






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 				_UpperCamelCase      :


      def __init__(						self       ,    __UpperCamelCase       ,    __UpperCamelCase=1_3       ,    __UpperCamelCase=1_0       ,    __UpperCamelCase=3       ,    __UpperCamelCase=2       ,    __UpperCamelCase=2       ,    __UpperCamelCase=True       ,    __UpperCamelCase=True       ,    __UpperCamelCase=3_2       ,    __UpperCamelCase=5       ,    __UpperCamelCase=4       ,    __UpperCamelCase=3_7       ,    __UpperCamelCase="gelu"       ,    __UpperCamelCase=0.1       ,    __UpperCamelCase=0.1       ,    __UpperCamelCase=1_0       ,    __UpperCamelCase=0.0_2       ,    __UpperCamelCase="divided_space_time"       ,    __UpperCamelCase=None       ,    )->						Optional[Any]:
           __lowerCAmelCase             =       parent
           __lowerCAmelCase             =       batch_size
           __lowerCAmelCase             =       image_size
           __lowerCAmelCase             =       num_channels
           __lowerCAmelCase             =       patch_size
           __lowerCAmelCase             =       num_frames
           __lowerCAmelCase             =       is_training
           __lowerCAmelCase             =       use_labels
           __lowerCAmelCase             =       hidden_size
           __lowerCAmelCase             =       num_hidden_layers
           __lowerCAmelCase             =       num_attention_heads
           __lowerCAmelCase             =       intermediate_size
           __lowerCAmelCase             =       hidden_act
           __lowerCAmelCase             =       hidden_dropout_prob
           __lowerCAmelCase             =       attention_probs_dropout_prob
           __lowerCAmelCase             =       attention_type
           __lowerCAmelCase             =       initializer_range
           __lowerCAmelCase             =       scope
           __lowerCAmelCase             =       num_labels

           # in TimeSformer, the number of spatial tokens equals num_frames * num_patches per frame + 1 CLS token
           __lowerCAmelCase             =       (image_size // patch_size) ** 2
           __lowerCAmelCase             =       (num_frames) * self.num_patches_per_frame + 1


      def 			__UpperCAmelCase		(						self						)->						Optional[int]:
           __lowerCAmelCase             =       floats_tensor(
               [self.batch_size, self.num_frames, self.num_channels, self.image_size, self.image_size]						)

           __lowerCAmelCase             =       None
           if self.use_labels:
                __lowerCAmelCase             =       ids_tensor([self.batch_size]       ,    self.num_labels						)

           __lowerCAmelCase             =       self.get_config()

           return config, pixel_values, labels


      def 			__UpperCAmelCase		(						self						)->						Tuple:
           __lowerCAmelCase             =       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       ,    )
           __lowerCAmelCase             =       self.num_labels
           return config


      def 			__UpperCAmelCase		(						self       ,    __UpperCamelCase       ,    __UpperCamelCase       ,    __UpperCamelCase						)->						Optional[int]:
           __lowerCAmelCase             =       TimesformerModel(config=__UpperCamelCase						)
           model.to(__UpperCamelCase						)
           model.eval()
           __lowerCAmelCase             =       model(__UpperCamelCase						)
           self.parent.assertEqual(result.last_hidden_state.shape       ,    (self.batch_size, self.seq_length, self.hidden_size)						)


      def 			__UpperCAmelCase		(						self       ,    __UpperCamelCase       ,    __UpperCamelCase       ,    __UpperCamelCase						)->						Dict:
           __lowerCAmelCase             =       TimesformerForVideoClassification(__UpperCamelCase						)
           model.to(__UpperCamelCase						)
           model.eval()

           __lowerCAmelCase             =       model(__UpperCamelCase						)

           # verify the logits shape
           __lowerCAmelCase             =       torch.Size((self.batch_size, self.num_labels)						)
           self.parent.assertEqual(result.logits.shape       ,    __UpperCamelCase						)


      def 			__UpperCAmelCase		(						self						)->						Dict:
           __lowerCAmelCase             =       self.prepare_config_and_inputs()
           __lowerCAmelCase      ,			__lowerCAmelCase      ,			__lowerCAmelCase             =       config_and_inputs
           __lowerCAmelCase             =       {"pixel_values": pixel_values}
           return config, inputs_dict



