


'''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


a_  :				int						= logging.get_logger(__name__)





class      snake_case    (    lowercase			):





  """simple docstring"""




  _lowerCamelCase		    =  ["pixel_values"]






  def __init__(    self			,  UpperCamelCase = True			,  UpperCamelCase = None			,  UpperCamelCase = PILImageResampling.BILINEAR			,  UpperCamelCase = True			,  UpperCamelCase = None			,  UpperCamelCase = True			,  UpperCamelCase = 1 / 255			,  UpperCamelCase = True			,  UpperCamelCase = None			,  UpperCamelCase = None			,  **UpperCamelCase			,  ):







   """simple docstring"""
   super().__init__(**UpperCamelCase     )
   lowerCamelCase_      =  size if size is not None else {"shortest_edge": 256}
   lowerCamelCase_      =  get_size_dict(UpperCamelCase			,  default_to_square=UpperCamelCase     )
   lowerCamelCase_      =  crop_size if crop_size is not None else {"height": 224, "width": 224}
   lowerCamelCase_      =  get_size_dict(UpperCamelCase     )
   lowerCamelCase_      =  do_resize
   lowerCamelCase_      =  size
   lowerCamelCase_      =  resample
   lowerCamelCase_      =  do_center_crop
   lowerCamelCase_      =  crop_size
   lowerCamelCase_      =  do_rescale
   lowerCamelCase_      =  rescale_factor
   lowerCamelCase_      =  do_normalize
   lowerCamelCase_      =  image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
   lowerCamelCase_      =  image_std if image_std is not None else IMAGENET_STANDARD_STD






  def   snake_case		(    self			,  UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase = PILImageResampling.BICUBIC			,  UpperCamelCase = None			,  **UpperCamelCase			,  ):







   """simple docstring"""
   lowerCamelCase_      =  get_size_dict(UpperCamelCase			,  default_to_square=UpperCamelCase     )
   if "shortest_edge" not in size:
    raise ValueError(f'''The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}'''     )
   lowerCamelCase_      =  get_resize_output_image_size(UpperCamelCase			,  size=size["shortest_edge"]			,  default_to_square=UpperCamelCase     )
   return resize(UpperCamelCase			,  size=UpperCamelCase			,  resample=UpperCamelCase			,  data_format=UpperCamelCase			,  **UpperCamelCase     )






  def   snake_case		(    self			,  UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase = None			,  **UpperCamelCase			,  ):







   """simple docstring"""
   lowerCamelCase_      =  get_size_dict(UpperCamelCase     )
   return center_crop(UpperCamelCase			,  size=(size["height"], size["width"])			,  data_format=UpperCamelCase			,  **UpperCamelCase     )






  def   snake_case		(    self			,  UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase = None			,  **UpperCamelCase     ):







   """simple docstring"""
   return rescale(UpperCamelCase			,  scale=UpperCamelCase			,  data_format=UpperCamelCase			,  **UpperCamelCase     )






  def   snake_case		(    self			,  UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase = None			,  **UpperCamelCase			,  ):







   """simple docstring"""
   return normalize(UpperCamelCase			,  mean=UpperCamelCase			,  std=UpperCamelCase			,  data_format=UpperCamelCase			,  **UpperCamelCase     )







  def   snake_case		(    self			,  UpperCamelCase			,  UpperCamelCase = None			,  UpperCamelCase = None			,  UpperCamelCase = None			,  UpperCamelCase = None			,  UpperCamelCase = None			,  UpperCamelCase = None			,  UpperCamelCase = None			,  UpperCamelCase = None			,  UpperCamelCase = None			,  UpperCamelCase = None			,  UpperCamelCase = None			,  UpperCamelCase = ChannelDimension.FIRST			,  **UpperCamelCase			,  ):







   """simple docstring"""
   lowerCamelCase_      =  do_resize if do_resize is not None else self.do_resize
   lowerCamelCase_      =  size if size is not None else self.size
   lowerCamelCase_      =  get_size_dict(UpperCamelCase			,  default_to_square=UpperCamelCase     )
   lowerCamelCase_      =  resample if resample is not None else self.resample
   lowerCamelCase_      =  do_center_crop if do_center_crop is not None else self.do_center_crop
   lowerCamelCase_      =  crop_size if crop_size is not None else self.crop_size
   lowerCamelCase_      =  get_size_dict(UpperCamelCase     )
   lowerCamelCase_      =  do_rescale if do_rescale is not None else self.do_rescale
   lowerCamelCase_      =  rescale_factor if rescale_factor is not None else self.rescale_factor
   lowerCamelCase_      =  do_normalize if do_normalize is not None else self.do_normalize
   lowerCamelCase_      =  image_mean if image_mean is not None else self.image_mean
   lowerCamelCase_      =  image_std if image_std is not None else self.image_std

   lowerCamelCase_      =  make_list_of_images(UpperCamelCase     )

   if not valid_images(UpperCamelCase     ):
    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.
   lowerCamelCase_      =  [to_numpy_array(UpperCamelCase     ) for image in images]

