

'''simple docstring'''







import os
import tempfile
import unittest

from transformers import DistilBertConfig, is_torch_available
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
      import torch

      from transformers import (
          DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
          DistilBertForMaskedLM,
          DistilBertForMultipleChoice,
          DistilBertForQuestionAnswering,
          DistilBertForSequenceClassification,
          DistilBertForTokenClassification,
          DistilBertModel,
      )


class        A__   (   _snake_case      ):



    def __init__(       self		,					UpperCamelCase__		,					UpperCamelCase__=13		,					UpperCamelCase__=7		,					UpperCamelCase__=True		,					UpperCamelCase__=True		,					UpperCamelCase__=False		,					UpperCamelCase__=True		,					UpperCamelCase__=99		,					UpperCamelCase__=32		,					UpperCamelCase__=5		,					UpperCamelCase__=4		,					UpperCamelCase__=37		,					UpperCamelCase__="gelu"		,					UpperCamelCase__=0.1		,					UpperCamelCase__=0.1		,					UpperCamelCase__=512		,					UpperCamelCase__=16		,					UpperCamelCase__=2		,					UpperCamelCase__=0.02		,					UpperCamelCase__=3		,					UpperCamelCase__=4		,					UpperCamelCase__=None		,					)    ->   Dict:





           '''simple docstring'''
           A_						       =   parent
           A_						       =   batch_size
           A_						       =   seq_length
           A_						       =   is_training
           A_						       =   use_input_mask
           A_						       =   use_token_type_ids
           A_						       =   use_labels
           A_						       =   vocab_size
           A_						       =   hidden_size
           A_						       =   num_hidden_layers
           A_						       =   num_attention_heads
           A_						       =   intermediate_size
           A_						       =   hidden_act
           A_						       =   hidden_dropout_prob
           A_						       =   attention_probs_dropout_prob
           A_						       =   max_position_embeddings
           A_						       =   type_vocab_size
           A_						       =   type_sequence_label_size
           A_						       =   initializer_range
           A_						       =   num_labels
           A_						       =   num_choices
           A_						       =   scope



    def        snake_case_      (       self							)    ->   List[Any]:





           '''simple docstring'''
           A_						       =   ids_tensor([self.batch_size, self.seq_length]		,					self.vocab_size							)

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

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

           A_						       =   self.get_config()

           return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels



    def        snake_case_      (       self							)    ->   List[str]:





           '''simple docstring'''
           return DistilBertConfig(
               vocab_size=self.vocab_size		,					dim=self.hidden_size		,					n_layers=self.num_hidden_layers		,					n_heads=self.num_attention_heads		,					hidden_dim=self.intermediate_size		,					hidden_act=self.hidden_act		,					dropout=self.hidden_dropout_prob		,					attention_dropout=self.attention_probs_dropout_prob		,					max_position_embeddings=self.max_position_embeddings		,					initializer_range=self.initializer_range		,					)



    def        snake_case_      (       self		,					UpperCamelCase__		,					UpperCamelCase__		,					UpperCamelCase__		,					UpperCamelCase__		,					UpperCamelCase__		,					UpperCamelCase__							)    ->   Union[str, Any]:





           '''simple docstring'''
           A_						       =   DistilBertModel(config=UpperCamelCase__							)
           model.to(UpperCamelCase__							)
           model.eval()
           A_						       =   model(UpperCamelCase__		,					UpperCamelCase__							)
           A_						       =   model(UpperCamelCase__							)
           self.parent.assertEqual(result.last_hidden_state.shape		,					(self.batch_size, self.seq_length, self.hidden_size)							)



    def        snake_case_      (       self		,					UpperCamelCase__		,					UpperCamelCase__		,					UpperCamelCase__		,					UpperCamelCase__		,					UpperCamelCase__		,					UpperCamelCase__							)    ->   Union[str, Any]:





           '''simple docstring'''
           A_						       =   DistilBertForMaskedLM(config=UpperCamelCase__							)
           model.to(UpperCamelCase__							)
           model.eval()
           A_						       =   model(UpperCamelCase__		,					attention_mask=UpperCamelCase__		,					labels=UpperCamelCase__							)
           self.parent.assertEqual(result.logits.shape		,					(self.batch_size, self.seq_length, self.vocab_size)							)



    def        snake_case_      (       self		,					UpperCamelCase__		,					UpperCamelCase__		,					UpperCamelCase__		,					UpperCamelCase__		,					UpperCamelCase__		,					UpperCamelCase__							)    ->   Dict:





           '''simple docstring'''
           A_						       =   DistilBertForQuestionAnswering(config=UpperCamelCase__							)
           model.to(UpperCamelCase__							)
           model.eval()
           A_						       =   model(
               UpperCamelCase__		,					attention_mask=UpperCamelCase__		,					start_positions=UpperCamelCase__		,					end_positions=UpperCamelCase__							)
           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        snake_case_      (       self		,					UpperCamelCase__		,					UpperCamelCase__		,					UpperCamelCase__		,					UpperCamelCase__		,					UpperCamelCase__		,					UpperCamelCase__							)    ->   Union[str, Any]:





           '''simple docstring'''
           A_						       =   self.num_labels
           A_						       =   DistilBertForSequenceClassification(UpperCamelCase__							)
           model.to(UpperCamelCase__							)
           model.eval()
           A_						       =   model(UpperCamelCase__		,					attention_mask=UpperCamelCase__		,					labels=UpperCamelCase__							)
           self.parent.assertEqual(result.logits.shape		,					(self.batch_size, self.num_labels)							)



    def        snake_case_      (       self		,					UpperCamelCase__		,					UpperCamelCase__		,					UpperCamelCase__		,					UpperCamelCase__		,					UpperCamelCase__		,					UpperCamelCase__							)    ->   Any:





           '''simple docstring'''
           A_						       =   self.num_labels
           A_						       =   DistilBertForTokenClassification(config=UpperCamelCase__							)
           model.to(UpperCamelCase__							)
           model.eval()

           A_						       =   model(UpperCamelCase__		,					attention_mask=UpperCamelCase__		,					labels=UpperCamelCase__							)
           self.parent.assertEqual(result.logits.shape		,					(self.batch_size, self.seq_length, self.num_labels)							)



    def        snake_case_      (       self		,					UpperCamelCase__		,					UpperCamelCase__		,					UpperCamelCase__		,					UpperCamelCase__		,					UpperCamelCase__		,					UpperCamelCase__							)    ->   List[Any]:





           '''simple docstring'''
           A_						       =   self.num_choices
           A_						       =   DistilBertForMultipleChoice(config=UpperCamelCase__							)
           model.to(UpperCamelCase__							)
           model.eval()
           A_						       =   input_ids.unsqueeze(1							).expand(-1		,					self.num_choices		,					-1							).contiguous()
           A_						       =   input_mask.unsqueeze(1							).expand(-1		,					self.num_choices		,					-1							).contiguous()
           A_						       =   model(
               UpperCamelCase__		,					attention_mask=UpperCamelCase__		,					labels=UpperCamelCase__		,					)
           self.parent.assertEqual(result.logits.shape		,					(self.batch_size, self.num_choices)							)







    def        snake_case_      (       self							)    ->   Tuple:





           '''simple docstring'''
           A_						       =   self.prepare_config_and_inputs()
           ((A_)       ,      (A_)       ,      (A_)       ,      (A_)       ,      (A_)       ,      (A_))						       =   config_and_inputs
           A_						       =   {"""input_ids""": input_ids, """attention_mask""": input_mask}
           return config, inputs_dict


