



import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import Callable, Dict, List, Tuple

import timm
import torch
import torch.nn as nn
from classy_vision.models.regnet import RegNet, RegNetParams, RegNetYaagf, RegNetYaagf, RegNetYaaagf
from huggingface_hub import cached_download, hf_hub_url
from torch import Tensor
from vissl.models.model_helpers import get_trunk_forward_outputs

from transformers import AutoImageProcessor, RegNetConfig, RegNetForImageClassification, RegNetModel
from transformers.utils import logging


logging.set_verbosity_info()
lowercase_										=  logging.get_logger()
@dataclass
class 					_snake_case      :
				UpperCamelCase__					:       nn.Module
				UpperCamelCase__					:       List[nn.Module]  		=field(default_factory=__lowercase)
				UpperCamelCase__					:       list  		=field(default_factory=__lowercase)




				def 				A__   (    self       :    str,       __lowercase       :    Tuple,       __lowercase       :    int,       __lowercase       :    List[Any]    ):
							lowercase__       	=					len(list(m.modules()    )    ) == 1 or isinstance(_lowerCAmelCase,       nn.Convad    ) or isinstance(_lowerCAmelCase,       nn.BatchNormad    )
							if has_not_submodules:
										self.traced.append(_lowerCAmelCase    )




				def __call__(    self       :    int,       __lowercase       :    str    ):




							for m in self.module.modules():
										self.handles.append(m.register_forward_hook(self._forward_hook    )    )
							self.module(_lowerCAmelCase    )
							[x.remove() for x in self.handles]
							return self




				@property
				def 				A__   (    self       :    Optional[Any]    ):
							return list(filter(lambda __lowercase    : len(list(x.state_dict().keys()    )    ) > 0,       self.traced    )    )
@dataclass
class 					_snake_case      :
				UpperCamelCase__					:       nn.Module
				UpperCamelCase__					:       nn.Module
				UpperCamelCase__					:       int  		=1
				UpperCamelCase__					:       List  		=field(default_factory=__lowercase)
				UpperCamelCase__					:       List  		=field(default_factory=__lowercase)
				UpperCamelCase__					:       bool  		=True




				def __call__(    self       :    List[str],       __lowercase       :    List[Any]    ):
							lowercase__       	=					Tracker(self.dest    )(_lowerCAmelCase    ).parametrized
							lowercase__       	=					Tracker(self.src    )(_lowerCAmelCase    ).parametrized

							lowercase__       	=					list(filter(lambda __lowercase    : type(_lowerCAmelCase    ) not in self.src_skip,       _lowerCAmelCase    )    )
							lowercase__       	=					list(filter(lambda __lowercase    : type(_lowerCAmelCase    ) not in self.dest_skip,       _lowerCAmelCase    )    )

							if len(_lowerCAmelCase    ) != len(_lowerCAmelCase    ) and self.raise_if_mismatch:
										raise Exception(
										    F'''Numbers of operations are different. Source module has {len(_lowerCAmelCase    )} operations while'''
										    F''' destination module has {len(_lowerCAmelCase    )}.'''    )

							for dest_m, src_m in zip(_lowerCAmelCase,       _lowerCAmelCase    ):
										dest_m.load_state_dict(src_m.state_dict()    )
										if self.verbose == 1:
													print(F'''Transfered from={src_m} to={dest_m}'''    )
class 					_snake_case      (       nn.Module):




				def __init__(    self       :    List[Any],       __lowercase       :    List[str]    ):
							super().__init__()

							lowercase__       	=					[]
							# - get the stem
							feature_blocks.append(("conv1", model.stem)    )
							# - get all the feature blocks
							for k, v in model.trunk_output.named_children():
										assert k.startswith("block"    ), F'''Unexpected layer name {k}'''
										lowercase__       	=					len(_lowerCAmelCase    ) + 1
										feature_blocks.append((F'''res{block_index}''', v)    )

							lowercase__       	=					nn.ModuleDict(_lowerCAmelCase    )




				def 				A__   (    self       :    List[str],       __lowercase       :    List[str]    ):
							return get_trunk_forward_outputs(
							    _lowerCAmelCase,       out_feat_keys=_lowerCAmelCase,       feature_blocks=self._feature_blocks,       )
class 					_snake_case      (       __lowercase):




				def 				A__   (    self       :    Union[str, Any],       __lowercase       :    Tuple    ):
							lowercase__       	=					x.split("-"    )
							return x_split[0] + x_split[1] + "_" + "".join(x_split[2:]    )




