


import argparse
import pathlib
from pathlib import Path
from tempfile import TemporaryDirectory

import esm as esm_module
import torch
from esm.esmfold.va.misc import batch_encode_sequences as esmfold_encode_sequences
from esm.esmfold.va.pretrained import esmfold_va

from transformers.models.esm.configuration_esm import EsmConfig, EsmFoldConfig
from transformers.models.esm.modeling_esm import (
    EsmForMaskedLM,
    EsmForSequenceClassification,
    EsmIntermediate,
    EsmLayer,
    EsmOutput,
    EsmSelfAttention,
    EsmSelfOutput,
)
from transformers.models.esm.modeling_esmfold import EsmForProteinFolding
from transformers.models.esm.tokenization_esm import EsmTokenizer
from transformers.utils import logging


logging.set_verbosity_info()
__magic_name__   :   Tuple  							=logging.get_logger(__name__)

__magic_name__   :   List[str]  							=[
    (
        'protein1',
        'MNGTEGPNFYVPFSNATGVVRSPFEYPQYYLAEPWQFSMLAAYMFLLIVLGFPINFLTLYVTVQHKKLRTPLNYILLNLAVADLFMVLGGFTSTLYTSLHGYFVFGPTGCNLEGFFATLGGEIALWSLVVLAIERYVVVCKPMSNFRFGENHAIMGVAFTWVMALACAAPPLAGWSRYIPEGLQCSCGIDYYTLKPEVNNESFVIYMFVVHFTIPMIIIFFCYGQLVFTVKEAAAQQQESATTQKAEKEVTRMVIIMVIAFLICWVPYASVAFYIFTHQGSNFGPIFMTIPAFFAKSAAIYNPVIYIMMNKQFRNCMLTTICCGKNPLGDDEASATVSKTETSQVAPA',
    ),
    ('protein2', 'MKTVRQERLKSIVRILERSKEPVSGAQLAEELSVSRQVIVQDIAYLRSLGYNIVATPRGYVLA'),
    ('protein3', 'MKTVRQERLKSI<mask>RILERSKEPVSGAQLAEELS<mask>SRQVIVQDIAYLRSLGYN<mask>VATPRGYVLAGG'),
    ('protein4', 'MKTVRQERLKSI<mask>RILERSKEPVSGAQLAEELS<mask>SRQVIVQDIAYLRSLGYN<mask>VATPRGYVLA'),
]

__magic_name__   :   str  							={
    'esm1b_t33_650M_UR50S': esm_module.pretrained.esm1b_t33_650M_UR50S,
    'esm1v_t33_650M_UR90S_1': esm_module.pretrained.esm1v_t33_650M_UR90S_1,
    'esm1v_t33_650M_UR90S_2': esm_module.pretrained.esm1v_t33_650M_UR90S_2,
    'esm1v_t33_650M_UR90S_3': esm_module.pretrained.esm1v_t33_650M_UR90S_3,
    'esm1v_t33_650M_UR90S_4': esm_module.pretrained.esm1v_t33_650M_UR90S_4,
    'esm1v_t33_650M_UR90S_5': esm_module.pretrained.esm1v_t33_650M_UR90S_5,
    'esm2_t48_15B_UR50D': esm_module.pretrained.esm2_t48_15B_UR50D,
    'esm2_t36_3B_UR50D': esm_module.pretrained.esm2_t36_3B_UR50D,
    'esm2_t33_650M_UR50D': esm_module.pretrained.esm2_t33_650M_UR50D,
    'esm2_t30_150M_UR50D': esm_module.pretrained.esm2_t30_150M_UR50D,
    'esm2_t12_35M_UR50D': esm_module.pretrained.esm2_t12_35M_UR50D,
    'esm2_t6_8M_UR50D': esm_module.pretrained.esm2_t6_8M_UR50D,
    'esmfold_v1': esmfold_va,
}

