAMING_SNAKE_CASE										=   "." * max(0			,  spaces - 2       ) + "# {:" + str(50 - spaces       ) + "s}"
        SCREAMING_SNAKE_CASE										=   fmt.format(A__       )

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





def 	__a     (      A__			:  Tuple			,  A__			:  List[Any]			,  A__			:  Tuple			,  A__			:  Optional[Any]			,  A__			:  Dict       ):
    # Permutes layout of param tensor to [num_splits * num_heads * hidden_size, :]
    # for compatibility with later versions of NVIDIA Megatron-LM.
    # The inverse operation is performed inside Megatron-LM to read checkpoints:
    # https://github.com/NVIDIA/Megatron-LM/blob/v2.4/megatron/checkpointing.py#L209
    # If param is the weight tensor of the self-attention block, the returned tensor
    # will have to be transposed one more time to be read by HuggingFace GPT2.
    SCREAMING_SNAKE_CASE										=   param.size()
    if checkpoint_version == 1.0:
        # version 1.0 stores [num_heads * hidden_size * num_splits, :]
        SCREAMING_SNAKE_CASE										=   (num_heads, hidden_size, num_splits) + input_shape[1:]
        SCREAMING_SNAKE_CASE										=   param.view(*A__       )
        SCREAMING_SNAKE_CASE										=   param.transpose(0			,  2       )
        SCREAMING_SNAKE_CASE										=   param.transpose(1			,  2       ).contiguous()
    elif checkpoint_version >= 2.0:
        # other versions store [num_heads * num_splits * hidden_size, :]
        SCREAMING_SNAKE_CASE										=   (num_heads, num_splits, hidden_size) + input_shape[1:]
        SCREAMING_SNAKE_CASE										=   param.view(*A__       )
        SCREAMING_SNAKE_CASE										=   param.transpose(0			,  1       ).contiguous()
    SCREAMING_SNAKE_CASE										=   param.view(*A__       )
    return param





def 	__a     (      A__			:  Optional[Any]			,  A__			:  Tuple			,  A__			:  Any       ):
    # The converted output model.
    SCREAMING_SNAKE_CASE										=   {}

    # old versions did not store training args
    SCREAMING_SNAKE_CASE										=   input_state_dict.get("args"			,  A__       )
    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))

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

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

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

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

    # The position embeddings.
    SCREAMING_SNAKE_CASE										=   embeddings["position_embeddings"]["weight"]
    # Read the causal mask dimension (seqlen). [max_sequence_length, hidden_size]
    SCREAMING_SNAKE_CASE										=   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.
    SCREAMING_SNAKE_CASE										=   pos_embeddings

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

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

    # The simple map of names for "automated" rules.
    SCREAMING_SNAKE_CASE										=   {
        "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.
        SCREAMING_SNAKE_CASE										=   layer_re.match(A__       )

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

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

        # The name of the layer.
        SCREAMING_SNAKE_CASE										=   F"transformer.h.{layer_idx}"

        # For layernorm(s), simply store the layer norm.
        if op_name.endswith("layernorm"       ):
            SCREAMING_SNAKE_CASE										=   "ln_1" if op_name.startswith("input"       ) else "ln_2"
            SCREAMING_SNAKE_CASE										=   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.
            SCREAMING_SNAKE_CASE										=   torch.tril(torch.ones((n_positions, n_positions)			,  dtype=torch.floataa       )       ).view(
                1			,  1			,  A__			,  A__       )
            SCREAMING_SNAKE_CASE										=   causal_mask

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

            SCREAMING_SNAKE_CASE										=   fix_query_key_value_ordering(A__			,  A__			,  3			,  A__			,  A__       )
            # Megatron stores (3*D) x D but transformers-GPT2 expects D x 3*D.
            SCREAMING_SNAKE_CASE										=   out_val.transpose(0			,  1       ).contiguous()
            # Store.
            SCREAMING_SNAKE_CASE										=   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":
            SCREAMING_SNAKE_CASE										=   fix_query_key_value_ordering(A__			,  A__			,  3			,  A__			,  A__       )
            # Store. No change of shape.
            SCREAMING_SNAKE_CASE										=   out_val

