





'''simple docstring'''




import argparse
import os.path as osp
import re

import torch
from safetensors.torch import load_file, save_file


# =================#
# UNet Conversion #
# =================#

__UpperCAmelCase            =       [
    # (stable-diffusion, HF Diffusers)
    ("""time_embed.0.weight""", """time_embedding.linear_1.weight"""),
    ("""time_embed.0.bias""", """time_embedding.linear_1.bias"""),
    ("""time_embed.2.weight""", """time_embedding.linear_2.weight"""),
    ("""time_embed.2.bias""", """time_embedding.linear_2.bias"""),
    ("""input_blocks.0.0.weight""", """conv_in.weight"""),
    ("""input_blocks.0.0.bias""", """conv_in.bias"""),
    ("""out.0.weight""", """conv_norm_out.weight"""),
    ("""out.0.bias""", """conv_norm_out.bias"""),
    ("""out.2.weight""", """conv_out.weight"""),
    ("""out.2.bias""", """conv_out.bias"""),
]

__UpperCAmelCase            =       [
    # (stable-diffusion, HF Diffusers)
    ("""in_layers.0""", """norm1"""),
    ("""in_layers.2""", """conv1"""),
    ("""out_layers.0""", """norm2"""),
    ("""out_layers.3""", """conv2"""),
    ("""emb_layers.1""", """time_emb_proj"""),
    ("""skip_connection""", """conv_shortcut"""),
]

__UpperCAmelCase            =       []
# hardcoded number of downblocks and resnets/attentions...
# would need smarter logic for other networks.
for i in range(4):
							# loop over downblocks/upblocks

							for j in range(2):
														# loop over resnets/attentions for downblocks
														__UpperCAmelCase            =       f'''down_blocks.{i}.resnets.{j}.'''
														__UpperCAmelCase            =       f'''input_blocks.{3*i + j + 1}.0.'''
														unet_conversion_map_layer.append((sd_down_res_prefix, hf_down_res_prefix))

														if i < 3:
																					# no attention layers in down_blocks.3
																					__UpperCAmelCase            =       f'''down_blocks.{i}.attentions.{j}.'''
																					__UpperCAmelCase            =       f'''input_blocks.{3*i + j + 1}.1.'''
																					unet_conversion_map_layer.append((sd_down_atn_prefix, hf_down_atn_prefix))

							for j in range(3):
														# loop over resnets/attentions for upblocks
														__UpperCAmelCase            =       f'''up_blocks.{i}.resnets.{j}.'''
														__UpperCAmelCase            =       f'''output_blocks.{3*i + j}.0.'''
														unet_conversion_map_layer.append((sd_up_res_prefix, hf_up_res_prefix))

														if i > 0:
																					# no attention layers in up_blocks.0
																					__UpperCAmelCase            =       f'''up_blocks.{i}.attentions.{j}.'''
																					__UpperCAmelCase            =       f'''output_blocks.{3*i + j}.1.'''
																					unet_conversion_map_layer.append((sd_up_atn_prefix, hf_up_atn_prefix))

							if i < 3:
														# no downsample in down_blocks.3
														__UpperCAmelCase            =       f'''down_blocks.{i}.downsamplers.0.conv.'''
														__UpperCAmelCase            =       f'''input_blocks.{3*(i+1)}.0.op.'''
														unet_conversion_map_layer.append((sd_downsample_prefix, hf_downsample_prefix))

														# no upsample in up_blocks.3
														__UpperCAmelCase            =       f'''up_blocks.{i}.upsamplers.0.'''
														__UpperCAmelCase            =       f'''output_blocks.{3*i + 2}.{1 if i == 0 else 2}.'''
														unet_conversion_map_layer.append((sd_upsample_prefix, hf_upsample_prefix))

