



'''simple docstring'''




import argparse

import requests
import torch

# pip3 install salesforce-lavis
# I'm actually installing a slightly modified version: pip3 install git+https://github.com/nielsrogge/LAVIS.git@fix_lavis
from lavis.models import load_model_and_preprocess
from PIL import Image

from transformers import (
    AutoTokenizer,
    BlipaConfig,
    BlipaForConditionalGeneration,
    BlipaProcessor,
    BlipaVisionConfig,
    BlipImageProcessor,
    OPTConfig,
    TaConfig,
)
from transformers.utils.constants import OPENAI_CLIP_MEAN, OPENAI_CLIP_STD


def        lowerCAmelCase_     (			):
		__SCREAMING_SNAKE_CASE :	Optional[int]					 =	"""https://storage.googleapis.com/sfr-vision-language-research/LAVIS/assets/merlion.png"""
		__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	Image.open(requests.get(_lowerCamelCase ,   stream=_lowerCamelCase      ).raw      ).convert("""RGB"""      )

		return image


def        lowerCAmelCase_     (			_lowerCamelCase:     Union[str, Any]      ):
		__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	[]
		# fmt: off

		# vision encoder
		rename_keys.append(("""visual_encoder.cls_token""", """vision_model.embeddings.class_embedding""")      )
		rename_keys.append(("""visual_encoder.pos_embed""", """vision_model.embeddings.position_embedding""")      )
		rename_keys.append(("""visual_encoder.patch_embed.proj.weight""", """vision_model.embeddings.patch_embedding.weight""")      )
		rename_keys.append(("""visual_encoder.patch_embed.proj.bias""", """vision_model.embeddings.patch_embedding.bias""")      )
		rename_keys.append(("""ln_vision.weight""", """vision_model.post_layernorm.weight""")      )
		rename_keys.append(("""ln_vision.bias""", """vision_model.post_layernorm.bias""")      )

		for i in range(config.vision_config.num_hidden_layers      ):
				rename_keys.append((F"visual_encoder.blocks.{i}.norm1.weight", F"vision_model.encoder.layers.{i}.layer_norm1.weight")      )
				rename_keys.append((F"visual_encoder.blocks.{i}.norm1.bias", F"vision_model.encoder.layers.{i}.layer_norm1.bias")      )
				rename_keys.append((F"visual_encoder.blocks.{i}.norm2.weight", F"vision_model.encoder.layers.{i}.layer_norm2.weight")      )
				rename_keys.append((F"visual_encoder.blocks.{i}.norm2.bias", F"vision_model.encoder.layers.{i}.layer_norm2.bias")      )
				rename_keys.append((F"visual_encoder.blocks.{i}.attn.qkv.weight", F"vision_model.encoder.layers.{i}.self_attn.qkv.weight")      )
				rename_keys.append((F"visual_encoder.blocks.{i}.attn.proj.weight", F"vision_model.encoder.layers.{i}.self_attn.projection.weight",)      )
				rename_keys.append((F"visual_encoder.blocks.{i}.attn.proj.bias", F"vision_model.encoder.layers.{i}.self_attn.projection.bias")      )
				rename_keys.append((F"visual_encoder.blocks.{i}.mlp.fc1.weight", F"vision_model.encoder.layers.{i}.mlp.fc1.weight")      )
				rename_keys.append((F"visual_encoder.blocks.{i}.mlp.fc1.bias", F"vision_model.encoder.layers.{i}.mlp.fc1.bias")      )
				rename_keys.append((F"visual_encoder.blocks.{i}.mlp.fc2.weight", F"vision_model.encoder.layers.{i}.mlp.fc2.weight")      )
				rename_keys.append((F"visual_encoder.blocks.{i}.mlp.fc2.bias", F"vision_model.encoder.layers.{i}.mlp.fc2.bias")      )

		# QFormer
		rename_keys.append(("""Qformer.bert.embeddings.LayerNorm.weight""", """qformer.layernorm.weight""")      )
		rename_keys.append(("""Qformer.bert.embeddings.LayerNorm.bias""", """qformer.layernorm.bias""")      )

