

from typing import Optional, Tuple, Union

import tensorflow as tf

from ...activations_tf import ACTaFN
from ...modeling_tf_outputs import (
    TFBaseModelOutputWithNoAttention,
    TFBaseModelOutputWithPoolingAndNoAttention,
    TFImageClassifierOutputWithNoAttention,
)
from ...modeling_tf_utils import TFPreTrainedModel, TFSequenceClassificationLoss, keras_serializable, unpack_inputs
from ...tf_utils import shape_list
from ...utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging
from .configuration_resnet import ResNetConfig


_lowerCamelCase							:  Union[str, Any]     =				logging.get_logger(__name__)

# General docstring
_lowerCamelCase							:  Any     =				"ResNetConfig"

# Base docstring
_lowerCamelCase							:  Union[str, Any]     =				"microsoft/resnet-50"
_lowerCamelCase							:  Tuple     =				[1, 2_0_4_8, 7, 7]

# Image classification docstring
_lowerCamelCase							:  Any     =				"microsoft/resnet-50"
_lowerCamelCase							:  int     =				"tiger cat"

_lowerCamelCase							:  List[str]     =				[
    "microsoft/resnet-50",
    # See all resnet models at https://huggingface.co/models?filter=resnet
]





class    __UpperCAmelCase	(			tf.keras.layers.Layer		):



   def __init__(				self				:	List[str],						__A				:	int,						__A				:	int = 3,						__A				:	int = 1,						__A				:	str = "relu",						**__A				:	List[Any]		):
     super().__init__(**__A		)
     UpperCAmelCase			:				Optional[Any]										=					kernel_size // 2
     UpperCAmelCase			:				List[Any]										=					tf.keras.layers.ConvaD(
         __A,						kernel_size=__A,						strides=__A,						padding='''valid''',						use_bias=__A,						name='''convolution'''		)
     # Use same default momentum and epsilon as PyTorch equivalent
     UpperCAmelCase			:				str										=					tf.keras.layers.BatchNormalization(epsilon=1E-5,						momentum=0.9,						name='''normalization'''		)
     UpperCAmelCase			:				int										=					ACTaFN[activation] if activation is not None else tf.keras.layers.Activation('''linear'''		)



   def 			__magic_name__       (				self				:	str,						__A				:	tf.Tensor		):
     # Pad to match that done in the PyTorch Conv2D model
     UpperCAmelCase			:				Optional[Any]										=					(self.pad_value, self.pad_value)
     UpperCAmelCase			:				Union[str, Any]										=					tf.pad(__A,						[(0, 0), height_pad, width_pad, (0, 0)]		)
     UpperCAmelCase			:				Optional[int]										=					self.conv(__A		)
     return hidden_state





   def 			__magic_name__       (				self				:	Optional[int],						__A				:	tf.Tensor,						__A				:	bool = False		):
     UpperCAmelCase			:				Optional[int]										=					self.convolution(__A		)
     UpperCAmelCase			:				Any										=					self.normalization(__A,						training=__A		)
     UpperCAmelCase			:				Dict										=					self.activation(__A		)
     return hidden_state






class    __UpperCAmelCase	(			tf.keras.layers.Layer		):



   def __init__(				self				:	Dict,						__A				:	ResNetConfig,						**__A				:	Any		):
     super().__init__(**__A		)
     UpperCAmelCase			:				List[str]										=					TFResNetConvLayer(
         config.embedding_size,						kernel_size=7,						stride=2,						activation=config.hidden_act,						name='''embedder''',						)
     UpperCAmelCase			:				List[Any]										=					tf.keras.layers.MaxPoolaD(pool_size=3,						strides=2,						padding='''valid''',						name='''pooler'''		)
     UpperCAmelCase			:				List[str]										=					config.num_channels





