




import json
import os
import unittest

from transformers.models.fsmt.tokenization_fsmt import VOCAB_FILES_NAMES, FSMTTokenizer
from transformers.testing_utils import slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


# using a different tiny model than the one used for default params defined in init to ensure proper testing
__lowerCAmelCase						:							Optional[int]			     =						"""stas/tiny-wmt19-en-ru"""




class        snake_case__				(__snake_case       ,			unittest.TestCase  ):
				"""simple docstring"""



				SCREAMING_SNAKE_CASE_			:				Union[str, Any]									=   FSMTTokenizer
				SCREAMING_SNAKE_CASE_			:				int									=   False





				def 				__UpperCAmelCase				( self							:	Any				)   ->	Union[str, Any]:
							super().setUp()

							# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
							a       =     [
							    '''l''',
							    '''o''',
							    '''w''',
							    '''e''',
							    '''r''',
							    '''s''',
							    '''t''',
							    '''i''',
							    '''d''',
							    '''n''',
							    '''w</w>''',
							    '''r</w>''',
							    '''t</w>''',
							    '''lo''',
							    '''low''',
							    '''er</w>''',
							    '''low</w>''',
							    '''lowest</w>''',
							    '''newer</w>''',
							    '''wider</w>''',
							    '''<unk>''',
							]
							a       =     dict(zip(a_   , range(len(a_				)				)				)				)
							a       =     ['''l o 123''', '''lo w 1456''', '''e r</w> 1789''', '''''']

							a       =     ['''en''', '''ru''']
							a       =     {
							    '''langs''': self.langs,
							    '''src_vocab_size''': 10,
							    '''tgt_vocab_size''': 20,
							}

							a       =     os.path.join(self.tmpdirname   , VOCAB_FILES_NAMES["src_vocab_file"]				)
							a       =     os.path.join(self.tmpdirname   , VOCAB_FILES_NAMES["tgt_vocab_file"]				)
							a       =     os.path.join(self.tmpdirname   , "tokenizer_config.json"				)
							a       =     os.path.join(self.tmpdirname   , VOCAB_FILES_NAMES["merges_file"]				)
							with open(self.src_vocab_file   , "w"				) as fp:
										fp.write(json.dumps(a_				)				)
							with open(self.tgt_vocab_file   , "w"				) as fp:
										fp.write(json.dumps(a_				)				)
							with open(self.merges_file   , "w"				) as fp:
										fp.write("\n".join(a_				)				)
							with open(a_   , "w"				) as fp:
										fp.write(json.dumps(a_				)				)





				@cached_property
				def 				__UpperCAmelCase				( self							:	Dict				)   ->	int:
							return FSMTTokenizer.from_pretrained("facebook/wmt19-ru-en"				)





				@cached_property
				def 				__UpperCAmelCase				( self							:	Union[str, Any]				)   ->	Dict:
							return FSMTTokenizer.from_pretrained("facebook/wmt19-en-ru"				)





				def 				__UpperCAmelCase				( self							:	Any				)   ->	List[Any]:
							a       =     FSMTTokenizer.from_pretrained(a_				)
							self.assertListEqual([tokenizer.src_lang, tokenizer.tgt_lang]   , ["en", "ru"]				)
							self.assertEqual(tokenizer.src_vocab_size   , 21				)
							self.assertEqual(tokenizer.tgt_vocab_size   , 21				)





				def 				__UpperCAmelCase				( self							:	int				)   ->	Union[str, Any]:
							a       =     FSMTTokenizer(self.langs   , self.src_vocab_file   , self.tgt_vocab_file   , self.merges_file				)

							a       =     '''lower'''
							a       =     ['''low''', '''er</w>''']
							a       =     tokenizer.tokenize(a_				)
							self.assertListEqual(a_   , a_				)

							a       =     tokens + ['''<unk>''']
							a       =     [14, 15, 20]
							self.assertListEqual(tokenizer.convert_tokens_to_ids(a_				)   , a_				)





