


'''simple docstring'''







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 					lowercase_     :


    def __init__(     self  ,					a  ,					a=13  ,					a=10  ,					a=3  ,					a=2  ,					a=2  ,					a=True  ,					a=True  ,					a=32  ,					a=5  ,					a=4  ,					a=37  ,					a="gelu"  ,					a=0.1  ,					a=0.1  ,					a=10  ,					a=0.02  ,					a="divided_space_time"  ,					a=None  ,					):
          UpperCamelCase__										=				parent
          UpperCamelCase__										=				batch_size
          UpperCamelCase__										=				image_size
          UpperCamelCase__										=				num_channels
          UpperCamelCase__										=				patch_size
          UpperCamelCase__										=				num_frames
          UpperCamelCase__										=				is_training
          UpperCamelCase__										=				use_labels
          UpperCamelCase__										=				hidden_size
          UpperCamelCase__										=				num_hidden_layers
          UpperCamelCase__										=				num_attention_heads
          UpperCamelCase__										=				intermediate_size
          UpperCamelCase__										=				hidden_act
          UpperCamelCase__										=				hidden_dropout_prob
          UpperCamelCase__										=				attention_probs_dropout_prob
          UpperCamelCase__										=				attention_type
          UpperCamelCase__										=				initializer_range
          UpperCamelCase__										=				scope
          UpperCamelCase__										=				num_labels

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


    def    __a							(     self    ):
          UpperCamelCase__										=				floats_tensor(
              [self.batch_size, self.num_frames, self.num_channels, self.image_size, self.image_size]    )

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

          UpperCamelCase__										=				self.get_config()

          return config, pixel_values, labels


    def    __a							(     self    ):
          UpperCamelCase__										=				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  ,					)
          UpperCamelCase__										=				self.num_labels
          return config


    def    __a							(     self  ,					a  ,					a  ,					a    ):
          UpperCamelCase__										=				TimesformerModel(config=lowercase_    )
          model.to(lowercase_    )
          model.eval()
          UpperCamelCase__										=				model(lowercase_    )
          self.parent.assertEqual(result.last_hidden_state.shape  ,					(self.batch_size, self.seq_length, self.hidden_size)    )


    def    __a							(     self  ,					a  ,					a  ,					a    ):
          UpperCamelCase__										=				TimesformerForVideoClassification(lowercase_    )
          model.to(lowercase_    )
          model.eval()

          UpperCamelCase__										=				model(lowercase_    )

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


    def    __a							(     self    ):
          UpperCamelCase__										=				self.prepare_config_and_inputs()
          UpperCamelCase__										=				config_and_inputs
          UpperCamelCase__										=				{"pixel_values": pixel_values}
          return config, inputs_dict

@require_torch
class 					lowercase_     (    __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    __a							(     self    ):
          UpperCamelCase__										=				TimesformerModelTester(self    )
          UpperCamelCase__										=				ConfigTester(
              self  ,					config_class=lowercase_  ,					has_text_modality=lowercase_  ,					hidden_size=37    )


    def    __a							(     self  ,					a  ,					a  ,					a=False    ):
          UpperCamelCase__										=				copy.deepcopy(lowercase_    )

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

          return inputs_dict


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


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


    def    __a							(     self    ):
          UpperCamelCase__										=				self.model_tester.prepare_config_and_inputs_for_common()

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


    def    __a							(     self    ):
          UpperCamelCase__										=				self.model_tester.prepare_config_and_inputs_for_common()

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

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


    def    __a							(     self    ):
          UpperCamelCase__										=				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*lowercase_    )


    def    __a							(     self    ):
          UpperCamelCase__										=				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_video_classification(*lowercase_    )


    @slow
    def    __a							(     self    ):
          for model_name in TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                UpperCamelCase__										=				TimesformerModel.from_pretrained(lowercase_    )
                self.assertIsNotNone(lowercase_    )


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

          else:
                UpperCamelCase__										=				self.model_tester.prepare_config_and_inputs_for_common()
                UpperCamelCase__										=				True

