





# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from ..models.auto import AutoModelForSeqaSeqLM, AutoTokenizer
from .base import PipelineTool


snake_case_				=			{
    'Acehnese Arabic': 'ace_Arab',
    'Acehnese Latin': 'ace_Latn',
    'Mesopotamian Arabic': 'acm_Arab',
    'Ta\'izzi-Adeni Arabic': 'acq_Arab',
    'Tunisian Arabic': 'aeb_Arab',
    'Afrikaans': 'afr_Latn',
    'South Levantine Arabic': 'ajp_Arab',
    'Akan': 'aka_Latn',
    'Amharic': 'amh_Ethi',
    'North Levantine Arabic': 'apc_Arab',
    'Modern Standard Arabic': 'arb_Arab',
    'Modern Standard Arabic Romanized': 'arb_Latn',
    'Najdi Arabic': 'ars_Arab',
    'Moroccan Arabic': 'ary_Arab',
    'Egyptian Arabic': 'arz_Arab',
    'Assamese': 'asm_Beng',
    'Asturian': 'ast_Latn',
    'Awadhi': 'awa_Deva',
    'Central Aymara': 'ayr_Latn',
    'South Azerbaijani': 'azb_Arab',
    'North Azerbaijani': 'azj_Latn',
    'Bashkir': 'bak_Cyrl',
    'Bambara': 'bam_Latn',
    'Balinese': 'ban_Latn',
    'Belarusian': 'bel_Cyrl',
    'Bemba': 'bem_Latn',
    'Bengali': 'ben_Beng',
    'Bhojpuri': 'bho_Deva',
    'Banjar Arabic': 'bjn_Arab',
    'Banjar Latin': 'bjn_Latn',
    'Standard Tibetan': 'bod_Tibt',
    'Bosnian': 'bos_Latn',
    'Buginese': 'bug_Latn',
    'Bulgarian': 'bul_Cyrl',
    'Catalan': 'cat_Latn',
    'Cebuano': 'ceb_Latn',
    'Czech': 'ces_Latn',
    'Chokwe': 'cjk_Latn',
    'Central Kurdish': 'ckb_Arab',
    'Crimean Tatar': 'crh_Latn',
    'Welsh': 'cym_Latn',
    'Danish': 'dan_Latn',
    'German': 'deu_Latn',
    'Southwestern Dinka': 'dik_Latn',
    'Dyula': 'dyu_Latn',
    'Dzongkha': 'dzo_Tibt',
    'Greek': 'ell_Grek',
    'English': 'eng_Latn',
    'Esperanto': 'epo_Latn',
    'Estonian': 'est_Latn',
    'Basque': 'eus_Latn',
    'Ewe': 'ewe_Latn',
    'Faroese': 'fao_Latn',
    'Fijian': 'fij_Latn',
    'Finnish': 'fin_Latn',
    'Fon': 'fon_Latn',
    'French': 'fra_Latn',
    'Friulian': 'fur_Latn',
    'Nigerian Fulfulde': 'fuv_Latn',
    'Scottish Gaelic': 'gla_Latn',
    'Irish': 'gle_Latn',
    'Galician': 'glg_Latn',
    'Guarani': 'grn_Latn',
    'Gujarati': 'guj_Gujr',
    'Haitian Creole': 'hat_Latn',
    'Hausa': 'hau_Latn',
    'Hebrew': 'heb_Hebr',
    'Hindi': 'hin_Deva',
    'Chhattisgarhi': 'hne_Deva',
    'Croatian': 'hrv_Latn',
    'Hungarian': 'hun_Latn',
    'Armenian': 'hye_Armn',
    'Igbo': 'ibo_Latn',
    'Ilocano': 'ilo_Latn',
    'Indonesian': 'ind_Latn',
    'Icelandic': 'isl_Latn',
    'Italian': 'ita_Latn',
    'Javanese': 'jav_Latn',
    'Japanese': 'jpn_Jpan',
    'Kabyle': 'kab_Latn',
    'Jingpho': 'kac_Latn',
    'Kamba': 'kam_Latn',
    'Kannada': 'kan_Knda',
    'Kashmiri Arabic': 'kas_Arab',
    'Kashmiri Devanagari': 'kas_Deva',
    'Georgian': 'kat_Geor',
    'Central Kanuri Arabic': 'knc_Arab',
    'Central Kanuri Latin': 'knc_Latn',
    'Kazakh': 'kaz_Cyrl',
    'Kabiyè': 'kbp_Latn',
    'Kabuverdianu': 'kea_Latn',
    'Khmer': 'khm_Khmr',
    'Kikuyu': 'kik_Latn',
    'Kinyarwanda': 'kin_Latn',
    'Kyrgyz': 'kir_Cyrl',
    'Kimbundu': 'kmb_Latn',
    'Northern Kurdish': 'kmr_Latn',
