
"""simple docstring"""


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

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

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






def  _snake_case			(						snake_case__				:       Tuple       ):
	A  				=				'https://storage.googleapis.com/scenic-bucket/vivit/kinetics_400/vivit_base_16x2_unfactorized/checkpoint'

	with open(snake_case__		, 'wb'       ) as f:
		with requests.get(snake_case__		, stream=snake_case__       ) as req:
			for chunk in req.iter_content(chunk_size=2048       ):
				f.write(snake_case__       )






def  _snake_case			(						):
	A  				=				VivitConfig()

	A  				=				400
	A  				=				'huggingface/label-files'
	A  				=				'kinetics400-id2label.json'

	A  				=				json.load(open(hf_hub_download(snake_case__		, snake_case__		, repo_type='dataset'       )		, 'r'       )       )
	A  				=				{int(snake_case__       ): v for k, v in idalabel.items()}
	A  				=				idalabel
	A  				=				{v: k for k, v in idalabel.items()}
	return config






def  _snake_case			(						):
	A  				=				hf_hub_download(
	    repo_id='hf-internal-testing/spaghetti-video'		, filename='eating_spaghetti_32_frames.npy'		, repo_type='dataset'       )
	A  				=				np.load(snake_case__       )
	return list(snake_case__       )






def  _snake_case			(						snake_case__				:       np.ndarray       ):
	if np.ndim(snake_case__       ) == 2:
		return transform_attention_bias(snake_case__       )

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

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






def  _snake_case			(						snake_case__				:       np.ndarray       ):
	return current.flatten()






def  _snake_case			(						snake_case__				:       np.ndarray       ):
	return np.reshape(snake_case__		, (current.shape[0], current.shape[1] * current.shape[2])       ).T






def  _snake_case			(						snake_case__				:       np.ndarray       ):
	return np.reshape(snake_case__		, (current.shape[0] * current.shape[1], current.shape[2])       ).T






def  _snake_case			(						snake_case__				:       str		, snake_case__				:       List[str]       ):
	A  				=				state_dict['optimizer']['target']['Transformer'][F'encoderblock_{i}']

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

	return new_state






def  _snake_case			(						snake_case__				:       Dict       ):
	return sum([1 if 'encoderblock_' in k else 0 for k in state_dict['optimizer']['target']['Transformer'].keys()]       )






def  _snake_case			(						snake_case__				:       Union[str, Any]		, snake_case__				:       Optional[int]=False       ):
	A  				=				get_n_layers(snake_case__       )

	A  				=				OrderedDict()

	A  				=				state_dict['optimizer']['target']['Transformer']['encoder_norm']['bias']
	A  				=				state_dict['optimizer']['target']['Transformer']['encoder_norm']['scale']

	A  				=				np.transpose(
	    state_dict['optimizer']['target']['embedding']['kernel']		, (4, 3, 0, 1, 2)       )
	A  				=				state_dict['optimizer']['target']['embedding']['bias']

	A  				=				state_dict['optimizer']['target']['cls']
	A  				=				state_dict['optimizer']['target']['Transformer']['posembed_input'][
	    'pos_embedding'
	]

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

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

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






def  _snake_case			(						):
	A  				=				VivitImageProcessor()

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

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

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

	return extractor






def  _snake_case			(						snake_case__				:       str       ):
	A  				=				'checkpoint'

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

	A  				=				restore_checkpoint(snake_case__		, snake_case__       )
	A  				=				transform_state(snake_case__		, classification_head=snake_case__       )

	A  				=				get_vivit_config()

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

	A  				=				VivitForVideoClassification(snake_case__       )
	model.load_state_dict(snake_case__       )
	model.eval()

	A  				=				get_processor()

	A  				=				prepare_video()
	A  				=				extractor(snake_case__		, return_tensors='pt'       )

	A  				=				model(**snake_case__       )