				def __getitem__(    self       :    Union[str, Any],       __lowercase       :    int    ):




							if x not in self:
										lowercase__       	=					self.convert_name_to_timm(_lowerCAmelCase    )
										lowercase__       	=					partial(lambda: (timm.create_model(_lowerCAmelCase,       pretrained=_lowerCAmelCase    ).eval(), None)    )

							else:
										lowercase__       	=					super().__getitem__(_lowerCAmelCase    )

							return val
class 					_snake_case      (       __lowercase):




				def __getitem__(    self       :    Dict,       __lowercase       :    Union[str, Any]    ):




							if "seer" in x and "in1k" not in x:
										lowercase__       	=					RegNetModel
							else:
										lowercase__       	=					RegNetForImageClassification
							return val



def __lowerCAmelCase			(  SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_	):





			for from_key, to_key in keys:
						lowercase__       	=					from_state_dict[from_key].clone()
						print(f'''Copied key={from_key} to={to_key}'''	)
			return to_state_dict



def __lowerCAmelCase			(  SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_ = True    ,	):
			print(f'''Converting {name}...'''	)
			with torch.no_grad():
						lowercase__       	=					from_model_func()
						lowercase__       	=					our_model_func(UpperCamelCase__	).eval()
						lowercase__       	=					ModuleTransfer(src=UpperCamelCase__    ,	dest=UpperCamelCase__    ,	raise_if_mismatch=UpperCamelCase__	)
						lowercase__       	=					torch.randn((1, 3, 224, 224)	)
						module_transfer(UpperCamelCase__	)

			if from_state_dict is not None:
						lowercase__       	=					[]
						# for seer - in1k finetuned we have to manually copy the head
						if "seer" in name and "in1k" in name:
									lowercase__       	=					[('''0.clf.0.weight''', '''classifier.1.weight'''), ('''0.clf.0.bias''', '''classifier.1.bias''')]
						lowercase__       	=					manually_copy_vissl_head(UpperCamelCase__    ,	our_model.state_dict()    ,	UpperCamelCase__	)
						our_model.load_state_dict(UpperCamelCase__	)

			lowercase__       	=					our_model(UpperCamelCase__    ,	output_hidden_states=UpperCamelCase__	)
			lowercase__       	=					(
			    our_outputs.logits if isinstance(UpperCamelCase__    ,	UpperCamelCase__	) else our_outputs.last_hidden_state
			)

			lowercase__       	=					from_model(UpperCamelCase__	)
			lowercase__       	=					from_output[-1] if type(UpperCamelCase__	) is list else from_output

			# now since I don't want to use any config files, vissl seer model doesn't actually have an head, so let's just check the last hidden state
			if "seer" in name and "in1k" in name:
						lowercase__       	=					our_outputs.hidden_states[-1]

			assert torch.allclose(UpperCamelCase__    ,	UpperCamelCase__	), "The model logits don't match the original one."

			if push_to_hub:
						our_model.push_to_hub(
						    repo_path_or_name=save_directory / name    ,	commit_message="Add model"    ,	use_temp_dir=UpperCamelCase__    ,	)

						lowercase__       	=					224 if '''seer''' not in name else 384
						# we can use the convnext one
						lowercase__       	=					AutoImageProcessor.from_pretrained("facebook/convnext-base-224-22k-1k"    ,	size=UpperCamelCase__	)
						image_processor.push_to_hub(
						    repo_path_or_name=save_directory / name    ,	commit_message="Add image processor"    ,	use_temp_dir=UpperCamelCase__    ,	)

						print(f'''Pushed {name}'''	)



def __lowerCAmelCase			(  SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_ = None    ,	SCREAMING_SNAKE_CASE_ = True	):
			lowercase__       	=					'''imagenet-1k-id2label.json'''
			lowercase__       	=					1000
			lowercase__       	=					(1, num_labels)

			lowercase__       	=					'''huggingface/label-files'''
			lowercase__       	=					num_labels
			lowercase__       	=					json.load(open(cached_download(hf_hub_url(UpperCamelCase__    ,	UpperCamelCase__    ,	repo_type="dataset"	)	)    ,	"r"	)	)
			lowercase__       	=					{int(UpperCamelCase__	): v for k, v in idalabel.items()}

			lowercase__       	=					idalabel
			lowercase__       	=					{v: k for k, v in idalabel.items()}

			lowercase__       	=					partial(UpperCamelCase__    ,	num_labels=UpperCamelCase__    ,	idalabel=UpperCamelCase__    ,	labelaid=UpperCamelCase__	)

