





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       A__     ( unittest.TestCase    ):





      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       UpperCamelCase			:      Tuple 			=     tempfile.mkdtemp()

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

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

       UpperCamelCase			:      Dict 			=     {
           "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],
       }
       UpperCamelCase			:      str 			=     os.path.join(self.tmpdirname ,							A_     )
       with open(self.image_processor_file ,							"w" ,							encoding="utf-8"     ) as fp:
        json.dump(A_ ,							A_     )





      def  __UpperCamelCase(				self ,							**A_     ):
       '''simple docstring'''




       return CLIPTokenizer.from_pretrained(self.tmpdirname ,							**A_     )





      def  __UpperCamelCase(				self ,							**A_     ):
       '''simple docstring'''




       return CLIPTokenizerFast.from_pretrained(self.tmpdirname ,							**A_     )





      def  __UpperCamelCase(				self ,							**A_     ):
       '''simple docstring'''




       return ViTImageProcessor.from_pretrained(self.tmpdirname ,							**A_     )





      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       shutil.rmtree(self.tmpdirname     )





      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       UpperCamelCase			:      List[str] 			=     [np.random.randint(255 ,							size=(3, 30, 400) ,							dtype=np.uinta     )]

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

       return image_inputs





      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       UpperCamelCase			:      Dict 			=     self.get_tokenizer()
       UpperCamelCase			:      Dict 			=     self.get_rust_tokenizer()
       UpperCamelCase			:      List[Any] 			=     self.get_image_processor()

       UpperCamelCase			:      Any 			=     CLIPSegProcessor(tokenizer=A_ ,							image_processor=A_     )
       processor_slow.save_pretrained(self.tmpdirname     )
       UpperCamelCase			:      int 			=     CLIPSegProcessor.from_pretrained(self.tmpdirname ,							use_fast=A_     )

       UpperCamelCase			:      str 			=     CLIPSegProcessor(tokenizer=A_ ,							image_processor=A_     )
       processor_fast.save_pretrained(self.tmpdirname     )
       UpperCamelCase			:      int 			=     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 ,							A_     )
       self.assertIsInstance(processor_fast.tokenizer ,							A_     )

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





      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       UpperCamelCase			:      Optional[Any] 			=     CLIPSegProcessor(tokenizer=self.get_tokenizer() ,							image_processor=self.get_image_processor()     )
       processor.save_pretrained(self.tmpdirname     )

       UpperCamelCase			:      Union[str, Any] 			=     self.get_tokenizer(bos_token="(BOS)" ,							eos_token="(EOS)"     )
       UpperCamelCase			:      Tuple 			=     self.get_image_processor(do_normalize=A_ ,							padding_value=1.0     )

       UpperCamelCase			:      List[str] 			=     CLIPSegProcessor.from_pretrained(
           self.tmpdirname ,							bos_token="(BOS)" ,							eos_token="(EOS)" ,							do_normalize=A_ ,							padding_value=1.0     )

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

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





      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       UpperCamelCase			:      Any 			=     self.get_image_processor()
       UpperCamelCase			:      str 			=     self.get_tokenizer()

       UpperCamelCase			:      List[Any] 			=     CLIPSegProcessor(tokenizer=A_ ,							image_processor=A_     )

       UpperCamelCase			:      Union[str, Any] 			=     self.prepare_image_inputs()

       UpperCamelCase			:      Optional[int] 			=     image_processor(A_ ,							return_tensors="np"     )
       UpperCamelCase			:      str 			=     processor(images=A_ ,							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  __UpperCamelCase(				self     ):
       '''simple docstring'''




       UpperCamelCase			:      Union[str, Any] 			=     self.get_image_processor()
       UpperCamelCase			:      List[str] 			=     self.get_tokenizer()

       UpperCamelCase			:      Tuple 			=     CLIPSegProcessor(tokenizer=A_ ,							image_processor=A_     )

       UpperCamelCase			:      Tuple 			=     "lower newer"

       UpperCamelCase			:      List[str] 			=     processor(text=A_     )

       UpperCamelCase			:      Optional[Any] 			=     tokenizer(A_     )

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





      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       UpperCamelCase			:      str 			=     self.get_image_processor()
       UpperCamelCase			:      Tuple 			=     self.get_tokenizer()

