


"""simple docstring"""

import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path

from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError

from transformers import AutoImageProcessor, ViTImageProcessor
from transformers.testing_utils import TOKEN, USER, get_tests_dir, is_staging_test


sys.path.append(str(Path(__file__).parent.parent / "utils"))

from test_module.custom_image_processing import CustomImageProcessor  # noqa E402


UpperCAmelCase__					=get_tests_dir("fixtures")





class  lowerCamelCase__    (		unittest.TestCase     ):
      def SCREAMING_SNAKE_CASE_						(		self						:						Dict				):






       '''simple docstring'''


       __lowercase						=						mock.Mock()
       __lowercase						=						5_0_0
       __lowercase						=						{}
       __lowercase						=						HTTPError
       __lowercase						=						{}

       # Download this model to make sure it's in the cache.
       __lowercase						=						ViTImageProcessor.from_pretrained("""hf-internal-testing/tiny-random-vit"""				)
       # Under the mock environment we get a 500 error when trying to reach the model.
       with mock.patch("""requests.Session.request"""   ,							return_value=A_				) as mock_head:
        __lowercase						=						ViTImageProcessor.from_pretrained("""hf-internal-testing/tiny-random-vit"""				)
        # This check we did call the fake head request
        mock_head.assert_called()
      def SCREAMING_SNAKE_CASE_						(		self						:						List[str]				):






       '''simple docstring'''


       __lowercase						=						ViTImageProcessor.from_pretrained(
           """https://huggingface.co/hf-internal-testing/tiny-random-vit/resolve/main/preprocessor_config.json"""				)






      def SCREAMING_SNAKE_CASE_						(		self						:						Optional[Any]				):






       '''simple docstring'''


       with self.assertRaises(A_				):
        # config is in subfolder, the following should not work without specifying the subfolder
        __lowercase						=						AutoImageProcessor.from_pretrained("""hf-internal-testing/stable-diffusion-all-variants"""				)

       __lowercase						=						AutoImageProcessor.from_pretrained(
           """hf-internal-testing/stable-diffusion-all-variants"""   ,							subfolder="""feature_extractor"""				)

       self.assertIsNotNone(A_				)






@is_staging_test
class  lowerCamelCase__    (		unittest.TestCase     ):
      @classmethod
      def SCREAMING_SNAKE_CASE_						(		cls						:						int				):






       '''simple docstring'''


       __lowercase						=						TOKEN
       HfFolder.save_token(A_				)
      @classmethod
      def SCREAMING_SNAKE_CASE_						(		cls						:						Any				):






       '''simple docstring'''


       try:
        delete_repo(token=cls._token   ,							repo_id="""test-image-processor"""				)
       except HTTPError:
        pass

       try:
        delete_repo(token=cls._token   ,							repo_id="""valid_org/test-image-processor-org"""				)
       except HTTPError:
        pass

       try:
        delete_repo(token=cls._token   ,							repo_id="""test-dynamic-image-processor"""				)
       except HTTPError:
        pass
      def SCREAMING_SNAKE_CASE_						(		self						:						Optional[int]				):






       '''simple docstring'''


       __lowercase						=						ViTImageProcessor.from_pretrained(A_				)
       image_processor.push_to_hub("""test-image-processor"""   ,							use_auth_token=self._token				)

       __lowercase						=						ViTImageProcessor.from_pretrained(F'''{USER}/test-image-processor'''				)
       for k, v in image_processor.__dict__.items():
        self.assertEqual(A_   ,							getattr(A_   ,							A_				)				)

       # Reset repo
       delete_repo(token=self._token   ,							repo_id="""test-image-processor"""				)

       # Push to hub via save_pretrained
       with tempfile.TemporaryDirectory() as tmp_dir:
        image_processor.save_pretrained(
            A_   ,							repo_id="""test-image-processor"""   ,							push_to_hub=A_   ,							use_auth_token=self._token				)

       __lowercase						=						ViTImageProcessor.from_pretrained(F'''{USER}/test-image-processor'''				)
       for k, v in image_processor.__dict__.items():
        self.assertEqual(A_   ,							getattr(A_   ,							A_				)				)
      def SCREAMING_SNAKE_CASE_						(		self						:						Any				):






       '''simple docstring'''