__magic_name__   :   Tuple  							=list('ARNDCQEGHILKMFPSTWYV')

__magic_name__   :   List[str]  							=restypes + ['X']
__magic_name__   :   Optional[int]  							=restypes_with_x + ['<pad>', '<mask>', '<cls>', '<sep>', '<eos>']




def      __snake_case       (       ):
				'''simple docstring'''




				with TemporaryDirectory() as tempdir:
								__magic_name__	  = '''\n'''.join(SCREAMING_SNAKE_CASE__						)
								__magic_name__	  = Path(SCREAMING_SNAKE_CASE__						) / '''vocab.txt'''
								vocab_file.write_text(SCREAMING_SNAKE_CASE__						)
								__magic_name__	  = EsmTokenizer(vocab_file=str(SCREAMING_SNAKE_CASE__						)						)
				__magic_name__	  = 0  # Overlaps with 'A' but that seems to be what they want
				return hf_tokenizer




def      __snake_case       (       lowerCamelCase_ :				Union[str, Any]	,  lowerCamelCase_ :				int						):
				'''simple docstring'''




				__magic_name__	  = our_module.load_state_dict(original_module.state_dict()						)
				if status.missing_keys:
								raise ValueError(F'Missing keys: {status.missing_keys}'						)
				if status.unexpected_keys:
								raise ValueError(F'Unexpected keys: {status.unexpected_keys}'						)




def      __snake_case       (       lowerCamelCase_ :				Optional[int]	,  lowerCamelCase_ :				str	,  lowerCamelCase_ :				Optional[int]	,  lowerCamelCase_ :				Tuple	,  lowerCamelCase_ :				List[str]						):
				'''simple docstring'''




				if model.startswith("esmfold"						):
								__magic_name__	  = MODEL_MAPPING[model]()
				else:
								__magic_name__	  = MODEL_MAPPING[model]()
				esm.eval()  # disable dropout

				if model.startswith("esmfold"						):
								__magic_name__	  = esm.esm.embed_dim
								__magic_name__	  = esm.esm.num_layers
								__magic_name__	  = esm.esm.attention_heads
								__magic_name__	  = 4 * embed_dim
								__magic_name__	  = esm.esm.token_dropout
								__magic_name__	  = False  # This code path does not exist in ESM-2
								__magic_name__	  = '''rotary'''
								__magic_name__	  = True
								__magic_name__	  = EsmFoldConfig()
								for key, val in esm.cfg.items():
												if hasattr(SCREAMING_SNAKE_CASE__	,  SCREAMING_SNAKE_CASE__						) and key != "trunk":
																setattr(SCREAMING_SNAKE_CASE__	,  SCREAMING_SNAKE_CASE__	,  SCREAMING_SNAKE_CASE__						)
								for key, val in esm.cfg.trunk.items():
												if hasattr(esmfold_config.trunk	,  SCREAMING_SNAKE_CASE__						) and key != "structure_module":
																setattr(esmfold_config.trunk	,  SCREAMING_SNAKE_CASE__	,  SCREAMING_SNAKE_CASE__						)
								for key, val in esm.cfg.trunk.structure_module.items():
												if hasattr(esmfold_config.trunk.structure_module	,  SCREAMING_SNAKE_CASE__						):
																setattr(esmfold_config.trunk.structure_module	,  SCREAMING_SNAKE_CASE__	,  SCREAMING_SNAKE_CASE__						)
				elif hasattr(SCREAMING_SNAKE_CASE__	,  "args"						):
								# Indicates an ESM-1b or ESM-1v model
								__magic_name__	  = esm.args.embed_dim
								__magic_name__	  = esm.args.layers
								__magic_name__	  = esm.args.attention_heads
								__magic_name__	  = esm.args.ffn_embed_dim
								__magic_name__	  = esm.args.token_dropout
								__magic_name__	  = True if esm.emb_layer_norm_before else False
								__magic_name__	  = '''absolute'''
								__magic_name__	  = False
								__magic_name__	  = None
				else:
								# Indicates an ESM-2 model
								__magic_name__	  = esm.embed_dim
								__magic_name__	  = esm.num_layers
								__magic_name__	  = esm.attention_heads
								__magic_name__	  = 4 * embed_dim  # This is hardcoded in ESM-2
								__magic_name__	  = esm.token_dropout
								__magic_name__	  = False  # This code path does not exist in ESM-2
								__magic_name__	  = '''rotary'''
								__magic_name__	  = False
								__magic_name__	  = None

				if is_folding_model:
								__magic_name__	  = esm.esm.alphabet
				__magic_name__	  = tuple(alphabet.all_toks						)
				__magic_name__	  = alphabet.mask_idx
				__magic_name__	  = alphabet.padding_idx

				if is_folding_model:
								__magic_name__	  = esm.esm
				else:
								__magic_name__	  = esm

