

'''simple docstring'''






import argparse
import json

import gdown
import numpy as np
import torch
from huggingface_hub import hf_hub_download

from transformers import (
    VideoMAEConfig,
    VideoMAEForPreTraining,
    VideoMAEForVideoClassification,
    VideoMAEImageProcessor,
)


def 	_A  (					A__   ):






 """simple docstring"""



 __lowercase													=				VideoMAEConfig()

 set_architecture_configs(__SCREAMING_SNAKE_CASE  ,       __SCREAMING_SNAKE_CASE   )

 if "finetuned" not in model_name:
  __lowercase													=				False

 if "finetuned" in model_name:
  __lowercase													=				"huggingface/label-files"
  if "kinetics" in model_name:
   __lowercase													=				400
   __lowercase													=				"kinetics400-id2label.json"
  elif "ssv2" in model_name:
   __lowercase													=				174
   __lowercase													=				"something-something-v2-id2label.json"
  else:
   raise ValueError('''Model name should either contain \'kinetics\' or \'ssv2\' in case it\'s fine-tuned.'''   )
  __lowercase													=				json.load(open(hf_hub_download(__SCREAMING_SNAKE_CASE  ,       __SCREAMING_SNAKE_CASE  ,       repo_type='''dataset'''   )  ,       '''r'''   )   )
  __lowercase													=				{int(__SCREAMING_SNAKE_CASE   ): v for k, v in idalabel.items()}
  __lowercase													=				idalabel
  __lowercase													=				{v: k for k, v in idalabel.items()}

 return config


def 	_A  (					A__  ,       A__   ):






 """simple docstring"""



 if "small" in model_name:
  __lowercase													=				384
  __lowercase													=				1536
  __lowercase													=				12
  __lowercase													=				16
  __lowercase													=				12
  __lowercase													=				3
  __lowercase													=				192
  __lowercase													=				768
 elif "large" in model_name:
  __lowercase													=				1024
  __lowercase													=				4096
  __lowercase													=				24
  __lowercase													=				16
  __lowercase													=				12
  __lowercase													=				8
  __lowercase													=				512
  __lowercase													=				2048
 elif "huge" in model_name:
  __lowercase													=				1280
  __lowercase													=				5120
  __lowercase													=				32
  __lowercase													=				16
  __lowercase													=				12
  __lowercase													=				8
  __lowercase													=				640
  __lowercase													=				2560
 elif "base" not in model_name:
  raise ValueError('''Model name should include either \"small\", \"base\", \"large\", or \"huge\"'''   )


def 	_A  (					A__   ):






 """simple docstring"""



 if "encoder." in name:
  __lowercase													=				name.replace('''encoder.'''  ,       ''''''   )
 if "cls_token" in name:
  __lowercase													=				name.replace('''cls_token'''  ,       '''videomae.embeddings.cls_token'''   )
 if "decoder_pos_embed" in name:
  __lowercase													=				name.replace('''decoder_pos_embed'''  ,       '''decoder.decoder_pos_embed'''   )
 if "pos_embed" in name and "decoder" not in name:
  __lowercase													=				name.replace('''pos_embed'''  ,       '''videomae.embeddings.position_embeddings'''   )
 if "patch_embed.proj" in name:
  __lowercase													=				name.replace('''patch_embed.proj'''  ,       '''videomae.embeddings.patch_embeddings.projection'''   )
 if "patch_embed.norm" in name:
  __lowercase													=				name.replace('''patch_embed.norm'''  ,       '''videomae.embeddings.norm'''   )
 if "decoder.blocks" in name:
  __lowercase													=				name.replace('''decoder.blocks'''  ,       '''decoder.decoder_layers'''   )
 if "blocks" in name:
  __lowercase													=				name.replace('''blocks'''  ,       '''videomae.encoder.layer'''   )
 if "attn.proj" in name:
  __lowercase													=				name.replace('''attn.proj'''  ,       '''attention.output.dense'''   )
 if "attn" in name and "bias" not in name:
  __lowercase													=				name.replace('''attn'''  ,       '''attention.self'''   )
 if "attn" in name:
  __lowercase													=				name.replace('''attn'''  ,       '''attention.attention'''   )
 if "norm1" in name:
  __lowercase													=				name.replace('''norm1'''  ,       '''layernorm_before'''   )
 if "norm2" in name:
  __lowercase													=				name.replace('''norm2'''  ,       '''layernorm_after'''   )
 if "mlp.fc1" in name:
  __lowercase													=				name.replace('''mlp.fc1'''  ,       '''intermediate.dense'''   )
 if "mlp.fc2" in name:
  __lowercase													=				name.replace('''mlp.fc2'''  ,       '''output.dense'''   )
 if "decoder_embed" in name:
  __lowercase													=				name.replace('''decoder_embed'''  ,       '''decoder.decoder_embed'''   )
 if "decoder_norm" in name:
  __lowercase													=				name.replace('''decoder_norm'''  ,       '''decoder.decoder_norm'''   )
 if "decoder_pred" in name:
  __lowercase													=				name.replace('''decoder_pred'''  ,       '''decoder.decoder_pred'''   )
 if "norm.weight" in name and "decoder" not in name and "fc" not in name:
  __lowercase													=				name.replace('''norm.weight'''  ,       '''videomae.layernorm.weight'''   )
 if "norm.bias" in name and "decoder" not in name and "fc" not in name:
  __lowercase													=				name.replace('''norm.bias'''  ,       '''videomae.layernorm.bias'''   )
 if "head" in name and "decoder" not in name:
  __lowercase													=				name.replace('''head'''  ,       '''classifier'''   )