				@slow
				def 				__UpperCAmelCase				( self							:	str				)   ->	Any:
							a       =     self.tokenizer_ru_en

							a       =     tokenizer.encode("sequence builders"   , add_special_tokens=a_				)
							a       =     tokenizer.encode("multi-sequence build"   , add_special_tokens=a_				)

							a       =     tokenizer.build_inputs_with_special_tokens(a_				)
							a       =     tokenizer.build_inputs_with_special_tokens(a_   , a_				)

							assert encoded_sentence == text + [2]
							assert encoded_pair == text + [2] + text_a + [2]





				@slow
				def 				__UpperCAmelCase				( self							:	List[Any]				)   ->	Any:
							a       =     self.tokenizer_en_ru
							a       =     self.tokenizer_ru_en

							a       =     [
							    [
							        '''Here\'s a little song I wrote. Don\'t worry, be happy.''',
							        [24_70, 39, 11, 23_49, 72_22, 70, 59_79, 7, 84_50, 10_50, 1_31_60, 5, 26, 64_45, 7, 2],
							    ],
							    ['''This is it. No more. I\'m done!''', [1_32, 21, 37, 7, 14_34, 86, 7, 70, 64_76, 13_05, 4_27, 2]],
							]

							# if data needs to be recreated or added, run:
							# import torch
							# model = torch.hub.load("pytorch/fairseq", "transformer.wmt19.en-ru", checkpoint_file="model4.pt", tokenizer="moses", bpe="fastbpe")
							# for src_text, _ in targets: print(f"""[\n"{src_text}",\n {model.encode(src_text).tolist()}\n],""")

							for src_text, tgt_input_ids in targets:
										a       =     tokenizer_enc.encode(a_   , return_tensors=a_				)
										self.assertListEqual(a_   , a_				)

										# and decode backward, using the reversed languages model
										a       =     tokenizer_dec.decode(a_   , skip_special_tokens=a_				)
										self.assertEqual(a_   , a_				)





				@slow
				def 				__UpperCAmelCase				( self							:	Tuple				)   ->	Optional[int]:
							a       =     FSMTTokenizer.from_pretrained("facebook/wmt19-ru-en"   , do_lower_case=a_				)
							a       =     tokenizer.tokenize("USA is United States of America"				)
							a       =     ['''us''', '''a</w>''', '''is</w>''', '''un''', '''i''', '''ted</w>''', '''st''', '''ates</w>''', '''of</w>''', '''am''', '''er''', '''ica</w>''']
							self.assertListEqual(a_   , a_				)





				@unittest.skip("FSMTConfig.__init__  requires non-optional args"				)
				def 				__UpperCAmelCase				( self							:	str				)   ->	Optional[Any]:
							pass





				@unittest.skip("FSMTConfig.__init__  requires non-optional args"				)
				def 				__UpperCAmelCase				( self							:	str				)   ->	Union[str, Any]:
							pass










"""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 						lowercase				(	_snake_case		: Optional[Any]	)	->Union[str, Any]:
	"""simple docstring"""





	__snake_case      :		str         =							'''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=2_048	):
				f.write(_snake_case	)






def 						lowercase				(	)	->VivitConfig:
	"""simple docstring"""





	__snake_case      :		int         =							VivitConfig()

	__snake_case      :		Tuple         =							400
	__snake_case      :		Tuple         =							'''huggingface/label-files'''
	__snake_case      :		List[str]         =							'''kinetics400-id2label.json'''

	__snake_case      :		Dict         =							json.load(open(hf_hub_download(_snake_case	,  _snake_case	,  repo_type='''dataset'''	)	,  '''r'''	)	)
	__snake_case      :		List[str]         =							{int(_snake_case	): v for k, v in idalabel.items()}
	__snake_case      :		Tuple         =							idalabel
	__snake_case      :		Optional[int]         =							{v: k for k, v in idalabel.items()}
	return config






def 						lowercase				(	)	->Optional[int]:
	"""simple docstring"""





