

"""simple docstring"""


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					:Optional[Any]    = logging.get_logger(__name__)




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

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

      SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	json.load(open(hf_hub_download(__lowerCAmelCase  ,					__lowerCAmelCase  ,					repo_type="""dataset"""							)  ,					"""r"""							)							)
      SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	{int(__lowerCAmelCase							): v for k, v in idalabel.items()}

      return config




def  _lowercase					(    __lowerCAmelCase							)       ->				Optional[int]:
      SCREAMING_SNAKE_CASE__   :      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  _lowercase					(    __lowerCAmelCase  ,					__lowerCAmelCase  ,					__lowerCAmelCase							)       ->				List[str]:
      SCREAMING_SNAKE_CASE__   :      int      		=	dct.pop(__lowerCAmelCase							)
      SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	val




def  _lowercase					(    __lowerCAmelCase  ,					__lowerCAmelCase							)       ->				Any:
      SCREAMING_SNAKE_CASE__   :      int      		=	[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__   :      Tuple      		=	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__   :      Any      		=	state_dict.pop(F'''backbone.layers.{i}.blocks.{j}.attn.qkv.weight'''							)
                  SCREAMING_SNAKE_CASE__   :      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
                  SCREAMING_SNAKE_CASE__   :      List[str]      		=	in_proj_weight[:dim, :]
                  SCREAMING_SNAKE_CASE__   :      List[str]      		=	in_proj_bias[: dim]
                  SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	in_proj_weight[
                      dim : dim * 2, :
                  ]
                  SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	in_proj_bias[
                      dim : dim * 2
                  ]
                  SCREAMING_SNAKE_CASE__   :      Any      		=	in_proj_weight[
                      -dim :, :
                  ]
                  SCREAMING_SNAKE_CASE__   :      List[str]      		=	in_proj_bias[-dim :]
                  # fmt: on




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




def  _lowercase					(    )       ->				str:
      SCREAMING_SNAKE_CASE__   :      List[str]      		=	"""http://images.cocodataset.org/val2017/000000039769.jpg"""
      SCREAMING_SNAKE_CASE__   :      Tuple      		=	Image.open(requests.get(__lowerCAmelCase  ,					stream=__lowerCAmelCase							).raw							)
      return im




@torch.no_grad()
def  _lowercase					(    __lowerCAmelCase  ,					__lowerCAmelCase  ,					__lowerCAmelCase  ,					__lowerCAmelCase = False							)       ->				str:
      SCREAMING_SNAKE_CASE__   :      Dict      		=	get_maskformer_config(__lowerCAmelCase							)

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

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

      # rename keys
      SCREAMING_SNAKE_CASE__   :      List[str]      		=	create_rename_keys(__lowerCAmelCase							)
      for src, dest in rename_keys:
            rename_key(__lowerCAmelCase  ,					__lowerCAmelCase  ,					__lowerCAmelCase							)
      read_in_swin_q_k_v(__lowerCAmelCase  ,					config.backbone_config							)
      read_in_decoder_q_k_v(__lowerCAmelCase  ,					__lowerCAmelCase							)

      # update to torch tensors
      for key, value in state_dict.items():
            SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	torch.from_numpy(__lowerCAmelCase							)

      # load 🤗 model
      SCREAMING_SNAKE_CASE__   :      List[str]      		=	MaskFormerForInstanceSegmentation(__lowerCAmelCase							)
      model.eval()

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

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

      # verify results
      SCREAMING_SNAKE_CASE__   :      List[Any]      		=	prepare_img()
      if "vistas" in model_name:
            SCREAMING_SNAKE_CASE__   :      str      		=	65
      elif "cityscapes" in model_name:
            SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	6_5535
      else:
            SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	255
      SCREAMING_SNAKE_CASE__   :      Dict      		=	True if """ade""" in model_name else False
      SCREAMING_SNAKE_CASE__   :      List[str]      		=	MaskFormerImageProcessor(ignore_index=__lowerCAmelCase  ,					reduce_labels=__lowerCAmelCase							)

      SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	image_processor(__lowerCAmelCase  ,					return_tensors="""pt"""							)

      SCREAMING_SNAKE_CASE__   :      int      		=	model(**__lowerCAmelCase							)

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

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

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

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


if __name__ == "__main__":
  a					:Union[str, Any]    = argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      "--model_name",
      default="maskformer-swin-tiny-ade",
      type=str,
      help=("Name of the MaskFormer model you\'d like to convert",),
  )
  parser.add_argument(
      "--checkpoint_path",
      default="/Users/nielsrogge/Documents/MaskFormer_checkpoints/MaskFormer-Swin-tiny-ADE20k/model.pkl",
      type=str,
      help="Path to the original state dict (.pth file).",
  )
  parser.add_argument(
      "--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
  )
  parser.add_argument(
      "--push_to_hub", action="store_true", help="Whether or not to push the converted model to the 🤗 hub."
  )

  a					:Optional[Any]    = parser.parse_args()
  convert_maskformer_checkpoint(
      args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
  )