   def 			__magic_name__       (				self				:	List[str],						__A				:	tf.Tensor,						__A				:	bool = False		):
     UpperCAmelCase					,		UpperCAmelCase					,		UpperCAmelCase					,		UpperCAmelCase			:				int										=					shape_list(__A		)
     if tf.executing_eagerly() and num_channels != self.num_channels:
       raise ValueError(
           '''Make sure that the channel dimension of the pixel values match with the one set in the configuration.'''		)
     UpperCAmelCase			:				List[str]										=					pixel_values
     UpperCAmelCase			:				Optional[Any]										=					self.embedder(__A		)
     UpperCAmelCase			:				Optional[int]										=					tf.pad(__A,						[[0, 0], [1, 1], [1, 1], [0, 0]]		)
     UpperCAmelCase			:				Optional[int]										=					self.pooler(__A		)
     return hidden_state






class    __UpperCAmelCase	(			tf.keras.layers.Layer		):



   def __init__(				self				:	Dict,						__A				:	int,						__A				:	int = 2,						**__A				:	Tuple		):
     super().__init__(**__A		)
     UpperCAmelCase			:				Tuple										=					tf.keras.layers.ConvaD(
         __A,						kernel_size=1,						strides=__A,						use_bias=__A,						name='''convolution'''		)
     # Use same default momentum and epsilon as PyTorch equivalent
     UpperCAmelCase			:				Dict										=					tf.keras.layers.BatchNormalization(epsilon=1E-5,						momentum=0.9,						name='''normalization'''		)





   def 			__magic_name__       (				self				:	Tuple,						__A				:	tf.Tensor,						__A				:	bool = False		):
     UpperCAmelCase			:				Tuple										=					x
     UpperCAmelCase			:				str										=					self.convolution(__A		)
     UpperCAmelCase			:				Tuple										=					self.normalization(__A,						training=__A		)
     return hidden_state






class    __UpperCAmelCase	(			tf.keras.layers.Layer		):



   def __init__(				self				:	Tuple,						__A				:	int,						__A				:	int,						__A				:	int = 1,						__A				:	str = "relu",						**__A				:	Union[str, Any]		):
     super().__init__(**__A		)
     UpperCAmelCase			:				Optional[int]										=					in_channels != out_channels or stride != 1
     UpperCAmelCase			:				List[str]										=					TFResNetConvLayer(__A,						stride=__A,						name='''layer.0'''		)
     UpperCAmelCase			:				Union[str, Any]										=					TFResNetConvLayer(__A,						activation=__A,						name='''layer.1'''		)
     UpperCAmelCase			:				Dict										=					(
         TFResNetShortCut(__A,						stride=__A,						name='''shortcut'''		)
         if should_apply_shortcut
         else tf.keras.layers.Activation('''linear''',						name='''shortcut'''		)
     )
     UpperCAmelCase			:				str										=					ACTaFN[activation]





   def 			__magic_name__       (				self				:	Optional[int],						__A				:	tf.Tensor,						__A				:	bool = False		):
     UpperCAmelCase			:				Tuple										=					hidden_state
     UpperCAmelCase			:				Tuple										=					self.conva(__A,						training=__A		)
     UpperCAmelCase			:				Dict										=					self.conva(__A,						training=__A		)
     UpperCAmelCase			:				List[Any]										=					self.shortcut(__A,						training=__A		)
     hidden_state += residual
     UpperCAmelCase			:				str										=					self.activation(__A		)
     return hidden_state






class    __UpperCAmelCase	(			tf.keras.layers.Layer		):



