





import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    MobileViTConfig,
    MobileViTForImageClassification,
    MobileViTForSemanticSegmentation,
    MobileViTImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
lowerCAmelCase							:       Union[str, Any]					 =	logging.get_logger(__name__)






def 		lowerCAmelCase  (	UpperCamelCase__   :   Optional[Any]					)      ->    Dict:

						"""simple docstring"""





						__SCREAMING_SNAKE_CASE:				Optional[int]           =   MobileViTConfig()

						# size of the architecture
						if "mobilevit_s" in mobilevit_name:
												__SCREAMING_SNAKE_CASE:				int           =   [144, 192, 240]
												__SCREAMING_SNAKE_CASE:				Any           =   [16, 32, 64, 96, 128, 160, 640]
						elif "mobilevit_xs" in mobilevit_name:
												__SCREAMING_SNAKE_CASE:				Dict           =   [96, 120, 144]
												__SCREAMING_SNAKE_CASE:				int           =   [16, 32, 48, 64, 80, 96, 384]
						elif "mobilevit_xxs" in mobilevit_name:
												__SCREAMING_SNAKE_CASE:				Optional[Any]           =   [64, 80, 96]
												__SCREAMING_SNAKE_CASE:				Optional[int]           =   [16, 16, 24, 48, 64, 80, 320]
												__SCREAMING_SNAKE_CASE:				int           =   0.05
												__SCREAMING_SNAKE_CASE:				Optional[int]           =   2.0

						if mobilevit_name.startswith('''deeplabv3_'''					):
												__SCREAMING_SNAKE_CASE:				List[str]           =   512
												__SCREAMING_SNAKE_CASE:				int           =   16
												__SCREAMING_SNAKE_CASE:				Optional[Any]           =   21
												__SCREAMING_SNAKE_CASE:				Any           =   '''pascal-voc-id2label.json'''
						else:
												__SCREAMING_SNAKE_CASE:				List[Any]           =   1_000
												__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   '''imagenet-1k-id2label.json'''

						__SCREAMING_SNAKE_CASE:				str           =   '''huggingface/label-files'''
						__SCREAMING_SNAKE_CASE:				List[str]           =   json.load(open(hf_hub_download(UpperCamelCase__       ,			UpperCamelCase__       ,			repo_type='''dataset'''					)       ,			'''r'''					)					)
						__SCREAMING_SNAKE_CASE:				Any           =   {int(UpperCamelCase__					): v for k, v in idalabel.items()}
						__SCREAMING_SNAKE_CASE:				Dict           =   idalabel
						__SCREAMING_SNAKE_CASE:				Dict           =   {v: k for k, v in idalabel.items()}

						return config






def 		lowerCAmelCase  (	UpperCamelCase__   :   List[str]       ,			UpperCamelCase__   :   Any=False					)      ->    Any:

						"""simple docstring"""





						for i in range(1       ,			6					):
												if F"""layer_{i}.""" in name:
																		__SCREAMING_SNAKE_CASE:				Tuple           =   name.replace(F"""layer_{i}."""       ,			F"""encoder.layer.{i - 1}."""					)

						if "conv_1." in name:
												__SCREAMING_SNAKE_CASE:				Tuple           =   name.replace('''conv_1.'''       ,			'''conv_stem.'''					)
						if ".block." in name:
												__SCREAMING_SNAKE_CASE:				Any           =   name.replace('''.block.'''       ,			'''.'''					)

						if "exp_1x1" in name:
												__SCREAMING_SNAKE_CASE:				int           =   name.replace('''exp_1x1'''       ,			'''expand_1x1'''					)
						if "red_1x1" in name:
												__SCREAMING_SNAKE_CASE:				List[Any]           =   name.replace('''red_1x1'''       ,			'''reduce_1x1'''					)
						if ".local_rep.conv_3x3." in name:
												__SCREAMING_SNAKE_CASE:				int           =   name.replace('''.local_rep.conv_3x3.'''       ,			'''.conv_kxk.'''					)
						if ".local_rep.conv_1x1." in name:
												__SCREAMING_SNAKE_CASE:				Optional[int]           =   name.replace('''.local_rep.conv_1x1.'''       ,			'''.conv_1x1.'''					)
						if ".norm." in name:
												__SCREAMING_SNAKE_CASE:				Tuple           =   name.replace('''.norm.'''       ,			'''.normalization.'''					)
						if ".conv." in name:
												__SCREAMING_SNAKE_CASE:				List[Any]           =   name.replace('''.conv.'''       ,			'''.convolution.'''					)
						if ".conv_proj." in name:
												__SCREAMING_SNAKE_CASE:				str           =   name.replace('''.conv_proj.'''       ,			'''.conv_projection.'''					)

