




import unittest

import numpy as np

from transformers import RoFormerConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
		import jax.numpy as jnp

		from transformers.models.roformer.modeling_flax_roformer import (
		    FlaxRoFormerForMaskedLM,
		    FlaxRoFormerForMultipleChoice,
		    FlaxRoFormerForQuestionAnswering,
		    FlaxRoFormerForSequenceClassification,
		    FlaxRoFormerForTokenClassification,
		    FlaxRoFormerModel,
		)
class 						UpperCAmelCase  (							unittest.TestCase     ):







						'''simple docstring'''





						def __init__(		self				:	List[Any]			,      __lowercase				:	int			,      __lowercase				:	Tuple=13			,      __lowercase				:	Dict=7			,      __lowercase				:	List[Any]=True			,      __lowercase				:	Optional[Any]=True			,      __lowercase				:	Optional[int]=True			,      __lowercase				:	Tuple=True			,      __lowercase				:	Tuple=99			,      __lowercase				:	int=32			,      __lowercase				:	Optional[Any]=5			,      __lowercase				:	Optional[Any]=4			,      __lowercase				:	Tuple=37			,      __lowercase				:	int="gelu"			,      __lowercase				:	List[str]=0.1			,      __lowercase				:	str=0.1			,      __lowercase				:	int=5_12			,      __lowercase				:	List[Any]=16			,      __lowercase				:	Dict=2			,      __lowercase				:	int=0.02			,      __lowercase				:	Optional[int]=4			,      ):






							"""simple docstring"""






							snake_case_						       =	parent
							snake_case_						       =	batch_size
							snake_case_						       =	seq_length
							snake_case_						       =	is_training
							snake_case_						       =	use_attention_mask
							snake_case_						       =	use_token_type_ids
							snake_case_						       =	use_labels
							snake_case_						       =	vocab_size
							snake_case_						       =	hidden_size
							snake_case_						       =	num_hidden_layers
							snake_case_						       =	num_attention_heads
							snake_case_						       =	intermediate_size
							snake_case_						       =	hidden_act
							snake_case_						       =	hidden_dropout_prob
							snake_case_						       =	attention_probs_dropout_prob
							snake_case_						       =	max_position_embeddings
							snake_case_						       =	type_vocab_size
							snake_case_						       =	type_sequence_label_size
							snake_case_						       =	initializer_range
							snake_case_						       =	num_choices






						def 			snake_case__		(		self				:	Tuple		):






							"""simple docstring"""






							snake_case_						       =	ids_tensor([self.batch_size, self.seq_length]			,      self.vocab_size		)

							snake_case_						       =	None
							if self.use_attention_mask:
								snake_case_						       =	random_attention_mask([self.batch_size, self.seq_length]		)

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

							snake_case_						       =	RoFormerConfig(
							    vocab_size=self.vocab_size			,      hidden_size=self.hidden_size			,      num_hidden_layers=self.num_hidden_layers			,      num_attention_heads=self.num_attention_heads			,      intermediate_size=self.intermediate_size			,      hidden_act=self.hidden_act			,      hidden_dropout_prob=self.hidden_dropout_prob			,      attention_probs_dropout_prob=self.attention_probs_dropout_prob			,      max_position_embeddings=self.max_position_embeddings			,      type_vocab_size=self.type_vocab_size			,      is_decoder=UpperCamelCase__			,      initializer_range=self.initializer_range			,      )

							return config, input_ids, token_type_ids, attention_mask






						def 			snake_case__		(		self				:	Optional[int]		):






							"""simple docstring"""






							snake_case_						       =	self.prepare_config_and_inputs()
							snake_case_       , snake_case_       , snake_case_       , snake_case_						       =	config_and_inputs
							snake_case_						       =	{"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": attention_mask}
							return config, inputs_dict







@require_flax
class 						UpperCAmelCase  (							_snake_case			,  unittest.TestCase     ):







						'''simple docstring'''





						lowerCAmelCase_							=  True

						lowerCAmelCase_							=  (
						    (
						        FlaxRoFormerModel,
						        FlaxRoFormerForMaskedLM,
						        FlaxRoFormerForSequenceClassification,
						        FlaxRoFormerForTokenClassification,
						        FlaxRoFormerForMultipleChoice,
						        FlaxRoFormerForQuestionAnswering,
						    )
						    if is_flax_available()
						    else ()
						)






						def 			snake_case__		(		self				:	List[str]		):






							"""simple docstring"""






							snake_case_						       =	FlaxRoFormerModelTester(self		)






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






							"""simple docstring"""






							for model_class_name in self.all_model_classes:
								snake_case_						       =	model_class_name.from_pretrained("junnyu/roformer_chinese_small"			,      from_pt=UpperCamelCase__		)
								snake_case_						       =	model(np.ones((1, 1)		)		)
								self.assertIsNotNone(UpperCamelCase__		)







@require_flax
class 						UpperCAmelCase  (							unittest.TestCase     ):







						'''simple docstring'''





						@slow
						def 			snake_case__		(		self				:	Dict		):






							"""simple docstring"""






							snake_case_						       =	FlaxRoFormerForMaskedLM.from_pretrained("junnyu/roformer_chinese_base"		)
							snake_case_						       =	jnp.array([[0, 1, 2, 3, 4, 5]]		)
							snake_case_						       =	model(UpperCamelCase__		)[0]

							snake_case_						       =	5_00_00

							snake_case_						       =	(1, 6, vocab_size)
							self.assertEqual(output.shape			,      UpperCamelCase__		)

							snake_case_						       =	jnp.array(
							    [[[-0.1205, -1.0265, 0.2922], [-1.5134, 0.1974, 0.1519], [-5.0135, -3.9003, -0.8404]]]		)

							self.assertTrue(jnp.allclose(output[:, :3, :3]			,      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)
