


import argparse
import os.path as osp
import re

import torch
from safetensors.torch import load_file, save_file


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

A							:					Dict     						=     [
    # (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'),
]

A							:					Union[str, Any]     						=     [
    # (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'),
]

A							:					List[str]     						=     []
# 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
		A							:					Tuple     						=     f'''down_blocks.{i}.resnets.{j}.'''
		A							:					Dict     						=     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
			A							:					List[Any]     						=     f'''down_blocks.{i}.attentions.{j}.'''
			A							:					Any     						=     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
		A							:					List[Any]     						=     f'''up_blocks.{i}.resnets.{j}.'''
		A							:					Optional[Any]     						=     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
			A							:					Any     						=     f'''up_blocks.{i}.attentions.{j}.'''
			A							:					Union[str, Any]     						=     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
		A							:					Tuple     						=     f'''down_blocks.{i}.downsamplers.0.conv.'''
		A							:					Dict     						=     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
		A							:					Any     						=     f'''up_blocks.{i}.upsamplers.0.'''
		A							:					Tuple     						=     f'''output_blocks.{3*i + 2}.{1 if i == 0 else 2}.'''
		unet_conversion_map_layer.append((sd_upsample_prefix, hf_upsample_prefix))

A							:					Dict     						=     'mid_block.attentions.0.'
A							:					Tuple     						=     'middle_block.1.'
unet_conversion_map_layer.append((sd_mid_atn_prefix, hf_mid_atn_prefix))

for j in range(2):
	A							:					Dict     						=     f'''mid_block.resnets.{j}.'''
	A							:					List[str]     						=     f'''middle_block.{2*j}.'''
	unet_conversion_map_layer.append((sd_mid_res_prefix, hf_mid_res_prefix))





def 	_lowerCAmelCase       (    _lowerCAmelCase )						->	List[Any]:


						'''simple docstring'''






						__snake_case				=		{k: k for k in unet_state_dict.keys()}
						for sd_name, hf_name in unet_conversion_map:
												__snake_case				=		sd_name
						for k, v in mapping.items():
												if "resnets" in k:
																		for sd_part, hf_part in unet_conversion_map_resnet:
																								__snake_case				=		v.replace(_lowerCAmelCase      ,	_lowerCAmelCase )
																		__snake_case				=		v
						for k, v in mapping.items():
												for sd_part, hf_part in unet_conversion_map_layer:
																		__snake_case				=		v.replace(_lowerCAmelCase      ,	_lowerCAmelCase )
												__snake_case				=		v
						__snake_case				=		{v: unet_state_dict[k] for k, v in mapping.items()}
						return new_state_dict


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

A							:					str     						=     [
    # (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):
		A							:					Union[str, Any]     						=     f'''encoder.down_blocks.{i}.resnets.{j}.'''
		A							:					List[Any]     						=     f'''encoder.down.{i}.block.{j}.'''
		vae_conversion_map.append((sd_down_prefix, hf_down_prefix))

	if i < 3:
		A							:					List[str]     						=     f'''down_blocks.{i}.downsamplers.0.'''
		A							:					Union[str, Any]     						=     f'''down.{i}.downsample.'''
		vae_conversion_map.append((sd_downsample_prefix, hf_downsample_prefix))

		A							:					Optional[Any]     						=     f'''up_blocks.{i}.upsamplers.0.'''
		A							:					Dict     						=     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):
		A							:					List[Any]     						=     f'''decoder.up_blocks.{i}.resnets.{j}.'''
		A							:					Tuple     						=     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):
	A							:					Optional[Any]     						=     f'''mid_block.resnets.{i}.'''
	A							:					Optional[Any]     						=     f'''mid.block_{i+1}.'''
	vae_conversion_map.append((sd_mid_res_prefix, hf_mid_res_prefix))


A							:					Optional[Any]     						=     [
    # (stable-diffusion, HF Diffusers)
    ('norm.', 'group_norm.'),
    ('q.', 'query.'),
    ('k.', 'key.'),
    ('v.', 'value.'),
    ('proj_out.', 'proj_attn.'),
]





def 	_lowerCAmelCase       (    _lowerCAmelCase )						->	List[str]:


						'''simple docstring'''






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





def 	_lowerCAmelCase       (    _lowerCAmelCase )						->	Optional[int]:


						'''simple docstring'''






						__snake_case				=		{k: k for k in vae_state_dict.keys()}
						for k, v in mapping.items():
												for sd_part, hf_part in vae_conversion_map:
																		__snake_case				=		v.replace(_lowerCAmelCase      ,	_lowerCAmelCase )
												__snake_case				=		v
						for k, v in mapping.items():
												if "attentions" in k:
																		for sd_part, hf_part in vae_conversion_map_attn:
																								__snake_case				=		v.replace(_lowerCAmelCase      ,	_lowerCAmelCase )
																		__snake_case				=		v
						__snake_case				=		{v: vae_state_dict[k] for k, v in mapping.items()}
						__snake_case				=		["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''' )
																								__snake_case				=		reshape_weight_for_sd(_lowerCAmelCase )
						return new_state_dict


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


A							:					str     						=     [
    # (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'),
]
A							:					List[str]     						=     {re.escape(x[1]): x[0] for x in textenc_conversion_lst}
A							:					Union[str, Any]     						=     re.compile('|'.join(protected.keys()))

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





def 	_lowerCAmelCase       (    _lowerCAmelCase )						->	Any:


						'''simple docstring'''






						__snake_case				=		{}
						__snake_case				=		{}
						__snake_case				=		{}
						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" )
												):
																		__snake_case				=		k[: -len(".q_proj.weight" )]
																		__snake_case				=		k[-len("q_proj.weight" )]
																		if k_pre not in capture_qkv_weight:
																								__snake_case				=		[None, None, None]
																		__snake_case				=		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" )
												):
																		__snake_case				=		k[: -len(".q_proj.bias" )]
																		__snake_case				=		k[-len("q_proj.bias" )]
																		if k_pre not in capture_qkv_bias:
																								__snake_case				=		[None, None, None]
																		__snake_case				=		v
																		continue

												__snake_case				=		textenc_pattern.sub(lambda _lowerCAmelCase : protected[re.escape(m.group(0 ) )]      ,	_lowerCAmelCase )
												__snake_case				=		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" )
												__snake_case				=		textenc_pattern.sub(lambda _lowerCAmelCase : protected[re.escape(m.group(0 ) )]      ,	_lowerCAmelCase )
												__snake_case				=		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" )
												__snake_case				=		textenc_pattern.sub(lambda _lowerCAmelCase : protected[re.escape(m.group(0 ) )]      ,	_lowerCAmelCase )
												__snake_case				=		torch.cat(_lowerCAmelCase )

						return new_state_dict





def 	_lowerCAmelCase       (    _lowerCAmelCase )						->	Optional[int]:


						'''simple docstring'''






						return text_enc_dict


if __name__ == "__main__":
	A							:					Optional[int]     						=     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.'
	)

	A							:					int     						=     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
	A							:					Dict     						=     osp.join(args.model_path, 'unet', 'diffusion_pytorch_model.safetensors')
	A							:					Dict     						=     osp.join(args.model_path, 'vae', 'diffusion_pytorch_model.safetensors')
	A							:					Dict     						=     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):
		A							:					str     						=     load_file(unet_path, device='cpu')
	else:
		A							:					Any     						=     osp.join(args.model_path, 'unet', 'diffusion_pytorch_model.bin')
		A							:					List[Any]     						=     torch.load(unet_path, map_location='cpu')

	if osp.exists(vae_path):
		A							:					List[Any]     						=     load_file(vae_path, device='cpu')
	else:
		A							:					Tuple     						=     osp.join(args.model_path, 'vae', 'diffusion_pytorch_model.bin')
		A							:					Union[str, Any]     						=     torch.load(vae_path, map_location='cpu')

	if osp.exists(text_enc_path):
		A							:					int     						=     load_file(text_enc_path, device='cpu')
	else:
		A							:					Dict     						=     osp.join(args.model_path, 'text_encoder', 'pytorch_model.bin')
		A							:					Optional[int]     						=     torch.load(text_enc_path, map_location='cpu')