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

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

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

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

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

    # It should be done!
    return output_state_dict





def 	__a     (      ):
    # Create the argument parser.
    SCREAMING_SNAKE_CASE										=   argparse.ArgumentParser()
    parser.add_argument("--print-checkpoint-structure"			,  action="store_true"       )
    parser.add_argument(
        "path_to_checkpoint"			,  type=A__			,  help="Path to the checkpoint file (.zip archive or direct .pt file)"			,  )
    parser.add_argument(
        "--config_file"			,  default=""			,  type=A__			,  help="An optional config json file describing the pre-trained model."			,  )
    SCREAMING_SNAKE_CASE										=   parser.parse_args()

    # Extract the basename.
    SCREAMING_SNAKE_CASE										=   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:
                SCREAMING_SNAKE_CASE										=   torch.load(A__			,  map_location="cpu"       )
    else:
        SCREAMING_SNAKE_CASE										=   torch.load(args.path_to_checkpoint			,  map_location="cpu"       )

    SCREAMING_SNAKE_CASE										=   input_state_dict.get("args"			,  A__       )

    # 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:
                SCREAMING_SNAKE_CASE										=   "gelu_fast"
            elif ds_args.openai_gelu:
                SCREAMING_SNAKE_CASE										=   "gelu_new"
            else:
                SCREAMING_SNAKE_CASE										=   "gelu"
        else:
            # in the very early days this used to be "gelu_new"
            SCREAMING_SNAKE_CASE										=   "gelu_new"

        # Spell out all parameters in case the defaults change.
        SCREAMING_SNAKE_CASE										=   GPTaConfig(
            vocab_size=50257			,  n_positions=1024			,  n_embd=1024			,  n_layer=24			,  n_head=16			,  n_inner=4096			,  activation_function=A__			,  resid_pdrop=0.1			,  embd_pdrop=0.1			,  attn_pdrop=0.1			,  layer_norm_epsilon=1E-5			,  initializer_range=0.0_2			,  summary_type="cls_index"			,  summary_use_proj=A__			,  summary_activation=A__			,  summary_proj_to_labels=A__			,  summary_first_dropout=0.1			,  scale_attn_weights=A__			,  use_cache=A__			,  bos_token_id=50256			,  eos_token_id=50256			,  )
    else:
        SCREAMING_SNAKE_CASE										=   GPTaConfig.from_json_file(args.config_file       )

    SCREAMING_SNAKE_CASE										=   ["GPT2LMHeadModel"]

    # Convert.
    print("Converting"       )
    SCREAMING_SNAKE_CASE										=   convert_megatron_checkpoint(A__			,  A__			,  A__       )

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

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

    SCREAMING_SNAKE_CASE										=   AutoTokenizer.from_pretrained(A__       )
    SCREAMING_SNAKE_CASE										=   type(A__       ).__name__
    SCREAMING_SNAKE_CASE										=   tokenizer_class

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

    # Save tokenizer based on args
    print(F"Adding {tokenizer_class} tokenizer files"       )
    tokenizer.save_pretrained(A__       )

    # Store the state_dict to file.
    SCREAMING_SNAKE_CASE										=   os.path.join(A__			,  "pytorch_model.bin"       )
    print(F"Saving checkpoint to \"{output_checkpoint_file}\""       )
    torch.save(A__			,  A__       )


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

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__												=						logging.get_logger(__name__)



def   __UpperCAmelCase					(      __a :     str      )     ->	List[Any]:

					"""simple docstring"""

