




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()
__UpperCamelCase   :			List[Any]          = logging.get_logger(__name__)





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

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

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

	return config





def 						__A						(      __lowerCamelCase       ) ->			Tuple:
	a									=				[]
	# 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 						__A						(      __lowerCamelCase				,     __lowerCamelCase				,     __lowerCamelCase       ) ->			str:
	a									=				dct.pop(__lowerCamelCase       )
	a									=				val





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





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





def 						__A						(      ) ->			torch.Tensor:
	a									=				"""http://images.cocodataset.org/val2017/000000039769.jpg"""
	a									=				Image.open(requests.get(__lowerCamelCase				,     stream=__lowerCamelCase       ).raw       )
	return im





@torch.no_grad()
def 						__A						(      __lowerCamelCase				,     __lowerCamelCase				,     __lowerCamelCase				,     __lowerCamelCase = False       ) ->			List[str]:
	a									=				get_maskformer_config(__lowerCamelCase       )

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

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

	# rename keys
	a									=				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():
		a									=				torch.from_numpy(__lowerCamelCase       )

	# load 🤗 model
	a									=				MaskFormerForInstanceSegmentation(__lowerCamelCase       )
	model.eval()

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

	a							,					a									=				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
	a									=				prepare_img()
	if "vistas" in model_name:
		a									=				65
	elif "cityscapes" in model_name:
		a									=				6_5535
	else:
		a									=				255
	a									=				True if """ade""" in model_name else False
	a									=				MaskFormerImageProcessor(ignore_index=__lowerCamelCase				,     reduce_labels=__lowerCamelCase       )

	a									=				image_processor(__lowerCamelCase				,     return_tensors="""pt"""       )

	a									=				model(**__lowerCamelCase       )

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

	if model_name == "maskformer-swin-tiny-ade":
		a									=				torch.tensor(
		    [[3.6353, -4.4770, -2.6065], [0.5081, -4.2394, -3.5343], [2.1909, -5.0353, -1.9323]]       )
	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__":
		__UpperCamelCase   :			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."
		)

		__UpperCamelCase   :			int          = parser.parse_args()
		convert_maskformer_checkpoint(
		    args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
		)







import os
import shutil
import sys
import tempfile
import unittest
from pathlib import Path

import pytest

import transformers
from transformers import (
    BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP,
    AutoTokenizer,
    BertConfig,
    BertTokenizer,
    BertTokenizerFast,
    CTRLTokenizer,
    GPTaTokenizer,
    GPTaTokenizerFast,
    PreTrainedTokenizerFast,
    RobertaTokenizer,
    RobertaTokenizerFast,
    is_tokenizers_available,
)
from transformers.models.auto.configuration_auto import CONFIG_MAPPING, AutoConfig
from transformers.models.auto.tokenization_auto import (
    TOKENIZER_MAPPING,
    get_tokenizer_config,
    tokenizer_class_from_name,
)
from transformers.models.roberta.configuration_roberta import RobertaConfig
from transformers.testing_utils import (
    DUMMY_DIFF_TOKENIZER_IDENTIFIER,
    DUMMY_UNKNOWN_IDENTIFIER,
    SMALL_MODEL_IDENTIFIER,
    RequestCounter,
    require_tokenizers,
    slow,
)


sys.path.append(str(Path(__file__).parent.parent.parent.parent / "utils"))

from test_module.custom_configuration import CustomConfig  # noqa E402
from test_module.custom_tokenization import CustomTokenizer  # noqa E402


if is_tokenizers_available():
		from test_module.custom_tokenization_fast import CustomTokenizerFast

class 						__lowerCAmelCase      (      unittest.TestCase					):
							def  lowerCamelCase__				(		self		:Optional[Any]							):







								'''simple docstring'''







								a									=				0
							@slow
							def  lowerCamelCase__				(		self		:Tuple							):







