






'''simple docstring'''




# 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

lowerCAmelCase__          =						'tiny-wmt19-en-ru'

# Build

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

with tempfile.TemporaryDirectory() as tmpdirname:
		lowerCAmelCase__          =						Path(tmpdirname)
		lowerCAmelCase__          =						build_dir / VOCAB_FILES_NAMES['src_vocab_file']
		lowerCAmelCase__          =						build_dir / VOCAB_FILES_NAMES['tgt_vocab_file']
		lowerCAmelCase__          =						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))

		lowerCAmelCase__          =						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,
		)

lowerCAmelCase__          =						FSMTConfig(
    langs=['ru', 'en'],
    src_vocab_size=1000,
    tgt_vocab_size=1000,
    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,
)

lowerCAmelCase__          =						FSMTForConditionalGeneration(config)
print(f'''num of params {tiny_model.num_parameters()}''')

# Test
lowerCAmelCase__          =						tokenizer(['Making tiny model'], return_tensors='pt')
lowerCAmelCase__          =						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












'''simple docstring'''




import numpy as np
from PIL import Image







def       __UpperCAmelCase						(  lowerCamelCase_     ,      lowerCamelCase_     ,      lowerCamelCase_)							->     np.ndarray:
		UpperCamelCase__				:					List[Any]					=					np.array(lowerCamelCase_)
		if arr.shape[0] != arr.shape[1]:
				raise ValueError('The input array is not a square matrix')
		UpperCamelCase__				:					Tuple					=					0
		UpperCamelCase__				:					int					=					0
		UpperCamelCase__				:					Optional[int]					=					0
		UpperCamelCase__				:					str					=					0

		# compute the shape of the output matrix
		UpperCamelCase__				:					int					=					(arr.shape[0] - size) // stride + 1
		# initialize the output matrix with zeros of shape maxpool_shape
		UpperCamelCase__				:					Dict					=					np.zeros((maxpool_shape, maxpool_shape))

		while i < arr.shape[0]:
				if i + size > arr.shape[0]:
						# if the end of the matrix is reached, break
						break
				while j < arr.shape[1]:
						# if the end of the matrix is reached, break
						if j + size > arr.shape[1]:
								break
						# compute the maximum of the pooling matrix
						UpperCamelCase__				:					Dict					=					np.max(arr[i : i + size, j : j + size])
						# shift the pooling matrix by stride of column pixels
						j += stride
						mat_j += 1

				# shift the pooling matrix by stride of row pixels
				i += stride
				mat_i += 1

				# reset the column index to 0
				UpperCamelCase__				:					List[Any]					=					0
				UpperCamelCase__				:					Optional[int]					=					0

		return updated_arr







def       __UpperCAmelCase						(  lowerCamelCase_     ,      lowerCamelCase_     ,      lowerCamelCase_)							->     np.ndarray:
		UpperCamelCase__				:					Tuple					=					np.array(lowerCamelCase_)
		if arr.shape[0] != arr.shape[1]:
				raise ValueError('The input array is not a square matrix')
		UpperCamelCase__				:					Optional[int]					=					0
		UpperCamelCase__				:					int					=					0
		UpperCamelCase__				:					List[str]					=					0
		UpperCamelCase__				:					List[Any]					=					0

		# compute the shape of the output matrix
		UpperCamelCase__				:					str					=					(arr.shape[0] - size) // stride + 1
		# initialize the output matrix with zeros of shape avgpool_shape
		UpperCamelCase__				:					Union[str, Any]					=					np.zeros((avgpool_shape, avgpool_shape))

		while i < arr.shape[0]:
				# if the end of the matrix is reached, break
				if i + size > arr.shape[0]:
						break
				while j < arr.shape[1]:
						# if the end of the matrix is reached, break
						if j + size > arr.shape[1]:
								break
						# compute the average of the pooling matrix
						UpperCamelCase__				:					List[Any]					=					int(np.average(arr[i : i + size, j : j + size]))
						# shift the pooling matrix by stride of column pixels
						j += stride
						mat_j += 1

				# shift the pooling matrix by stride of row pixels
				i += stride
				mat_i += 1
				# reset the column index to 0
				UpperCamelCase__				:					Union[str, Any]					=					0
				UpperCamelCase__				:					Optional[Any]					=					0

		return updated_arr


# Main Function
if __name__ == "__main__":
		from doctest import testmod

		testmod(name='avgpooling', verbose=True)

		# Loading the image
		lowerCAmelCase__          =						Image.open('path_to_image')

		# Converting the image to numpy array and maxpooling, displaying the result
		# Ensure that the image is a square matrix

		Image.fromarray(maxpooling(np.array(image), size=3, stride=2)).show()

		# Converting the image to numpy array and averagepooling, displaying the result
		# Ensure that the image is a square matrix

		Image.fromarray(avgpooling(np.array(image), size=3, stride=2)).show()