   def __init__(				self				:	Union[str, Any],						__A				:	int,						__A				:	int,						__A				:	int = 1,						__A				:	str = "relu",						__A				:	int = 4,						**__A				:	Union[str, Any],						):
     super().__init__(**__A		)
     UpperCAmelCase			:				Dict										=					in_channels != out_channels or stride != 1
     UpperCAmelCase			:				Optional[int]										=					out_channels // reduction
     UpperCAmelCase			:				List[str]										=					TFResNetConvLayer(__A,						kernel_size=1,						name='''layer.0'''		)
     UpperCAmelCase			:				Union[str, Any]										=					TFResNetConvLayer(__A,						stride=__A,						name='''layer.1'''		)
     UpperCAmelCase			:				int										=					TFResNetConvLayer(__A,						kernel_size=1,						activation=__A,						name='''layer.2'''		)
     UpperCAmelCase			:				Tuple										=					(
         TFResNetShortCut(__A,						stride=__A,						name='''shortcut'''		)
         if should_apply_shortcut
         else tf.keras.layers.Activation('''linear''',						name='''shortcut'''		)
     )
     UpperCAmelCase			:				Union[str, Any]										=					ACTaFN[activation]





   def 			__magic_name__       (				self				:	str,						__A				:	tf.Tensor,						__A				:	bool = False		):
     UpperCAmelCase			:				Union[str, Any]										=					hidden_state
     UpperCAmelCase			:				Union[str, Any]										=					self.conva(__A,						training=__A		)
     UpperCAmelCase			:				List[str]										=					self.conva(__A,						training=__A		)
     UpperCAmelCase			:				List[Any]										=					self.conva(__A,						training=__A		)
     UpperCAmelCase			:				str										=					self.shortcut(__A,						training=__A		)
     hidden_state += residual
     UpperCAmelCase			:				Optional[Any]										=					self.activation(__A		)
     return hidden_state






class    __UpperCAmelCase	(			tf.keras.layers.Layer		):



   def __init__(				self				:	List[Any],						__A				:	ResNetConfig,						__A				:	int,						__A				:	int,						__A				:	int = 2,						__A				:	int = 2,						**__A				:	Optional[int]		):
     super().__init__(**__A		)

     UpperCAmelCase			:				Dict										=					TFResNetBottleNeckLayer if config.layer_type == '''bottleneck''' else TFResNetBasicLayer

     UpperCAmelCase			:				int										=					[layer(__A,						__A,						stride=__A,						activation=config.hidden_act,						name='''layers.0'''		)]
     layers += [
         layer(__A,						__A,						activation=config.hidden_act,						name=F'''layers.{i + 1}'''		)
         for i in range(depth - 1		)
     ]
     UpperCAmelCase			:				str										=					layers





   def 			__magic_name__       (				self				:	List[str],						__A				:	tf.Tensor,						__A				:	bool = False		):
     for layer in self.stage_layers:
       UpperCAmelCase			:				Optional[int]										=					layer(__A,						training=__A		)
     return hidden_state






class    __UpperCAmelCase	(			tf.keras.layers.Layer		):



   def __init__(				self				:	Optional[int],						__A				:	ResNetConfig,						**__A				:	Dict		):
     super().__init__(**__A		)
     # based on `downsample_in_first_stage` the first layer of the first stage may or may not downsample the input
     UpperCAmelCase			:				Union[str, Any]										=					[
         TFResNetStage(
             __A,						config.embedding_size,						config.hidden_sizes[0],						stride=2 if config.downsample_in_first_stage else 1,						depth=config.depths[0],						name='''stages.0''',						)
     ]
     for i, (in_channels, out_channels, depth) in enumerate(
         zip(config.hidden_sizes,						config.hidden_sizes[1:],						config.depths[1:]		)		):
       self.stages.append(TFResNetStage(__A,						__A,						__A,						depth=__A,						name=F'''stages.{i + 1}'''		)		)





   def 			__magic_name__       (				self				:	Dict,						__A				:	tf.Tensor,						__A				:	bool = False,						__A				:	bool = True,						__A				:	bool = False,						):
     UpperCAmelCase			:				Union[str, Any]										=					() if output_hidden_states else None

     for stage_module in self.stages:
       if output_hidden_states:
         UpperCAmelCase			:				Tuple										=					hidden_states + (hidden_state,)