	A  				=				torch.Size([1, 400]       )
	A  				=				torch.tensor([-1.0543, 2.0764, -0.2104, 0.4439, -0.9658]       )

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

	model.save_pretrained(snake_case__       )
	extractor.save_pretrained(snake_case__       )


if __name__ == "__main__":
		_lowercase							     =			argparse.ArgumentParser()

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

		_lowercase							     =			parser.parse_args()
		convert(args.output_model_name)

"""simple docstring"""


import itertools
import random
import unittest

import numpy as np

from transformers import EncodecFeatureExtractor
from transformers.testing_utils import require_torch
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_torch_available():
		import torch


_lowercase											=			random.Random()






def  _snake_case			(						snake_case__				:       List[str]		, snake_case__				:       Union[str, Any]=1.0		, snake_case__				:       int=None		, snake_case__				:       Dict=None       ):
	if rng is None:
		A  				=				global_rng

	A  				=				[]
	for batch_idx in range(shape[0]       ):
		values.append([]       )
		for _ in range(shape[1]       ):
			values[-1].append(rng.random() * scale       )

	return values







@require_torch
class 			lowerCAmelCase_			( unittest.TestCase      ):



						'''simple docstring'''


						def __init__(	self       : int					,A_       : str					,A_       : List[str]=7					,A_       : Tuple=400					,A_       : Union[str, Any]=2000					,A_       : Optional[Any]=1					,A_       : Optional[int]=0.0					,A_       : str=2_4000					,A_       : Optional[int]=True					,)       ->				Dict:
							A  				=				parent
							A  				=				batch_size
							A  				=				min_seq_length
							A  				=				max_seq_length
							A  				=				(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
							A  				=				feature_size
							A  				=				padding_value
							A  				=				sampling_rate
							A  				=				return_attention_mask


						def   _SCREAMING_SNAKE_CASE (	self       : Optional[Any] )       ->				Optional[int]:
							return {
							    "feature_size": self.feature_size,
							    "padding_value": self.padding_value,
							    "sampling_rate": self.sampling_rate,
							    "return_attention_mask": self.return_attention_mask,
							}




						def   _SCREAMING_SNAKE_CASE (	self       : Dict					,A_       : List[str]=False					,A_       : str=False )       ->				Optional[Any]:
							def _flatten(A_       : int ):
								return list(itertools.chain(*A_ ) )

							if equal_length:
								A  				=				floats_list((self.batch_size, self.max_seq_length) )
							else:
								# make sure that inputs increase in size
								A  				=				[
								    _flatten(floats_list((x, self.feature_size) ) )
								    for x in range(self.min_seq_length					,self.max_seq_length					,self.seq_length_diff )
								]

							if numpify:
								A  				=				[np.asarray(A_ ) for x in audio_inputs]

							return audio_inputs







@require_torch
class 			lowerCAmelCase_			( _lowercase						,   unittest.TestCase      ):



						'''simple docstring'''


						_lowerCamelCase:      Union[str, Any] =      EncodecFeatureExtractor


						def   _SCREAMING_SNAKE_CASE (	self       : List[str] )       ->				List[Any]:
							A  				=				EnCodecFeatureExtractionTester(self )


						def   _SCREAMING_SNAKE_CASE (	self       : Tuple )       ->				str:
							# Tests that all call wrap to encode_plus and batch_encode_plus
							A  				=				self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
							# create three inputs of length 800, 1000, and 1200
							A  				=				[floats_list((1, x) )[0] for x in range(800					,1400					,200 )]
							A  				=				[np.asarray(A_ ) for audio_input in audio_inputs]

							# Test not batched input
							A  				=				feat_extract(audio_inputs[0]					,return_tensors='np' ).input_values
							A  				=				feat_extract(np_audio_inputs[0]					,return_tensors='np' ).input_values
							self.assertTrue(np.allclose(A_					,A_					,atol=1e-3 ) )

