




import argparse
import json
from pathlib import Path

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

from transformers import DetrConfig, DetrForObjectDetection, DetrForSegmentation, DetrImageProcessor, ResNetConfig
from transformers.utils import logging


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





def 		__A		(	__lowerCamelCase    )      ->       Optional[Any]:
	# initialize config
	if "resnet-50" in model_name:
		a										=							ResNetConfig.from_pretrained("""microsoft/resnet-50"""    )
	elif "resnet-101" in model_name:
		a										=							ResNetConfig.from_pretrained("""microsoft/resnet-101"""    )
	else:
		raise ValueError("""Model name should include either resnet50 or resnet101"""    )

	a										=							DetrConfig(use_timm_backbone=__lowerCamelCase							,			backbone_config=__lowerCamelCase    )

	# set label attributes
	a										=							"""panoptic""" in model_name
	if is_panoptic:
		a										=							250
	else:
		a										=							91
		a										=							"""huggingface/label-files"""
		a										=							"""coco-detection-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()}
		a										=							idalabel
		a										=							{v: k for k, v in idalabel.items()}

	return config, is_panoptic





def 		__A		(	__lowerCamelCase    )      ->       Optional[Any]:
	# here we list all keys to be renamed (original name on the left, our name on the right)
	a										=							[]

	# stem
	# fmt: off
	rename_keys.append(("""backbone.0.body.conv1.weight""", """backbone.conv_encoder.model.embedder.embedder.convolution.weight""")    )
	rename_keys.append(("""backbone.0.body.bn1.weight""", """backbone.conv_encoder.model.embedder.embedder.normalization.weight""")    )
	rename_keys.append(("""backbone.0.body.bn1.bias""", """backbone.conv_encoder.model.embedder.embedder.normalization.bias""")    )
	rename_keys.append(("""backbone.0.body.bn1.running_mean""", """backbone.conv_encoder.model.embedder.embedder.normalization.running_mean""")    )
	rename_keys.append(("""backbone.0.body.bn1.running_var""", """backbone.conv_encoder.model.embedder.embedder.normalization.running_var""")    )
	# stages
	for stage_idx in range(len(config.backbone_config.depths    )    ):
		for layer_idx in range(config.backbone_config.depths[stage_idx]    ):
			# shortcut
			if layer_idx == 0:
				rename_keys.append(
				    (
				        f'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.0.weight',
				        f'backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.convolution.weight',
				    )    )
				rename_keys.append(
				    (
				        f'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.weight',
				        f'backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.weight',
				    )    )
				rename_keys.append(
				    (
				        f'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.bias',
				        f'backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.bias',
				    )    )
				rename_keys.append(
				    (
				        f'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_mean',
				        f'backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_mean',
				    )    )
				rename_keys.append(
				    (
				        f'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_var',
				        f'backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_var',
				    )    )
			# 3 convs
			for i in range(3    ):
				rename_keys.append(
				    (
				        f'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.conv{i+1}.weight',
				        f'backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.convolution.weight',
				    )    )
				rename_keys.append(
				    (
				        f'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.weight',
				        f'backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.weight',
				    )    )
				rename_keys.append(
				    (
				        f'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.bias',
				        f'backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.bias',
				    )    )
				rename_keys.append(
				    (
				        f'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_mean',
				        f'backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_mean',
				    )    )
				rename_keys.append(
				    (
				        f'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_var',
				        f'backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_var',
				    )    )
    # fmt: on