	# Convert the UNet model
	A							:					List[str]     						=     convert_unet_state_dict(unet_state_dict)
	A							:					Any     						=     {'model.diffusion_model.' + k: v for k, v in unet_state_dict.items()}

	# Convert the VAE model
	A							:					str     						=     convert_vae_state_dict(vae_state_dict)
	A							:					Dict     						=     {'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
	A							:					Any     						=     '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
		A							:					Dict     						=     {'transformer.' + k: v for k, v in text_enc_dict.items()}
		A							:					str     						=     convert_text_enc_state_dict_vaa(text_enc_dict)
		A							:					Optional[int]     						=     {'cond_stage_model.model.' + k: v for k, v in text_enc_dict.items()}
	else:
		A							:					str     						=     convert_text_enc_state_dict(text_enc_dict)
		A							:					str     						=     {'cond_stage_model.transformer.' + k: v for k, v in text_enc_dict.items()}

	# Put together new checkpoint
	A							:					List[Any]     						=     {**unet_state_dict, **vae_state_dict, **text_enc_dict}
	if args.half:
		A							:					str     						=     {k: v.half() for k, v in state_dict.items()}

	if args.use_safetensors:
		save_file(state_dict, args.checkpoint_path)
	else:
		A							:					Union[str, Any]     						=     {'state_dict': state_dict}
		torch.save(state_dict, args.checkpoint_path)









import unittest

from transformers import DebertaVaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
	import torch

	from transformers import (
	    DebertaVaForMaskedLM,
	    DebertaVaForMultipleChoice,
	    DebertaVaForQuestionAnswering,
	    DebertaVaForSequenceClassification,
	    DebertaVaForTokenClassification,
	    DebertaVaModel,
	)
	from transformers.models.deberta_va.modeling_deberta_va import DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST






class     UpperCamelCase(  _a   ):


					def __init__(						self	:		Dict     ,    SCREAMING_SNAKE_CASE	:		Dict     ,    SCREAMING_SNAKE_CASE	:		Dict=1_3     ,    SCREAMING_SNAKE_CASE	:		str=7     ,    SCREAMING_SNAKE_CASE	:		Optional[int]=True     ,    SCREAMING_SNAKE_CASE	:		str=True     ,    SCREAMING_SNAKE_CASE	:		Union[str, Any]=True     ,    SCREAMING_SNAKE_CASE	:		Dict=True     ,    SCREAMING_SNAKE_CASE	:		Dict=9_9     ,    SCREAMING_SNAKE_CASE	:		str=3_2     ,    SCREAMING_SNAKE_CASE	:		Optional[int]=5     ,    SCREAMING_SNAKE_CASE	:		Dict=4     ,    SCREAMING_SNAKE_CASE	:		Optional[Any]=3_7     ,    SCREAMING_SNAKE_CASE	:		Optional[Any]="gelu"     ,    SCREAMING_SNAKE_CASE	:		Optional[int]=0.1     ,    SCREAMING_SNAKE_CASE	:		int=0.1     ,    SCREAMING_SNAKE_CASE	:		int=5_1_2     ,    SCREAMING_SNAKE_CASE	:		Optional[Any]=1_6     ,    SCREAMING_SNAKE_CASE	:		Union[str, Any]=2     ,    SCREAMING_SNAKE_CASE	:		Optional[int]=0.02     ,    SCREAMING_SNAKE_CASE	:		str=False     ,    SCREAMING_SNAKE_CASE	:		Union[str, Any]=True     ,    SCREAMING_SNAKE_CASE	:		Tuple="None"     ,    SCREAMING_SNAKE_CASE	:		List[str]=3     ,    SCREAMING_SNAKE_CASE	:		str=4     ,    SCREAMING_SNAKE_CASE	:		List[str]=None     ,    )			->	Tuple:




											'''simple docstring'''

