



"""simple docstring"""






import unittest

import numpy as np
import timeout_decorator  # noqa

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

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor


if is_flax_available():
		import os

		# The slow tests are often failing with OOM error on GPU
		# This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
		# but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
		_lowercase				:				int   		=		"platform"

		import jax
		import jax.numpy as jnp

		from transformers.models.blenderbot_small.modeling_flax_blenderbot_small import (
		    FlaxBlenderbotSmallForConditionalGeneration,
		    FlaxBlenderbotSmallModel,
		    shift_tokens_right,
		)






def   snake_case__     (	__lowerCamelCase       :					str							,	__lowerCamelCase       :					List[Any]							,	__lowerCamelCase       :					Optional[Any]=None							,	__lowerCamelCase       :					Optional[Any]=None							,	__lowerCamelCase       :					List[str]=None							,	__lowerCamelCase       :					Any=None							,	__lowerCamelCase       :					Optional[Any]=None							,	__lowerCamelCase       :					int=None							,	):
			"""simple docstring"""
			if attention_mask is None:
						lowerCamelCase__	:	Tuple				=np.where(input_ids != config.pad_token_id							,	1							,	0			)
			if decoder_attention_mask is None:
						lowerCamelCase__	:	Optional[int]				=np.where(decoder_input_ids != config.pad_token_id							,	1							,	0			)
			if head_mask is None:
						lowerCamelCase__	:	int				=np.ones((config.encoder_layers, config.encoder_attention_heads)			)
			if decoder_head_mask is None:
						lowerCamelCase__	:	Dict				=np.ones((config.decoder_layers, config.decoder_attention_heads)			)
			if cross_attn_head_mask is None:
						lowerCamelCase__	:	int				=np.ones((config.decoder_layers, config.decoder_attention_heads)			)
			return {
			    "input_ids": input_ids,
			    "decoder_input_ids": decoder_input_ids,
			    "attention_mask": attention_mask,
			    "decoder_attention_mask": attention_mask,
			}

class  __SCREAMING_SNAKE_CASE			:


					'''simple docstring'''





					def __init__( self							:						Tuple,						lowerCamelCase							:						Union[str, Any],						lowerCamelCase							:						Dict=13,						lowerCamelCase							:						Union[str, Any]=7,						lowerCamelCase							:						Dict=True,						lowerCamelCase							:						str=False,						lowerCamelCase							:						Any=99,						lowerCamelCase							:						str=16,						lowerCamelCase							:						Optional[int]=2,						lowerCamelCase							:						Tuple=4,						lowerCamelCase							:						Dict=4,						lowerCamelCase							:						Any="gelu",						lowerCamelCase							:						Dict=0.1,						lowerCamelCase							:						Tuple=0.1,						lowerCamelCase							:						Optional[Any]=32,						lowerCamelCase							:						str=2,						lowerCamelCase							:						Tuple=1,						lowerCamelCase							:						List[str]=0,						lowerCamelCase							:						Any=0.02,						)->      Dict:
								lowerCamelCase__	:	Dict				=parent
								lowerCamelCase__	:	int				=batch_size
								lowerCamelCase__	:	Tuple				=seq_length
								lowerCamelCase__	:	Optional[Any]				=is_training
								lowerCamelCase__	:	str				=use_labels
								lowerCamelCase__	:	Optional[int]				=vocab_size
								lowerCamelCase__	:	Dict				=hidden_size
								lowerCamelCase__	:	str				=num_hidden_layers
								lowerCamelCase__	:	List[str]				=num_attention_heads
								lowerCamelCase__	:	int				=intermediate_size
								lowerCamelCase__	:	Union[str, Any]				=hidden_act
								lowerCamelCase__	:	List[Any]				=hidden_dropout_prob
								lowerCamelCase__	:	List[Any]				=attention_probs_dropout_prob
								lowerCamelCase__	:	Tuple				=max_position_embeddings
								lowerCamelCase__	:	Optional[int]				=eos_token_id
								lowerCamelCase__	:	Tuple				=pad_token_id
								lowerCamelCase__	:	Union[str, Any]				=bos_token_id
								lowerCamelCase__	:	int				=initializer_range