		# fmt: on
		return rename_keys


def        lowerCAmelCase_     (			_lowerCamelCase:     List[str] ,   _lowerCamelCase:     List[str] ,   _lowerCamelCase:     Dict      ):
		__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	dct.pop(_lowerCamelCase      )
		__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	val


def        lowerCAmelCase_     (			_lowerCamelCase:     Any ,   _lowerCamelCase:     List[str]      ):
		for i in range(config.vision_config.num_hidden_layers      ):
				# read in original q and v biases
				__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	state_dict.pop(F"visual_encoder.blocks.{i}.attn.q_bias"      )
				__SCREAMING_SNAKE_CASE :	List[Any]					 =	state_dict.pop(F"visual_encoder.blocks.{i}.attn.v_bias"      )

				# next, set bias in the state dict
				__SCREAMING_SNAKE_CASE :	Optional[int]					 =	torch.cat((q_bias, torch.zeros_like(_lowerCamelCase ,   requires_grad=_lowerCamelCase      ), v_bias)      )
				__SCREAMING_SNAKE_CASE :	Any					 =	qkv_bias


def        lowerCAmelCase_     (			_lowerCamelCase:     Tuple ,   _lowerCamelCase:     Union[str, Any]      ):
		__SCREAMING_SNAKE_CASE :	Tuple					 =	3_64 if """coco""" in model_name else 2_24
		__SCREAMING_SNAKE_CASE :	List[str]					 =	BlipaVisionConfig(image_size=_lowerCamelCase      ).to_dict()

		# make sure the models have proper bos_token_id and eos_token_id set (important for generation)
		# seems like flan-T5 models don't have bos_token_id properly set?
		if "opt-2.7b" in model_name:
				__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	OPTConfig.from_pretrained("""facebook/opt-2.7b""" ,   eos_token_id=_lowerCamelCase      ).to_dict()
		elif "opt-6.7b" in model_name:
				__SCREAMING_SNAKE_CASE :	List[Any]					 =	OPTConfig.from_pretrained("""facebook/opt-6.7b""" ,   eos_token_id=_lowerCamelCase      ).to_dict()
		elif "t5-xl" in model_name:
				__SCREAMING_SNAKE_CASE :	Dict					 =	TaConfig.from_pretrained("""google/flan-t5-xl""" ,   dense_act_fn="""gelu""" ,   bos_token_id=1      ).to_dict()
		elif "t5-xxl" in model_name:
				__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	TaConfig.from_pretrained("""google/flan-t5-xxl""" ,   dense_act_fn="""gelu""" ,   bos_token_id=1      ).to_dict()

		__SCREAMING_SNAKE_CASE :	Optional[int]					 =	BlipaConfig(vision_config=_lowerCamelCase ,   text_config=_lowerCamelCase      )

		return config, image_size


@torch.no_grad()
def        lowerCAmelCase_     (			_lowerCamelCase:     List[Any] ,   _lowerCamelCase:     List[Any]=None ,   _lowerCamelCase:     List[str]=False      ):
		__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	(
		    AutoTokenizer.from_pretrained("""facebook/opt-2.7b"""      )
		    if """opt""" in model_name
		    else AutoTokenizer.from_pretrained("""google/flan-t5-xl"""      )
		)
		__SCREAMING_SNAKE_CASE :	Optional[int]					 =	tokenizer("""\n""" ,   add_special_tokens=_lowerCamelCase      ).input_ids[0]
		__SCREAMING_SNAKE_CASE :	str					 =	get_blipa_config(_lowerCamelCase ,   eos_token_id=_lowerCamelCase      )

		__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	BlipaForConditionalGeneration(_lowerCamelCase      ).eval()

		__SCREAMING_SNAKE_CASE :	Tuple					 =	{
		    """blip2-opt-2.7b""": ("""blip2_opt""", """pretrain_opt2.7b"""),
		    """blip2-opt-6.7b""": ("""blip2_opt""", """pretrain_opt6.7b"""),
		    """blip2-opt-2.7b-coco""": ("""blip2_opt""", """caption_coco_opt2.7b"""),
		    """blip2-opt-6.7b-coco""": ("""blip2_opt""", """caption_coco_opt6.7b"""),
		    """blip2-flan-t5-xl""": ("""blip2_t5""", """pretrain_flant5xl"""),
		    """blip2-flan-t5-xl-coco""": ("""blip2_t5""", """caption_coco_flant5xl"""),
		    """blip2-flan-t5-xxl""": ("""blip2_t5""", """pretrain_flant5xxl"""),
		}