											__snake_case				=		parent
											__snake_case				=		batch_size
											__snake_case				=		seq_length
											__snake_case				=		is_training
											__snake_case				=		use_input_mask
											__snake_case				=		use_token_type_ids
											__snake_case				=		use_labels
											__snake_case				=		vocab_size
											__snake_case				=		hidden_size
											__snake_case				=		num_hidden_layers
											__snake_case				=		num_attention_heads
											__snake_case				=		intermediate_size
											__snake_case				=		hidden_act
											__snake_case				=		hidden_dropout_prob
											__snake_case				=		attention_probs_dropout_prob
											__snake_case				=		max_position_embeddings
											__snake_case				=		type_vocab_size
											__snake_case				=		type_sequence_label_size
											__snake_case				=		initializer_range
											__snake_case				=		num_labels
											__snake_case				=		num_choices
											__snake_case				=		relative_attention
											__snake_case				=		position_biased_input
											__snake_case				=		pos_att_type
											__snake_case				=		scope


					def 							SCREAMING_SNAKE_CASE_  (						self	:		List[Any]						)			->	Dict:




											'''simple docstring'''

											__snake_case				=		ids_tensor([self.batch_size, self.seq_length]     ,    self.vocab_size						)

											__snake_case				=		None
											if self.use_input_mask:
																	__snake_case				=		ids_tensor([self.batch_size, self.seq_length]     ,    vocab_size=2						)

											__snake_case				=		None
											if self.use_token_type_ids:
																	__snake_case				=		ids_tensor([self.batch_size, self.seq_length]     ,    self.type_vocab_size						)

											__snake_case				=		None
											__snake_case				=		None
											__snake_case				=		None
											if self.use_labels:
																	__snake_case				=		ids_tensor([self.batch_size]     ,    self.type_sequence_label_size						)
																	__snake_case				=		ids_tensor([self.batch_size, self.seq_length]     ,    self.num_labels						)
																	__snake_case				=		ids_tensor([self.batch_size]     ,    self.num_choices						)

											__snake_case				=		self.get_config()

											return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels


					def 							SCREAMING_SNAKE_CASE_  (						self	:		Any						)			->	int:




											'''simple docstring'''

											return DebertaVaConfig(
											    vocab_size=self.vocab_size     ,    hidden_size=self.hidden_size     ,    num_hidden_layers=self.num_hidden_layers     ,    num_attention_heads=self.num_attention_heads     ,    intermediate_size=self.intermediate_size     ,    hidden_act=self.hidden_act     ,    hidden_dropout_prob=self.hidden_dropout_prob     ,    attention_probs_dropout_prob=self.attention_probs_dropout_prob     ,    max_position_embeddings=self.max_position_embeddings     ,    type_vocab_size=self.type_vocab_size     ,    initializer_range=self.initializer_range     ,    relative_attention=self.relative_attention     ,    position_biased_input=self.position_biased_input     ,    pos_att_type=self.pos_att_type     ,    )


					def 							SCREAMING_SNAKE_CASE_  (						self	:		Optional[int]     ,    SCREAMING_SNAKE_CASE	:		str						)			->	Any:




											'''simple docstring'''

											self.parent.assertListEqual(list(result.loss.size()						)     ,    []						)


					def 							SCREAMING_SNAKE_CASE_  (						self	:		List[Any]     ,    SCREAMING_SNAKE_CASE	:		Optional[Any]     ,    SCREAMING_SNAKE_CASE	:		Dict     ,    SCREAMING_SNAKE_CASE	:		Dict     ,    SCREAMING_SNAKE_CASE	:		Optional[int]     ,    SCREAMING_SNAKE_CASE	:		List[str]     ,    SCREAMING_SNAKE_CASE	:		Dict     ,    SCREAMING_SNAKE_CASE	:		str						)			->	List[str]:




											'''simple docstring'''