 return name


def 	_A  (					A__  ,       A__   ):






 """simple docstring"""



 for key in orig_state_dict.copy().keys():
  __lowercase													=				orig_state_dict.pop(__SCREAMING_SNAKE_CASE   )

  if key.startswith('''encoder.'''   ):
   __lowercase													=				key.replace('''encoder.'''  ,       ''''''   )

  if "qkv" in key:
   __lowercase													=				key.split('''.'''   )
   if key.startswith('''decoder.blocks'''   ):
    __lowercase													=				config.decoder_hidden_size
    __lowercase													=				int(key_split[2]   )
    __lowercase													=				"decoder.decoder_layers."
    if "weight" in key:
     __lowercase													=				val[:dim, :]
     __lowercase													=				val[dim : dim * 2, :]
     __lowercase													=				val[-dim:, :]
   else:
    __lowercase													=				config.hidden_size
    __lowercase													=				int(key_split[1]   )
    __lowercase													=				"videomae.encoder.layer."
    if "weight" in key:
     __lowercase													=				val[:dim, :]
     __lowercase													=				val[dim : dim * 2, :]
     __lowercase													=				val[-dim:, :]
  else:
   __lowercase													=				val

 return orig_state_dict


def 	_A  (					):






 """simple docstring"""



 __lowercase													=				hf_hub_download(
     repo_id='''hf-internal-testing/spaghetti-video'''  ,       filename='''eating_spaghetti.npy'''  ,       repo_type='''dataset'''   )
 __lowercase													=				np.load(__SCREAMING_SNAKE_CASE   )
 return list(__SCREAMING_SNAKE_CASE   )


def 	_A  (					A__  ,       A__  ,       A__  ,       A__   ):






 """simple docstring"""



 __lowercase													=				get_videomae_config(__SCREAMING_SNAKE_CASE   )

 if "finetuned" in model_name:
  __lowercase													=				VideoMAEForVideoClassification(__SCREAMING_SNAKE_CASE   )
 else:
  __lowercase													=				VideoMAEForPreTraining(__SCREAMING_SNAKE_CASE   )

 # download original checkpoint, hosted on Google Drive
 __lowercase													=				"pytorch_model.bin"
 gdown.cached_download(__SCREAMING_SNAKE_CASE  ,       __SCREAMING_SNAKE_CASE  ,       quiet=__SCREAMING_SNAKE_CASE   )
 __lowercase													=				torch.load(__SCREAMING_SNAKE_CASE  ,       map_location='''cpu'''   )
 if "model" in files:
  __lowercase													=				files["model"]
 else:
  __lowercase													=				files["module"]
 __lowercase													=				convert_state_dict(__SCREAMING_SNAKE_CASE  ,       __SCREAMING_SNAKE_CASE   )

 model.load_state_dict(__SCREAMING_SNAKE_CASE   )
 model.eval()