@require_torch
class        A__   (   _snake_case    ,    _snake_case    ,    unittest.TestCase      ):
    lowercase						       =		(
        (
            DistilBertModel,
            DistilBertForMaskedLM,
            DistilBertForMultipleChoice,
            DistilBertForQuestionAnswering,
            DistilBertForSequenceClassification,
            DistilBertForTokenClassification,
        )
        if is_torch_available()
        else None
    )
    lowercase						       =		(
        {
            "feature-extraction": DistilBertModel,
            "fill-mask": DistilBertForMaskedLM,
            "question-answering": DistilBertForQuestionAnswering,
            "text-classification": DistilBertForSequenceClassification,
            "token-classification": DistilBertForTokenClassification,
            "zero-shot": DistilBertForSequenceClassification,
        }
        if is_torch_available()
        else {}
    )
    lowercase						       =		True
    lowercase						       =		True
    lowercase						       =		True
    lowercase						       =		True



    def        snake_case_      (       self							)    ->   Tuple:





           '''simple docstring'''
           A_						       =   DistilBertModelTester(self							)
           A_						       =   ConfigTester(self		,					config_class=UpperCamelCase__		,					dim=37							)



    def        snake_case_      (       self							)    ->   List[Any]:





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



    def        snake_case_      (       self							)    ->   Tuple:





           '''simple docstring'''
           A_						       =   self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_distilbert_model(*UpperCamelCase__							)



    def        snake_case_      (       self							)    ->   List[Any]:





           '''simple docstring'''
           A_						       =   self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_distilbert_for_masked_lm(*UpperCamelCase__							)



    def        snake_case_      (       self							)    ->   Union[str, Any]:





           '''simple docstring'''
           A_						       =   self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_distilbert_for_question_answering(*UpperCamelCase__							)



    def        snake_case_      (       self							)    ->   Any:





           '''simple docstring'''
           A_						       =   self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_distilbert_for_sequence_classification(*UpperCamelCase__							)



    def        snake_case_      (       self							)    ->   Dict:





           '''simple docstring'''
           A_						       =   self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_distilbert_for_token_classification(*UpperCamelCase__							)



    def        snake_case_      (       self							)    ->   Union[str, Any]:





           '''simple docstring'''
           A_						       =   self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_distilbert_for_multiple_choice(*UpperCamelCase__							)



    @slow
    def        snake_case_      (       self							)    ->   Tuple:





           '''simple docstring'''
           for model_name in DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                  A_						       =   DistilBertModel.from_pretrained(UpperCamelCase__							)
                  self.assertIsNotNone(UpperCamelCase__							)







    @slow
    @require_torch_gpu
    def        snake_case_      (       self							)    ->   str:





           '''simple docstring'''
           A_       ,      A_						       =   self.model_tester.prepare_config_and_inputs_for_common()
           for model_class in self.all_model_classes:
                  # BertForMultipleChoice behaves incorrectly in JIT environments.
                  if model_class == DistilBertForMultipleChoice:
                         return

                  A_						       =   True
                  A_						       =   model_class(config=UpperCamelCase__							)

                  A_						       =   self._prepare_for_class(UpperCamelCase__		,					UpperCamelCase__							)
                  A_						       =   torch.jit.trace(
                      UpperCamelCase__		,					(inputs_dict["""input_ids"""].to("""cpu"""							), inputs_dict["""attention_mask"""].to("""cpu"""							))							)

                  with tempfile.TemporaryDirectory() as tmp:
                         torch.jit.save(UpperCamelCase__		,					os.path.join(UpperCamelCase__		,					"""traced_model.pt"""							)							)
                         A_						       =   torch.jit.load(os.path.join(UpperCamelCase__		,					"""traced_model.pt"""							)		,					map_location=UpperCamelCase__							)
                         loaded(inputs_dict["""input_ids"""].to(UpperCamelCase__							)		,					inputs_dict["""attention_mask"""].to(UpperCamelCase__							)							)


@require_torch
class        A__   (   unittest.TestCase      ):



    @slow
    def        snake_case_      (       self							)    ->   int:





           '''simple docstring'''
           A_						       =   DistilBertModel.from_pretrained("""distilbert-base-uncased"""							)
           A_						       =   torch.tensor([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]]							)
           A_						       =   torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]							)
           with torch.no_grad():
                  A_						       =   model(UpperCamelCase__		,					attention_mask=UpperCamelCase__							)[0]
           A_						       =   torch.Size((1, 11, 768)							)
           self.assertEqual(output.shape		,					UpperCamelCase__							)
           A_						       =   torch.tensor(
               [[[-0.1639, 0.3299, 0.1648], [-0.1746, 0.3289, 0.1710], [-0.1884, 0.3357, 0.1810]]]							)

           self.assertTrue(torch.allclose(output[:, 1:4, 1:4]		,					UpperCamelCase__		,					atol=1e-4							)							)



'''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    UpperCAmelCase__     (   UpperCAmelCase__					)      ->      Union[str, Any]:
       A_						       =   VideoMAEConfig()

       set_architecture_configs(UpperCAmelCase__,							UpperCAmelCase__					)

       if "finetuned" not in model_name:
              A_						       =   False

       if "finetuned" in model_name:
              A_						       =   """huggingface/label-files"""
              if "kinetics" in model_name:
                     A_						       =   4_00
                     A_						       =   """kinetics400-id2label.json"""
              elif "ssv2" in model_name:
                     A_						       =   1_74
                     A_						       =   """something-something-v2-id2label.json"""
              else:
                     raise ValueError("""Model name should either contain 'kinetics' or 'ssv2' in case it's fine-tuned."""					)
              A_						       =   json.load(open(hf_hub_download(UpperCAmelCase__,							UpperCAmelCase__,							repo_type="""dataset"""					),							"""r"""					)					)
              A_						       =   {int(UpperCAmelCase__					): v for k, v in idalabel.items()}
              A_						       =   idalabel
              A_						       =   {v: k for k, v in idalabel.items()}

       return config
def    UpperCAmelCase__     (   UpperCAmelCase__,							UpperCAmelCase__					)      ->      Dict:
       if "small" in model_name:
              A_						       =   3_84
              A_						       =   15_36
              A_						       =   12
              A_						       =   16
              A_						       =   12
              A_						       =   3
              A_						       =   1_92
              A_						       =   7_68
       elif "large" in model_name:
              A_						       =   10_24
              A_						       =   40_96
              A_						       =   24
              A_						       =   16
              A_						       =   12
              A_						       =   8
              A_						       =   5_12
              A_						       =   20_48
       elif "huge" in model_name:
              A_						       =   12_80
              A_						       =   51_20
              A_						       =   32
              A_						       =   16
              A_						       =   12
              A_						       =   8
              A_						       =   6_40
              A_						       =   25_60
       elif "base" not in model_name:
              raise ValueError("""Model name should include either \"small\", \"base\", \"large\", or \"huge\""""					)
def    UpperCAmelCase__     (   UpperCAmelCase__					)      ->      List[Any]:
       if "encoder." in name:
              A_						       =   name.replace("""encoder.""",							""""""					)
       if "cls_token" in name:
              A_						       =   name.replace("""cls_token""",							"""videomae.embeddings.cls_token"""					)
       if "decoder_pos_embed" in name:
              A_						       =   name.replace("""decoder_pos_embed""",							"""decoder.decoder_pos_embed"""					)
       if "pos_embed" in name and "decoder" not in name:
              A_						       =   name.replace("""pos_embed""",							"""videomae.embeddings.position_embeddings"""					)
       if "patch_embed.proj" in name:
              A_						       =   name.replace("""patch_embed.proj""",							"""videomae.embeddings.patch_embeddings.projection"""					)
       if "patch_embed.norm" in name:
              A_						       =   name.replace("""patch_embed.norm""",							"""videomae.embeddings.norm"""					)
       if "decoder.blocks" in name:
              A_						       =   name.replace("""decoder.blocks""",							"""decoder.decoder_layers"""					)
       if "blocks" in name:
              A_						       =   name.replace("""blocks""",							"""videomae.encoder.layer"""					)
       if "attn.proj" in name:
              A_						       =   name.replace("""attn.proj""",							"""attention.output.dense"""					)
       if "attn" in name and "bias" not in name:
              A_						       =   name.replace("""attn""",							"""attention.self"""					)
       if "attn" in name:
              A_						       =   name.replace("""attn""",							"""attention.attention"""					)
       if "norm1" in name:
              A_						       =   name.replace("""norm1""",							"""layernorm_before"""					)
       if "norm2" in name:
              A_						       =   name.replace("""norm2""",							"""layernorm_after"""					)
       if "mlp.fc1" in name:
              A_						       =   name.replace("""mlp.fc1""",							"""intermediate.dense"""					)
       if "mlp.fc2" in name:
              A_						       =   name.replace("""mlp.fc2""",							"""output.dense"""					)
       if "decoder_embed" in name:
              A_						       =   name.replace("""decoder_embed""",							"""decoder.decoder_embed"""					)
       if "decoder_norm" in name:
              A_						       =   name.replace("""decoder_norm""",							"""decoder.decoder_norm"""					)
       if "decoder_pred" in name:
              A_						       =   name.replace("""decoder_pred""",							"""decoder.decoder_pred"""					)
       if "norm.weight" in name and "decoder" not in name and "fc" not in name:
              A_						       =   name.replace("""norm.weight""",							"""videomae.layernorm.weight"""					)
       if "norm.bias" in name and "decoder" not in name and "fc" not in name:
              A_						       =   name.replace("""norm.bias""",							"""videomae.layernorm.bias"""					)
       if "head" in name and "decoder" not in name:
              A_						       =   name.replace("""head""",							"""classifier"""					)

