import argparse
import json
from collections import OrderedDict
from pathlib import Path

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

from transformers import (
    ConditionalDetrConfig,
    ConditionalDetrForObjectDetection,
    ConditionalDetrForSegmentation,
    ConditionalDetrImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
A__           =							logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
A__           =							[]
for i in range(6):
		# 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}.cross_attn.out_proj.weight',
		        F'decoder.layers.{i}.encoder_attn.out_proj.weight',
		    )
		)
		rename_keys.append(
		    (
		        F'transformer.decoder.layers.{i}.cross_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'))

		# q, k, v projections in self/cross-attention in decoder for conditional DETR
		rename_keys.append(
		    (F'transformer.decoder.layers.{i}.sa_qcontent_proj.weight', F'decoder.layers.{i}.sa_qcontent_proj.weight')
		)
		rename_keys.append(
		    (F'transformer.decoder.layers.{i}.sa_kcontent_proj.weight', F'decoder.layers.{i}.sa_kcontent_proj.weight')
		)
		rename_keys.append(
		    (F'transformer.decoder.layers.{i}.sa_qpos_proj.weight', F'decoder.layers.{i}.sa_qpos_proj.weight')
		)
		rename_keys.append(
		    (F'transformer.decoder.layers.{i}.sa_kpos_proj.weight', F'decoder.layers.{i}.sa_kpos_proj.weight')
		)
		rename_keys.append((F'transformer.decoder.layers.{i}.sa_v_proj.weight', F'decoder.layers.{i}.sa_v_proj.weight'))
		rename_keys.append(
		    (F'transformer.decoder.layers.{i}.ca_qcontent_proj.weight', F'decoder.layers.{i}.ca_qcontent_proj.weight')
		)
		# rename_keys.append((f"transformer.decoder.layers.{i}.ca_qpos_proj.weight", f"decoder.layers.{i}.ca_qpos_proj.weight"))
		rename_keys.append(
		    (F'transformer.decoder.layers.{i}.ca_kcontent_proj.weight', F'decoder.layers.{i}.ca_kcontent_proj.weight')
		)
		rename_keys.append(
		    (F'transformer.decoder.layers.{i}.ca_kpos_proj.weight', F'decoder.layers.{i}.ca_kpos_proj.weight')
		)
		rename_keys.append((F'transformer.decoder.layers.{i}.ca_v_proj.weight', F'decoder.layers.{i}.ca_v_proj.weight'))
		rename_keys.append(
		    (F'transformer.decoder.layers.{i}.ca_qpos_sine_proj.weight', F'decoder.layers.{i}.ca_qpos_sine_proj.weight')
		)

		rename_keys.append(
		    (F'transformer.decoder.layers.{i}.sa_qcontent_proj.bias', F'decoder.layers.{i}.sa_qcontent_proj.bias')
		)
		rename_keys.append(
		    (F'transformer.decoder.layers.{i}.sa_kcontent_proj.bias', F'decoder.layers.{i}.sa_kcontent_proj.bias')
		)
		rename_keys.append((F'transformer.decoder.layers.{i}.sa_qpos_proj.bias', F'decoder.layers.{i}.sa_qpos_proj.bias'))
		rename_keys.append((F'transformer.decoder.layers.{i}.sa_kpos_proj.bias', F'decoder.layers.{i}.sa_kpos_proj.bias'))
		rename_keys.append((F'transformer.decoder.layers.{i}.sa_v_proj.bias', F'decoder.layers.{i}.sa_v_proj.bias'))
		rename_keys.append(
		    (F'transformer.decoder.layers.{i}.ca_qcontent_proj.bias', F'decoder.layers.{i}.ca_qcontent_proj.bias')
		)
		# rename_keys.append((f"transformer.decoder.layers.{i}.ca_qpos_proj.bias", f"decoder.layers.{i}.ca_qpos_proj.bias"))
		rename_keys.append(
		    (F'transformer.decoder.layers.{i}.ca_kcontent_proj.bias', F'decoder.layers.{i}.ca_kcontent_proj.bias')
		)
		rename_keys.append((F'transformer.decoder.layers.{i}.ca_kpos_proj.bias', F'decoder.layers.{i}.ca_kpos_proj.bias'))
		rename_keys.append((F'transformer.decoder.layers.{i}.ca_v_proj.bias', F'decoder.layers.{i}.ca_v_proj.bias'))
		rename_keys.append(
		    (F'transformer.decoder.layers.{i}.ca_qpos_sine_proj.bias', F'decoder.layers.{i}.ca_qpos_sine_proj.bias')
		)

