
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   :





   '''simple docstring'''


   def __init__(					self     :							Tuple			,			lowerCAmelCase_     :							Any			,			lowerCAmelCase_     :							Dict=1_3			,			lowerCAmelCase_     :							Optional[Any]=1_0			,			lowerCAmelCase_     :							Any=3			,			lowerCAmelCase_     :							Optional[Any]=2			,			lowerCAmelCase_     :							Dict=2			,			lowerCAmelCase_     :							Optional[Any]=True			,			lowerCAmelCase_     :							str=True			,			lowerCAmelCase_     :							int=3_2			,			lowerCAmelCase_     :							Dict=5			,			lowerCAmelCase_     :							int=4			,			lowerCAmelCase_     :							Any=3_7			,			lowerCAmelCase_     :							Union[str, Any]="gelu"			,			lowerCAmelCase_     :							Tuple=0.1			,			lowerCAmelCase_     :							Dict=0.1			,			lowerCAmelCase_     :							Dict=1_0			,			lowerCAmelCase_     :							List[str]=0.02			,			lowerCAmelCase_     :							Optional[Any]="divided_space_time"			,			lowerCAmelCase_     :							Dict=None			,			):

      """simple docstring"""
      _A:  str					=       parent
      _A:  Any					=       batch_size
      _A:  Dict					=       image_size
      _A:  Tuple					=       num_channels
      _A:  str					=       patch_size
      _A:  Any					=       num_frames
      _A:  Dict					=       is_training
      _A:  str					=       use_labels
      _A:  Optional[int]					=       hidden_size
      _A:  List[str]					=       num_hidden_layers
      _A:  Any					=       num_attention_heads
      _A:  Tuple					=       intermediate_size
      _A:  Optional[Any]					=       hidden_act
      _A:  Any					=       hidden_dropout_prob
      _A:  Optional[int]					=       attention_probs_dropout_prob
      _A:  int					=       attention_type
      _A:  Any					=       initializer_range
      _A:  List[str]					=       scope
      _A:  int					=       num_labels

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


   def  __magic_name__				(					self     :							Dict     ):

      """simple docstring"""
      _A:  Optional[Any]					=       floats_tensor(
          [self.batch_size, self.num_frames, self.num_channels, self.image_size, self.image_size]     )

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

      _A:  List[Any]					=       self.get_config()

      return config, pixel_values, labels


   def  __magic_name__				(					self     :							Optional[Any]     ):

      """simple docstring"""
      _A:  str					=       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:  Union[str, Any]					=       self.num_labels
      return config


   def  __magic_name__				(					self     :							Union[str, Any]			,			lowerCAmelCase_     :							List[Any]			,			lowerCAmelCase_     :							List[str]			,			lowerCAmelCase_     :							List[str]     ):

      """simple docstring"""
      _A:  List[Any]					=       TimesformerModel(config=lowerCAmelCase_     )
      model.to(lowerCAmelCase_     )
      model.eval()
      _A:  Union[str, Any]					=       model(lowerCAmelCase_     )
      self.parent.assertEqual(result.last_hidden_state.shape			,			(self.batch_size, self.seq_length, self.hidden_size)     )


   def  __magic_name__				(					self     :							Optional[int]			,			lowerCAmelCase_     :							Union[str, Any]			,			lowerCAmelCase_     :							int			,			lowerCAmelCase_     :							Tuple     ):

      """simple docstring"""
      _A:  str					=       TimesformerForVideoClassification(lowerCAmelCase_     )
      model.to(lowerCAmelCase_     )
      model.eval()

      _A:  Optional[Any]					=       model(lowerCAmelCase_     )

      # verify the logits shape
      _A:  List[Any]					=       torch.Size((self.batch_size, self.num_labels)     )
      self.parent.assertEqual(result.logits.shape			,			lowerCAmelCase_     )


   def  __magic_name__				(					self     :							Optional[int]     ):

      """simple docstring"""
      _A:  Tuple					=       self.prepare_config_and_inputs()
      _A			,  _A			,  _A:  List[str]					=       config_and_inputs
      _A:  List[str]					=       {'''pixel_values''': pixel_values}
      return config, inputs_dict






@require_torch
class UpperCAmelCase   (						SCREAMING_SNAKE_CASE__							,							SCREAMING_SNAKE_CASE__							,							unittest.TestCase   ):