				__magic_name__	  = EsmConfig(
				    vocab_size=original_esm_model.embed_tokens.num_embeddings	,  mask_token_id=SCREAMING_SNAKE_CASE__	,  hidden_size=SCREAMING_SNAKE_CASE__	,  num_hidden_layers=SCREAMING_SNAKE_CASE__	,  num_attention_heads=SCREAMING_SNAKE_CASE__	,  intermediate_size=SCREAMING_SNAKE_CASE__	,  max_position_embeddings=1026	,  layer_norm_eps=1e-5	,  attention_probs_dropout_prob=0.0	,  hidden_dropout_prob=0.0	,  pad_token_id=SCREAMING_SNAKE_CASE__	,  emb_layer_norm_before=SCREAMING_SNAKE_CASE__	,  token_dropout=SCREAMING_SNAKE_CASE__	,  position_embedding_type=SCREAMING_SNAKE_CASE__	,  is_folding_model=SCREAMING_SNAKE_CASE__	,  esmfold_config=SCREAMING_SNAKE_CASE__	,  vocab_list=SCREAMING_SNAKE_CASE__	,  )
				if classification_head:
								__magic_name__	  = esm.classification_heads['''mnli'''].out_proj.weight.shape[0]
				print("Our ESM config:"	,  SCREAMING_SNAKE_CASE__						)

				if model.startswith("esmfold"						):
								__magic_name__	  = EsmForProteinFolding
				elif classification_head:
								__magic_name__	  = EsmForSequenceClassification
				else:
								__magic_name__	  = EsmForMaskedLM
				__magic_name__	  = model_class(SCREAMING_SNAKE_CASE__						)
				model.eval()

				# Now let's copy all the weights.
				# Embeddings
				__magic_name__	  = original_esm_model.embed_tokens.weight
				if position_embedding_type == "absolute":
								__magic_name__	  = original_esm_model.embed_positions.weight

				if config.emb_layer_norm_before:
								__magic_name__	  = original_esm_model.emb_layer_norm_before.weight
								__magic_name__	  = original_esm_model.emb_layer_norm_before.bias

				__magic_name__	  = original_esm_model.emb_layer_norm_after.weight
				__magic_name__	  = original_esm_model.emb_layer_norm_after.bias

				for i in range(config.num_hidden_layers						):
								# Encoder: start of layer
								__magic_name__	  = model.esm.encoder.layer[i]
								# esm_layer: TransformerSentenceEncoderLayer = original_esm_model.layers[i]
								__magic_name__	  = original_esm_model.layers[i]

								# self attention
								__magic_name__	  = layer.attention.self
								assert (
								    esm_layer.self_attn.k_proj.weight.data.shape
								    == esm_layer.self_attn.q_proj.weight.data.shape
								    == esm_layer.self_attn.v_proj.weight.data.shape
								    == torch.Size((config.hidden_size, config.hidden_size)						)
								)

								__magic_name__	  = esm_layer.self_attn.q_proj.weight
								__magic_name__	  = esm_layer.self_attn.q_proj.bias
								__magic_name__	  = esm_layer.self_attn.k_proj.weight
								__magic_name__	  = esm_layer.self_attn.k_proj.bias
								__magic_name__	  = esm_layer.self_attn.v_proj.weight
								__magic_name__	  = esm_layer.self_attn.v_proj.bias

								if getattr(esm_layer.self_attn	,  "rot_emb"	,  SCREAMING_SNAKE_CASE__						) is not None:
												# Matt: Although inv_freq is not a trainable weight, it is computed at model init and cached.
												# During the training of ESM-2 the model was converted to float16 precision, which also converts
												# the inv_freq tensor, and the loss of precision remains even if the model is loaded later as float32.
												# If we recompute inv_freq without this loss of precision then we will get subtly different rotary
												# embeddings, which are enough to cause significant discrepancies in model outputs. To avoid this,
												# we make sure the new model copies the data from the old inv_freq.
												__magic_name__	  = esm_layer.self_attn.rot_emb.inv_freq