							# Test batched
							A  				=				feat_extract(A_					,padding=A_					,return_tensors='np' ).input_values
							A  				=				feat_extract(A_					,padding=A_					,return_tensors='np' ).input_values
							for enc_seq_a, enc_seq_a in zip(A_					,A_ ):
								self.assertTrue(np.allclose(A_					,A_					,atol=1e-3 ) )


						def   _SCREAMING_SNAKE_CASE (	self       : Tuple )       ->				Dict:
							A  				=				self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
							A  				=				np.random.rand(100 ).astype(np.floataa )
							A  				=				np_audio_inputs.tolist()

							for inputs in [py_audio_inputs, np_audio_inputs]:
								A  				=				feature_extractor.pad([{'input_values': inputs}]					,return_tensors='np' )
								self.assertTrue(np_processed.input_values.dtype == np.floataa )
								A  				=				feature_extractor.pad([{'input_values': inputs}]					,return_tensors='pt' )
								self.assertTrue(pt_processed.input_values.dtype == torch.floataa )


						def   _SCREAMING_SNAKE_CASE (	self       : Optional[int]					,A_       : int )       ->				List[Any]:
							from datasets import load_dataset

							A  				=				load_dataset('hf-internal-testing/librispeech_asr_dummy'					,'clean'					,split='validation' )
							# automatic decoding with librispeech
							A  				=				ds.sort('id' ).select(range(A_ ) )[:num_samples]['audio']

							return [x["array"] for x in audio_samples]


						def   _SCREAMING_SNAKE_CASE (	self       : List[Any] )       ->				Optional[int]:
							# fmt: off
							A  				=				torch.tensor(
							    [2.3_804e-03, 2.0_752e-03, 1.9_836e-03, 2.1_057e-03, 1.6_174e-03,
							     3.0_518e-04, 9.1_553e-05, 3.3_569e-04, 9.7_656e-04, 1.8_311e-03,
							     2.0_142e-03, 2.1_057e-03, 1.7_395e-03, 4.5_776e-04, -3.9_673e-04,
							     4.5_776e-04, 1.0_071e-03, 9.1_553e-05, 4.8_828e-04, 1.1_597e-03,
							     7.3_242e-04, 9.4_604e-04, 1.8_005e-03, 1.8_311e-03, 8.8_501e-04,
							     4.2_725e-04, 4.8_828e-04, 7.3_242e-04, 1.0_986e-03, 2.1_057e-03] )
							# fmt: on
							A  				=				self._load_datasamples(1 )
							A  				=				EncodecFeatureExtractor()
							A  				=				feature_extractor(A_					,return_tensors='pt' ).input_values
							self.assertEquals(input_values.shape					,(1, 1, 9_3680) )
							self.assertTrue(torch.allclose(input_values[0, 0, :30]					,A_					,atol=1e-6 ) )


						def   _SCREAMING_SNAKE_CASE (	self       : Union[str, Any] )       ->				List[Any]:
							# fmt: off
							A  				=				torch.tensor(
							    [2.3_804e-03, 2.0_752e-03, 1.9_836e-03, 2.1_057e-03, 1.6_174e-03,
							     3.0_518e-04, 9.1_553e-05, 3.3_569e-04, 9.7_656e-04, 1.8_311e-03,
							     2.0_142e-03, 2.1_057e-03, 1.7_395e-03, 4.5_776e-04, -3.9_673e-04,
							     4.5_776e-04, 1.0_071e-03, 9.1_553e-05, 4.8_828e-04, 1.1_597e-03,
							     7.3_242e-04, 9.4_604e-04, 1.8_005e-03, 1.8_311e-03, 8.8_501e-04,
							     4.2_725e-04, 4.8_828e-04, 7.3_242e-04, 1.0_986e-03, 2.1_057e-03] )
							# fmt: on
							A  				=				self._load_datasamples(1 )
							A  				=				[np.tile(input_audio[0][None]					,reps=(2, 1) )]
							input_audio[0][1] *= 0.5
							A  				=				EncodecFeatureExtractor(feature_size=2 )
							A  				=				feature_extractor(A_					,return_tensors='pt' ).input_values
							self.assertEquals(input_values.shape					,(1, 2, 9_3680) )
							self.assertTrue(torch.allclose(input_values[0, 0, :30]					,A_					,atol=1e-6 ) )
							self.assertTrue(torch.allclose(input_values[0, 1, :30]					,EXPECTED_INPUT_VALUES * 0.5					,atol=1e-6 ) )