					def 	snake_case ( self							:						Optional[int]  )->      str:
								lowerCamelCase__	:	Optional[Any]				=np.clip(ids_tensor([self.batch_size, self.seq_length - 1],						self.vocab_size  ),						3,						self.vocab_size  )
								lowerCamelCase__	:	str				=np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1),						dtype=np.intaa  )),						-1  )

								lowerCamelCase__	:	List[str]				=shift_tokens_right(lowerCamelCase,						1,						2  )

								lowerCamelCase__	:	List[Any]				=BlenderbotSmallConfig(
								    vocab_size=self.vocab_size,						d_model=self.hidden_size,						encoder_layers=self.num_hidden_layers,						decoder_layers=self.num_hidden_layers,						encoder_attention_heads=self.num_attention_heads,						decoder_attention_heads=self.num_attention_heads,						encoder_ffn_dim=self.intermediate_size,						decoder_ffn_dim=self.intermediate_size,						dropout=self.hidden_dropout_prob,						attention_dropout=self.attention_probs_dropout_prob,						max_position_embeddings=self.max_position_embeddings,						eos_token_id=self.eos_token_id,						bos_token_id=self.bos_token_id,						pad_token_id=self.pad_token_id,						initializer_range=self.initializer_range,						use_cache=lowerCamelCase,						)
								lowerCamelCase__	:	Union[str, Any]				=prepare_blenderbot_inputs_dict(lowerCamelCase,						lowerCamelCase,						lowerCamelCase  )
								return config, inputs_dict



					def 	snake_case ( self							:						Optional[Any]  )->      Union[str, Any]:
								lowerCamelCase__			,     lowerCamelCase__	:	Any				=self.prepare_config_and_inputs()
								return config, inputs_dict



					def 	snake_case ( self							:						Optional[Any],						lowerCamelCase							:						List[Any],						lowerCamelCase							:						Optional[int],						lowerCamelCase							:						Union[str, Any]  )->      List[Any]:
								lowerCamelCase__	:	Union[str, Any]				=20
								lowerCamelCase__	:	str				=model_class_name(lowerCamelCase  )

								lowerCamelCase__	:	Optional[int]				=model.encode(inputs_dict['''input_ids''']  )

								lowerCamelCase__			,     lowerCamelCase__	:	Any				=(
								    inputs_dict['''decoder_input_ids'''],
								    inputs_dict['''decoder_attention_mask'''],
								)

								lowerCamelCase__	:	Optional[int]				=model.init_cache(decoder_input_ids.shape[0],						lowerCamelCase,						lowerCamelCase  )
								lowerCamelCase__	:	Optional[Any]				=jnp.ones((decoder_input_ids.shape[0], max_decoder_length),						dtype='''i4'''  )

								lowerCamelCase__	:	Any				=jnp.broadcast_to(
								    jnp.arange(decoder_input_ids.shape[-1] - 1  )[None, :],						(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1),						)
								lowerCamelCase__	:	List[Any]				=model.decode(
								    decoder_input_ids[:, :-1],						lowerCamelCase,						decoder_attention_mask=lowerCamelCase,						past_key_values=lowerCamelCase,						decoder_position_ids=lowerCamelCase,						)

								lowerCamelCase__	:	Any				=jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]],						dtype='''i4'''  )
								lowerCamelCase__	:	str				=model.decode(
								    decoder_input_ids[:, -1:],						lowerCamelCase,						decoder_attention_mask=lowerCamelCase,						past_key_values=outputs_cache.past_key_values,						decoder_position_ids=lowerCamelCase,						)

								lowerCamelCase__	:	List[str]				=model.decode(lowerCamelCase,						lowerCamelCase  )

								lowerCamelCase__	:	Dict				=np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])  )  )
								self.parent.assertTrue(diff < 1E-3,						msg=F'''Max diff is {diff}'''  )




					def 	snake_case ( self							:						List[Any],						lowerCamelCase							:						str,						lowerCamelCase							:						List[Any],						lowerCamelCase							:						int  )->      Optional[Any]:
								lowerCamelCase__	:	Tuple				=20
								lowerCamelCase__	:	Optional[int]				=model_class_name(lowerCamelCase  )