		__SCREAMING_SNAKE_CASE :	Dict					 =	model_name_to_original[model_name]

		# load original model
		print("""Loading original model..."""      )
		__SCREAMING_SNAKE_CASE :	List[Any]					 =	"""cuda""" if torch.cuda.is_available() else """cpu"""
		__SCREAMING_SNAKE_CASE :	Dict					 =	load_model_and_preprocess(
		    name=_lowerCamelCase ,   model_type=_lowerCamelCase ,   is_eval=_lowerCamelCase ,   device=_lowerCamelCase      )
		original_model.eval()
		print("""Done!"""      )

		# update state dict keys
		__SCREAMING_SNAKE_CASE :	List[Any]					 =	original_model.state_dict()
		__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	create_rename_keys(_lowerCamelCase      )
		for src, dest in rename_keys:
				rename_key(_lowerCamelCase ,   _lowerCamelCase ,   _lowerCamelCase      )

		# some keys can be renamed efficiently
		for key, val in state_dict.copy().items():
				__SCREAMING_SNAKE_CASE :	Optional[int]					 =	state_dict.pop(_lowerCamelCase      )
				if key.startswith("""Qformer.bert"""      ):
						__SCREAMING_SNAKE_CASE :	Any					 =	key.replace("""Qformer.bert""" ,   """qformer"""      )
				if "attention.self" in key:
						__SCREAMING_SNAKE_CASE :	List[Any]					 =	key.replace("""self""" ,   """attention"""      )
				if "opt_proj" in key:
						__SCREAMING_SNAKE_CASE :	List[str]					 =	key.replace("""opt_proj""" ,   """language_projection"""      )
				if "t5_proj" in key:
						__SCREAMING_SNAKE_CASE :	List[str]					 =	key.replace("""t5_proj""" ,   """language_projection"""      )
				if key.startswith("""opt"""      ):
						__SCREAMING_SNAKE_CASE :	str					 =	key.replace("""opt""" ,   """language"""      )
				if key.startswith("""t5"""      ):
						__SCREAMING_SNAKE_CASE :	int					 =	key.replace("""t5""" ,   """language"""      )
				__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	val

		# read in qv biases
		read_in_q_v_bias(_lowerCamelCase ,   _lowerCamelCase      )

		__SCREAMING_SNAKE_CASE :	str					 =	hf_model.load_state_dict(_lowerCamelCase ,   strict=_lowerCamelCase      )
		assert len(_lowerCamelCase      ) == 0
		assert unexpected_keys == ["qformer.embeddings.position_ids"]

		__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	load_demo_image()
		__SCREAMING_SNAKE_CASE :	int					 =	vis_processors["""eval"""](_lowerCamelCase      ).unsqueeze(0      ).to(_lowerCamelCase      )
		__SCREAMING_SNAKE_CASE :	str					 =	tokenizer(["""\n"""] ,   return_tensors="""pt"""      ).input_ids.to(_lowerCamelCase      )

		# create processor
		__SCREAMING_SNAKE_CASE :	Tuple					 =	BlipImageProcessor(
		    size={"""height""": image_size, """width""": image_size} ,   image_mean=_lowerCamelCase ,   image_std=_lowerCamelCase      )
		__SCREAMING_SNAKE_CASE :	str					 =	BlipaProcessor(image_processor=_lowerCamelCase ,   tokenizer=_lowerCamelCase      )
		__SCREAMING_SNAKE_CASE :	List[Any]					 =	processor(images=_lowerCamelCase ,   return_tensors="""pt"""      ).pixel_values.to(_lowerCamelCase      )