__UpperCAmelCase            =       """mid_block.attentions.0."""
__UpperCAmelCase            =       """middle_block.1."""
unet_conversion_map_layer.append((sd_mid_atn_prefix, hf_mid_atn_prefix))

for j in range(2):
							__UpperCAmelCase            =       f'''mid_block.resnets.{j}.'''
							__UpperCAmelCase            =       f'''middle_block.{2*j}.'''
							unet_conversion_map_layer.append((sd_mid_res_prefix, hf_mid_res_prefix))



def 		__A		(  lowerCamelCase_	):


		"""simple docstring"""



		SCREAMING_SNAKE_CASE :				Any		=			{k: k for k in unet_state_dict.keys()}
		for sd_name, hf_name in unet_conversion_map:
				SCREAMING_SNAKE_CASE :				int		=			sd_name
		for k, v in mapping.items():
				if "resnets" in k:
						for sd_part, hf_part in unet_conversion_map_resnet:
								SCREAMING_SNAKE_CASE :				Dict		=			v.replace(lowerCamelCase_			,			lowerCamelCase_	)
						SCREAMING_SNAKE_CASE :				Dict		=			v
		for k, v in mapping.items():
				for sd_part, hf_part in unet_conversion_map_layer:
						SCREAMING_SNAKE_CASE :				List[Any]		=			v.replace(lowerCamelCase_			,			lowerCamelCase_	)
				SCREAMING_SNAKE_CASE :				Any		=			v
		SCREAMING_SNAKE_CASE :				Optional[Any]		=			{v: unet_state_dict[k] for k, v in mapping.items()}
		return new_state_dict


# ================#
# VAE Conversion #
# ================#

__UpperCAmelCase            =       [
    # (stable-diffusion, HF Diffusers)
    ("""nin_shortcut""", """conv_shortcut"""),
    ("""norm_out""", """conv_norm_out"""),
    ("""mid.attn_1.""", """mid_block.attentions.0."""),
]

for i in range(4):
							# down_blocks have two resnets
							for j in range(2):
														__UpperCAmelCase            =       f'''encoder.down_blocks.{i}.resnets.{j}.'''
														__UpperCAmelCase            =       f'''encoder.down.{i}.block.{j}.'''
														vae_conversion_map.append((sd_down_prefix, hf_down_prefix))

							if i < 3:
														__UpperCAmelCase            =       f'''down_blocks.{i}.downsamplers.0.'''
														__UpperCAmelCase            =       f'''down.{i}.downsample.'''
														vae_conversion_map.append((sd_downsample_prefix, hf_downsample_prefix))

														__UpperCAmelCase            =       f'''up_blocks.{i}.upsamplers.0.'''
														__UpperCAmelCase            =       f'''up.{3-i}.upsample.'''
														vae_conversion_map.append((sd_upsample_prefix, hf_upsample_prefix))

							# up_blocks have three resnets
							# also, up blocks in hf are numbered in reverse from sd
							for j in range(3):
														__UpperCAmelCase            =       f'''decoder.up_blocks.{i}.resnets.{j}.'''
														__UpperCAmelCase            =       f'''decoder.up.{3-i}.block.{j}.'''
														vae_conversion_map.append((sd_up_prefix, hf_up_prefix))

# this part accounts for mid blocks in both the encoder and the decoder
for i in range(2):
							__UpperCAmelCase            =       f'''mid_block.resnets.{i}.'''
							__UpperCAmelCase            =       f'''mid.block_{i+1}.'''
							vae_conversion_map.append((sd_mid_res_prefix, hf_mid_res_prefix))


__UpperCAmelCase            =       [
    # (stable-diffusion, HF Diffusers)
    ("""norm.""", """group_norm."""),
    ("""q.""", """query."""),
    ("""k.""", """key."""),
    ("""v.""", """value."""),
    ("""proj_out.""", """proj_attn."""),
]



def 		__A		(  lowerCamelCase_	):


		"""simple docstring"""



		return w.reshape(*w.shape			,			1			,			1	)



def 		__A		(  lowerCamelCase_	):