						for i in range(0       ,			2					):
												for j in range(0       ,			4					):
																		if F""".{i}.{j}.""" in name:
																								__SCREAMING_SNAKE_CASE:				Optional[Any]           =   name.replace(F""".{i}.{j}."""       ,			F""".{i}.layer.{j}."""					)

						for i in range(2       ,			6					):
												for j in range(0       ,			4					):
																		if F""".{i}.{j}.""" in name:
																								__SCREAMING_SNAKE_CASE:				int           =   name.replace(F""".{i}.{j}."""       ,			F""".{i}."""					)
																								if "expand_1x1" in name:
																														__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   name.replace('''expand_1x1'''       ,			'''downsampling_layer.expand_1x1'''					)
																								if "conv_3x3" in name:
																														__SCREAMING_SNAKE_CASE:				Optional[Any]           =   name.replace('''conv_3x3'''       ,			'''downsampling_layer.conv_3x3'''					)
																								if "reduce_1x1" in name:
																														__SCREAMING_SNAKE_CASE:				Optional[Any]           =   name.replace('''reduce_1x1'''       ,			'''downsampling_layer.reduce_1x1'''					)

						for i in range(2       ,			5					):
												if F""".global_rep.{i}.weight""" in name:
																		__SCREAMING_SNAKE_CASE:				Tuple           =   name.replace(F""".global_rep.{i}.weight"""       ,			'''.layernorm.weight'''					)
												if F""".global_rep.{i}.bias""" in name:
																		__SCREAMING_SNAKE_CASE:				Tuple           =   name.replace(F""".global_rep.{i}.bias"""       ,			'''.layernorm.bias'''					)

						if ".global_rep." in name:
												__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   name.replace('''.global_rep.'''       ,			'''.transformer.'''					)
						if ".pre_norm_mha.0." in name:
												__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   name.replace('''.pre_norm_mha.0.'''       ,			'''.layernorm_before.'''					)
						if ".pre_norm_mha.1.out_proj." in name:
												__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   name.replace('''.pre_norm_mha.1.out_proj.'''       ,			'''.attention.output.dense.'''					)
						if ".pre_norm_ffn.0." in name:
												__SCREAMING_SNAKE_CASE:				Tuple           =   name.replace('''.pre_norm_ffn.0.'''       ,			'''.layernorm_after.'''					)
						if ".pre_norm_ffn.1." in name:
												__SCREAMING_SNAKE_CASE:				List[str]           =   name.replace('''.pre_norm_ffn.1.'''       ,			'''.intermediate.dense.'''					)
						if ".pre_norm_ffn.4." in name:
												__SCREAMING_SNAKE_CASE:				List[str]           =   name.replace('''.pre_norm_ffn.4.'''       ,			'''.output.dense.'''					)
						if ".transformer." in name:
												__SCREAMING_SNAKE_CASE:				Dict           =   name.replace('''.transformer.'''       ,			'''.transformer.layer.'''					)

						if ".aspp_layer." in name:
												__SCREAMING_SNAKE_CASE:				int           =   name.replace('''.aspp_layer.'''       ,			'''.'''					)
						if ".aspp_pool." in name:
												__SCREAMING_SNAKE_CASE:				Optional[Any]           =   name.replace('''.aspp_pool.'''       ,			'''.'''					)
						if "seg_head." in name:
												__SCREAMING_SNAKE_CASE:				str           =   name.replace('''seg_head.'''       ,			'''segmentation_head.'''					)
						if "segmentation_head.classifier.classifier." in name:
												__SCREAMING_SNAKE_CASE:				List[Any]           =   name.replace('''segmentation_head.classifier.classifier.'''       ,			'''segmentation_head.classifier.'''					)

						if "classifier.fc." in name:
												__SCREAMING_SNAKE_CASE:				Optional[int]           =   name.replace('''classifier.fc.'''       ,			'''classifier.'''					)
						elif (not base_model) and ("segmentation_head." not in name):
												__SCREAMING_SNAKE_CASE:				Dict           =   '''mobilevit.''' + name

						return name






def 		lowerCAmelCase  (	UpperCamelCase__   :   Any       ,			UpperCamelCase__   :   Optional[int]       ,			UpperCamelCase__   :   Dict=False					)      ->    Tuple:

						"""simple docstring"""





						if base_model:
												__SCREAMING_SNAKE_CASE:				Tuple           =   ''''''
						else:
												__SCREAMING_SNAKE_CASE:				List[Any]           =   '''mobilevit.'''