								'''simple docstring'''







								for model_name in (x for x in BERT_PRETRAINED_CONFIG_ARCHIVE_MAP.keys() if "japanese" not in x):
									a									=				AutoTokenizer.from_pretrained(__magic_name__							)
									self.assertIsNotNone(__magic_name__							)
									self.assertIsInstance(__magic_name__						, (BertTokenizer, BertTokenizerFast)							)
									self.assertGreater(len(__magic_name__							)						, 0							)

								for model_name in GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP.keys():
									a									=				AutoTokenizer.from_pretrained(__magic_name__							)
									self.assertIsNotNone(__magic_name__							)
									self.assertIsInstance(__magic_name__						, (GPTaTokenizer, GPTaTokenizerFast)							)
									self.assertGreater(len(__magic_name__							)						, 0							)
							def  lowerCamelCase__				(		self		:Union[str, Any]							):







								'''simple docstring'''







								a									=				AutoTokenizer.from_pretrained(__magic_name__							)
								self.assertIsInstance(__magic_name__						, (BertTokenizer, BertTokenizerFast)							)
								self.assertEqual(tokenizer.vocab_size						, 12							)
							def  lowerCamelCase__				(		self		:Dict							):







								'''simple docstring'''







								a									=				AutoTokenizer.from_pretrained(__magic_name__							)
								self.assertIsInstance(__magic_name__						, (RobertaTokenizer, RobertaTokenizerFast)							)
								self.assertEqual(tokenizer.vocab_size						, 20							)
							def  lowerCamelCase__				(		self		:str							):







								'''simple docstring'''







								a									=				AutoConfig.from_pretrained(__magic_name__							)
								self.assertIsInstance(__magic_name__						, __magic_name__							)
								# Check that tokenizer_type ≠ model_type
								a									=				AutoTokenizer.from_pretrained(__magic_name__						, config=__magic_name__							)
								self.assertIsInstance(__magic_name__						, (BertTokenizer, BertTokenizerFast)							)
								self.assertEqual(tokenizer.vocab_size						, 12							)
							def  lowerCamelCase__				(		self		:int							):







								'''simple docstring'''







								with tempfile.TemporaryDirectory() as tmp_dir:
									shutil.copy("""./tests/fixtures/vocab.txt"""						, os.path.join(__magic_name__						, """vocab.txt"""							)							)

									a									=				AutoTokenizer.from_pretrained(__magic_name__						, tokenizer_type="""bert"""						, use_fast=__magic_name__							)
									self.assertIsInstance(__magic_name__						, __magic_name__							)

								with tempfile.TemporaryDirectory() as tmp_dir:
									shutil.copy("""./tests/fixtures/vocab.json"""						, os.path.join(__magic_name__						, """vocab.json"""							)							)
									shutil.copy("""./tests/fixtures/merges.txt"""						, os.path.join(__magic_name__						, """merges.txt"""							)							)

									a									=				AutoTokenizer.from_pretrained(__magic_name__						, tokenizer_type="""gpt2"""						, use_fast=__magic_name__							)
									self.assertIsInstance(__magic_name__						, __magic_name__							)
							@require_tokenizers
							def  lowerCamelCase__				(		self		:List[str]							):







								'''simple docstring'''







								with tempfile.TemporaryDirectory() as tmp_dir:
									shutil.copy("""./tests/fixtures/vocab.txt"""						, os.path.join(__magic_name__						, """vocab.txt"""							)							)

									a									=				AutoTokenizer.from_pretrained(__magic_name__						, tokenizer_type="""bert"""							)
									self.assertIsInstance(__magic_name__						, __magic_name__							)

								with tempfile.TemporaryDirectory() as tmp_dir:
									shutil.copy("""./tests/fixtures/vocab.json"""						, os.path.join(__magic_name__						, """vocab.json"""							)							)
									shutil.copy("""./tests/fixtures/merges.txt"""						, os.path.join(__magic_name__						, """merges.txt"""							)							)

									a									=				AutoTokenizer.from_pretrained(__magic_name__						, tokenizer_type="""gpt2"""							)
									self.assertIsInstance(__magic_name__						, __magic_name__							)
							def  lowerCamelCase__				(		self		:Dict							):







								'''simple docstring'''







								with pytest.raises(__magic_name__							):
									AutoTokenizer.from_pretrained("""./"""						, tokenizer_type="""xxx"""							)
							@require_tokenizers
							def  lowerCamelCase__				(		self		:Tuple							):







