'''simple docstring'''





import unittest
from pathlib import Path
from tempfile import TemporaryDirectory

from transformers import AutoConfig, TFGPTaLMHeadModel, is_keras_nlp_available, is_tf_available
from transformers.models.gpta.tokenization_gpta import GPTaTokenizer
from transformers.testing_utils import require_keras_nlp, require_tf, slow


if is_tf_available():
    import tensorflow as tf

if is_keras_nlp_available():
    from transformers.models.gpta import TFGPTaTokenizer


SCREAMING_SNAKE_CASE_:     Union[str, Any]  =['gpt2']
SCREAMING_SNAKE_CASE_:     Optional[int]  ='gpt2'

if is_tf_available():




    class        __A      ( tf.Module	):



        def __init__(self    :    Union[str, Any]   ,			__a    :    Union[str, Any] ):
             super().__init__()
             UpperCAmelCase_           =   tokenizer
             UpperCAmelCase_           =   AutoConfig.from_pretrained(__a )
             UpperCAmelCase_           =   TFGPTaLMHeadModel.from_config(__a )






        @tf.function(input_signature=(tf.TensorSpec((None,)   ,			tf.string   ,			name="text" ),) )
        def 					_lowercase						(self    :    Tuple   ,			__a    :    List[str] ):
             UpperCAmelCase_           =   self.tokenizer(__a )
             UpperCAmelCase_           =   tokenized["input_ids"].to_tensor()

             UpperCAmelCase_           =   tf.cast(input_ids_dense > 0   ,			tf.intaa )
             # input_mask = tf.reshape(input_mask, [-1, MAX_SEQ_LEN])

             UpperCAmelCase_           =   self.model(input_ids=__a   ,			attention_mask=__a )["logits"]

             return outputs




@require_tf
@require_keras_nlp
class        __A      ( unittest.TestCase	):



    def 					_lowercase						(self    :    List[Any] ):
         super().setUp()

         UpperCAmelCase_           =   [GPTaTokenizer.from_pretrained(__a ) for checkpoint in (TOKENIZER_CHECKPOINTS)]
         UpperCAmelCase_           =   [TFGPTaTokenizer.from_pretrained(__a ) for checkpoint in TOKENIZER_CHECKPOINTS]
         assert len(self.tokenizers ) == len(self.tf_tokenizers )

         UpperCAmelCase_           =   [
             "This is a straightforward English test sentence.",
             "This one has some weird characters\rto\nsee\r\nif  those\u00E9break things.",
             "Now we're going to add some Chinese: 一 二 三 一二三",
             "And some much more rare Chinese: 齉 堃 齉堃",
             "Je vais aussi écrire en français pour tester les accents",
             "Classical Irish also has some unusual characters, so in they go: Gaelaċ, ꝼ",
         ]
         UpperCAmelCase_           =   list(zip(self.test_sentences   ,			self.test_sentences[::-1] ) )



    def 					_lowercase						(self    :    List[str] ):
         for tokenizer, tf_tokenizer in zip(self.tokenizers   ,			self.tf_tokenizers ):
              for test_inputs in self.test_sentences:
                   UpperCAmelCase_           =   tokenizer([test_inputs]   ,			return_tensors="tf" )
                   UpperCAmelCase_           =   tf_tokenizer([test_inputs] )

                   for key in python_outputs.keys():
                        # convert them to numpy to avoid messing with ragged tensors
                        UpperCAmelCase_           =   python_outputs[key].numpy()
                        UpperCAmelCase_           =   tf_outputs[key].numpy()

                        self.assertTrue(tf.reduce_all(python_outputs_values.shape == tf_outputs_values.shape ) )
                        self.assertTrue(tf.reduce_all(tf.cast(__a   ,			tf.intaa ) == tf_outputs_values ) )