 # verify model on basic input
 __lowercase													=				VideoMAEImageProcessor(image_mean=[0.5, 0.5, 0.5]  ,       image_std=[0.5, 0.5, 0.5]   )
 __lowercase													=				prepare_video()
 __lowercase													=				image_processor(__SCREAMING_SNAKE_CASE  ,       return_tensors='''pt'''   )

 if "finetuned" not in model_name:
  __lowercase													=				hf_hub_download(repo_id='''hf-internal-testing/bool-masked-pos'''  ,       filename='''bool_masked_pos.pt'''   )
  __lowercase													=				torch.load(__SCREAMING_SNAKE_CASE   )

 __lowercase													=				model(**__SCREAMING_SNAKE_CASE   )
 __lowercase													=				outputs.logits

 __lowercase													=				[
     "videomae-small-finetuned-kinetics",
     "videomae-small-finetuned-ssv2",
     # Kinetics-400 checkpoints (short = pretrained only for 800 epochs instead of 1600)
     "videomae-base-short",
     "videomae-base-short-finetuned-kinetics",
     "videomae-base",
     "videomae-base-finetuned-kinetics",
     "videomae-large",
     "videomae-large-finetuned-kinetics",
     "videomae-huge-finetuned-kinetics",
     # Something-Something-v2 checkpoints (short = pretrained only for 800 epochs instead of 2400)
     "videomae-base-short-ssv2",
     "videomae-base-short-finetuned-ssv2",
     "videomae-base-ssv2",
     "videomae-base-finetuned-ssv2",
 ]

 # NOTE: logits were tested with image_mean and image_std equal to [0.5, 0.5, 0.5] and [0.5, 0.5, 0.5]
 if model_name == "videomae-small-finetuned-kinetics":
  __lowercase													=				torch.Size([1, 400]   )
  __lowercase													=				torch.tensor([-0.9_2_9_1, -0.4_0_6_1, -0.9_3_0_7]   )
 elif model_name == "videomae-small-finetuned-ssv2":
  __lowercase													=				torch.Size([1, 174]   )
  __lowercase													=				torch.tensor([0.2_6_7_1, -0.4_6_8_9, -0.8_2_3_5]   )
 elif model_name == "videomae-base":
  __lowercase													=				torch.Size([1, 1408, 1536]   )
  __lowercase													=				torch.tensor([[0.7_7_3_9, 0.7_9_6_8, 0.7_0_8_9], [0.6_7_0_1, 0.7_4_8_7, 0.6_2_0_9], [0.4_2_8_7, 0.5_1_5_8, 0.4_7_7_3]]   )
 elif model_name == "videomae-base-short":
  __lowercase													=				torch.Size([1, 1408, 1536]   )
  __lowercase													=				torch.tensor([[0.7_9_9_4, 0.9_6_1_2, 0.8_5_0_8], [0.7_4_0_1, 0.8_9_5_8, 0.8_3_0_2], [0.5_8_6_2, 0.7_4_6_8, 0.7_3_2_5]]   )
  # we verified the loss both for normalized and unnormalized targets for this one
  __lowercase													=				torch.tensor([0.5_1_4_2]   ) if config.norm_pix_loss else torch.tensor([0.6_4_6_9]   )
 elif model_name == "videomae-large":
  __lowercase													=				torch.Size([1, 1408, 1536]   )
  __lowercase													=				torch.tensor([[0.7_1_4_9, 0.7_9_9_7, 0.6_9_6_6], [0.6_7_6_8, 0.7_8_6_9, 0.6_9_4_8], [0.5_1_3_9, 0.6_2_2_1, 0.5_6_0_5]]   )
 elif model_name == "videomae-large-finetuned-kinetics":
  __lowercase													=				torch.Size([1, 400]   )
  __lowercase													=				torch.tensor([0.0_7_7_1, 0.0_0_1_1, -0.3_6_2_5]   )
 elif model_name == "videomae-huge-finetuned-kinetics":
  __lowercase													=				torch.Size([1, 400]   )
  __lowercase													=				torch.tensor([0.2_4_3_3, 0.1_6_3_2, -0.4_8_9_4]   )
 elif model_name == "videomae-base-short-finetuned-kinetics":
  __lowercase													=				torch.Size([1, 400]   )
  __lowercase													=				torch.tensor([0.6_5_8_8, 0.0_9_9_0, -0.2_4_9_3]   )
 elif model_name == "videomae-base-finetuned-kinetics":
  __lowercase													=				torch.Size([1, 400]   )
  __lowercase													=				torch.tensor([0.3_6_6_9, -0.0_6_8_8, -0.2_4_2_1]   )
 elif model_name == "videomae-base-short-ssv2":
  __lowercase													=				torch.Size([1, 1408, 1536]   )
  __lowercase													=				torch.tensor([[0.4_7_1_2, 0.5_2_9_6, 0.5_7_8_6], [0.2_2_7_8, 0.2_7_2_9, 0.4_0_2_6], [0.0_3_5_2, 0.0_7_3_0, 0.2_5_0_6]]   )
 elif model_name == "videomae-base-short-finetuned-ssv2":
  __lowercase													=				torch.Size([1, 174]   )
  __lowercase													=				torch.tensor([-0.0_5_3_7, -0.1_5_3_9, -0.3_2_6_6]   )
 elif model_name == "videomae-base-ssv2":
  __lowercase													=				torch.Size([1, 1408, 1536]   )
  __lowercase													=				torch.tensor([[0.8_1_3_1, 0.8_7_2_7, 0.8_5_4_6], [0.7_3_6_6, 0.9_3_7_7, 0.8_8_7_0], [0.5_9_3_5, 0.8_8_7_4, 0.8_5_6_4]]   )
 elif model_name == "videomae-base-finetuned-ssv2":
  __lowercase													=				torch.Size([1, 174]   )
  __lowercase													=				torch.tensor([0.1_9_6_1, -0.8_3_3_7, -0.6_3_8_9]   )
 else:
  raise ValueError(F"Model name not supported. Should be one of {model_names}"   )

