import copy
import inspect
import unittest

import numpy as np
from huggingface_hub import hf_hub_download

from transformers import TimesformerConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

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


if is_torch_available():
      import torch
      from torch import nn

      from transformers import (
          MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING,
          TimesformerForVideoClassification,
          TimesformerModel,
      )
      from transformers.models.timesformer.modeling_timesformer import TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
      from transformers import VideoMAEImageProcessor



class   snake_case_       :






   def __init__(      self				:     Optional[int]	,						lowercase_				:     str	,						lowercase_				:     Dict=13	,						lowercase_				:     Optional[Any]=10	,						lowercase_				:     int=3	,						lowercase_				:     str=2	,						lowercase_				:     Tuple=2	,						lowercase_				:     int=True	,						lowercase_				:     int=True	,						lowercase_				:     Optional[int]=32	,						lowercase_				:     List[Any]=5	,						lowercase_				:     Optional[Any]=4	,						lowercase_				:     List[Any]=37	,						lowercase_				:     Tuple="gelu"	,						lowercase_				:     int=0.1	,						lowercase_				:     List[str]=0.1	,						lowercase_				:     str=10	,						lowercase_				:     Optional[int]=0.02	,						lowercase_				:     List[str]="divided_space_time"	,						lowercase_				:     Union[str, Any]=None	,						)					->						List[str]:
       lowercase__ :   int    					=				parent
       lowercase__ :   str    					=				batch_size
       lowercase__ :   Optional[int]    					=				image_size
       lowercase__ :   Tuple    					=				num_channels
       lowercase__ :   List[str]    					=				patch_size
       lowercase__ :   Optional[Any]    					=				num_frames
       lowercase__ :   Any    					=				is_training
       lowercase__ :   Union[str, Any]    					=				use_labels
       lowercase__ :   List[str]    					=				hidden_size
       lowercase__ :   Union[str, Any]    					=				num_hidden_layers
       lowercase__ :   List[str]    					=				num_attention_heads
       lowercase__ :   int    					=				intermediate_size
       lowercase__ :   Dict    					=				hidden_act
       lowercase__ :   Optional[int]    					=				hidden_dropout_prob
       lowercase__ :   List[str]    					=				attention_probs_dropout_prob
       lowercase__ :   int    					=				attention_type
       lowercase__ :   Optional[int]    					=				initializer_range
       lowercase__ :   Optional[int]    					=				scope
       lowercase__ :   Optional[Any]    					=				num_labels

       # in TimeSformer, the number of spatial tokens equals num_frames * num_patches per frame + 1 CLS token
       lowercase__ :   Optional[int]    					=				(image_size // patch_size) ** 2
       lowercase__ :   Tuple    					=				(num_frames) * self.num_patches_per_frame + 1






   def       __UpperCamelCase     (      self				:     List[Any]  )					->						Union[str, Any]:
       lowercase__ :   List[str]    					=				floats_tensor(
           [self.batch_size, self.num_frames, self.num_channels, self.image_size, self.image_size]  )

       lowercase__ :   int    					=				None
       if self.use_labels:
           lowercase__ :   Any    					=				ids_tensor([self.batch_size]	,						self.num_labels  )

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

       return config, pixel_values, labels






   def       __UpperCamelCase     (      self				:     Union[str, Any]  )					->						Optional[Any]:
       lowercase__ :   Dict    					=				TimesformerConfig(
           image_size=self.image_size	,						patch_size=self.patch_size	,						num_channels=self.num_channels	,						num_frames=self.num_frames	,						hidden_size=self.hidden_size	,						num_hidden_layers=self.num_hidden_layers	,						num_attention_heads=self.num_attention_heads	,						intermediate_size=self.intermediate_size	,						hidden_act=self.hidden_act	,						hidden_dropout_prob=self.hidden_dropout_prob	,						attention_probs_dropout_prob=self.attention_probs_dropout_prob	,						initializer_range=self.initializer_range	,						attention_type=self.attention_type	,						)
       lowercase__ :   int    					=				self.num_labels
       return config