		# make sure processor creates exact same pixel values
		assert torch.allclose(_lowerCamelCase ,   _lowerCamelCase      )

		original_model.to(_lowerCamelCase      )
		hf_model.to(_lowerCamelCase      )
		with torch.no_grad():
				if "opt" in model_name:
						__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	original_model({"""image""": original_pixel_values, """text_input""": [""""""]}      ).logits
						__SCREAMING_SNAKE_CASE :	List[Any]					 =	hf_model(_lowerCamelCase ,   _lowerCamelCase      ).logits
				else:
						__SCREAMING_SNAKE_CASE :	str					 =	original_model(
						    {"""image""": original_pixel_values, """text_input""": ["""\n"""], """text_output""": ["""\n"""]}      ).logits
						__SCREAMING_SNAKE_CASE :	List[Any]					 =	input_ids.masked_fill(input_ids == tokenizer.pad_token_id ,   -1_00      )
						__SCREAMING_SNAKE_CASE :	int					 =	hf_model(_lowerCamelCase ,   _lowerCamelCase ,   labels=_lowerCamelCase      ).logits

		assert original_logits.shape == logits.shape
		print("""First values of original logits:""" ,   original_logits[0, :3, :3]      )
		print("""First values of HF logits:""" ,   logits[0, :3, :3]      )

		# assert values
		if model_name == "blip2-flan-t5-xl":
				__SCREAMING_SNAKE_CASE :	List[Any]					 =	torch.tensor(
				    [[-41.58_50, -4.44_40, -8.99_22], [-47.43_22, -5.91_43, -1.73_40]] ,   device=_lowerCamelCase      )
				assert torch.allclose(logits[0, :3, :3] ,   _lowerCamelCase ,   atol=1E-4      )
		elif model_name == "blip2-flan-t5-xl-coco":
				__SCREAMING_SNAKE_CASE :	Optional[int]					 =	torch.tensor(
				    [[-57.01_09, -9.89_67, -12.62_80], [-68.65_78, -12.71_91, -10.50_65]] ,   device=_lowerCamelCase      )
		else:
				# cast to same type
				__SCREAMING_SNAKE_CASE :	Tuple					 =	logits.dtype
				assert torch.allclose(original_logits.to(_lowerCamelCase      ) ,   _lowerCamelCase ,   atol=1E-2      )
		print("""Looks ok!"""      )

		print("""Generating a caption..."""      )
		__SCREAMING_SNAKE_CASE :	List[str]					 =	""""""
		__SCREAMING_SNAKE_CASE :	List[Any]					 =	tokenizer(_lowerCamelCase ,   return_tensors="""pt"""      ).input_ids.to(_lowerCamelCase      )

		__SCREAMING_SNAKE_CASE :	Dict					 =	original_model.generate({"""image""": original_pixel_values}      )
		__SCREAMING_SNAKE_CASE :	Tuple					 =	hf_model.generate(
		    _lowerCamelCase ,   _lowerCamelCase ,   do_sample=_lowerCamelCase ,   num_beams=5 ,   max_length=30 ,   min_length=1 ,   top_p=0.9 ,   repetition_penalty=1.0 ,   length_penalty=1.0 ,   temperature=1 ,   )
		print("""Original generation:""" ,   _lowerCamelCase      )
		__SCREAMING_SNAKE_CASE :	List[str]					 =	input_ids.shape[1]
		__SCREAMING_SNAKE_CASE :	str					 =	processor.batch_decode(outputs[:, prompt_length:] ,   skip_special_tokens=_lowerCamelCase      )
		__SCREAMING_SNAKE_CASE :	List[Any]					 =	[text.strip() for text in output_text]
		print("""HF generation:""" ,   _lowerCamelCase      )

		if pytorch_dump_folder_path is not None:
				processor.save_pretrained(_lowerCamelCase      )
				hf_model.save_pretrained(_lowerCamelCase      )

		if push_to_hub:
				processor.push_to_hub(F"nielsr/{model_name}"      )
				hf_model.push_to_hub(F"nielsr/{model_name}"      )


if __name__ == "__main__":
						UpperCamelCase__ :			Any												=				argparse.ArgumentParser()
						UpperCamelCase__ :			str												=				[
						    '''blip2-opt-2.7b''',
						    '''blip2-opt-6.7b''',
						    '''blip2-opt-2.7b-coco''',
						    '''blip2-opt-6.7b-coco''',
						    '''blip2-flan-t5-xl''',
						    '''blip2-flan-t5-xl-coco''',
						    '''blip2-flan-t5-xxl''',
						]
						parser.add_argument(
						    '''--model_name''',
						    default='''blip2-opt-2.7b''',
						    choices=choices,
						    type=str,
						    help='''Path to hf config.json of model to convert''',
						)
						parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
						parser.add_argument(
						    '''--push_to_hub''',
						    action='''store_true''',
						    help='''Whether to push the model and processor to the hub after converting''',
						)

