




import argparse

import requests
import torch

# pip3 install salesforce-lavis
# I'm actually installing a slightly modified version: pip3 install git+https://github.com/nielsrogge/LAVIS.git@fix_lavis
from lavis.models import load_model_and_preprocess
from PIL import Image

from transformers import (
    AutoTokenizer,
    BlipaConfig,
    BlipaForConditionalGeneration,
    BlipaProcessor,
    BlipaVisionConfig,
    BlipImageProcessor,
    OPTConfig,
    TaConfig,
)
from transformers.utils.constants import OPENAI_CLIP_MEAN, OPENAI_CLIP_STD







def lowerCamelCase__       (      ):

					'''simple docstring'''


					UpperCAmelCase_      :							List[Any]       =      'https://storage.googleapis.com/sfr-vision-language-research/LAVIS/assets/merlion.png'
					UpperCAmelCase_      :							int       =      Image.open(requests.get(_UpperCAmelCase			,						stream=_UpperCAmelCase				).raw				).convert('''RGB'''				)

					return image







def lowerCamelCase__       (      _lowercase				):

					'''simple docstring'''


					UpperCAmelCase_      :							str       =      []
					# fmt: off

					# vision encoder
					rename_keys.append(('''visual_encoder.cls_token''', '''vision_model.embeddings.class_embedding''')				)
					rename_keys.append(('''visual_encoder.pos_embed''', '''vision_model.embeddings.position_embedding''')				)
					rename_keys.append(('''visual_encoder.patch_embed.proj.weight''', '''vision_model.embeddings.patch_embedding.weight''')				)
					rename_keys.append(('''visual_encoder.patch_embed.proj.bias''', '''vision_model.embeddings.patch_embedding.bias''')				)
					rename_keys.append(('''ln_vision.weight''', '''vision_model.post_layernorm.weight''')				)
					rename_keys.append(('''ln_vision.bias''', '''vision_model.post_layernorm.bias''')				)

					for i in range(config.vision_config.num_hidden_layers				):
										rename_keys.append((f'''visual_encoder.blocks.{i}.norm1.weight''', f'''vision_model.encoder.layers.{i}.layer_norm1.weight''')				)
										rename_keys.append((f'''visual_encoder.blocks.{i}.norm1.bias''', f'''vision_model.encoder.layers.{i}.layer_norm1.bias''')				)
										rename_keys.append((f'''visual_encoder.blocks.{i}.norm2.weight''', f'''vision_model.encoder.layers.{i}.layer_norm2.weight''')				)
										rename_keys.append((f'''visual_encoder.blocks.{i}.norm2.bias''', f'''vision_model.encoder.layers.{i}.layer_norm2.bias''')				)
										rename_keys.append((f'''visual_encoder.blocks.{i}.attn.qkv.weight''', f'''vision_model.encoder.layers.{i}.self_attn.qkv.weight''')				)
										rename_keys.append((f'''visual_encoder.blocks.{i}.attn.proj.weight''', f'''vision_model.encoder.layers.{i}.self_attn.projection.weight''',)				)
										rename_keys.append((f'''visual_encoder.blocks.{i}.attn.proj.bias''', f'''vision_model.encoder.layers.{i}.self_attn.projection.bias''')				)
										rename_keys.append((f'''visual_encoder.blocks.{i}.mlp.fc1.weight''', f'''vision_model.encoder.layers.{i}.mlp.fc1.weight''')				)
										rename_keys.append((f'''visual_encoder.blocks.{i}.mlp.fc1.bias''', f'''vision_model.encoder.layers.{i}.mlp.fc1.bias''')				)
										rename_keys.append((f'''visual_encoder.blocks.{i}.mlp.fc2.weight''', f'''vision_model.encoder.layers.{i}.mlp.fc2.weight''')				)
										rename_keys.append((f'''visual_encoder.blocks.{i}.mlp.fc2.bias''', f'''vision_model.encoder.layers.{i}.mlp.fc2.bias''')				)

					# QFormer
					rename_keys.append(('''Qformer.bert.embeddings.LayerNorm.weight''', '''qformer.layernorm.weight''')				)
					rename_keys.append(('''Qformer.bert.embeddings.LayerNorm.bias''', '''qformer.layernorm.bias''')				)

					# fmt: on
					return rename_keys







def lowerCamelCase__       (      _lowercase			,						_lowercase			,						_lowercase				):

					'''simple docstring'''