	for i in range(config.encoder_layers    ):
		# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
		rename_keys.append(
		    (
		        f'transformer.encoder.layers.{i}.self_attn.out_proj.weight',
		        f'encoder.layers.{i}.self_attn.out_proj.weight',
		    )    )
		rename_keys.append(
		    (f'transformer.encoder.layers.{i}.self_attn.out_proj.bias', f'encoder.layers.{i}.self_attn.out_proj.bias')    )
		rename_keys.append((f'transformer.encoder.layers.{i}.linear1.weight', f'encoder.layers.{i}.fc1.weight')    )
		rename_keys.append((f'transformer.encoder.layers.{i}.linear1.bias', f'encoder.layers.{i}.fc1.bias')    )
		rename_keys.append((f'transformer.encoder.layers.{i}.linear2.weight', f'encoder.layers.{i}.fc2.weight')    )
		rename_keys.append((f'transformer.encoder.layers.{i}.linear2.bias', f'encoder.layers.{i}.fc2.bias')    )
		rename_keys.append(
		    (f'transformer.encoder.layers.{i}.norm1.weight', f'encoder.layers.{i}.self_attn_layer_norm.weight')    )
		rename_keys.append(
		    (f'transformer.encoder.layers.{i}.norm1.bias', f'encoder.layers.{i}.self_attn_layer_norm.bias')    )
		rename_keys.append(
		    (f'transformer.encoder.layers.{i}.norm2.weight', f'encoder.layers.{i}.final_layer_norm.weight')    )
		rename_keys.append((f'transformer.encoder.layers.{i}.norm2.bias', f'encoder.layers.{i}.final_layer_norm.bias')    )
		# decoder layers: 2 times output projection, 2 feedforward neural networks and 3 layernorms
		rename_keys.append(
		    (
		        f'transformer.decoder.layers.{i}.self_attn.out_proj.weight',
		        f'decoder.layers.{i}.self_attn.out_proj.weight',
		    )    )
		rename_keys.append(
		    (f'transformer.decoder.layers.{i}.self_attn.out_proj.bias', f'decoder.layers.{i}.self_attn.out_proj.bias')    )
		rename_keys.append(
		    (
		        f'transformer.decoder.layers.{i}.multihead_attn.out_proj.weight',
		        f'decoder.layers.{i}.encoder_attn.out_proj.weight',
		    )    )
		rename_keys.append(
		    (
		        f'transformer.decoder.layers.{i}.multihead_attn.out_proj.bias',
		        f'decoder.layers.{i}.encoder_attn.out_proj.bias',
		    )    )
		rename_keys.append((f'transformer.decoder.layers.{i}.linear1.weight', f'decoder.layers.{i}.fc1.weight')    )
		rename_keys.append((f'transformer.decoder.layers.{i}.linear1.bias', f'decoder.layers.{i}.fc1.bias')    )
		rename_keys.append((f'transformer.decoder.layers.{i}.linear2.weight', f'decoder.layers.{i}.fc2.weight')    )
		rename_keys.append((f'transformer.decoder.layers.{i}.linear2.bias', f'decoder.layers.{i}.fc2.bias')    )
		rename_keys.append(
		    (f'transformer.decoder.layers.{i}.norm1.weight', f'decoder.layers.{i}.self_attn_layer_norm.weight')    )
		rename_keys.append(
		    (f'transformer.decoder.layers.{i}.norm1.bias', f'decoder.layers.{i}.self_attn_layer_norm.bias')    )
		rename_keys.append(
		    (f'transformer.decoder.layers.{i}.norm2.weight', f'decoder.layers.{i}.encoder_attn_layer_norm.weight')    )
		rename_keys.append(
		    (f'transformer.decoder.layers.{i}.norm2.bias', f'decoder.layers.{i}.encoder_attn_layer_norm.bias')    )
		rename_keys.append(
		    (f'transformer.decoder.layers.{i}.norm3.weight', f'decoder.layers.{i}.final_layer_norm.weight')    )
		rename_keys.append((f'transformer.decoder.layers.{i}.norm3.bias', f'decoder.layers.{i}.final_layer_norm.bias')    )

	# convolutional projection + query embeddings + layernorm of decoder + class and bounding box heads
	rename_keys.extend(
	    [
	        ("""input_proj.weight""", """input_projection.weight"""),
	        ("""input_proj.bias""", """input_projection.bias"""),
	        ("""query_embed.weight""", """query_position_embeddings.weight"""),
	        ("""transformer.decoder.norm.weight""", """decoder.layernorm.weight"""),
	        ("""transformer.decoder.norm.bias""", """decoder.layernorm.bias"""),
	        ("""class_embed.weight""", """class_labels_classifier.weight"""),
	        ("""class_embed.bias""", """class_labels_classifier.bias"""),
	        ("""bbox_embed.layers.0.weight""", """bbox_predictor.layers.0.weight"""),
	        ("""bbox_embed.layers.0.bias""", """bbox_predictor.layers.0.bias"""),
	        ("""bbox_embed.layers.1.weight""", """bbox_predictor.layers.1.weight"""),
	        ("""bbox_embed.layers.1.bias""", """bbox_predictor.layers.1.bias"""),
	        ("""bbox_embed.layers.2.weight""", """bbox_predictor.layers.2.weight"""),
	        ("""bbox_embed.layers.2.bias""", """bbox_predictor.layers.2.bias"""),
	    ]    )

	return rename_keys





def 		__A		(	__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase    )      ->       Dict:
	a										=							state_dict.pop(__lowerCamelCase    )
	a										=							val





def 		__A		(	__lowerCamelCase							,			__lowerCamelCase=False    )      ->       Union[str, Any]:
	a										=							""""""
	if is_panoptic:
		a										=							"""detr."""