						UpperCamelCase__ :			Optional[int]												=				parser.parse_args()

						convert_blipa_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)









'''simple docstring'''




import unittest

from transformers import BertGenerationTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_torch, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


UpperCamelCase__ :			Any												=				'''▁'''

UpperCamelCase__ :			Dict												=				get_tests_dir('''fixtures/test_sentencepiece.model''')






@require_sentencepiece
class 		_UpperCamelCase						(							lowerCamelCase__					,      unittest.TestCase					):


			'''simple docstring'''

			_A   :	Dict    					=	BertGenerationTokenizer
			_A   :	List[Any]    					=	False
			_A   :	Union[str, Any]    					=	True

			def UpperCamelCase__      (							self  :				Optional[Any]		):





					"""simple docstring"""






					super().setUp()

					__SCREAMING_SNAKE_CASE :	Dict					 =	BertGenerationTokenizer(lowerCAmelCase__							,  keep_accents=lowerCAmelCase__		)
					tokenizer.save_pretrained(self.tmpdirname		)

			def UpperCamelCase__      (							self  :				Optional[Any]		):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE :	int					 =	"""<s>"""
					__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	1

					self.assertEqual(self.get_tokenizer()._convert_token_to_id(lowerCAmelCase__		)							,  lowerCAmelCase__		)
					self.assertEqual(self.get_tokenizer()._convert_id_to_token(lowerCAmelCase__		)							,  lowerCAmelCase__		)

			def UpperCamelCase__      (							self  :				Union[str, Any]		):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE :	Dict					 =	list(self.get_tokenizer().get_vocab().keys()		)

					self.assertEqual(vocab_keys[0]							,  """<unk>"""		)
					self.assertEqual(vocab_keys[1]							,  """<s>"""		)
					self.assertEqual(vocab_keys[-1]							,  """<pad>"""		)
					self.assertEqual(len(lowerCAmelCase__		)							,  1_0_0_2		)

			def UpperCamelCase__      (							self  :				Optional[int]		):





					"""simple docstring"""






					self.assertEqual(self.get_tokenizer().vocab_size							,  1_0_0_0		)

			def UpperCamelCase__      (							self  :				Optional[Any]		):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	BertGenerationTokenizer(lowerCAmelCase__							,  keep_accents=lowerCAmelCase__		)

					__SCREAMING_SNAKE_CASE :	Any					 =	tokenizer.tokenize("""This is a test"""		)
					self.assertListEqual(lowerCAmelCase__							,  ["""▁This""", """▁is""", """▁a""", """▁t""", """est"""]		)

					self.assertListEqual(
					    tokenizer.convert_tokens_to_ids(lowerCAmelCase__		)							,  [2_8_5, 4_6, 1_0, 1_7_0, 3_8_2]							,  )

					__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	tokenizer.tokenize("""I was born in 92000, and this is falsé."""		)
					self.assertListEqual(
					    lowerCAmelCase__							,  [
					        SPIECE_UNDERLINE + """I""",
					        SPIECE_UNDERLINE + """was""",
					        SPIECE_UNDERLINE + """b""",
					        """or""",
					        """n""",
					        SPIECE_UNDERLINE + """in""",
					        SPIECE_UNDERLINE + """""",
					        """9""",
					        """2""",
					        """0""",
					        """0""",
					        """0""",
					        """,""",
					        SPIECE_UNDERLINE + """and""",
					        SPIECE_UNDERLINE + """this""",
					        SPIECE_UNDERLINE + """is""",
					        SPIECE_UNDERLINE + """f""",
					        """al""",
					        """s""",
					        """é""",
					        """.""",
					    ]							,  )
					__SCREAMING_SNAKE_CASE :	Any					 =	tokenizer.convert_tokens_to_ids(lowerCAmelCase__		)
					self.assertListEqual(
					    lowerCAmelCase__							,  [8, 2_1, 8_4, 5_5, 2_4, 1_9, 7, 0, 6_0_2, 3_4_7, 3_4_7, 3_4_7, 3, 1_2, 6_6, 4_6, 7_2, 8_0, 6, 0, 4]							,  )

