



from dataclasses import dataclass
from typing import Tuple

import numpy as np
import torch







@dataclass
class  lowerCamelCase__    :




		'''simple docstring'''



		snake_case_			=42  # [batch_size x 3]
		snake_case_			=42  # [batch_size x 3]
		snake_case_			=42  # [batch_size x 3]
		snake_case_			=42  # [batch_size x 3]
		snake_case_			=42
		snake_case_			=42
		snake_case_			=42
		snake_case_			=42
		snake_case_			=42



		def 	lowerCAmelCase__   (self				)  ->    str:

						"""simple docstring"""






						assert self.x.shape[0] == self.y.shape[0] == self.z.shape[0] == self.origin.shape[0]
						assert self.x.shape[1] == self.y.shape[1] == self.z.shape[1] == self.origin.shape[1] == 3
						assert len(self.x.shape				) == len(self.y.shape				) == len(self.z.shape				) == len(self.origin.shape				) == 2



		def 	lowerCAmelCase__   (self				)  ->    Dict:

						"""simple docstring"""






						return torch.from_numpy(np.array([self.width, self.height]	,dtype=np.floataa				)				)



		def 	lowerCAmelCase__   (self				)  ->    str:

						"""simple docstring"""






						return torch.from_numpy(np.array([self.x_fov, self.y_fov]	,dtype=np.floataa				)				)



		def 	lowerCAmelCase__   (self				)  ->    torch.Tensor:

						"""simple docstring"""






						lowerCAmelCase__  :       str				=  torch.arange(self.height * self.width				)
						lowerCAmelCase__  :       Any				=  torch.stack(
						    [
						        pixel_indices % self.width,
						        torch.div(__UpperCamelCase	,self.width	,rounding_mode='''trunc'''				),
						    ]	,axis=1	,)
						return coords



		@property
		def 	lowerCAmelCase__   (self				)  ->    int:

						"""simple docstring"""






						lowerCAmelCase__		,	*lowerCAmelCase__  :       str				=  self.shape
						lowerCAmelCase__  :       str				=  int(np.prod(__UpperCamelCase				)				)

						lowerCAmelCase__  :       List[Any]				=  self.get_image_coords()
						lowerCAmelCase__  :       List[Any]				=  torch.broadcast_to(coords.unsqueeze(0				)	,[batch_size * inner_batch_size, *coords.shape]				)
						lowerCAmelCase__  :       Optional[int]				=  self.get_camera_rays(__UpperCamelCase				)

						lowerCAmelCase__  :       Union[str, Any]				=  rays.view(__UpperCamelCase	,inner_batch_size * self.height * self.width	,2	,3				)

						return rays



		def 	lowerCAmelCase__   (self	,__lowerCamelCase				)  ->    torch.Tensor:

						"""simple docstring"""






						lowerCAmelCase__		,	*lowerCAmelCase__		,	lowerCAmelCase__  :       str				=  coords.shape
						assert n_coords == 2
						assert batch_size == self.origin.shape[0]

						lowerCAmelCase__  :       List[str]				=  coords.view(__UpperCamelCase	,-1	,2				)

						lowerCAmelCase__  :       Tuple				=  self.resolution()
						lowerCAmelCase__  :       int				=  self.fov()

						lowerCAmelCase__  :       Tuple				=  (flat.float() / (res - 1)) * 2 - 1
						lowerCAmelCase__  :       str				=  fracs * torch.tan(fov / 2				)

						lowerCAmelCase__  :       Tuple				=  fracs.view(__UpperCamelCase	,-1	,2				)
						lowerCAmelCase__  :       List[str]				=  (
						    self.z.view(__UpperCamelCase	,1	,3				)
						    + self.x.view(__UpperCamelCase	,1	,3				) * fracs[:, :, :1]
						    + self.y.view(__UpperCamelCase	,1	,3				) * fracs[:, :, 1:]
						)
						lowerCAmelCase__  :       Optional[int]				=  directions / directions.norm(dim=-1	,keepdim=__UpperCamelCase				)
						lowerCAmelCase__  :       str				=  torch.stack(
						    [
						        torch.broadcast_to(self.origin.view(__UpperCamelCase	,1	,3				)	,[batch_size, directions.shape[1], 3]				),
						        directions,
						    ]	,dim=2	,)
						return rays.view(__UpperCamelCase	,*__UpperCamelCase	,2	,3				)



		def 	lowerCAmelCase__   (self	,__lowerCamelCase	,__lowerCamelCase				)  ->    "DifferentiableProjectiveCamera":