 # verify logits
 assert logits.shape == expected_shape
 if "finetuned" in model_name:
  assert torch.allclose(logits[0, :3]  ,       __SCREAMING_SNAKE_CASE  ,       atol=1e-4   )
 else:
  print('''Logits:'''  ,       logits[0, :3, :3]   )
  assert torch.allclose(logits[0, :3, :3]  ,       __SCREAMING_SNAKE_CASE  ,       atol=1e-4   )
 print('''Logits ok!'''   )

 # verify loss, if applicable
 if model_name == "videomae-base-short":
  __lowercase													=				outputs.loss
  assert torch.allclose(__SCREAMING_SNAKE_CASE  ,       __SCREAMING_SNAKE_CASE  ,       atol=1e-4   )
  print('''Loss ok!'''   )

 if pytorch_dump_folder_path is not None:
  print(F"Saving model and image processor to {pytorch_dump_folder_path}"   )
  image_processor.save_pretrained(__SCREAMING_SNAKE_CASE   )
  model.save_pretrained(__SCREAMING_SNAKE_CASE   )

 if push_to_hub:
  print('''Pushing to the hub...'''   )
  model.push_to_hub(__SCREAMING_SNAKE_CASE  ,       organization='''nielsr'''   )


if __name__ == "__main__":
  lowerCAmelCase__      		=			argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      '''--checkpoint_url''',
      default='''https://drive.google.com/u/1/uc?id=1tEhLyskjb755TJ65ptsrafUG2llSwQE1&amp;export=download&amp;confirm=t&amp;uuid=aa3276eb-fb7e-482a-adec-dc7171df14c4''',
      type=str,
      help=(
          '''URL of the original PyTorch checkpoint (on Google Drive) you\'d like to convert. Should be a direct'''
          ''' download link.'''
      ),
  )
  parser.add_argument(
      '''--pytorch_dump_folder_path''',
      default='''/Users/nielsrogge/Documents/VideoMAE/Test''',
      type=str,
      help='''Path to the output PyTorch model directory.''',
  )
  parser.add_argument('''--model_name''', default='''videomae-base''', type=str, help='''Name of the model.''')
  parser.add_argument(
      '''--push_to_hub''', action='''store_true''', help='''Whether or not to push the converted model to the 🤗 hub.'''
  )

  lowerCAmelCase__      		=			parser.parse_args()
  convert_videomae_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.model_name, args.push_to_hub)