# convolutional projection + query embeddings + layernorm of decoder + class and bounding box heads
# for conditional DETR, also convert reference point head and query scale MLP
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"""),
        ("""transformer.decoder.ref_point_head.layers.0.weight""", """decoder.ref_point_head.layers.0.weight"""),
        ("""transformer.decoder.ref_point_head.layers.0.bias""", """decoder.ref_point_head.layers.0.bias"""),
        ("""transformer.decoder.ref_point_head.layers.1.weight""", """decoder.ref_point_head.layers.1.weight"""),
        ("""transformer.decoder.ref_point_head.layers.1.bias""", """decoder.ref_point_head.layers.1.bias"""),
        ("""transformer.decoder.query_scale.layers.0.weight""", """decoder.query_scale.layers.0.weight"""),
        ("""transformer.decoder.query_scale.layers.0.bias""", """decoder.query_scale.layers.0.bias"""),
        ("""transformer.decoder.query_scale.layers.1.weight""", """decoder.query_scale.layers.1.weight"""),
        ("""transformer.decoder.query_scale.layers.1.bias""", """decoder.query_scale.layers.1.bias"""),
        ("""transformer.decoder.layers.0.ca_qpos_proj.weight""", """decoder.layers.0.ca_qpos_proj.weight"""),
        ("""transformer.decoder.layers.0.ca_qpos_proj.bias""", """decoder.layers.0.ca_qpos_proj.bias"""),
    ]
)





def  _lowerCamelCase					(   a_				: List[Any]						,			a_				: int						,			a_				: Dict):
			lowerCamelCase						:List[str]						  =     state_dict.pop(_lowerCamelCase)
			lowerCamelCase						:Tuple						  =     val





def  _lowerCamelCase					(   a_				: str):
			lowerCamelCase						:Tuple						  =     OrderedDict()
			for key, value in state_dict.items():
						if "backbone.0.body" in key:
									lowerCamelCase						:Dict						  =     key.replace('''backbone.0.body'''						,			'''backbone.conv_encoder.model''')
									lowerCamelCase						:Dict						  =     value
						else:
									lowerCamelCase						:Any						  =     value

			return new_state_dict





def  _lowerCamelCase					(   a_				: Optional[int]						,			a_				: Tuple=False):
			lowerCamelCase						:Optional[Any]						  =     ''
			if is_panoptic:
						lowerCamelCase						:Optional[Any]						  =     'conditional_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)
						lowerCamelCase						:Optional[int]						  =     state_dict.pop(F"{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight")
						lowerCamelCase						:Tuple						  =     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
						lowerCamelCase						:Dict						  =     in_proj_weight[:2_56, :]
						lowerCamelCase						:List[Any]						  =     in_proj_bias[:2_56]
						lowerCamelCase						:Dict						  =     in_proj_weight[2_56:5_12, :]
						lowerCamelCase						:Union[str, Any]						  =     in_proj_bias[2_56:5_12]
						lowerCamelCase						:Any						  =     in_proj_weight[-2_56:, :]
						lowerCamelCase						:Any						  =     in_proj_bias[-2_56:]





def  _lowerCamelCase					(   ):
			lowerCamelCase						:Any						  =     'http://images.cocodataset.org/val2017/000000039769.jpg'
			lowerCamelCase						:Union[str, Any]						  =     Image.open(requests.get(_lowerCamelCase						,			stream=_lowerCamelCase).raw)

			return im





@torch.no_grad()
def  _lowerCamelCase					(   a_				: Union[str, Any]						,			a_				: Optional[int]):
			lowerCamelCase						:List[Any]						  =     ConditionalDetrConfig()
			# set backbone and dilation attributes
			if "resnet101" in model_name:
						lowerCamelCase						:Dict						  =     'resnet101'
			if "dc5" in model_name:
						lowerCamelCase						:List[Any]						  =     True
			lowerCamelCase						:Dict						  =     'panoptic' in model_name
			if is_panoptic:
						lowerCamelCase						:List[str]						  =     2_50
			else:
						lowerCamelCase						:str						  =     91
						lowerCamelCase						:Any						  =     'huggingface/label-files'
						lowerCamelCase						:List[str]						  =     'coco-detection-id2label.json'
						lowerCamelCase						:int						  =     json.load(open(hf_hub_download(_lowerCamelCase						,			_lowerCamelCase						,			repo_type='''dataset''')						,			'''r'''))
						lowerCamelCase						:str						  =     {int(_lowerCamelCase): v for k, v in idalabel.items()}
						lowerCamelCase						:Union[str, Any]						  =     idalabel
						lowerCamelCase						:Any						  =     {v: k for k, v in idalabel.items()}