	# first: transformer encoder
	for i in range(6    ):
		# read in weights + bias of input projection layer (in PyTorch's MultiHeadAttention, this is a single matrix + bias)
		a										=							state_dict.pop(f'{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight'    )
		a										=							state_dict.pop(f'{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_bias'    )
		# next, add query, keys and values (in that order) to the state dict
		a										=							in_proj_weight[:256, :]
		a										=							in_proj_bias[:256]
		a										=							in_proj_weight[256:512, :]
		a										=							in_proj_bias[256:512]
		a										=							in_proj_weight[-256:, :]
		a										=							in_proj_bias[-256:]
	# next: transformer decoder (which is a bit more complex because it also includes cross-attention)
	for i in range(6    ):
		# read in weights + bias of input projection layer of self-attention
		a										=							state_dict.pop(f'{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight'    )
		a										=							state_dict.pop(f'{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_bias'    )
		# next, add query, keys and values (in that order) to the state dict
		a										=							in_proj_weight[:256, :]
		a										=							in_proj_bias[:256]
		a										=							in_proj_weight[256:512, :]
		a										=							in_proj_bias[256:512]
		a										=							in_proj_weight[-256:, :]
		a										=							in_proj_bias[-256:]
		# read in weights + bias of input projection layer of cross-attention
		a										=							state_dict.pop(
		    f'{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight'    )
		a										=							state_dict.pop(f'{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_bias'    )
		# next, add query, keys and values (in that order) of cross-attention to the state dict
		a										=							in_proj_weight_cross_attn[:256, :]
		a										=							in_proj_bias_cross_attn[:256]
		a										=							in_proj_weight_cross_attn[256:512, :]
		a										=							in_proj_bias_cross_attn[256:512]
		a										=							in_proj_weight_cross_attn[-256:, :]
		a										=							in_proj_bias_cross_attn[-256:]





def 		__A		(	)      ->       List[str]:
	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=None							,			__lowerCamelCase=False    )      ->       Any:
	a				, a										=							get_detr_config(__lowerCamelCase    )

	# load original model from torch hub
	a										=							{
	    """detr-resnet-50""": """detr_resnet50""",
	    """detr-resnet-101""": """detr_resnet101""",
	}
	logger.info(f'Converting model {model_name}...'    )
	a										=							torch.hub.load("""facebookresearch/detr"""							,			model_name_to_original_name[model_name]							,			pretrained=__lowerCamelCase    ).eval()
	a										=							detr.state_dict()
	# rename keys
	for src, dest in create_rename_keys(__lowerCamelCase    ):
		if is_panoptic:
			a										=							"""detr.""" + src
		rename_key(__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase    )
	# query, key and value matrices need special treatment
	read_in_q_k_v(__lowerCamelCase							,			is_panoptic=__lowerCamelCase    )
	# important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
	a										=							"""detr.model.""" if is_panoptic else """model."""
	for key in state_dict.copy().keys():
		if is_panoptic:
			if (
			    key.startswith("""detr"""    )
			    and not key.startswith("""class_labels_classifier"""    )
			    and not key.startswith("""bbox_predictor"""    )
			):
				a										=							state_dict.pop(__lowerCamelCase    )
				a										=							val
			elif "class_labels_classifier" in key or "bbox_predictor" in key:
				a										=							state_dict.pop(__lowerCamelCase    )
				a										=							val
			elif key.startswith("""bbox_attention"""    ) or key.startswith("""mask_head"""    ):
				continue
			else:
				a										=							state_dict.pop(__lowerCamelCase    )
				a										=							val
		else:
			if not key.startswith("""class_labels_classifier"""    ) and not key.startswith("""bbox_predictor"""    ):
				a										=							state_dict.pop(__lowerCamelCase    )
				a										=							val

