



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 ,						a__ ,						a__=1_3 ,						a__=1_0 ,						a__=3 ,						a__=2 ,						a__=2 ,						a__=True ,						a__=True ,						a__=3_2 ,						a__=5 ,						a__=4 ,						a__=3_7 ,						a__="gelu" ,						a__=0.1 ,						a__=0.1 ,						a__=1_0 ,						a__=0.0_2 ,						a__="divided_space_time" ,						a__=None ,						):
         A__		 =					parent
         A__		 =					batch_size
         A__		 =					image_size
         A__		 =					num_channels
         A__		 =					patch_size
         A__		 =					num_frames
         A__		 =					is_training
         A__		 =					use_labels
         A__		 =					hidden_size
         A__		 =					num_hidden_layers
         A__		 =					num_attention_heads
         A__		 =					intermediate_size
         A__		 =					hidden_act
         A__		 =					hidden_dropout_prob
         A__		 =					attention_probs_dropout_prob
         A__		 =					attention_type
         A__		 =					initializer_range
         A__		 =					scope
         A__		 =					num_labels

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




     def 		snake_case_		(				self):
         A__		 =					floats_tensor(
             [self.batch_size, self.num_frames, self.num_channels, self.image_size, self.image_size])

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

         A__		 =					self.get_config()

         return config, pixel_values, labels




     def 		snake_case_		(				self):
         A__		 =					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 ,						)
         A__		 =					self.num_labels
         return config




     def 		snake_case_		(				self ,						a__ ,						a__ ,						a__):
         A__		 =					TimesformerModel(config=a__)
         model.to(a__)
         model.eval()
         A__		 =					model(a__)
         self.parent.assertEqual(result.last_hidden_state.shape ,						(self.batch_size, self.seq_length, self.hidden_size))




     def 		snake_case_		(				self ,						a__ ,						a__ ,						a__):
         A__		 =					TimesformerForVideoClassification(a__)
         model.to(a__)
         model.eval()

         A__		 =					model(a__)

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




     def 		snake_case_		(				self):
         A__		 =					self.prepare_config_and_inputs()
         A__ ,	A__ ,	A__		 =					config_and_inputs
         A__		 =					{'''pixel_values''': pixel_values}
         return config, inputs_dict







@require_torch
class     _UpperCAmelCase			(       A__			,      A__			,      unittest.TestCase     ):
     UpperCamelCase__		    =				(TimesformerModel, TimesformerForVideoClassification) if is_torch_available() else ()
     UpperCamelCase__		    =				(
         {'''feature-extraction''': TimesformerModel, '''video-classification''': TimesformerForVideoClassification}
         if is_torch_available()
         else {}
     )

     UpperCamelCase__		    =				False
     UpperCamelCase__		    =				False
     UpperCamelCase__		    =				False
     UpperCamelCase__		    =				False




     def 		snake_case_		(				self):
         A__		 =					TimesformerModelTester(self)
         A__		 =					ConfigTester(
             self ,						config_class=a__ ,						has_text_modality=a__ ,						hidden_size=3_7)




     def 		snake_case_		(				self ,						a__ ,						a__ ,						a__=False):
         A__		 =					copy.deepcopy(a__)

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

         return inputs_dict




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




     @unittest.skip(reason='''TimeSformer does not use inputs_embeds''')
     def 		snake_case_		(				self):
         pass




     def 		snake_case_		(				self):
         A__ ,	A__		 =					self.model_tester.prepare_config_and_inputs_for_common()

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




     def 		snake_case_		(				self):
         A__ ,	A__		 =					self.model_tester.prepare_config_and_inputs_for_common()

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

             A__		 =					['''pixel_values''']
             self.assertListEqual(arg_names[:1] ,						a__)




     def 		snake_case_		(				self):
         A__		 =					self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*a__)




     def 		snake_case_		(				self):
         A__		 =					self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_video_classification(*a__)




     @slow
     def 		snake_case_		(				self):
         for model_name in TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             A__		 =					TimesformerModel.from_pretrained(a__)
             self.assertIsNotNone(a__)




     def 		snake_case_		(				self):
         if not self.has_attentions:
             pass

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

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

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

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

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

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

                 A__		 =					outputs.attentions

                 self.assertEqual(len(a__) ,						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 		snake_case_		(				self):
         def check_hidden_states_output(a__ ,						a__ ,						a__):
             A__		 =					model_class(a__)
             model.to(a__)
             model.eval()

             with torch.no_grad():
                 A__		 =					model(**self._prepare_for_class(a__ ,						a__))

             A__		 =					outputs.hidden_states
             A__		 =					self.model_tester.num_hidden_layers + 1
             self.assertEqual(len(a__) ,						a__)

             A__		 =					self.model_tester.seq_length

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

         A__ ,	A__		 =					self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
             A__		 =					True
             check_hidden_states_output(a__ ,						a__ ,						a__)

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

             check_hidden_states_output(a__ ,						a__ ,						a__)







def 		lowerCAmelCase__    (					)->  Any:
    A__		 =					hf_hub_download(
        repo_id='''hf-internal-testing/spaghetti-video''' ,       filename='''eating_spaghetti.npy''' ,       repo_type='''dataset'''	)
    A__		 =					np.load(UpperCamelCase_	)
    return list(UpperCamelCase_	)



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




     @cached_property
     def 		snake_case_		(				self):
         # 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 		snake_case_		(				self):
         A__		 =					TimesformerForVideoClassification.from_pretrained('''facebook/timesformer-base-finetuned-k400''').to(
             a__)

         A__		 =					self.default_image_processor
         A__		 =					prepare_video()
         A__		 =					image_processor(video[:8] ,						return_tensors='''pt''').to(a__)

         # forward pass
         with torch.no_grad():
             A__		 =					model(**a__)

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

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

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





from __future__ import annotations



def 		lowerCAmelCase__    (					UpperCamelCase_				:       str ,       UpperCamelCase_				:       list[str] | None = None	)->  list[list[str]]:
    A__		 =					word_bank or []
    # create a table
    A__		 =					len(UpperCamelCase_	) + 1

    A__		 =					[]
    for _ in range(UpperCamelCase_	):
        table.append([]	)
    # seed value
    A__		 =					[[]]  # because empty string has empty combination

    # iterate through the indices
    for i in range(UpperCamelCase_	):
        # condition
        if table[i] != []:
            for word in word_bank:
                # slice condition
                if target[i : i + len(UpperCamelCase_	)] == word:
                    A__		 =					[
                        [word, *way] for way in table[i]
                    ]
                    # adds the word to every combination the current position holds
                    # now,push that combination to the table[i+len(word)]
                    table[i + len(UpperCamelCase_	)] += new_combinations

    # combinations are in reverse order so reverse for better output
    for combination in table[len(UpperCamelCase_	)]:
        combination.reverse()

    return table[len(UpperCamelCase_	)]


if __name__ == "__main__":
   print(all_construct("jwajalapa", ["jwa", "j", "w", "a", "la", "lapa"]))
   print(all_construct("rajamati", ["s", "raj", "amat", "raja", "ma", "i", "t"]))
   print(
       all_construct(
           "hexagonosaurus",
           ["h", "ex", "hex", "ag", "ago", "ru", "auru", "rus", "go", "no", "o", "s"],
       )
   )