'''simple docstring'''






from __future__ import annotations

import unittest

from transformers import FunnelConfig, is_tf_available
from transformers.testing_utils import require_tf

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
 import tensorflow as tf

 from transformers import (
     TFFunnelBaseModel,
     TFFunnelForMaskedLM,
     TFFunnelForMultipleChoice,
     TFFunnelForPreTraining,
     TFFunnelForQuestionAnswering,
     TFFunnelForSequenceClassification,
     TFFunnelForTokenClassification,
     TFFunnelModel,
 )






class     lowercase_			:




  """simple docstring"""




  def __init__(   self     :  Union[str, Any]      ,lowercase__     :  Union[str, Any]      ,lowercase__     :  List[Any]=1_3      ,lowercase__     :  Union[str, Any]=7      ,lowercase__     :  List[Any]=True      ,lowercase__     :  str=True      ,lowercase__     :  Any=True      ,lowercase__     :  Union[str, Any]=True      ,lowercase__     :  Tuple=9_9      ,lowercase__     :  List[str]=[1, 1, 2]      ,lowercase__     :  List[Any]=1      ,lowercase__     :  List[Any]=3_2      ,lowercase__     :  int=4      ,lowercase__     :  Tuple=8      ,lowercase__     :  Tuple=3_7      ,lowercase__     :  str="gelu_new"      ,lowercase__     :  Dict=0.1      ,lowercase__     :  Optional[int]=0.1      ,lowercase__     :  Optional[Any]=0.0      ,lowercase__     :  Tuple=5_1_2      ,lowercase__     :  Any=3      ,lowercase__     :  List[str]=0.0_2      ,lowercase__     :  List[Any]=3      ,lowercase__     :  List[Any]=4      ,lowercase__     :  Optional[Any]=None      ,lowercase__     :  Tuple=False      ,):
   __lowercase													=				parent
   __lowercase													=				batch_size
   __lowercase													=				seq_length
   __lowercase													=				is_training
   __lowercase													=				use_input_mask
   __lowercase													=				use_token_type_ids
   __lowercase													=				use_labels
   __lowercase													=				vocab_size
   __lowercase													=				block_sizes
   __lowercase													=				num_decoder_layers
   __lowercase													=				d_model
   __lowercase													=				n_head
   __lowercase													=				d_head
   __lowercase													=				d_inner
   __lowercase													=				hidden_act
   __lowercase													=				hidden_dropout
   __lowercase													=				attention_dropout
   __lowercase													=				activation_dropout
   __lowercase													=				max_position_embeddings
   __lowercase													=				type_vocab_size
   __lowercase													=				2
   __lowercase													=				num_labels
   __lowercase													=				num_choices
   __lowercase													=				scope
   __lowercase													=				initializer_std

   # Used in the tests to check the size of the first attention layer
   __lowercase													=				n_head
   # Used in the tests to check the size of the first hidden state
   __lowercase													=				self.d_model
   # Used in the tests to check the number of output hidden states/attentions
   __lowercase													=				sum(self.block_sizes    ) + (0 if base else self.num_decoder_layers)
   # FunnelModel adds two hidden layers: input embeddings and the sum of the upsampled encoder hidden state with
   # the last hidden state of the first block (which is the first hidden state of the decoder).
   if not base:
    __lowercase													=				self.num_hidden_layers + 2






  def       SCREAMING_SNAKE_CASE   (   self     :  List[Any]    ):
   __lowercase													=				ids_tensor([self.batch_size, self.seq_length]      ,self.vocab_size    )

   __lowercase													=				None
   if self.use_input_mask:
    __lowercase													=				random_attention_mask([self.batch_size, self.seq_length]    )

   __lowercase													=				None
   if self.use_token_type_ids:
    __lowercase													=				ids_tensor([self.batch_size, self.seq_length]      ,self.type_vocab_size    )