			lowercase__       	=					{
			    '''regnet-x-002''': ImageNetPreTrainedConfig(
			        depths=[1, 1, 4, 7]    ,	hidden_sizes=[24, 56, 152, 368]    ,	groups_width=8    ,	layer_type="x"	),
			    '''regnet-x-004''': ImageNetPreTrainedConfig(
			        depths=[1, 2, 7, 12]    ,	hidden_sizes=[32, 64, 160, 384]    ,	groups_width=16    ,	layer_type="x"	),
			    '''regnet-x-006''': ImageNetPreTrainedConfig(
			        depths=[1, 3, 5, 7]    ,	hidden_sizes=[48, 96, 240, 528]    ,	groups_width=24    ,	layer_type="x"	),
			    '''regnet-x-008''': ImageNetPreTrainedConfig(
			        depths=[1, 3, 7, 5]    ,	hidden_sizes=[64, 128, 288, 672]    ,	groups_width=16    ,	layer_type="x"	),
			    '''regnet-x-016''': ImageNetPreTrainedConfig(
			        depths=[2, 4, 10, 2]    ,	hidden_sizes=[72, 168, 408, 912]    ,	groups_width=24    ,	layer_type="x"	),
			    '''regnet-x-032''': ImageNetPreTrainedConfig(
			        depths=[2, 6, 15, 2]    ,	hidden_sizes=[96, 192, 432, 1008]    ,	groups_width=48    ,	layer_type="x"	),
			    '''regnet-x-040''': ImageNetPreTrainedConfig(
			        depths=[2, 5, 14, 2]    ,	hidden_sizes=[80, 240, 560, 1360]    ,	groups_width=40    ,	layer_type="x"	),
			    '''regnet-x-064''': ImageNetPreTrainedConfig(
			        depths=[2, 4, 10, 1]    ,	hidden_sizes=[168, 392, 784, 1624]    ,	groups_width=56    ,	layer_type="x"	),
			    '''regnet-x-080''': ImageNetPreTrainedConfig(
			        depths=[2, 5, 15, 1]    ,	hidden_sizes=[80, 240, 720, 1920]    ,	groups_width=120    ,	layer_type="x"	),
			    '''regnet-x-120''': ImageNetPreTrainedConfig(
			        depths=[2, 5, 11, 1]    ,	hidden_sizes=[224, 448, 896, 2240]    ,	groups_width=112    ,	layer_type="x"	),
			    '''regnet-x-160''': ImageNetPreTrainedConfig(
			        depths=[2, 6, 13, 1]    ,	hidden_sizes=[256, 512, 896, 2048]    ,	groups_width=128    ,	layer_type="x"	),
			    '''regnet-x-320''': ImageNetPreTrainedConfig(
			        depths=[2, 7, 13, 1]    ,	hidden_sizes=[336, 672, 1344, 2520]    ,	groups_width=168    ,	layer_type="x"	),
			    # y variant
			    '''regnet-y-002''': ImageNetPreTrainedConfig(depths=[1, 1, 4, 7]    ,	hidden_sizes=[24, 56, 152, 368]    ,	groups_width=8	),
			    '''regnet-y-004''': ImageNetPreTrainedConfig(
			        depths=[1, 3, 6, 6]    ,	hidden_sizes=[48, 104, 208, 440]    ,	groups_width=8	),
			    '''regnet-y-006''': ImageNetPreTrainedConfig(
			        depths=[1, 3, 7, 4]    ,	hidden_sizes=[48, 112, 256, 608]    ,	groups_width=16	),
			    '''regnet-y-008''': ImageNetPreTrainedConfig(
			        depths=[1, 3, 8, 2]    ,	hidden_sizes=[64, 128, 320, 768]    ,	groups_width=16	),
			    '''regnet-y-016''': ImageNetPreTrainedConfig(
			        depths=[2, 6, 17, 2]    ,	hidden_sizes=[48, 120, 336, 888]    ,	groups_width=24	),
