




import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
     from PIL import Image

     from transformers import CLIPSegProcessor, ViTImageProcessor




@require_vision
class       __lowerCAmelCase				(							unittest.TestCase):






       def     SCREAMING_SNAKE_CASE			(      self:    Tuple ):
         lowercase			:Optional[int]							=  tempfile.mkdtemp()

         # fmt: off
         lowercase			:Tuple							=  ["l", "o", "w", "e", "r", "s", "t", "i", "d", "n", "lo", "l</w>", "w</w>", "r</w>", "t</w>", "low</w>", "er</w>", "lowest</w>", "newer</w>", "wider", "<unk>", "<|startoftext|>", "<|endoftext|>"]
         # fmt: on
         lowercase			:Tuple							=  dict(zip(_lowerCAmelCase				,    range(len(_lowerCAmelCase ) ) ) )
         lowercase			:Optional[Any]							=  ["#version: 0.2", "l o", "lo w</w>", "e r</w>", ""]
         lowercase			:List[Any]							=  {"unk_token": "<unk>"}

         lowercase			:Optional[int]							=  os.path.join(self.tmpdirname				,    VOCAB_FILES_NAMES["vocab_file"] )
         lowercase			:Optional[int]							=  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(_lowerCAmelCase ) + "\n" )
         with open(self.merges_file				,    "w"				,    encoding="utf-8" ) as fp:
           fp.write("\n".join(_lowerCAmelCase ) )

         lowercase			:List[str]							=  {
             "do_resize": True,
             "size": 20,
             "do_center_crop": True,
             "crop_size": 18,
             "do_normalize": True,
             "image_mean": [0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73],
             "image_std": [0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11],
         }
         lowercase			:Tuple							=  os.path.join(self.tmpdirname				,    _lowerCAmelCase )
         with open(self.image_processor_file				,    "w"				,    encoding="utf-8" ) as fp:
           json.dump(_lowerCAmelCase				,    _lowerCAmelCase )






       def     SCREAMING_SNAKE_CASE			(      self:    List[Any]				,    **_lowerCAmelCase:    Optional[int] ):
         return CLIPTokenizer.from_pretrained(self.tmpdirname				,    **_lowerCAmelCase )






       def     SCREAMING_SNAKE_CASE			(      self:    List[str]				,    **_lowerCAmelCase:    Optional[Any] ):
         return CLIPTokenizerFast.from_pretrained(self.tmpdirname				,    **_lowerCAmelCase )






       def     SCREAMING_SNAKE_CASE			(      self:    int				,    **_lowerCAmelCase:    int ):
         return ViTImageProcessor.from_pretrained(self.tmpdirname				,    **_lowerCAmelCase )






       def     SCREAMING_SNAKE_CASE			(      self:    Any ):
         shutil.rmtree(self.tmpdirname )






       def     SCREAMING_SNAKE_CASE			(      self:    Optional[Any] ):
         lowercase			:Optional[int]							=  [np.random.randint(2_55				,    size=(3, 30, 4_00)				,    dtype=np.uinta )]

         lowercase			:List[str]							=  [Image.fromarray(np.moveaxis(_lowerCAmelCase				,    0				,    -1 ) ) for x in image_inputs]

         return image_inputs






       def     SCREAMING_SNAKE_CASE			(      self:    List[str] ):
         lowercase			:Union[str, Any]							=  self.get_tokenizer()
         lowercase			:Union[str, Any]							=  self.get_rust_tokenizer()
         lowercase			:Dict							=  self.get_image_processor()

         lowercase			:int							=  CLIPSegProcessor(tokenizer=_lowerCAmelCase				,    image_processor=_lowerCAmelCase )
         processor_slow.save_pretrained(self.tmpdirname )
         lowercase			:Dict							=  CLIPSegProcessor.from_pretrained(self.tmpdirname				,    use_fast=_lowerCAmelCase )

         lowercase			:List[Any]							=  CLIPSegProcessor(tokenizer=_lowerCAmelCase				,    image_processor=_lowerCAmelCase )
         processor_fast.save_pretrained(self.tmpdirname )
         lowercase			:Dict							=  CLIPSegProcessor.from_pretrained(self.tmpdirname )