			# load image processor
			lowerCamelCase						:Optional[int]						  =     'coco_panoptic' if is_panoptic else 'coco_detection'
			lowerCamelCase						:str						  =     ConditionalDetrImageProcessor(format=_lowerCamelCase)

			# prepare image
			lowerCamelCase						:List[str]						  =     prepare_img()
			lowerCamelCase						:int						  =     image_processor(images=_lowerCamelCase						,			return_tensors='''pt''')
			lowerCamelCase						:Dict						  =     encoding['pixel_values']

			logger.info(F"Converting model {model_name}...")

			# load original model from torch hub
			lowerCamelCase						:Any						  =     torch.hub.load('''DeppMeng/ConditionalDETR'''						,			_lowerCamelCase						,			pretrained=_lowerCamelCase).eval()
			lowerCamelCase						:Union[str, Any]						  =     conditional_detr.state_dict()
			# rename keys
			for src, dest in rename_keys:
						if is_panoptic:
									lowerCamelCase						:Optional[Any]						  =     'conditional_detr.' + src
						rename_key(_lowerCamelCase						,			_lowerCamelCase						,			_lowerCamelCase)
			lowerCamelCase						:Union[str, Any]						  =     rename_backbone_keys(_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
			lowerCamelCase						:Dict						  =     'conditional_detr.model.' if is_panoptic else 'model.'
			for key in state_dict.copy().keys():
						if is_panoptic:
									if (
									    key.startswith('''conditional_detr''')
									    and not key.startswith('''class_labels_classifier''')
									    and not key.startswith('''bbox_predictor''')
									):
												lowerCamelCase						:List[Any]						  =     state_dict.pop(_lowerCamelCase)
												lowerCamelCase						:Tuple						  =     val
									elif "class_labels_classifier" in key or "bbox_predictor" in key:
												lowerCamelCase						:Any						  =     state_dict.pop(_lowerCamelCase)
												lowerCamelCase						:Dict						  =     val
									elif key.startswith('''bbox_attention''') or key.startswith('''mask_head'''):
												continue
									else:
												lowerCamelCase						:str						  =     state_dict.pop(_lowerCamelCase)
												lowerCamelCase						:List[str]						  =     val
						else:
									if not key.startswith('''class_labels_classifier''') and not key.startswith('''bbox_predictor'''):
												lowerCamelCase						:Dict						  =     state_dict.pop(_lowerCamelCase)
												lowerCamelCase						:List[Any]						  =     val
    # finally, create HuggingFace model and load state dict
			lowerCamelCase						:str						  =     ConditionalDetrForSegmentation(_lowerCamelCase) if is_panoptic else ConditionalDetrForObjectDetection(_lowerCamelCase)
			model.load_state_dict(_lowerCamelCase)
			model.eval()
			model.push_to_hub(repo_id=_lowerCamelCase						,			organization='''DepuMeng'''						,			commit_message='''Add model''')
			# verify our conversion
			lowerCamelCase						:List[Any]						  =     conditional_detr(_lowerCamelCase)
			lowerCamelCase						:Dict						  =     model(_lowerCamelCase)
			assert torch.allclose(outputs.logits						,			original_outputs['''pred_logits''']						,			atol=1e-4)
			assert torch.allclose(outputs.pred_boxes						,			original_outputs['''pred_boxes''']						,			atol=1e-4)
			if is_panoptic:
						assert torch.allclose(outputs.pred_masks						,			original_outputs['''pred_masks''']						,			atol=1e-4)