								lowerCamelCase__	:	List[Any]				=model.encode(inputs_dict['''input_ids''']  )

								lowerCamelCase__			,     lowerCamelCase__	:	Tuple				=(
								    inputs_dict['''decoder_input_ids'''],
								    inputs_dict['''decoder_attention_mask'''],
								)

								lowerCamelCase__	:	Tuple				=jnp.concatenate(
								    [
								        decoder_attention_mask,
								        jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])  ),
								    ],						axis=-1,						)

								lowerCamelCase__	:	Tuple				=model.init_cache(decoder_input_ids.shape[0],						lowerCamelCase,						lowerCamelCase  )
								lowerCamelCase__	:	List[Any]				=jnp.broadcast_to(
								    jnp.arange(decoder_input_ids.shape[-1] - 1  )[None, :],						(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1),						)

								lowerCamelCase__	:	Union[str, Any]				=model.decode(
								    decoder_input_ids[:, :-1],						lowerCamelCase,						decoder_attention_mask=lowerCamelCase,						past_key_values=lowerCamelCase,						decoder_position_ids=lowerCamelCase,						)
								lowerCamelCase__	:	Any				=jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]],						dtype='''i4'''  )
								lowerCamelCase__	:	Tuple				=model.decode(
								    decoder_input_ids[:, -1:],						lowerCamelCase,						past_key_values=outputs_cache.past_key_values,						decoder_attention_mask=lowerCamelCase,						decoder_position_ids=lowerCamelCase,						)

								lowerCamelCase__	:	List[Any]				=model.decode(lowerCamelCase,						lowerCamelCase,						decoder_attention_mask=lowerCamelCase  )

								lowerCamelCase__	:	Dict				=np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])  )  )
								self.parent.assertTrue(diff < 1E-3,						msg=F'''Max diff is {diff}'''  )





@require_flax
class  __SCREAMING_SNAKE_CASE			(   unittest.TestCase     ):


					'''simple docstring'''





					_a         =							9_9



					def 	snake_case ( self							:						Optional[Any]  )->      str:
								lowerCamelCase__	:	str				=np.array(
								    [
								        [71, 82, 18, 33, 46, 91, 2],
								        [68, 34, 26, 58, 30, 82, 2],
								        [5, 97, 17, 39, 94, 40, 2],
								        [76, 83, 94, 25, 70, 78, 2],
								        [87, 59, 41, 35, 48, 66, 2],
								        [55, 13, 16, 58, 5, 2, 1],  # note padding
								        [64, 27, 31, 51, 12, 75, 2],
								        [52, 64, 86, 17, 83, 39, 2],
								        [48, 61, 9, 24, 71, 82, 2],
								        [26, 1, 60, 48, 22, 13, 2],
								        [21, 5, 62, 28, 14, 76, 2],
								        [45, 98, 37, 86, 59, 48, 2],
								        [70, 70, 50, 9, 28, 0, 2],
								    ],						dtype=np.intaa,						)

								lowerCamelCase__	:	List[str]				=input_ids.shape[0]
								lowerCamelCase__	:	Dict				=BlenderbotSmallConfig(
								    vocab_size=self.vocab_size,						d_model=24,						encoder_layers=2,						decoder_layers=2,						encoder_attention_heads=2,						decoder_attention_heads=2,						encoder_ffn_dim=32,						decoder_ffn_dim=32,						max_position_embeddings=48,						eos_token_id=2,						pad_token_id=1,						bos_token_id=0,						)
								return config, input_ids, batch_size



					def 	snake_case ( self							:						Optional[Any]  )->      Any:
								lowerCamelCase__			,     lowerCamelCase__			,     lowerCamelCase__	:	Optional[int]				=self._get_config_and_data()
								lowerCamelCase__	:	Optional[int]				=FlaxBlenderbotSmallForConditionalGeneration(lowerCamelCase  )
								lowerCamelCase__	:	List[str]				=lm_model(input_ids=lowerCamelCase  )
								lowerCamelCase__	:	Tuple				=(batch_size, input_ids.shape[1], config.vocab_size)
								self.assertEqual(outputs['''logits'''].shape,						lowerCamelCase  )