    'Kikongo': 'kon_Latn',
    'Korean': 'kor_Hang',
    'Lao': 'lao_Laoo',
    'Ligurian': 'lij_Latn',
    'Limburgish': 'lim_Latn',
    'Lingala': 'lin_Latn',
    'Lithuanian': 'lit_Latn',
    'Lombard': 'lmo_Latn',
    'Latgalian': 'ltg_Latn',
    'Luxembourgish': 'ltz_Latn',
    'Luba-Kasai': 'lua_Latn',
    'Ganda': 'lug_Latn',
    'Luo': 'luo_Latn',
    'Mizo': 'lus_Latn',
    'Standard Latvian': 'lvs_Latn',
    'Magahi': 'mag_Deva',
    'Maithili': 'mai_Deva',
    'Malayalam': 'mal_Mlym',
    'Marathi': 'mar_Deva',
    'Minangkabau Arabic ': 'min_Arab',
    'Minangkabau Latin': 'min_Latn',
    'Macedonian': 'mkd_Cyrl',
    'Plateau Malagasy': 'plt_Latn',
    'Maltese': 'mlt_Latn',
    'Meitei Bengali': 'mni_Beng',
    'Halh Mongolian': 'khk_Cyrl',
    'Mossi': 'mos_Latn',
    'Maori': 'mri_Latn',
    'Burmese': 'mya_Mymr',
    'Dutch': 'nld_Latn',
    'Norwegian Nynorsk': 'nno_Latn',
    'Norwegian Bokmål': 'nob_Latn',
    'Nepali': 'npi_Deva',
    'Northern Sotho': 'nso_Latn',
    'Nuer': 'nus_Latn',
    'Nyanja': 'nya_Latn',
    'Occitan': 'oci_Latn',
    'West Central Oromo': 'gaz_Latn',
    'Odia': 'ory_Orya',
    'Pangasinan': 'pag_Latn',
    'Eastern Panjabi': 'pan_Guru',
    'Papiamento': 'pap_Latn',
    'Western Persian': 'pes_Arab',
    'Polish': 'pol_Latn',
    'Portuguese': 'por_Latn',
    'Dari': 'prs_Arab',
    'Southern Pashto': 'pbt_Arab',
    'Ayacucho Quechua': 'quy_Latn',
    'Romanian': 'ron_Latn',
    'Rundi': 'run_Latn',
    'Russian': 'rus_Cyrl',
    'Sango': 'sag_Latn',
    'Sanskrit': 'san_Deva',
    'Santali': 'sat_Olck',
    'Sicilian': 'scn_Latn',
    'Shan': 'shn_Mymr',
    'Sinhala': 'sin_Sinh',
    'Slovak': 'slk_Latn',
    'Slovenian': 'slv_Latn',
    'Samoan': 'smo_Latn',
    'Shona': 'sna_Latn',
    'Sindhi': 'snd_Arab',
    'Somali': 'som_Latn',
    'Southern Sotho': 'sot_Latn',
    'Spanish': 'spa_Latn',
    'Tosk Albanian': 'als_Latn',
    'Sardinian': 'srd_Latn',
    'Serbian': 'srp_Cyrl',
    'Swati': 'ssw_Latn',
    'Sundanese': 'sun_Latn',
    'Swedish': 'swe_Latn',
    'Swahili': 'swh_Latn',
    'Silesian': 'szl_Latn',
    'Tamil': 'tam_Taml',
    'Tatar': 'tat_Cyrl',
    'Telugu': 'tel_Telu',
    'Tajik': 'tgk_Cyrl',
    'Tagalog': 'tgl_Latn',
    'Thai': 'tha_Thai',
    'Tigrinya': 'tir_Ethi',
    'Tamasheq Latin': 'taq_Latn',
    'Tamasheq Tifinagh': 'taq_Tfng',
    'Tok Pisin': 'tpi_Latn',
    'Tswana': 'tsn_Latn',
    'Tsonga': 'tso_Latn',
    'Turkmen': 'tuk_Latn',
    'Tumbuka': 'tum_Latn',
    'Turkish': 'tur_Latn',
    'Twi': 'twi_Latn',
    'Central Atlas Tamazight': 'tzm_Tfng',
    'Uyghur': 'uig_Arab',
    'Ukrainian': 'ukr_Cyrl',
    'Umbundu': 'umb_Latn',
    'Urdu': 'urd_Arab',
    'Northern Uzbek': 'uzn_Latn',
    'Venetian': 'vec_Latn',
    'Vietnamese': 'vie_Latn',
    'Waray': 'war_Latn',
    'Wolof': 'wol_Latn',
    'Xhosa': 'xho_Latn',
    'Eastern Yiddish': 'ydd_Hebr',
    'Yoruba': 'yor_Latn',
    'Yue Chinese': 'yue_Hant',
    'Chinese Simplified': 'zho_Hans',
    'Chinese Traditional': 'zho_Hant',
    'Standard Malay': 'zsm_Latn',
    'Zulu': 'zul_Latn',
}