					_a      :       Tuple   =						SwinConfig.from_pretrained(
					    '''microsoft/swin-tiny-patch4-window7-224'''						,out_features=['''stage1''', '''stage2''', '''stage3''', '''stage4''']      )
					_a      :       Dict   =						MaskFormerConfig(backbone_config=__a      )

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

					_a      :       Optional[int]   =						json.load(open(hf_hub_download(__a						,__a						,repo_type='''dataset'''      )						,'''r'''      )      )
					_a      :       Tuple   =						{int(__a      ): v for k, v in idalabel.items()}

					return config



def   __UpperCAmelCase					(      __a :     Optional[Any]      )     ->	Tuple:

					"""simple docstring"""

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

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

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

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

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

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

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

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

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

					return rename_keys



def   __UpperCAmelCase					(      __a :     List[str]						,__a :     List[Any]						,__a :     Optional[Any]      )     ->	Union[str, Any]:

					"""simple docstring"""

					_a      :       str   =						dct.pop(__a      )
					_a      :       str   =						val



def   __UpperCAmelCase					(      __a :     List[Any]						,__a :     Union[str, Any]      )     ->	Union[str, Any]:

					"""simple docstring"""

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



def   __UpperCAmelCase					(      __a :     List[str]						,__a :     List[Any]      )     ->	List[Any]:

					"""simple docstring"""

					_a      :       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)
										_a      :       Union[str, Any]   =						state_dict.pop(F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_weight"""      )
										_a      :       List[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
										_a      :       Union[str, Any]   =						in_proj_weight[: hidden_size, :]
										_a      :       List[Any]   =						in_proj_bias[:config.hidden_size]
										_a      :       Dict   =						in_proj_weight[hidden_size : hidden_size * 2, :]
										_a      :       Any   =						in_proj_bias[hidden_size : hidden_size * 2]
										_a      :       Tuple   =						in_proj_weight[-hidden_size :, :]
										_a      :       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)
										_a      :       List[Any]   =						state_dict.pop(F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_weight"""      )
										_a      :       List[str]   =						state_dict.pop(F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_bias"""      )
										# next, add query, keys and values (in that order) to the state dict
										_a      :       Optional[Any]   =						in_proj_weight[: hidden_size, :]
										_a      :       Any   =						in_proj_bias[:config.hidden_size]
										_a      :       List[str]   =						in_proj_weight[hidden_size : hidden_size * 2, :]
										_a      :       Optional[Any]   =						in_proj_bias[hidden_size : hidden_size * 2]
										_a      :       List[str]   =						in_proj_weight[-hidden_size :, :]
										_a      :       int   =						in_proj_bias[-hidden_size :]
					# fmt: on



def   __UpperCAmelCase					(      )     ->	torch.Tensor:

					"""simple docstring"""

					_a      :       str   =						'''http://images.cocodataset.org/val2017/000000039769.jpg'''
					_a      :       Dict   =						Image.open(requests.get(__a						,stream=__a      ).raw      )
					return im



@torch.no_grad()
def   __UpperCAmelCase					(      __a :     str						,__a :     str						,__a :     str						,__a :     bool = False      )     ->	Union[str, Any]:

					"""simple docstring"""

					_a      :       Optional[Any]   =						get_maskformer_config(__a      )

					# load original state_dict
					with open(__a						,'''rb'''      ) as f:
										_a      :       str   =						pickle.load(__a      )
					_a      :       Union[str, Any]   =						data['''model''']

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

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

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

					# load 🤗 model
					_a      :       Dict   =						MaskFormerForInstanceSegmentation(__a      )
					model.eval()

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

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

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

					_a      :       Optional[Any]   =						image_processor(__a						,return_tensors='''pt'''      )

					_a      :       int   =						model(**__a      )

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

					if model_name == "maskformer-swin-tiny-ade":
										_a      :       Union[str, Any]   =						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]						,__a						,atol=1E-4      )
					print('''Looks ok!'''      )

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

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


if __name__ == "__main__":
			a__												=						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
			)