					def 	snake_case ( self							:						List[Any]  )->      Any:
								lowerCamelCase__	:	str				=BlenderbotSmallConfig(
								    vocab_size=self.vocab_size,						d_model=14,						encoder_layers=2,						decoder_layers=2,						encoder_attention_heads=2,						decoder_attention_heads=2,						encoder_ffn_dim=8,						decoder_ffn_dim=8,						max_position_embeddings=48,						)
								lowerCamelCase__	:	List[Any]				=FlaxBlenderbotSmallForConditionalGeneration(lowerCamelCase  )
								lowerCamelCase__	:	Optional[int]				=np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]],						dtype=np.intaa  )
								lowerCamelCase__	:	Optional[int]				=np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]],						dtype=np.intaa  )
								lowerCamelCase__	:	Tuple				=lm_model(input_ids=lowerCamelCase,						decoder_input_ids=lowerCamelCase  )
								lowerCamelCase__	:	str				=(*summary.shape, config.vocab_size)
								self.assertEqual(outputs['''logits'''].shape,						lowerCamelCase  )




					def 	snake_case ( self							:						Union[str, Any]  )->      Optional[int]:
								lowerCamelCase__	:	Optional[Any]				=np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]],						dtype=np.intaa  )
								lowerCamelCase__	:	Any				=shift_tokens_right(lowerCamelCase,						1,						2  )
								lowerCamelCase__	:	Optional[Any]				=np.equal(lowerCamelCase,						1  ).astype(np.floataa  ).sum()
								lowerCamelCase__	:	int				=np.equal(lowerCamelCase,						1  ).astype(np.floataa  ).sum()
								self.assertEqual(shifted.shape,						input_ids.shape  )
								self.assertEqual(lowerCamelCase,						n_pad_before - 1  )
								self.assertTrue(np.equal(shifted[:, 0],						2  ).all()  )





@require_flax
class  __SCREAMING_SNAKE_CASE			(   lowerCAmelCase_	,     unittest.TestCase	,     lowerCAmelCase_     ):


					'''simple docstring'''





					_a         =							True
					_a         =							(
					    (
					        FlaxBlenderbotSmallModel,
					        FlaxBlenderbotSmallForConditionalGeneration,
					    )
					    if is_flax_available()
					    else ()
					)
					_a         =							(FlaxBlenderbotSmallForConditionalGeneration,) if is_flax_available() else ()



					def 	snake_case ( self							:						Any  )->      Any:
								lowerCamelCase__	:	Optional[Any]				=FlaxBlenderbotSmallModelTester(self  )



					def 	snake_case ( self							:						Union[str, Any]  )->      Dict:
								lowerCamelCase__			,     lowerCamelCase__	:	int				=self.model_tester.prepare_config_and_inputs()
								for model_class in self.all_model_classes:
											self.model_tester.check_use_cache_forward(lowerCamelCase,						lowerCamelCase,						lowerCamelCase  )



					def 	snake_case ( self							:						Union[str, Any]  )->      List[Any]:
								lowerCamelCase__			,     lowerCamelCase__	:	Dict				=self.model_tester.prepare_config_and_inputs()
								for model_class in self.all_model_classes:
											self.model_tester.check_use_cache_forward_with_attn_mask(lowerCamelCase,						lowerCamelCase,						lowerCamelCase  )



					def 	snake_case ( self							:						Any  )->      Tuple:
								lowerCamelCase__			,     lowerCamelCase__	:	Tuple				=self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
											with self.subTest(model_class.__name__  ):
														lowerCamelCase__	:	Dict				=self._prepare_for_class(lowerCamelCase,						lowerCamelCase  )
														lowerCamelCase__	:	Tuple				=model_class(lowerCamelCase  )