			    '''regnet-y-032''': ImageNetPreTrainedConfig(
			        depths=[2, 5, 13, 1]    ,	hidden_sizes=[72, 216, 576, 1512]    ,	groups_width=24	),
			    '''regnet-y-040''': ImageNetPreTrainedConfig(
			        depths=[2, 6, 12, 2]    ,	hidden_sizes=[128, 192, 512, 1088]    ,	groups_width=64	),
			    '''regnet-y-064''': ImageNetPreTrainedConfig(
			        depths=[2, 7, 14, 2]    ,	hidden_sizes=[144, 288, 576, 1296]    ,	groups_width=72	),
			    '''regnet-y-080''': ImageNetPreTrainedConfig(
			        depths=[2, 4, 10, 1]    ,	hidden_sizes=[168, 448, 896, 2016]    ,	groups_width=56	),
			    '''regnet-y-120''': ImageNetPreTrainedConfig(
			        depths=[2, 5, 11, 1]    ,	hidden_sizes=[224, 448, 896, 2240]    ,	groups_width=112	),
			    '''regnet-y-160''': ImageNetPreTrainedConfig(
			        depths=[2, 4, 11, 1]    ,	hidden_sizes=[224, 448, 1232, 3024]    ,	groups_width=112	),
			    '''regnet-y-320''': ImageNetPreTrainedConfig(
			        depths=[2, 5, 12, 1]    ,	hidden_sizes=[232, 696, 1392, 3712]    ,	groups_width=232	),
			    # models created by SEER -> https://arxiv.org/abs/2202.08360
			    '''regnet-y-320-seer''': RegNetConfig(depths=[2, 5, 12, 1]    ,	hidden_sizes=[232, 696, 1392, 3712]    ,	groups_width=232	),
			    '''regnet-y-640-seer''': RegNetConfig(depths=[2, 5, 12, 1]    ,	hidden_sizes=[328, 984, 1968, 4920]    ,	groups_width=328	),
			    '''regnet-y-1280-seer''': RegNetConfig(
			        depths=[2, 7, 17, 1]    ,	hidden_sizes=[528, 1056, 2904, 7392]    ,	groups_width=264	),
			    '''regnet-y-2560-seer''': RegNetConfig(
			        depths=[3, 7, 16, 1]    ,	hidden_sizes=[640, 1696, 2544, 5088]    ,	groups_width=640	),
			    '''regnet-y-10b-seer''': ImageNetPreTrainedConfig(
			        depths=[2, 7, 17, 1]    ,	hidden_sizes=[2020, 4040, 1_1110, 2_8280]    ,	groups_width=1010	),
			    # finetuned on imagenet
			    '''regnet-y-320-seer-in1k''': ImageNetPreTrainedConfig(
			        depths=[2, 5, 12, 1]    ,	hidden_sizes=[232, 696, 1392, 3712]    ,	groups_width=232	),
			    '''regnet-y-640-seer-in1k''': ImageNetPreTrainedConfig(
			        depths=[2, 5, 12, 1]    ,	hidden_sizes=[328, 984, 1968, 4920]    ,	groups_width=328	),
			    '''regnet-y-1280-seer-in1k''': ImageNetPreTrainedConfig(
			        depths=[2, 7, 17, 1]    ,	hidden_sizes=[528, 1056, 2904, 7392]    ,	groups_width=264	),
			    '''regnet-y-2560-seer-in1k''': ImageNetPreTrainedConfig(
			        depths=[3, 7, 16, 1]    ,	hidden_sizes=[640, 1696, 2544, 5088]    ,	groups_width=640	),
			    '''regnet-y-10b-seer-in1k''': ImageNetPreTrainedConfig(
			        depths=[2, 7, 17, 1]    ,	hidden_sizes=[2020, 4040, 1_1110, 2_8280]    ,	groups_width=1010	),
			}