								# LayerNorm changes for pre-activation
								__magic_name__	  = esm_layer.self_attn_layer_norm.weight
								__magic_name__	  = esm_layer.self_attn_layer_norm.bias
								__magic_name__	  = esm_layer.final_layer_norm.weight
								__magic_name__	  = esm_layer.final_layer_norm.bias

								# self-attention output
								__magic_name__	  = layer.attention.output
								assert self_output.dense.weight.shape == esm_layer.self_attn.out_proj.weight.shape
								__magic_name__	  = esm_layer.self_attn.out_proj.weight
								__magic_name__	  = esm_layer.self_attn.out_proj.bias

								# intermediate
								__magic_name__	  = layer.intermediate
								assert intermediate.dense.weight.shape == esm_layer.fca.weight.shape
								__magic_name__	  = esm_layer.fca.weight
								__magic_name__	  = esm_layer.fca.bias

								# output
								__magic_name__	  = layer.output
								assert bert_output.dense.weight.shape == esm_layer.fca.weight.shape
								__magic_name__	  = esm_layer.fca.weight
								__magic_name__	  = esm_layer.fca.bias
								# end of layer

				if is_folding_model:
								__magic_name__	  = esm.esm_s_combine.data
								__magic_name__	  = esm.afa_to_esm.data
								transfer_and_check_weights(esm.embedding	,  model.embedding						)
								transfer_and_check_weights(esm.esm_s_mlp	,  model.esm_s_mlp						)
								transfer_and_check_weights(esm.trunk	,  model.trunk						)
								transfer_and_check_weights(esm.distogram_head	,  model.distogram_head						)
								transfer_and_check_weights(esm.ptm_head	,  model.ptm_head						)
								transfer_and_check_weights(esm.lm_head	,  model.lm_head						)
								transfer_and_check_weights(esm.lddt_head	,  model.lddt_head						)

				elif classification_head:
								__magic_name__	  = esm.esm.classification_heads['''mnli'''].dense.weight
								__magic_name__	  = esm.classification_heads['''mnli'''].dense.bias
								__magic_name__	  = esm.classification_heads['''mnli'''].out_proj.weight
								__magic_name__	  = esm.classification_heads['''mnli'''].out_proj.bias
				else:
								# LM Head
								__magic_name__	  = esm.lm_head.dense.weight
								__magic_name__	  = esm.lm_head.dense.bias
								__magic_name__	  = esm.lm_head.layer_norm.weight
								__magic_name__	  = esm.lm_head.layer_norm.bias
								__magic_name__	  = esm.lm_head.weight
								__magic_name__	  = esm.lm_head.bias

				# Contact prediction head
				transfer_and_check_weights(esm.contact_head	,  model.esm.contact_head						)

				# Prepare data (first 2 sequences from ESMStructuralSplitDataset superfamily / 4)
				if is_folding_model:
								# Folding models aren't trained on masked inputs and don't like mask tokens.
								__magic_name__	  = SAMPLE_DATA[:2]
				else:
								__magic_name__	  = SAMPLE_DATA

				if is_folding_model:
								__magic_name__	  = get_esmfold_tokenizer()
								__magic_name__	  = hf_tokenizer(
								    [row[1] for row in sample_data]	,  return_tensors="pt"	,  padding=SCREAMING_SNAKE_CASE__	,  add_special_tokens=SCREAMING_SNAKE_CASE__						)
								__magic_name__	  = esmfold_encode_sequences([row[1] for row in sample_data]						)
								__magic_name__	  = torch.all(hf_tokens["input_ids"] == esmfold_aas						) and torch.all(
								    hf_tokens["attention_mask"] == esmfold_mask						)
				else:
								# Let's check that we get the same results.
								__magic_name__	  = alphabet.get_batch_converter()
								__magic_name__	  = batch_converter(SCREAMING_SNAKE_CASE__						)
								# Prepare tokenizer and make sure it matches
								with TemporaryDirectory() as tempdir:
												__magic_name__	  = '''\n'''.join(alphabet.all_toks						)
												__magic_name__	  = Path(SCREAMING_SNAKE_CASE__						) / '''vocab.txt'''
												vocab_file.write_text(SCREAMING_SNAKE_CASE__						)
												__magic_name__	  = EsmTokenizer(vocab_file=str(SCREAMING_SNAKE_CASE__						)						)