       UpperCAmelCase			:				Tuple										=					stage_module(__A,						training=__A		)

     if output_hidden_states:
       UpperCAmelCase			:				int										=					hidden_states + (hidden_state,)

     if not return_dict:
       return tuple(v for v in [hidden_state, hidden_states] if v is not None		)

     return TFBaseModelOutputWithNoAttention(last_hidden_state=__A,						hidden_states=__A		)






class    __UpperCAmelCase	(			lowerCamelCase__		):
   UpperCamelCase  	=	ResNetConfig
   UpperCamelCase  	=	"""resnet"""
   UpperCamelCase  	=	"""pixel_values"""



   @property
   def 			__magic_name__       (				self				:	Union[str, Any]		):
     return {"pixel_values": tf.TensorSpec(shape=(None, self.config.num_channels, 2_2_4, 2_2_4),						dtype=tf.floataa		)}






_lowerCamelCase							:  Union[str, Any]     =				R"\n    This model is a TensorFlow\n    [tf.keras.layers.Layer](https://www.tensorflow.org/api_docs/python/tf/keras/layers/Layer) sub-class. Use it as a\n    regular TensorFlow Module and refer to the TensorFlow documentation for all matter related to general usage and\n    behavior.\n\n    Parameters:\n        config ([`ResNetConfig`]): Model configuration class with all the parameters of the model.\n            Initializing with a config file does not load the weights associated with the model, only the\n            configuration. Check out the [`~TFPreTrainedModel.from_pretrained`] method to load the model weights.\n"


_lowerCamelCase							:  List[Any]     =				R"\n    Args:\n        pixel_values (`tf.Tensor` of shape `(batch_size, num_channels, height, width)`):\n            Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See\n            [`ConvNextImageProcessor.__call__`] for details.\n\n        output_hidden_states (`bool`, *optional*):\n            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for\n            more detail.\n        return_dict (`bool`, *optional*):\n            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.\n"





@keras_serializable
class    __UpperCAmelCase	(			tf.keras.layers.Layer		):
   UpperCamelCase  	=	ResNetConfig



   def __init__(				self				:	List[Any],						__A				:	ResNetConfig,						**__A				:	str		):
     super().__init__(**__A		)
     UpperCAmelCase			:				List[Any]										=					config
     UpperCAmelCase			:				Tuple										=					TFResNetEmbeddings(__A,						name='''embedder'''		)
     UpperCAmelCase			:				Union[str, Any]										=					TFResNetEncoder(__A,						name='''encoder'''		)
     UpperCAmelCase			:				Dict										=					tf.keras.layers.GlobalAveragePoolingaD(keepdims=__A		)





   @unpack_inputs
   def 			__magic_name__       (				self				:	Union[str, Any],						__A				:	tf.Tensor,						__A				:	Optional[bool] = None,						__A				:	Optional[bool] = None,						__A				:	bool = False,						):
     UpperCAmelCase			:				List[str]										=					(
         output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
     )
     UpperCAmelCase			:				int										=					return_dict if return_dict is not None else self.config.use_return_dict

     # TF 2.0 image layers can't use NCHW format when running on CPU.
     # We transpose to NHWC format and then transpose back after the full forward pass.
     # (batch_size, num_channels, height, width) -> (batch_size, height, width, num_channels)
     UpperCAmelCase			:				Optional[int]										=					tf.transpose(__A,						perm=[0, 2, 3, 1]		)
     UpperCAmelCase			:				List[Any]										=					self.embedder(__A,						training=__A		)

     UpperCAmelCase			:				int										=					self.encoder(
         __A,						output_hidden_states=__A,						return_dict=__A,						training=__A		)

     UpperCAmelCase			:				int										=					encoder_outputs[0]

     UpperCAmelCase			:				Tuple										=					self.pooler(__A		)