					__SCREAMING_SNAKE_CASE :	Any					 =	tokenizer.convert_ids_to_tokens(lowerCAmelCase__		)
					self.assertListEqual(
					    lowerCAmelCase__							,  [
					        SPIECE_UNDERLINE + """I""",
					        SPIECE_UNDERLINE + """was""",
					        SPIECE_UNDERLINE + """b""",
					        """or""",
					        """n""",
					        SPIECE_UNDERLINE + """in""",
					        SPIECE_UNDERLINE + """""",
					        """<unk>""",
					        """2""",
					        """0""",
					        """0""",
					        """0""",
					        """,""",
					        SPIECE_UNDERLINE + """and""",
					        SPIECE_UNDERLINE + """this""",
					        SPIECE_UNDERLINE + """is""",
					        SPIECE_UNDERLINE + """f""",
					        """al""",
					        """s""",
					        """<unk>""",
					        """.""",
					    ]							,  )

			@cached_property
			def UpperCamelCase__      (							self  :				List[str]		):





					"""simple docstring"""






					return BertGenerationTokenizer.from_pretrained("""google/bert_for_seq_generation_L-24_bbc_encoder"""		)

			@slow
			def UpperCamelCase__      (							self  :				Tuple		):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE :	Any					 =	"""Hello World!"""
					__SCREAMING_SNAKE_CASE :	Dict					 =	[1_8_5_3_6, 2_2_6_0, 1_0_1]

					self.assertListEqual(lowerCAmelCase__							,  self.big_tokenizer.encode(lowerCAmelCase__		)		)

			@slow
			def UpperCamelCase__      (							self  :				Dict		):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE :	List[Any]					 =	(
					    """This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) \" [ ] ! : - . Also we will"""
					    """ add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth"""
					)
					__SCREAMING_SNAKE_CASE :	Optional[int]					 =	[
					    8_7_1,
					    4_1_9,
					    3_5_8,
					    9_4_6,
					    9_9_1,
					    2_5_2_1,
					    4_5_2,
					    3_5_8,
					    1_3_5_7,
					    3_8_7,
					    7_7_5_1,
					    3_5_3_6,
					    1_1_2,
					    9_8_5,
					    4_5_6,
					    1_2_6,
					    8_6_5,
					    9_3_8,
					    5_4_0_0,
					    5_7_3_4,
					    4_5_8,
					    1_3_6_8,
					    4_6_7,
					    7_8_6,
					    2_4_6_2,
					    5_2_4_6,
					    1_1_5_9,
					    6_3_3,
					    8_6_5,
					    4_5_1_9,
					    4_5_7,
					    5_8_2,
					    8_5_2,
					    2_5_5_7,
					    4_2_7,
					    9_1_6,
					    5_0_8,
					    4_0_5,
					    3_4_3_2_4,
					    4_9_7,
					    3_9_1,
					    4_0_8,
					    1_1_3_4_2,
					    1_2_4_4,
					    3_8_5,
					    1_0_0,
					    9_3_8,
					    9_8_5,
					    4_5_6,
					    5_7_4,
					    3_6_2,
					    1_2_5_9_7,
					    3_2_0_0,
					    3_1_2_9,
					    1_1_7_2,
					]

					self.assertListEqual(lowerCAmelCase__							,  self.big_tokenizer.encode(lowerCAmelCase__		)		)

			@require_torch
			@slow
			def UpperCamelCase__      (							self  :				str		):





					"""simple docstring"""






					import torch

					from transformers import BertGenerationConfig, BertGenerationEncoder

					# Build sequence
					__SCREAMING_SNAKE_CASE :	List[Any]					 =	list(self.big_tokenizer.get_vocab().keys()		)[:1_0]
					__SCREAMING_SNAKE_CASE :	int					 =	""" """.join(lowerCAmelCase__		)
					__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	self.big_tokenizer.encode_plus(lowerCAmelCase__							,  return_tensors="""pt"""							,  return_token_type_ids=lowerCAmelCase__		)
					__SCREAMING_SNAKE_CASE :	Optional[int]					 =	self.big_tokenizer.batch_encode_plus(
					    [sequence + """ """ + sequence]							,  return_tensors="""pt"""							,  return_token_type_ids=lowerCAmelCase__		)

