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 CLIPImageProcessor, CLIPProcessor


@require_vision
class   lowerCamelCase					(unittest.TestCase					):
  """simple docstring"""







  def    __A     (							self :       Optional[int]   )  ->	Dict:
        SCREAMING_SNAKE_CASE_          =  tempfile.mkdtemp()

        # fmt: off
        SCREAMING_SNAKE_CASE_          =  ["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
        SCREAMING_SNAKE_CASE_          =  dict(zip(__magic_name__  ,     range(len(__magic_name__   )   )   )   )
        SCREAMING_SNAKE_CASE_          =  ["#version: 0.2", "l o", "lo w</w>", "e r</w>", ""]
        SCREAMING_SNAKE_CASE_          =  {"unk_token": "<unk>"}

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

        SCREAMING_SNAKE_CASE_          =  {
            "do_resize": True,
            "size": 20,
            "do_center_crop": True,
            "crop_size": 18,
            "do_normalize": True,
            "image_mean": [0.4814_5466, 0.457_8275, 0.4082_1073],
            "image_std": [0.2686_2954, 0.2613_0258, 0.2757_7711],
        }
        SCREAMING_SNAKE_CASE_          =  os.path.join(self.tmpdirname  ,     __magic_name__   )
        with open(self.image_processor_file  ,     "w"  ,     encoding="utf-8"   ) as fp:
              json.dump(__magic_name__  ,     __magic_name__   )







  def    __A     (							self :       Dict  ,     **__magic_name__ :       Optional[Any]   )  ->	str:
        return CLIPTokenizer.from_pretrained(self.tmpdirname  ,     **__magic_name__   )







  def    __A     (							self :       Optional[Any]  ,     **__magic_name__ :       str   )  ->	str:
        return CLIPTokenizerFast.from_pretrained(self.tmpdirname  ,     **__magic_name__   )







  def    __A     (							self :       int  ,     **__magic_name__ :       Any   )  ->	Union[str, Any]:
        return CLIPImageProcessor.from_pretrained(self.tmpdirname  ,     **__magic_name__   )







  def    __A     (							self :       Union[str, Any]   )  ->	Any:
        shutil.rmtree(self.tmpdirname   )







  def    __A     (							self :       List[Any]   )  ->	Optional[int]:
        SCREAMING_SNAKE_CASE_          =  [np.random.randint(255  ,     size=(3, 30, 400)  ,     dtype=np.uinta   )]

        SCREAMING_SNAKE_CASE_          =  [Image.fromarray(np.moveaxis(__magic_name__  ,     0  ,     -1   )   ) for x in image_inputs]

        return image_inputs







  def    __A     (							self :       Union[str, Any]   )  ->	Any:
        SCREAMING_SNAKE_CASE_          =  self.get_tokenizer()
        SCREAMING_SNAKE_CASE_          =  self.get_rust_tokenizer()
        SCREAMING_SNAKE_CASE_          =  self.get_image_processor()

        SCREAMING_SNAKE_CASE_          =  CLIPProcessor(tokenizer=__magic_name__  ,     image_processor=__magic_name__   )
        processor_slow.save_pretrained(self.tmpdirname   )
        SCREAMING_SNAKE_CASE_          =  CLIPProcessor.from_pretrained(self.tmpdirname  ,     use_fast=__magic_name__   )

        SCREAMING_SNAKE_CASE_          =  CLIPProcessor(tokenizer=__magic_name__  ,     image_processor=__magic_name__   )
        processor_fast.save_pretrained(self.tmpdirname   )
        SCREAMING_SNAKE_CASE_          =  CLIPProcessor.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  ,     __magic_name__   )
        self.assertIsInstance(processor_fast.tokenizer  ,     __magic_name__   )

        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  ,     __magic_name__   )
        self.assertIsInstance(processor_fast.image_processor  ,     __magic_name__   )







  def    __A     (							self :       Union[str, Any]   )  ->	Dict:
        SCREAMING_SNAKE_CASE_          =  CLIPProcessor(tokenizer=self.get_tokenizer()  ,     image_processor=self.get_image_processor()   )
        processor.save_pretrained(self.tmpdirname   )

        SCREAMING_SNAKE_CASE_          =  self.get_tokenizer(bos_token="(BOS)"  ,     eos_token="(EOS)"   )
        SCREAMING_SNAKE_CASE_          =  self.get_image_processor(do_normalize=__magic_name__  ,     padding_value=1.0   )

        SCREAMING_SNAKE_CASE_          =  CLIPProcessor.from_pretrained(
            self.tmpdirname  ,     bos_token="(BOS)"  ,     eos_token="(EOS)"  ,     do_normalize=__magic_name__  ,     padding_value=1.0   )

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

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







  def    __A     (							self :       Dict   )  ->	Union[str, Any]:
        SCREAMING_SNAKE_CASE_          =  self.get_image_processor()
        SCREAMING_SNAKE_CASE_          =  self.get_tokenizer()

        SCREAMING_SNAKE_CASE_          =  CLIPProcessor(tokenizer=__magic_name__  ,     image_processor=__magic_name__   )

        SCREAMING_SNAKE_CASE_          =  self.prepare_image_inputs()

        SCREAMING_SNAKE_CASE_          =  image_processor(__magic_name__  ,     return_tensors="np"   )
        SCREAMING_SNAKE_CASE_          =  processor(images=__magic_name__  ,     return_tensors="np"   )

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







  def    __A     (							self :       List[str]   )  ->	Tuple:
        SCREAMING_SNAKE_CASE_          =  self.get_image_processor()
        SCREAMING_SNAKE_CASE_          =  self.get_tokenizer()