       __lowercase						=						ViTImageProcessor.from_pretrained(A_				)
       image_processor.push_to_hub("""valid_org/test-image-processor"""   ,							use_auth_token=self._token				)

       __lowercase						=						ViTImageProcessor.from_pretrained("""valid_org/test-image-processor"""				)
       for k, v in image_processor.__dict__.items():
        self.assertEqual(A_   ,							getattr(A_   ,							A_				)				)

       # Reset repo
       delete_repo(token=self._token   ,							repo_id="""valid_org/test-image-processor"""				)

       # Push to hub via save_pretrained
       with tempfile.TemporaryDirectory() as tmp_dir:
        image_processor.save_pretrained(
            A_   ,							repo_id="""valid_org/test-image-processor-org"""   ,							push_to_hub=A_   ,							use_auth_token=self._token				)

       __lowercase						=						ViTImageProcessor.from_pretrained("""valid_org/test-image-processor-org"""				)
       for k, v in image_processor.__dict__.items():
        self.assertEqual(A_   ,							getattr(A_   ,							A_				)				)






      def SCREAMING_SNAKE_CASE_						(		self						:						Any				):






       '''simple docstring'''


       CustomImageProcessor.register_for_auto_class()
       __lowercase						=						CustomImageProcessor.from_pretrained(A_				)

       image_processor.push_to_hub("""test-dynamic-image-processor"""   ,							use_auth_token=self._token				)

       # This has added the proper auto_map field to the config
       self.assertDictEqual(
           image_processor.auto_map   ,							{"""AutoImageProcessor""": """custom_image_processing.CustomImageProcessor"""}   ,							)

       __lowercase						=						AutoImageProcessor.from_pretrained(
           F'''{USER}/test-dynamic-image-processor'''   ,							trust_remote_code=A_				)
       # Can't make an isinstance check because the new_image_processor is from the CustomImageProcessor class of a dynamic module
       self.assertEqual(new_image_processor.__class__.__name__   ,							"""CustomImageProcessor"""				)




"""simple docstring"""

import unittest

from transformers import BarthezTokenizer, BarthezTokenizerFast, BatchEncoding
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow

from ...test_tokenization_common import TokenizerTesterMixin





@require_tokenizers
@require_sentencepiece
@slow  # see https://github.com/huggingface/transformers/issues/11457
class  lowerCamelCase__    (		_a      , unittest.TestCase     ):
      a			:  Union[str, Any]           =		BarthezTokenizer
      a			:  Any           =		BarthezTokenizerFast
      a			:  Tuple           =		True
      a			:  List[Any]           =		True
      def SCREAMING_SNAKE_CASE_						(		self						:						List[str]				):






       '''simple docstring'''


       super().setUp()

       __lowercase						=						BarthezTokenizerFast.from_pretrained("""moussaKam/mbarthez"""				)
       tokenizer.save_pretrained(self.tmpdirname				)
       tokenizer.save_pretrained(self.tmpdirname   ,							legacy_format=A_				)
       __lowercase						=						tokenizer
      def SCREAMING_SNAKE_CASE_						(		self						:						Tuple				):






       '''simple docstring'''


       __lowercase						=						"""<pad>"""
       __lowercase						=						1

       self.assertEqual(self.get_tokenizer()._convert_token_to_id(A_				)   ,							A_				)
       self.assertEqual(self.get_tokenizer()._convert_id_to_token(A_				)   ,							A_				)
      def SCREAMING_SNAKE_CASE_						(		self						:						List[str]				):






       '''simple docstring'''


       __lowercase						=						list(self.get_tokenizer().get_vocab().keys()				)

       self.assertEqual(vocab_keys[0]   ,							"""<s>"""				)
       self.assertEqual(vocab_keys[1]   ,							"""<pad>"""				)
       self.assertEqual(vocab_keys[-1]   ,							"""<mask>"""				)
       self.assertEqual(len(A_				)   ,							1_0_1_1_2_2				)
      def SCREAMING_SNAKE_CASE_						(		self						:						Union[str, Any]				):






       '''simple docstring'''


       self.assertEqual(self.get_tokenizer().vocab_size   ,							1_0_1_1_2_2				)
      @require_torch
      def SCREAMING_SNAKE_CASE_						(		self						:						str				):






       '''simple docstring'''