		"""simple docstring"""



		SCREAMING_SNAKE_CASE :				Optional[Any]		=			{k: k for k in vae_state_dict.keys()}
		for k, v in mapping.items():
				for sd_part, hf_part in vae_conversion_map:
						SCREAMING_SNAKE_CASE :				str		=			v.replace(lowerCamelCase_			,			lowerCamelCase_	)
				SCREAMING_SNAKE_CASE :				str		=			v
		for k, v in mapping.items():
				if "attentions" in k:
						for sd_part, hf_part in vae_conversion_map_attn:
								SCREAMING_SNAKE_CASE :				str		=			v.replace(lowerCamelCase_			,			lowerCamelCase_	)
						SCREAMING_SNAKE_CASE :				Any		=			v
		SCREAMING_SNAKE_CASE :				List[Any]		=			{v: vae_state_dict[k] for k, v in mapping.items()}
		SCREAMING_SNAKE_CASE :				Tuple		=			["""q""", """k""", """v""", """proj_out"""]
		for k, v in new_state_dict.items():
				for weight_name in weights_to_convert:
						if f'''mid.attn_1.{weight_name}.weight''' in k:
								print(f'''Reshaping {k} for SD format'''	)
								SCREAMING_SNAKE_CASE :				Union[str, Any]		=			reshape_weight_for_sd(lowerCamelCase_	)
		return new_state_dict


# =========================#
# Text Encoder Conversion #
# =========================#


__UpperCAmelCase            =       [
    # (stable-diffusion, HF Diffusers)
    ("""resblocks.""", """text_model.encoder.layers."""),
    ("""ln_1""", """layer_norm1"""),
    ("""ln_2""", """layer_norm2"""),
    (""".c_fc.""", """.fc1."""),
    (""".c_proj.""", """.fc2."""),
    (""".attn""", """.self_attn"""),
    ("""ln_final.""", """transformer.text_model.final_layer_norm."""),
    ("""token_embedding.weight""", """transformer.text_model.embeddings.token_embedding.weight"""),
    ("""positional_embedding""", """transformer.text_model.embeddings.position_embedding.weight"""),
]
__UpperCAmelCase            =       {re.escape(x[1]): x[0] for x in textenc_conversion_lst}
__UpperCAmelCase            =       re.compile("""|""".join(protected.keys()))

# Ordering is from https://github.com/pytorch/pytorch/blob/master/test/cpp/api/modules.cpp
__UpperCAmelCase            =       {"""q""": 0, """k""": 1, """v""": 2}



def 		__A		(  lowerCamelCase_	):


		"""simple docstring"""



		SCREAMING_SNAKE_CASE :				int		=			{}
		SCREAMING_SNAKE_CASE :				Dict		=			{}
		SCREAMING_SNAKE_CASE :				Optional[int]		=			{}
		for k, v in text_enc_dict.items():
				if (
				    k.endswith(""".self_attn.q_proj.weight"""	)
				    or k.endswith(""".self_attn.k_proj.weight"""	)
				    or k.endswith(""".self_attn.v_proj.weight"""	)
				):
						SCREAMING_SNAKE_CASE :				List[str]		=			k[: -len(""".q_proj.weight"""	)]
						SCREAMING_SNAKE_CASE :				int		=			k[-len("""q_proj.weight"""	)]
						if k_pre not in capture_qkv_weight:
								SCREAMING_SNAKE_CASE :				Optional[Any]		=			[None, None, None]
						SCREAMING_SNAKE_CASE :				str		=			v
						continue

				if (
				    k.endswith(""".self_attn.q_proj.bias"""	)
				    or k.endswith(""".self_attn.k_proj.bias"""	)
				    or k.endswith(""".self_attn.v_proj.bias"""	)
				):
						SCREAMING_SNAKE_CASE :				Dict		=			k[: -len(""".q_proj.bias"""	)]
						SCREAMING_SNAKE_CASE :				Optional[Any]		=			k[-len("""q_proj.bias"""	)]
						if k_pre not in capture_qkv_bias:
								SCREAMING_SNAKE_CASE :				int		=			[None, None, None]
						SCREAMING_SNAKE_CASE :				str		=			v
						continue