														@jax.jit
														def encode_jitted(lowerCamelCase							:						List[Any],						lowerCamelCase							:						List[str]=None,						**lowerCamelCase							:						Any  ):
																	return model.encode(input_ids=lowerCamelCase,						attention_mask=lowerCamelCase  )

														with self.subTest('''JIT Enabled'''  ):
																	lowerCamelCase__	:	Tuple				=encode_jitted(**lowerCamelCase  ).to_tuple()

														with self.subTest('''JIT Disabled'''  ):
																	with jax.disable_jit():
																				lowerCamelCase__	:	Dict				=encode_jitted(**lowerCamelCase  ).to_tuple()

														self.assertEqual(len(lowerCamelCase  ),						len(lowerCamelCase  )  )
														for jitted_output, output in zip(lowerCamelCase,						lowerCamelCase  ):
																	self.assertEqual(jitted_output.shape,						output.shape  )



					def 	snake_case ( self							:						Optional[int]  )->      int:
								lowerCamelCase__			,     lowerCamelCase__	:	Dict				=self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
											with self.subTest(model_class.__name__  ):
														lowerCamelCase__	:	int				=model_class(lowerCamelCase  )
														lowerCamelCase__	:	Optional[int]				=model.encode(inputs_dict['''input_ids'''],						inputs_dict['''attention_mask''']  )

														lowerCamelCase__	:	str				={
														    '''decoder_input_ids''': inputs_dict['''decoder_input_ids'''],
														    '''decoder_attention_mask''': inputs_dict['''decoder_attention_mask'''],
														    '''encoder_outputs''': encoder_outputs,
														}

														@jax.jit
														def decode_jitted(lowerCamelCase							:						Union[str, Any],						lowerCamelCase							:						Dict,						lowerCamelCase							:						Union[str, Any]  ):
																	return model.decode(
																	    decoder_input_ids=lowerCamelCase,						decoder_attention_mask=lowerCamelCase,						encoder_outputs=lowerCamelCase,						)

														with self.subTest('''JIT Enabled'''  ):
																	lowerCamelCase__	:	str				=decode_jitted(**lowerCamelCase  ).to_tuple()

														with self.subTest('''JIT Disabled'''  ):
																	with jax.disable_jit():
																				lowerCamelCase__	:	List[str]				=decode_jitted(**lowerCamelCase  ).to_tuple()

														self.assertEqual(len(lowerCamelCase  ),						len(lowerCamelCase  )  )
														for jitted_output, output in zip(lowerCamelCase,						lowerCamelCase  ):
																	self.assertEqual(jitted_output.shape,						output.shape  )




					@slow
					def 	snake_case ( self							:						Dict  )->      Tuple:
								for model_class_name in self.all_model_classes:
											lowerCamelCase__	:	Dict				=model_class_name.from_pretrained('''facebook/blenderbot_small-90M'''  )
											# FlaxBlenderbotForSequenceClassification expects eos token in input_ids
											lowerCamelCase__	:	str				=np.ones((1, 1)  ) * model.config.eos_token_id
											lowerCamelCase__	:	Union[str, Any]				=model(lowerCamelCase  )
											self.assertIsNotNone(lowerCamelCase  )









"""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   snake_case__     (	__lowerCamelCase       :					Optional[Any]			):
			"""simple docstring"""
			lowerCamelCase__	:	str				=VideoMAEConfig()

			set_architecture_configs(__lowerCamelCase							,	__lowerCamelCase			)

			if "finetuned" not in model_name:
						lowerCamelCase__	:	int				=False

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

			return config






def   snake_case__     (	__lowerCamelCase       :					List[str]							,	__lowerCamelCase       :					Optional[Any]			):
			"""simple docstring"""
			if "small" in model_name:
						lowerCamelCase__	:	Optional[Any]				=384
						lowerCamelCase__	:	List[Any]				=1536
						lowerCamelCase__	:	int				=12
						lowerCamelCase__	:	Dict				=16
						lowerCamelCase__	:	List[Any]				=12
						lowerCamelCase__	:	Optional[Any]				=3
						lowerCamelCase__	:	Union[str, Any]				=192
						lowerCamelCase__	:	str				=768
			elif "large" in model_name:
						lowerCamelCase__	:	Union[str, Any]				=1024
						lowerCamelCase__	:	str				=4096
						lowerCamelCase__	:	int				=24
						lowerCamelCase__	:	Dict				=16
						lowerCamelCase__	:	Union[str, Any]				=12
						lowerCamelCase__	:	List[Any]				=8
						lowerCamelCase__	:	int				=512
						lowerCamelCase__	:	Optional[Any]				=2048
			elif "huge" in model_name:
						lowerCamelCase__	:	Optional[int]				=1280
						lowerCamelCase__	:	Optional[int]				=5120
						lowerCamelCase__	:	List[Any]				=32
						lowerCamelCase__	:	List[Any]				=16
						lowerCamelCase__	:	Optional[Any]				=12
						lowerCamelCase__	:	Dict				=8
						lowerCamelCase__	:	List[Any]				=640
						lowerCamelCase__	:	Any				=2560
			elif "base" not in model_name:
						raise ValueError('''Model name should include either "small", "base", "large", or "huge"'''			)






