

import unittest

from transformers import TrOCRConfig
from transformers.testing_utils import is_torch_available, require_torch, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
					import torch

					from transformers.models.trocr.modeling_trocr import TrOCRDecoder, TrOCRForCausalLM

@require_torch
class 			a					:


						def __init__(    self  ,   A_  ,   A_=99  ,   A_=13  ,   A_=16  ,   A_=7  ,   A_=True  ,   A_=True  ,   A_=True  ,   A_=False  ,   A_=True  ,   A_=2  ,   A_=32  ,   A_=4  ,   A_=4  ,   A_=30  ,   A_=0  ,   A_=1  ,   A_=2  ,   A_=None  ,   ):







										'''simple docstring'''


										_UpperCAmelCase    :						Tuple							=      parent
										_UpperCAmelCase    :						Dict							=      batch_size
										_UpperCAmelCase    :						Optional[Any]							=      decoder_seq_length
										# For common tests
										_UpperCAmelCase    :						List[str]							=      self.decoder_seq_length
										_UpperCAmelCase    :						Union[str, Any]							=      is_training
										_UpperCAmelCase    :						int							=      use_attention_mask
										_UpperCAmelCase    :						Dict							=      use_labels

										_UpperCAmelCase    :						Union[str, Any]							=      vocab_size
										_UpperCAmelCase    :						str							=      d_model
										_UpperCAmelCase    :						Tuple							=      d_model
										_UpperCAmelCase    :						Tuple							=      decoder_layers
										_UpperCAmelCase    :						int							=      decoder_layers
										_UpperCAmelCase    :						Optional[Any]							=      decoder_ffn_dim
										_UpperCAmelCase    :						str							=      decoder_attention_heads
										_UpperCAmelCase    :						Optional[int]							=      decoder_attention_heads
										_UpperCAmelCase    :						Any							=      eos_token_id
										_UpperCAmelCase    :						Tuple							=      bos_token_id
										_UpperCAmelCase    :						List[Any]							=      pad_token_id
										_UpperCAmelCase    :						Optional[Any]							=      decoder_start_token_id
										_UpperCAmelCase    :						List[str]							=      use_cache
										_UpperCAmelCase    :						int							=      max_position_embeddings

										_UpperCAmelCase    :						int							=      None
										_UpperCAmelCase    :						Optional[Any]							=      decoder_seq_length
										_UpperCAmelCase    :						Union[str, Any]							=      2
										_UpperCAmelCase    :						List[Any]							=      1


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						Optional[Any]							=      ids_tensor([self.batch_size, self.decoder_seq_length]  ,   self.vocab_size		)

										_UpperCAmelCase    :						List[Any]							=      None
										if self.use_attention_mask:
														_UpperCAmelCase    :						Optional[Any]							=      ids_tensor([self.batch_size, self.decoder_seq_length]  ,   vocab_size=2		)

										_UpperCAmelCase    :						List[str]							=      None
										if self.use_labels:
														_UpperCAmelCase    :						List[str]							=      ids_tensor([self.batch_size, self.decoder_seq_length]  ,   self.vocab_size		)

										_UpperCAmelCase    :						Optional[int]							=      TrOCRConfig(
										    vocab_size=self.vocab_size  ,   d_model=self.d_model  ,   decoder_layers=self.decoder_layers  ,   decoder_ffn_dim=self.decoder_ffn_dim  ,   decoder_attention_heads=self.decoder_attention_heads  ,   eos_token_id=self.eos_token_id  ,   bos_token_id=self.bos_token_id  ,   use_cache=self.use_cache  ,   pad_token_id=self.pad_token_id  ,   decoder_start_token_id=self.decoder_start_token_id  ,   max_position_embeddings=self.max_position_embeddings  ,   )

										return (config, input_ids, attention_mask, lm_labels)


						def     _UpperCAmelCase			(    self  ,   A_  ,   A_  ,   A_  ,   A_  ,   ):







										'''simple docstring'''