class 		SCREAMING_SNAKE_CASE__				(       _UpperCAmelCase  ):
      A_						:				List[Any]							=    'facebook/nllb-200-distilled-600M'
      A_						:				Dict							=    (
          'This is a tool that translates text from a language to another. It takes three inputs: `text`, which should '
          'be the text to translate, `src_lang`, which should be the language of the text to translate and `tgt_lang`, '
          'which should be the language for the desired ouput language. Both `src_lang` and `tgt_lang` are written in '
          'plain English, such as \'Romanian\', or \'Albanian\'. It returns the text translated in `tgt_lang`.'
      )
      A_						:				List[Any]							=    'translator'
      A_						:				List[Any]							=    AutoTokenizer
      A_						:				Union[str, Any]							=    AutoModelForSeqaSeqLM
      A_						:				Any							=    LANGUAGE_CODES

      A_						:				Union[str, Any]							=    ['text', 'text', 'text']
      A_						:				int							=    ['text']


      def    a (self       :      Optional[Any]				,			a__       :      int				,			a__       :      Tuple				,			a__       :      List[str]      ):





         """simple docstring"""



         if src_lang not in self.lang_to_code:
            raise ValueError(f"""{src_lang} is not a supported language."""      )
         if tgt_lang not in self.lang_to_code:
            raise ValueError(f"""{tgt_lang} is not a supported language."""      )
         __snake_case							       =   self.lang_to_code[src_lang]
         __snake_case							       =   self.lang_to_code[tgt_lang]
         return self.pre_processor._build_translation_inputs(
             a__				,			return_tensors='''pt'''				,			src_lang=a__				,			tgt_lang=a__      )


      def    a (self       :      Any				,			a__       :      List[str]      ):





         """simple docstring"""



         return self.model.generate(**a__      )


      def    a (self       :      str				,			a__       :      Optional[Any]      ):





         """simple docstring"""



         return self.post_processor.decode(outputs[0].tolist()				,			skip_special_tokens=a__      )








import inspect
import unittest

from transformers import ConvNextConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
    import torch

    from transformers import ConvNextBackbone, ConvNextForImageClassification, ConvNextModel
    from transformers.models.convnext.modeling_convnext import CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
    from PIL import Image

    from transformers import AutoImageProcessor

class 		SCREAMING_SNAKE_CASE__				:


      def __init__(self       :      Any				,			a__       :      List[Any]				,			a__       :      Dict=13				,			a__       :      str=32				,			a__       :      Tuple=3				,			a__       :      Optional[Any]=4				,			a__       :      Optional[int]=[10, 20, 30, 40]				,			a__       :      List[Any]=[2, 2, 3, 2]				,			a__       :      List[Any]=True				,			a__       :      int=True				,			a__       :      List[Any]=37				,			a__       :      Any="gelu"				,			a__       :      int=10				,			a__       :      Dict=0.0_2				,			a__       :      Dict=["stage2", "stage3", "stage4"]				,			a__       :      Tuple=[2, 3, 4]				,			a__       :      List[str]=None				,			):





         """simple docstring"""



         __snake_case							       =   parent
         __snake_case							       =   batch_size
         __snake_case							       =   image_size
         __snake_case							       =   num_channels
         __snake_case							       =   num_stages
         __snake_case							       =   hidden_sizes
         __snake_case							       =   depths
         __snake_case							       =   is_training
         __snake_case							       =   use_labels
         __snake_case							       =   intermediate_size
         __snake_case							       =   hidden_act
         __snake_case							       =   num_labels
         __snake_case							       =   initializer_range
         __snake_case							       =   out_features
         __snake_case							       =   out_indices
         __snake_case							       =   scope


      def    a (self       :      Dict      ):





         """simple docstring"""



         __snake_case							       =   floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]      )

         __snake_case							       =   None
         if self.use_labels:
            __snake_case							       =   ids_tensor([self.batch_size]				,			self.num_labels      )