def   snake_case__     (	__lowerCamelCase       :					Any			):
			"""simple docstring"""
			if "encoder." in name:
						lowerCamelCase__	:	Optional[int]				=name.replace('''encoder.'''							,	''''''			)
			if "cls_token" in name:
						lowerCamelCase__	:	List[Any]				=name.replace('''cls_token'''							,	'''videomae.embeddings.cls_token'''			)
			if "decoder_pos_embed" in name:
						lowerCamelCase__	:	Tuple				=name.replace('''decoder_pos_embed'''							,	'''decoder.decoder_pos_embed'''			)
			if "pos_embed" in name and "decoder" not in name:
						lowerCamelCase__	:	Any				=name.replace('''pos_embed'''							,	'''videomae.embeddings.position_embeddings'''			)
			if "patch_embed.proj" in name:
						lowerCamelCase__	:	Optional[Any]				=name.replace('''patch_embed.proj'''							,	'''videomae.embeddings.patch_embeddings.projection'''			)
			if "patch_embed.norm" in name:
						lowerCamelCase__	:	List[Any]				=name.replace('''patch_embed.norm'''							,	'''videomae.embeddings.norm'''			)
			if "decoder.blocks" in name:
						lowerCamelCase__	:	Tuple				=name.replace('''decoder.blocks'''							,	'''decoder.decoder_layers'''			)
			if "blocks" in name:
						lowerCamelCase__	:	Dict				=name.replace('''blocks'''							,	'''videomae.encoder.layer'''			)
			if "attn.proj" in name:
						lowerCamelCase__	:	Union[str, Any]				=name.replace('''attn.proj'''							,	'''attention.output.dense'''			)
			if "attn" in name and "bias" not in name:
						lowerCamelCase__	:	List[str]				=name.replace('''attn'''							,	'''attention.self'''			)
			if "attn" in name:
						lowerCamelCase__	:	Union[str, Any]				=name.replace('''attn'''							,	'''attention.attention'''			)
			if "norm1" in name:
						lowerCamelCase__	:	Tuple				=name.replace('''norm1'''							,	'''layernorm_before'''			)
			if "norm2" in name:
						lowerCamelCase__	:	Optional[int]				=name.replace('''norm2'''							,	'''layernorm_after'''			)
			if "mlp.fc1" in name:
						lowerCamelCase__	:	List[Any]				=name.replace('''mlp.fc1'''							,	'''intermediate.dense'''			)
			if "mlp.fc2" in name:
						lowerCamelCase__	:	int				=name.replace('''mlp.fc2'''							,	'''output.dense'''			)
			if "decoder_embed" in name:
						lowerCamelCase__	:	Any				=name.replace('''decoder_embed'''							,	'''decoder.decoder_embed'''			)
			if "decoder_norm" in name:
						lowerCamelCase__	:	Optional[Any]				=name.replace('''decoder_norm'''							,	'''decoder.decoder_norm'''			)
			if "decoder_pred" in name:
						lowerCamelCase__	:	Any				=name.replace('''decoder_pred'''							,	'''decoder.decoder_pred'''			)
			if "norm.weight" in name and "decoder" not in name and "fc" not in name:
						lowerCamelCase__	:	str				=name.replace('''norm.weight'''							,	'''videomae.layernorm.weight'''			)
			if "norm.bias" in name and "decoder" not in name and "fc" not in name:
						lowerCamelCase__	:	Optional[int]				=name.replace('''norm.bias'''							,	'''videomae.layernorm.bias'''			)
			if "head" in name and "decoder" not in name:
						lowerCamelCase__	:	List[str]				=name.replace('''head'''							,	'''classifier'''			)

