



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

# Build

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

with tempfile.TemporaryDirectory() as tmpdirname:
							lowerCAmelCase_     :       List[str]								=						Path(tmpdirname)
							lowerCAmelCase_     :       Optional[Any]								=						build_dir / VOCAB_FILES_NAMES['src_vocab_file']
							lowerCAmelCase_     :       List[str]								=						build_dir / VOCAB_FILES_NAMES['tgt_vocab_file']
							lowerCAmelCase_     :       List[str]								=						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_     :       List[str]								=						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_     :       Optional[Any]								=						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_     :       Any								=						FSMTForConditionalGeneration(config)
print(f'num of params {tiny_model.num_parameters()}')

# Test
lowerCAmelCase_     :       Any								=						tokenizer(['Making tiny model'], return_tensors='pt')
lowerCAmelCase_     :       int								=						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 warnings
from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class 	lowerCamelCase_							(					snake_case_			):
						_lowerCAmelCase     :   int	     =				['image_processor', 'tokenizer']
						_lowerCAmelCase     :   Union[str, Any]	     =				'LayoutLMv3ImageProcessor'
						_lowerCAmelCase     :   List[Any]	     =				('LayoutLMv3Tokenizer', 'LayoutLMv3TokenizerFast')







						def __init__(  self   :      str				,    lowerCAmelCase__   :      int=None				,    lowerCAmelCase__   :      str=None				,    **lowerCAmelCase__   :      List[str]				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Tuple     					=			None
													if "feature_extractor" in kwargs:
																				warnings.warn(
																				    '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
																				    ''' instead.'''				,    lowerCAmelCase__				,    )
																				SCREAMING_SNAKE_CASE					:      List[str]     					=			kwargs.pop('''feature_extractor'''				)

													SCREAMING_SNAKE_CASE					:      Any     					=			image_processor if image_processor is not None else feature_extractor
													if image_processor is None:
																				raise ValueError('''You need to specify an `image_processor`.'''				)
													if tokenizer is None:
																				raise ValueError('''You need to specify a `tokenizer`.'''				)

													super().__init__(lowerCAmelCase__				,    lowerCAmelCase__				)







						def __call__(  self   :      Dict				,    lowerCAmelCase__   :      Any				,    lowerCAmelCase__   :      Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None				,    lowerCAmelCase__   :      Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None				,    lowerCAmelCase__   :      Union[List[List[int]], List[List[List[int]]]] = None				,    lowerCAmelCase__   :      Optional[Union[List[int], List[List[int]]]] = None				,    lowerCAmelCase__   :      bool = True				,    lowerCAmelCase__   :      Union[bool, str, PaddingStrategy] = False				,    lowerCAmelCase__   :      Union[bool, str, TruncationStrategy] = None				,    lowerCAmelCase__   :      Optional[int] = None				,    lowerCAmelCase__   :      int = 0				,    lowerCAmelCase__   :      Optional[int] = None				,    lowerCAmelCase__   :      Optional[bool] = None				,    lowerCAmelCase__   :      Optional[bool] = None				,    lowerCAmelCase__   :      bool = False				,    lowerCAmelCase__   :      bool = False				,    lowerCAmelCase__   :      bool = False				,    lowerCAmelCase__   :      bool = False				,    lowerCAmelCase__   :      bool = True				,    lowerCAmelCase__   :      Optional[Union[str, TensorType]] = None				,    **lowerCAmelCase__   :      Any				,    ):

													"""simple docstring"""
													# verify input
													if self.image_processor.apply_ocr and (boxes is not None):
																				raise ValueError(
																				    '''You cannot provide bounding boxes if you initialized the image processor with apply_ocr set to True.'''				)

													if self.image_processor.apply_ocr and (word_labels is not None):
																				raise ValueError(
																				    '''You cannot provide word labels if you initialized the image processor with apply_ocr set to True.'''				)