			# 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)
			image_processor.save_pretrained(_lowerCamelCase)


if __name__ == "__main__":
		A__           =							argparse.ArgumentParser()

		parser.add_argument(
		    """--model_name""",
		    default="""conditional_detr_resnet50""",
		    type=str,
		    help="""Name of the CONDITIONAL_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."""
		)
		A__           =							parser.parse_args()
		convert_conditional_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)




import json
import os
import unittest

from transformers import BatchEncoding, LEDTokenizer, LEDTokenizerFast
from transformers.models.led.tokenization_led import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class   _lowerCAmelCase		(     __SCREAMING_SNAKE_CASE      ,   unittest.TestCase				):
							_UpperCAmelCase						    =     LEDTokenizer
							_UpperCAmelCase						    =     LEDTokenizerFast
							_UpperCAmelCase						    =     True







							def 				snake_case  (				self     :    Any		):
										super().setUp()
										lowerCamelCase						:Optional[int]						  =     [
										    '''l''',
										    '''o''',
										    '''w''',
										    '''e''',
										    '''r''',
										    '''s''',
										    '''t''',
										    '''i''',
										    '''d''',
										    '''n''',
										    '''\u0120''',
										    '''\u0120l''',
										    '''\u0120n''',
										    '''\u0120lo''',
										    '''\u0120low''',
										    '''er''',
										    '''\u0120lowest''',
										    '''\u0120newer''',
										    '''\u0120wider''',
										    '''<unk>''',
										]
										lowerCamelCase						:Any						  =     dict(zip(__snake_case	,					range(len(__snake_case		)		)		)		)
										lowerCamelCase						:List[str]						  =     ['''#version: 0.2''', '''\u0120 l''', '''\u0120l o''', '''\u0120lo w''', '''e r''', '''''']
										lowerCamelCase						:int						  =     {'''unk_token''': '''<unk>'''}

										lowerCamelCase						:Optional[int]						  =     os.path.join(self.tmpdirname	,					VOCAB_FILES_NAMES['''vocab_file''']		)
										lowerCamelCase						:Any						  =     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(__snake_case		) + '''\n'''		)
										with open(self.merges_file	,					'''w'''	,					encoding='''utf-8'''		) as fp:
													fp.write('''\n'''.join(__snake_case		)		)







							def 				snake_case  (				self     :    int	,					**__snake_case     :    int		):
										kwargs.update(self.special_tokens_map		)
										return self.tokenizer_class.from_pretrained(self.tmpdirname	,					**__snake_case		)







							def 				snake_case  (				self     :    Dict	,					**__snake_case     :    Any		):
										kwargs.update(self.special_tokens_map		)
										return self.rust_tokenizer_class.from_pretrained(self.tmpdirname	,					**__snake_case		)







							def 				snake_case  (				self     :    Optional[Any]	,					__snake_case     :    Union[str, Any]		):
										return "lower newer", "lower newer"







							@cached_property
							def 				snake_case  (				self     :    Any		):
										return LEDTokenizer.from_pretrained('''allenai/led-base-16384'''		)







							@cached_property
							def 				snake_case  (				self     :    int		):
										return LEDTokenizerFast.from_pretrained('''allenai/led-base-16384'''		)







							@require_torch
							def 				snake_case  (				self     :    str		):
										lowerCamelCase						:Any						  =     ['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
										lowerCamelCase						:List[Any]						  =     [0, 250, 251, 17818, 13, 39186, 1938, 4, 2]

										for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
													lowerCamelCase						:Optional[Any]						  =     tokenizer(__snake_case	,					max_length=len(__snake_case		)	,					padding=__snake_case	,					return_tensors='''pt'''		)
													self.assertIsInstance(__snake_case	,					__snake_case		)

													self.assertEqual((2, 9)	,					batch.input_ids.shape		)
													self.assertEqual((2, 9)	,					batch.attention_mask.shape		)
													lowerCamelCase						:List[Any]						  =     batch.input_ids.tolist()[0]
													self.assertListEqual(__snake_case	,					__snake_case		)







							@require_torch
							def 				snake_case  (				self     :    Tuple		):
										lowerCamelCase						:Dict						  =     ['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
										for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
													lowerCamelCase						:Union[str, Any]						  =     tokenizer(__snake_case	,					padding=__snake_case	,					return_tensors='''pt'''		)
													self.assertIn('''input_ids'''	,					__snake_case		)
													self.assertIn('''attention_mask'''	,					__snake_case		)
													self.assertNotIn('''labels'''	,					__snake_case		)
													self.assertNotIn('''decoder_attention_mask'''	,					__snake_case		)







							@require_torch
							def 				snake_case  (				self     :    Union[str, Any]		):
										lowerCamelCase						:Union[str, Any]						  =     [
										    '''Summary of the text.''',
										    '''Another summary.''',
										]
										for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
													lowerCamelCase						:List[Any]						  =     tokenizer(text_target=__snake_case	,					max_length=32	,					padding='''max_length'''	,					return_tensors='''pt'''		)
													self.assertEqual(32	,					targets['''input_ids'''].shape[1]		)