   def       __UpperCamelCase     (      self				:     Tuple	,						lowercase_				:     Dict	,						lowercase_				:     Dict	,						lowercase_				:     List[str]  )					->						Optional[int]:
       lowercase__ :   Optional[Any]    					=				TimesformerModel(config=lowercase_  )
       model.to(lowercase_  )
       model.eval()
       lowercase__ :   Dict    					=				model(lowercase_  )
       self.parent.assertEqual(result.last_hidden_state.shape	,						(self.batch_size, self.seq_length, self.hidden_size)  )






   def       __UpperCamelCase     (      self				:     Tuple	,						lowercase_				:     Union[str, Any]	,						lowercase_				:     Union[str, Any]	,						lowercase_				:     Union[str, Any]  )					->						Tuple:
       lowercase__ :   Tuple    					=				TimesformerForVideoClassification(lowercase_  )
       model.to(lowercase_  )
       model.eval()

       lowercase__ :   Tuple    					=				model(lowercase_  )

       # verify the logits shape
       lowercase__ :   Optional[int]    					=				torch.Size((self.batch_size, self.num_labels)  )
       self.parent.assertEqual(result.logits.shape	,						lowercase_  )







   def       __UpperCamelCase     (      self				:     Optional[int]  )					->						Optional[int]:
       lowercase__ :   str    					=				self.prepare_config_and_inputs()
       lowercase__					,   lowercase__					,   lowercase__ :   List[str]    					=				config_and_inputs
       lowercase__ :   Dict    					=				{"pixel_values": pixel_values}
       return config, inputs_dict



@require_torch
class   snake_case_       (		__A  ,__A  ,unittest.TestCase       ):
   __A	:		int         =				(TimesformerModel, TimesformerForVideoClassification) if is_torch_available() else ()
   __A	:		Dict         =				(
       {"feature-extraction": TimesformerModel, "video-classification": TimesformerForVideoClassification}
       if is_torch_available()
       else {}
   )

   __A	:		str         =				False
   __A	:		Optional[Any]         =				False
   __A	:		Tuple         =				False
   __A	:		Union[str, Any]         =				False






   def       __UpperCamelCase     (      self				:     Union[str, Any]  )					->						Dict:
       lowercase__ :   Optional[Any]    					=				TimesformerModelTester(self  )
       lowercase__ :   Tuple    					=				ConfigTester(
           self	,						config_class=lowercase_	,						has_text_modality=lowercase_	,						hidden_size=37  )






   def       __UpperCamelCase     (      self				:     Optional[Any]	,						lowercase_				:     Tuple	,						lowercase_				:     Tuple	,						lowercase_				:     Any=False  )					->						Union[str, Any]:
       lowercase__ :   Dict    					=				copy.deepcopy(lowercase_  )

       if return_labels:
           if model_class in get_values(lowercase_  ):
               lowercase__ :   List[str]    					=				torch.zeros(
                   self.model_tester.batch_size	,						dtype=torch.long	,						device=lowercase_  )

       return inputs_dict






   def       __UpperCamelCase     (      self				:     str  )					->						List[Any]:
       self.config_tester.run_common_tests()






   @unittest.skip(reason="TimeSformer does not use inputs_embeds"  )
   def       __UpperCamelCase     (      self				:     str  )					->						Optional[int]:
       pass






   def       __UpperCamelCase     (      self				:     Union[str, Any]  )					->						List[str]:
       lowercase__					,   lowercase__ :   List[Any]    					=				self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
           lowercase__ :   Dict    					=				model_class(lowercase_  )
           self.assertIsInstance(model.get_input_embeddings()	,						(nn.Module)  )
           lowercase__ :   Dict    					=				model.get_output_embeddings()
           self.assertTrue(x is None or isinstance(lowercase_	,						nn.Linear  )  )






