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()
__lowercase							 =					logging.get_logger(__name__)



def lowerCamelCase  (   SCREAMING_SNAKE_CASE	):
    '''simple docstring'''







    __UpperCamelCase     :List[str]			  =		SwinConfig.from_pretrained(
        '''microsoft/swin-tiny-patch4-window7-224'''  ,			out_features=['''stage1''', '''stage2''', '''stage3''', '''stage4''']	)
    __UpperCamelCase     :str			  =		MaskFormerConfig(backbone_config=SCREAMING_SNAKE_CASE	)

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

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

    return config



def lowerCamelCase  (   SCREAMING_SNAKE_CASE	):
    '''simple docstring'''







    __UpperCamelCase     :int			  =		[]
    # 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  (   SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	):
    '''simple docstring'''







    __UpperCamelCase     :List[str]			  =		dct.pop(SCREAMING_SNAKE_CASE	)
    __UpperCamelCase     :Dict			  =		val



def lowerCamelCase  (   SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	):
    '''simple docstring'''







    __UpperCamelCase     :int			  =		[int(backbone_config.embed_dim * 2**i	) for i in range(len(backbone_config.depths	)	)]
    for i in range(len(backbone_config.depths	)	):
        __UpperCamelCase     :int			  =		num_features[i]
        for j in range(backbone_config.depths[i]	):
            # fmt: off
            # read in weights + bias of input projection layer (in original implementation, this is a single matrix + bias)
            __UpperCamelCase     :Any			  =		state_dict.pop(f"""backbone.layers.{i}.blocks.{j}.attn.qkv.weight"""	)
            __UpperCamelCase     :List[Any]			  =		state_dict.pop(f"""backbone.layers.{i}.blocks.{j}.attn.qkv.bias"""	)
            # next, add query, keys and values (in that order) to the state dict
            __UpperCamelCase     :int			  =		in_proj_weight[:dim, :]
            __UpperCamelCase     :Union[str, Any]			  =		in_proj_bias[: dim]
            __UpperCamelCase     :List[Any]			  =		in_proj_weight[
                dim : dim * 2, :
            ]
            __UpperCamelCase     :Union[str, Any]			  =		in_proj_bias[
                dim : dim * 2
            ]
            __UpperCamelCase     :Optional[Any]			  =		in_proj_weight[
                -dim :, :
            ]
            __UpperCamelCase     :Any			  =		in_proj_bias[-dim :]
            # fmt: on



def lowerCamelCase  (   SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	):
    '''simple docstring'''







    __UpperCamelCase     :Dict			  =		config.decoder_config.hidden_size
    for idx in range(config.decoder_config.decoder_layers	):
        # read in weights + bias of self-attention input projection layer (in the original implementation, this is a single matrix + bias)
        __UpperCamelCase     :Union[str, Any]			  =		state_dict.pop(f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_weight"""	)
        __UpperCamelCase     :Optional[int]			  =		state_dict.pop(f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_bias"""	)
        # next, add query, keys and values (in that order) to the state dict
        __UpperCamelCase     :Optional[Any]			  =		in_proj_weight[: hidden_size, :]
        __UpperCamelCase     :Tuple			  =		in_proj_bias[:config.hidden_size]
        __UpperCamelCase     :Any			  =		in_proj_weight[hidden_size : hidden_size * 2, :]
        __UpperCamelCase     :Dict			  =		in_proj_bias[hidden_size : hidden_size * 2]
        __UpperCamelCase     :List[str]			  =		in_proj_weight[-hidden_size :, :]
        __UpperCamelCase     :Optional[Any]			  =		in_proj_bias[-hidden_size :]
        # read in weights + bias of cross-attention input projection layer (in the original implementation, this is a single matrix + bias)
        __UpperCamelCase     :Dict			  =		state_dict.pop(f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_weight"""	)
        __UpperCamelCase     :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
        __UpperCamelCase     :List[str]			  =		in_proj_weight[: hidden_size, :]
        __UpperCamelCase     :str			  =		in_proj_bias[:config.hidden_size]
        __UpperCamelCase     :Optional[int]			  =		in_proj_weight[hidden_size : hidden_size * 2, :]
        __UpperCamelCase     :Union[str, Any]			  =		in_proj_bias[hidden_size : hidden_size * 2]
        __UpperCamelCase     :Optional[Any]			  =		in_proj_weight[-hidden_size :, :]
        __UpperCamelCase     :Optional[int]			  =		in_proj_bias[-hidden_size :]
    # fmt: on