						"""simple docstring"""






						assert width * self.height == height * self.width, "The aspect ratio should not change."
						return DifferentiableProjectiveCamera(
						    origin=self.origin	,x=self.x	,y=self.y	,z=self.z	,width=__UpperCamelCase	,height=__UpperCamelCase	,x_fov=self.x_fov	,y_fov=self.y_fov	,)






def 			lowerCAmelCase__	(					lowerCamelCase_	:	List[Any]):





				'''simple docstring'''

				lowerCAmelCase__  :       Optional[int]				=  []
				lowerCAmelCase__  :       Dict				=  []
				lowerCAmelCase__  :       Optional[Any]				=  []
				lowerCAmelCase__  :       Any				=  []
				for theta in np.linspace(0 ,2 * np.pi ,num=20):
								lowerCAmelCase__  :       int				=  np.array([np.sin(_A), np.cos(_A), -0.5])
								z /= np.sqrt(np.sum(z**2))
								lowerCAmelCase__  :       str				=  -z * 4
								lowerCAmelCase__  :       Dict				=  np.array([np.cos(_A), -np.sin(_A), 0.0])
								lowerCAmelCase__  :       int				=  np.cross(_A ,_A)
								origins.append(_A)
								xs.append(_A)
								ys.append(_A)
								zs.append(_A)
				return DifferentiableProjectiveCamera(
				    origin=torch.from_numpy(np.stack(_A ,axis=0)).float() ,x=torch.from_numpy(np.stack(_A ,axis=0)).float() ,y=torch.from_numpy(np.stack(_A ,axis=0)).float() ,z=torch.from_numpy(np.stack(_A ,axis=0)).float() ,width=_A ,height=_A ,x_fov=0.7 ,y_fov=0.7 ,shape=(1, len(_A)) ,)






# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny vocab first, and then a tiny model - so the outcome is truly tiny -
# all files ~60KB. As compared to taking a full-size model, reducing to the minimum its layers and
# emb dimensions, but keeping the full vocab + merges files, leading to ~3MB in total for all files.
# The latter is done by `fsmt-make-super-tiny-model.py`.
#
# It will be used then as "stas/tiny-wmt19-en-ru"

from pathlib import Path
import json
import tempfile

from transformers import FSMTTokenizer, FSMTConfig, FSMTForConditionalGeneration
from transformers.models.fsmt.tokenization_fsmt import VOCAB_FILES_NAMES

__snake_case       :	List[str]            ='tiny-wmt19-en-ru'

# Build

# borrowed from a test
__snake_case       :	Any            =[
    '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>',
]
__snake_case       :	Union[str, Any]            =dict(zip(vocab, range(len(vocab))))
__snake_case       :	List[Any]            =['l o 123', 'lo w 1456', 'e r</w> 1789', '']

with tempfile.TemporaryDirectory() as tmpdirname:
							__snake_case       :	Optional[Any]            =Path(tmpdirname)
							__snake_case       :	int            =build_dir / VOCAB_FILES_NAMES['src_vocab_file']
							__snake_case       :	str            =build_dir / VOCAB_FILES_NAMES['tgt_vocab_file']
							__snake_case       :	Optional[int]            =build_dir / VOCAB_FILES_NAMES['merges_file']
							with open(src_vocab_file, 'w') as fp:
														fp.write(json.dumps(vocab_tokens))
							with open(tgt_vocab_file, 'w') as fp:
														fp.write(json.dumps(vocab_tokens))
							with open(merges_file, 'w') as fp:
														fp.write('\n'.join(merges))

							__snake_case       :	Union[str, Any]            =FSMTTokenizer(
							    langs=['en', 'ru'],
							    src_vocab_size=len(vocab),
							    tgt_vocab_size=len(vocab),
							    src_vocab_file=src_vocab_file,
							    tgt_vocab_file=tgt_vocab_file,
							    merges_file=merges_file,
							)

__snake_case       :	Dict            =FSMTConfig(
    langs=['ru', 'en'],
    src_vocab_size=1_0_0_0,
    tgt_vocab_size=1_0_0_0,
    d_model=4,
    encoder_layers=1,
    decoder_layers=1,
    encoder_ffn_dim=4,
    decoder_ffn_dim=4,
    encoder_attention_heads=1,
    decoder_attention_heads=1,
)

__snake_case       :	Optional[Any]            =FSMTForConditionalGeneration(config)
print(f"""num of params {tiny_model.num_parameters()}""")

# Test
__snake_case       :	Dict            =tokenizer(['Making tiny model'], return_tensors='pt')
__snake_case       :	Dict            =tiny_model(**batch)

print('test output:', len(outputs.logits[0]))

# Save
tiny_model.half()  # makes it smaller
tiny_model.save_pretrained(mname_tiny)
tokenizer.save_pretrained(mname_tiny)

print(f"""Generated {mname_tiny}""")

# Upload
# transformers-cli upload tiny-wmt19-en-ru