   '''simple docstring'''
   __UpperCamelCase		:    List[Any] 				=    (TimesformerModel, TimesformerForVideoClassification) if is_torch_available() else ()
   __UpperCamelCase		:    int 				=    (
       {'''feature-extraction''': TimesformerModel, '''video-classification''': TimesformerForVideoClassification}
       if is_torch_available()
       else {}
   )

   __UpperCamelCase		:    str 				=    False
   __UpperCamelCase		:    Any 				=    False
   __UpperCamelCase		:    Dict 				=    False
   __UpperCamelCase		:    Any 				=    False


   def  __magic_name__				(					self     :							Optional[Any]     ):

      """simple docstring"""
      _A:  Tuple					=       TimesformerModelTester(self     )
      _A:  int					=       ConfigTester(
          self			,			config_class=lowerCAmelCase_			,			has_text_modality=lowerCAmelCase_			,			hidden_size=3_7     )


   def  __magic_name__				(					self     :							List[Any]			,			lowerCAmelCase_     :							Tuple			,			lowerCAmelCase_     :							List[Any]			,			lowerCAmelCase_     :							Dict=False     ):

      """simple docstring"""
      _A:  Tuple					=       copy.deepcopy(lowerCAmelCase_     )

      if return_labels:
         if model_class in get_values(lowerCAmelCase_     ):
            _A:  Tuple					=       torch.zeros(
                self.model_tester.batch_size			,			dtype=torch.long			,			device=lowerCAmelCase_     )

      return inputs_dict


   def  __magic_name__				(					self     :							Optional[int]     ):

      """simple docstring"""
      self.config_tester.run_common_tests()


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

      """simple docstring"""
      pass


   def  __magic_name__				(					self     :							Any     ):

      """simple docstring"""
      _A			,  _A:  int					=       self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
         _A:  Optional[int]					=       model_class(lowerCAmelCase_     )
         self.assertIsInstance(model.get_input_embeddings()			,			(nn.Module)     )
         _A:  Any					=       model.get_output_embeddings()
         self.assertTrue(x is None or isinstance(lowerCAmelCase_			,			nn.Linear     )     )


   def  __magic_name__				(					self     :							int     ):

      """simple docstring"""
      _A			,  _A:  int					=       self.model_tester.prepare_config_and_inputs_for_common()

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

         _A:  int					=       ['''pixel_values''']
         self.assertListEqual(arg_names[:1]			,			lowerCAmelCase_     )


   def  __magic_name__				(					self     :							Tuple     ):

      """simple docstring"""
      _A:  Dict					=       self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*lowerCAmelCase_     )


   def  __magic_name__				(					self     :							List[Any]     ):

      """simple docstring"""
      _A:  List[str]					=       self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_video_classification(*lowerCAmelCase_     )


   @slow
   def  __magic_name__				(					self     :							Union[str, Any]     ):

      """simple docstring"""
      for model_name in TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
         _A:  List[Any]					=       TimesformerModel.from_pretrained(lowerCAmelCase_     )
         self.assertIsNotNone(lowerCAmelCase_     )


   def  __magic_name__				(					self     :							Dict     ):

      """simple docstring"""
      if not self.has_attentions:
         pass

      else:
         _A			,  _A:  Dict					=       self.model_tester.prepare_config_and_inputs_for_common()
         _A:  List[Any]					=       True

         for model_class in self.all_model_classes:
            _A:  Optional[Any]					=       self.model_tester.seq_length
            _A:  Optional[Any]					=       self.model_tester.num_frames

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

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

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

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

            _A:  Optional[Any]					=       outputs.attentions

            self.assertEqual(len(lowerCAmelCase_     )			,			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  __magic_name__				(					self     :							Union[str, Any]     ):

      """simple docstring"""
      def check_hidden_states_output(lowerCAmelCase_     :							Optional[int]			,			lowerCAmelCase_     :							int			,			lowerCAmelCase_     :							Optional[Any]     ):
         _A:  Union[str, Any]					=       model_class(lowerCAmelCase_     )
         model.to(lowerCAmelCase_     )
         model.eval()

         with torch.no_grad():
            _A:  Dict					=       model(**self._prepare_for_class(lowerCAmelCase_			,			lowerCAmelCase_     )     )

         _A:  Optional[Any]					=       outputs.hidden_states
         _A:  str					=       self.model_tester.num_hidden_layers + 1
         self.assertEqual(len(lowerCAmelCase_     )			,			lowerCAmelCase_     )