     # Transpose all the outputs to the NCHW format
     # (batch_size, height, width, num_channels) -> (batch_size, num_channels, height, width)
     UpperCAmelCase			:				Optional[Any]										=					tf.transpose(__A,						(0, 3, 1, 2)		)
     UpperCAmelCase			:				Tuple										=					tf.transpose(__A,						(0, 3, 1, 2)		)
     UpperCAmelCase			:				Tuple										=					()
     for hidden_state in encoder_outputs[1:]:
       UpperCAmelCase			:				int										=					hidden_states + tuple(tf.transpose(__A,						(0, 3, 1, 2)		) for h in hidden_state		)

     if not return_dict:
       return (last_hidden_state, pooled_output) + hidden_states

     UpperCAmelCase			:				Optional[int]										=					hidden_states if output_hidden_states else None

     return TFBaseModelOutputWithPoolingAndNoAttention(
         last_hidden_state=__A,						pooler_output=__A,						hidden_states=__A,						)






@add_start_docstrings(
    """The bare ResNet model outputting raw features without any specific head on top."""     ,    lowerCamelCase__     ,    )
class    __UpperCAmelCase	(			lowerCamelCase__		):



   def __init__(				self				:	Dict,						__A				:	ResNetConfig,						**__A				:	List[Any]		):
     super().__init__(__A,						**__A		)
     UpperCAmelCase			:				Optional[int]										=					TFResNetMainLayer(config=__A,						name='''resnet'''		)





   @add_start_docstrings_to_model_forward(__A		)
   @add_code_sample_docstrings(
       checkpoint=_CHECKPOINT_FOR_DOC,						output_type=__A,						config_class=_CONFIG_FOR_DOC,						modality='''vision''',						expected_output=_EXPECTED_OUTPUT_SHAPE,						)
   @unpack_inputs
   def 			__magic_name__       (				self				:	Union[str, Any],						__A				:	tf.Tensor,						__A				:	Optional[bool] = None,						__A				:	Optional[bool] = None,						__A				:	bool = False,						):
     UpperCAmelCase			:				Tuple										=					(
         output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
     )
     UpperCAmelCase			:				List[Any]										=					return_dict if return_dict is not None else self.config.use_return_dict

     UpperCAmelCase			:				str										=					self.resnet(
         pixel_values=__A,						output_hidden_states=__A,						return_dict=__A,						training=__A,						)
     return resnet_outputs






@add_start_docstrings(
    """
    ResNet Model with an image classification head on top (a linear layer on top of the pooled features), e.g. for
    ImageNet.
    """     ,    lowerCamelCase__     ,    )
class    __UpperCAmelCase	(			lowerCamelCase__     ,    lowerCamelCase__		):



   def __init__(				self				:	str,						__A				:	ResNetConfig,						**__A				:	Optional[int]		):
     super().__init__(__A,						**__A		)
     UpperCAmelCase			:				Union[str, Any]										=					config.num_labels
     UpperCAmelCase			:				List[Any]										=					TFResNetMainLayer(__A,						name='''resnet'''		)
     # classification head
     UpperCAmelCase			:				Tuple										=					(
         tf.keras.layers.Dense(config.num_labels,						name='''classifier.1'''		)
         if config.num_labels > 0
         else tf.keras.layers.Activation('''linear''',						name='''classifier.1'''		)
     )



   def 			__magic_name__       (				self				:	int,						__A				:	tf.Tensor		):
     UpperCAmelCase			:				List[str]										=					tf.keras.layers.Flatten()(__A		)
     UpperCAmelCase			:				Dict										=					self.classifier_layer(__A		)
     return logits





   @add_start_docstrings_to_model_forward(__A		)
   @add_code_sample_docstrings(
       checkpoint=_IMAGE_CLASS_CHECKPOINT,						output_type=__A,						config_class=_CONFIG_FOR_DOC,						expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT,						)
   @unpack_inputs
   def 			__magic_name__       (				self				:	Optional[int],						__A				:	tf.Tensor = None,						__A				:	tf.Tensor = None,						__A				:	bool = None,						__A				:	bool = None,						__A				:	bool = False,						):
     UpperCAmelCase			:				Any										=					return_dict if return_dict is not None else self.config.use_return_dict