													# first, apply the image processor
													SCREAMING_SNAKE_CASE					:      List[Any]     					=			self.image_processor(images=lowerCAmelCase__				,    return_tensors=lowerCAmelCase__				)

													# second, apply the tokenizer
													if text is not None and self.image_processor.apply_ocr and text_pair is None:
																				if isinstance(lowerCAmelCase__				,    lowerCAmelCase__				):
																											SCREAMING_SNAKE_CASE					:      List[Any]     					=			[text]  # add batch dimension (as the image processor always adds a batch dimension)
																				SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			features['''words''']

													SCREAMING_SNAKE_CASE					:      Tuple     					=			self.tokenizer(
													    text=text if text is not None else features['''words''']				,    text_pair=text_pair if text_pair is not None else None				,    boxes=boxes if boxes is not None else features['''boxes''']				,    word_labels=lowerCAmelCase__				,    add_special_tokens=lowerCAmelCase__				,    padding=lowerCAmelCase__				,    truncation=lowerCAmelCase__				,    max_length=lowerCAmelCase__				,    stride=lowerCAmelCase__				,    pad_to_multiple_of=lowerCAmelCase__				,    return_token_type_ids=lowerCAmelCase__				,    return_attention_mask=lowerCAmelCase__				,    return_overflowing_tokens=lowerCAmelCase__				,    return_special_tokens_mask=lowerCAmelCase__				,    return_offsets_mapping=lowerCAmelCase__				,    return_length=lowerCAmelCase__				,    verbose=lowerCAmelCase__				,    return_tensors=lowerCAmelCase__				,    **lowerCAmelCase__				,    )

													# add pixel values
													SCREAMING_SNAKE_CASE					:      List[Any]     					=			features.pop('''pixel_values'''				)
													if return_overflowing_tokens is True:
																				SCREAMING_SNAKE_CASE					:      Any     					=			self.get_overflowing_images(lowerCAmelCase__				,    encoded_inputs['''overflow_to_sample_mapping''']				)
													SCREAMING_SNAKE_CASE					:      Any     					=			images

													return encoded_inputs







						def 					__lowercase    (  self   :      str				,    lowerCAmelCase__   :      Optional[Any]				,    lowerCAmelCase__   :      Optional[int]				):

													"""simple docstring"""
													# in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
													SCREAMING_SNAKE_CASE					:      Dict     					=			[]
													for sample_idx in overflow_to_sample_mapping:
																				images_with_overflow.append(images[sample_idx]				)

													if len(lowerCAmelCase__				) != len(lowerCAmelCase__				):
																				raise ValueError(
																				    '''Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got'''
																				    F""" {len(lowerCAmelCase__				)} and {len(lowerCAmelCase__				)}"""				)

													return images_with_overflow







						def 					__lowercase    (  self   :      Any				,    *lowerCAmelCase__   :      Union[str, Any]				,    **lowerCAmelCase__   :      List[Any]				):

													"""simple docstring"""
													return self.tokenizer.batch_decode(*lowerCAmelCase__				,    **lowerCAmelCase__				)







						def 					__lowercase    (  self   :      Optional[int]				,    *lowerCAmelCase__   :      Optional[Any]				,    **lowerCAmelCase__   :      Tuple				):

													"""simple docstring"""
													return self.tokenizer.decode(*lowerCAmelCase__				,    **lowerCAmelCase__				)







						@property
						def 					__lowercase    (  self   :      Union[str, Any]				):

													"""simple docstring"""
													return ["input_ids", "bbox", "attention_mask", "pixel_values"]







						@property
						def 					__lowercase    (  self   :      Union[str, Any]				):

													"""simple docstring"""
													warnings.warn(
													    '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'''				,    lowerCAmelCase__				,    )
													return self.image_processor_class







						@property
						def 					__lowercase    (  self   :      List[Any]				):

													"""simple docstring"""
													warnings.warn(
													    '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'''				,    lowerCAmelCase__				,    )
													return self.image_processor




