




'''simple docstring'''





import copy
import inspect
import unittest

import numpy as np
from huggingface_hub import hf_hub_download

from transformers import VivitConfig
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


if is_torch_available():
      import torch
      from torch import nn

      from transformers import MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING, VivitForVideoClassification, VivitModel
      from transformers.models.vivit.modeling_vivit import VIVIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
      from transformers import VivitImageProcessor





class 						SCREAMING_SNAKE_CASE__		:
     def __init__(	self  :		str					,	__lowerCamelCase  :		List[str]					,	__lowerCamelCase  :		List[str]=2					,	__lowerCamelCase  :		Any=True					,	__lowerCamelCase  :		List[str]=True					,	__lowerCamelCase  :		Dict=10					,	__lowerCamelCase  :		int=10					,	__lowerCamelCase  :		str=8					,	__lowerCamelCase  :		Tuple=[2, 4, 4]					,	__lowerCamelCase  :		Any=3					,	__lowerCamelCase  :		Optional[int]=7_68					,	__lowerCamelCase  :		int=5					,	__lowerCamelCase  :		Optional[Any]=4					,	__lowerCamelCase  :		Tuple=37					,	__lowerCamelCase  :		Optional[Any]="gelu_fast"					,	__lowerCamelCase  :		Optional[Any]=0.0					,	__lowerCamelCase  :		str=0.0					,	__lowerCamelCase  :		Optional[Any]=0.02					,	__lowerCamelCase  :		int=1e-06					,	__lowerCamelCase  :		int=True					,	__lowerCamelCase  :		Tuple=None					,	):



       """simple docstring"""
       lowerCAmelCase__								=			parent
       lowerCAmelCase__								=			batch_size
       lowerCAmelCase__								=			is_training
       lowerCAmelCase__								=			use_labels
       lowerCAmelCase__								=			num_labels
       lowerCAmelCase__								=			image_size
       lowerCAmelCase__								=			num_frames
       lowerCAmelCase__								=			tubelet_size
       lowerCAmelCase__								=			num_channels
       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__								=			initializer_range
       lowerCAmelCase__								=			layer_norm_eps
       lowerCAmelCase__								=			qkv_bias
       lowerCAmelCase__								=			scope

       lowerCAmelCase__								=			(
           (self.image_size // self.tubelet_size[2])
           * (self.image_size // self.tubelet_size[1])
           * (self.num_frames // self.tubelet_size[0])
       ) + 1  # CLS token
     def 					A__			(	self  :		List[Any]	):



       """simple docstring"""
       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 					A__			(	self  :		List[Any]	):



       """simple docstring"""
       lowerCAmelCase__								=			VivitConfig(
           num_frames=self.num_frames					,	image_size=self.image_size					,	tubelet_size=self.tubelet_size					,	num_channels=self.num_channels					,	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					,	layer_norm_eps=self.layer_norm_eps					,	qkv_bias=self.qkv_bias					,	)
       lowerCAmelCase__								=			self.num_labels
       return config
     def 					A__			(	self  :		Any					,	__lowerCamelCase  :		Union[str, Any]					,	__lowerCamelCase  :		Optional[int]					,	__lowerCamelCase  :		List[str]	):



       """simple docstring"""
       lowerCAmelCase__								=			VivitModel(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 					A__			(	self  :		List[str]					,	__lowerCamelCase  :		str					,	__lowerCamelCase  :		Dict					,	__lowerCamelCase  :		Optional[int]	):



       """simple docstring"""
       lowerCAmelCase__								=			VivitForVideoClassification(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 					A__			(	self  :		Any	):



       """simple docstring"""
       lowerCAmelCase__								=			self.prepare_config_and_inputs()
       lowerCAmelCase__								=			config_and_inputs
       lowerCAmelCase__								=			{'''pixel_values''': pixel_values}
       return config, inputs_dict





@require_torch
class 						SCREAMING_SNAKE_CASE__		(__SCREAMING_SNAKE_CASE						,				unittest.TestCase ):
     lowercase_   :			Tuple   =   (VivitModel, VivitForVideoClassification) if is_torch_available() else ()

     lowercase_   :			Dict   =   False
     lowercase_   :			Union[str, Any]   =   False
     lowercase_   :			List[Any]   =   False
     lowercase_   :			Tuple   =   False
     def 					A__			(	self  :		Any	):