def lowerCamelCase  (   ):
    '''simple docstring'''







    __UpperCamelCase     :Union[str, Any]			  =		'''http://images.cocodataset.org/val2017/000000039769.jpg'''
    __UpperCamelCase     :List[str]			  =		Image.open(requests.get(SCREAMING_SNAKE_CASE  ,			stream=SCREAMING_SNAKE_CASE	).raw	)
    return im



@torch.no_grad()
def lowerCamelCase  (   SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE = False	):
    '''simple docstring'''







    __UpperCamelCase     :str			  =		get_maskformer_config(SCREAMING_SNAKE_CASE	)

    # load original state_dict
    with open(SCREAMING_SNAKE_CASE  ,			'''rb'''	) as f:
        __UpperCamelCase     :str			  =		pickle.load(SCREAMING_SNAKE_CASE	)
    __UpperCamelCase     :int			  =		data['''model''']

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

    # rename keys
    __UpperCamelCase     :Optional[Any]			  =		create_rename_keys(SCREAMING_SNAKE_CASE	)
    for src, dest in rename_keys:
        rename_key(SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	)
    read_in_swin_q_k_v(SCREAMING_SNAKE_CASE  ,			config.backbone_config	)
    read_in_decoder_q_k_v(SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	)

    # update to torch tensors
    for key, value in state_dict.items():
        __UpperCamelCase     :Dict			  =		torch.from_numpy(SCREAMING_SNAKE_CASE	)

    # load 🤗 model
    __UpperCamelCase     :Optional[int]			  =		MaskFormerForInstanceSegmentation(SCREAMING_SNAKE_CASE	)
    model.eval()

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

    __UpperCamelCase  ,       __UpperCamelCase     :Optional[int]			  =		model.load_state_dict(SCREAMING_SNAKE_CASE  ,			strict=SCREAMING_SNAKE_CASE	)
    assert missing_keys == [
        "model.pixel_level_module.encoder.model.layernorm.weight",
        "model.pixel_level_module.encoder.model.layernorm.bias",
    ]
    assert len(SCREAMING_SNAKE_CASE	) == 0, f"""Unexpected keys: {unexpected_keys}"""

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

    __UpperCamelCase     :Union[str, Any]			  =		image_processor(SCREAMING_SNAKE_CASE  ,			return_tensors='''pt'''	)

    __UpperCamelCase     :str			  =		model(**SCREAMING_SNAKE_CASE	)

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

    if model_name == "maskformer-swin-tiny-ade":
        __UpperCamelCase     :List[Any]			  =		torch.tensor(
            [[3.6_353, -4.4_770, -2.6_065], [0.5_081, -4.2_394, -3.5_343], [2.1_909, -5.0_353, -1.9_323]]	)
    assert torch.allclose(outputs.class_queries_logits[0, :3, :3]  ,			SCREAMING_SNAKE_CASE  ,			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(SCREAMING_SNAKE_CASE	).mkdir(exist_ok=SCREAMING_SNAKE_CASE	)
        model.save_pretrained(SCREAMING_SNAKE_CASE	)
        image_processor.save_pretrained(SCREAMING_SNAKE_CASE	)

    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__":
    __lowercase							 =					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.'''
    )