								__magic_name__	  = hf_tokenizer([row[1] for row in sample_data]	,  return_tensors="pt"	,  padding=SCREAMING_SNAKE_CASE__						)
								__magic_name__	  = torch.all(hf_tokens["input_ids"] == batch_tokens						)

				print("Do both models tokenizers output the same tokens?"	,  "🔥" if success else "💩"						)
				if not success:
								raise Exception("Tokenization does not match!"						)

				with torch.no_grad():
								if is_folding_model:
												# Let's test the model in parts
												# ESMFold always converts the ESM stem to float16, which requires float16 ops
												# that don't exist on CPU. Therefore, to test it we need to run it on GPU. However,
												# ESMFold is what we in the community call a "big boy" and so we desperately avoid putting both the
												# original and the converted model on the GPU at the same time.
												__magic_name__	  = esm.cuda().infer([row[1] for row in sample_data]						)
												__magic_name__	  = model.cuda()(
												    input_ids=hf_tokens["input_ids"].cuda()	,  attention_mask=hf_tokens["attention_mask"].cuda()						)
								else:
												__magic_name__	  = model(**SCREAMING_SNAKE_CASE__	,  output_hidden_states=SCREAMING_SNAKE_CASE__						)
												__magic_name__	  = our_output['''logits''']
												if classification_head:
																__magic_name__	  = esm.model.classification_heads['''mnli'''](esm.extract_features(SCREAMING_SNAKE_CASE__						)						)
												else:
																__magic_name__	  = esm(hf_tokens["input_ids"]	,  repr_layers=list(range(999						)						)						)
																__magic_name__	  = their_output['''logits''']

								if is_folding_model:
												__magic_name__	  = torch.max(torch.abs(our_output["positions"] - their_output["positions"]						)						).item()
												__magic_name__	  = torch.allclose(our_output["positions"]	,  their_output["positions"]	,  atol=1e-5						)
								else:
												__magic_name__	  = torch.max(torch.abs(our_output - their_output						)						).item()
												__magic_name__	  = torch.allclose(SCREAMING_SNAKE_CASE__	,  SCREAMING_SNAKE_CASE__	,  atol=1e-5						)

								print(F'max_absolute_diff = {max_absolute_diff}'						)  # ~ 1e-5
								print("Do both models output the same tensors?"	,  "🔥" if success else "💩"						)

								if not success:
												raise Exception("Something went wRoNg"						)

								if not is_folding_model:
												# Let's check contact prediction too
												__magic_name__	  = model.predict_contacts(hf_tokens["input_ids"]	,  hf_tokens["attention_mask"]						)
												__magic_name__	  = esm.predict_contacts(hf_tokens["input_ids"]						)
												__magic_name__	  = torch.max(torch.abs(our_output - their_output						)						).item()
												__magic_name__	  = torch.allclose(SCREAMING_SNAKE_CASE__	,  SCREAMING_SNAKE_CASE__	,  atol=1e-5						)

												print("Contact prediction testing:"						)
												print(F'max_absolute_diff = {max_absolute_diff}'						)  # ~ 1e-5
												print("Do both models output the same tensors?"	,  "🔥" if success else "💩"						)

												if not success:
																raise Exception("Something went wRoNg"						)

								pathlib.Path(SCREAMING_SNAKE_CASE__						).mkdir(parents=SCREAMING_SNAKE_CASE__	,  exist_ok=SCREAMING_SNAKE_CASE__						)
								print(F'Saving model to {pytorch_dump_folder_path}'						)
								model.save_pretrained(SCREAMING_SNAKE_CASE__						)

								del esm  # Free up some memory before continuing

				print(F'Saving tokenizer to {pytorch_dump_folder_path}'						)
				hf_tokenizer.save_pretrained(SCREAMING_SNAKE_CASE__						)