				SCREAMING_SNAKE_CASE :				Union[str, Any]		=			textenc_pattern.sub(lambda lowerCamelCase_	: protected[re.escape(m.group(0	)	)]			,			lowerCamelCase_	)
				SCREAMING_SNAKE_CASE :				Union[str, Any]		=			v

		for k_pre, tensors in capture_qkv_weight.items():
				if None in tensors:
						raise Exception("""CORRUPTED MODEL: one of the q-k-v values for the text encoder was missing"""	)
				SCREAMING_SNAKE_CASE :				Any		=			textenc_pattern.sub(lambda lowerCamelCase_	: protected[re.escape(m.group(0	)	)]			,			lowerCamelCase_	)
				SCREAMING_SNAKE_CASE :				str		=			torch.cat(lowerCamelCase_	)

		for k_pre, tensors in capture_qkv_bias.items():
				if None in tensors:
						raise Exception("""CORRUPTED MODEL: one of the q-k-v values for the text encoder was missing"""	)
				SCREAMING_SNAKE_CASE :				Any		=			textenc_pattern.sub(lambda lowerCamelCase_	: protected[re.escape(m.group(0	)	)]			,			lowerCamelCase_	)
				SCREAMING_SNAKE_CASE :				List[Any]		=			torch.cat(lowerCamelCase_	)

		return new_state_dict



def 		__A		(  lowerCamelCase_	):


		"""simple docstring"""



		return text_enc_dict


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

							parser.add_argument("""--model_path""", default=None, type=str, required=True, help="""Path to the model to convert.""")
							parser.add_argument("""--checkpoint_path""", default=None, type=str, required=True, help="""Path to the output model.""")
							parser.add_argument("""--half""", action="""store_true""", help="""Save weights in half precision.""")
							parser.add_argument(
							    """--use_safetensors""", action="""store_true""", help="""Save weights use safetensors, default is ckpt."""
							)

							__UpperCAmelCase            =       parser.parse_args()

							assert args.model_path is not None, "Must provide a model path!"

							assert args.checkpoint_path is not None, "Must provide a checkpoint path!"

							# Path for safetensors
							__UpperCAmelCase            =       osp.join(args.model_path, """unet""", """diffusion_pytorch_model.safetensors""")
							__UpperCAmelCase            =       osp.join(args.model_path, """vae""", """diffusion_pytorch_model.safetensors""")
							__UpperCAmelCase            =       osp.join(args.model_path, """text_encoder""", """model.safetensors""")

							# Load models from safetensors if it exists, if it doesn't pytorch
							if osp.exists(unet_path):
														__UpperCAmelCase            =       load_file(unet_path, device="""cpu""")
							else:
														__UpperCAmelCase            =       osp.join(args.model_path, """unet""", """diffusion_pytorch_model.bin""")
														__UpperCAmelCase            =       torch.load(unet_path, map_location="""cpu""")

							if osp.exists(vae_path):
														__UpperCAmelCase            =       load_file(vae_path, device="""cpu""")
							else:
														__UpperCAmelCase            =       osp.join(args.model_path, """vae""", """diffusion_pytorch_model.bin""")
														__UpperCAmelCase            =       torch.load(vae_path, map_location="""cpu""")

							if osp.exists(text_enc_path):
														__UpperCAmelCase            =       load_file(text_enc_path, device="""cpu""")
							else:
														__UpperCAmelCase            =       osp.join(args.model_path, """text_encoder""", """pytorch_model.bin""")
														__UpperCAmelCase            =       torch.load(text_enc_path, map_location="""cpu""")