         __snake_case							       =   self.get_config()
         return config, pixel_values, labels


      def    a (self       :      List[str]      ):





         """simple docstring"""



         return ConvNextConfig(
             num_channels=self.num_channels				,			hidden_sizes=self.hidden_sizes				,			depths=self.depths				,			num_stages=self.num_stages				,			hidden_act=self.hidden_act				,			is_decoder=a__				,			initializer_range=self.initializer_range				,			out_features=self.out_features				,			out_indices=self.out_indices				,			num_labels=self.num_labels				,			)


      def    a (self       :      str				,			a__       :      Union[str, Any]				,			a__       :      List[str]				,			a__       :      List[Any]      ):





         """simple docstring"""



         __snake_case							       =   ConvNextModel(config=a__      )
         model.to(a__      )
         model.eval()
         __snake_case							       =   model(a__      )
         # expected last hidden states: B, C, H // 32, W // 32
         self.parent.assertEqual(
             result.last_hidden_state.shape				,			(self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)				,			)


      def    a (self       :      Optional[Any]				,			a__       :      List[Any]				,			a__       :      str				,			a__       :      List[Any]      ):





         """simple docstring"""



         __snake_case							       =   ConvNextForImageClassification(a__      )
         model.to(a__      )
         model.eval()
         __snake_case							       =   model(a__				,			labels=a__      )
         self.parent.assertEqual(result.logits.shape				,			(self.batch_size, self.num_labels)      )


      def    a (self       :      Tuple				,			a__       :      List[Any]				,			a__       :      List[str]				,			a__       :      List[str]      ):





         """simple docstring"""



         __snake_case							       =   ConvNextBackbone(config=a__      )
         model.to(a__      )
         model.eval()
         __snake_case							       =   model(a__      )

         # verify hidden states
         self.parent.assertEqual(len(result.feature_maps      )				,			len(config.out_features      )      )
         self.parent.assertListEqual(list(result.feature_maps[0].shape      )				,			[self.batch_size, self.hidden_sizes[1], 4, 4]      )

         # verify channels
         self.parent.assertEqual(len(model.channels      )				,			len(config.out_features      )      )
         self.parent.assertListEqual(model.channels				,			config.hidden_sizes[1:]      )

         # verify backbone works with out_features=None
         __snake_case							       =   None
         __snake_case							       =   ConvNextBackbone(config=a__      )
         model.to(a__      )
         model.eval()
         __snake_case							       =   model(a__      )

         # verify feature maps
         self.parent.assertEqual(len(result.feature_maps      )				,			1      )
         self.parent.assertListEqual(list(result.feature_maps[0].shape      )				,			[self.batch_size, self.hidden_sizes[-1], 1, 1]      )

         # verify channels
         self.parent.assertEqual(len(model.channels      )				,			1      )
         self.parent.assertListEqual(model.channels				,			[config.hidden_sizes[-1]]      )


      def    a (self       :      Tuple      ):





         """simple docstring"""



         __snake_case							       =   self.prepare_config_and_inputs()
         __snake_case       ,	__snake_case       ,	__snake_case							       =   config_and_inputs
         __snake_case							       =   {'''pixel_values''': pixel_values}
         return config, inputs_dict






@require_torch
class 		SCREAMING_SNAKE_CASE__				(       _UpperCAmelCase						,      _UpperCAmelCase						,      unittest.TestCase  ):
      A_						:				Dict							=    (
          (
              ConvNextModel,
              ConvNextForImageClassification,
              ConvNextBackbone,
          )
          if is_torch_available()
          else ()
      )
      A_						:				Optional[Any]							=    (
          {'feature-extraction': ConvNextModel, 'image-classification': ConvNextForImageClassification}
          if is_torch_available()
          else {}
      )

      A_						:				Dict							=    True
      A_						:				Optional[Any]							=    False
      A_						:				int							=    False
      A_						:				int							=    False
      A_						:				List[str]							=    False


      def    a (self       :      List[str]      ):





         """simple docstring"""



         __snake_case							       =   ConvNextModelTester(self      )
         __snake_case							       =   ConfigTester(self				,			config_class=a__				,			has_text_modality=a__				,			hidden_size=37      )


      def    a (self       :      Tuple      ):





         """simple docstring"""



         self.create_and_test_config_common_properties()
         self.config_tester.create_and_test_config_to_json_string()
         self.config_tester.create_and_test_config_to_json_file()
         self.config_tester.create_and_test_config_from_and_save_pretrained()
         self.config_tester.create_and_test_config_with_num_labels()
         self.config_tester.check_config_can_be_init_without_params()
         self.config_tester.check_config_arguments_init()


      def    a (self       :      str      ):