			lowercase__       	=					NameToOurModelFuncMap()
			lowercase__       	=					NameToFromModelFuncMap()
			# add seer weights logic

			def load_using_classy_vision(SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_	) -> Tuple[nn.Module, Dict]:
						lowercase__       	=					torch.hub.load_state_dict_from_url(UpperCamelCase__    ,	model_dir=str(UpperCamelCase__	)    ,	map_location="cpu"	)
						lowercase__       	=					model_func()
						# check if we have a head, if yes add it
						lowercase__       	=					files['''classy_state_dict''']['''base_model''']['''model''']
						lowercase__       	=					model_state_dict['''trunk''']
						model.load_state_dict(UpperCamelCase__	)
						return model.eval(), model_state_dict["heads"]

			# pretrained
			lowercase__       	=					partial(
			    UpperCamelCase__    ,	"https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet32d/seer_regnet32gf_model_iteration244000.torch"    ,	lambda: FakeRegNetVisslWrapper(RegNetYaagf()	)    ,	)

			lowercase__       	=					partial(
			    UpperCamelCase__    ,	"https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet64/seer_regnet64gf_model_final_checkpoint_phase0.torch"    ,	lambda: FakeRegNetVisslWrapper(RegNetYaagf()	)    ,	)

			lowercase__       	=					partial(
			    UpperCamelCase__    ,	"https://dl.fbaipublicfiles.com/vissl/model_zoo/swav_ig1b_regnet128Gf_cnstant_bs32_node16_sinkhorn10_proto16k_syncBN64_warmup8k/model_final_checkpoint_phase0.torch"    ,	lambda: FakeRegNetVisslWrapper(RegNetYaaagf()	)    ,	)

			lowercase__       	=					partial(
			    UpperCamelCase__    ,	"https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet10B/model_iteration124500_conso.torch"    ,	lambda: FakeRegNetVisslWrapper(
			        RegNet(RegNetParams(depth=27    ,	group_width=1010    ,	w_a=1744    ,	w_a=620.83    ,	w_m=2.52	)	)	)    ,	)

			# IN1K finetuned
			lowercase__       	=					partial(
			    UpperCamelCase__    ,	"https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet32_finetuned_in1k_model_final_checkpoint_phase78.torch"    ,	lambda: FakeRegNetVisslWrapper(RegNetYaagf()	)    ,	)

			lowercase__       	=					partial(
			    UpperCamelCase__    ,	"https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet64_finetuned_in1k_model_final_checkpoint_phase78.torch"    ,	lambda: FakeRegNetVisslWrapper(RegNetYaagf()	)    ,	)

			lowercase__       	=					partial(
			    UpperCamelCase__    ,	"https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet128_finetuned_in1k_model_final_checkpoint_phase78.torch"    ,	lambda: FakeRegNetVisslWrapper(RegNetYaaagf()	)    ,	)

			lowercase__       	=					partial(
			    UpperCamelCase__    ,	"https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_10b_finetuned_in1k_model_phase28_conso.torch"    ,	lambda: FakeRegNetVisslWrapper(
			        RegNet(RegNetParams(depth=27    ,	group_width=1010    ,	w_a=1744    ,	w_a=620.83    ,	w_m=2.52	)	)	)    ,	)

			if model_name:
						convert_weight_and_push(
						    UpperCamelCase__    ,	names_to_from_model_map[model_name]    ,	names_to_ours_model_map[model_name]    ,	names_to_config[model_name]    ,	UpperCamelCase__    ,	UpperCamelCase__    ,	)
			else:
						for model_name, config in names_to_config.items():
									convert_weight_and_push(
									    UpperCamelCase__    ,	names_to_from_model_map[model_name]    ,	names_to_ours_model_map[model_name]    ,	UpperCamelCase__    ,	UpperCamelCase__    ,	UpperCamelCase__    ,	)
			return config, expected_shape


if __name__ == "__main__":
							lowercase_										=  argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    """--model_name""",
							    default=None,
							    type=str,
							    help=(
							        """The name of the model you wish to convert, it must be one of the supported regnet* architecture,"""
							        """ currently: regnetx-*, regnety-*. If `None`, all of them will the converted."""
							    ),
							)
							parser.add_argument(
							    """--pytorch_dump_folder_path""",
							    default=None,
							    type=Path,
							    required=True,
							    help="""Path to the output PyTorch model directory.""",
							)
							parser.add_argument(
							    """--push_to_hub""",
							    default=True,
							    type=bool,
							    required=False,
							    help="""If True, push model and image processor to the hub.""",
							)

							lowercase_										=  parser.parse_args()

							lowercase_										=  args.pytorch_dump_folder_path
							pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
							convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)











import unittest

from transformers import EsmConfig, is_torch_available
from transformers.testing_utils import TestCasePlus, require_torch, slow, torch_device

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


if is_torch_available():
							import torch

							from transformers import EsmForMaskedLM, EsmForSequenceClassification, EsmForTokenClassification, EsmModel
							from transformers.models.esm.modeling_esm import (
							    ESM_PRETRAINED_MODEL_ARCHIVE_LIST,
							    EsmEmbeddings,
							    create_position_ids_from_input_ids,
							)





class 				a			:






				def __init__(  self			, _lowerCAmelCase			, _lowerCAmelCase=13			, _lowerCAmelCase=7			, _lowerCAmelCase=False			, _lowerCAmelCase=True			, _lowerCAmelCase=False			, _lowerCAmelCase=True			, _lowerCAmelCase=33			, _lowerCAmelCase=32			, _lowerCAmelCase=5			, _lowerCAmelCase=4			, _lowerCAmelCase=37			, _lowerCAmelCase="gelu"			, _lowerCAmelCase=0.1			, _lowerCAmelCase=0.1			, _lowerCAmelCase=512			, _lowerCAmelCase=16			, _lowerCAmelCase=2			, _lowerCAmelCase=0.02			, _lowerCAmelCase=3			, _lowerCAmelCase=4			, _lowerCAmelCase=None			, ):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   parent
										__SCREAMING_SNAKE_CASE:				Any           =   batch_size
										__SCREAMING_SNAKE_CASE:				Dict           =   seq_length
										__SCREAMING_SNAKE_CASE:				List[Any]           =   is_training
										__SCREAMING_SNAKE_CASE:				List[Any]           =   use_input_mask
										__SCREAMING_SNAKE_CASE:				str           =   use_token_type_ids
										__SCREAMING_SNAKE_CASE:				Any           =   use_labels
										__SCREAMING_SNAKE_CASE:				Optional[int]           =   vocab_size
										__SCREAMING_SNAKE_CASE:				Dict           =   hidden_size
										__SCREAMING_SNAKE_CASE:				Dict           =   num_hidden_layers
										__SCREAMING_SNAKE_CASE:				Optional[int]           =   num_attention_heads
										__SCREAMING_SNAKE_CASE:				str           =   intermediate_size
										__SCREAMING_SNAKE_CASE:				str           =   hidden_act
										__SCREAMING_SNAKE_CASE:				Optional[int]           =   hidden_dropout_prob
										__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   attention_probs_dropout_prob
										__SCREAMING_SNAKE_CASE:				Any           =   max_position_embeddings
										__SCREAMING_SNAKE_CASE:				Optional[Any]           =   type_vocab_size
										__SCREAMING_SNAKE_CASE:				List[Any]           =   type_sequence_label_size
										__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   initializer_range
										__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   num_labels
										__SCREAMING_SNAKE_CASE:				List[str]           =   num_choices
										__SCREAMING_SNAKE_CASE:				List[Any]           =   scope






				def 			snake_case_    (  self					):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				List[str]           =   ids_tensor([self.batch_size, self.seq_length]			, self.vocab_size					)

										__SCREAMING_SNAKE_CASE:				int           =   None
										if self.use_input_mask:
																__SCREAMING_SNAKE_CASE:				Tuple           =   random_attention_mask([self.batch_size, self.seq_length]					)

										__SCREAMING_SNAKE_CASE:				Optional[Any]           =   None
										__SCREAMING_SNAKE_CASE:				Optional[int]           =   None
										__SCREAMING_SNAKE_CASE:				Optional[Any]           =   None
										if self.use_labels:
																__SCREAMING_SNAKE_CASE:				List[Any]           =   ids_tensor([self.batch_size]			, self.type_sequence_label_size					)
																__SCREAMING_SNAKE_CASE:				List[str]           =   ids_tensor([self.batch_size, self.seq_length]			, self.num_labels					)
																__SCREAMING_SNAKE_CASE:				str           =   ids_tensor([self.batch_size]			, self.num_choices					)

										__SCREAMING_SNAKE_CASE:				Optional[int]           =   self.get_config()

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






				def 			snake_case_    (  self					):

										"""simple docstring"""




										return EsmConfig(
										    vocab_size=self.vocab_size			, hidden_size=self.hidden_size			, pad_token_id=1			, 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			, )






				def 			snake_case_    (  self			, _lowerCAmelCase			, _lowerCAmelCase			, _lowerCAmelCase			, _lowerCAmelCase			, _lowerCAmelCase			, _lowerCAmelCase					):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				List[Any]           =   EsmModel(config=_lowerCAmelCase					)
										model.to(_lowerCAmelCase					)
										model.eval()
										__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   model(_lowerCAmelCase			, attention_mask=_lowerCAmelCase					)
										__SCREAMING_SNAKE_CASE:				Tuple           =   model(_lowerCAmelCase					)
										__SCREAMING_SNAKE_CASE:				int           =   model(_lowerCAmelCase					)

										self.parent.assertEqual(result.last_hidden_state.shape			, (self.batch_size, self.seq_length, self.hidden_size)					)
										self.parent.assertEqual(result.pooler_output.shape			, (self.batch_size, self.hidden_size)					)