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










"""simple docstring"""
import argparse
import json
import os
from collections import OrderedDict

import numpy as np
import tensorflow as tf
import torch
def    A_  (			_lowercase	):







						'''simple docstring'''


						snake_case_ :Union[str, Any]						=			os.path.join(args.tf_model_dir,					"""parameters.json"""	)
						snake_case_ :Any						=			json.loads(open(_lowercase	).read()	)
						if not params:
												raise ValueError(
												    f"""It seems that the json file at {parameter_file} is empty. Make sure you have a correct json file."""	)
						if not args.output.endswith(""".pt"""	):
												snake_case_ :Optional[int]						=			args.output + """.pt"""
						snake_case_ :List[str]						=			OrderedDict()
						with tf.device("""/CPU:0"""	):
												snake_case_ :Dict						=			tf.train.load_checkpoint(args.tf_model_dir	)
												snake_case_ :str						=			reader.get_variable_to_shape_map()
												for key_name in shapes.keys():
																		snake_case_ :List[Any]						=			reader.get_tensor(_lowercase	).astype(np.floataa	)
																		if key_name.endswith("""/adam_m"""	) or key_name.endswith("""/adam_v"""	):
																								continue
																		if key_name.startswith("""pasts/"""	):
																								if key_name.startswith("""pasts/mlp"""	):
																														snake_case_ :Any						=			int(key_name[9]	)
																								elif key_name.startswith("""pasts/out"""	):
																														snake_case_ :Optional[int]						=			8
																								snake_case_ :List[str]						=			"""model.sqout.%d.weight""" % (player * 2)  # enter to nn.Sequencial with Tanh, so 2 at a time
																								snake_case_ :Optional[Any]						=			vnp.transpose([1, 0]	).copy()  # Mesh-Tensorflow is a diagonal matrix
																								snake_case_ :List[str]						=			torch.tensor(_lowercase	)
																		elif key_name.startswith("""model/moe"""	):
																								snake_case_ :Tuple						=			int(key_name[9:].split("""/"""	)[0]	)
																								if key_name.endswith("""/switch_gating/kernel"""	):
																														snake_case_ :Union[str, Any]						=			"""model.blocks.%d.feed_forward.mlp.router.classifier.weight""" % player
																														snake_case_ :Optional[Any]						=			vnp.transpose([1, 0]	).copy()  # Mesh-Tensorflow is a diagonal matrix
																														snake_case_ :Optional[Any]						=			torch.tensor(_lowercase	)
																								elif key_name.endswith("""/softmlp/kernel"""	):
																														snake_case_ :List[Any]						=			"""model.blocks.%d.feed_forward.soft_bypass_mlp.weight""" % player
																														snake_case_ :Optional[int]						=			vnp.transpose([1, 0]	).copy()  # Mesh-Tensorflow is a diagonal matrix
																														snake_case_ :Optional[Any]						=			torch.tensor(_lowercase	)
																								elif key_name.endswith("""/wo/kernel"""	) or key_name.endswith("""/wi/kernel"""	):
																														snake_case_ :Dict						=			key_name[-9:-7]
																														for i in range(16	):
																																				snake_case_ :str						=			"""model.blocks.%d.feed_forward.mlp.experts.expert_%d.%s.weight""" % (player, i, nlayer)
																																				snake_case_ :Tuple						=			(
																																				    vnp[i].transpose([1, 0]	).copy()
																																				)  # In Mesh-Tensorflow, it is one array, so it is divided
																																				snake_case_ :Optional[int]						=			torch.tensor(_lowercase	)
																		elif key_name.startswith("""model/mlp"""	):
																								snake_case_ :Optional[int]						=			int(key_name[9:].split("""/"""	)[0]	)
																								if key_name.endswith("""/p1/kernel"""	):
																														snake_case_ :Union[str, Any]						=			"""model.blocks.%d.feed_forward.mlp.wi.weight""" % player
																														snake_case_ :Dict						=			vnp.transpose([1, 0]	).copy()  # Mesh-Tensorflow is a diagonal matrix