							# Convert the UNet model
							__UpperCAmelCase            =       convert_unet_state_dict(unet_state_dict)
							__UpperCAmelCase            =       {"""model.diffusion_model.""" + k: v for k, v in unet_state_dict.items()}

							# Convert the VAE model
							__UpperCAmelCase            =       convert_vae_state_dict(vae_state_dict)
							__UpperCAmelCase            =       {"""first_stage_model.""" + k: v for k, v in vae_state_dict.items()}

							# Easiest way to identify v2.0 model seems to be that the text encoder (OpenCLIP) is deeper
							__UpperCAmelCase            =       """text_model.encoder.layers.22.layer_norm2.bias""" in text_enc_dict

							if is_vaa_model:
														# Need to add the tag 'transformer' in advance so we can knock it out from the final layer-norm
														__UpperCAmelCase            =       {"""transformer.""" + k: v for k, v in text_enc_dict.items()}
														__UpperCAmelCase            =       convert_text_enc_state_dict_vaa(text_enc_dict)
														__UpperCAmelCase            =       {"""cond_stage_model.model.""" + k: v for k, v in text_enc_dict.items()}
							else:
														__UpperCAmelCase            =       convert_text_enc_state_dict(text_enc_dict)
														__UpperCAmelCase            =       {"""cond_stage_model.transformer.""" + k: v for k, v in text_enc_dict.items()}

							# Put together new checkpoint
							__UpperCAmelCase            =       {**unet_state_dict, **vae_state_dict, **text_enc_dict}
							if args.half:
														__UpperCAmelCase            =       {k: v.half() for k, v in state_dict.items()}

							if args.use_safetensors:
														save_file(state_dict, args.checkpoint_path)
							else:
														__UpperCAmelCase            =       {"""state_dict""": state_dict}
														torch.save(state_dict, args.checkpoint_path)










'''simple docstring'''




import unittest

from transformers import BigBirdTokenizer, BigBirdTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


__UpperCAmelCase            =       """▁"""

__UpperCAmelCase            =       get_tests_dir("""fixtures/test_sentencepiece.model""")




@require_sentencepiece
@require_tokenizers
class      UpperCamelCase__					(					lowercase_						,					unittest.TestCase			):




					"""simple docstring"""
					SCREAMING_SNAKE_CASE__				=	BigBirdTokenizer
					SCREAMING_SNAKE_CASE__				=	BigBirdTokenizerFast
					SCREAMING_SNAKE_CASE__				=	True
					SCREAMING_SNAKE_CASE__				=	True


					def lowerCamelCase_			(						self				:			List[Any]						):

							'''simple docstring'''



							super().setUp()

							SCREAMING_SNAKE_CASE :				List[Any]		=			self.tokenizer_class(lowerCamelCase_      ,						keep_accents=lowerCamelCase_						)
							tokenizer.save_pretrained(self.tmpdirname						)


					def lowerCamelCase_			(						self				:			List[Any]						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				Union[str, Any]		=			"""<s>"""
							SCREAMING_SNAKE_CASE :				str		=			1

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


					def lowerCamelCase_			(						self				:			List[str]						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				Optional[Any]		=			list(self.get_tokenizer().get_vocab().keys()						)

							self.assertEqual(vocab_keys[0]      ,						"""<unk>"""						)
							self.assertEqual(vocab_keys[1]      ,						"""<s>"""						)
							self.assertEqual(vocab_keys[-1]      ,						"""[MASK]"""						)
							self.assertEqual(len(lowerCamelCase_						)      ,						10_04						)


					def lowerCamelCase_			(						self				:			str						):

							'''simple docstring'''



							self.assertEqual(self.get_tokenizer().vocab_size      ,						10_00						)


					def lowerCamelCase_			(						self				:			str						):

							'''simple docstring'''



							if not self.test_rust_tokenizer:
									return

							SCREAMING_SNAKE_CASE :				List[Any]		=			self.get_tokenizer()
							SCREAMING_SNAKE_CASE :				str		=			self.get_rust_tokenizer()

							SCREAMING_SNAKE_CASE :				Optional[Any]		=			"""I was born in 92000, and this is falsé."""