				if push_to_repo:
								model.push_to_hub(repo_id=SCREAMING_SNAKE_CASE__	,  use_auth_token=SCREAMING_SNAKE_CASE__						)
								hf_tokenizer.push_to_hub(repo_id=SCREAMING_SNAKE_CASE__	,  use_auth_token=SCREAMING_SNAKE_CASE__						)


if __name__ == "__main__":
						__magic_name__   :   Optional[int]  							=argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    '--pytorch_dump_folder_path', type=str, required=True, help='Path to the output PyTorch model.'
						)
						parser.add_argument(
						    '--classification_head', action='store_true', help='Whether to convert a final classification head.'
						)
						parser.add_argument('--model', default=None, type=str, required=True, help='Name of model to convert.')
						parser.add_argument('--push_to_repo', type=str, help='Repo to upload to (including username!).')
						parser.add_argument('--auth_token', type=str, help='HuggingFace auth token.')
						__magic_name__   :   Optional[int]  							=parser.parse_args()
						convert_esm_checkpoint_to_pytorch(
						    args.model, args.pytorch_dump_folder_path, args.classification_head, args.push_to_repo, args.auth_token
						)







'''simple docstring'''
from __future__ import annotations

import unittest

from transformers import DebertaConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
						import tensorflow as tf

						from transformers import (
						    TFDebertaForMaskedLM,
						    TFDebertaForQuestionAnswering,
						    TFDebertaForSequenceClassification,
						    TFDebertaForTokenClassification,
						    TFDebertaModel,
						)






class 		UpperCamelCase_		:





						"""simple docstring"""




						def __init__( self				:					Dict	,				_lowerCamelCase				:					str	,				_lowerCamelCase				:					Dict=13	,				_lowerCamelCase				:					Union[str, Any]=7	,				_lowerCamelCase				:					Union[str, Any]=True	,				_lowerCamelCase				:					Optional[Any]=True	,				_lowerCamelCase				:					Tuple=True	,				_lowerCamelCase				:					Any=True	,				_lowerCamelCase				:					int=99	,				_lowerCamelCase				:					Any=32	,				_lowerCamelCase				:					Any=2	,				_lowerCamelCase				:					str=4	,				_lowerCamelCase				:					str=37	,				_lowerCamelCase				:					Optional[int]="gelu"	,				_lowerCamelCase				:					Optional[Any]=0.1	,				_lowerCamelCase				:					Dict=0.1	,				_lowerCamelCase				:					int=5_12	,				_lowerCamelCase				:					Union[str, Any]=16	,				_lowerCamelCase				:					int=2	,				_lowerCamelCase				:					List[Any]=0.02	,				_lowerCamelCase				:					str=3	,				_lowerCamelCase				:					Tuple=4	,				_lowerCamelCase				:					int=None	,				)							->					Optional[int]:
										__magic_name__	  = parent
										__magic_name__	  = 13
										__magic_name__	  = 7
										__magic_name__	  = True
										__magic_name__	  = True
										__magic_name__	  = True
										__magic_name__	  = True
										__magic_name__	  = 99
										__magic_name__	  = 32
										__magic_name__	  = 2
										__magic_name__	  = 4
										__magic_name__	  = 37
										__magic_name__	  = "gelu"
										__magic_name__	  = 0.1
										__magic_name__	  = 0.1
										__magic_name__	  = 5_12
										__magic_name__	  = 16
										__magic_name__	  = False
										__magic_name__	  = -1
										__magic_name__	  = True
										__magic_name__	  = 2
										__magic_name__	  = 0.02
										__magic_name__	  = 3
										__magic_name__	  = 4
										__magic_name__	  = None
						def 					__A					( self				:					Any  )							->					Union[str, Any]:
										__magic_name__	  = ids_tensor([self.batch_size, self.seq_length]	,				self.vocab_size  )

										__magic_name__	  = None
										if self.use_input_mask:
														__magic_name__	  = random_attention_mask([self.batch_size, self.seq_length]  )

