

import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_funnel import FunnelTokenizer


__lowercase				      =						logging.get_logger(__name__)

__lowercase				      =						{"""vocab_file""": """vocab.txt""", """tokenizer_file""": """tokenizer.json"""}

__lowercase				      =						[
    """small""",
    """small-base""",
    """medium""",
    """medium-base""",
    """intermediate""",
    """intermediate-base""",
    """large""",
    """large-base""",
    """xlarge""",
    """xlarge-base""",
]

__lowercase				      =						{
    """vocab_file""": {
        """funnel-transformer/small""": """https://huggingface.co/funnel-transformer/small/resolve/main/vocab.txt""",
        """funnel-transformer/small-base""": """https://huggingface.co/funnel-transformer/small-base/resolve/main/vocab.txt""",
        """funnel-transformer/medium""": """https://huggingface.co/funnel-transformer/medium/resolve/main/vocab.txt""",
        """funnel-transformer/medium-base""": (
            """https://huggingface.co/funnel-transformer/medium-base/resolve/main/vocab.txt"""
        ),
        """funnel-transformer/intermediate""": (
            """https://huggingface.co/funnel-transformer/intermediate/resolve/main/vocab.txt"""
        ),
        """funnel-transformer/intermediate-base""": (
            """https://huggingface.co/funnel-transformer/intermediate-base/resolve/main/vocab.txt"""
        ),
        """funnel-transformer/large""": """https://huggingface.co/funnel-transformer/large/resolve/main/vocab.txt""",
        """funnel-transformer/large-base""": """https://huggingface.co/funnel-transformer/large-base/resolve/main/vocab.txt""",
        """funnel-transformer/xlarge""": """https://huggingface.co/funnel-transformer/xlarge/resolve/main/vocab.txt""",
        """funnel-transformer/xlarge-base""": (
            """https://huggingface.co/funnel-transformer/xlarge-base/resolve/main/vocab.txt"""
        ),
    },
    """tokenizer_file""": {
        """funnel-transformer/small""": """https://huggingface.co/funnel-transformer/small/resolve/main/tokenizer.json""",
        """funnel-transformer/small-base""": (
            """https://huggingface.co/funnel-transformer/small-base/resolve/main/tokenizer.json"""
        ),
        """funnel-transformer/medium""": """https://huggingface.co/funnel-transformer/medium/resolve/main/tokenizer.json""",
        """funnel-transformer/medium-base""": (
            """https://huggingface.co/funnel-transformer/medium-base/resolve/main/tokenizer.json"""
        ),
        """funnel-transformer/intermediate""": (
            """https://huggingface.co/funnel-transformer/intermediate/resolve/main/tokenizer.json"""
        ),
        """funnel-transformer/intermediate-base""": (
            """https://huggingface.co/funnel-transformer/intermediate-base/resolve/main/tokenizer.json"""
        ),
        """funnel-transformer/large""": """https://huggingface.co/funnel-transformer/large/resolve/main/tokenizer.json""",
        """funnel-transformer/large-base""": (
            """https://huggingface.co/funnel-transformer/large-base/resolve/main/tokenizer.json"""
        ),
        """funnel-transformer/xlarge""": """https://huggingface.co/funnel-transformer/xlarge/resolve/main/tokenizer.json""",
        """funnel-transformer/xlarge-base""": (
            """https://huggingface.co/funnel-transformer/xlarge-base/resolve/main/tokenizer.json"""
        ),
    },
}
__lowercase				      =						{f'funnel-transformer/{name}': 512 for name in _model_names}
__lowercase				      =						{f'funnel-transformer/{name}': {"""do_lower_case""": True} for name in _model_names}

class    _lowercase				(					_SCREAMING_SNAKE_CASE			):
   _lowercase     :       str  					=    VOCAB_FILES_NAMES
   _lowercase     :       Tuple  					=    PRETRAINED_VOCAB_FILES_MAP
   _lowercase     :       Any  					=    PRETRAINED_INIT_CONFIGURATION
   _lowercase     :       List[str]  					=    FunnelTokenizer
   _lowercase     :       Any  					=    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   _lowercase     :       int  					=    2