			return name






def   snake_case__     (	__lowerCamelCase       :					int							,	__lowerCamelCase       :					int			):
			"""simple docstring"""
			for key in orig_state_dict.copy().keys():
						lowerCamelCase__	:	Dict				=orig_state_dict.pop(__lowerCamelCase			)

						if key.startswith('''encoder.'''			):
									lowerCamelCase__	:	Optional[int]				=key.replace('''encoder.'''							,	''''''			)

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

			return orig_state_dict






def   snake_case__     (	):
			"""simple docstring"""
			lowerCamelCase__	:	List[Any]				=hf_hub_download(
			    repo_id='''hf-internal-testing/spaghetti-video'''							,	filename='''eating_spaghetti.npy'''							,	repo_type='''dataset'''			)
			lowerCamelCase__	:	Optional[Any]				=np.load(__lowerCamelCase			)
			return list(__lowerCamelCase			)






def   snake_case__     (	__lowerCamelCase       :					Optional[Any]							,	__lowerCamelCase       :					Union[str, Any]							,	__lowerCamelCase       :					List[Any]							,	__lowerCamelCase       :					Optional[int]			):
			"""simple docstring"""
			lowerCamelCase__	:	str				=get_videomae_config(__lowerCamelCase			)

			if "finetuned" in model_name:
						lowerCamelCase__	:	Tuple				=VideoMAEForVideoClassification(__lowerCamelCase			)
			else:
						lowerCamelCase__	:	int				=VideoMAEForPreTraining(__lowerCamelCase			)

			# download original checkpoint, hosted on Google Drive
			lowerCamelCase__	:	Union[str, Any]				='''pytorch_model.bin'''
			gdown.cached_download(__lowerCamelCase							,	__lowerCamelCase							,	quiet=__lowerCamelCase			)
			lowerCamelCase__	:	Optional[Any]				=torch.load(__lowerCamelCase							,	map_location='''cpu'''			)
			if "model" in files:
						lowerCamelCase__	:	Dict				=files['''model''']
			else:
						lowerCamelCase__	:	str				=files['''module''']
			lowerCamelCase__	:	Optional[Any]				=convert_state_dict(__lowerCamelCase							,	__lowerCamelCase			)

			model.load_state_dict(__lowerCamelCase			)
			model.eval()

			# verify model on basic input
			lowerCamelCase__	:	Dict				=VideoMAEImageProcessor(image_mean=[0.5, 0.5, 0.5]							,	image_std=[0.5, 0.5, 0.5]			)
			lowerCamelCase__	:	int				=prepare_video()
			lowerCamelCase__	:	Tuple				=image_processor(__lowerCamelCase							,	return_tensors='''pt'''			)

			if "finetuned" not in model_name:
						lowerCamelCase__	:	Tuple				=hf_hub_download(repo_id='''hf-internal-testing/bool-masked-pos'''							,	filename='''bool_masked_pos.pt'''			)
						lowerCamelCase__	:	Union[str, Any]				=torch.load(__lowerCamelCase			)

			lowerCamelCase__	:	int				=model(**__lowerCamelCase			)
			lowerCamelCase__	:	Dict				=outputs.logits