   __lowercase													=				None
   __lowercase													=				None
   __lowercase													=				None
   if self.use_labels:
    __lowercase													=				ids_tensor([self.batch_size]      ,self.type_sequence_label_size    )
    __lowercase													=				ids_tensor([self.batch_size, self.seq_length]      ,self.num_labels    )
    __lowercase													=				ids_tensor([self.batch_size]      ,self.num_choices    )

   __lowercase													=				FunnelConfig(
       vocab_size=self.vocab_size      ,block_sizes=self.block_sizes      ,num_decoder_layers=self.num_decoder_layers      ,d_model=self.d_model      ,n_head=self.n_head      ,d_head=self.d_head      ,d_inner=self.d_inner      ,hidden_act=self.hidden_act      ,hidden_dropout=self.hidden_dropout      ,attention_dropout=self.attention_dropout      ,activation_dropout=self.activation_dropout      ,max_position_embeddings=self.max_position_embeddings      ,type_vocab_size=self.type_vocab_size      ,initializer_std=self.initializer_std      ,)

   return (
       config,
       input_ids,
       token_type_ids,
       input_mask,
       sequence_labels,
       token_labels,
       choice_labels,
   )






  def       SCREAMING_SNAKE_CASE   (   self     :  int      ,lowercase__     :  List[str]      ,lowercase__     :  Union[str, Any]      ,lowercase__     :  int      ,lowercase__     :  List[str]      ,lowercase__     :  Optional[int]      ,lowercase__     :  List[Any]      ,lowercase__     :  List[str]      ,):
   __lowercase													=				TFFunnelModel(config=lowercase__    )
   __lowercase													=				{'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
   __lowercase													=				model(lowercase__    )

   __lowercase													=				[input_ids, input_mask]
   __lowercase													=				model(lowercase__    )

   __lowercase													=				model(lowercase__    )
   self.parent.assertEqual(result.last_hidden_state.shape      ,(self.batch_size, self.seq_length, self.d_model)    )

   __lowercase													=				False
   __lowercase													=				TFFunnelModel(config=lowercase__    )
   __lowercase													=				model(lowercase__    )
   self.parent.assertEqual(result.last_hidden_state.shape      ,(self.batch_size, self.seq_length, self.d_model)    )

   __lowercase													=				False
   __lowercase													=				TFFunnelModel(config=lowercase__    )
   __lowercase													=				model(lowercase__    )
   self.parent.assertEqual(result.last_hidden_state.shape      ,(self.batch_size, self.seq_length, self.d_model)    )






  def       SCREAMING_SNAKE_CASE   (   self     :  int      ,lowercase__     :  List[Any]      ,lowercase__     :  Tuple      ,lowercase__     :  Dict      ,lowercase__     :  Any      ,lowercase__     :  Optional[Any]      ,lowercase__     :  Union[str, Any]      ,lowercase__     :  Dict      ,):
   __lowercase													=				TFFunnelBaseModel(config=lowercase__    )
   __lowercase													=				{'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
   __lowercase													=				model(lowercase__    )

   __lowercase													=				[input_ids, input_mask]
   __lowercase													=				model(lowercase__    )

   __lowercase													=				model(lowercase__    )
   self.parent.assertEqual(result.last_hidden_state.shape      ,(self.batch_size, 2, self.d_model)    )

   __lowercase													=				False
   __lowercase													=				TFFunnelBaseModel(config=lowercase__    )
   __lowercase													=				model(lowercase__    )
   self.parent.assertEqual(result.last_hidden_state.shape      ,(self.batch_size, 3, self.d_model)    )