   def __init__(				self	:    List[str]			,						lowerCamelCase__	:    Dict=None			,						lowerCamelCase__	:    Tuple=None			,						lowerCamelCase__	:    Any=True			,						lowerCamelCase__	:    List[Any]="<unk>"			,						lowerCamelCase__	:    Dict="<sep>"			,						lowerCamelCase__	:    str="<pad>"			,						lowerCamelCase__	:    Optional[int]="<cls>"			,						lowerCamelCase__	:    Dict="<mask>"			,						lowerCamelCase__	:    Any="<s>"			,						lowerCamelCase__	:    Union[str, Any]="</s>"			,						lowerCamelCase__	:    Optional[int]=True			,						lowerCamelCase__	:    Dict=True			,						lowerCamelCase__	:    List[Any]=None			,						lowerCamelCase__	:    Optional[Any]="##"			,						**lowerCamelCase__	:    Optional[Any]			,						) ->			Any:
         """simple docstring"""





         super().__init__(
             _lowercase			,						tokenizer_file=_lowercase			,						do_lower_case=_lowercase			,						unk_token=_lowercase			,						sep_token=_lowercase			,						pad_token=_lowercase			,						cls_token=_lowercase			,						mask_token=_lowercase			,						bos_token=_lowercase			,						eos_token=_lowercase			,						clean_text=_lowercase			,						tokenize_chinese_chars=_lowercase			,						strip_accents=_lowercase			,						wordpieces_prefix=_lowercase			,						**_lowercase			,						)

         A_							       =		json.loads(self.backend_tokenizer.normalizer.__getstate__()       )
         if (
             normalizer_state.get('''lowercase'''			,						_lowercase       ) != do_lower_case
             or normalizer_state.get('''strip_accents'''			,						_lowercase       ) != strip_accents
             or normalizer_state.get('''handle_chinese_chars'''			,						_lowercase       ) != tokenize_chinese_chars
         ):
               A_							       =		getattr(_lowercase			,						normalizer_state.pop('''type'''       )       )
               A_							       =		do_lower_case
               A_							       =		strip_accents
               A_							       =		tokenize_chinese_chars
               A_							       =		normalizer_class(**_lowercase       )

         A_							       =		do_lower_case

   def     UpperCamelCase					(				self	:    Union[str, Any]			,						lowerCamelCase__	:    Union[str, Any]			,						lowerCamelCase__	:    str=None       ) ->			Any:
         """simple docstring"""





         A_							       =		[self.cls_token_id] + token_ids_a + [self.sep_token_id]

         if token_ids_a:
               output += token_ids_a + [self.sep_token_id]

         return output

   def     UpperCamelCase					(				self	:    Union[str, Any]			,						lowerCamelCase__	:    Union[str, Any]			,						lowerCamelCase__	:    Tuple = None       ) ->			Union[str, Any]:
         """simple docstring"""





         A_							       =		[self.sep_token_id]
         A_							       =		[self.cls_token_id]
         if token_ids_a is None:
               return len(cls       ) * [self.cls_token_type_id] + len(token_ids_a + sep       ) * [0]
         return len(cls       ) * [self.cls_token_type_id] + len(token_ids_a + sep       ) * [0] + len(token_ids_a + sep       ) * [1]





   def     UpperCamelCase					(				self	:    List[str]			,						lowerCamelCase__	:    Tuple			,						lowerCamelCase__	:    Optional[Any] = None       ) ->			Optional[Any]:
         """simple docstring"""





         A_							       =		self._tokenizer.model.save(_lowercase			,						name=_lowercase       )
         return tuple(_lowercase       )