			lowerCamelCase__	:	List[str]				=[
			    '''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":
						lowerCamelCase__	:	Union[str, Any]				=torch.Size([1, 400]			)
						lowerCamelCase__	:	str				=torch.tensor([-0.92_91, -0.40_61, -0.93_07]			)
			elif model_name == "videomae-small-finetuned-ssv2":
						lowerCamelCase__	:	int				=torch.Size([1, 174]			)
						lowerCamelCase__	:	Dict				=torch.tensor([0.26_71, -0.46_89, -0.82_35]			)
			elif model_name == "videomae-base":
						lowerCamelCase__	:	List[str]				=torch.Size([1, 1408, 1536]			)
						lowerCamelCase__	:	Dict				=torch.tensor([[0.77_39, 0.79_68, 0.70_89], [0.67_01, 0.74_87, 0.62_09], [0.42_87, 0.51_58, 0.47_73]]			)
			elif model_name == "videomae-base-short":
						lowerCamelCase__	:	List[Any]				=torch.Size([1, 1408, 1536]			)
						lowerCamelCase__	:	List[str]				=torch.tensor([[0.79_94, 0.96_12, 0.85_08], [0.74_01, 0.89_58, 0.83_02], [0.58_62, 0.74_68, 0.73_25]]			)
						# we verified the loss both for normalized and unnormalized targets for this one
						lowerCamelCase__	:	str				=torch.tensor([0.51_42]			) if config.norm_pix_loss else torch.tensor([0.64_69]			)
			elif model_name == "videomae-large":
						lowerCamelCase__	:	Union[str, Any]				=torch.Size([1, 1408, 1536]			)
						lowerCamelCase__	:	List[Any]				=torch.tensor([[0.71_49, 0.79_97, 0.69_66], [0.67_68, 0.78_69, 0.69_48], [0.51_39, 0.62_21, 0.56_05]]			)
			elif model_name == "videomae-large-finetuned-kinetics":
						lowerCamelCase__	:	Any				=torch.Size([1, 400]			)
						lowerCamelCase__	:	str				=torch.tensor([0.07_71, 0.00_11, -0.36_25]			)
			elif model_name == "videomae-huge-finetuned-kinetics":
						lowerCamelCase__	:	Any				=torch.Size([1, 400]			)
						lowerCamelCase__	:	Optional[int]				=torch.tensor([0.24_33, 0.16_32, -0.48_94]			)
			elif model_name == "videomae-base-short-finetuned-kinetics":
						lowerCamelCase__	:	List[str]				=torch.Size([1, 400]			)
						lowerCamelCase__	:	Dict				=torch.tensor([0.65_88, 0.09_90, -0.24_93]			)
			elif model_name == "videomae-base-finetuned-kinetics":
						lowerCamelCase__	:	str				=torch.Size([1, 400]			)
						lowerCamelCase__	:	Any				=torch.tensor([0.36_69, -0.06_88, -0.24_21]			)
			elif model_name == "videomae-base-short-ssv2":
						lowerCamelCase__	:	Tuple				=torch.Size([1, 1408, 1536]			)
						lowerCamelCase__	:	Dict				=torch.tensor([[0.47_12, 0.52_96, 0.57_86], [0.22_78, 0.27_29, 0.40_26], [0.03_52, 0.07_30, 0.25_06]]			)
			elif model_name == "videomae-base-short-finetuned-ssv2":
						lowerCamelCase__	:	Optional[int]				=torch.Size([1, 174]			)
						lowerCamelCase__	:	Any				=torch.tensor([-0.05_37, -0.15_39, -0.32_66]			)
			elif model_name == "videomae-base-ssv2":
						lowerCamelCase__	:	Dict				=torch.Size([1, 1408, 1536]			)
						lowerCamelCase__	:	str				=torch.tensor([[0.81_31, 0.87_27, 0.85_46], [0.73_66, 0.93_77, 0.88_70], [0.59_35, 0.88_74, 0.85_64]]			)
			elif model_name == "videomae-base-finetuned-ssv2":
						lowerCamelCase__	:	str				=torch.Size([1, 174]			)
						lowerCamelCase__	:	int				=torch.tensor([0.19_61, -0.83_37, -0.63_89]			)
			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]							,	__lowerCamelCase							,	atol=1e-4			)
			else:
						print('''Logits:'''							,	logits[0, :3, :3]			)
						assert torch.allclose(logits[0, :3, :3]							,	__lowerCamelCase							,	atol=1e-4			)
			print('''Logits ok!'''			)

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

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


if __name__ == "__main__":
		_lowercase				:				Any   		=		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."
		)

		_lowercase				:				Union[str, Any]   		=		parser.parse_args()
		convert_videomae_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.model_name, args.push_to_hub)