						for key in orig_state_dict.copy().keys():
												__SCREAMING_SNAKE_CASE:				int           =   orig_state_dict.pop(UpperCamelCase__					)

												if key[:8] == "encoder.":
																		__SCREAMING_SNAKE_CASE:				Optional[int]           =   key[8:]

												if "qkv" in key:
																		__SCREAMING_SNAKE_CASE:				Dict           =   key.split('''.'''					)
																		__SCREAMING_SNAKE_CASE:				List[Any]           =   int(key_split[0][6:]					) - 1
																		__SCREAMING_SNAKE_CASE:				Optional[int]           =   int(key_split[3]					)
																		__SCREAMING_SNAKE_CASE:				Dict           =   model.get_submodule(F"""{model_prefix}encoder.layer.{layer_num}"""					)
																		__SCREAMING_SNAKE_CASE:				str           =   layer.transformer.layer[transformer_num].attention.attention.all_head_size
																		__SCREAMING_SNAKE_CASE:				Optional[int]           =   (
																		    F"""{model_prefix}encoder.layer.{layer_num}.transformer.layer.{transformer_num}.attention.attention."""
																		)
																		if "weight" in key:
																								__SCREAMING_SNAKE_CASE:				int           =   val[:dim, :]
																								__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   val[dim : dim * 2, :]
																								__SCREAMING_SNAKE_CASE:				List[str]           =   val[-dim:, :]
																		else:
																								__SCREAMING_SNAKE_CASE:				List[Any]           =   val[:dim]
																								__SCREAMING_SNAKE_CASE:				Optional[Any]           =   val[dim : dim * 2]
																								__SCREAMING_SNAKE_CASE:				int           =   val[-dim:]
												else:
																		__SCREAMING_SNAKE_CASE:				int           =   val

						return orig_state_dict






def 		lowerCAmelCase  (	)      ->    Optional[int]:

						"""simple docstring"""





						__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   '''http://images.cocodataset.org/val2017/000000039769.jpg'''
						__SCREAMING_SNAKE_CASE:				Optional[int]           =   Image.open(requests.get(UpperCamelCase__       ,			stream=UpperCamelCase__					).raw					)
						return im






@torch.no_grad()
def 		lowerCAmelCase  (	UpperCamelCase__   :   Optional[int]       ,			UpperCamelCase__   :   Optional[int]       ,			UpperCamelCase__   :   Optional[int]       ,			UpperCamelCase__   :   Union[str, Any]=False					)      ->    Optional[Any]:

						"""simple docstring"""





						__SCREAMING_SNAKE_CASE:				Optional[Any]           =   get_mobilevit_config(UpperCamelCase__					)

						# load original state_dict
						__SCREAMING_SNAKE_CASE:				Optional[Any]           =   torch.load(UpperCamelCase__       ,			map_location='''cpu'''					)

						# load 🤗 model
						if mobilevit_name.startswith('''deeplabv3_'''					):
												__SCREAMING_SNAKE_CASE:				int           =   MobileViTForSemanticSegmentation(UpperCamelCase__					).eval()
						else:
												__SCREAMING_SNAKE_CASE:				Tuple           =   MobileViTForImageClassification(UpperCamelCase__					).eval()

						__SCREAMING_SNAKE_CASE:				Tuple           =   convert_state_dict(UpperCamelCase__       ,			UpperCamelCase__					)
						model.load_state_dict(UpperCamelCase__					)

						# Check outputs on an image, prepared by MobileViTImageProcessor
						__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   MobileViTImageProcessor(crop_size=config.image_size       ,			size=config.image_size + 32					)
						__SCREAMING_SNAKE_CASE:				Optional[Any]           =   image_processor(images=prepare_img()       ,			return_tensors='''pt'''					)
						__SCREAMING_SNAKE_CASE:				Any           =   model(**UpperCamelCase__					)
						__SCREAMING_SNAKE_CASE:				str           =   outputs.logits

						if mobilevit_name.startswith('''deeplabv3_'''					):
												assert logits.shape == (1, 21, 32, 32)