"""simple docstring"""


from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging


a					:Optional[Any]    = logging.get_logger(__name__)

a					:Union[str, Any]    = {
    "t5-small": "https://huggingface.co/t5-small/resolve/main/config.json",
    "t5-base": "https://huggingface.co/t5-base/resolve/main/config.json",
    "t5-large": "https://huggingface.co/t5-large/resolve/main/config.json",
    "t5-3b": "https://huggingface.co/t5-3b/resolve/main/config.json",
    "t5-11b": "https://huggingface.co/t5-11b/resolve/main/config.json",
}

class      __a							(UpperCamelCase_):






     '''simple docstring'''
     _SCREAMING_SNAKE_CASE						:List[Any]       = """t5"""
     _SCREAMING_SNAKE_CASE						:List[str]       = ["""past_key_values"""]
     _SCREAMING_SNAKE_CASE						:Any       = {"""hidden_size""": """d_model""", """num_attention_heads""": """num_heads""", """num_hidden_layers""": """num_layers"""}

     def __init__(						self	,    _a=32_128	,    _a=512	,    _a=64	,    _a=2_048	,    _a=6	,    _a=None	,    _a=8	,    _a=32	,    _a=128	,    _a=0.1	,    _a=1E-6	,    _a=1.0	,    _a="relu"	,    _a=True	,    _a=True	,    _a=0	,    _a=1	,    **_a	,    )  ->      Optional[int]:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	vocab_size
           SCREAMING_SNAKE_CASE__   :      Tuple      		=	d_model
           SCREAMING_SNAKE_CASE__   :      int      		=	d_kv
           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	d_ff
           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	num_layers
           SCREAMING_SNAKE_CASE__   :      int      		=	(
               num_decoder_layers if num_decoder_layers is not None else self.num_layers
           )  # default = symmetry
           SCREAMING_SNAKE_CASE__   :      Tuple      		=	num_heads
           SCREAMING_SNAKE_CASE__   :      Dict      		=	relative_attention_num_buckets
           SCREAMING_SNAKE_CASE__   :      str      		=	relative_attention_max_distance
           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	dropout_rate
           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	layer_norm_epsilon
           SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	initializer_factor
           SCREAMING_SNAKE_CASE__   :      Tuple      		=	feed_forward_proj
           SCREAMING_SNAKE_CASE__   :      str      		=	use_cache

           SCREAMING_SNAKE_CASE__   :      List[str]      		=	self.feed_forward_proj.split("""-"""  )
           SCREAMING_SNAKE_CASE__   :      Dict      		=	act_info[-1]
           SCREAMING_SNAKE_CASE__   :      str      		=	act_info[0] == """gated"""

           if len(_a  ) > 1 and act_info[0] != "gated" or len(_a  ) > 2:
                 raise ValueError(
                     f'''`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer.'''
                     """Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. """
                     """'gated-gelu' or 'relu'"""  )

           # for backwards compatibility
           if feed_forward_proj == "gated-gelu":
                 SCREAMING_SNAKE_CASE__   :      List[Any]      		=	"""gelu_new"""

           super().__init__(
               pad_token_id=_a	,    eos_token_id=_a	,    is_encoder_decoder=_a	,    **_a	,    )





class      __a							(UpperCamelCase_):






     '''simple docstring'''

     @property
     def 					_a			(						self  )  ->      Mapping[str, Mapping[int, str]]:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      int      		=	{
               """input_ids""": {0: """batch""", 1: """encoder_sequence"""},
               """attention_mask""": {0: """batch""", 1: """encoder_sequence"""},
           }
           if self.use_past:
                 SCREAMING_SNAKE_CASE__   :      Tuple      		=	"""past_encoder_sequence + sequence"""
                 SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	{0: """batch"""}
                 SCREAMING_SNAKE_CASE__   :      Tuple      		=	{0: """batch""", 1: """past_decoder_sequence + sequence"""}
           else:
                 SCREAMING_SNAKE_CASE__   :      str      		=	{0: """batch""", 1: """decoder_sequence"""}
                 SCREAMING_SNAKE_CASE__   :      Dict      		=	{0: """batch""", 1: """decoder_sequence"""}

           if self.use_past:
                 self.fill_with_past_key_values_(_a	,    direction="""inputs"""  )

           return common_inputs





     @property
     def 					_a			(						self  )  ->      int:


           """simple docstring"""


           return 13