											__snake_case				=		DebertaVaModel(config=SCREAMING_SNAKE_CASE						)
											model.to(SCREAMING_SNAKE_CASE						)
											model.eval()
											__snake_case				=		model(SCREAMING_SNAKE_CASE     ,    attention_mask=SCREAMING_SNAKE_CASE     ,    token_type_ids=SCREAMING_SNAKE_CASE						)[0]
											__snake_case				=		model(SCREAMING_SNAKE_CASE     ,    token_type_ids=SCREAMING_SNAKE_CASE						)[0]
											__snake_case				=		model(SCREAMING_SNAKE_CASE						)[0]

											self.parent.assertListEqual(list(sequence_output.size()						)     ,    [self.batch_size, self.seq_length, self.hidden_size]						)


					def 							SCREAMING_SNAKE_CASE_  (						self	:		List[Any]     ,    SCREAMING_SNAKE_CASE	:		Dict     ,    SCREAMING_SNAKE_CASE	:		Optional[int]     ,    SCREAMING_SNAKE_CASE	:		Dict     ,    SCREAMING_SNAKE_CASE	:		Dict     ,    SCREAMING_SNAKE_CASE	:		Union[str, Any]     ,    SCREAMING_SNAKE_CASE	:		Any     ,    SCREAMING_SNAKE_CASE	:		Optional[Any]						)			->	str:




											'''simple docstring'''

											__snake_case				=		DebertaVaForMaskedLM(config=SCREAMING_SNAKE_CASE						)
											model.to(SCREAMING_SNAKE_CASE						)
											model.eval()
											__snake_case				=		model(SCREAMING_SNAKE_CASE     ,    attention_mask=SCREAMING_SNAKE_CASE     ,    token_type_ids=SCREAMING_SNAKE_CASE     ,    labels=SCREAMING_SNAKE_CASE						)

											self.parent.assertEqual(result.logits.shape     ,    (self.batch_size, self.seq_length, self.vocab_size)						)


					def 							SCREAMING_SNAKE_CASE_  (						self	:		Union[str, Any]     ,    SCREAMING_SNAKE_CASE	:		Tuple     ,    SCREAMING_SNAKE_CASE	:		str     ,    SCREAMING_SNAKE_CASE	:		Union[str, Any]     ,    SCREAMING_SNAKE_CASE	:		int     ,    SCREAMING_SNAKE_CASE	:		Union[str, Any]     ,    SCREAMING_SNAKE_CASE	:		Any     ,    SCREAMING_SNAKE_CASE	:		Tuple						)			->	int:




											'''simple docstring'''

											__snake_case				=		self.num_labels
											__snake_case				=		DebertaVaForSequenceClassification(SCREAMING_SNAKE_CASE						)
											model.to(SCREAMING_SNAKE_CASE						)
											model.eval()
											__snake_case				=		model(SCREAMING_SNAKE_CASE     ,    attention_mask=SCREAMING_SNAKE_CASE     ,    token_type_ids=SCREAMING_SNAKE_CASE     ,    labels=SCREAMING_SNAKE_CASE						)
											self.parent.assertListEqual(list(result.logits.size()						)     ,    [self.batch_size, self.num_labels]						)
											self.check_loss_output(SCREAMING_SNAKE_CASE						)


					def 							SCREAMING_SNAKE_CASE_  (						self	:		Dict     ,    SCREAMING_SNAKE_CASE	:		Any     ,    SCREAMING_SNAKE_CASE	:		Dict     ,    SCREAMING_SNAKE_CASE	:		Union[str, Any]     ,    SCREAMING_SNAKE_CASE	:		int     ,    SCREAMING_SNAKE_CASE	:		str     ,    SCREAMING_SNAKE_CASE	:		Optional[Any]     ,    SCREAMING_SNAKE_CASE	:		List[Any]						)			->	Union[str, Any]:




											'''simple docstring'''

											__snake_case				=		self.num_labels
											__snake_case				=		DebertaVaForTokenClassification(config=SCREAMING_SNAKE_CASE						)
											model.to(SCREAMING_SNAKE_CASE						)
											model.eval()
											__snake_case				=		model(SCREAMING_SNAKE_CASE     ,    attention_mask=SCREAMING_SNAKE_CASE     ,    token_type_ids=SCREAMING_SNAKE_CASE     ,    labels=SCREAMING_SNAKE_CASE						)
											self.parent.assertEqual(result.logits.shape     ,    (self.batch_size, self.seq_length, self.num_labels)						)


					def 							SCREAMING_SNAKE_CASE_  (						self	:		Dict     ,    SCREAMING_SNAKE_CASE	:		Optional[int]     ,    SCREAMING_SNAKE_CASE	:		Dict     ,    SCREAMING_SNAKE_CASE	:		int     ,    SCREAMING_SNAKE_CASE	:		str     ,    SCREAMING_SNAKE_CASE	:		int     ,    SCREAMING_SNAKE_CASE	:		int     ,    SCREAMING_SNAKE_CASE	:		Union[str, Any]						)			->	int:




											'''simple docstring'''

											__snake_case				=		DebertaVaForQuestionAnswering(config=SCREAMING_SNAKE_CASE						)
											model.to(SCREAMING_SNAKE_CASE						)
											model.eval()
											__snake_case				=		model(
											    SCREAMING_SNAKE_CASE     ,    attention_mask=SCREAMING_SNAKE_CASE     ,    token_type_ids=SCREAMING_SNAKE_CASE     ,    start_positions=SCREAMING_SNAKE_CASE     ,    end_positions=SCREAMING_SNAKE_CASE     ,    )
											self.parent.assertEqual(result.start_logits.shape     ,    (self.batch_size, self.seq_length)						)
											self.parent.assertEqual(result.end_logits.shape     ,    (self.batch_size, self.seq_length)						)


					def 							SCREAMING_SNAKE_CASE_  (						self	:		Tuple     ,    SCREAMING_SNAKE_CASE	:		List[Any]     ,    SCREAMING_SNAKE_CASE	:		str     ,    SCREAMING_SNAKE_CASE	:		List[str]     ,    SCREAMING_SNAKE_CASE	:		List[str]     ,    SCREAMING_SNAKE_CASE	:		Optional[Any]     ,    SCREAMING_SNAKE_CASE	:		Any     ,    SCREAMING_SNAKE_CASE	:		Optional[int]						)			->	Union[str, Any]:




											'''simple docstring'''

											__snake_case				=		DebertaVaForMultipleChoice(config=SCREAMING_SNAKE_CASE						)
											model.to(SCREAMING_SNAKE_CASE						)
											model.eval()
											__snake_case				=		input_ids.unsqueeze(1						).expand(-1     ,    self.num_choices     ,    -1						).contiguous()
											__snake_case				=		token_type_ids.unsqueeze(1						).expand(-1     ,    self.num_choices     ,    -1						).contiguous()
											__snake_case				=		input_mask.unsqueeze(1						).expand(-1     ,    self.num_choices     ,    -1						).contiguous()
											__snake_case				=		model(
											    SCREAMING_SNAKE_CASE     ,    attention_mask=SCREAMING_SNAKE_CASE     ,    token_type_ids=SCREAMING_SNAKE_CASE     ,    labels=SCREAMING_SNAKE_CASE     ,    )
											self.parent.assertEqual(result.logits.shape     ,    (self.batch_size, self.num_choices)						)






					def 							SCREAMING_SNAKE_CASE_  (						self	:		Union[str, Any]						)			->	List[Any]:




											'''simple docstring'''

											__snake_case				=		self.prepare_config_and_inputs()
											(
											    (
											    __snake_case
											)		,					(
											    __snake_case
											)		,					(
											    __snake_case
											)		,					(
											    __snake_case
											)		,					(
											    __snake_case
											)		,					(
											    __snake_case
											)		,					(
											    __snake_case
											)		,					
											)				=		config_and_inputs
											__snake_case				=		{"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
											return config, inputs_dict






@require_torch
class     UpperCamelCase(  _a     ,							_a     ,							unittest.TestCase   ):
					snake_case_		:				Union[str, Any]										=	(
					    (
					        DebertaVaModel,
					        DebertaVaForMaskedLM,
					        DebertaVaForSequenceClassification,
					        DebertaVaForTokenClassification,
					        DebertaVaForQuestionAnswering,
					        DebertaVaForMultipleChoice,
					    )
					    if is_torch_available()
					    else ()
					)
					snake_case_		:				Optional[Any]										=	(
					    {
					        """feature-extraction""": DebertaVaModel,
					        """fill-mask""": DebertaVaForMaskedLM,
					        """question-answering""": DebertaVaForQuestionAnswering,
					        """text-classification""": DebertaVaForSequenceClassification,
					        """token-classification""": DebertaVaForTokenClassification,
					        """zero-shot""": DebertaVaForSequenceClassification,
					    }
					    if is_torch_available()
					    else {}
					)

					snake_case_		:				Dict										=	True
					snake_case_		:				Union[str, Any]										=	False
					snake_case_		:				Tuple										=	False
					snake_case_		:				List[str]										=	False
					snake_case_		:				List[Any]										=	False


					def 							SCREAMING_SNAKE_CASE_  (						self	:		Dict						)			->	Dict:




											'''simple docstring'''

											__snake_case				=		DebertaVaModelTester(self						)
											__snake_case				=		ConfigTester(self     ,    config_class=SCREAMING_SNAKE_CASE     ,    hidden_size=3_7						)


					def 							SCREAMING_SNAKE_CASE_  (						self	:		Any						)			->	Dict:




											'''simple docstring'''

											self.config_tester.run_common_tests()


					def 							SCREAMING_SNAKE_CASE_  (						self	:		Any						)			->	str:




											'''simple docstring'''

											__snake_case				=		self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_deberta_model(*SCREAMING_SNAKE_CASE						)


					def 							SCREAMING_SNAKE_CASE_  (						self	:		Union[str, Any]						)			->	str:




											'''simple docstring'''

											__snake_case				=		self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_deberta_for_sequence_classification(*SCREAMING_SNAKE_CASE						)


					def 							SCREAMING_SNAKE_CASE_  (						self	:		Any						)			->	Optional[Any]:




											'''simple docstring'''

											__snake_case				=		self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_deberta_for_masked_lm(*SCREAMING_SNAKE_CASE						)


					def 							SCREAMING_SNAKE_CASE_  (						self	:		Tuple						)			->	List[str]:




											'''simple docstring'''

											__snake_case				=		self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_deberta_for_question_answering(*SCREAMING_SNAKE_CASE						)


					def 							SCREAMING_SNAKE_CASE_  (						self	:		Optional[Any]						)			->	str:




											'''simple docstring'''

											__snake_case				=		self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_deberta_for_token_classification(*SCREAMING_SNAKE_CASE						)


					def 							SCREAMING_SNAKE_CASE_  (						self	:		int						)			->	Dict:




											'''simple docstring'''

											__snake_case				=		self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_deberta_for_multiple_choice(*SCREAMING_SNAKE_CASE						)






					@slow
					def 							SCREAMING_SNAKE_CASE_  (						self	:		str						)			->	Union[str, Any]:




											'''simple docstring'''

											for model_name in DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																	__snake_case				=		DebertaVaModel.from_pretrained(SCREAMING_SNAKE_CASE						)
																	self.assertIsNotNone(SCREAMING_SNAKE_CASE						)






@require_torch
@require_sentencepiece
@require_tokenizers
class     UpperCamelCase(  unittest.TestCase   ):


					@unittest.skip(reason="Model not available yet"						)
					def 							SCREAMING_SNAKE_CASE_  (						self	:		Optional[int]						)			->	str:




											'''simple docstring'''

											pass






					@slow
					def 							SCREAMING_SNAKE_CASE_  (						self	:		str						)			->	Tuple:




											'''simple docstring'''

											__snake_case				=		DebertaVaModel.from_pretrained("microsoft/deberta-v2-xlarge"						)

											__snake_case				=		torch.tensor([[0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9, 4_6_0_7_8, 1_5_8_8, 2]]						)
											__snake_case				=		torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]						)
											with torch.no_grad():
																	__snake_case				=		model(SCREAMING_SNAKE_CASE     ,    attention_mask=SCREAMING_SNAKE_CASE						)[0]
											# compare the actual values for a slice.
											__snake_case				=		torch.tensor(
											    [[[0.2356, 0.1948, 0.0369], [-0.1063, 0.3586, -0.5152], [-0.6399, -0.0259, -0.2525]]]						)
											self.assertTrue(torch.allclose(output[:, 1:4, 1:4]     ,    SCREAMING_SNAKE_CASE     ,    atol=1e-4						)     ,    f'''{output[:, 1:4, 1:4]}'''						)