       __lowercase						=						["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
       __lowercase						=						[0, 5_7, 3_0_1_8, 7_0_3_0_7, 9_1, 2]

       __lowercase						=						self.tokenizer(
           A_   ,							max_length=len(A_				)   ,							padding=A_   ,							truncation=A_   ,							return_tensors="""pt"""				)
       self.assertIsInstance(A_   ,							A_				)

       self.assertEqual((2, 6)   ,							batch.input_ids.shape				)
       self.assertEqual((2, 6)   ,							batch.attention_mask.shape				)
       __lowercase						=						batch.input_ids.tolist()[0]
       self.assertListEqual(A_   ,							A_				)
      def SCREAMING_SNAKE_CASE_						(		self						:						int				):






       '''simple docstring'''


       if not self.test_rust_tokenizer:
        return

       __lowercase						=						self.get_tokenizer()
       __lowercase						=						self.get_rust_tokenizer()

       __lowercase						=						"""I was born in 92000, and this is falsé."""

       __lowercase						=						tokenizer.tokenize(A_				)
       __lowercase						=						rust_tokenizer.tokenize(A_				)
       self.assertListEqual(A_   ,							A_				)

       __lowercase						=						tokenizer.encode(A_   ,							add_special_tokens=A_				)
       __lowercase						=						rust_tokenizer.encode(A_   ,							add_special_tokens=A_				)
       self.assertListEqual(A_   ,							A_				)

       __lowercase						=						self.get_rust_tokenizer()
       __lowercase						=						tokenizer.encode(A_				)
       __lowercase						=						rust_tokenizer.encode(A_				)
       self.assertListEqual(A_   ,							A_				)






      @slow
      def SCREAMING_SNAKE_CASE_						(		self						:						Union[str, Any]				):






       '''simple docstring'''


       __lowercase						=						{"""input_ids""": [[0, 4_9_0, 1_4_3_2_8, 4_5_0_7, 3_5_4, 4_7, 4_3_6_6_9, 9_5, 2_5, 7_8_1_1_7, 2_0_2_1_5, 1_9_7_7_9, 1_9_0, 2_2, 4_0_0, 4, 3_5_3_4_3, 8_0_3_1_0, 6_0_3, 8_6, 2_4_9_3_7, 1_0_5, 3_3_4_3_8, 9_4_7_6_2, 1_9_6, 3_9_6_4_2, 7, 1_5, 1_5_9_3_3, 1_7_3, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 1_0_5_3_4, 8_7, 2_5, 6_6, 3_3_5_8, 1_9_6, 5_5_2_8_9, 8, 8_2_9_6_1, 8_1, 2_2_0_4, 7_5_2_0_3, 7, 1_5, 7_6_3, 1_2_9_5_6, 2_1_6, 1_7_8, 1_4_3_2_8, 9_5_9_5, 1_3_7_7, 6_9_6_9_3, 7, 4_4_8, 7_1_0_2_1, 1_9_6, 1_8_1_0_6, 1_4_3_7, 1_3_9_7_4, 1_0_8, 9_0_8_3, 4, 4_9_3_1_5, 7, 3_9, 8_6, 1_3_2_6, 2_7_9_3, 4_6_3_3_3, 4, 4_4_8, 1_9_6, 7_4_5_8_8, 7, 4_9_3_1_5, 7, 3_9, 2_1, 8_2_2, 3_8_4_7_0, 7_4, 2_1, 6_6_7_2_3, 6_2_4_8_0, 8, 2_2_0_5_0, 5, 2]], """attention_mask""": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}  # noqa: E501
       # fmt: on

       # moussaKam/mbarthez is a french model. So we also use french texts.
       __lowercase						=						[
           """Le transformeur est un modèle d'apprentissage profond introduit en 2017, """
           """utilisé principalement dans le domaine du traitement automatique des langues (TAL).""",
           """À l'instar des réseaux de neurones récurrents (RNN), les transformeurs sont conçus """
           """pour gérer des données séquentielles, telles que le langage naturel, pour des tâches """
           """telles que la traduction et la synthèse de texte.""",
       ]

       self.tokenizer_integration_test_util(
           expected_encoding=A_   ,							model_name="""moussaKam/mbarthez"""   ,							revision="""c2e4ecbca5e3cd2c37fe1ac285ca4fbdf1366fb6"""   ,							sequences=A_   ,							)