   __lowercase													=				False
   __lowercase													=				TFFunnelBaseModel(config=lowercase__    )
   __lowercase													=				model(lowercase__    )
   self.parent.assertEqual(result.last_hidden_state.shape      ,(self.batch_size, 2, self.d_model)    )






  def       SCREAMING_SNAKE_CASE   (   self     :  Tuple      ,lowercase__     :  Any      ,lowercase__     :  Any      ,lowercase__     :  List[Any]      ,lowercase__     :  Optional[int]      ,lowercase__     :  Dict      ,lowercase__     :  int      ,lowercase__     :  List[str]      ,):
   __lowercase													=				TFFunnelForPreTraining(config=lowercase__    )
   __lowercase													=				{'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
   __lowercase													=				model(lowercase__    )
   self.parent.assertEqual(result.logits.shape      ,(self.batch_size, self.seq_length)    )






  def       SCREAMING_SNAKE_CASE   (   self     :  Union[str, Any]      ,lowercase__     :  Union[str, Any]      ,lowercase__     :  Optional[Any]      ,lowercase__     :  Dict      ,lowercase__     :  List[Any]      ,lowercase__     :  Tuple      ,lowercase__     :  Optional[Any]      ,lowercase__     :  Tuple      ,):
   __lowercase													=				TFFunnelForMaskedLM(config=lowercase__    )
   __lowercase													=				{'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
   __lowercase													=				model(lowercase__    )
   self.parent.assertEqual(result.logits.shape      ,(self.batch_size, self.seq_length, self.vocab_size)    )






  def       SCREAMING_SNAKE_CASE   (   self     :  List[Any]      ,lowercase__     :  Tuple      ,lowercase__     :  Optional[Any]      ,lowercase__     :  List[Any]      ,lowercase__     :  List[Any]      ,lowercase__     :  str      ,lowercase__     :  Union[str, Any]      ,lowercase__     :  Tuple      ,):
   __lowercase													=				self.num_labels
   __lowercase													=				TFFunnelForSequenceClassification(config=lowercase__    )
   __lowercase													=				{'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
   __lowercase													=				model(lowercase__    )
   self.parent.assertEqual(result.logits.shape      ,(self.batch_size, self.num_labels)    )






  def       SCREAMING_SNAKE_CASE   (   self     :  Dict      ,lowercase__     :  List[str]      ,lowercase__     :  Optional[Any]      ,lowercase__     :  List[str]      ,lowercase__     :  List[str]      ,lowercase__     :  List[str]      ,lowercase__     :  str      ,lowercase__     :  Tuple      ,):
   __lowercase													=				self.num_choices
   __lowercase													=				TFFunnelForMultipleChoice(config=lowercase__    )
   __lowercase													=				tf.tile(tf.expand_dims(lowercase__      ,1    )      ,(1, self.num_choices, 1)    )
   __lowercase													=				tf.tile(tf.expand_dims(lowercase__      ,1    )      ,(1, self.num_choices, 1)    )
   __lowercase													=				tf.tile(tf.expand_dims(lowercase__      ,1    )      ,(1, self.num_choices, 1)    )
   __lowercase													=				{
       '''input_ids''': multiple_choice_inputs_ids,
       '''attention_mask''': multiple_choice_input_mask,
       '''token_type_ids''': multiple_choice_token_type_ids,
   }
   __lowercase													=				model(lowercase__    )
   self.parent.assertEqual(result.logits.shape      ,(self.batch_size, self.num_choices)    )






  def       SCREAMING_SNAKE_CASE   (   self     :  Optional[int]      ,lowercase__     :  Optional[Any]      ,lowercase__     :  List[Any]      ,lowercase__     :  List[str]      ,lowercase__     :  List[Any]      ,lowercase__     :  Dict      ,lowercase__     :  Any      ,lowercase__     :  Union[str, Any]      ,):
   __lowercase													=				self.num_labels
   __lowercase													=				TFFunnelForTokenClassification(config=lowercase__    )
   __lowercase													=				{'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
   __lowercase													=				model(lowercase__    )
   self.parent.assertEqual(result.logits.shape      ,(self.batch_size, self.seq_length, self.num_labels)    )






  def       SCREAMING_SNAKE_CASE   (   self     :  List[Any]      ,lowercase__     :  Optional[Any]      ,lowercase__     :  Optional[int]      ,lowercase__     :  Dict      ,lowercase__     :  List[str]      ,lowercase__     :  str      ,lowercase__     :  Tuple      ,lowercase__     :  Any      ,):
   __lowercase													=				TFFunnelForQuestionAnswering(config=lowercase__    )
   __lowercase													=				{'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
   __lowercase													=				model(lowercase__    )
   self.parent.assertEqual(result.start_logits.shape      ,(self.batch_size, self.seq_length)    )
   self.parent.assertEqual(result.end_logits.shape      ,(self.batch_size, self.seq_length)    )






  def       SCREAMING_SNAKE_CASE   (   self     :  Any    ):
   __lowercase													=				self.prepare_config_and_inputs()
   (
       (
       __lowercase
   )						,			(
       __lowercase
   )						,			(
       __lowercase
   )						,			(
       __lowercase
   )						,			(
       __lowercase
   )						,			(
       __lowercase
   )						,			(
       __lowercase
   )						,			
   )													=				config_and_inputs
   __lowercase													=				{'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
   return config, inputs_dict