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

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

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

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

   lowerCamelCase_      =  [to_channel_dimension_format(UpperCamelCase			,  UpperCamelCase     ) for image in images]

   lowerCamelCase_      =  {"pixel_values": images}
   return BatchFeature(data=UpperCamelCase			,  tensor_type=UpperCamelCase     )







'''simple docstring'''

import json
import os
import unittest

from transformers.models.blenderbot_small.tokenization_blenderbot_small import (
    VOCAB_FILES_NAMES,
    BlenderbotSmallTokenizer,
)

from ...test_tokenization_common import TokenizerTesterMixin





class      snake_case    (    lowercase			,						unittest.TestCase			):





  """simple docstring"""




  _lowerCamelCase		    =  BlenderbotSmallTokenizer
  _lowerCamelCase		    =  False






  def   snake_case		(    self     ):







   """simple docstring"""
   super().setUp()

   lowerCamelCase_      =  ["__start__", "adapt", "act", "ap@@", "te", "__end__", "__unk__"]
   lowerCamelCase_      =  dict(zip(UpperCamelCase			,  range(len(UpperCamelCase     )     )     )     )

   lowerCamelCase_      =  ["#version: 0.2", "a p", "t e</w>", "ap t</w>", "a d", "ad apt</w>", "a c", "ac t</w>", ""]
   lowerCamelCase_      =  {"unk_token": "__unk__", "bos_token": "__start__", "eos_token": "__end__"}

   lowerCamelCase_      =  os.path.join(self.tmpdirname			,  VOCAB_FILES_NAMES["vocab_file"]     )
   lowerCamelCase_      =  os.path.join(self.tmpdirname			,  VOCAB_FILES_NAMES["merges_file"]     )
   with open(self.vocab_file			,  "w"			,  encoding="utf-8"     ) as fp:
    fp.write(json.dumps(UpperCamelCase     ) + "\n"     )
   with open(self.merges_file			,  "w"			,  encoding="utf-8"     ) as fp:
    fp.write("\n".join(UpperCamelCase     )     )






  def   snake_case		(    self			,  **UpperCamelCase     ):







   """simple docstring"""
   kwargs.update(self.special_tokens_map     )
   return BlenderbotSmallTokenizer.from_pretrained(self.tmpdirname			,  **UpperCamelCase     )






  def   snake_case		(    self			,  UpperCamelCase     ):







   """simple docstring"""
   lowerCamelCase_      =  "adapt act apte"
   lowerCamelCase_      =  "adapt act apte"
   return input_text, output_text






  def   snake_case		(    self     ):







   """simple docstring"""
   lowerCamelCase_      =  BlenderbotSmallTokenizer(self.vocab_file			,  self.merges_file			,  **self.special_tokens_map     )
   lowerCamelCase_      =  "adapt act apte"
   lowerCamelCase_      =  ["adapt", "act", "ap@@", "te"]
   lowerCamelCase_      =  tokenizer.tokenize(UpperCamelCase     )
   self.assertListEqual(UpperCamelCase			,  UpperCamelCase     )

   lowerCamelCase_      =  [tokenizer.bos_token] + tokens + [tokenizer.eos_token]

   lowerCamelCase_      =  [0, 1, 2, 3, 4, 5]
   self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCamelCase     )			,  UpperCamelCase     )






  def   snake_case		(    self     ):







   """simple docstring"""
   lowerCamelCase_      =  BlenderbotSmallTokenizer.from_pretrained("facebook/blenderbot-90M"     )
   assert tok("sam"     ).input_ids == [1384]
   lowerCamelCase_      =  "I am a small frog."
   lowerCamelCase_      =  tok([src_text]			,  padding=UpperCamelCase			,  truncation=UpperCamelCase     )["input_ids"]
   lowerCamelCase_      =  tok.batch_decode(UpperCamelCase			,  skip_special_tokens=UpperCamelCase			,  clean_up_tokenization_spaces=UpperCamelCase     )[0]
   assert src_text != decoded  # I wish it did!
   assert decoded == "i am a small frog ."







  def   snake_case		(    self     ):







   """simple docstring"""
   lowerCamelCase_      =  BlenderbotSmallTokenizer.from_pretrained("facebook/blenderbot-90M"     )
   lowerCamelCase_      =  "I am a small frog ."
   lowerCamelCase_      =  "."
   lowerCamelCase_      =  tok(UpperCamelCase     )["input_ids"]
   lowerCamelCase_      =  tok(UpperCamelCase     )["input_ids"]

   assert encoded[-1] == encoded_dot[0]