   def       __UpperCamelCase     (      self				:     Optional[int]  )					->						List[str]:
       lowercase__					,   lowercase__ :   List[Any]    					=				self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
           lowercase__ :   Tuple    					=				model_class(lowercase_  )
           lowercase__ :   Optional[Any]    					=				inspect.signature(model.forward  )
           # signature.parameters is an OrderedDict => so arg_names order is deterministic
           lowercase__ :   Tuple    					=				[*signature.parameters.keys()]

           lowercase__ :   Any    					=				["pixel_values"]
           self.assertListEqual(arg_names[:1]	,						lowercase_  )






   def       __UpperCamelCase     (      self				:     Union[str, Any]  )					->						Union[str, Any]:
       lowercase__ :   Optional[int]    					=				self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*lowercase_  )






   def       __UpperCamelCase     (      self				:     List[Any]  )					->						Dict:
       lowercase__ :   List[str]    					=				self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_video_classification(*lowercase_  )






   @slow
   def       __UpperCamelCase     (      self				:     Tuple  )					->						Tuple:
       for model_name in TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
           lowercase__ :   Any    					=				TimesformerModel.from_pretrained(lowercase_  )
           self.assertIsNotNone(lowercase_  )






   def       __UpperCamelCase     (      self				:     List[str]  )					->						Tuple:
       if not self.has_attentions:
           pass

       else:
           lowercase__					,   lowercase__ :   int    					=				self.model_tester.prepare_config_and_inputs_for_common()
           lowercase__ :   str    					=				True

           for model_class in self.all_model_classes:
               lowercase__ :   Tuple    					=				self.model_tester.seq_length
               lowercase__ :   Any    					=				self.model_tester.num_frames

               lowercase__ :   Optional[int]    					=				True
               lowercase__ :   List[Any]    					=				False
               lowercase__ :   Tuple    					=				True
               lowercase__ :   Union[str, Any]    					=				model_class(lowercase_  )
               model.to(lowercase_  )
               model.eval()
               with torch.no_grad():
                   lowercase__ :   str    					=				model(**self._prepare_for_class(lowercase_	,						lowercase_  )  )
               lowercase__ :   int    					=				outputs.attentions
               self.assertEqual(len(lowercase_  )	,						self.model_tester.num_hidden_layers  )

               # check that output_attentions also work using config
               del inputs_dict["output_attentions"]
               lowercase__ :   Dict    					=				True
               lowercase__ :   Union[str, Any]    					=				model_class(lowercase_  )
               model.to(lowercase_  )
               model.eval()
               with torch.no_grad():
                   lowercase__ :   Optional[int]    					=				model(**self._prepare_for_class(lowercase_	,						lowercase_  )  )
               lowercase__ :   Optional[Any]    					=				outputs.attentions
               self.assertEqual(len(lowercase_  )	,						self.model_tester.num_hidden_layers  )

               # attentions has shape (batch_size x num_frames) x num_heads x (num_patches per frame + 1) x (num_patches per frame + 1)
               self.assertListEqual(
                   list(attentions[0].shape[-3:]  )	,						[self.model_tester.num_attention_heads, seq_len // num_frames + 1, seq_len // num_frames + 1]	,						)
               lowercase__ :   Optional[Any]    					=				len(lowercase_  )

               # Check attention is always last and order is fine
               lowercase__ :   Any    					=				True
               lowercase__ :   List[str]    					=				True
               lowercase__ :   Union[str, Any]    					=				model_class(lowercase_  )
               model.to(lowercase_  )
               model.eval()
               with torch.no_grad():
                   lowercase__ :   int    					=				model(**self._prepare_for_class(lowercase_	,						lowercase_  )  )

               self.assertEqual(out_len + 1	,						len(lowercase_  )  )

               lowercase__ :   Tuple    					=				outputs.attentions

               self.assertEqual(len(lowercase_  )	,						self.model_tester.num_hidden_layers  )