@require_tf
class     lowercase_			(lowerCamelCase__       , lowerCamelCase__       , unittest.TestCase   ):




  """simple docstring"""




  SCREAMING_SNAKE_CASE					:						int           =    (
      (
          TFFunnelModel,
          TFFunnelForMaskedLM,
          TFFunnelForPreTraining,
          TFFunnelForQuestionAnswering,
          TFFunnelForTokenClassification,
      )
      if is_tf_available()
      else ()
  )
  SCREAMING_SNAKE_CASE					:						Union[str, Any]           =    (
      {
          'feature-extraction': (TFFunnelBaseModel, TFFunnelModel),
          'fill-mask': TFFunnelForMaskedLM,
          'question-answering': TFFunnelForQuestionAnswering,
          'text-classification': TFFunnelForSequenceClassification,
          'token-classification': TFFunnelForTokenClassification,
          'zero-shot': TFFunnelForSequenceClassification,
      }
      if is_tf_available()
      else {}
  )
  SCREAMING_SNAKE_CASE					:						Tuple           =    False
  SCREAMING_SNAKE_CASE					:						Any           =    False






  def       SCREAMING_SNAKE_CASE   (   self     :  str    ):
   __lowercase													=				TFFunnelModelTester(self    )
   __lowercase													=				ConfigTester(self      ,config_class=lowercase__    )






  def       SCREAMING_SNAKE_CASE   (   self     :  str    ):
   self.config_tester.run_common_tests()






  def       SCREAMING_SNAKE_CASE   (   self     :  Optional[int]    ):
   __lowercase													=				self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_model(*lowercase__    )






  def       SCREAMING_SNAKE_CASE   (   self     :  List[Any]    ):
   __lowercase													=				self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_for_pretraining(*lowercase__    )






  def       SCREAMING_SNAKE_CASE   (   self     :  List[str]    ):
   __lowercase													=				self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_for_masked_lm(*lowercase__    )






  def       SCREAMING_SNAKE_CASE   (   self     :  List[Any]    ):
   __lowercase													=				self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_for_token_classification(*lowercase__    )






  def       SCREAMING_SNAKE_CASE   (   self     :  int    ):
   __lowercase													=				self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_for_question_answering(*lowercase__    )






@require_tf
class     lowercase_			(lowerCamelCase__       , unittest.TestCase   ):




  """simple docstring"""




  SCREAMING_SNAKE_CASE					:						List[Any]           =    (
      (TFFunnelBaseModel, TFFunnelForMultipleChoice, TFFunnelForSequenceClassification) if is_tf_available() else ()
  )
  SCREAMING_SNAKE_CASE					:						Dict           =    False
  SCREAMING_SNAKE_CASE					:						List[str]           =    False






  def       SCREAMING_SNAKE_CASE   (   self     :  str    ):
   __lowercase													=				TFFunnelModelTester(self      ,base=lowercase__    )
   __lowercase													=				ConfigTester(self      ,config_class=lowercase__    )






  def       SCREAMING_SNAKE_CASE   (   self     :  Any    ):
   self.config_tester.run_common_tests()






  def       SCREAMING_SNAKE_CASE   (   self     :  List[str]    ):
   __lowercase													=				self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_base_model(*lowercase__    )






  def       SCREAMING_SNAKE_CASE   (   self     :  Dict    ):
   __lowercase													=				self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_for_sequence_classification(*lowercase__    )






  def       SCREAMING_SNAKE_CASE   (   self     :  Any    ):
   __lowercase													=				self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_for_multiple_choice(*lowercase__    )

