

from datetime import datetime

import requests

def 	snake_case__			(    SCREAMING_SNAKE_CASE_		:    str			):



     '''simple docstring'''





     lowercase__    :   List[Any]      =       'https://downloadgram.net/wp-json/wppress/video-downloader/video?url='
     lowercase__    :   Union[str, Any]      =       requests.get(base_url + url			).json()[0]['urls'][0]['src']
     return requests.get(SCREAMING_SNAKE_CASE_			).content


if __name__ == "__main__":
       snake_case_			  = input('''Enter Video/IGTV url: ''').strip()
       snake_case_			  = F'''{datetime.now():%Y-%m-%d_%H:%M:%S}.mp4'''
       with open(file_name, '''wb''') as fp:
              fp.write(download_video(url))
       print(F'''Done. Video saved to disk as {file_name}.''')




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       SCREAMING_SNAKE_CASE__					:





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





    def       snake_case_				(      self):
         lowercase__    :   List[Any]      =       ids_tensor([self.batch_size, self.seq_length]						,   self.vocab_size)

         lowercase__    :   List[Any]      =       None
         if self.use_input_mask:
              lowercase__    :   Union[str, Any]      =       random_attention_mask([self.batch_size, self.seq_length])

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

         lowercase__    :   int      =       self.get_config()

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





    def       snake_case_				(      self):
         lowercase__    :   str      =       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=a						,   esmfold_config={'trunk': {'num_blocks': 2}, 'fp16_esm': False}						,   )
         return config





    def       snake_case_				(      self						,   a						,   a						,   a						,   a						,   a						,   a):
         lowercase__    :   Dict      =       EsmForProteinFolding(config=a).float()
         model.to(a)
         model.eval()
         lowercase__    :   Union[str, Any]      =       model(a						,   attention_mask=a)
         lowercase__    :   Dict      =       model(a)
         lowercase__    :   int      =       model(a)

         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       snake_case_				(      self):
         lowercase__    :   List[str]      =       self.prepare_config_and_inputs()
         (
             (
             lowercase__
         )      , (
             lowercase__
         )      , (
             lowercase__
         )      , (
             lowercase__
         )      , (
             lowercase__
         )      , (
             lowercase__
         )      , 
         )    :   int      =       config_and_inputs
         lowercase__    :   Optional[int]      =       {'input_ids': input_ids, 'attention_mask': input_mask}
         return config, inputs_dict



@require_torch
class       SCREAMING_SNAKE_CASE__					(__snake_case		,       __snake_case		,       unittest.TestCase     ):
    __lowerCamelCase						:       Dict														=     False

    __lowerCamelCase						:       Dict														=     (EsmForProteinFolding,) if is_torch_available() else ()
    __lowerCamelCase						:       Union[str, Any]														=     ()
    __lowerCamelCase						:       List[Any]														=     {} if is_torch_available() else {}
    __lowerCamelCase						:       Optional[Any]														=     False





    def       snake_case_				(      self):
         lowercase__    :   Tuple      =       EsmFoldModelTester(self)
         lowercase__    :   List[Any]      =       ConfigTester(self						,   config_class=a						,   hidden_size=37)





    def       snake_case_				(      self):
         self.config_tester.run_common_tests()





    def       snake_case_				(      self):
         lowercase__    :   Optional[int]      =       self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*a)





    @unittest.skip('Does not support attention outputs')
    def       snake_case_				(      self):
         pass





    @unittest.skip
    def       snake_case_				(      self):
         pass





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





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





    @unittest.skip('ESMFold does not support passing input embeds!')
    def       snake_case_				(      self):
         pass





    @unittest.skip('ESMFold does not support head pruning.')
    def       snake_case_				(      self):
         pass





    @unittest.skip('ESMFold does not support head pruning.')
    def       snake_case_				(      self):
         pass





    @unittest.skip('ESMFold does not support head pruning.')
    def       snake_case_				(      self):
         pass





    @unittest.skip('ESMFold does not support head pruning.')
    def       snake_case_				(      self):
         pass





    @unittest.skip('ESMFold does not support head pruning.')
    def       snake_case_				(      self):
         pass





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





    @unittest.skip('ESMfold does not output hidden states in the normal way.')
    def       snake_case_				(      self):
         pass





    @unittest.skip('ESMFold only has one output format.')
    def       snake_case_				(      self):
         pass





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





    @unittest.skip('ESMFold does not support input chunking.')
    def       snake_case_				(      self):
         pass





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





    @unittest.skip('ESMFold doesn\'t support torchscript compilation.')
    def       snake_case_				(      self):
         pass





    @unittest.skip('ESMFold doesn\'t support torchscript compilation.')
    def       snake_case_				(      self):
         pass





    @unittest.skip('ESMFold doesn\'t support torchscript compilation.')
    def       snake_case_				(      self):
         pass





    @unittest.skip('ESMFold doesn\'t support data parallel.')
    def       snake_case_				(      self):
         pass





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



@require_torch
class       SCREAMING_SNAKE_CASE__					(__snake_case     ):





    @slow
    def       snake_case_				(      self):
         lowercase__    :   Dict      =       EsmForProteinFolding.from_pretrained('facebook/esmfold_v1').float()
         model.eval()
         lowercase__    :   Optional[Any]      =       torch.tensor([[0, 6, 4, 13, 5, 4, 16, 12, 11, 7, 2]])
         lowercase__    :   Optional[int]      =       model(a)['positions']
         lowercase__    :   Dict      =       torch.tensor([2.5_828, 0.7_993, -10.9_334]						,   dtype=torch.floataa)
         self.assertTrue(torch.allclose(position_outputs[0, 0, 0, 0]						,   a						,   atol=1e-4))