					UpperCAmelCase_      :							Tuple       =      dct.pop(_UpperCAmelCase				)
					UpperCAmelCase_      :							Tuple       =      val







def lowerCamelCase__       (      _lowercase			,						_lowercase				):

					'''simple docstring'''


					for i in range(config.vision_config.num_hidden_layers				):
										# read in original q and v biases
										UpperCAmelCase_      :							Optional[int]       =      state_dict.pop(f'''visual_encoder.blocks.{i}.attn.q_bias'''				)
										UpperCAmelCase_      :							int       =      state_dict.pop(f'''visual_encoder.blocks.{i}.attn.v_bias'''				)

										# next, set bias in the state dict
										UpperCAmelCase_      :							List[Any]       =      torch.cat((q_bias, torch.zeros_like(_UpperCAmelCase			,						requires_grad=_UpperCAmelCase				), v_bias)				)
										UpperCAmelCase_      :							Optional[Any]       =      qkv_bias







def lowerCamelCase__       (      _lowercase			,						_lowercase				):

					'''simple docstring'''


					UpperCAmelCase_      :							List[str]       =      364 if 'coco' in model_name else 224
					UpperCAmelCase_      :							int       =      BlipaVisionConfig(image_size=_UpperCAmelCase				).to_dict()

					# make sure the models have proper bos_token_id and eos_token_id set (important for generation)
					# seems like flan-T5 models don't have bos_token_id properly set?
					if "opt-2.7b" in model_name:
										UpperCAmelCase_      :							Optional[int]       =      OPTConfig.from_pretrained('''facebook/opt-2.7b'''			,						eos_token_id=_UpperCAmelCase				).to_dict()
					elif "opt-6.7b" in model_name:
										UpperCAmelCase_      :							List[str]       =      OPTConfig.from_pretrained('''facebook/opt-6.7b'''			,						eos_token_id=_UpperCAmelCase				).to_dict()
					elif "t5-xl" in model_name:
										UpperCAmelCase_      :							int       =      TaConfig.from_pretrained('''google/flan-t5-xl'''			,						dense_act_fn='''gelu'''			,						bos_token_id=1				).to_dict()
					elif "t5-xxl" in model_name:
										UpperCAmelCase_      :							Optional[Any]       =      TaConfig.from_pretrained('''google/flan-t5-xxl'''			,						dense_act_fn='''gelu'''			,						bos_token_id=1				).to_dict()

					UpperCAmelCase_      :							int       =      BlipaConfig(vision_config=_UpperCAmelCase			,						text_config=_UpperCAmelCase				)

					return config, image_size







@torch.no_grad()
def lowerCamelCase__       (      _lowercase			,						_lowercase=None			,						_lowercase=False				):

					'''simple docstring'''


					UpperCAmelCase_      :							Any       =      (
					    AutoTokenizer.from_pretrained('''facebook/opt-2.7b'''				)
					    if 'opt' in model_name
					    else AutoTokenizer.from_pretrained('''google/flan-t5-xl'''				)
					)
					UpperCAmelCase_      :							Any       =      tokenizer('''\n'''			,						add_special_tokens=_UpperCAmelCase				).input_ids[0]
					UpperCAmelCase_      :							Union[str, Any]       =      get_blipa_config(_UpperCAmelCase			,						eos_token_id=_UpperCAmelCase				)

					UpperCAmelCase_      :							Any       =      BlipaForConditionalGeneration(_UpperCAmelCase				).eval()

					UpperCAmelCase_      :							Any       =      {
					    'blip2-opt-2.7b': ('blip2_opt', 'pretrain_opt2.7b'),
					    'blip2-opt-6.7b': ('blip2_opt', 'pretrain_opt6.7b'),
					    'blip2-opt-2.7b-coco': ('blip2_opt', 'caption_coco_opt2.7b'),
					    'blip2-opt-6.7b-coco': ('blip2_opt', 'caption_coco_opt6.7b'),
					    'blip2-flan-t5-xl': ('blip2_t5', 'pretrain_flant5xl'),
					    'blip2-flan-t5-xl-coco': ('blip2_t5', 'caption_coco_flant5xl'),
					    'blip2-flan-t5-xxl': ('blip2_t5', 'pretrain_flant5xxl'),
					}

					UpperCAmelCase_      :							Optional[int]       =      model_name_to_original[model_name]