       UpperCamelCase			:      Union[str, Any] 			=     CLIPSegProcessor(tokenizer=A_ ,							image_processor=A_     )

       UpperCamelCase			:      int 			=     "lower newer"
       UpperCamelCase			:      Any 			=     self.prepare_image_inputs()

       UpperCamelCase			:      Optional[int] 			=     processor(text=A_ ,							images=A_     )

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

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





      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       UpperCamelCase			:      List[str] 			=     self.get_image_processor()
       UpperCamelCase			:      str 			=     self.get_tokenizer()

       UpperCamelCase			:      List[Any] 			=     CLIPSegProcessor(tokenizer=A_ ,							image_processor=A_     )

       UpperCamelCase			:      Optional[Any] 			=     self.prepare_image_inputs()
       UpperCamelCase			:      Optional[Any] 			=     self.prepare_image_inputs()

       UpperCamelCase			:      Optional[int] 			=     processor(images=A_ ,							visual_prompt=A_     )

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

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





      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       UpperCamelCase			:      str 			=     self.get_image_processor()
       UpperCamelCase			:      List[Any] 			=     self.get_tokenizer()

       UpperCamelCase			:      List[Any] 			=     CLIPSegProcessor(tokenizer=A_ ,							image_processor=A_     )

       UpperCamelCase			:      Optional[int] 			=     [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

       UpperCamelCase			:      Optional[int] 			=     processor.batch_decode(A_     )
       UpperCamelCase			:      Optional[int] 			=     tokenizer.batch_decode(A_     )

       self.assertListEqual(A_ ,							A_     )







from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


__lowerCamelCase :  Tuple										=  logging.get_logger(__name__)

__lowerCamelCase :  str										=  {
    """camembert-base""": """https://huggingface.co/camembert-base/resolve/main/config.json""",
    """umberto-commoncrawl-cased-v1""": (
        """https://huggingface.co/Musixmatch/umberto-commoncrawl-cased-v1/resolve/main/config.json"""
    ),
    """umberto-wikipedia-uncased-v1""": (
        """https://huggingface.co/Musixmatch/umberto-wikipedia-uncased-v1/resolve/main/config.json"""
    ),
}



class       A__     ( __snake_case    ):
      _UpperCAmelCase			:Union[str, Any]							=     'camembert'





      def __init__(				self ,							A_=3_0522 ,							A_=768 ,							A_=12 ,							A_=12 ,							A_=3072 ,							A_="gelu" ,							A_=0.1 ,							A_=0.1 ,							A_=512 ,							A_=2 ,							A_=0.02 ,							A_=1e-12 ,							A_=1 ,							A_=0 ,							A_=2 ,							A_="absolute" ,							A_=True ,							A_=None ,							**A_ ,							):
       '''simple docstring'''




       super().__init__(pad_token_id=A_ ,							bos_token_id=A_ ,							eos_token_id=A_ ,							**A_     )

       UpperCamelCase			:      List[str] 			=     vocab_size
       UpperCamelCase			:      Union[str, Any] 			=     hidden_size
       UpperCamelCase			:      Any 			=     num_hidden_layers
       UpperCamelCase			:      Union[str, Any] 			=     num_attention_heads
       UpperCamelCase			:      Dict 			=     hidden_act
       UpperCamelCase			:      str 			=     intermediate_size
       UpperCamelCase			:      str 			=     hidden_dropout_prob
       UpperCamelCase			:      Dict 			=     attention_probs_dropout_prob
       UpperCamelCase			:      Union[str, Any] 			=     max_position_embeddings
       UpperCamelCase			:      Optional[Any] 			=     type_vocab_size
       UpperCamelCase			:      int 			=     initializer_range
       UpperCamelCase			:      List[str] 			=     layer_norm_eps
       UpperCamelCase			:      Dict 			=     position_embedding_type
       UpperCamelCase			:      int 			=     use_cache
       UpperCamelCase			:      List[str] 			=     classifier_dropout



class       A__     ( __snake_case    ):





      @property
      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       if self.task == "multiple-choice":
        UpperCamelCase			:      Optional[int] 			=     {0: "batch", 1: "choice", 2: "sequence"}
       else:
        UpperCamelCase			:      str 			=     {0: "batch", 1: "sequence"}
       return OrderedDict(
           [
               ("input_ids", dynamic_axis),
               ("attention_mask", dynamic_axis),
           ]     )