										_UpperCAmelCase    :						Any							=      True
										_UpperCAmelCase    :						int							=      TrOCRDecoder(config=A_		).to(A_		).eval()
										_UpperCAmelCase    :						List[Any]							=      input_ids[:2]

										input_ids[input_ids == 0] += 1
										# first forward pass
										_UpperCAmelCase    :						List[str]							=      model(A_  ,   use_cache=A_		)
										_UpperCAmelCase    :						Union[str, Any]							=      model(A_		)
										_UpperCAmelCase    :						Optional[Any]							=      model(A_  ,   use_cache=A_		)

										self.parent.assertTrue(len(A_		) == len(A_		)		)
										self.parent.assertTrue(len(A_		) == len(A_		) + 1		)

										_UpperCAmelCase    :						List[Any]							=      outputs["past_key_values"]

										# create hypothetical next token and extent to next_input_ids
										_UpperCAmelCase    :						Any							=      ids_tensor((2, 1)  ,   config.vocab_size - 1		) + 1

										# append to next input_ids and
										_UpperCAmelCase    :						List[Any]							=      torch.cat([input_ids, next_tokens]  ,   dim=-1		)

										_UpperCAmelCase    :						int							=      model(A_		)["last_hidden_state"]
										_UpperCAmelCase    :						Any							=      model(A_  ,   past_key_values=A_		)["last_hidden_state"]

										# select random slice
										_UpperCAmelCase    :						Tuple							=      ids_tensor((1,)  ,   output_from_past.shape[-1]		).item()
										_UpperCAmelCase    :						Tuple							=      output_from_no_past[:, next_input_ids.shape[-1] - 1, random_slice_idx].detach()
										_UpperCAmelCase    :						Tuple							=      output_from_past[:, 0, random_slice_idx].detach()

										# test that outputs are equal for slice
										assert torch.allclose(A_  ,   A_  ,   atol=1e-3		)





						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						Optional[Any]							=      self.prepare_config_and_inputs()
										_UpperCAmelCase						,							_UpperCAmelCase						,							_UpperCAmelCase						,							_UpperCAmelCase    :						Optional[int]							=      config_and_inputs

										_UpperCAmelCase    :						Dict							=      {"input_ids": input_ids, "attention_mask": attention_mask}
										return config, inputs_dict







@require_torch
class 			a					(		UpperCAmelCase							,	UpperCAmelCase							,	UpperCAmelCase							,	unittest.TestCase			):
						_lowercase        =     (TrOCRDecoder, TrOCRForCausalLM) if is_torch_available() else ()
						_lowercase        =     (TrOCRForCausalLM,) if is_torch_available() else ()
						_lowercase        =     {"text-generation": TrOCRForCausalLM} if is_torch_available() else {}
						_lowercase        =     True
						_lowercase        =     False


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						Optional[int]							=      TrOCRStandaloneDecoderModelTester(self  ,   is_training=A_		)
										_UpperCAmelCase    :						int							=      ConfigTester(self  ,   config_class=A_		)


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										pass


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										pass


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										pass


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										self.config_tester.run_common_tests()


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						Any							=      self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_decoder_model_past(*A_		)


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										return





						@unittest.skip("The model doesn't support left padding"		)  # and it's not used enough to be worth fixing :)
						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										pass





# 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


SCREAMING_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 			a					(		UpperCAmelCase			):
						_lowercase        =     "facebook/nllb-200-distilled-600M"
						_lowercase        =     (
						    "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`."
						)
						_lowercase        =     "translator"
						_lowercase        =     AutoTokenizer
						_lowercase        =     AutoModelForSeqaSeqLM
						_lowercase        =     LANGUAGE_CODES

						_lowercase        =     ["text", "text", "text"]
						_lowercase        =     ["text"]


						def     _UpperCAmelCase			(    self  ,   A_  ,   A_  ,   A_		):







										'''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.'		)
										_UpperCAmelCase    :						int							=      self.lang_to_code[src_lang]
										_UpperCAmelCase    :						int							=      self.lang_to_code[tgt_lang]
										return self.pre_processor._build_translation_inputs(
										    A_  ,   return_tensors="pt"  ,   src_lang=A_  ,   tgt_lang=A_		)


						def     _UpperCAmelCase			(    self  ,   A_		):







										'''simple docstring'''


										return self.model.generate(**A_		)





						def     _UpperCAmelCase			(    self  ,   A_		):







										'''simple docstring'''


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