					# load original model
					print('''Loading original model...'''				)
					UpperCAmelCase_      :							Dict       =      'cuda' if torch.cuda.is_available() else 'cpu'
					UpperCAmelCase_      :							List[str]       =      load_model_and_preprocess(
					    name=_UpperCAmelCase			,						model_type=_UpperCAmelCase			,						is_eval=_UpperCAmelCase			,						device=_UpperCAmelCase				)
					original_model.eval()
					print('''Done!'''				)

					# update state dict keys
					UpperCAmelCase_      :							int       =      original_model.state_dict()
					UpperCAmelCase_      :							str       =      create_rename_keys(_UpperCAmelCase				)
					for src, dest in rename_keys:
										rename_key(_UpperCAmelCase			,						_UpperCAmelCase			,						_UpperCAmelCase				)

					# some keys can be renamed efficiently
					for key, val in state_dict.copy().items():
										UpperCAmelCase_      :							Dict       =      state_dict.pop(_UpperCAmelCase				)
										if key.startswith('''Qformer.bert'''				):
															UpperCAmelCase_      :							List[Any]       =      key.replace('''Qformer.bert'''			,						'''qformer'''				)
										if "attention.self" in key:
															UpperCAmelCase_      :							List[Any]       =      key.replace('''self'''			,						'''attention'''				)
										if "opt_proj" in key:
															UpperCAmelCase_      :							Any       =      key.replace('''opt_proj'''			,						'''language_projection'''				)
										if "t5_proj" in key:
															UpperCAmelCase_      :							List[Any]       =      key.replace('''t5_proj'''			,						'''language_projection'''				)
										if key.startswith('''opt'''				):
															UpperCAmelCase_      :							Optional[Any]       =      key.replace('''opt'''			,						'''language'''				)
										if key.startswith('''t5'''				):
															UpperCAmelCase_      :							Optional[Any]       =      key.replace('''t5'''			,						'''language'''				)
										UpperCAmelCase_      :							Tuple       =      val

					# read in qv biases
					read_in_q_v_bias(_UpperCAmelCase			,						_UpperCAmelCase				)

					UpperCAmelCase_      :							str       =      hf_model.load_state_dict(_UpperCAmelCase			,						strict=_UpperCAmelCase				)
					assert len(_UpperCAmelCase				) == 0
					assert unexpected_keys == ["qformer.embeddings.position_ids"]

					UpperCAmelCase_      :							List[Any]       =      load_demo_image()
					UpperCAmelCase_      :							Optional[Any]       =      vis_processors['eval'](_UpperCAmelCase				).unsqueeze(0				).to(_UpperCAmelCase				)
					UpperCAmelCase_      :							str       =      tokenizer(['''\n''']			,						return_tensors='''pt'''				).input_ids.to(_UpperCAmelCase				)

					# create processor
					UpperCAmelCase_      :							List[Any]       =      BlipImageProcessor(
					    size={'''height''': image_size, '''width''': image_size}			,						image_mean=_UpperCAmelCase			,						image_std=_UpperCAmelCase				)
					UpperCAmelCase_      :							Union[str, Any]       =      BlipaProcessor(image_processor=_UpperCAmelCase			,						tokenizer=_UpperCAmelCase				)
					UpperCAmelCase_      :							Tuple       =      processor(images=_UpperCAmelCase			,						return_tensors='''pt'''				).pixel_values.to(_UpperCAmelCase				)

					# make sure processor creates exact same pixel values
					assert torch.allclose(_UpperCAmelCase			,						_UpperCAmelCase				)

					original_model.to(_UpperCAmelCase				)
					hf_model.to(_UpperCAmelCase				)
					with torch.no_grad():
										if "opt" in model_name:
															UpperCAmelCase_      :							Any       =      original_model({'''image''': original_pixel_values, '''text_input''': ['''''']}				).logits
															UpperCAmelCase_      :							str       =      hf_model(_UpperCAmelCase			,						_UpperCAmelCase				).logits
										else:
															UpperCAmelCase_      :							Tuple       =      original_model(
															    {'''image''': original_pixel_values, '''text_input''': ['''\n'''], '''text_output''': ['''\n''']}				).logits
															UpperCAmelCase_      :							Dict       =      input_ids.masked_fill(input_ids == tokenizer.pad_token_id			,						-100				)
															UpperCAmelCase_      :							Tuple       =      hf_model(_UpperCAmelCase			,						_UpperCAmelCase			,						labels=_UpperCAmelCase				).logits

					assert original_logits.shape == logits.shape
					print('''First values of original logits:'''			,						original_logits[0, :3, :3]				)
					print('''First values of HF logits:'''			,						logits[0, :3, :3]				)