							@require_torch
							def 				snake_case  (				self     :    List[Any]		):
										for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
													lowerCamelCase						:Optional[Any]						  =     tokenizer(
													    ['''I am a small frog''' * 1024, '''I am a small frog''']	,					padding=__snake_case	,					truncation=__snake_case	,					return_tensors='''pt'''		)
													self.assertIsInstance(__snake_case	,					__snake_case		)
													self.assertEqual(batch.input_ids.shape	,					(2, 5122)		)







							@require_torch
							def 				snake_case  (				self     :    Optional[int]		):
										lowerCamelCase						:Union[str, Any]						  =     ['''A long paragraph for summarization.''']
										lowerCamelCase						:Any						  =     [
										    '''Summary of the text.''',
										]
										for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
													lowerCamelCase						:Union[str, Any]						  =     tokenizer(__snake_case	,					return_tensors='''pt'''		)
													lowerCamelCase						:Any						  =     tokenizer(text_target=__snake_case	,					return_tensors='''pt'''		)
													lowerCamelCase						:Optional[int]						  =     inputs['''input_ids''']
													lowerCamelCase						:Any						  =     targets['''input_ids''']
													self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item()		)
													self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item()		)
													self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item()		)
													self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item()		)







							@require_torch
							def 				snake_case  (				self     :    Dict		):
										for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
													lowerCamelCase						:Optional[int]						  =     ['''Summary of the text.''', '''Another summary.''']
													lowerCamelCase						:List[Any]						  =     [[0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, -1, -1]]

													lowerCamelCase						:Optional[int]						  =     tokenizer(__snake_case	,					padding=__snake_case		)
													lowerCamelCase						:Union[str, Any]						  =     [[0] * len(__snake_case		) for x in encoded_output['''input_ids''']]
													lowerCamelCase						:str						  =     tokenizer.pad(__snake_case		)
													self.assertSequenceEqual(outputs['''global_attention_mask''']	,					__snake_case		)







							def 				snake_case  (				self     :    Tuple		):
										pass







							def 				snake_case  (				self     :    Optional[int]		):
										for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
													with self.subTest(F"{tokenizer.__class__.__name__} ({pretrained_name})"		):
																lowerCamelCase						:Optional[Any]						  =     self.rust_tokenizer_class.from_pretrained(__snake_case	,					**__snake_case		)
																lowerCamelCase						:Tuple						  =     self.tokenizer_class.from_pretrained(__snake_case	,					**__snake_case		)
																lowerCamelCase						:int						  =     '''A, <mask> AllenNLP sentence.'''
																lowerCamelCase						:str						  =     tokenizer_r.encode_plus(__snake_case	,					add_special_tokens=__snake_case	,					return_token_type_ids=__snake_case		)
																lowerCamelCase						:str						  =     tokenizer_p.encode_plus(__snake_case	,					add_special_tokens=__snake_case	,					return_token_type_ids=__snake_case		)
																self.assertEqual(sum(tokens_r['''token_type_ids''']		)	,					sum(tokens_p['''token_type_ids''']		)		)
																self.assertEqual(
																    sum(tokens_r['''attention_mask''']		) / len(tokens_r['''attention_mask''']		)	,					sum(tokens_p['''attention_mask''']		) / len(tokens_p['''attention_mask''']		)	,					)

																lowerCamelCase						:Optional[int]						  =     tokenizer_r.convert_ids_to_tokens(tokens_r['''input_ids''']		)
																lowerCamelCase						:Tuple						  =     tokenizer_p.convert_ids_to_tokens(tokens_p['''input_ids''']		)
																self.assertSequenceEqual(tokens_p['''input_ids''']	,					[0, 250, 6, 50264, 3823, 487, 21992, 3645, 4, 2]		)
																self.assertSequenceEqual(tokens_r['''input_ids''']	,					[0, 250, 6, 50264, 3823, 487, 21992, 3645, 4, 2]		)

																self.assertSequenceEqual(
																    __snake_case	,					['''<s>''', '''A''', ''',''', '''<mask>''', '''ĠAllen''', '''N''', '''LP''', '''Ġsentence''', '''.''', '''</s>''']		)
																self.assertSequenceEqual(
																    __snake_case	,					['''<s>''', '''A''', ''',''', '''<mask>''', '''ĠAllen''', '''N''', '''LP''', '''Ġsentence''', '''.''', '''</s>''']		)