'''simple docstring'''



import argparse
import json

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import ConvNextConfig, SegformerImageProcessor, UperNetConfig, UperNetForSemanticSegmentation


def A (__lowerCamelCase :List[Any]						):
						_lowerCAmelCase    		=					384
						if "tiny" in model_name:
												_lowerCAmelCase    		=					[3, 3, 9, 3]
												_lowerCAmelCase    		=					[96, 192, 384, 768]
						if "small" in model_name:
												_lowerCAmelCase    		=					[3, 3, 27, 3]
												_lowerCAmelCase    		=					[96, 192, 384, 768]
						if "base" in model_name:
												_lowerCAmelCase    		=					[3, 3, 27, 3]
												_lowerCAmelCase    		=					[128, 256, 512, 1024]
												_lowerCAmelCase    		=					512
						if "large" in model_name:
												_lowerCAmelCase    		=					[3, 3, 27, 3]
												_lowerCAmelCase    		=					[192, 384, 768, 1536]
												_lowerCAmelCase    		=					768
						if "xlarge" in model_name:
												_lowerCAmelCase    		=					[3, 3, 27, 3]
												_lowerCAmelCase    		=					[256, 512, 1024, 2048]
												_lowerCAmelCase    		=					1024

						# set label information
						_lowerCAmelCase    		=					150
						_lowerCAmelCase    		=					"""huggingface/label-files"""
						_lowerCAmelCase    		=					"""ade20k-id2label.json"""
						_lowerCAmelCase    		=					json.load(open(hf_hub_download(__lowerCamelCase					,      __lowerCamelCase					,      repo_type="""dataset"""						)					,      """r"""						)						)
						_lowerCAmelCase    		=					{int(__lowerCamelCase						): v for k, v in idalabel.items()}
						_lowerCAmelCase    		=					{v: k for k, v in idalabel.items()}

						_lowerCAmelCase    		=					ConvNextConfig(
						    depths=__lowerCamelCase					,      hidden_sizes=__lowerCamelCase					,      out_features=["""stage1""", """stage2""", """stage3""", """stage4"""]						)
						_lowerCAmelCase    		=					UperNetConfig(
						    backbone_config=__lowerCamelCase					,      auxiliary_in_channels=__lowerCamelCase					,      num_labels=__lowerCamelCase					,      idalabel=__lowerCamelCase					,      labelaid=__lowerCamelCase					,      )

						return config


def A (__lowerCamelCase :Optional[Any]						):
						_lowerCAmelCase    		=					[]

						# fmt: off
						# stem
						rename_keys.append(("""backbone.downsample_layers.0.0.weight""", """backbone.embeddings.patch_embeddings.weight""")						)
						rename_keys.append(("""backbone.downsample_layers.0.0.bias""", """backbone.embeddings.patch_embeddings.bias""")						)
						rename_keys.append(("""backbone.downsample_layers.0.1.weight""", """backbone.embeddings.layernorm.weight""")						)
						rename_keys.append(("""backbone.downsample_layers.0.1.bias""", """backbone.embeddings.layernorm.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.stages.{i}.{j}.gamma', f'backbone.encoder.stages.{i}.layers.{j}.layer_scale_parameter')						)
																		rename_keys.append((f'backbone.stages.{i}.{j}.depthwise_conv.weight', f'backbone.encoder.stages.{i}.layers.{j}.dwconv.weight')						)
																		rename_keys.append((f'backbone.stages.{i}.{j}.depthwise_conv.bias', f'backbone.encoder.stages.{i}.layers.{j}.dwconv.bias')						)
																		rename_keys.append((f'backbone.stages.{i}.{j}.norm.weight', f'backbone.encoder.stages.{i}.layers.{j}.layernorm.weight')						)
																		rename_keys.append((f'backbone.stages.{i}.{j}.norm.bias', f'backbone.encoder.stages.{i}.layers.{j}.layernorm.bias')						)
																		rename_keys.append((f'backbone.stages.{i}.{j}.pointwise_conv1.weight', f'backbone.encoder.stages.{i}.layers.{j}.pwconv1.weight')						)
																		rename_keys.append((f'backbone.stages.{i}.{j}.pointwise_conv1.bias', f'backbone.encoder.stages.{i}.layers.{j}.pwconv1.bias')						)
																		rename_keys.append((f'backbone.stages.{i}.{j}.pointwise_conv2.weight', f'backbone.encoder.stages.{i}.layers.{j}.pwconv2.weight')						)
																		rename_keys.append((f'backbone.stages.{i}.{j}.pointwise_conv2.bias', f'backbone.encoder.stages.{i}.layers.{j}.pwconv2.bias')						)
												if i > 0:
																		rename_keys.append((f'backbone.downsample_layers.{i}.0.weight', f'backbone.encoder.stages.{i}.downsampling_layer.0.weight')						)
																		rename_keys.append((f'backbone.downsample_layers.{i}.0.bias', f'backbone.encoder.stages.{i}.downsampling_layer.0.bias')						)
																		rename_keys.append((f'backbone.downsample_layers.{i}.1.weight', f'backbone.encoder.stages.{i}.downsampling_layer.1.weight')						)
																		rename_keys.append((f'backbone.downsample_layers.{i}.1.bias', f'backbone.encoder.stages.{i}.downsampling_layer.1.bias')						)