					# assert values
					if model_name == "blip2-flan-t5-xl":
										UpperCAmelCase_      :							str       =      torch.tensor(
										    [[-41.5850, -4.4440, -8.9922], [-47.4322, -5.9143, -1.7340]]			,						device=_UpperCAmelCase				)
										assert torch.allclose(logits[0, :3, :3]			,						_UpperCAmelCase			,						atol=1E-4				)
					elif model_name == "blip2-flan-t5-xl-coco":
										UpperCAmelCase_      :							Any       =      torch.tensor(
										    [[-57.0109, -9.8967, -12.6280], [-68.6578, -12.7191, -10.5065]]			,						device=_UpperCAmelCase				)
					else:
										# cast to same type
										UpperCAmelCase_      :							Dict       =      logits.dtype
										assert torch.allclose(original_logits.to(_UpperCAmelCase				)			,						_UpperCAmelCase			,						atol=1E-2				)
					print('''Looks ok!'''				)

					print('''Generating a caption...'''				)
					UpperCAmelCase_      :							str       =      ''
					UpperCAmelCase_      :							List[str]       =      tokenizer(_UpperCAmelCase			,						return_tensors='''pt'''				).input_ids.to(_UpperCAmelCase				)

					UpperCAmelCase_      :							Any       =      original_model.generate({'''image''': original_pixel_values}				)
					UpperCAmelCase_      :							Union[str, Any]       =      hf_model.generate(
					    _UpperCAmelCase			,						_UpperCAmelCase			,						do_sample=_UpperCAmelCase			,						num_beams=5			,						max_length=30			,						min_length=1			,						top_p=0.9			,						repetition_penalty=1.0			,						length_penalty=1.0			,						temperature=1			,						)
					print('''Original generation:'''			,						_UpperCAmelCase				)
					UpperCAmelCase_      :							str       =      input_ids.shape[1]
					UpperCAmelCase_      :							Dict       =      processor.batch_decode(outputs[:, prompt_length:]			,						skip_special_tokens=_UpperCAmelCase				)
					UpperCAmelCase_      :							Optional[int]       =      [text.strip() for text in output_text]
					print('''HF generation:'''			,						_UpperCAmelCase				)

					if pytorch_dump_folder_path is not None:
										processor.save_pretrained(_UpperCAmelCase				)
										hf_model.save_pretrained(_UpperCAmelCase				)

					if push_to_hub:
										processor.push_to_hub(f'''nielsr/{model_name}'''				)
										hf_model.push_to_hub(f'''nielsr/{model_name}'''				)


if __name__ == "__main__":
					__a          =			argparse.ArgumentParser()
					__a          =			[
					    'blip2-opt-2.7b',
					    'blip2-opt-6.7b',
					    'blip2-opt-2.7b-coco',
					    'blip2-opt-6.7b-coco',
					    'blip2-flan-t5-xl',
					    'blip2-flan-t5-xl-coco',
					    'blip2-flan-t5-xxl',
					]
					parser.add_argument(
					    '--model_name',
					    default='blip2-opt-2.7b',
					    choices=choices,
					    type=str,
					    help='Path to hf config.json of model to convert',
					)
					parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
					parser.add_argument(
					    '--push_to_hub',
					    action='store_true',
					    help='Whether to push the model and processor to the hub after converting',
					)

					__a          =			parser.parse_args()

					convert_blipa_checkpoint(args.model_name, 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.models.esm.modeling_esmfold import EsmForProteinFolding

class   __a:






    """simple docstring"""