								'''simple docstring'''







								for tokenizer_class in [BertTokenizer, BertTokenizerFast, AutoTokenizer]:
									a									=				tokenizer_class.from_pretrained("""wietsedv/bert-base-dutch-cased"""							)
									self.assertIsInstance(__magic_name__						, (BertTokenizer, BertTokenizerFast)							)

									if isinstance(__magic_name__						, __magic_name__							):
										self.assertEqual(tokenizer.basic_tokenizer.do_lower_case						, __magic_name__							)
									else:
										self.assertEqual(tokenizer.do_lower_case						, __magic_name__							)

									self.assertEqual(tokenizer.model_max_length						, 512							)
							@require_tokenizers
							def  lowerCamelCase__				(		self		:Union[str, Any]							):







								'''simple docstring'''







								for tokenizer_class in [BertTokenizer, BertTokenizerFast, AutoTokenizer]:
									with self.assertRaisesRegex(
									    __magic_name__						, """julien-c/herlolip-not-exists is not a local folder and is not a valid model identifier"""						, ):
										a									=				tokenizer_class.from_pretrained("""julien-c/herlolip-not-exists"""							)
							def  lowerCamelCase__				(		self		:Union[str, Any]							):







								'''simple docstring'''







								a									=				TOKENIZER_MAPPING.values()
								a									=				[]

								for slow_tok, fast_tok in tokenizers:
									if slow_tok is not None:
										tokenizer_names.append(slow_tok.__name__							)

									if fast_tok is not None:
										tokenizer_names.append(fast_tok.__name__							)

								for tokenizer_name in tokenizer_names:
									# must find the right class
									tokenizer_class_from_name(__magic_name__							)
							@require_tokenizers
							def  lowerCamelCase__				(		self		:str							):







								'''simple docstring'''







								self.assertIsInstance(AutoTokenizer.from_pretrained("""bert-base-cased"""						, use_fast=__magic_name__							)						, __magic_name__							)
								self.assertIsInstance(AutoTokenizer.from_pretrained("""bert-base-cased"""							)						, __magic_name__							)
							@require_tokenizers
							def  lowerCamelCase__				(		self		:str							):







								'''simple docstring'''







								a									=				AutoTokenizer.from_pretrained("""distilbert-base-uncased"""						, do_lower_case=__magic_name__							)
								a									=				"""Hello, world. How are you?"""
								a									=				tokenizer.tokenize(__magic_name__							)
								self.assertEqual("""[UNK]"""						, tokens[0]							)

								a									=				AutoTokenizer.from_pretrained("""microsoft/mpnet-base"""						, do_lower_case=__magic_name__							)
								a									=				tokenizer.tokenize(__magic_name__							)
								self.assertEqual("""[UNK]"""						, tokens[0]							)
							@require_tokenizers
							def  lowerCamelCase__				(		self		:int							):







								'''simple docstring'''







								a									=				AutoTokenizer.from_pretrained("""robot-test/dummy-tokenizer-fast-with-model-config"""							)
								self.assertEqual(type(__magic_name__							)						, __magic_name__							)
								self.assertEqual(tokenizer.model_max_length						, 512							)
								self.assertEqual(tokenizer.vocab_size						, 3_0000							)
								self.assertEqual(tokenizer.unk_token						, """[UNK]"""							)
								self.assertEqual(tokenizer.padding_side						, """right"""							)
								self.assertEqual(tokenizer.truncation_side						, """right"""							)
							def  lowerCamelCase__				(		self		:int							):







								'''simple docstring'''







								a									=				AutoTokenizer.from_pretrained(__magic_name__							)
								self.assertIsInstance(__magic_name__						, (BertTokenizer, BertTokenizerFast)							)
								with tempfile.TemporaryDirectory() as tmp_dir:
									tokenizer.save_pretrained(__magic_name__							)
									a									=				AutoTokenizer.from_pretrained(__magic_name__							)

								self.assertIsInstance(__magic_name__						, tokenizer.__class__							)
								self.assertEqual(tokenizera.vocab_size						, 12							)
							def  lowerCamelCase__				(		self		:Union[str, Any]							):