         self.assertEqual(processor_slow.tokenizer.get_vocab()				,    tokenizer_slow.get_vocab() )
         self.assertEqual(processor_fast.tokenizer.get_vocab()				,    tokenizer_fast.get_vocab() )
         self.assertEqual(tokenizer_slow.get_vocab()				,    tokenizer_fast.get_vocab() )
         self.assertIsInstance(processor_slow.tokenizer				,    _lowerCAmelCase )
         self.assertIsInstance(processor_fast.tokenizer				,    _lowerCAmelCase )

         self.assertEqual(processor_slow.image_processor.to_json_string()				,    image_processor.to_json_string() )
         self.assertEqual(processor_fast.image_processor.to_json_string()				,    image_processor.to_json_string() )
         self.assertIsInstance(processor_slow.image_processor				,    _lowerCAmelCase )
         self.assertIsInstance(processor_fast.image_processor				,    _lowerCAmelCase )






       def     SCREAMING_SNAKE_CASE			(      self:    str ):
         lowercase			:Any							=  CLIPSegProcessor(tokenizer=self.get_tokenizer()				,    image_processor=self.get_image_processor() )
         processor.save_pretrained(self.tmpdirname )

         lowercase			:str							=  self.get_tokenizer(bos_token="(BOS)"				,    eos_token="(EOS)" )
         lowercase			:int							=  self.get_image_processor(do_normalize=_lowerCAmelCase				,    padding_value=1.0 )

         lowercase			:Optional[Any]							=  CLIPSegProcessor.from_pretrained(
             self.tmpdirname				,    bos_token="(BOS)"				,    eos_token="(EOS)"				,    do_normalize=_lowerCAmelCase				,    padding_value=1.0 )

         self.assertEqual(processor.tokenizer.get_vocab()				,    tokenizer_add_kwargs.get_vocab() )
         self.assertIsInstance(processor.tokenizer				,    _lowerCAmelCase )

         self.assertEqual(processor.image_processor.to_json_string()				,    image_processor_add_kwargs.to_json_string() )
         self.assertIsInstance(processor.image_processor				,    _lowerCAmelCase )






       def     SCREAMING_SNAKE_CASE			(      self:    Optional[int] ):
         lowercase			:List[Any]							=  self.get_image_processor()
         lowercase			:Tuple							=  self.get_tokenizer()

         lowercase			:Any							=  CLIPSegProcessor(tokenizer=_lowerCAmelCase				,    image_processor=_lowerCAmelCase )

         lowercase			:int							=  self.prepare_image_inputs()

         lowercase			:Optional[int]							=  image_processor(_lowerCAmelCase				,    return_tensors="np" )
         lowercase			:str							=  processor(images=_lowerCAmelCase				,    return_tensors="np" )

         for key in input_feat_extract.keys():
           self.assertAlmostEqual(input_feat_extract[key].sum()				,    input_processor[key].sum()				,    delta=1e-2 )






       def     SCREAMING_SNAKE_CASE			(      self:    Optional[int] ):
         lowercase			:Union[str, Any]							=  self.get_image_processor()
         lowercase			:Tuple							=  self.get_tokenizer()

         lowercase			:Optional[Any]							=  CLIPSegProcessor(tokenizer=_lowerCAmelCase				,    image_processor=_lowerCAmelCase )

         lowercase			:str							=  "lower newer"

         lowercase			:List[str]							=  processor(text=_lowerCAmelCase )

         lowercase			:Tuple							=  tokenizer(_lowerCAmelCase )

         for key in encoded_tok.keys():
           self.assertListEqual(encoded_tok[key]				,    encoded_processor[key] )






       def     SCREAMING_SNAKE_CASE			(      self:    Tuple ):
         lowercase			:int							=  self.get_image_processor()
         lowercase			:int							=  self.get_tokenizer()

         lowercase			:Optional[int]							=  CLIPSegProcessor(tokenizer=_lowerCAmelCase				,    image_processor=_lowerCAmelCase )

         lowercase			:Optional[int]							=  "lower newer"
         lowercase			:List[Any]							=  self.prepare_image_inputs()

         lowercase			:str							=  processor(text=_lowerCAmelCase				,    images=_lowerCAmelCase )

         self.assertListEqual(list(inputs.keys() )				,    ["input_ids", "attention_mask", "pixel_values"] )