							SCREAMING_SNAKE_CASE :				Tuple		=			tokenizer.tokenize(lowerCamelCase_						)
							SCREAMING_SNAKE_CASE :				List[Any]		=			rust_tokenizer.tokenize(lowerCamelCase_						)
							self.assertListEqual(lowerCamelCase_      ,						lowerCamelCase_						)

							SCREAMING_SNAKE_CASE :				int		=			tokenizer.encode(lowerCamelCase_      ,						add_special_tokens=lowerCamelCase_						)
							SCREAMING_SNAKE_CASE :				Optional[Any]		=			rust_tokenizer.encode(lowerCamelCase_      ,						add_special_tokens=lowerCamelCase_						)
							self.assertListEqual(lowerCamelCase_      ,						lowerCamelCase_						)

							SCREAMING_SNAKE_CASE :				List[str]		=			self.get_rust_tokenizer()
							SCREAMING_SNAKE_CASE :				Optional[Any]		=			tokenizer.encode(lowerCamelCase_						)
							SCREAMING_SNAKE_CASE :				Optional[Any]		=			rust_tokenizer.encode(lowerCamelCase_						)
							self.assertListEqual(lowerCamelCase_      ,						lowerCamelCase_						)


					def lowerCamelCase_			(						self				:			str						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				int		=			BigBirdTokenizer(lowerCamelCase_      ,						keep_accents=lowerCamelCase_						)

							SCREAMING_SNAKE_CASE :				int		=			tokenizer.tokenize("""This is a test"""						)
							self.assertListEqual(lowerCamelCase_      ,						["""▁This""", """▁is""", """▁a""", """▁t""", """est"""]						)

							self.assertListEqual(
							    tokenizer.convert_tokens_to_ids(lowerCamelCase_						)      ,						[2_85, 46, 10, 1_70, 3_82]      ,						)

							SCREAMING_SNAKE_CASE :				List[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 :				int		=			tokenizer.convert_tokens_to_ids(lowerCamelCase_						)
							self.assertListEqual(
							    lowerCamelCase_      ,						[8, 21, 84, 55, 24, 19, 7, 0, 6_02, 3_47, 3_47, 3_47, 3, 12, 66, 46, 72, 80, 6, 0, 4]      ,						)

							SCREAMING_SNAKE_CASE :				int		=			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 lowerCamelCase_			(						self				:			Optional[Any]						):

							'''simple docstring'''



							return BigBirdTokenizer.from_pretrained("""google/bigbird-roberta-base"""						)


					@slow
					def lowerCamelCase_			(						self				:			List[Any]						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				List[str]		=			"""Hello World!"""
							SCREAMING_SNAKE_CASE :				Tuple		=			[65, 1_85_36, 22_60, 1_01, 66]

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


					@slow
					def lowerCamelCase_			(						self				:			Dict						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				Dict		=			(
							    """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"""
							)
							# fmt: off
							SCREAMING_SNAKE_CASE :				Dict		=			[65, 8_71, 4_19, 3_58, 9_46, 9_91, 25_21, 4_52, 3_58, 13_57, 3_87, 77_51, 35_36, 1_12, 9_85, 4_56, 1_26, 8_65, 9_38, 54_00, 57_34, 4_58, 13_68, 4_67, 7_86, 24_62, 52_46, 11_59, 6_33, 8_65, 45_19, 4_57, 5_82, 8_52, 25_57, 4_27, 9_16, 5_08, 4_05, 3_43_24, 4_97, 3_91, 4_08, 1_13_42, 12_44, 3_85, 1_00, 9_38, 9_85, 4_56, 5_74, 3_62, 1_25_97, 32_00, 31_29, 11_72, 66]  # noqa: E231
							# fmt: on
							self.assertListEqual(lowerCamelCase_      ,						self.big_tokenizer.encode(lowerCamelCase_						)						)


