"""simple docstring"""


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,
    get_resize_output_image_size,
    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, logging


UpperCAmelCase__			=    logging.get_logger(__name__)


class 							a					(		SCREAMING_SNAKE_CASE_ ):
    _snake_case  :	Optional[Any]  				=				['pixel_values']


    def __init__(       self						:	List[Any] ,					__lowerCAmelCase						:	bool = True ,					__lowerCAmelCase						:	Optional[Dict[str, int]] = None ,					__lowerCAmelCase						:	PILImageResampling = PILImageResampling.BILINEAR ,					__lowerCAmelCase						:	bool = True ,					__lowerCAmelCase						:	Dict[str, int] = None ,					__lowerCAmelCase						:	bool = True ,					__lowerCAmelCase						:	Union[int, float] = 1 / 255 ,					__lowerCAmelCase						:	bool = True ,					__lowerCAmelCase						:	Optional[Union[float, List[float]]] = None ,					__lowerCAmelCase						:	Optional[Union[float, List[float]]] = None ,					**__lowerCAmelCase						:	Tuple ,					):
      super().__init__(**__SCREAMING_SNAKE_CASE       )
      _UpperCAmelCase    			=   size if size is not None else {'''shortest_edge''': 256}
      _UpperCAmelCase    			=   get_size_dict(__SCREAMING_SNAKE_CASE ,					default_to_square=__SCREAMING_SNAKE_CASE       )
      _UpperCAmelCase    			=   crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
      _UpperCAmelCase    			=   get_size_dict(__SCREAMING_SNAKE_CASE       )
      _UpperCAmelCase    			=   do_resize
      _UpperCAmelCase    			=   size
      _UpperCAmelCase    			=   resample
      _UpperCAmelCase    			=   do_center_crop
      _UpperCAmelCase    			=   crop_size
      _UpperCAmelCase    			=   do_rescale
      _UpperCAmelCase    			=   rescale_factor
      _UpperCAmelCase    			=   do_normalize
      _UpperCAmelCase    			=   image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
      _UpperCAmelCase    			=   image_std if image_std is not None else IMAGENET_STANDARD_STD


    def   lowerCAmelCase_	(       self						:	Tuple ,					__lowerCAmelCase						:	np.ndarray ,					__lowerCAmelCase						:	Dict[str, int] ,					__lowerCAmelCase						:	PILImageResampling = PILImageResampling.BICUBIC ,					__lowerCAmelCase						:	Optional[Union[str, ChannelDimension]] = None ,					**__lowerCAmelCase						:	str ,					):
      _UpperCAmelCase    			=   get_size_dict(__SCREAMING_SNAKE_CASE ,					default_to_square=__SCREAMING_SNAKE_CASE       )
      if "shortest_edge" not in size:
        raise ValueError(f'''The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}'''       )
      _UpperCAmelCase    			=   get_resize_output_image_size(__SCREAMING_SNAKE_CASE ,					size=size["""shortest_edge"""] ,					default_to_square=__SCREAMING_SNAKE_CASE       )
      return resize(__SCREAMING_SNAKE_CASE ,					size=__SCREAMING_SNAKE_CASE ,					resample=__SCREAMING_SNAKE_CASE ,					data_format=__SCREAMING_SNAKE_CASE ,					**__SCREAMING_SNAKE_CASE       )


    def   lowerCAmelCase_	(       self						:	Any ,					__lowerCAmelCase						:	np.ndarray ,					__lowerCAmelCase						:	Dict[str, int] ,					__lowerCAmelCase						:	Optional[Union[str, ChannelDimension]] = None ,					**__lowerCAmelCase						:	Any ,					):
      _UpperCAmelCase    			=   get_size_dict(__SCREAMING_SNAKE_CASE       )
      return center_crop(__SCREAMING_SNAKE_CASE ,					size=(size["""height"""], size["""width"""]) ,					data_format=__SCREAMING_SNAKE_CASE ,					**__SCREAMING_SNAKE_CASE       )


    def   lowerCAmelCase_	(       self						:	List[str] ,					__lowerCAmelCase						:	np.ndarray ,					__lowerCAmelCase						:	float ,					__lowerCAmelCase						:	Optional[Union[str, ChannelDimension]] = None ,					**__lowerCAmelCase						:	str       ):
      return rescale(__SCREAMING_SNAKE_CASE ,					scale=__SCREAMING_SNAKE_CASE ,					data_format=__SCREAMING_SNAKE_CASE ,					**__SCREAMING_SNAKE_CASE       )