               # attentions has shape (batch_size x num_frames) x num_heads x (num_patches per frame + 1) x (num_patches per frame + 1)
               self.assertListEqual(
                   list(self_attentions[0].shape[-3:]  )	,						[self.model_tester.num_attention_heads, seq_len // num_frames + 1, seq_len // num_frames + 1]	,						)







   def       __UpperCamelCase     (      self				:     Union[str, Any]  )					->						Tuple:
       def check_hidden_states_output(lowercase_				:     str	,						lowercase_				:     Any	,						lowercase_				:     str  ):
           lowercase__ :   str    					=				model_class(lowercase_  )
           model.to(lowercase_  )
           model.eval()

           with torch.no_grad():
               lowercase__ :   Dict    					=				model(**self._prepare_for_class(lowercase_	,						lowercase_  )  )

           lowercase__ :   Any    					=				outputs.hidden_states
           lowercase__ :   Any    					=				self.model_tester.num_hidden_layers + 1
           self.assertEqual(len(lowercase_  )	,						lowercase_  )

           lowercase__ :   List[Any]    					=				self.model_tester.seq_length

           self.assertListEqual(
               list(hidden_states[0].shape[-2:]  )	,						[seq_length, self.model_tester.hidden_size]	,						)

       lowercase__					,   lowercase__ :   int    					=				self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
           lowercase__ :   str    					=				True
           check_hidden_states_output(lowercase_	,						lowercase_	,						lowercase_  )

           # check that output_hidden_states also work using config
           del inputs_dict["output_hidden_states"]
           lowercase__ :   Tuple    					=				True

           check_hidden_states_output(lowercase_	,						lowercase_	,						lowercase_  )



def 			lowercase_				(			):
    lowercase__ :   Optional[Any]    					=				hf_hub_download(
        repo_id="hf-internal-testing/spaghetti-video"		,	filename="eating_spaghetti.npy"		,	repo_type="dataset")
    lowercase__ :   Optional[Any]    					=				np.load(_lowerCamelCase)
    return list(_lowerCamelCase)



@require_torch
@require_vision
class   snake_case_       (		unittest.TestCase       ):






   @cached_property
   def       __UpperCamelCase     (      self				:     Optional[int]  )					->						str:
       # logits were tested with a different mean and std, so we use the same here
       return (
           VideoMAEImageProcessor(image_mean=[0.5, 0.5, 0.5]	,						image_std=[0.5, 0.5, 0.5]  )
           if is_vision_available()
           else None
       )







   @slow
   def       __UpperCamelCase     (      self				:     List[str]  )					->						Union[str, Any]:
       lowercase__ :   Optional[int]    					=				TimesformerForVideoClassification.from_pretrained("facebook/timesformer-base-finetuned-k400"  ).to(
           lowercase_  )

       lowercase__ :   str    					=				self.default_image_processor
       lowercase__ :   str    					=				prepare_video()
       lowercase__ :   Any    					=				image_processor(video[:8]	,						return_tensors="pt"  ).to(lowercase_  )

       # forward pass
       with torch.no_grad():
           lowercase__ :   Optional[int]    					=				model(**lowercase_  )

       # verify the logits
       lowercase__ :   Optional[Any]    					=				torch.Size((1, 4_00)  )
       self.assertEqual(outputs.logits.shape	,						lowercase_  )

       lowercase__ :   List[str]    					=				torch.tensor([-0.30_16, -0.77_13, -0.42_05]  ).to(lowercase_  )

       self.assertTrue(torch.allclose(outputs.logits[0, :3]	,						lowercase_	,						atol=1E-4  )  )










"""simple docstring"""




from datetime import datetime

import requests






def       _SCREAMING_SNAKE_CASE		(	_lowercase						:    str					) ->bytes:






	'''simple docstring'''






	a      :		Dict	=				"https://downloadgram.net/wp-json/wppress/video-downloader/video?url="
	a      :		Dict	=				requests.get(base_url + url					).json()[0]["urls"][0]["src"]
	return requests.get(_lowercase					).content


if __name__ == "__main__":
		a						: str     	=			input('''Enter Video/IGTV url: ''').strip()
		a						: Any     	=			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}.''')