								'''simple docstring'''







								a									=				AutoTokenizer.from_pretrained("""ctrl"""							)
								# There is no fast CTRL so this always gives us a slow tokenizer.
								self.assertIsInstance(__magic_name__						, __magic_name__							)
							def  lowerCamelCase__				(		self		:str							):







								'''simple docstring'''







								a									=				get_tokenizer_config("""bert-base-cased"""							)
								a									=				config.pop("""_commit_hash"""						, __magic_name__							)
								# If we ever update bert-base-cased tokenizer config, this dict here will need to be updated.
								self.assertEqual(__magic_name__						, {"""do_lower_case""": False}							)

								# This model does not have a tokenizer_config so we get back an empty dict.
								a									=				get_tokenizer_config(__magic_name__							)
								self.assertDictEqual(__magic_name__						, {}							)

								# A tokenizer saved with `save_pretrained` always creates a tokenizer config.
								a									=				AutoTokenizer.from_pretrained(__magic_name__							)
								with tempfile.TemporaryDirectory() as tmp_dir:
									tokenizer.save_pretrained(__magic_name__							)
									a									=				get_tokenizer_config(__magic_name__							)

								# Check the class of the tokenizer was properly saved (note that it always saves the slow class).
								self.assertEqual(config["""tokenizer_class"""]						, """BertTokenizer"""							)
							def  lowerCamelCase__				(		self		:Union[str, Any]							):







								'''simple docstring'''







								try:
									AutoConfig.register("""custom"""						, __magic_name__							)

									AutoTokenizer.register(__magic_name__						, slow_tokenizer_class=__magic_name__							)
									# Trying to register something existing in the Transformers library will raise an error
									with self.assertRaises(__magic_name__							):
										AutoTokenizer.register(__magic_name__						, slow_tokenizer_class=__magic_name__							)

									a									=				CustomTokenizer.from_pretrained(__magic_name__							)
									with tempfile.TemporaryDirectory() as tmp_dir:
										tokenizer.save_pretrained(__magic_name__							)

										a									=				AutoTokenizer.from_pretrained(__magic_name__							)
										self.assertIsInstance(__magic_name__						, __magic_name__							)

								finally:
									if "custom" in CONFIG_MAPPING._extra_content:
										del CONFIG_MAPPING._extra_content["custom"]
									if CustomConfig in TOKENIZER_MAPPING._extra_content:
										del TOKENIZER_MAPPING._extra_content[CustomConfig]
							@require_tokenizers
							def  lowerCamelCase__				(		self		:Tuple							):







								'''simple docstring'''







								try:
									AutoConfig.register("""custom"""						, __magic_name__							)

									# Can register in two steps
									AutoTokenizer.register(__magic_name__						, slow_tokenizer_class=__magic_name__							)
									self.assertEqual(TOKENIZER_MAPPING[CustomConfig]						, (CustomTokenizer, None)							)
									AutoTokenizer.register(__magic_name__						, fast_tokenizer_class=__magic_name__							)
									self.assertEqual(TOKENIZER_MAPPING[CustomConfig]						, (CustomTokenizer, CustomTokenizerFast)							)

									del TOKENIZER_MAPPING._extra_content[CustomConfig]
									# Can register in one step
									AutoTokenizer.register(
									    __magic_name__						, slow_tokenizer_class=__magic_name__						, fast_tokenizer_class=__magic_name__							)
									self.assertEqual(TOKENIZER_MAPPING[CustomConfig]						, (CustomTokenizer, CustomTokenizerFast)							)

									# Trying to register something existing in the Transformers library will raise an error
									with self.assertRaises(__magic_name__							):
										AutoTokenizer.register(__magic_name__						, fast_tokenizer_class=__magic_name__							)