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

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

										__magic_name__	  = DebertaConfig(
										    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	,				relative_attention=self.relative_attention	,				max_relative_positions=self.max_relative_positions	,				position_biased_input=self.position_biased_input	,				initializer_range=self.initializer_range	,				return_dict=_lowerCamelCase	,				)

										return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
						def 					__A					( self				:					Optional[Any]	,				_lowerCamelCase				:					Optional[Any]	,				_lowerCamelCase				:					Optional[int]	,				_lowerCamelCase				:					Optional[int]	,				_lowerCamelCase				:					Union[str, Any]	,				_lowerCamelCase				:					List[Any]	,				_lowerCamelCase				:					Tuple	,				_lowerCamelCase				:					Union[str, Any]  )							->					Optional[int]:
										__magic_name__	  = TFDebertaModel(config=_lowerCamelCase  )
										__magic_name__	  = {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}

										__magic_name__	  = [input_ids, input_mask]
										__magic_name__	  = model(_lowerCamelCase  )

										__magic_name__	  = model(_lowerCamelCase  )

										self.parent.assertEqual(result.last_hidden_state.shape	,				(self.batch_size, self.seq_length, self.hidden_size)  )
						def 					__A					( self				:					Optional[int]	,				_lowerCamelCase				:					Optional[int]	,				_lowerCamelCase				:					int	,				_lowerCamelCase				:					str	,				_lowerCamelCase				:					Union[str, Any]	,				_lowerCamelCase				:					Union[str, Any]	,				_lowerCamelCase				:					Union[str, Any]	,				_lowerCamelCase				:					Tuple  )							->					Dict:
										__magic_name__	  = TFDebertaForMaskedLM(config=_lowerCamelCase  )
										__magic_name__	  = {
										    "input_ids": input_ids,
										    "attention_mask": input_mask,
										    "token_type_ids": token_type_ids,
										}
										__magic_name__	  = model(_lowerCamelCase  )
										self.parent.assertEqual(result.logits.shape	,				(self.batch_size, self.seq_length, self.vocab_size)  )
						def 					__A					( self				:					List[str]	,				_lowerCamelCase				:					Tuple	,				_lowerCamelCase				:					Dict	,				_lowerCamelCase				:					Optional[int]	,				_lowerCamelCase				:					List[Any]	,				_lowerCamelCase				:					List[Any]	,				_lowerCamelCase				:					str	,				_lowerCamelCase				:					Tuple  )							->					str:
										__magic_name__	  = self.num_labels
										__magic_name__	  = TFDebertaForSequenceClassification(config=_lowerCamelCase  )
										__magic_name__	  = {
										    "input_ids": input_ids,
										    "attention_mask": input_mask,
										    "token_type_ids": token_type_ids,
										}

										__magic_name__	  = model(_lowerCamelCase  )
										self.parent.assertEqual(result.logits.shape	,				(self.batch_size, self.num_labels)  )
						def 					__A					( self				:					List[str]	,				_lowerCamelCase				:					Optional[int]	,				_lowerCamelCase				:					Any	,				_lowerCamelCase				:					List[str]	,				_lowerCamelCase				:					Optional[int]	,				_lowerCamelCase				:					List[str]	,				_lowerCamelCase				:					int	,				_lowerCamelCase				:					List[str]  )							->					Tuple:
										__magic_name__	  = self.num_labels
										__magic_name__	  = TFDebertaForTokenClassification(config=_lowerCamelCase  )
										__magic_name__	  = {
										    "input_ids": input_ids,
										    "attention_mask": input_mask,
										    "token_type_ids": token_type_ids,
										}
										__magic_name__	  = model(_lowerCamelCase  )
										self.parent.assertEqual(result.logits.shape	,				(self.batch_size, self.seq_length, self.num_labels)  )
						def 					__A					( self				:					str	,				_lowerCamelCase				:					int	,				_lowerCamelCase				:					Optional[int]	,				_lowerCamelCase				:					Union[str, Any]	,				_lowerCamelCase				:					List[str]	,				_lowerCamelCase				:					List[Any]	,				_lowerCamelCase				:					List[str]	,				_lowerCamelCase				:					List[str]  )							->					Tuple:
										__magic_name__	  = TFDebertaForQuestionAnswering(config=_lowerCamelCase  )
										__magic_name__	  = {
										    "input_ids": input_ids,
										    "attention_mask": input_mask,
										    "token_type_ids": token_type_ids,
										}