    # finally, create HuggingFace model and load state dict
	a										=							DetrForSegmentation(__lowerCamelCase    ) if is_panoptic else DetrForObjectDetection(__lowerCamelCase    )
	model.load_state_dict(__lowerCamelCase    )
	model.eval()

	# verify our conversion on an image
	a										=							"""coco_panoptic""" if is_panoptic else """coco_detection"""
	a										=							DetrImageProcessor(format=__lowerCamelCase    )

	a										=							processor(images=prepare_img()							,			return_tensors="""pt"""    )
	a										=							encoding["""pixel_values"""]

	a										=							detr(__lowerCamelCase    )
	a										=							model(__lowerCamelCase    )

	assert torch.allclose(outputs.logits							,			original_outputs["""pred_logits"""]							,			atol=1E-3    )
	assert torch.allclose(outputs.pred_boxes							,			original_outputs["""pred_boxes"""]							,			atol=1E-3    )
	if is_panoptic:
		assert torch.allclose(outputs.pred_masks							,			original_outputs["""pred_masks"""]							,			atol=1E-4    )
	print("""Looks ok!"""    )

	if pytorch_dump_folder_path is not None:
		# Save model and image processor
		logger.info(f'Saving PyTorch model and image processor to {pytorch_dump_folder_path}...'    )
		Path(__lowerCamelCase    ).mkdir(exist_ok=__lowerCamelCase    )
		model.save_pretrained(__lowerCamelCase    )
		processor.save_pretrained(__lowerCamelCase    )

	if push_to_hub:
		# Upload model and image processor to the hub
		logger.info("""Uploading PyTorch model and image processor to the hub..."""    )
		model.push_to_hub(f'nielsr/{model_name}'    )
		processor.push_to_hub(f'nielsr/{model_name}'    )


if __name__ == "__main__":
		__UpperCamelCase					:       List[Any]    					=      argparse.ArgumentParser()

		parser.add_argument(
		    "--model_name",
		    default="detr-resnet-50",
		    type=str,
		    choices=["detr-resnet-50", "detr-resnet-101"],
		    help="Name of the DETR model you'd like to convert.",
		)
		parser.add_argument(
		    "--pytorch_dump_folder_path", default=None, type=str, help="Path to the folder to output PyTorch model."
		)
		parser.add_argument("--push_to_hub", action="store_true", help="Whether to push the model to the hub or not.")
		__UpperCamelCase					:       List[str]    					=      parser.parse_args()
		convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)







import json
import os
import unittest

from transformers import AutoTokenizer, GPTaTokenizer, GPTaTokenizerFast
from transformers.models.gpta.tokenization_gpta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin

@require_tokenizers
class 		__lowerCAmelCase	(	__magic_name__      ,       unittest.TestCase ):
							UpperCamelCase__					     =  GPTaTokenizer
							UpperCamelCase__					     =  GPTaTokenizerFast
							UpperCamelCase__					     =  True
							UpperCamelCase__					     =  {'''add_prefix_space''': True}
							UpperCamelCase__					     =  False
							def       lowerCamelCase__							(						self						:Optional[Any]				):







								'''simple docstring'''







								super().setUp()

								# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
								a										=							[
								    """l""",
								    """o""",
								    """w""",
								    """e""",
								    """r""",
								    """s""",
								    """t""",
								    """i""",
								    """d""",
								    """n""",
								    """\u0120""",
								    """\u0120l""",
								    """\u0120n""",
								    """\u0120lo""",
								    """\u0120low""",
								    """er""",
								    """\u0120lowest""",
								    """\u0120newer""",
								    """\u0120wider""",
								    """<unk>""",
								    """<|endoftext|>""",
								]
								a										=							dict(zip(__magic_name__		,      range(len(__magic_name__				)				)				)				)
								a										=							["""#version: 0.2""", """\u0120 l""", """\u0120l o""", """\u0120lo w""", """e r""", """"""]
								a										=							{"""unk_token""": """<unk>"""}

								a										=							os.path.join(self.tmpdirname		,      VOCAB_FILES_NAMES["""vocab_file"""]				)
								a										=							os.path.join(self.tmpdirname		,      VOCAB_FILES_NAMES["""merges_file"""]				)
								with open(self.vocab_file		,      """w"""		,      encoding="""utf-8"""				) as fp:
									fp.write(json.dumps(__magic_name__				) + """\n"""				)
								with open(self.merges_file		,      """w"""		,      encoding="""utf-8"""				) as fp:
									fp.write("""\n""".join(__magic_name__				)				)
							def       lowerCamelCase__							(						self						:Dict		,      **__magic_name__						:List[str]				):