    def __init__(				self       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE=13       ,_SCREAMING_SNAKE_CASE=7       ,_SCREAMING_SNAKE_CASE=False       ,_SCREAMING_SNAKE_CASE=True       ,_SCREAMING_SNAKE_CASE=False       ,_SCREAMING_SNAKE_CASE=False       ,_SCREAMING_SNAKE_CASE=19       ,_SCREAMING_SNAKE_CASE=32       ,_SCREAMING_SNAKE_CASE=5       ,_SCREAMING_SNAKE_CASE=4       ,_SCREAMING_SNAKE_CASE=37       ,_SCREAMING_SNAKE_CASE="gelu"       ,_SCREAMING_SNAKE_CASE=0.1       ,_SCREAMING_SNAKE_CASE=0.1       ,_SCREAMING_SNAKE_CASE=512       ,_SCREAMING_SNAKE_CASE=16       ,_SCREAMING_SNAKE_CASE=2       ,_SCREAMING_SNAKE_CASE=0.02       ,_SCREAMING_SNAKE_CASE=3       ,_SCREAMING_SNAKE_CASE=4       ,_SCREAMING_SNAKE_CASE=None       ,)			->		Dict:
         UpperCAmelCase_      :							Optional[Any]       =      parent
         UpperCAmelCase_      :							Dict       =      batch_size
         UpperCAmelCase_      :							Optional[int]       =      seq_length
         UpperCAmelCase_      :							Union[str, Any]       =      is_training
         UpperCAmelCase_      :							Any       =      use_input_mask
         UpperCAmelCase_      :							Tuple       =      use_token_type_ids
         UpperCAmelCase_      :							Optional[int]       =      use_labels
         UpperCAmelCase_      :							List[Any]       =      vocab_size
         UpperCAmelCase_      :							List[Any]       =      hidden_size
         UpperCAmelCase_      :							str       =      num_hidden_layers
         UpperCAmelCase_      :							Dict       =      num_attention_heads
         UpperCAmelCase_      :							Optional[Any]       =      intermediate_size
         UpperCAmelCase_      :							str       =      hidden_act
         UpperCAmelCase_      :							int       =      hidden_dropout_prob
         UpperCAmelCase_      :							List[Any]       =      attention_probs_dropout_prob
         UpperCAmelCase_      :							Optional[int]       =      max_position_embeddings
         UpperCAmelCase_      :							Optional[int]       =      type_vocab_size
         UpperCAmelCase_      :							Any       =      type_sequence_label_size
         UpperCAmelCase_      :							int       =      initializer_range
         UpperCAmelCase_      :							Any       =      num_labels
         UpperCAmelCase_      :							Optional[Any]       =      num_choices
         UpperCAmelCase_      :							List[str]       =      scope





    def 							a__				(				self  )			->		Optional[Any]:
         UpperCAmelCase_      :							List[Any]       =      ids_tensor([self.batch_size, self.seq_length]       ,self.vocab_size  )

         UpperCAmelCase_      :							str       =      None
         if self.use_input_mask:
              UpperCAmelCase_      :							Optional[int]       =      random_attention_mask([self.batch_size, self.seq_length]  )

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

         UpperCAmelCase_      :							Optional[Any]       =      self.get_config()

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





    def 							a__				(				self  )			->		List[str]:
         UpperCAmelCase_      :							Union[str, Any]       =      EsmConfig(
             vocab_size=33       ,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       ,is_folding_model=_SCREAMING_SNAKE_CASE       ,esmfold_config={'''trunk''': {'''num_blocks''': 2}, '''fp16_esm''': False}       ,)
         return config





    def 							a__				(				self       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE  )			->		int:
         UpperCAmelCase_      :							Optional[int]       =      EsmForProteinFolding(config=_SCREAMING_SNAKE_CASE  ).float()
         model.to(_SCREAMING_SNAKE_CASE  )
         model.eval()
         UpperCAmelCase_      :							Union[str, Any]       =      model(_SCREAMING_SNAKE_CASE       ,attention_mask=_SCREAMING_SNAKE_CASE  )
         UpperCAmelCase_      :							List[str]       =      model(_SCREAMING_SNAKE_CASE  )
         UpperCAmelCase_      :							Any       =      model(_SCREAMING_SNAKE_CASE  )

         self.parent.assertEqual(result.positions.shape       ,(8, self.batch_size, self.seq_length, 14, 3)  )
         self.parent.assertEqual(result.angles.shape       ,(8, self.batch_size, self.seq_length, 7, 2)  )





    def 							a__				(				self  )			->		Optional[Any]:
         UpperCAmelCase_      :							int       =      self.prepare_config_and_inputs()
         (
             (
             UpperCAmelCase_
         ),   (
             UpperCAmelCase_
         ),   (
             UpperCAmelCase_
         ),   (
             UpperCAmelCase_
         ),   (
             UpperCAmelCase_
         ),   (
             UpperCAmelCase_
         ),   
         )      :							Optional[Any]       =      config_and_inputs
         UpperCAmelCase_      :							int       =      {'''input_ids''': input_ids, '''attention_mask''': input_mask}
         return config, inputs_dict


@require_torch
class   __a(					_a    ,						_a    ,						unittest.TestCase       ):