												rename_keys.append((f'backbone.norm{i}.weight', f'backbone.hidden_states_norms.stage{i+1}.weight')						)
												rename_keys.append((f'backbone.norm{i}.bias', f'backbone.hidden_states_norms.stage{i+1}.bias')						)

						# decode head
						rename_keys.extend(
						    [
						        ("""decode_head.conv_seg.weight""", """decode_head.classifier.weight"""),
						        ("""decode_head.conv_seg.bias""", """decode_head.classifier.bias"""),
						        ("""auxiliary_head.conv_seg.weight""", """auxiliary_head.classifier.weight"""),
						        ("""auxiliary_head.conv_seg.bias""", """auxiliary_head.classifier.bias"""),
						    ]						)
						# fmt: on

						return rename_keys


def A (__lowerCamelCase :Optional[Any]					,      __lowerCamelCase :Dict					,      __lowerCamelCase :Tuple						):
						_lowerCAmelCase    		=					dct.pop(__lowerCamelCase						)
						_lowerCAmelCase    		=					val


def A (__lowerCamelCase :Union[str, Any]					,      __lowerCamelCase :Optional[Any]					,      __lowerCamelCase :Any						):
						_lowerCAmelCase    		=					{
						    """upernet-convnext-tiny""": """https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_tiny_fp16_512x512_160k_ade20k/upernet_convnext_tiny_fp16_512x512_160k_ade20k_20220227_124553-cad485de.pth""",
						    """upernet-convnext-small""": """https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_small_fp16_512x512_160k_ade20k/upernet_convnext_small_fp16_512x512_160k_ade20k_20220227_131208-1b1e394f.pth""",
						    """upernet-convnext-base""": """https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_base_fp16_512x512_160k_ade20k/upernet_convnext_base_fp16_512x512_160k_ade20k_20220227_181227-02a24fc6.pth""",
						    """upernet-convnext-large""": """https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_large_fp16_640x640_160k_ade20k/upernet_convnext_large_fp16_640x640_160k_ade20k_20220226_040532-e57aa54d.pth""",
						    """upernet-convnext-xlarge""": """https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_xlarge_fp16_640x640_160k_ade20k/upernet_convnext_xlarge_fp16_640x640_160k_ade20k_20220226_080344-95fc38c2.pth""",
						}
						_lowerCAmelCase    		=					model_name_to_url[model_name]
						_lowerCAmelCase    		=					torch.hub.load_state_dict_from_url(__lowerCamelCase					,      map_location="""cpu"""						)["""state_dict"""]

						_lowerCAmelCase    		=					get_upernet_config(__lowerCamelCase						)
						_lowerCAmelCase    		=					UperNetForSemanticSegmentation(__lowerCamelCase						)
						model.eval()