								'''simple docstring'''







								kwargs.update(self.special_tokens_map				)
								return GPTaTokenizer.from_pretrained(self.tmpdirname		,      **__magic_name__				)
							def       lowerCamelCase__							(						self						:List[str]		,      **__magic_name__						:Optional[Any]				):







								'''simple docstring'''







								kwargs.update(self.special_tokens_map				)
								return GPTaTokenizerFast.from_pretrained(self.tmpdirname		,      **__magic_name__				)
							def       lowerCamelCase__							(						self						:Dict		,      __magic_name__						:List[str]				):







								'''simple docstring'''







								a										=							"""lower newer"""
								a										=							"""lower newer"""
								return input_text, output_text
							def       lowerCamelCase__							(						self						:int				):







								'''simple docstring'''







								a										=							GPTaTokenizer(self.vocab_file		,      self.merges_file		,      **self.special_tokens_map				)
								a										=							"""lower newer"""
								a										=							["""\u0120low""", """er""", """\u0120""", """n""", """e""", """w""", """er"""]
								a										=							tokenizer.tokenize(__magic_name__		,      add_prefix_space=__magic_name__				)
								self.assertListEqual(__magic_name__		,      __magic_name__				)

								a										=							tokens + [tokenizer.unk_token]
								a										=							[14, 15, 10, 9, 3, 2, 15, 19]
								self.assertListEqual(tokenizer.convert_tokens_to_ids(__magic_name__				)		,      __magic_name__				)
							def       lowerCamelCase__							(						self						:int				):







								'''simple docstring'''







								if not self.test_rust_tokenizer:
									return

								a										=							self.get_tokenizer()
								a										=							self.get_rust_tokenizer(add_prefix_space=__magic_name__				)

								a										=							"""lower newer"""

								# Testing tokenization
								a										=							tokenizer.tokenize(__magic_name__		,      add_prefix_space=__magic_name__				)
								a										=							rust_tokenizer.tokenize(__magic_name__				)
								self.assertListEqual(__magic_name__		,      __magic_name__				)

								# Testing conversion to ids without special tokens
								a										=							tokenizer.encode(__magic_name__		,      add_special_tokens=__magic_name__		,      add_prefix_space=__magic_name__				)
								a										=							rust_tokenizer.encode(__magic_name__		,      add_special_tokens=__magic_name__				)
								self.assertListEqual(__magic_name__		,      __magic_name__				)

								# Testing conversion to ids with special tokens
								a										=							self.get_rust_tokenizer(add_prefix_space=__magic_name__				)
								a										=							tokenizer.encode(__magic_name__		,      add_prefix_space=__magic_name__				)
								a										=							rust_tokenizer.encode(__magic_name__				)
								self.assertListEqual(__magic_name__		,      __magic_name__				)

								# Testing the unknown token
								a										=							tokens + [rust_tokenizer.unk_token]
								a										=							[14, 15, 10, 9, 3, 2, 15, 19]
								self.assertListEqual(rust_tokenizer.convert_tokens_to_ids(__magic_name__				)		,      __magic_name__				)
							def       lowerCamelCase__							(						self						:Optional[int]		,      *__magic_name__						:Tuple		,      **__magic_name__						:str				):







								'''simple docstring'''







								pass
							def       lowerCamelCase__							(						self						:Optional[Any]		,      __magic_name__						:Union[str, Any]=15				):







								'''simple docstring'''







								for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
									with self.subTest(F'{tokenizer.__class__.__name__} ({pretrained_name})'				):
										a										=							self.rust_tokenizer_class.from_pretrained(__magic_name__		,      **__magic_name__				)

										# Simple input
										a										=							"""This is a simple input"""
										a										=							["""This is a simple input 1""", """This is a simple input 2"""]
										a										=							("""This is a simple input""", """This is a pair""")
										a										=							[
										    ("""This is a simple input 1""", """This is a simple input 2"""),
										    ("""This is a simple pair 1""", """This is a simple pair 2"""),
										]