       """simple docstring"""
       lowerCAmelCase__								=			VivitModelTester(self	)
       lowerCAmelCase__								=			ConfigTester(self					,	config_class=UpperCamelCase__					,	has_text_modality=UpperCamelCase__					,	hidden_size=37	)
     def 					A__			(	self  :		Union[str, Any]					,	__lowerCamelCase  :		int					,	__lowerCamelCase  :		List[Any]					,	__lowerCamelCase  :		int=False	):



       """simple docstring"""
       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 					A__			(	self  :		Optional[Any]	):



       """simple docstring"""
       self.config_tester.run_common_tests()
     @unittest.skip(reason='''Vivit does not use inputs_embeds'''	)
     def 					A__			(	self  :		List[str]	):



       """simple docstring"""
       pass
     def 					A__			(	self  :		Dict	):



       """simple docstring"""
       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 					A__			(	self  :		Optional[Any]	):



       """simple docstring"""
       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''', '''head_mask''']
         self.assertListEqual(arg_names[:2]					,	UpperCamelCase__	)
     def 					A__			(	self  :		Optional[Any]	):



       """simple docstring"""
       lowerCAmelCase__								=			self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*UpperCamelCase__	)
     def 					A__			(	self  :		str	):



       """simple docstring"""
       lowerCAmelCase__								=			self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_video_classification(*UpperCamelCase__	)
     @slow
     def 					A__			(	self  :		Dict	):



       """simple docstring"""







       for model_name in VIVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
         lowerCAmelCase__								=			VivitModel.from_pretrained(UpperCamelCase__	)
         self.assertIsNotNone(UpperCamelCase__	)
     def 					A__			(	self  :		List[Any]	):



       """simple docstring"""
       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__								=			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	)

         self.assertListEqual(
             list(attentions[0].shape[-3:]	)					,	[self.model_tester.num_attention_heads, seq_len, seq_len]					,	)
         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	)

         self.assertListEqual(
             list(self_attentions[0].shape[-3:]	)					,	[self.model_tester.num_attention_heads, seq_len, seq_len]					,	)
     def 					A__			(	self  :		Optional[Any]	):



       """simple docstring"""







       def check_hidden_states_output(__lowerCamelCase  :		str					,	__lowerCamelCase  :		Optional[int]					,	__lowerCamelCase  :		Tuple	):
         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__								=			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__	)



     @unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.'''	)
     def 					A__			(	self  :		Dict	):



       """simple docstring"""
       pass



def 		a_       (   ):
  lowerCAmelCase__								=			hf_hub_download(
      repo_id='''hf-internal-testing/spaghetti-video'''      ,   filename='''eating_spaghetti_32_frames.npy'''      ,   repo_type='''dataset'''							)
  lowerCAmelCase__								=			np.load(SCREAMING_SNAKE_CASE__							)
  return list(SCREAMING_SNAKE_CASE__							)





@require_torch
@require_vision
class 						SCREAMING_SNAKE_CASE__		(unittest.TestCase ):
     @cached_property
     def 					A__			(	self  :		List[Any]	):



       """simple docstring"""
       return VivitImageProcessor() if is_vision_available() else None



     @slow
     def 					A__			(	self  :		List[str]	):



       """simple docstring"""
       lowerCAmelCase__								=			VivitForVideoClassification.from_pretrained('''google/vivit-b-16x2-kinetics400'''	).to(UpperCamelCase__	)

       lowerCAmelCase__								=			self.default_image_processor
       lowerCAmelCase__								=			prepare_video()
       lowerCAmelCase__								=			image_processor(UpperCamelCase__					,	return_tensors='''pt'''	).to(UpperCamelCase__	)

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

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

       # taken from original model
       lowerCAmelCase__								=			torch.tensor([-1.0543, 2.0764, -0.2104, 0.4439, -0.9658]	).to(UpperCamelCase__	)

       self.assertTrue(torch.allclose(outputs.logits[0, :5]					,	UpperCamelCase__					,	atol=1e-4	)	)







from dataclasses import dataclass
from typing import Optional, Tuple, Union

import flax
import jax.numpy as jnp
from scipy import integrate

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import (
    CommonSchedulerState,
    FlaxKarrasDiffusionSchedulers,
    FlaxSchedulerMixin,
    FlaxSchedulerOutput,
    broadcast_to_shape_from_left,
)