						# replace "bn" => "batch_norm"
						for key in state_dict.copy().keys():
												_lowerCAmelCase    		=					state_dict.pop(__lowerCamelCase						)
												if "bn" in key:
																		_lowerCAmelCase    		=					key.replace("""bn"""					,      """batch_norm"""						)
												_lowerCAmelCase    		=					val

						# rename keys
						_lowerCAmelCase    		=					create_rename_keys(__lowerCamelCase						)
						for src, dest in rename_keys:
												rename_key(__lowerCamelCase					,      __lowerCamelCase					,      __lowerCamelCase						)

						model.load_state_dict(__lowerCamelCase						)

						# verify on image
						_lowerCAmelCase    		=					"""https://huggingface.co/datasets/hf-internal-testing/fixtures_ade20k/resolve/main/ADE_val_00000001.jpg"""
						_lowerCAmelCase    		=					Image.open(requests.get(__lowerCamelCase					,      stream=__lowerCamelCase						).raw						).convert("""RGB"""						)

						_lowerCAmelCase    		=					SegformerImageProcessor()
						_lowerCAmelCase    		=					processor(__lowerCamelCase					,      return_tensors="""pt"""						).pixel_values

						with torch.no_grad():
												_lowerCAmelCase    		=					model(__lowerCamelCase						)

						if model_name == "upernet-convnext-tiny":
												_lowerCAmelCase    		=					torch.tensor(
												    [[-8.8_110, -8.8_110, -8.6_521], [-8.8_110, -8.8_110, -8.6_521], [-8.7_746, -8.7_746, -8.6_130]]						)
						elif model_name == "upernet-convnext-small":
												_lowerCAmelCase    		=					torch.tensor(
												    [[-8.8_236, -8.8_236, -8.6_771], [-8.8_236, -8.8_236, -8.6_771], [-8.7_638, -8.7_638, -8.6_240]]						)
						elif model_name == "upernet-convnext-base":
												_lowerCAmelCase    		=					torch.tensor(
												    [[-8.8_558, -8.8_558, -8.6_905], [-8.8_558, -8.8_558, -8.6_905], [-8.7_669, -8.7_669, -8.6_021]]						)
						elif model_name == "upernet-convnext-large":
												_lowerCAmelCase    		=					torch.tensor(
												    [[-8.6_660, -8.6_660, -8.6_210], [-8.6_660, -8.6_660, -8.6_210], [-8.6_310, -8.6_310, -8.5_964]]						)
						elif model_name == "upernet-convnext-xlarge":
												_lowerCAmelCase    		=					torch.tensor(
												    [[-8.4_980, -8.4_980, -8.3_977], [-8.4_980, -8.4_980, -8.3_977], [-8.4_379, -8.4_379, -8.3_412]]						)
						print("""Logits:"""					,      outputs.logits[0, 0, :3, :3]						)
						assert torch.allclose(outputs.logits[0, 0, :3, :3]					,      __lowerCamelCase					,      atol=1e-4						)
						print("""Looks ok!"""						)

						if pytorch_dump_folder_path is not None:
												print(f'Saving model {model_name} to {pytorch_dump_folder_path}'						)
												model.save_pretrained(__lowerCamelCase						)
												print(f'Saving processor to {pytorch_dump_folder_path}'						)
												processor.save_pretrained(__lowerCamelCase						)

						if push_to_hub:
												print(f'Pushing model and processor for {model_name} to hub'						)
												model.push_to_hub(f'openmmlab/{model_name}'						)
												processor.push_to_hub(f'openmmlab/{model_name}'						)


if __name__ == "__main__":
						_lowercase          =						argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    """--model_name""",
						    default="""upernet-convnext-tiny""",
						    type=str,
						    choices=[F"""upernet-convnext-{size}""" for size in ["""tiny""", """small""", """base""", """large""", """xlarge"""]],
						    help="""Name of the ConvNext UperNet model you'd like to convert.""",
						)
						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_upernet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