	__snake_case      :		List[Any]         =							hf_hub_download(
	    repo_id='''hf-internal-testing/spaghetti-video'''	,  filename='''eating_spaghetti_32_frames.npy'''	,  repo_type='''dataset'''	)
	__snake_case      :		str         =							np.load(_snake_case	)
	return list(_snake_case	)






def 						lowercase				(	_snake_case		: np.ndarray	)	->str:
	"""simple docstring"""





	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 						lowercase				(	_snake_case		: np.ndarray	)	->Tuple:
	"""simple docstring"""





	return current.flatten()






def 						lowercase				(	_snake_case		: np.ndarray	)	->List[str]:
	"""simple docstring"""





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






def 						lowercase				(	_snake_case		: np.ndarray	)	->Optional[Any]:
	"""simple docstring"""





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






def 						lowercase				(	_snake_case		: Dict	,  _snake_case		: Tuple	)	->Tuple:
	"""simple docstring"""





	__snake_case      :		int         =							state_dict['''optimizer''']['''target''']['''Transformer'''][f"""encoderblock_{i}"""]

	__snake_case      :		Optional[int]         =							f"""encoder.layer.{i}."""
	__snake_case      :		Dict         =							{
	    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 						lowercase				(	_snake_case		: int	)	->List[str]:
	"""simple docstring"""





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






def 						lowercase				(	_snake_case		: Optional[Any]	,  _snake_case		: Optional[int]=False	)	->Union[str, Any]:
	"""simple docstring"""





	__snake_case      :		Tuple         =							get_n_layers(_snake_case	)

	__snake_case      :		str         =							OrderedDict()

	__snake_case      :		List[Any]         =							state_dict['''optimizer''']['''target''']['''Transformer''']['''encoder_norm''']['''bias''']
	__snake_case      :		List[str]         =							state_dict['''optimizer''']['''target''']['''Transformer''']['''encoder_norm''']['''scale''']

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

	__snake_case      :		Union[str, Any]         =							state_dict['''optimizer''']['''target''']['''cls''']
	__snake_case      :		List[str]         =							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:
		__snake_case      :		List[Any]         =							{'''vivit.''' + k: v for k, v in new_state.items()}
		__snake_case      :		str         =							np.transpose(state_dict['''optimizer''']['''target''']['''output_projection''']['''kernel''']	)
		__snake_case      :		int         =							np.transpose(state_dict['''optimizer''']['''target''']['''output_projection''']['''bias''']	)

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






def 						lowercase				(	)	->VivitImageProcessor:
	"""simple docstring"""





	__snake_case      :		List[str]         =							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 						lowercase				(	_snake_case		: str	)	->Optional[Any]:
	"""simple docstring"""





	__snake_case      :		Tuple         =							'''checkpoint'''

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

	__snake_case      :		List[Any]         =							restore_checkpoint(_snake_case	,  _snake_case	)
	__snake_case      :		Union[str, Any]         =							transform_state(_snake_case	,  classification_head=_snake_case	)

	__snake_case      :		Optional[Any]         =							get_vivit_config()

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

	__snake_case      :		List[str]         =							VivitForVideoClassification(_snake_case	)
	model.load_state_dict(_snake_case	)
	model.eval()

	__snake_case      :		Tuple         =							get_processor()

	__snake_case      :		Any         =							prepare_video()
	__snake_case      :		List[str]         =							extractor(_snake_case	,  return_tensors='''pt'''	)

	__snake_case      :		Union[str, Any]         =							model(**_snake_case	)

	__snake_case      :		List[str]         =							torch.Size([1, 400]	)
	__snake_case      :		Optional[int]         =							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__":
			SCREAMING_SNAKE_CASE					:       Optional[Any]							   =							argparse.ArgumentParser()

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

			SCREAMING_SNAKE_CASE					:       List[str]							   =							parser.parse_args()
			convert(args.output_model_name)