         # test if it raises when no input is passed
         with pytest.raises(_lowerCAmelCase ):
           processor()






       def     SCREAMING_SNAKE_CASE			(      self:    Union[str, Any] ):
         lowercase			:Optional[Any]							=  self.get_image_processor()
         lowercase			:Union[str, Any]							=  self.get_tokenizer()

         lowercase			:List[Any]							=  CLIPSegProcessor(tokenizer=_lowerCAmelCase				,    image_processor=_lowerCAmelCase )

         lowercase			:Dict							=  self.prepare_image_inputs()
         lowercase			:Dict							=  self.prepare_image_inputs()

         lowercase			:Dict							=  processor(images=_lowerCAmelCase				,    visual_prompt=_lowerCAmelCase )

         self.assertListEqual(list(inputs.keys() )				,    ["pixel_values", "conditional_pixel_values"] )

         # test if it raises when no input is passed
         with pytest.raises(_lowerCAmelCase ):
           processor()






       def     SCREAMING_SNAKE_CASE			(      self:    str ):
         lowercase			:Dict							=  self.get_image_processor()
         lowercase			:Any							=  self.get_tokenizer()

         lowercase			:Tuple							=  CLIPSegProcessor(tokenizer=_lowerCAmelCase				,    image_processor=_lowerCAmelCase )

         lowercase			:Optional[Any]							=  [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

         lowercase			:Optional[Any]							=  processor.batch_decode(_lowerCAmelCase )
         lowercase			:List[Any]							=  tokenizer.batch_decode(_lowerCAmelCase )

         self.assertListEqual(_lowerCAmelCase				,    _lowerCAmelCase )









from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


_UpperCAmelCase		:  List[str]	=   {
    "configuration_blenderbot": [
        "BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "BlenderbotConfig",
        "BlenderbotOnnxConfig",
    ],
    "tokenization_blenderbot": ["BlenderbotTokenizer"],
}

try:
     if not is_tokenizers_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _UpperCAmelCase		:  Optional[int]	=   ["BlenderbotTokenizerFast"]

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _UpperCAmelCase		:  int	=   [
         "BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST",
         "BlenderbotForCausalLM",
         "BlenderbotForConditionalGeneration",
         "BlenderbotModel",
         "BlenderbotPreTrainedModel",
     ]


try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _UpperCAmelCase		:  Any	=   [
         "TFBlenderbotForConditionalGeneration",
         "TFBlenderbotModel",
         "TFBlenderbotPreTrainedModel",
     ]


try:
     if not is_flax_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _UpperCAmelCase		:  Optional[int]	=   [
         "FlaxBlenderbotForConditionalGeneration",
         "FlaxBlenderbotModel",
         "FlaxBlenderbotPreTrainedModel",
     ]


if TYPE_CHECKING:
     from .configuration_blenderbot import (
         BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP,
         BlenderbotConfig,
         BlenderbotOnnxConfig,
     )
     from .tokenization_blenderbot import BlenderbotTokenizer

     try:
          if not is_tokenizers_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_blenderbot_fast import BlenderbotTokenizerFast

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_blenderbot import (
              BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST,
              BlenderbotForCausalLM,
              BlenderbotForConditionalGeneration,
              BlenderbotModel,
              BlenderbotPreTrainedModel,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_blenderbot import (
              TFBlenderbotForConditionalGeneration,
              TFBlenderbotModel,
              TFBlenderbotPreTrainedModel,
          )

     try:
          if not is_flax_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_flax_blenderbot import (
              FlaxBlenderbotForConditionalGeneration,
              FlaxBlenderbotModel,
              FlaxBlenderbotPreTrainedModel,
          )

else:
     import sys

     _UpperCAmelCase		:  Union[str, Any]	=   _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