         """simple docstring"""



         return


      @unittest.skip(reason='''ConvNext does not use inputs_embeds'''      )
      def    a (self       :      int      ):





         """simple docstring"""



         pass


      @unittest.skip(reason='''ConvNext does not support input and output embeddings'''      )
      def    a (self       :      Dict      ):





         """simple docstring"""



         pass


      @unittest.skip(reason='''ConvNext does not use feedforward chunking'''      )
      def    a (self       :      List[Any]      ):





         """simple docstring"""



         pass


      def    a (self       :      Optional[Any]      ):





         """simple docstring"""



         __snake_case       ,	__snake_case							       =   self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
            __snake_case							       =   model_class(a__      )
            __snake_case							       =   inspect.signature(model.forward      )
            # signature.parameters is an OrderedDict => so arg_names order is deterministic
            __snake_case							       =   [*signature.parameters.keys()]

            __snake_case							       =   ['''pixel_values''']
            self.assertListEqual(arg_names[:1]				,			a__      )


      def    a (self       :      List[Any]      ):





         """simple docstring"""



         __snake_case							       =   self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*a__      )


      def    a (self       :      Dict      ):





         """simple docstring"""



         __snake_case							       =   self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_backbone(*a__      )


      def    a (self       :      Dict      ):





         """simple docstring"""



         def check_hidden_states_output(a__       :      List[str]				,			a__       :      str				,			a__       :      Tuple      ):
            __snake_case							       =   model_class(a__      )
            model.to(a__      )
            model.eval()

            with torch.no_grad():
               __snake_case							       =   model(**self._prepare_for_class(a__				,			a__      )      )

            __snake_case							       =   outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

            __snake_case							       =   self.model_tester.num_stages
            self.assertEqual(len(a__      )				,			expected_num_stages + 1      )

            # ConvNext's feature maps are of shape (batch_size, num_channels, height, width)
            self.assertListEqual(
                list(hidden_states[0].shape[-2:]      )				,			[self.model_tester.image_size // 4, self.model_tester.image_size // 4]				,			)

         __snake_case       ,	__snake_case							       =   self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
            __snake_case							       =   True
            check_hidden_states_output(a__				,			a__				,			a__      )

            # check that output_hidden_states also work using config
            del inputs_dict["output_hidden_states"]
            __snake_case							       =   True

            check_hidden_states_output(a__				,			a__				,			a__      )


      def    a (self       :      Optional[Any]      ):





         """simple docstring"""



         __snake_case							       =   self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_image_classification(*a__      )


      @slow
      def    a (self       :      Any      ):





         """simple docstring"""



         for model_name in CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
            __snake_case							       =   ConvNextModel.from_pretrained(a__      )
            self.assertIsNotNone(a__      )






def lowerCamelCase__    (							)      ->				List[str]:
   __snake_case							       =   Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''       )
   return image

@require_torch
@require_vision
class 		SCREAMING_SNAKE_CASE__				(       unittest.TestCase  ):


      @cached_property
      def    a (self       :      Tuple      ):





         """simple docstring"""



         return AutoImageProcessor.from_pretrained('''facebook/convnext-tiny-224'''      ) if is_vision_available() else None


      @slow
      def    a (self       :      Optional[Any]      ):





         """simple docstring"""



         __snake_case							       =   ConvNextForImageClassification.from_pretrained('''facebook/convnext-tiny-224'''      ).to(a__      )

         __snake_case							       =   self.default_image_processor
         __snake_case							       =   prepare_img()
         __snake_case							       =   image_processor(images=a__				,			return_tensors='''pt'''      ).to(a__      )

         # forward pass
         with torch.no_grad():
            __snake_case							       =   model(**a__      )

         # verify the logits
         __snake_case							       =   torch.Size((1, 1000)      )
         self.assertEqual(outputs.logits.shape				,			a__      )

         __snake_case							       =   torch.tensor([-0.0_2_6_0, -0.4_7_3_9, 0.1_9_1_1]      ).to(a__      )

         self.assertTrue(torch.allclose(outputs.logits[0, :3]				,			a__				,			atol=1E-4      )      )






@require_torch
class 		SCREAMING_SNAKE_CASE__				(       unittest.TestCase						,      _UpperCAmelCase  ):
      A_						:				Union[str, Any]							=    (ConvNextBackbone,) if is_torch_available() else ()
      A_						:				List[Any]							=    ConvNextConfig

      A_						:				Optional[Any]							=    False


      def    a (self       :      Optional[int]      ):





         """simple docstring"""



         __snake_case							       =   ConvNextModelTester(self      )