    @slow
    def 					_lowercase						(self    :    Optional[Any] ):
         for tf_tokenizer in self.tf_tokenizers:
              UpperCAmelCase_           =   tf.function(__a )
              for test_inputs in self.test_sentences:
                   UpperCAmelCase_           =   tf.constant(__a )
                   UpperCAmelCase_           =   compiled_tokenizer(__a )
                   UpperCAmelCase_           =   tf_tokenizer(__a )

                   for key in eager_outputs.keys():
                        self.assertTrue(tf.reduce_all(eager_outputs[key] == compiled_outputs[key] ) )



    @slow
    def 					_lowercase						(self    :    Tuple ):
         for tf_tokenizer in self.tf_tokenizers:
              UpperCAmelCase_           =   ModelToSave(tokenizer=__a )
              UpperCAmelCase_           =   tf.convert_to_tensor([self.test_sentences[0]] )
              UpperCAmelCase_           =   model.serving(__a )  # Build model with some sample inputs
              with TemporaryDirectory() as tempdir:
                   UpperCAmelCase_           =   Path(__a ) / "saved.model"
                   tf.saved_model.save(__a   ,			__a   ,			signatures={"serving_default": model.serving} )
                   UpperCAmelCase_           =   tf.saved_model.load(__a )
              UpperCAmelCase_           =   loaded_model.signatures["serving_default"](__a )["output_0"]
              # We may see small differences because the loaded model is compiled, so we need an epsilon for the test
              self.assertTrue(tf.reduce_all(out == loaded_output ) )



    @slow
    def 					_lowercase						(self    :    Optional[int] ):
         for tf_tokenizer in self.tf_tokenizers:
              UpperCAmelCase_           =   tf.convert_to_tensor([self.test_sentences[0]] )
              UpperCAmelCase_           =   tf_tokenizer(__a )  # Build model with some sample inputs

              UpperCAmelCase_           =   tf_tokenizer.get_config()
              UpperCAmelCase_           =   TFGPTaTokenizer.from_config(__a )
              UpperCAmelCase_           =   model_from_config(__a )

              for key in from_config_output.keys():
                   self.assertTrue(tf.reduce_all(from_config_output[key] == out[key] ) )






    @slow
    def 					_lowercase						(self    :    Tuple ):
         for tf_tokenizer in self.tf_tokenizers:
              # for the test to run
              UpperCAmelCase_           =   123123

              for max_length in [3, 5, 1024]:
                   UpperCAmelCase_           =   tf.convert_to_tensor([self.test_sentences[0]] )
                   UpperCAmelCase_           =   tf_tokenizer(__a   ,			max_length=__a )

                   UpperCAmelCase_           =   out["input_ids"].numpy().shape[1]

                   assert out_length == max_length











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    _lowercase  (    self							)   ->						Dict:



        '''simple docstring'''




        a__	:					Any										=tempfile.mkdtemp()

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

        a__	:					Optional[Any]										=os.path.join(self.tmpdirname		,      VOCAB_FILES_NAMES["vocab_file"]							)
        a__	:					Tuple										=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__							)							)

        a__	:					Optional[Any]										={
            "do_resize": True,
            "size": 2_0,
            "do_center_crop": True,
            "crop_size": 1_8,
            "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],
        }
        a__	:					Dict										=os.path.join(self.tmpdirname		,      lowerCAmelCase__							)
        with open(self.image_processor_file		,      "w"		,      encoding="utf-8"							) as fp:
          json.dump(lowerCAmelCase__		,      lowerCAmelCase__							)







      def    _lowercase  (    self		,      **lowerCAmelCase__							)   ->						Union[str, Any]:



        '''simple docstring'''




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







      def    _lowercase  (    self		,      **lowerCAmelCase__							)   ->						List[Any]:



        '''simple docstring'''




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







      def    _lowercase  (    self		,      **lowerCAmelCase__							)   ->						Any:



        '''simple docstring'''




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







      def    _lowercase  (    self							)   ->						Optional[int]:



        '''simple docstring'''




        shutil.rmtree(self.tmpdirname							)







      def    _lowercase  (    self							)   ->						Any:



        '''simple docstring'''




        a__	:					Optional[Any]										=[np.random.randint(2_5_5		,      size=(3, 3_0, 4_0_0)		,      dtype=np.uinta							)]

        a__	:					List[Any]										=[Image.fromarray(np.moveaxis(lowerCAmelCase__		,      0		,      -1							)							) for x in image_inputs]

        return image_inputs







      def    _lowercase  (    self							)   ->						Dict:



        '''simple docstring'''




        a__	:					Union[str, Any]										=self.get_tokenizer()
        a__	:					int										=self.get_rust_tokenizer()
        a__	:					List[str]										=self.get_image_processor()

        a__	:					Dict										=CLIPSegProcessor(tokenizer=lowerCAmelCase__		,      image_processor=lowerCAmelCase__							)
        processor_slow.save_pretrained(self.tmpdirname							)
        a__	:					Optional[Any]										=CLIPSegProcessor.from_pretrained(self.tmpdirname		,      use_fast=lowerCAmelCase__							)

        a__	:					Tuple										=CLIPSegProcessor(tokenizer=lowerCAmelCase__		,      image_processor=lowerCAmelCase__							)
        processor_fast.save_pretrained(self.tmpdirname							)
        a__	:					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    _lowercase  (    self							)   ->						Any:



        '''simple docstring'''




        a__	:					List[str]										=CLIPSegProcessor(tokenizer=self.get_tokenizer()		,      image_processor=self.get_image_processor()							)
        processor.save_pretrained(self.tmpdirname							)

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

        a__	:					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    _lowercase  (    self							)   ->						List[Any]:



        '''simple docstring'''




        a__	:					str										=self.get_image_processor()
        a__	:					Optional[int]										=self.get_tokenizer()

        a__	:					Dict										=CLIPSegProcessor(tokenizer=lowerCAmelCase__		,      image_processor=lowerCAmelCase__							)

        a__	:					str										=self.prepare_image_inputs()

        a__	:					Any										=image_processor(lowerCAmelCase__		,      return_tensors="np"							)
        a__	:					Optional[int]										=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    _lowercase  (    self							)   ->						Union[str, Any]:



        '''simple docstring'''




        a__	:					Optional[int]										=self.get_image_processor()
        a__	:					List[Any]										=self.get_tokenizer()

        a__	:					Optional[int]										=CLIPSegProcessor(tokenizer=lowerCAmelCase__		,      image_processor=lowerCAmelCase__							)

        a__	:					Union[str, Any]										="lower newer"

        a__	:					List[str]										=processor(text=lowerCAmelCase__							)

        a__	:					str										=tokenizer(lowerCAmelCase__							)

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







      def    _lowercase  (    self							)   ->						Optional[int]:



        '''simple docstring'''




        a__	:					Any										=self.get_image_processor()
        a__	:					Dict										=self.get_tokenizer()

        a__	:					Union[str, Any]										=CLIPSegProcessor(tokenizer=lowerCAmelCase__		,      image_processor=lowerCAmelCase__							)

        a__	:					Dict										="lower newer"
        a__	:					int										=self.prepare_image_inputs()

        a__	:					Any										=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    _lowercase  (    self							)   ->						str:



        '''simple docstring'''




        a__	:					Union[str, Any]										=self.get_image_processor()
        a__	:					Optional[Any]										=self.get_tokenizer()

        a__	:					str										=CLIPSegProcessor(tokenizer=lowerCAmelCase__		,      image_processor=lowerCAmelCase__							)

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

        a__	:					Tuple										=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    _lowercase  (    self							)   ->						Tuple:



        '''simple docstring'''




        a__	:					Optional[int]										=self.get_image_processor()
        a__	:					Any										=self.get_tokenizer()

        a__	:					Tuple										=CLIPSegProcessor(tokenizer=lowerCAmelCase__		,      image_processor=lowerCAmelCase__							)

        a__	:					Dict										=[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

        a__	:					Optional[Any]										=processor.batch_decode(lowerCAmelCase__							)
        a__	:					Dict										=tokenizer.batch_decode(lowerCAmelCase__							)

        self.assertListEqual(lowerCAmelCase__		,      lowerCAmelCase__							)