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

      snake_case_	=				False
      snake_case_	=				False
      snake_case_	=				False
      snake_case_	=				False


      def 			__UpperCAmelCase		(						self						)->						Dict:
           __lowerCAmelCase             =       TimesformerModelTester(self						)
           __lowerCAmelCase             =       ConfigTester(
               self       ,    config_class=__UpperCamelCase       ,    has_text_modality=__UpperCamelCase       ,    hidden_size=3_7						)


      def 			__UpperCAmelCase		(						self       ,    __UpperCamelCase       ,    __UpperCamelCase       ,    __UpperCamelCase=False						)->						List[str]:
           __lowerCAmelCase             =       copy.deepcopy(__UpperCamelCase						)

           if return_labels:
                if model_class in get_values(__UpperCamelCase						):
                     __lowerCAmelCase             =       torch.zeros(
                         self.model_tester.batch_size       ,    dtype=torch.long       ,    device=__UpperCamelCase						)

           return inputs_dict


      def 			__UpperCAmelCase		(						self						)->						Any:
           self.config_tester.run_common_tests()


      @unittest.skip(reason="TimeSformer does not use inputs_embeds"						)
      def 			__UpperCAmelCase		(						self						)->						str:
           pass


      def 			__UpperCAmelCase		(						self						)->						int:
           __lowerCAmelCase      ,			__lowerCAmelCase             =       self.model_tester.prepare_config_and_inputs_for_common()

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


      def 			__UpperCAmelCase		(						self						)->						List[Any]:
           __lowerCAmelCase      ,			__lowerCAmelCase             =       self.model_tester.prepare_config_and_inputs_for_common()

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

                __lowerCAmelCase             =       ["pixel_values"]
                self.assertListEqual(arg_names[:1]       ,    __UpperCamelCase						)


      def 			__UpperCAmelCase		(						self						)->						List[Any]:
           __lowerCAmelCase             =       self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*__UpperCamelCase						)


      def 			__UpperCAmelCase		(						self						)->						Union[str, Any]:
           __lowerCAmelCase             =       self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_video_classification(*__UpperCamelCase						)


      @slow
      def 			__UpperCAmelCase		(						self						)->						Union[str, Any]:
           for model_name in TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                __lowerCAmelCase             =       TimesformerModel.from_pretrained(__UpperCamelCase						)
                self.assertIsNotNone(__UpperCamelCase						)


      def 			__UpperCAmelCase		(						self						)->						str:
           if not self.has_attentions:
                pass

           else:
                __lowerCAmelCase      ,			__lowerCAmelCase             =       self.model_tester.prepare_config_and_inputs_for_common()
                __lowerCAmelCase             =       True

                for model_class in self.all_model_classes:
                     __lowerCAmelCase             =       self.model_tester.seq_length
                     __lowerCAmelCase             =       self.model_tester.num_frames

                     __lowerCAmelCase             =       True
                     __lowerCAmelCase             =       False
                     __lowerCAmelCase             =       True
                     __lowerCAmelCase             =       model_class(__UpperCamelCase						)
                     model.to(__UpperCamelCase						)
                     model.eval()
                     with torch.no_grad():
                          __lowerCAmelCase             =       model(**self._prepare_for_class(__UpperCamelCase       ,    __UpperCamelCase						)						)
                     __lowerCAmelCase             =       outputs.attentions
                     self.assertEqual(len(__UpperCamelCase						)       ,    self.model_tester.num_hidden_layers						)

                     # check that output_attentions also work using config
                     del inputs_dict["output_attentions"]
                     __lowerCAmelCase             =       True
                     __lowerCAmelCase             =       model_class(__UpperCamelCase						)
                     model.to(__UpperCamelCase						)
                     model.eval()
                     with torch.no_grad():
                          __lowerCAmelCase             =       model(**self._prepare_for_class(__UpperCamelCase       ,    __UpperCamelCase						)						)
                     __lowerCAmelCase             =       outputs.attentions
                     self.assertEqual(len(__UpperCamelCase						)       ,    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]       ,    )
                     __lowerCAmelCase             =       len(__UpperCamelCase						)