									# We pass through a bert tokenizer fast cause there is no converter slow to fast for our new toknizer
									# and that model does not have a tokenizer.json
									with tempfile.TemporaryDirectory() as tmp_dir:
										a									=				BertTokenizerFast.from_pretrained(__magic_name__							)
										bert_tokenizer.save_pretrained(__magic_name__							)
										a									=				CustomTokenizerFast.from_pretrained(__magic_name__							)

									with tempfile.TemporaryDirectory() as tmp_dir:
										tokenizer.save_pretrained(__magic_name__							)

										a									=				AutoTokenizer.from_pretrained(__magic_name__							)
										self.assertIsInstance(__magic_name__						, __magic_name__							)

										a									=				AutoTokenizer.from_pretrained(__magic_name__						, use_fast=__magic_name__							)
										self.assertIsInstance(__magic_name__						, __magic_name__							)

								finally:
									if "custom" in CONFIG_MAPPING._extra_content:
										del CONFIG_MAPPING._extra_content["custom"]
									if CustomConfig in TOKENIZER_MAPPING._extra_content:
										del TOKENIZER_MAPPING._extra_content[CustomConfig]
							def  lowerCamelCase__				(		self		:int							):







								'''simple docstring'''







								with self.assertRaises(__magic_name__							):
									a									=				AutoTokenizer.from_pretrained("""hf-internal-testing/test_dynamic_tokenizer"""							)
								# If remote code is disabled, we can't load this config.
								with self.assertRaises(__magic_name__							):
									a									=				AutoTokenizer.from_pretrained(
									    """hf-internal-testing/test_dynamic_tokenizer"""						, trust_remote_code=__magic_name__							)

								a									=				AutoTokenizer.from_pretrained("""hf-internal-testing/test_dynamic_tokenizer"""						, trust_remote_code=__magic_name__							)
								self.assertTrue(tokenizer.special_attribute_present							)
								# Test tokenizer can be reloaded.
								with tempfile.TemporaryDirectory() as tmp_dir:
									tokenizer.save_pretrained(__magic_name__							)
									a									=				AutoTokenizer.from_pretrained(__magic_name__						, trust_remote_code=__magic_name__							)
								self.assertTrue(reloaded_tokenizer.special_attribute_present							)

								if is_tokenizers_available():
									self.assertEqual(tokenizer.__class__.__name__						, """NewTokenizerFast"""							)
									self.assertEqual(reloaded_tokenizer.__class__.__name__						, """NewTokenizerFast"""							)

									# Test we can also load the slow version
									a									=				AutoTokenizer.from_pretrained(
									    """hf-internal-testing/test_dynamic_tokenizer"""						, trust_remote_code=__magic_name__						, use_fast=__magic_name__							)
									self.assertTrue(tokenizer.special_attribute_present							)
									self.assertEqual(tokenizer.__class__.__name__						, """NewTokenizer"""							)
									# Test tokenizer can be reloaded.
									with tempfile.TemporaryDirectory() as tmp_dir:
										tokenizer.save_pretrained(__magic_name__							)
										a									=				AutoTokenizer.from_pretrained(__magic_name__						, trust_remote_code=__magic_name__						, use_fast=__magic_name__							)
									self.assertEqual(reloaded_tokenizer.__class__.__name__						, """NewTokenizer"""							)
									self.assertTrue(reloaded_tokenizer.special_attribute_present							)
								else:
									self.assertEqual(tokenizer.__class__.__name__						, """NewTokenizer"""							)
									self.assertEqual(reloaded_tokenizer.__class__.__name__						, """NewTokenizer"""							)
							@require_tokenizers
							def  lowerCamelCase__				(		self		:str							):







								'''simple docstring'''







								class 						__lowerCAmelCase      (      __magic_name__					):
															UpperCamelCase__   	=    False







								class 						__lowerCAmelCase      (      __magic_name__					):
															UpperCamelCase__   	=    NewTokenizer
															UpperCamelCase__   	=    False

								try:
									AutoConfig.register("""custom"""						, __magic_name__							)
									AutoTokenizer.register(__magic_name__						, slow_tokenizer_class=__magic_name__							)
									AutoTokenizer.register(__magic_name__						, fast_tokenizer_class=__magic_name__							)
									# If remote code is not set, the default is to use local
									a									=				AutoTokenizer.from_pretrained("""hf-internal-testing/test_dynamic_tokenizer"""							)
									self.assertEqual(tokenizer.__class__.__name__						, """NewTokenizerFast"""							)
									self.assertFalse(tokenizer.special_attribute_present							)
									a									=				AutoTokenizer.from_pretrained("""hf-internal-testing/test_dynamic_tokenizer"""						, use_fast=__magic_name__							)
									self.assertEqual(tokenizer.__class__.__name__						, """NewTokenizer"""							)
									self.assertFalse(tokenizer.special_attribute_present							)