    def   lowerCAmelCase_	(       self						:	List[Any] ,					__lowerCAmelCase						:	np.ndarray ,					__lowerCAmelCase						:	Union[float, List[float]] ,					__lowerCAmelCase						:	Union[float, List[float]] ,					__lowerCAmelCase						:	Optional[Union[str, ChannelDimension]] = None ,					**__lowerCAmelCase						:	Any ,					):
      return normalize(__SCREAMING_SNAKE_CASE ,					mean=__SCREAMING_SNAKE_CASE ,					std=__SCREAMING_SNAKE_CASE ,					data_format=__SCREAMING_SNAKE_CASE ,					**__SCREAMING_SNAKE_CASE       )


    def   lowerCAmelCase_	(       self						:	Optional[int] ,					__lowerCAmelCase						:	ImageInput ,					__lowerCAmelCase						:	Optional[bool] = None ,					__lowerCAmelCase						:	Dict[str, int] = None ,					__lowerCAmelCase						:	PILImageResampling = None ,					__lowerCAmelCase						:	bool = None ,					__lowerCAmelCase						:	Dict[str, int] = None ,					__lowerCAmelCase						:	Optional[bool] = None ,					__lowerCAmelCase						:	Optional[float] = None ,					__lowerCAmelCase						:	Optional[bool] = None ,					__lowerCAmelCase						:	Optional[Union[float, List[float]]] = None ,					__lowerCAmelCase						:	Optional[Union[float, List[float]]] = None ,					__lowerCAmelCase						:	Optional[Union[str, TensorType]] = None ,					__lowerCAmelCase						:	Union[str, ChannelDimension] = ChannelDimension.FIRST ,					**__lowerCAmelCase						:	Tuple ,					):
      _UpperCAmelCase    			=   do_resize if do_resize is not None else self.do_resize
      _UpperCAmelCase    			=   size if size is not None else self.size
      _UpperCAmelCase    			=   get_size_dict(__SCREAMING_SNAKE_CASE ,					default_to_square=__SCREAMING_SNAKE_CASE       )
      _UpperCAmelCase    			=   resample if resample is not None else self.resample
      _UpperCAmelCase    			=   do_center_crop if do_center_crop is not None else self.do_center_crop
      _UpperCAmelCase    			=   crop_size if crop_size is not None else self.crop_size
      _UpperCAmelCase    			=   get_size_dict(__SCREAMING_SNAKE_CASE       )
      _UpperCAmelCase    			=   do_rescale if do_rescale is not None else self.do_rescale
      _UpperCAmelCase    			=   rescale_factor if rescale_factor is not None else self.rescale_factor
      _UpperCAmelCase    			=   do_normalize if do_normalize is not None else self.do_normalize
      _UpperCAmelCase    			=   image_mean if image_mean is not None else self.image_mean
      _UpperCAmelCase    			=   image_std if image_std is not None else self.image_std

      _UpperCAmelCase    			=   make_list_of_images(__SCREAMING_SNAKE_CASE       )

      if not valid_images(__SCREAMING_SNAKE_CASE       ):
        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:
        raise ValueError("""Size 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.
      _UpperCAmelCase    			=   [to_numpy_array(__SCREAMING_SNAKE_CASE       ) for image in images]

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

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

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

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

      _UpperCAmelCase    			=   [to_channel_dimension_format(__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE       ) for image in images]

      _UpperCAmelCase    			=   {'''pixel_values''': images}
      return BatchFeature(data=__SCREAMING_SNAKE_CASE ,					tensor_type=__SCREAMING_SNAKE_CASE       )








'''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 model through reduction of a normal pre-trained model, but keeping the
# full vocab, merges file, and thus also resulting in a larger model due to a large vocab size.
# This gives ~3MB in total for all files.
#
# If you want a 50 times smaller than this see `fsmt-make-super-tiny-model.py`, which is slightly more complicated
#
#
# It will be used then as "stas/tiny-wmt19-en-de"

# Build
from transformers import FSMTTokenizer, FSMTConfig, FSMTForConditionalGeneration

__lowerCAmelCase									=   'facebook/wmt19-en-de'
__lowerCAmelCase									=   FSMTTokenizer.from_pretrained(mname)
# get the correct vocab sizes, etc. from the master model
__lowerCAmelCase									=   FSMTConfig.from_pretrained(mname)
config.update(
    dict(
        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
__lowerCAmelCase									=   'tiny-wmt19-en-de'
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-de