					@require_torch
					@slow
					def lowerCamelCase_			(						self				:			Optional[Any]						):

							'''simple docstring'''



							import torch

							from transformers import BigBirdConfig, BigBirdModel

							# Build sequence
							SCREAMING_SNAKE_CASE :				int		=			list(self.big_tokenizer.get_vocab().keys()						)[:10]
							SCREAMING_SNAKE_CASE :				List[str]		=			""" """.join(lowerCamelCase_						)
							SCREAMING_SNAKE_CASE :				int		=			self.big_tokenizer.encode_plus(lowerCamelCase_      ,						return_tensors="""pt"""      ,						return_token_type_ids=lowerCamelCase_						)
							SCREAMING_SNAKE_CASE :				Any		=			self.big_tokenizer.batch_encode_plus(
							    [sequence + """ """ + sequence]      ,						return_tensors="""pt"""      ,						return_token_type_ids=lowerCamelCase_						)

							SCREAMING_SNAKE_CASE :				Dict		=			BigBirdConfig(attention_type="""original_full"""						)
							SCREAMING_SNAKE_CASE :				Tuple		=			BigBirdModel(lowerCamelCase_						)

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

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


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

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				Dict		=			BigBirdTokenizer.from_pretrained("""google/bigbird-roberta-base"""						)
							SCREAMING_SNAKE_CASE :				List[Any]		=			tokenizer.decode(tokenizer("""Paris is the [MASK]."""						).input_ids						)

							self.assertTrue(decoded_text == """[CLS] Paris is the[MASK].[SEP]"""						)



					@slow
					def lowerCamelCase_			(						self				:			str						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				Optional[int]		=			{"""input_ids""": [[65, 3_92_86, 4_58, 3_63_35, 20_01, 4_56, 1_30_73, 1_32_66, 4_55, 1_13, 77_46, 17_41, 1_11_57, 3_91, 1_30_73, 1_32_66, 4_55, 1_13, 39_67, 3_54_12, 1_13, 49_36, 1_09, 38_70, 23_77, 1_13, 3_00_84, 4_57_20, 4_58, 1_34, 1_74_96, 1_12, 5_03, 1_16_72, 1_13, 1_18, 1_12, 56_65, 1_33_47, 3_86_87, 1_12, 14_96, 3_13_89, 1_12, 32_68, 4_72_64, 1_34, 9_62, 1_12, 1_63_77, 80_35, 2_31_30, 4_30, 1_21_69, 1_55_18, 2_85_92, 4_58, 1_46, 4_16_97, 1_09, 3_91, 1_21_69, 1_55_18, 1_66_89, 4_58, 1_46, 4_13_58, 1_09, 4_52, 7_26, 40_34, 1_11, 7_63, 3_54_12, 50_82, 3_88, 19_03, 1_11, 90_51, 3_91, 28_70, 4_89_18, 19_00, 11_23, 5_50, 9_98, 1_12, 95_86, 1_59_85, 4_55, 3_91, 4_10, 2_29_55, 3_76_36, 1_14, 66], [65, 4_48, 1_74_96, 4_19, 36_63, 3_85, 7_63, 1_13, 2_75_33, 28_70, 32_83, 1_30_43, 16_39, 2_47_13, 5_23, 6_56, 2_40_13, 1_85_50, 25_21, 5_17, 2_70_14, 2_12_44, 4_20, 12_12, 14_65, 3_91, 9_27, 48_33, 3_88, 5_78, 1_17_86, 1_14, 66, 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], [65, 4_84, 21_69, 76_87, 2_19_32, 1_81_46, 7_26, 3_63, 1_70_32, 33_91, 1_14, 66, 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, 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, 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/bigbird-roberta-base"""      ,						revision="""215c99f1600e06f83acce68422f2035b2b5c3510"""      ,						)