     UpperCAmelCase			:				List[str]										=					self.resnet(
         __A,						output_hidden_states=__A,						return_dict=__A,						training=__A		)

     UpperCAmelCase			:				str										=					outputs.pooler_output if return_dict else outputs[1]

     UpperCAmelCase			:				Optional[Any]										=					self.classifier(__A		)

     UpperCAmelCase			:				Union[str, Any]										=					None if labels is None else self.hf_compute_loss(__A,						__A		)

     if not return_dict:
       UpperCAmelCase			:				Optional[int]										=					(logits,) + outputs[2:]
       return (loss,) + output if loss is not None else output

     return TFImageClassifierOutputWithNoAttention(loss=__A,						logits=__A,						hidden_states=outputs.hidden_states		)








import argparse
import json
import os.path
from collections import OrderedDict

import numpy as np
import requests
import torch
from flax.training.checkpoints import restore_checkpoint
from huggingface_hub import hf_hub_download

from transformers import VivitConfig, VivitForVideoClassification, VivitImageProcessor
from transformers.image_utils import PILImageResampling

def      a__	(						UpperCAmelCase				:     Dict     )    ->		Any:
  UpperCAmelCase			:				List[str]										=					'''https://storage.googleapis.com/scenic-bucket/vivit/kinetics_400/vivit_base_16x2_unfactorized/checkpoint'''

  with open(UpperCAmelCase							,					'''wb'''     ) as f:
    with requests.get(UpperCAmelCase							,					stream=UpperCAmelCase     ) as req:
      for chunk in req.iter_content(chunk_size=2_048     ):
        f.write(UpperCAmelCase     )

def      a__	(						)    ->		VivitConfig:
  UpperCAmelCase			:				Any										=					VivitConfig()

  UpperCAmelCase			:				Any										=					400
  UpperCAmelCase			:				int										=					'''huggingface/label-files'''
  UpperCAmelCase			:				int										=					'''kinetics400-id2label.json'''

  UpperCAmelCase			:				Tuple										=					json.load(open(hf_hub_download(UpperCAmelCase							,					UpperCAmelCase							,					repo_type='''dataset'''     )							,					'''r'''     )     )
  UpperCAmelCase			:				Dict										=					{int(UpperCAmelCase     ): v for k, v in idalabel.items()}
  UpperCAmelCase			:				Optional[Any]										=					idalabel
  UpperCAmelCase			:				Tuple										=					{v: k for k, v in idalabel.items()}
  return config

def      a__	(						)    ->		List[Any]:
  UpperCAmelCase			:				int										=					hf_hub_download(
      repo_id='''hf-internal-testing/spaghetti-video'''							,					filename='''eating_spaghetti_32_frames.npy'''							,					repo_type='''dataset'''     )
  UpperCAmelCase			:				Tuple										=					np.load(UpperCAmelCase     )
  return list(UpperCAmelCase     )

def      a__	(						UpperCAmelCase				:     np.ndarray     )    ->		Any:
  if np.ndim(UpperCAmelCase     ) == 2:
    return transform_attention_bias(UpperCAmelCase     )

  elif np.ndim(UpperCAmelCase     ) == 3:
    return transform_attention_kernel(UpperCAmelCase     )

  else:
    raise Exception(f'''Invalid number of dimesions: {np.ndim(UpperCAmelCase     )}'''     )

def      a__	(						UpperCAmelCase				:     np.ndarray     )    ->		List[Any]:
  return current.flatten()

def      a__	(						UpperCAmelCase				:     np.ndarray     )    ->		Optional[Any]:
  return np.reshape(UpperCAmelCase							,					(current.shape[0], current.shape[1] * current.shape[2])     ).T

def      a__	(						UpperCAmelCase				:     np.ndarray     )    ->		Any:
  return np.reshape(UpperCAmelCase							,					(current.shape[0] * current.shape[1], current.shape[2])     ).T

def      a__	(						UpperCAmelCase				:     Dict							,					UpperCAmelCase				:     Union[str, Any]     )    ->		Dict:
  UpperCAmelCase			:				Any										=					state_dict['''optimizer''']['''target''']['''Transformer'''][f'''encoderblock_{i}''']