												if mobilevit_name == "deeplabv3_mobilevit_s":
																		__SCREAMING_SNAKE_CASE:				List[Any]           =   torch.tensor(
																		    [
																		        [[6.20_65, 6.12_92, 6.20_70], [6.10_79, 6.12_54, 6.17_47], [6.00_42, 6.10_71, 6.10_34]],
																		        [[-6.92_53, -6.86_53, -7.03_98], [-7.32_18, -7.39_83, -7.36_70], [-7.19_61, -7.24_82, -7.15_69]],
																		        [[-4.47_23, -4.43_48, -4.37_69], [-5.36_29, -5.46_32, -5.45_98], [-5.15_87, -5.34_02, -5.50_59]],
																		    ]					)
												elif mobilevit_name == "deeplabv3_mobilevit_xs":
																		__SCREAMING_SNAKE_CASE:				Optional[Any]           =   torch.tensor(
																		    [
																		        [[5.44_49, 5.57_33, 5.63_14], [5.18_15, 5.39_30, 5.59_63], [5.16_56, 5.43_33, 5.48_53]],
																		        [[-9.44_23, -9.77_66, -9.67_14], [-9.15_81, -9.57_20, -9.55_19], [-9.10_06, -9.64_58, -9.57_03]],
																		        [[-7.77_21, -7.37_16, -7.15_83], [-8.45_99, -8.06_24, -7.79_44], [-8.41_72, -7.83_66, -7.50_25]],
																		    ]					)
												elif mobilevit_name == "deeplabv3_mobilevit_xxs":
																		__SCREAMING_SNAKE_CASE:				List[str]           =   torch.tensor(
																		    [
																		        [[6.98_11, 6.97_43, 7.31_23], [7.17_77, 7.19_31, 7.39_38], [7.56_33, 7.80_50, 7.89_01]],
																		        [[-10.55_36, -10.23_32, -10.29_24], [-10.23_36, -9.86_24, -9.59_64], [-10.88_40, -10.81_58, -10.66_59]],
																		        [[-3.49_38, -3.06_31, -2.86_20], [-3.42_05, -2.81_35, -2.68_75], [-3.41_79, -2.79_45, -2.87_50]],
																		    ]					)
												else:
																		raise ValueError(F"""Unknown mobilevit_name: {mobilevit_name}"""					)

												assert torch.allclose(logits[0, :3, :3, :3]       ,			UpperCamelCase__       ,			atol=1E-4					)
						else:
												assert logits.shape == (1, 1_000)

												if mobilevit_name == "mobilevit_s":
																		__SCREAMING_SNAKE_CASE:				str           =   torch.tensor([-0.98_66, 0.23_92, -1.12_41]					)
												elif mobilevit_name == "mobilevit_xs":
																		__SCREAMING_SNAKE_CASE:				Optional[int]           =   torch.tensor([-2.47_61, -0.93_99, -1.95_87]					)
												elif mobilevit_name == "mobilevit_xxs":
																		__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   torch.tensor([-1.93_64, -1.23_27, -0.46_53]					)
												else:
																		raise ValueError(F"""Unknown mobilevit_name: {mobilevit_name}"""					)

												assert torch.allclose(logits[0, :3]       ,			UpperCamelCase__       ,			atol=1E-4					)

						Path(UpperCamelCase__					).mkdir(exist_ok=UpperCamelCase__					)
						print(F"""Saving model {mobilevit_name} to {pytorch_dump_folder_path}"""					)
						model.save_pretrained(UpperCamelCase__					)
						print(F"""Saving image processor to {pytorch_dump_folder_path}"""					)
						image_processor.save_pretrained(UpperCamelCase__					)

						if push_to_hub:
												__SCREAMING_SNAKE_CASE:				Any           =   {
												    '''mobilevit_s''': '''mobilevit-small''',
												    '''mobilevit_xs''': '''mobilevit-x-small''',
												    '''mobilevit_xxs''': '''mobilevit-xx-small''',
												    '''deeplabv3_mobilevit_s''': '''deeplabv3-mobilevit-small''',
												    '''deeplabv3_mobilevit_xs''': '''deeplabv3-mobilevit-x-small''',
												    '''deeplabv3_mobilevit_xxs''': '''deeplabv3-mobilevit-xx-small''',
												}

												print('''Pushing to the hub...'''					)
												__SCREAMING_SNAKE_CASE:				Dict           =   model_mapping[mobilevit_name]
												image_processor.push_to_hub(UpperCamelCase__       ,			organization='''apple'''					)
												model.push_to_hub(UpperCamelCase__       ,			organization='''apple'''					)


if __name__ == "__main__":
							lowerCAmelCase							:       Optional[Any]					 =	argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    """--mobilevit_name""",
							    default="""mobilevit_s""",
							    type=str,
							    help=(
							        """Name of the MobileViT model you'd like to convert. Should be one of 'mobilevit_s', 'mobilevit_xs',"""
							        """ 'mobilevit_xxs', 'deeplabv3_mobilevit_s', 'deeplabv3_mobilevit_xs', 'deeplabv3_mobilevit_xxs'."""
							    ),
							)
							parser.add_argument(
							    """--checkpoint_path""", required=True, type=str, help="""Path to the original state dict (.pt file)."""
							)
							parser.add_argument(
							    """--pytorch_dump_folder_path""", required=True, type=str, help="""Path to the output PyTorch model directory."""
							)
							parser.add_argument(
							    """--push_to_hub""", action="""store_true""", help="""Whether or not to push the converted model to the 🤗 hub."""
							)

							lowerCAmelCase							:       Optional[Any]					 =	parser.parse_args()
							convert_movilevit_checkpoint(
							    args.mobilevit_name, args.checkpoint_path, args.pytorch_dump_folder_path, 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					)					)