										# Simple input tests
										self.assertRaises(__magic_name__		,      tokenizer_r.encode		,      __magic_name__		,      max_length=__magic_name__		,      padding="""max_length"""				)

										# Simple input
										self.assertRaises(__magic_name__		,      tokenizer_r.encode_plus		,      __magic_name__		,      max_length=__magic_name__		,      padding="""max_length"""				)

										# Simple input
										self.assertRaises(
										    __magic_name__		,      tokenizer_r.batch_encode_plus		,      __magic_name__		,      max_length=__magic_name__		,      padding="""max_length"""		,      )

										# Pair input
										self.assertRaises(__magic_name__		,      tokenizer_r.encode		,      __magic_name__		,      max_length=__magic_name__		,      padding="""max_length"""				)

										# Pair input
										self.assertRaises(__magic_name__		,      tokenizer_r.encode_plus		,      __magic_name__		,      max_length=__magic_name__		,      padding="""max_length"""				)

										# Pair input
										self.assertRaises(
										    __magic_name__		,      tokenizer_r.batch_encode_plus		,      __magic_name__		,      max_length=__magic_name__		,      padding="""max_length"""		,      )
							def       lowerCamelCase__							(						self						:str				):







								'''simple docstring'''







								a										=							GPTaTokenizer.from_pretrained(self.tmpdirname		,      pad_token="""<pad>"""				)

								# Simple input
								a										=							"""This is a simple input"""
								a										=							["""This is a simple input looooooooong""", """This is a simple input"""]
								a										=							("""This is a simple input""", """This is a pair""")
								a										=							[
								    ("""This is a simple input loooooong""", """This is a simple input"""),
								    ("""This is a simple pair loooooong""", """This is a simple pair"""),
								]

								a										=							tokenizer.pad_token_id

								a										=							tokenizer(__magic_name__		,      padding="""max_length"""		,      max_length=30		,      return_tensors="""np"""				)
								a										=							tokenizer(__magic_name__		,      padding=__magic_name__		,      truncate=__magic_name__		,      return_tensors="""np"""				)
								a										=							tokenizer(*__magic_name__		,      padding="""max_length"""		,      max_length=60		,      return_tensors="""np"""				)
								a										=							tokenizer(__magic_name__		,      padding=__magic_name__		,      truncate=__magic_name__		,      return_tensors="""np"""				)

								# s
								# test single string max_length padding
								self.assertEqual(out_s["""input_ids"""].shape[-1]		,      30				)
								self.assertTrue(pad_token_id in out_s["""input_ids"""]				)
								self.assertTrue(0 in out_s["""attention_mask"""]				)

								# s2
								# test automatic padding
								self.assertEqual(out_sa["""input_ids"""].shape[-1]		,      33				)
								# long slice doesn't have padding
								self.assertFalse(pad_token_id in out_sa["""input_ids"""][0]				)
								self.assertFalse(0 in out_sa["""attention_mask"""][0]				)
								# short slice does have padding
								self.assertTrue(pad_token_id in out_sa["""input_ids"""][1]				)
								self.assertTrue(0 in out_sa["""attention_mask"""][1]				)

								# p
								# test single pair max_length padding
								self.assertEqual(out_p["""input_ids"""].shape[-1]		,      60				)
								self.assertTrue(pad_token_id in out_p["""input_ids"""]				)
								self.assertTrue(0 in out_p["""attention_mask"""]				)

								# p2
								# test automatic padding pair
								self.assertEqual(out_pa["""input_ids"""].shape[-1]		,      52				)
								# long slice pair doesn't have padding
								self.assertFalse(pad_token_id in out_pa["""input_ids"""][0]				)
								self.assertFalse(0 in out_pa["""attention_mask"""][0]				)
								# short slice pair does have padding
								self.assertTrue(pad_token_id in out_pa["""input_ids"""][1]				)
								self.assertTrue(0 in out_pa["""attention_mask"""][1]				)
							def       lowerCamelCase__							(						self						:Union[str, Any]				):