       return name
def    UpperCAmelCase__     (   UpperCAmelCase__,							UpperCAmelCase__					)      ->      Optional[int]:
       for key in orig_state_dict.copy().keys():
              A_						       =   orig_state_dict.pop(UpperCAmelCase__					)

              if key.startswith("""encoder."""					):
                     A_						       =   key.replace("""encoder.""",							""""""					)

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

       return orig_state_dict
def    UpperCAmelCase__     (   )      ->      Any:
       A_						       =   hf_hub_download(
           repo_id="""hf-internal-testing/spaghetti-video""",							filename="""eating_spaghetti.npy""",							repo_type="""dataset"""					)
       A_						       =   np.load(UpperCAmelCase__					)
       return list(UpperCAmelCase__					)
def    UpperCAmelCase__     (   UpperCAmelCase__,							UpperCAmelCase__,							UpperCAmelCase__,							UpperCAmelCase__					)      ->      int:
       A_						       =   get_videomae_config(UpperCAmelCase__					)

       if "finetuned" in model_name:
              A_						       =   VideoMAEForVideoClassification(UpperCAmelCase__					)
       else:
              A_						       =   VideoMAEForPreTraining(UpperCAmelCase__					)

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

       model.load_state_dict(UpperCAmelCase__					)
       model.eval()

       # verify model on basic input
       A_						       =   VideoMAEImageProcessor(image_mean=[0.5, 0.5, 0.5],							image_std=[0.5, 0.5, 0.5]					)
       A_						       =   prepare_video()
       A_						       =   image_processor(UpperCAmelCase__,							return_tensors="""pt"""					)

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

       A_						       =   model(**UpperCAmelCase__					)
       A_						       =   outputs.logits