				def 			snake_case_    (  self			, _lowerCAmelCase			, _lowerCAmelCase			, _lowerCAmelCase			, _lowerCAmelCase			, _lowerCAmelCase			, _lowerCAmelCase					):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				List[Any]           =   EsmForMaskedLM(config=_lowerCAmelCase					)
										model.to(_lowerCAmelCase					)
										model.eval()
										__SCREAMING_SNAKE_CASE:				Optional[Any]           =   model(_lowerCAmelCase			, attention_mask=_lowerCAmelCase			, labels=_lowerCAmelCase					)
										self.parent.assertEqual(result.logits.shape			, (self.batch_size, self.seq_length, self.vocab_size)					)






				def 			snake_case_    (  self			, _lowerCAmelCase			, _lowerCAmelCase			, _lowerCAmelCase			, _lowerCAmelCase			, _lowerCAmelCase			, _lowerCAmelCase					):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				Any           =   self.num_labels
										__SCREAMING_SNAKE_CASE:				List[Any]           =   EsmForTokenClassification(config=_lowerCAmelCase					)
										model.to(_lowerCAmelCase					)
										model.eval()
										__SCREAMING_SNAKE_CASE:				int           =   model(_lowerCAmelCase			, attention_mask=_lowerCAmelCase			, labels=_lowerCAmelCase					)
										self.parent.assertEqual(result.logits.shape			, (self.batch_size, self.seq_length, self.num_labels)					)






				def 			snake_case_    (  self					):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				str           =   self.prepare_config_and_inputs()
										(
										    (
										    __SCREAMING_SNAKE_CASE
										)       ,(
										    __SCREAMING_SNAKE_CASE
										)       ,(
										    __SCREAMING_SNAKE_CASE
										)       ,(
										    __SCREAMING_SNAKE_CASE
										)       ,(
										    __SCREAMING_SNAKE_CASE
										)       ,(
										    __SCREAMING_SNAKE_CASE
										)       ,
										):				int           =   config_and_inputs
										__SCREAMING_SNAKE_CASE:				Tuple           =   {'''input_ids''': input_ids, '''attention_mask''': input_mask}
										return config, inputs_dict





@require_torch
class 				a			(      __lowercase   ,__lowercase   ,unittest.TestCase			):
				SCREAMING_SNAKE_CASE__     :   Tuple		      =     False

				SCREAMING_SNAKE_CASE__     :   Optional[int]		      =     (
				    (
				        EsmForMaskedLM,
				        EsmModel,
				        EsmForSequenceClassification,
				        EsmForTokenClassification,
				    )
				    if is_torch_available()
				    else ()
				)
				SCREAMING_SNAKE_CASE__     :   int		      =     ()
				SCREAMING_SNAKE_CASE__     :   Optional[int]		      =     (
				    {
				        '''feature-extraction''': EsmModel,
				        '''fill-mask''': EsmForMaskedLM,
				        '''text-classification''': EsmForSequenceClassification,
				        '''token-classification''': EsmForTokenClassification,
				        '''zero-shot''': EsmForSequenceClassification,
				    }
				    if is_torch_available()
				    else {}
				)
				SCREAMING_SNAKE_CASE__     :   Any		      =     True






				def 			snake_case_    (  self					):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				List[str]           =   EsmModelTester(self					)
										__SCREAMING_SNAKE_CASE:				List[str]           =   ConfigTester(self			, config_class=_lowerCAmelCase			, hidden_size=37					)






				def 			snake_case_    (  self					):

										"""simple docstring"""




										self.config_tester.run_common_tests()






				def 			snake_case_    (  self					):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				Dict           =   self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*_lowerCAmelCase					)






				def 			snake_case_    (  self					):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				Optional[int]           =   self.model_tester.prepare_config_and_inputs()
										for type in ["absolute", "relative_key", "relative_key_query"]:
																__SCREAMING_SNAKE_CASE:				Any           =   type
																self.model_tester.create_and_check_model(*_lowerCAmelCase					)






				def 			snake_case_    (  self					):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				Optional[int]           =   self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_masked_lm(*_lowerCAmelCase					)






				def 			snake_case_    (  self					):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				Optional[int]           =   self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_token_classification(*_lowerCAmelCase					)






				@slow
				def 			snake_case_    (  self					):

										"""simple docstring"""




										for model_name in ESM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																__SCREAMING_SNAKE_CASE:				Optional[int]           =   EsmModel.from_pretrained(_lowerCAmelCase					)
																self.assertIsNotNone(_lowerCAmelCase					)






				def 			snake_case_    (  self					):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   self.model_tester.prepare_config_and_inputs()[0]
										__SCREAMING_SNAKE_CASE:				int           =   EsmEmbeddings(config=_lowerCAmelCase					)