  UpperCAmelCase			:				Dict										=					f'''encoder.layer.{i}.'''
  UpperCAmelCase			:				Optional[int]										=					{
      prefix + '''intermediate.dense.bias''': state['''MlpBlock_0''']['''Dense_0''']['''bias'''],
      prefix + '''intermediate.dense.weight''': np.transpose(state['''MlpBlock_0''']['''Dense_0''']['''kernel''']     ),
      prefix + '''output.dense.bias''': state['''MlpBlock_0''']['''Dense_1''']['''bias'''],
      prefix + '''output.dense.weight''': np.transpose(state['''MlpBlock_0''']['''Dense_1''']['''kernel''']     ),
      prefix + '''layernorm_before.bias''': state['''LayerNorm_0''']['''bias'''],
      prefix + '''layernorm_before.weight''': state['''LayerNorm_0''']['''scale'''],
      prefix + '''layernorm_after.bias''': state['''LayerNorm_1''']['''bias'''],
      prefix + '''layernorm_after.weight''': state['''LayerNorm_1''']['''scale'''],
      prefix
      + '''attention.attention.query.bias''': transform_attention(
          state['''MultiHeadDotProductAttention_0''']['''query''']['''bias''']     ),
      prefix
      + '''attention.attention.query.weight''': transform_attention(
          state['''MultiHeadDotProductAttention_0''']['''query''']['''kernel''']     ),
      prefix
      + '''attention.attention.key.bias''': transform_attention(state['''MultiHeadDotProductAttention_0''']['''key''']['''bias''']     ),
      prefix
      + '''attention.attention.key.weight''': transform_attention(
          state['''MultiHeadDotProductAttention_0''']['''key''']['''kernel''']     ),
      prefix
      + '''attention.attention.value.bias''': transform_attention(
          state['''MultiHeadDotProductAttention_0''']['''value''']['''bias''']     ),
      prefix
      + '''attention.attention.value.weight''': transform_attention(
          state['''MultiHeadDotProductAttention_0''']['''value''']['''kernel''']     ),
      prefix + '''attention.output.dense.bias''': state['''MultiHeadDotProductAttention_0''']['''out''']['''bias'''],
      prefix
      + '''attention.output.dense.weight''': transform_attention_output_weight(
          state['''MultiHeadDotProductAttention_0''']['''out''']['''kernel''']     ),
  }

  return new_state

def      a__	(						UpperCAmelCase				:     Any     )    ->		List[str]:
  return sum([1 if '''encoderblock_''' in k else 0 for k in state_dict['''optimizer''']['''target''']['''Transformer'''].keys()]     )

def      a__	(						UpperCAmelCase				:     Any							,					UpperCAmelCase				:     Dict=False     )    ->		Dict:
  UpperCAmelCase			:				int										=					get_n_layers(UpperCAmelCase     )

  UpperCAmelCase			:				List[str]										=					OrderedDict()

  UpperCAmelCase			:				int										=					state_dict['''optimizer''']['''target''']['''Transformer''']['''encoder_norm''']['''bias''']
  UpperCAmelCase			:				Tuple										=					state_dict['''optimizer''']['''target''']['''Transformer''']['''encoder_norm''']['''scale''']

  UpperCAmelCase			:				Union[str, Any]										=					np.transpose(
      state_dict['''optimizer''']['''target''']['''embedding''']['''kernel''']							,					(4, 3, 0, 1, 2)     )
  UpperCAmelCase			:				List[str]										=					state_dict['''optimizer''']['''target''']['''embedding''']['''bias''']