    """simple docstring"""





    lowerCAmelCase        =				False

    lowerCAmelCase        =				(EsmForProteinFolding,) if is_torch_available() else ()
    lowerCAmelCase        =				()
    lowerCAmelCase        =				{} if is_torch_available() else {}
    lowerCAmelCase        =				False





    def 							a__				(				self  )			->		List[str]:
         UpperCAmelCase_      :							Optional[int]       =      EsmFoldModelTester(self  )
         UpperCAmelCase_      :							int       =      ConfigTester(self       ,config_class=_SCREAMING_SNAKE_CASE       ,hidden_size=37  )





    def 							a__				(				self  )			->		Tuple:
         self.config_tester.run_common_tests()





    def 							a__				(				self  )			->		Dict:
         UpperCAmelCase_      :							Optional[Any]       =      self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*_SCREAMING_SNAKE_CASE  )





    @unittest.skip('''Does not support attention outputs'''  )
    def 							a__				(				self  )			->		Optional[int]:
         pass





    @unittest.skip
    def 							a__				(				self  )			->		Dict:
         pass





    @unittest.skip('''Esm does not support embedding resizing'''  )
    def 							a__				(				self  )			->		Union[str, Any]:
         pass





    @unittest.skip('''Esm does not support embedding resizing'''  )
    def 							a__				(				self  )			->		List[Any]:
         pass





    @unittest.skip('''ESMFold does not support passing input embeds!'''  )
    def 							a__				(				self  )			->		Any:
         pass





    @unittest.skip('''ESMFold does not support head pruning.'''  )
    def 							a__				(				self  )			->		Optional[Any]:
         pass





    @unittest.skip('''ESMFold does not support head pruning.'''  )
    def 							a__				(				self  )			->		Optional[Any]:
         pass





    @unittest.skip('''ESMFold does not support head pruning.'''  )
    def 							a__				(				self  )			->		Optional[int]:
         pass





    @unittest.skip('''ESMFold does not support head pruning.'''  )
    def 							a__				(				self  )			->		Optional[int]:
         pass





    @unittest.skip('''ESMFold does not support head pruning.'''  )
    def 							a__				(				self  )			->		Dict:
         pass





    @unittest.skip('''ESMFold does not output hidden states in the normal way.'''  )
    def 							a__				(				self  )			->		str:
         pass





    @unittest.skip('''ESMfold does not output hidden states in the normal way.'''  )
    def 							a__				(				self  )			->		Optional[Any]:
         pass





    @unittest.skip('''ESMFold only has one output format.'''  )
    def 							a__				(				self  )			->		Optional[int]:
         pass





    @unittest.skip('''This test doesn\'t work for ESMFold and doesn\'t test core functionality'''  )
    def 							a__				(				self  )			->		int:
         pass





    @unittest.skip('''ESMFold does not support input chunking.'''  )
    def 							a__				(				self  )			->		List[Any]:
         pass





    @unittest.skip('''ESMFold doesn\'t respect you and it certainly doesn\'t respect your initialization arguments.'''  )
    def 							a__				(				self  )			->		Tuple:
         pass





    @unittest.skip('''ESMFold doesn\'t support torchscript compilation.'''  )
    def 							a__				(				self  )			->		Optional[int]:
         pass





    @unittest.skip('''ESMFold doesn\'t support torchscript compilation.'''  )
    def 							a__				(				self  )			->		List[str]:
         pass





    @unittest.skip('''ESMFold doesn\'t support torchscript compilation.'''  )
    def 							a__				(				self  )			->		Tuple:
         pass





    @unittest.skip('''ESMFold doesn\'t support data parallel.'''  )
    def 							a__				(				self  )			->		Dict:
         pass





    @unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.'''  )
    def 							a__				(				self  )			->		List[Any]:
         pass


@require_torch
class   __a(					_a       ):






    """simple docstring"""





    @slow
    def 							a__				(				self  )			->		List[Any]:
         UpperCAmelCase_      :							List[str]       =      EsmForProteinFolding.from_pretrained('''facebook/esmfold_v1'''  ).float()
         model.eval()
         UpperCAmelCase_      :							str       =      torch.tensor([[0, 6, 4, 13, 5, 4, 16, 12, 11, 7, 2]]  )
         UpperCAmelCase_      :							Optional[int]       =      model(_SCREAMING_SNAKE_CASE  )['''positions''']
         UpperCAmelCase_      :							List[str]       =      torch.tensor([2.58_28, 0.79_93, -10.93_34]       ,dtype=torch.floataa  )
         self.assertTrue(torch.allclose(position_outputs[0, 0, 0, 0]       ,_SCREAMING_SNAKE_CASE       ,atol=1e-4  )  )