						def   _SCREAMING_SNAKE_CASE (	self       : Dict )       ->				List[str]:
							A  				=				self._load_datasamples(2 )
							# would be easier if the stride was like
							A  				=				EncodecFeatureExtractor(feature_size=1					,chunk_length_s=1					,overlap=0.01 )

							# pad and trunc raise an error ?
							with self.assertRaisesRegex(
							    A_					,'^Both padding and truncation were set. Make sure you only set one.$'					,):
								A  				=				feature_extractor(
								    A_					,padding='max_length'					,truncation=A_					,return_tensors='pt' ).input_values

							# truncate to chunk
							A  				=				feature_extractor(A_					,truncation=A_					,return_tensors='pt' ).input_values
							self.assertEquals(truncated_outputs.shape					,(2, 1, 7_1520) )  # 2 chunks

							# force truncate to max_length
							A  				=				feature_extractor(
							    A_					,truncation=A_					,max_length=4_8000					,return_tensors='pt' ).input_values
							self.assertEquals(truncated_outputs.shape					,(2, 1, 4_8000) )

							# pad to chunk
							A  				=				feature_extractor(A_					,padding=A_					,return_tensors='pt' ).input_values
							self.assertEquals(padded_outputs.shape					,(2, 1, 9_5280) )

							# pad to chunk
							A  				=				feature_extractor(A_					,return_tensors='pt' ).input_values
							self.assertEquals(truncated_outputs.shape					,(2, 1, 9_5280) )

							# force pad to max length
							A  				=				feature_extractor(
							    A_					,padding='max_length'					,max_length=10_0000					,return_tensors='pt' ).input_values
							self.assertEquals(truncated_outputs.shape					,(2, 1, 10_0000) )

							# force no pad
							with self.assertRaisesRegex(
							    A_					,'^Unable to create tensor, you should probably activate padding with \'padding=True\' to have batched tensors with the same length.$'					,):
								A  				=				feature_extractor(A_					,padding=A_					,return_tensors='pt' ).input_values

							A  				=				feature_extractor(input_audio[0]					,padding=A_					,return_tensors='pt' ).input_values
							self.assertEquals(truncated_outputs.shape					,(1, 1, 9_3680) )

							# no pad if no chunk_length_s
							A  				=				None
							with self.assertRaisesRegex(
							    A_					,'^Unable to create tensor, you should probably activate padding with \'padding=True\' to have batched tensors with the same length.$'					,):
								A  				=				feature_extractor(A_					,padding=A_					,return_tensors='pt' ).input_values

							A  				=				feature_extractor(input_audio[0]					,padding=A_					,return_tensors='pt' ).input_values
							self.assertEquals(truncated_outputs.shape					,(1, 1, 9_3680) )

							# no pad if no overlap
							A  				=				2
							A  				=				None
							with self.assertRaisesRegex(
							    A_					,'^Unable to create tensor, you should probably activate padding with \'padding=True\' to have batched tensors with the same length.$'					,):
								A  				=				feature_extractor(A_					,padding=A_					,return_tensors='pt' ).input_values

							A  				=				feature_extractor(input_audio[0]					,padding=A_					,return_tensors='pt' ).input_values
							self.assertEquals(truncated_outputs.shape					,(1, 1, 9_3680) )