								'''simple docstring'''







								a										=							"""$$$"""
								a										=							GPTaTokenizer.from_pretrained(self.tmpdirname		,      bos_token=__magic_name__		,      add_bos_token=__magic_name__				)

								a										=							"""This is a simple input"""
								a										=							["""This is a simple input 1""", """This is a simple input 2"""]

								a										=							tokenizer.bos_token_id

								a										=							tokenizer(__magic_name__				)
								a										=							tokenizer(__magic_name__				)

								self.assertEqual(out_s.input_ids[0]		,      __magic_name__				)
								self.assertTrue(all(o[0] == bos_token_id for o in out_sa.input_ids				)				)

								a										=							tokenizer.decode(out_s.input_ids				)
								a										=							tokenizer.batch_decode(out_sa.input_ids				)

								self.assertEqual(decode_s.split()[0]		,      __magic_name__				)
								self.assertTrue(all(d.split()[0] == bos_token for d in decode_sa				)				)
							def       lowerCamelCase__							(						self						:Optional[int]				):







								'''simple docstring'''







								pass





							def       lowerCamelCase__							(						self						:Any				):







								'''simple docstring'''







								a										=							[self.get_tokenizer(do_lower_case=__magic_name__		,      add_bos_token=__magic_name__				)]
								for tokenizer in tokenizers:
									with self.subTest(F'{tokenizer.__class__.__name__}'				):
										a										=							"""Encode this."""
										a										=							"""This one too please."""
										a										=							tokenizer.encode(__magic_name__		,      add_special_tokens=__magic_name__				)
										encoded_sequence += tokenizer.encode(__magic_name__		,      add_special_tokens=__magic_name__				)
										a										=							tokenizer.encode_plus(
										    __magic_name__		,      __magic_name__		,      add_special_tokens=__magic_name__		,      return_special_tokens_mask=__magic_name__		,      )
										a										=							encoded_sequence_dict["""input_ids"""]
										a										=							encoded_sequence_dict["""special_tokens_mask"""]
										self.assertEqual(len(__magic_name__				)		,      len(__magic_name__				)				)

										a										=							[
										    (x if not special_tokens_mask[i] else None) for i, x in enumerate(__magic_name__				)
										]
										a										=							[x for x in filtered_sequence if x is not None]
										self.assertEqual(__magic_name__		,      __magic_name__				)







@require_tokenizers
class 		__lowerCAmelCase	(	unittest.TestCase ):
							def       lowerCamelCase__							(						self						:Optional[int]				):







								'''simple docstring'''







								a										=							AutoTokenizer.from_pretrained("""facebook/opt-350m"""		,      from_slow=__magic_name__				)
								a										=							"""A photo of a cat"""

								a										=							tokenizer.encode(
								    __magic_name__		,      )
								self.assertEqual(__magic_name__		,      [2, 250, 1345, 9, 10, 4758]				)
								tokenizer.save_pretrained("""test_opt"""				)

								a										=							AutoTokenizer.from_pretrained("""./test_opt"""				)
								a										=							tokenizer.encode(
								    __magic_name__		,      )
								self.assertEqual(__magic_name__		,      [2, 250, 1345, 9, 10, 4758]				)
							def       lowerCamelCase__							(						self						:Union[str, Any]				):







								'''simple docstring'''







								a										=							AutoTokenizer.from_pretrained("""facebook/opt-350m"""		,      use_slow=__magic_name__				)
								a										=							"""A photo of a cat"""

								a										=							tokenizer.encode(
								    __magic_name__		,      )
								# Same as above
								self.assertEqual(__magic_name__		,      [2, 250, 1345, 9, 10, 4758]				)





							@unittest.skip("""This test is failing because of a bug in the fast tokenizer"""				)
							def       lowerCamelCase__							(						self						:str				):







								'''simple docstring'''







								a										=							AutoTokenizer.from_pretrained("""facebook/opt-350m"""		,      from_slow=__magic_name__				)

								a										=							"""bos"""
								a										=							tokenizer.get_vocab()["""bos"""]

								a										=							"""A photo of a cat"""
								a										=							tokenizer.encode(
								    __magic_name__		,      )
								# We changed the bos token
								self.assertEqual(__magic_name__		,      [3_1957, 250, 1345, 9, 10, 4758]				)
								tokenizer.save_pretrained("""./tok"""				)
								a										=							AutoTokenizer.from_pretrained("""./tok"""				)
								self.assertTrue(tokenizer.is_fast				)
								a										=							tokenizer.encode(
								    __magic_name__		,      )
								self.assertEqual(__magic_name__		,      [3_1957, 250, 1345, 9, 10, 4758]				)