       A_						       =   [
           """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":
              A_						       =   torch.Size([1, 4_00]					)
              A_						       =   torch.tensor([-0.9_291, -0.4_061, -0.9_307]					)
       elif model_name == "videomae-small-finetuned-ssv2":
              A_						       =   torch.Size([1, 1_74]					)
              A_						       =   torch.tensor([0.2_671, -0.4_689, -0.8_235]					)
       elif model_name == "videomae-base":
              A_						       =   torch.Size([1, 14_08, 15_36]					)
              A_						       =   torch.tensor([[0.7_739, 0.7_968, 0.7_089], [0.6_701, 0.7_487, 0.6_209], [0.4_287, 0.5_158, 0.4_773]]					)
       elif model_name == "videomae-base-short":
              A_						       =   torch.Size([1, 14_08, 15_36]					)
              A_						       =   torch.tensor([[0.7_994, 0.9_612, 0.8_508], [0.7_401, 0.8_958, 0.8_302], [0.5_862, 0.7_468, 0.7_325]]					)
              # we verified the loss both for normalized and unnormalized targets for this one
              A_						       =   torch.tensor([0.5_142]					) if config.norm_pix_loss else torch.tensor([0.6_469]					)
       elif model_name == "videomae-large":
              A_						       =   torch.Size([1, 14_08, 15_36]					)
              A_						       =   torch.tensor([[0.7_149, 0.7_997, 0.6_966], [0.6_768, 0.7_869, 0.6_948], [0.5_139, 0.6_221, 0.5_605]]					)
       elif model_name == "videomae-large-finetuned-kinetics":
              A_						       =   torch.Size([1, 4_00]					)
              A_						       =   torch.tensor([0.0_771, 0.0_011, -0.3_625]					)
       elif model_name == "videomae-huge-finetuned-kinetics":
              A_						       =   torch.Size([1, 4_00]					)
              A_						       =   torch.tensor([0.2_433, 0.1_632, -0.4_894]					)
       elif model_name == "videomae-base-short-finetuned-kinetics":
              A_						       =   torch.Size([1, 4_00]					)
              A_						       =   torch.tensor([0.6_588, 0.0_990, -0.2_493]					)
       elif model_name == "videomae-base-finetuned-kinetics":
              A_						       =   torch.Size([1, 4_00]					)
              A_						       =   torch.tensor([0.3_669, -0.0_688, -0.2_421]					)
       elif model_name == "videomae-base-short-ssv2":
              A_						       =   torch.Size([1, 14_08, 15_36]					)
              A_						       =   torch.tensor([[0.4_712, 0.5_296, 0.5_786], [0.2_278, 0.2_729, 0.4_026], [0.0_352, 0.0_730, 0.2_506]]					)
       elif model_name == "videomae-base-short-finetuned-ssv2":
              A_						       =   torch.Size([1, 1_74]					)
              A_						       =   torch.tensor([-0.0_537, -0.1_539, -0.3_266]					)
       elif model_name == "videomae-base-ssv2":
              A_						       =   torch.Size([1, 14_08, 15_36]					)
              A_						       =   torch.tensor([[0.8_131, 0.8_727, 0.8_546], [0.7_366, 0.9_377, 0.8_870], [0.5_935, 0.8_874, 0.8_564]]					)
       elif model_name == "videomae-base-finetuned-ssv2":
              A_						       =   torch.Size([1, 1_74]					)
              A_						       =   torch.tensor([0.1_961, -0.8_337, -0.6_389]					)
       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],							UpperCAmelCase__,							atol=1e-4					)
       else:
              print("""Logits:""",							logits[0, :3, :3]					)
              assert torch.allclose(logits[0, :3, :3],							UpperCAmelCase__,							atol=1e-4					)
       print("""Logits ok!"""					)

       # verify loss, if applicable
       if model_name == "videomae-base-short":
              A_						       =   outputs.loss
              assert torch.allclose(UpperCAmelCase__,							UpperCAmelCase__,							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(UpperCAmelCase__					)
              model.save_pretrained(UpperCAmelCase__					)

       if push_to_hub:
              print("""Pushing to the hub..."""					)
              model.push_to_hub(UpperCAmelCase__,							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)