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

                      UpperCamelCase__										=				True
                      UpperCamelCase__										=				False
                      UpperCamelCase__										=				True
                      UpperCamelCase__										=				model_class(lowercase_    )
                      model.to(lowercase_    )
                      model.eval()
                      with torch.no_grad():
                            UpperCamelCase__										=				model(**self._prepare_for_class(lowercase_  ,					lowercase_    )    )
                      UpperCamelCase__										=				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"]
                      UpperCamelCase__										=				True
                      UpperCamelCase__										=				model_class(lowercase_    )
                      model.to(lowercase_    )
                      model.eval()
                      with torch.no_grad():
                            UpperCamelCase__										=				model(**self._prepare_for_class(lowercase_  ,					lowercase_    )    )
                      UpperCamelCase__										=				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]  ,					)
                      UpperCamelCase__										=				len(lowercase_    )

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

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

                      UpperCamelCase__										=				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    __a							(     self    ):
          def check_hidden_states_output(a  ,					a  ,					a    ):
                UpperCamelCase__										=				model_class(lowercase_    )
                model.to(lowercase_    )
                model.eval()

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

                UpperCamelCase__										=				outputs.hidden_states
                UpperCamelCase__										=				self.model_tester.num_hidden_layers + 1
                self.assertEqual(len(lowercase_    )  ,					lowercase_    )

                UpperCamelCase__										=				self.model_tester.seq_length

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

          UpperCamelCase__										=				self.model_tester.prepare_config_and_inputs_for_common()

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

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

                check_hidden_states_output(lowercase_  ,					lowercase_  ,					lowercase_    )







def _UpperCamelCase       (     )							->		int:


      '''simple docstring'''



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



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


    @cached_property
    def    __a							(     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    __a							(     self    ):
          UpperCamelCase__										=				TimesformerForVideoClassification.from_pretrained("facebook/timesformer-base-finetuned-k400"    ).to(
              lowercase_    )

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

          # forward pass
          with torch.no_grad():
                UpperCamelCase__										=				model(**lowercase_    )

          # verify the logits
          UpperCamelCase__										=				torch.Size((1, 4_00)    )
          self.assertEqual(outputs.logits.shape  ,					lowercase_    )

          UpperCamelCase__										=				torch.tensor([-0.3016, -0.7713, -0.4205]    ).to(lowercase_    )

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






import warnings
from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType



class   snake_case_       (		__A       ):
   __A	:		Optional[Any]         =				["image_processor", "tokenizer"]
   __A	:		Tuple         =				"LayoutLMv3ImageProcessor"
   __A	:		List[Any]         =				("LayoutLMv3Tokenizer", "LayoutLMv3TokenizerFast")






   def __init__(      self				:     Union[str, Any]	,						lowercase_				:     int=None	,						lowercase_				:     str=None	,						**lowercase_				:     Optional[Any]  )					->						Optional[int]:
       lowercase__ :   Union[str, Any]    					=				None
       if "feature_extractor" in kwargs:
           warnings.warn(
               "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
               " instead."	,						lowercase_	,						)
           lowercase__ :   Optional[int]    					=				kwargs.pop("feature_extractor"  )

       lowercase__ :   int    					=				image_processor if image_processor is not None else feature_extractor
       if image_processor is None:
           raise ValueError("You need to specify an `image_processor`."  )
       if tokenizer is None:
           raise ValueError("You need to specify a `tokenizer`."  )

       super().__init__(lowercase_	,						lowercase_  )






   def __call__(      self				:     Dict	,						lowercase_				:     Optional[Any]	,						lowercase_				:     Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None	,						lowercase_				:     Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None	,						lowercase_				:     Union[List[List[int]], List[List[List[int]]]] = None	,						lowercase_				:     Optional[Union[List[int], List[List[int]]]] = None	,						lowercase_				:     bool = True	,						lowercase_				:     Union[bool, str, PaddingStrategy] = False	,						lowercase_				:     Union[bool, str, TruncationStrategy] = None	,						lowercase_				:     Optional[int] = None	,						lowercase_				:     int = 0	,						lowercase_				:     Optional[int] = None	,						lowercase_				:     Optional[bool] = None	,						lowercase_				:     Optional[bool] = None	,						lowercase_				:     bool = False	,						lowercase_				:     bool = False	,						lowercase_				:     bool = False	,						lowercase_				:     bool = False	,						lowercase_				:     bool = True	,						lowercase_				:     Optional[Union[str, TensorType]] = None	,						**lowercase_				:     Dict	,						)					->						BatchEncoding:
       # verify input
       if self.image_processor.apply_ocr and (boxes is not None):
           raise ValueError(
               "You cannot provide bounding boxes if you initialized the image processor with apply_ocr set to True."  )