         _A:  Optional[int]					=       self.model_tester.seq_length

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

      _A			,  _A:  Optional[Any]					=       self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
         _A:  Dict					=       True
         check_hidden_states_output(lowerCAmelCase_			,			lowerCAmelCase_			,			lowerCAmelCase_     )

         # check that output_hidden_states also work using config
         del inputs_dict["output_hidden_states"]
         _A:  Optional[int]					=       True

         check_hidden_states_output(lowerCAmelCase_			,			lowerCAmelCase_			,			lowerCAmelCase_     )






def   lowerCamelCase__							(					)      ->   Optional[Any]:
   _A:  List[str]					=       hf_hub_download(
       repo_id='''hf-internal-testing/spaghetti-video'''		,		filename='''eating_spaghetti.npy'''		,		repo_type='''dataset'''      )
   _A:  Dict					=       np.load(a      )
   return list(a      )






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





   '''simple docstring'''


   @cached_property
   def  __magic_name__				(					self     :							Union[str, Any]     ):

      """simple docstring"""
      # 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  __magic_name__				(					self     :							Union[str, Any]     ):

      """simple docstring"""
      _A:  List[Any]					=       TimesformerForVideoClassification.from_pretrained('''facebook/timesformer-base-finetuned-k400'''     ).to(
          lowerCAmelCase_     )

      _A:  List[Any]					=       self.default_image_processor
      _A:  List[Any]					=       prepare_video()
      _A:  int					=       image_processor(video[:8]			,			return_tensors='''pt'''     ).to(lowerCAmelCase_     )

      # forward pass
      with torch.no_grad():
         _A:  Dict					=       model(**lowerCAmelCase_     )

      # verify the logits
      _A:  List[Any]					=       torch.Size((1, 4_0_0)     )
      self.assertEqual(outputs.logits.shape			,			lowerCAmelCase_     )

      _A:  List[Any]					=       torch.tensor([-0.3016, -0.7713, -0.4205]     ).to(lowerCAmelCase_     )

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





UpperCAmelCase__		:     Optional[Any]     			=      {
    'A': ['B', 'C', 'E'],
    'B': ['A', 'D', 'E'],
    'C': ['A', 'F', 'G'],
    'D': ['B'],
    'E': ['A', 'B', 'D'],
    'F': ['C'],
    'G': ['C'],
}






def   lowerCamelCase__							(					a		,		a		,		a      )      ->   list[str]:
   _A:  Union[str, Any]					=       set()
   # keep track of all the paths to be checked
   _A:  Union[str, Any]					=       [[start]]

   # return path if start is goal
   if start == goal:
      return [start]

   # keeps looping until all possible paths have been checked
   while queue:
      # pop the first path from the queue
      _A:  Any					=       queue.pop(0      )
      # get the last node from the path
      _A:  Union[str, Any]					=       path[-1]
      if node not in explored:
         _A:  str					=       graph[node]
         # go through all neighbour nodes, construct a new path and
         # push it into the queue
         for neighbour in neighbours:
            _A:  Optional[int]					=       list(a      )
            new_path.append(a      )
            queue.append(a      )
            # return path if neighbour is goal
            if neighbour == goal:
               return new_path

            # mark node as explored
         explored.add(a      )

    # in case there's no path between the 2 nodes
   return []






def   lowerCamelCase__							(					a		,		a		,		a      )      ->   int:
   if not graph or start not in graph or target not in graph:
      return -1
   if start == target:
      return 0
   _A:  Any					=       [start]
   _A:  List[str]					=       set(a      )
   # Keep tab on distances from `start` node.
   _A:  Optional[int]					=       {start: 0, target: -1}
   while queue:
      _A:  Union[str, Any]					=       queue.pop(0      )
      if node == target:
         _A:  Dict					=       (
             dist[node] if dist[target] == -1 else min(dist[target]		,		dist[node]      )
         )
      for adjacent in graph[node]:
         if adjacent not in visited:
            visited.add(a      )
            queue.append(a      )
            _A:  List[Any]					=       dist[node] + 1
   return dist[target]


if __name__ == "__main__":
   print(bfs_shortest_path(demo_graph, 'G', 'D'))  # returns ['G', 'C', 'A', 'B', 'D']
   print(bfs_shortest_path_distance(demo_graph, 'G', 'D'))  # returns 4