																														snake_case_ :Optional[Any]						=			torch.tensor(_lowercase	)
																								elif key_name.endswith("""/p1/bias"""	):
																														snake_case_ :List[Any]						=			"""model.blocks.%d.feed_forward.mlp.wi.bias""" % player
																														snake_case_ :str						=			vnp.copy()  # same because it is one dimensional
																														snake_case_ :Optional[Any]						=			torch.tensor(_lowercase	)
																								elif key_name.endswith("""/p2/kernel"""	):
																														snake_case_ :Union[str, Any]						=			"""model.blocks.%d.feed_forward.mlp.wo.weight""" % player
																														snake_case_ :Dict						=			vnp.transpose([1, 0]	).copy()  # Mesh-Tensorflow is a diagonal matrix
																														snake_case_ :Tuple						=			torch.tensor(_lowercase	)
																								elif key_name.endswith("""/p2/bias"""	):
																														snake_case_ :Dict						=			"""model.blocks.%d.feed_forward.mlp.wo.bias""" % player
																														snake_case_ :Any						=			vnp.copy()  # same because it is one dimensional
																														snake_case_ :Optional[int]						=			torch.tensor(_lowercase	)
																		elif key_name.startswith("""model/ln"""	):
																								snake_case_ :Union[str, Any]						=			int(key_name[8:].split("""/"""	)[0]	)
																								if key_name.endswith("""/b"""	):
																														snake_case_ :str						=			"""model.blocks.%d.feed_forward.norm.bias""" % player
																														snake_case_ :Dict						=			vnp.copy()  # same because it is one dimensional
																														snake_case_ :int						=			torch.tensor(_lowercase	)
																								elif key_name.endswith("""/g"""	):
																														snake_case_ :Dict						=			"""model.blocks.%d.feed_forward.norm.weight""" % player
																														snake_case_ :Dict						=			vnp.copy()  # same because it is one dimensional
																														snake_case_ :Tuple						=			torch.tensor(_lowercase	)
																		elif key_name.startswith("""model/att"""	):
																								snake_case_ :List[str]						=			int(key_name[9:].split("""/"""	)[0]	)
																								if key_name.endswith("""/qkv/kernel"""	):
																														snake_case_ :Optional[int]						=			vnp.copy()  # Compute same dimension as Mesh-tensorflow using einsum
																														snake_case_ :Dict						=			state[:, 0, :, :]
																														snake_case_ :int						=			state[:, 1, :, :]
																														snake_case_ :List[str]						=			state[:, 2, :, :]
																														snake_case_ :str						=			(
																														    state_q.reshape([state_q.shape[0], state_q.shape[1] * state_q.shape[2]]	)
																														    .transpose([1, 0]	)
																														    .copy()
																														)  # Mesh-Tensorflow is a diagonal matrix
																														snake_case_ :Any						=			(
																														    state_k.reshape([state_k.shape[0], state_k.shape[1] * state_k.shape[2]]	)
																														    .transpose([1, 0]	)
																														    .copy()
																														)  # Mesh-Tensorflow is a diagonal matrix
																														snake_case_ :Optional[int]						=			(
																														    state_v.reshape([state_v.shape[0], state_v.shape[1] * state_v.shape[2]]	)
																														    .transpose([1, 0]	)
																														    .copy()
																														)  # Mesh-Tensorflow is a diagonal matrix
																														snake_case_ :int						=			"""model.blocks.%d.self_attn.self_attn.q_proj.weight""" % player
																														snake_case_ :int						=			torch.tensor(_lowercase	)
																														snake_case_ :Optional[Any]						=			"""model.blocks.%d.self_attn.self_attn.k_proj.weight""" % player
																														snake_case_ :Dict						=			torch.tensor(_lowercase	)