  UpperCAmelCase			:				Optional[int]										=					state_dict['''optimizer''']['''target''']['''cls''']
  UpperCAmelCase			:				str										=					state_dict['''optimizer''']['''target''']['''Transformer''']['''posembed_input'''][
      '''pos_embedding'''
  ]

  for i in range(UpperCAmelCase     ):
    new_state.update(transform_state_encoder_block(UpperCAmelCase							,					UpperCAmelCase     )     )

  if classification_head:
    UpperCAmelCase			:				List[Any]										=					{'''vivit.''' + k: v for k, v in new_state.items()}
    UpperCAmelCase			:				str										=					np.transpose(state_dict['''optimizer''']['''target''']['''output_projection''']['''kernel''']     )
    UpperCAmelCase			:				Dict										=					np.transpose(state_dict['''optimizer''']['''target''']['''output_projection''']['''bias''']     )

  return {k: torch.tensor(UpperCAmelCase     ) for k, v in new_state.items()}

def      a__	(						)    ->		VivitImageProcessor:
  UpperCAmelCase			:				Optional[int]										=					VivitImageProcessor()

  assert extractor.do_resize is True
  assert extractor.size == {"shortest_edge": 256}
  assert extractor.do_center_crop is True
  assert extractor.crop_size == {"width": 224, "height": 224}
  assert extractor.resample == PILImageResampling.BILINEAR

  # here: https://github.com/deepmind/dmvr/blob/master/dmvr/modalities.py
  # one can seen that add_image has default values for normalization_mean and normalization_std set to 0 and 1
  # which effectively means no normalization (and ViViT does not overwrite those when calling this func)
  assert extractor.do_normalize is False
  assert extractor.do_rescale is True
  assert extractor.rescale_factor == 1 / 255

  # zero-centering = True in original implementation
  assert extractor.do_zero_centering is True

  return extractor

def      a__	(						UpperCAmelCase				:     str     )    ->		Tuple:
  UpperCAmelCase			:				Union[str, Any]										=					'''checkpoint'''

  if not os.path.exists(UpperCAmelCase     ):
    download_checkpoint(UpperCAmelCase     )

  UpperCAmelCase			:				Tuple										=					restore_checkpoint(UpperCAmelCase							,					UpperCAmelCase     )
  UpperCAmelCase			:				Any										=					transform_state(UpperCAmelCase							,					classification_head=UpperCAmelCase     )

  UpperCAmelCase			:				Union[str, Any]										=					get_vivit_config()

  assert config.image_size == 224
  assert config.num_frames == 32

  UpperCAmelCase			:				Optional[int]										=					VivitForVideoClassification(UpperCAmelCase     )
  model.load_state_dict(UpperCAmelCase     )
  model.eval()

  UpperCAmelCase			:				List[Any]										=					get_processor()

  UpperCAmelCase			:				str										=					prepare_video()
  UpperCAmelCase			:				Union[str, Any]										=					extractor(UpperCAmelCase							,					return_tensors='''pt'''     )

  UpperCAmelCase			:				Optional[Any]										=					model(**UpperCAmelCase     )

  UpperCAmelCase			:				str										=					torch.Size([1, 400]     )
  UpperCAmelCase			:				int										=					torch.tensor([-1.0543, 2.0764, -0.2104, 0.4439, -0.9658]     )

  assert outputs.logits.shape == expected_shape
  assert torch.allclose(outputs.logits[0, :5]							,					UpperCAmelCase							,					atol=1E-4     ), outputs.logits[0, :5]

  model.save_pretrained(UpperCAmelCase     )
  extractor.save_pretrained(UpperCAmelCase     )


if __name__ == "__main__":
     _lowerCamelCase							:  Optional[Any]     =				argparse.ArgumentParser()

     parser.add_argument("--output_model_name", "-o", type=str, help="Output path for the converted HuggingFace model")

     _lowerCamelCase							:  str     =				parser.parse_args()
     convert(args.output_model_name)