										__magic_name__	  = model(_lowerCamelCase  )
										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 					__A					( self				:					Dict  )							->					Dict:
										__magic_name__	  = self.prepare_config_and_inputs()
										(
										    (
										    __magic_name__
										)  ,      (
										    __magic_name__
										)  ,      (
										    __magic_name__
										)  ,      (
										    __magic_name__
										)  ,      (
										    __magic_name__
										)  ,      (
										    __magic_name__
										)  ,      (
										    __magic_name__
										)  ,      
										)	  = config_and_inputs
										__magic_name__	  = {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
										return config, inputs_dict






@require_tf
class 		UpperCamelCase_		(					A   ,		A   ,		unittest.TestCase   ):





						"""simple docstring"""




						UpperCAmelCase__	:       Tuple         =     (
						    (
						        TFDebertaModel,
						        TFDebertaForMaskedLM,
						        TFDebertaForQuestionAnswering,
						        TFDebertaForSequenceClassification,
						        TFDebertaForTokenClassification,
						    )
						    if is_tf_available()
						    else ()
						)
						UpperCAmelCase__	:       Optional[Any]         =     (
						    {
						        '''feature-extraction''': TFDebertaModel,
						        '''fill-mask''': TFDebertaForMaskedLM,
						        '''question-answering''': TFDebertaForQuestionAnswering,
						        '''text-classification''': TFDebertaForSequenceClassification,
						        '''token-classification''': TFDebertaForTokenClassification,
						        '''zero-shot''': TFDebertaForSequenceClassification,
						    }
						    if is_tf_available()
						    else {}
						)

						UpperCAmelCase__	:       List[str]         =     False
						UpperCAmelCase__	:       List[str]         =     False
						def 					__A					( self				:					Optional[Any]  )							->					str:
										__magic_name__	  = TFDebertaModelTester(self  )
										__magic_name__	  = ConfigTester(self	,				config_class=_lowerCamelCase	,				hidden_size=37  )
						def 					__A					( self				:					int  )							->					Optional[Any]:
										self.config_tester.run_common_tests()
						def 					__A					( self				:					List[str]  )							->					Optional[Any]:
										__magic_name__	  = self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*_lowerCamelCase  )
						def 					__A					( self				:					List[str]  )							->					Dict:
										__magic_name__	  = self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_masked_lm(*_lowerCamelCase  )
						def 					__A					( self				:					Tuple  )							->					Union[str, Any]:
										__magic_name__	  = self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_question_answering(*_lowerCamelCase  )
						def 					__A					( self				:					List[Any]  )							->					Union[str, Any]:
										__magic_name__	  = self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_sequence_classification(*_lowerCamelCase  )
						def 					__A					( self				:					Union[str, Any]  )							->					str:
										__magic_name__	  = self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_token_classification(*_lowerCamelCase  )

						@slow
						def 					__A					( self				:					Optional[int]  )							->					Any:
										__magic_name__	  = TFDebertaModel.from_pretrained("kamalkraj/deberta-base"  )
										self.assertIsNotNone(_lowerCamelCase  )






@require_tf
class 		UpperCamelCase_		(					unittest.TestCase   ):





						"""simple docstring"""




						@unittest.skip(reason="Model not available yet"  )
						def 					__A					( self				:					str  )							->					str:
										pass

						@slow
						def 					__A					( self				:					str  )							->					Dict:
										__magic_name__	  = TFDebertaModel.from_pretrained("kamalkraj/deberta-base"  )
										__magic_name__	  = tf.constant([[0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69, 4_60_78, 15_88, 2]]  )
										__magic_name__	  = tf.constant([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]  )
										__magic_name__	  = model(_lowerCamelCase	,				attention_mask=_lowerCamelCase  )[0]

										__magic_name__	  = tf.constant(
										    [
										        [
										            [-0.59_855_896, -0.80_552_566, -0.8_462_135],
										            [1.4_484_025, -0.93_483_794, -0.80_593_085],
										            [0.3_122_741, 0.00_316_059, -1.4_131_377],
										        ]
										    ]  )
										tf.debugging.assert_near(output[:, 1:4, 1:4]	,				_lowerCamelCase	,				atol=1e-4  )