@flax.struct.dataclass
class 						SCREAMING_SNAKE_CASE__		:
     lowercase_   :			CommonSchedulerState

     # setable values
     lowercase_   :			jnp.ndarray
     lowercase_   :			jnp.ndarray
     lowercase_   :			jnp.ndarray
     lowercase_   :			Optional[int]   =   None

     # running values
     lowercase_   :			Optional[jnp.ndarray]   =   None
     @classmethod
     def 					A__			(	cls  :		List[str]					,	__lowerCamelCase  :		CommonSchedulerState					,	__lowerCamelCase  :		jnp.ndarray					,	__lowerCamelCase  :		jnp.ndarray					,	__lowerCamelCase  :		jnp.ndarray	):



       """simple docstring"""
       return cls(common=__lowerCamelCase					,	init_noise_sigma=__lowerCamelCase					,	timesteps=__lowerCamelCase					,	sigmas=__lowerCamelCase	)





@dataclass
class 						SCREAMING_SNAKE_CASE__		(_a ):
     lowercase_   :			LMSDiscreteSchedulerState





class 						SCREAMING_SNAKE_CASE__		(_a						,				_a ):
     lowercase_   :			int   =   [e.name for e in FlaxKarrasDiffusionSchedulers]

     lowercase_   :			jnp.dtype
     @property
     def 					A__			(	self  :		Optional[Any]	):



       """simple docstring"""
       return True
     @register_to_config
     def __init__(	self  :		int					,	__lowerCamelCase  :		int = 10_00					,	__lowerCamelCase  :		float = 0.0001					,	__lowerCamelCase  :		float = 0.02					,	__lowerCamelCase  :		str = "linear"					,	__lowerCamelCase  :		Optional[jnp.ndarray] = None					,	__lowerCamelCase  :		str = "epsilon"					,	__lowerCamelCase  :		jnp.dtype = jnp.floataa					,	):



       """simple docstring"""
       lowerCAmelCase__								=			dtype
     def 					A__			(	self  :		List[str]					,	__lowerCamelCase  :		Optional[CommonSchedulerState] = None	):



       """simple docstring"""
       if common is None:
         lowerCAmelCase__								=			CommonSchedulerState.create(self	)

       lowerCAmelCase__								=			jnp.arange(0					,	self.config.num_train_timesteps	).round()[::-1]
       lowerCAmelCase__								=			((1 - common.alphas_cumprod) / common.alphas_cumprod) ** 0.5

       # standard deviation of the initial noise distribution
       lowerCAmelCase__								=			sigmas.max()

       return LMSDiscreteSchedulerState.create(
           common=__lowerCamelCase					,	init_noise_sigma=__lowerCamelCase					,	timesteps=__lowerCamelCase					,	sigmas=__lowerCamelCase					,	)
     def 					A__			(	self  :		str					,	__lowerCamelCase  :		LMSDiscreteSchedulerState					,	__lowerCamelCase  :		jnp.ndarray					,	__lowerCamelCase  :		int	):



       """simple docstring"""
       ((lowerCAmelCase__)							,			)								=			jnp.where(state.timesteps == timestep					,	size=1	)
       lowerCAmelCase__								=			step_index[0]

       lowerCAmelCase__								=			state.sigmas[step_index]
       lowerCAmelCase__								=			sample / ((sigma**2 + 1) ** 0.5)
       return sample
     def 					A__			(	self  :		List[Any]					,	__lowerCamelCase  :		LMSDiscreteSchedulerState					,	__lowerCamelCase  :		Optional[int]					,	__lowerCamelCase  :		str					,	__lowerCamelCase  :		Any	):



       """simple docstring"""

       def lms_derivative(__lowerCamelCase  :		int	):
         lowerCAmelCase__								=			1.0
         for k in range(__lowerCamelCase	):
           if current_order == k:
             continue
           prod *= (tau - state.sigmas[t - k]) / (state.sigmas[t - current_order] - state.sigmas[t - k])
         return prod

       lowerCAmelCase__								=			integrate.quad(__lowerCamelCase					,	state.sigmas[t]					,	state.sigmas[t + 1]					,	epsrel=1e-4	)[0]

       return integrated_coeff
     def 					A__			(	self  :		str					,	__lowerCamelCase  :		LMSDiscreteSchedulerState					,	__lowerCamelCase  :		int					,	__lowerCamelCase  :		Tuple = ()	):



