



# 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





from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import center_crop, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
						import PIL


lowerCamelCase__			= logging.get_logger(__name__)






class    SCREAMING_SNAKE_CASE			(							lowerCamelCase__					):
							__lowerCamelCase	:	str    =['pixel_values']





							def __init__(   self		:		str      ,				__lowercase		:		bool = True      ,				__lowercase		:		Dict[str, int] = None      ,				__lowercase		:		PILImageResampling = PIL.Image.BICUBIC      ,				__lowercase		:		bool = True      ,				__lowercase		:		Dict[str, int] = None      ,				__lowercase		:		Union[int, float] = 1 / 255      ,				__lowercase		:		bool = True      ,				__lowercase		:		bool = True      ,				__lowercase		:		Optional[Union[float, List[float]]] = None      ,				__lowercase		:		Optional[Union[float, List[float]]] = None      ,				**__lowercase		:		Any      ,				):

											'''simple docstring'''
											super().__init__(**__lowercase	)
											__a       =		size if size is not None else {"""height""": 256, """width""": 256}
											__a       =		get_size_dict(__lowercase	)
											__a       =		crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
											__a       =		get_size_dict(__lowercase      ,				param_name="""crop_size"""	)

											__a       =		do_resize
											__a       =		size
											__a       =		resample
											__a       =		do_center_crop
											__a       =		crop_size
											__a       =		do_rescale
											__a       =		rescale_factor
											__a       =		do_normalize
											__a       =		image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
											__a       =		image_std if image_std is not None else IMAGENET_STANDARD_STD





							def 						UpperCamelCase_					(   self		:		List[Any]      ,				__lowercase		:		np.ndarray      ,				__lowercase		:		Dict[str, int]      ,				__lowercase		:		PILImageResampling = PIL.Image.BICUBIC      ,				__lowercase		:		Optional[Union[str, ChannelDimension]] = None      ,				**__lowercase		:		Union[str, Any]      ,				):

											'''simple docstring'''
											__a       =		get_size_dict(__lowercase	)
											if "height" not in size or "width" not in size:
															raise ValueError(F"The size dictionary must have keys 'height' and 'width'. Got {size.keys()}"	)
											return resize(
											    __lowercase      ,				size=(size["""height"""], size["""width"""])      ,				resample=__lowercase      ,				data_format=__lowercase      ,				**__lowercase	)





							def 						UpperCamelCase_					(   self		:		Dict      ,				__lowercase		:		np.ndarray      ,				__lowercase		:		Dict[str, int]      ,				__lowercase		:		Optional[Union[str, ChannelDimension]] = None      ,				**__lowercase		:		str      ,				):

											'''simple docstring'''
											__a       =		get_size_dict(__lowercase	)
											if "height" not in size or "width" not in size:
															raise ValueError(F"The size dictionary must have keys 'height' and 'width'. Got {size.keys()}"	)
											return center_crop(__lowercase      ,				size=(size["""height"""], size["""width"""])      ,				data_format=__lowercase      ,				**__lowercase	)





							def 						UpperCamelCase_					(   self		:		int      ,				__lowercase		:		np.ndarray      ,				__lowercase		:		Union[int, float]      ,				__lowercase		:		Optional[Union[str, ChannelDimension]] = None      ,				**__lowercase		:		List[str]      ,				):

											'''simple docstring'''
											return rescale(__lowercase      ,				scale=__lowercase      ,				data_format=__lowercase      ,				**__lowercase	)





							def 						UpperCamelCase_					(   self		:		Tuple      ,				__lowercase		:		np.ndarray      ,				__lowercase		:		Union[float, List[float]]      ,				__lowercase		:		Union[float, List[float]]      ,				__lowercase		:		Optional[Union[str, ChannelDimension]] = None      ,				**__lowercase		:		str      ,				):

											'''simple docstring'''
											return normalize(__lowercase      ,				mean=__lowercase      ,				std=__lowercase      ,				data_format=__lowercase      ,				**__lowercase	)







							def 						UpperCamelCase_					(   self		:		Optional[Any]      ,				__lowercase		:		ImageInput      ,				__lowercase		:		bool = None      ,				__lowercase		:		Dict[str, int] = None      ,				__lowercase		:		Dict=None      ,				__lowercase		:		bool = None      ,				__lowercase		:		Dict[str, int] = None      ,				__lowercase		:		bool = None      ,				__lowercase		:		float = None      ,				__lowercase		:		bool = None      ,				__lowercase		:		Optional[Union[float, List[float]]] = None      ,				__lowercase		:		Optional[Union[float, List[float]]] = None      ,				__lowercase		:		Optional[Union[str, TensorType]] = None      ,				__lowercase		:		ChannelDimension = ChannelDimension.FIRST      ,				**__lowercase		:		Optional[Any]      ,				):

											'''simple docstring'''
											__a       =		do_resize if do_resize is not None else self.do_resize
											__a       =		resample if resample is not None else self.resample
											__a       =		do_center_crop if do_center_crop is not None else self.do_center_crop
											__a       =		do_rescale if do_rescale is not None else self.do_rescale
											__a       =		rescale_factor if rescale_factor is not None else self.rescale_factor
											__a       =		do_normalize if do_normalize is not None else self.do_normalize
											__a       =		image_mean if image_mean is not None else self.image_mean
											__a       =		image_std if image_std is not None else self.image_std

											__a       =		size if size is not None else self.size
											__a       =		get_size_dict(__lowercase	)
											__a       =		crop_size if crop_size is not None else self.crop_size
											__a       =		get_size_dict(__lowercase      ,				param_name="""crop_size"""	)

											__a       =		make_list_of_images(__lowercase	)

											if not valid_images(__lowercase	):
															raise ValueError(
															    """Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
															    """torch.Tensor, tf.Tensor or jax.ndarray."""	)

											if do_resize and size is None or resample is None:
															raise ValueError("""Size and resample must be specified if do_resize is True."""	)

											if do_center_crop and crop_size is None:
															raise ValueError("""Crop size must be specified if do_center_crop is True."""	)

											if do_rescale and rescale_factor is None:
															raise ValueError("""Rescale factor must be specified if do_rescale is True."""	)

											if do_normalize and (image_mean is None or image_std is None):
															raise ValueError("""Image mean and std must be specified if do_normalize is True."""	)

											# All transformations expect numpy arrays.
											__a       =		[to_numpy_array(__lowercase	) for image in images]

											if do_resize:
															__a       =		[self.resize(image=__lowercase      ,				size=__lowercase      ,				resample=__lowercase	) for image in images]

											if do_center_crop:
															__a       =		[self.center_crop(image=__lowercase      ,				size=__lowercase	) for image in images]

											if do_rescale:
															__a       =		[self.rescale(image=__lowercase      ,				scale=__lowercase	) for image in images]

											if do_normalize:
															__a       =		[self.normalize(image=__lowercase      ,				mean=__lowercase      ,				std=__lowercase	) for image in images]

											__a       =		[to_channel_dimension_format(__lowercase      ,				__lowercase	) for image in images]

											__a       =		{"""pixel_values""": images}
											return BatchFeature(data=__lowercase      ,				tensor_type=__lowercase	)