       if self.image_processor.apply_ocr and (word_labels is not None):
           raise ValueError(
               "You cannot provide word labels if you initialized the image processor with apply_ocr set to True."  )

       # first, apply the image processor
       lowercase__ :   Union[str, Any]    					=				self.image_processor(images=lowercase_	,						return_tensors=lowercase_  )

       # second, apply the tokenizer
       if text is not None and self.image_processor.apply_ocr and text_pair is None:
           if isinstance(lowercase_	,						lowercase_  ):
               lowercase__ :   Optional[Any]    					=				[text]  # add batch dimension (as the image processor always adds a batch dimension)
           lowercase__ :   Any    					=				features["words"]

       lowercase__ :   Tuple    					=				self.tokenizer(
           text=text if text is not None else features["words"]	,						text_pair=text_pair if text_pair is not None else None	,						boxes=boxes if boxes is not None else features["boxes"]	,						word_labels=lowercase_	,						add_special_tokens=lowercase_	,						padding=lowercase_	,						truncation=lowercase_	,						max_length=lowercase_	,						stride=lowercase_	,						pad_to_multiple_of=lowercase_	,						return_token_type_ids=lowercase_	,						return_attention_mask=lowercase_	,						return_overflowing_tokens=lowercase_	,						return_special_tokens_mask=lowercase_	,						return_offsets_mapping=lowercase_	,						return_length=lowercase_	,						verbose=lowercase_	,						return_tensors=lowercase_	,						**lowercase_	,						)

       # add pixel values
       lowercase__ :   Optional[int]    					=				features.pop("pixel_values"  )
       if return_overflowing_tokens is True:
           lowercase__ :   Dict    					=				self.get_overflowing_images(lowercase_	,						encoded_inputs["overflow_to_sample_mapping"]  )
       lowercase__ :   str    					=				images

       return encoded_inputs






   def       __UpperCamelCase     (      self				:     List[Any]	,						lowercase_				:     Tuple	,						lowercase_				:     List[Any]  )					->						Dict:
       # in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
       lowercase__ :   Tuple    					=				[]
       for sample_idx in overflow_to_sample_mapping:
           images_with_overflow.append(images[sample_idx]  )

       if len(lowercase_  ) != len(lowercase_  ):
           raise ValueError(
               "Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got"
               F''' {len(lowercase_  )} and {len(lowercase_  )}'''  )

       return images_with_overflow






   def       __UpperCamelCase     (      self				:     int	,						*lowercase_				:     Union[str, Any]	,						**lowercase_				:     List[str]  )					->						Union[str, Any]:
       return self.tokenizer.batch_decode(*lowercase_	,						**lowercase_  )






   def       __UpperCamelCase     (      self				:     Union[str, Any]	,						*lowercase_				:     str	,						**lowercase_				:     int  )					->						Dict:
       return self.tokenizer.decode(*lowercase_	,						**lowercase_  )






   @property
   def       __UpperCamelCase     (      self				:     Any  )					->						Any:
       return ["input_ids", "bbox", "attention_mask", "pixel_values"]






   @property
   def       __UpperCamelCase     (      self				:     Optional[int]  )					->						Optional[Any]:
       warnings.warn(
           "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."	,						lowercase_	,						)
       return self.image_processor_class







   @property
   def       __UpperCamelCase     (      self				:     List[Any]  )					->						Tuple:
       warnings.warn(
           "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."	,						lowercase_	,						)
       return self.image_processor