       """simple docstring"""
       lowerCAmelCase__								=			jnp.linspace(self.config.num_train_timesteps - 1					,	0					,	__lowerCamelCase					,	dtype=self.dtype	)

       lowerCAmelCase__								=			jnp.floor(__lowerCamelCase	).astype(jnp.intaa	)
       lowerCAmelCase__								=			jnp.ceil(__lowerCamelCase	).astype(jnp.intaa	)

       lowerCAmelCase__								=			jnp.mod(__lowerCamelCase					,	1.0	)

       lowerCAmelCase__								=			((1 - state.common.alphas_cumprod) / state.common.alphas_cumprod) ** 0.5
       lowerCAmelCase__								=			(1 - frac) * sigmas[low_idx] + frac * sigmas[high_idx]
       lowerCAmelCase__								=			jnp.concatenate([sigmas, jnp.array([0.0]					,	dtype=self.dtype	)]	)

       lowerCAmelCase__								=			timesteps.astype(jnp.intaa	)

       # initial running values
       lowerCAmelCase__								=			jnp.zeros((0,) + shape					,	dtype=self.dtype	)

       return state.replace(
           timesteps=__lowerCamelCase					,	sigmas=__lowerCamelCase					,	num_inference_steps=__lowerCamelCase					,	derivatives=__lowerCamelCase					,	)
     def 					A__			(	self  :		Union[str, Any]					,	__lowerCamelCase  :		LMSDiscreteSchedulerState					,	__lowerCamelCase  :		jnp.ndarray					,	__lowerCamelCase  :		int					,	__lowerCamelCase  :		jnp.ndarray					,	__lowerCamelCase  :		int = 4					,	__lowerCamelCase  :		bool = True					,	):



       """simple docstring"""
       if state.num_inference_steps is None:
         raise ValueError(
             '''Number of inference steps is \'None\', you need to run \'set_timesteps\' after creating the scheduler'''	)

       lowerCAmelCase__								=			state.sigmas[timestep]

       # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
       if self.config.prediction_type == "epsilon":
         lowerCAmelCase__								=			sample - sigma * model_output
       elif self.config.prediction_type == "v_prediction":
         # * c_out + input * c_skip
         lowerCAmelCase__								=			model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
       else:
         raise ValueError(
             F"""prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`"""	)

       # 2. Convert to an ODE derivative
       lowerCAmelCase__								=			(sample - pred_original_sample) / sigma
       lowerCAmelCase__								=			state.replace(derivatives=jnp.append(state.derivatives					,	__lowerCamelCase	)	)
       if len(state.derivatives	) > order:
         lowerCAmelCase__								=			state.replace(derivatives=jnp.delete(state.derivatives					,	0	)	)

       # 3. Compute linear multistep coefficients
       lowerCAmelCase__								=			min(timestep + 1					,	__lowerCamelCase	)
       lowerCAmelCase__								=			[self.get_lms_coefficient(__lowerCamelCase					,	__lowerCamelCase					,	__lowerCamelCase					,	__lowerCamelCase	) for curr_order in range(__lowerCamelCase	)]

       # 4. Compute previous sample based on the derivatives path
       lowerCAmelCase__								=			sample + sum(
           coeff * derivative for coeff, derivative in zip(__lowerCamelCase					,	reversed(state.derivatives	)	)	)

       if not return_dict:
         return (prev_sample, state)

       return FlaxLMSSchedulerOutput(prev_sample=__lowerCamelCase					,	state=__lowerCamelCase	)
     def 					A__			(	self  :		Dict					,	__lowerCamelCase  :		LMSDiscreteSchedulerState					,	__lowerCamelCase  :		jnp.ndarray					,	__lowerCamelCase  :		jnp.ndarray					,	__lowerCamelCase  :		jnp.ndarray					,	):



       """simple docstring"""
       lowerCAmelCase__								=			state.sigmas[timesteps].flatten()
       lowerCAmelCase__								=			broadcast_to_shape_from_left(__lowerCamelCase					,	noise.shape	)

       lowerCAmelCase__								=			original_samples + noise * sigma

       return noisy_samples



     def __len__(	self  :		str	):



       """simple docstring"""
       return self.config.num_train_timesteps