                     # Check attention is always last and order is fine
                     __lowerCAmelCase             =       True
                     __lowerCAmelCase             =       True
                     __lowerCAmelCase             =       model_class(__UpperCamelCase						)
                     model.to(__UpperCamelCase						)
                     model.eval()
                     with torch.no_grad():
                          __lowerCAmelCase             =       model(**self._prepare_for_class(__UpperCamelCase       ,    __UpperCamelCase						)						)

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

                     __lowerCAmelCase             =       outputs.attentions

                     self.assertEqual(len(__UpperCamelCase						)       ,    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						)->						Optional[Any]:
           def check_hidden_states_output(__UpperCamelCase       ,    __UpperCamelCase       ,    __UpperCamelCase						):
                __lowerCAmelCase             =       model_class(__UpperCamelCase						)
                model.to(__UpperCamelCase						)
                model.eval()

                with torch.no_grad():
                     __lowerCAmelCase             =       model(**self._prepare_for_class(__UpperCamelCase       ,    __UpperCamelCase						)						)

                __lowerCAmelCase             =       outputs.hidden_states
                __lowerCAmelCase             =       self.model_tester.num_hidden_layers + 1
                self.assertEqual(len(__UpperCamelCase						)       ,    __UpperCamelCase						)

                __lowerCAmelCase             =       self.model_tester.seq_length

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

           __lowerCAmelCase      ,			__lowerCAmelCase             =       self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
                __lowerCAmelCase             =       True
                check_hidden_states_output(__UpperCamelCase       ,    __UpperCamelCase       ,    __UpperCamelCase						)

                # check that output_hidden_states also work using config
                del inputs_dict["output_hidden_states"]
                __lowerCAmelCase             =       True

                check_hidden_states_output(__UpperCamelCase       ,    __UpperCamelCase       ,    __UpperCamelCase						)





def    __lowerCAmelCase   (    ):
     __lowerCAmelCase             =       hf_hub_download(
         repo_id="hf-internal-testing/spaghetti-video"       ,			filename="eating_spaghetti.npy"       ,			repo_type="dataset"							)
     __lowerCAmelCase             =       np.load(__snake_case							)
     return list(__snake_case							)



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


      @cached_property
      def 			__UpperCAmelCase		(						self						)->						int:
           # 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						)->						Dict:
           __lowerCAmelCase             =       TimesformerForVideoClassification.from_pretrained("facebook/timesformer-base-finetuned-k400"						).to(
               __UpperCamelCase						)

           __lowerCAmelCase             =       self.default_image_processor
           __lowerCAmelCase             =       prepare_video()
           __lowerCAmelCase             =       image_processor(video[:8]       ,    return_tensors="pt"						).to(__UpperCamelCase						)

           # forward pass
           with torch.no_grad():
                __lowerCAmelCase             =       model(**__UpperCamelCase						)

           # verify the logits
           __lowerCAmelCase             =       torch.Size((1, 4_0_0)						)
           self.assertEqual(outputs.logits.shape       ,    __UpperCamelCase						)

           __lowerCAmelCase             =       torch.tensor([-0.3_0_1_6, -0.7_7_1_3, -0.4_2_0_5]						).to(__UpperCamelCase						)

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














from __future__ import annotations

import csv

import requests
from bsa import BeautifulSoup





def    __lowerCAmelCase   (    __snake_case = ""							):
     __lowerCAmelCase             =       url or "https://www.imdb.com/chart/top/?ref_=nv_mv_250"
     __lowerCAmelCase             =       BeautifulSoup(requests.get(__snake_case							).text       ,			"html.parser"							)
     __lowerCAmelCase             =       soup.find_all("td"       ,			attrs="titleColumn"							)
     __lowerCAmelCase             =       soup.find_all("td"       ,			class_="ratingColumn imdbRating"							)
     return {
         title.a.text: float(rating.strong.text							)
         for title, rating in zip(__snake_case       ,			__snake_case							)
     }





def    __lowerCAmelCase   (    __snake_case = "IMDb_Top_250_Movies.csv"							):
     __lowerCAmelCase             =       get_imdb_top_aaa_movies()
     with open(__snake_case       ,			"w"       ,			newline=""							) as out_file:
          __lowerCAmelCase             =       csv.writer(__snake_case							)
          writer.writerow(["Movie title", "IMDb rating"]							)
          for title, rating in movies.items():
               writer.writerow([title, rating]							)


if __name__ == "__main__":
 write_movies()