					__SCREAMING_SNAKE_CASE :	Tuple					 =	BertGenerationConfig()
					__SCREAMING_SNAKE_CASE :	Any					 =	BertGenerationEncoder(lowerCAmelCase__		)

					assert model.get_input_embeddings().weight.shape[0] >= self.big_tokenizer.vocab_size

					with torch.no_grad():
							model(**lowerCAmelCase__		)
							model(**lowerCAmelCase__		)



			@slow
			def UpperCamelCase__      (							self  :				List[str]		):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	{"""input_ids""": [[3_9_2_8_6, 4_5_8, 3_6_3_3_5, 2_0_0_1, 4_5_6, 1_3_0_7_3, 1_3_2_6_6, 4_5_5, 1_1_3, 7_7_4_6, 1_7_4_1, 1_1_1_5_7, 3_9_1, 1_3_0_7_3, 1_3_2_6_6, 4_5_5, 1_1_3, 3_9_6_7, 3_5_4_1_2, 1_1_3, 4_9_3_6, 1_0_9, 3_8_7_0, 2_3_7_7, 1_1_3, 3_0_0_8_4, 4_5_7_2_0, 4_5_8, 1_3_4, 1_7_4_9_6, 1_1_2, 5_0_3, 1_1_6_7_2, 1_1_3, 1_1_8, 1_1_2, 5_6_6_5, 1_3_3_4_7, 3_8_6_8_7, 1_1_2, 1_4_9_6, 3_1_3_8_9, 1_1_2, 3_2_6_8, 4_7_2_6_4, 1_3_4, 9_6_2, 1_1_2, 1_6_3_7_7, 8_0_3_5, 2_3_1_3_0, 4_3_0, 1_2_1_6_9, 1_5_5_1_8, 2_8_5_9_2, 4_5_8, 1_4_6, 4_1_6_9_7, 1_0_9, 3_9_1, 1_2_1_6_9, 1_5_5_1_8, 1_6_6_8_9, 4_5_8, 1_4_6, 4_1_3_5_8, 1_0_9, 4_5_2, 7_2_6, 4_0_3_4, 1_1_1, 7_6_3, 3_5_4_1_2, 5_0_8_2, 3_8_8, 1_9_0_3, 1_1_1, 9_0_5_1, 3_9_1, 2_8_7_0, 4_8_9_1_8, 1_9_0_0, 1_1_2_3, 5_5_0, 9_9_8, 1_1_2, 9_5_8_6, 1_5_9_8_5, 4_5_5, 3_9_1, 4_1_0, 2_2_9_5_5, 3_7_6_3_6, 1_1_4], [4_4_8, 1_7_4_9_6, 4_1_9, 3_6_6_3, 3_8_5, 7_6_3, 1_1_3, 2_7_5_3_3, 2_8_7_0, 3_2_8_3, 1_3_0_4_3, 1_6_3_9, 2_4_7_1_3, 5_2_3, 6_5_6, 2_4_0_1_3, 1_8_5_5_0, 2_5_2_1, 5_1_7, 2_7_0_1_4, 2_1_2_4_4, 4_2_0, 1_2_1_2, 1_4_6_5, 3_9_1, 9_2_7, 4_8_3_3, 3_8_8, 5_7_8, 1_1_7_8_6, 1_1_4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [4_8_4, 2_1_6_9, 7_6_8_7, 2_1_9_3_2, 1_8_1_4_6, 7_2_6, 3_6_3, 1_7_0_3_2, 3_3_9_1, 1_1_4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], """attention_mask""": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
					# fmt: on

					self.tokenizer_integration_test_util(
					    expected_encoding=lowerCAmelCase__							,  model_name="""google/bert_for_seq_generation_L-24_bbc_encoder"""							,  revision="""c817d1fd1be2ffa69431227a1fe320544943d4db"""							,  )