        SCREAMING_SNAKE_CASE_          =  CLIPProcessor(tokenizer=__magic_name__  ,     image_processor=__magic_name__   )

        SCREAMING_SNAKE_CASE_          =  "lower newer"

        SCREAMING_SNAKE_CASE_          =  processor(text=__magic_name__   )

        SCREAMING_SNAKE_CASE_          =  tokenizer(__magic_name__   )

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







  def    __A     (							self :       str   )  ->	List[Any]:
        SCREAMING_SNAKE_CASE_          =  self.get_image_processor()
        SCREAMING_SNAKE_CASE_          =  self.get_tokenizer()

        SCREAMING_SNAKE_CASE_          =  CLIPProcessor(tokenizer=__magic_name__  ,     image_processor=__magic_name__   )

        SCREAMING_SNAKE_CASE_          =  "lower newer"
        SCREAMING_SNAKE_CASE_          =  self.prepare_image_inputs()

        SCREAMING_SNAKE_CASE_          =  processor(text=__magic_name__  ,     images=__magic_name__   )

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

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







  def    __A     (							self :       Any   )  ->	str:
        SCREAMING_SNAKE_CASE_          =  self.get_image_processor()
        SCREAMING_SNAKE_CASE_          =  self.get_tokenizer()

        SCREAMING_SNAKE_CASE_          =  CLIPProcessor(tokenizer=__magic_name__  ,     image_processor=__magic_name__   )

        SCREAMING_SNAKE_CASE_          =  [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

        SCREAMING_SNAKE_CASE_          =  processor.batch_decode(__magic_name__   )
        SCREAMING_SNAKE_CASE_          =  tokenizer.batch_decode(__magic_name__   )

        self.assertListEqual(__magic_name__  ,     __magic_name__   )







  def    __A     (							self :       str   )  ->	List[str]:
        SCREAMING_SNAKE_CASE_          =  self.get_image_processor()
        SCREAMING_SNAKE_CASE_          =  self.get_tokenizer()

        SCREAMING_SNAKE_CASE_          =  CLIPProcessor(tokenizer=__magic_name__  ,     image_processor=__magic_name__   )

        SCREAMING_SNAKE_CASE_          =  "lower newer"
        SCREAMING_SNAKE_CASE_          =  self.prepare_image_inputs()

        SCREAMING_SNAKE_CASE_          =  processor(text=__magic_name__  ,     images=__magic_name__   )

        self.assertListEqual(list(inputs.keys()   )  ,     processor.model_input_names   )



from typing import TYPE_CHECKING

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


A     :  int					     =       {
    "configuration_roberta": ["ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP", "RobertaConfig", "RobertaOnnxConfig"],
    "tokenization_roberta": ["RobertaTokenizer"],
}

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A     :  str					     =       ["RobertaTokenizerFast"]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A     :  Tuple					     =       [
          "ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST",
          "RobertaForCausalLM",
          "RobertaForMaskedLM",
          "RobertaForMultipleChoice",
          "RobertaForQuestionAnswering",
          "RobertaForSequenceClassification",
          "RobertaForTokenClassification",
          "RobertaModel",
          "RobertaPreTrainedModel",
      ]

try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A     :  Any					     =       [
          "TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST",
          "TFRobertaForCausalLM",
          "TFRobertaForMaskedLM",
          "TFRobertaForMultipleChoice",
          "TFRobertaForQuestionAnswering",
          "TFRobertaForSequenceClassification",
          "TFRobertaForTokenClassification",
          "TFRobertaMainLayer",
          "TFRobertaModel",
          "TFRobertaPreTrainedModel",
      ]

try:
      if not is_flax_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A     :  int					     =       [
          "FlaxRobertaForCausalLM",
          "FlaxRobertaForMaskedLM",
          "FlaxRobertaForMultipleChoice",
          "FlaxRobertaForQuestionAnswering",
          "FlaxRobertaForSequenceClassification",
          "FlaxRobertaForTokenClassification",
          "FlaxRobertaModel",
          "FlaxRobertaPreTrainedModel",
      ]


if TYPE_CHECKING:
      from .configuration_roberta import ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, RobertaConfig, RobertaOnnxConfig
      from .tokenization_roberta import RobertaTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_roberta_fast import RobertaTokenizerFast

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_roberta import (
                ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
                RobertaForCausalLM,
                RobertaForMaskedLM,
                RobertaForMultipleChoice,
                RobertaForQuestionAnswering,
                RobertaForSequenceClassification,
                RobertaForTokenClassification,
                RobertaModel,
                RobertaPreTrainedModel,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_roberta import (
                TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFRobertaForCausalLM,
                TFRobertaForMaskedLM,
                TFRobertaForMultipleChoice,
                TFRobertaForQuestionAnswering,
                TFRobertaForSequenceClassification,
                TFRobertaForTokenClassification,
                TFRobertaMainLayer,
                TFRobertaModel,
                TFRobertaPreTrainedModel,
            )

      try:
            if not is_flax_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_flax_roberta import (
                FlaxRobertaForCausalLM,
                FlaxRobertaForMaskedLM,
                FlaxRobertaForMultipleChoice,
                FlaxRobertaForQuestionAnswering,
                FlaxRobertaForSequenceClassification,
                FlaxRobertaForTokenClassification,
                FlaxRobertaModel,
                FlaxRobertaPreTrainedModel,
            )

else:
      import sys

      A     :  Tuple					     =       _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


