





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()
_lowerCamelCase 					=      logging.get_logger(__name__)






def 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				str     ):
		SCREAMING_SNAKE_CASE__								=   SwinConfig.from_pretrained(
		    """microsoft/swin-tiny-patch4-window7-224"""       , out_features=["""stage1""", """stage2""", """stage3""", """stage4"""]     )
		SCREAMING_SNAKE_CASE__								=   MaskFormerConfig(backbone_config=UpperCamelCase__     )

		SCREAMING_SNAKE_CASE__								=   """huggingface/label-files"""
		if "ade20k-full" in model_name:
				# this should be ok
				SCREAMING_SNAKE_CASE__								=   847
				SCREAMING_SNAKE_CASE__								=   """maskformer-ade20k-full-id2label.json"""
		elif "ade" in model_name:
				# this should be ok
				SCREAMING_SNAKE_CASE__								=   150
				SCREAMING_SNAKE_CASE__								=   """ade20k-id2label.json"""
		elif "coco-stuff" in model_name:
				# this should be ok
				SCREAMING_SNAKE_CASE__								=   171
				SCREAMING_SNAKE_CASE__								=   """maskformer-coco-stuff-id2label.json"""
		elif "coco" in model_name:
				# TODO
				SCREAMING_SNAKE_CASE__								=   133
				SCREAMING_SNAKE_CASE__								=   """coco-panoptic-id2label.json"""
		elif "cityscapes" in model_name:
				# this should be ok
				SCREAMING_SNAKE_CASE__								=   19
				SCREAMING_SNAKE_CASE__								=   """cityscapes-id2label.json"""
		elif "vistas" in model_name:
				# this should be ok
				SCREAMING_SNAKE_CASE__								=   65
				SCREAMING_SNAKE_CASE__								=   """mapillary-vistas-id2label.json"""

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

		return config






def 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				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 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				int       , UpperCamelCase__:				Optional[int]       , UpperCamelCase__:				Optional[int]     ):
		SCREAMING_SNAKE_CASE__								=   dct.pop(UpperCamelCase__     )
		SCREAMING_SNAKE_CASE__								=   val






def 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				str       , UpperCamelCase__:				Union[str, 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 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				int       , UpperCamelCase__:				Optional[Any]     ):
		# 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 					SCREAMING_SNAKE_CASE__				(							):
		SCREAMING_SNAKE_CASE__								=   """http://images.cocodataset.org/val2017/000000039769.jpg"""
		SCREAMING_SNAKE_CASE__								=   Image.open(requests.get(UpperCamelCase__       , stream=UpperCamelCase__     ).raw     )
		return im






@torch.no_grad()
def 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				str       , UpperCamelCase__:				str       , UpperCamelCase__:				str       , UpperCamelCase__:				bool = False     ):
		SCREAMING_SNAKE_CASE__								=   get_maskformer_config(UpperCamelCase__     )

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

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

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

		# update to torch tensors
		for key, value in state_dict.items():
				SCREAMING_SNAKE_CASE__								=   torch.from_numpy(UpperCamelCase__     )

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

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

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

		# verify results
		SCREAMING_SNAKE_CASE__								=   prepare_img()
		if "vistas" in model_name:
				SCREAMING_SNAKE_CASE__								=   65
		elif "cityscapes" in model_name:
				SCREAMING_SNAKE_CASE__								=   65_535
		else:
				SCREAMING_SNAKE_CASE__								=   255
		SCREAMING_SNAKE_CASE__								=   True if """ade""" in model_name else False
		SCREAMING_SNAKE_CASE__								=   MaskFormerImageProcessor(ignore_index=UpperCamelCase__       , reduce_labels=UpperCamelCase__     )

		SCREAMING_SNAKE_CASE__								=   image_processor(UpperCamelCase__       , return_tensors="""pt"""     )

		SCREAMING_SNAKE_CASE__								=   model(**UpperCamelCase__     )

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

		if model_name == "maskformer-swin-tiny-ade":
				SCREAMING_SNAKE_CASE__								=   torch.tensor(
				    [[3.6_3_5_3, -4.4_7_7_0, -2.6_0_6_5], [0.5_0_8_1, -4.2_3_9_4, -3.5_3_4_3], [2.1_9_0_9, -5.0_3_5_3, -1.9_3_2_3]]     )
		assert torch.allclose(outputs.class_queries_logits[0, :3, :3]       , UpperCamelCase__       , 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(UpperCamelCase__     ).mkdir(exist_ok=UpperCamelCase__     )
				model.save_pretrained(UpperCamelCase__     )
				image_processor.save_pretrained(UpperCamelCase__     )

		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__":
	_lowerCamelCase 					=      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.'
	)

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





import torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import TaBlock, TaConfig, TaLayerNorm

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class 						UpperCAmelCase  (							UpperCAmelCase__			,  UpperCAmelCase__			,  UpperCAmelCase__     ):







						'''simple docstring'''





						@register_to_config
						def __init__(		self				:	Tuple			,      __lowercase				:	int			,      __lowercase				:	int			,      __lowercase				:	int			,      __lowercase				:	float			,      __lowercase				:	int			,      __lowercase				:	int			,      __lowercase				:	int			,      __lowercase				:	int			,      __lowercase				:	str			,      __lowercase				:	bool = False			,      ):






							"""simple docstring"""






							super().__init__()

							snake_case_						       =	nn.Embedding(__lowercase			,      __lowercase		)

							snake_case_						       =	nn.Embedding(__lowercase			,      __lowercase		)
							snake_case_						       =	False

							snake_case_						       =	nn.Dropout(p=__lowercase		)

							snake_case_						       =	TaConfig(
							    vocab_size=__lowercase			,      d_model=__lowercase			,      num_heads=__lowercase			,      d_kv=__lowercase			,      d_ff=__lowercase			,      dropout_rate=__lowercase			,      feed_forward_proj=__lowercase			,      is_decoder=__lowercase			,      is_encoder_decoder=__lowercase			,      )

							snake_case_						       =	nn.ModuleList()
							for lyr_num in range(__lowercase		):
								snake_case_						       =	TaBlock(__lowercase		)
								self.encoders.append(__lowercase		)

							snake_case_						       =	TaLayerNorm(__lowercase		)
							snake_case_						       =	nn.Dropout(p=__lowercase		)






						def 			snake_case__		(		self				:	str			,      __lowercase				:	Dict			,      __lowercase				:	str		):






							"""simple docstring"""






							snake_case_						       =	self.token_embedder(__lowercase		)

							snake_case_						       =	encoder_input_tokens.shape[1]
							snake_case_						       =	torch.arange(__lowercase			,      device=encoder_input_tokens.device		)
							x += self.position_encoding(__lowercase		)

							snake_case_						       =	self.dropout_pre(__lowercase		)

							# inverted the attention mask
							snake_case_						       =	encoder_input_tokens.size()
							snake_case_						       =	self.get_extended_attention_mask(__lowercase			,      __lowercase		)

							for lyr in self.encoders:
								snake_case_						       =	lyr(__lowercase			,      __lowercase		)[0]
							snake_case_						       =	self.layer_norm(__lowercase		)

							return self.dropout_post(__lowercase		), encoder_inputs_mask