										__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   torch.as_tensor([[12, 31, 13, model.padding_idx]]					)
										__SCREAMING_SNAKE_CASE:				Optional[Any]           =   torch.as_tensor(
										    [
										        [
										            0 + model.padding_idx + 1,
										            1 + model.padding_idx + 1,
										            2 + model.padding_idx + 1,
										            model.padding_idx,
										        ]
										    ]					)
										__SCREAMING_SNAKE_CASE:				Any           =   create_position_ids_from_input_ids(_lowerCAmelCase			, model.padding_idx					)
										self.assertEqual(position_ids.shape			, expected_positions.shape					)
										self.assertTrue(torch.all(torch.eq(_lowerCAmelCase			, _lowerCAmelCase					)					)					)






				def 			snake_case_    (  self					):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				Tuple           =   self.model_tester.prepare_config_and_inputs()[0]
										__SCREAMING_SNAKE_CASE:				Dict           =   EsmEmbeddings(config=_lowerCAmelCase					)

										__SCREAMING_SNAKE_CASE:				Optional[int]           =   torch.empty(2			, 4			, 30					)
										__SCREAMING_SNAKE_CASE:				List[str]           =   [
										    0 + embeddings.padding_idx + 1,
										    1 + embeddings.padding_idx + 1,
										    2 + embeddings.padding_idx + 1,
										    3 + embeddings.padding_idx + 1,
										]
										__SCREAMING_SNAKE_CASE:				str           =   torch.as_tensor([expected_single_positions, expected_single_positions]					)
										__SCREAMING_SNAKE_CASE:				Optional[Any]           =   embeddings.create_position_ids_from_inputs_embeds(_lowerCAmelCase					)
										self.assertEqual(position_ids.shape			, expected_positions.shape					)
										self.assertTrue(torch.all(torch.eq(_lowerCAmelCase			, _lowerCAmelCase					)					)					)






				@unittest.skip('''Esm does not support embedding resizing'''					)
				def 			snake_case_    (  self					):

										"""simple docstring"""




										pass






				@unittest.skip('''Esm does not support embedding resizing'''					)
				def 			snake_case_    (  self					):

										"""simple docstring"""




										pass






				@unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.'''					)
				def 			snake_case_    (  self					):

										"""simple docstring"""




										pass





@require_torch
class 				a			(      __lowercase			):






				@slow
				def 			snake_case_    (  self					):

										"""simple docstring"""




										with torch.no_grad():
																__SCREAMING_SNAKE_CASE:				Optional[int]           =   EsmForMaskedLM.from_pretrained('''facebook/esm2_t6_8M_UR50D'''					)
																model.eval()
																__SCREAMING_SNAKE_CASE:				Tuple           =   torch.tensor([[0, 1, 2, 3, 4, 5]]					)
																__SCREAMING_SNAKE_CASE:				str           =   model(_lowerCAmelCase					)[0]

																__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   33

																__SCREAMING_SNAKE_CASE:				Dict           =   torch.Size((1, 6, vocab_size)					)
																self.assertEqual(output.shape			, _lowerCAmelCase					)

																__SCREAMING_SNAKE_CASE:				Optional[int]           =   torch.tensor(
																    [[[8.9215, -10.5898, -6.4671], [-6.3967, -13.9114, -1.1212], [-7.7812, -13.9516, -3.7406]]]					)
																self.assertTrue(torch.allclose(output[:, :3, :3]			, _lowerCAmelCase			, atol=1e-4					)					)






				@slow
				def 			snake_case_    (  self					):

										"""simple docstring"""




										with torch.no_grad():
																__SCREAMING_SNAKE_CASE:				Dict           =   EsmModel.from_pretrained('''facebook/esm2_t6_8M_UR50D'''					)
																model.eval()

																__SCREAMING_SNAKE_CASE:				Dict           =   torch.tensor([[0, 6, 4, 13, 5, 4, 16, 12, 11, 7, 2]]					)
																__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   model(_lowerCAmelCase					)[0]
																# compare the actual values for a slice.
																__SCREAMING_SNAKE_CASE:				Optional[int]           =   torch.tensor(
																    [[[0.1444, 0.5413, 0.3248], [0.3034, 0.0053, 0.3108], [0.3228, -0.2499, 0.3415]]]					)
																self.assertTrue(torch.allclose(output[:, :3, :3]			, _lowerCAmelCase			, atol=1e-4					)					)