																														snake_case_ :Dict						=			"""model.blocks.%d.self_attn.self_attn.v_proj.weight""" % player
																														snake_case_ :Optional[Any]						=			torch.tensor(_lowercase	)
																								elif key_name.endswith("""/o/kernel"""	):
																														snake_case_ :str						=			"""model.blocks.%d.self_attn.self_attn.out_proj.weight""" % player
																														snake_case_ :str						=			(
																														    vnp.reshape([vnp.shape[0] * vnp.shape[1], vnp.shape[2]]	).transpose([1, 0]	).copy()
																														)  # Mesh-Tensorflow is a diagonal matrix
																														snake_case_ :Any						=			torch.tensor(_lowercase	)
																		elif key_name.startswith("""model/an"""	):
																								snake_case_ :Optional[int]						=			int(key_name[8:].split("""/"""	)[0]	)
																								if key_name.endswith("""/b"""	):
																														snake_case_ :Any						=			"""model.blocks.%d.self_attn.norm.bias""" % player
																														snake_case_ :Optional[int]						=			vnp.copy()  # same because it is one dimensional
																														snake_case_ :Tuple						=			torch.tensor(_lowercase	)
																								elif key_name.endswith("""/g"""	):
																														snake_case_ :Union[str, Any]						=			"""model.blocks.%d.self_attn.norm.weight""" % player
																														snake_case_ :Dict						=			vnp.copy()  # same because it is one dimensional
																														snake_case_ :Optional[int]						=			torch.tensor(_lowercase	)
																		elif (
																		    key_name.startswith("""model/wte"""	)
																		    or key_name.startswith("""model/wpe"""	)
																		    or key_name.startswith("""model/ete"""	)
																		):
																								snake_case_ :List[Any]						=			{"""wte""": """embed_tokens""", """wpe""": """position_embeddings""", """ete""": """extra_position_embeddings"""}[
																								    key_name[-3:]
																								]
																								snake_case_ :Optional[Any]						=			"""model.%s.weight""" % nlayer
																								snake_case_ :Any						=			vnp.copy()  # same in embedded
																								snake_case_ :List[Any]						=			torch.tensor(_lowercase	)
																								if key_name.startswith("""model/wte"""	):
																														snake_case_ :Tuple						=			"""lm_head.weight"""
																														snake_case_ :List[str]						=			vnp.copy()  # same in embedded
																														snake_case_ :List[Any]						=			torch.tensor(_lowercase	)
																		elif key_name.startswith("""model/wob"""	):
																								snake_case_ :str						=			"""final_logits_bias"""
																								snake_case_ :Any						=			vnp.copy()  # same in embedded
																								snake_case_ :List[Any]						=			state.reshape((1, -1)	)
																								snake_case_ :Union[str, Any]						=			torch.tensor(_lowercase	)
																		elif key_name == "model/dense/kernel":
																								snake_case_ :str						=			"""model.last_project.weight"""
																								snake_case_ :Dict						=			vnp.transpose([1, 0]	).copy()  # Mesh-Tensorflow is a diagonal matrix
																								snake_case_ :int						=			torch.tensor(_lowercase	)
																		elif key_name == "model/dense_1/bias":
																								snake_case_ :Optional[int]						=			"""model.last_project.bias"""
																								snake_case_ :Tuple						=			vnp.copy()  # same because it is one dimensional
																								snake_case_ :Any						=			torch.tensor(_lowercase	)
						torch.save(_lowercase,					args.output	)


if __name__ == "__main__":
						__a      =				argparse.ArgumentParser(
						    description="model converter.", formatter_class=argparse.ArgumentDefaultsHelpFormatter
						)
						parser.add_argument("--tf_model_dir", metavar="PATH", type=str, required=True, help="import model")
						parser.add_argument("--output", metavar="PATH", type=str, required=True, help="output model")
						__a      =				parser.parse_args()
						convert_tf_gptsan_to_pt(args)