									# If remote code is disabled, we load the local one.
									a									=				AutoTokenizer.from_pretrained(
									    """hf-internal-testing/test_dynamic_tokenizer"""						, trust_remote_code=__magic_name__							)
									self.assertEqual(tokenizer.__class__.__name__						, """NewTokenizerFast"""							)
									self.assertFalse(tokenizer.special_attribute_present							)
									a									=				AutoTokenizer.from_pretrained(
									    """hf-internal-testing/test_dynamic_tokenizer"""						, trust_remote_code=__magic_name__						, use_fast=__magic_name__							)
									self.assertEqual(tokenizer.__class__.__name__						, """NewTokenizer"""							)
									self.assertFalse(tokenizer.special_attribute_present							)

									# If remote is enabled, we load from the Hub
									a									=				AutoTokenizer.from_pretrained(
									    """hf-internal-testing/test_dynamic_tokenizer"""						, trust_remote_code=__magic_name__							)
									self.assertEqual(tokenizer.__class__.__name__						, """NewTokenizerFast"""							)
									self.assertTrue(tokenizer.special_attribute_present							)
									a									=				AutoTokenizer.from_pretrained(
									    """hf-internal-testing/test_dynamic_tokenizer"""						, trust_remote_code=__magic_name__						, use_fast=__magic_name__							)
									self.assertEqual(tokenizer.__class__.__name__						, """NewTokenizer"""							)
									self.assertTrue(tokenizer.special_attribute_present							)

								finally:
									if "custom" in CONFIG_MAPPING._extra_content:
										del CONFIG_MAPPING._extra_content["custom"]
									if CustomConfig in TOKENIZER_MAPPING._extra_content:
										del TOKENIZER_MAPPING._extra_content[CustomConfig]
							def  lowerCamelCase__				(		self		:Any							):







								'''simple docstring'''







								a									=				AutoTokenizer.from_pretrained(
								    """hf-internal-testing/test_dynamic_tokenizer_legacy"""						, trust_remote_code=__magic_name__							)
								self.assertTrue(tokenizer.special_attribute_present							)
								if is_tokenizers_available():
									self.assertEqual(tokenizer.__class__.__name__						, """NewTokenizerFast"""							)

									# Test we can also load the slow version
									a									=				AutoTokenizer.from_pretrained(
									    """hf-internal-testing/test_dynamic_tokenizer_legacy"""						, trust_remote_code=__magic_name__						, use_fast=__magic_name__							)
									self.assertTrue(tokenizer.special_attribute_present							)
									self.assertEqual(tokenizer.__class__.__name__						, """NewTokenizer"""							)
								else:
									self.assertEqual(tokenizer.__class__.__name__						, """NewTokenizer"""							)
							def  lowerCamelCase__				(		self		:Union[str, Any]							):







								'''simple docstring'''







								with self.assertRaisesRegex(
								    __magic_name__						, """bert-base is not a local folder and is not a valid model identifier"""							):
									a									=				AutoTokenizer.from_pretrained("""bert-base"""							)
							def  lowerCamelCase__				(		self		:List[str]							):







								'''simple docstring'''







								with self.assertRaisesRegex(
								    __magic_name__						, r"""aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)"""							):
									a									=				AutoTokenizer.from_pretrained(__magic_name__						, revision="""aaaaaa"""							)





							def  lowerCamelCase__				(		self		:str							):







								'''simple docstring'''







								a									=				AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-random-bert"""							)
								with RequestCounter() as counter:
									a									=				AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-random-bert"""							)
									self.assertEqual(counter.get_request_count						, 0							)
									self.assertEqual(counter.head_request_count						, 1							)
									self.assertEqual(counter.other_request_count						, 0							)

