





import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, BatchEncoding, PreTrainedTokenizer
from ...utils import logging


SCREAMING_SNAKE_CASE_						       =	logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_						       =	"""▁"""

SCREAMING_SNAKE_CASE_						       =	{"""vocab_file""": """sentencepiece.bpe.model"""}

SCREAMING_SNAKE_CASE_						       =	{
    """vocab_file""": {
        """facebook/nllb-200-distilled-600M""": (
            """https://huggingface.co/facebook/nllb-200-distilled-600M/blob/main/sentencepiece.bpe.model"""
        ),
    }
}

SCREAMING_SNAKE_CASE_						       =	{
    """facebook/nllb-200-distilled-600M""": 1_0_2_4,
}

# fmt: off
SCREAMING_SNAKE_CASE_						       =	["""ace_Arab""", """ace_Latn""", """acm_Arab""", """acq_Arab""", """aeb_Arab""", """afr_Latn""", """ajp_Arab""", """aka_Latn""", """amh_Ethi""", """apc_Arab""", """arb_Arab""", """ars_Arab""", """ary_Arab""", """arz_Arab""", """asm_Beng""", """ast_Latn""", """awa_Deva""", """ayr_Latn""", """azb_Arab""", """azj_Latn""", """bak_Cyrl""", """bam_Latn""", """ban_Latn""", """bel_Cyrl""", """bem_Latn""", """ben_Beng""", """bho_Deva""", """bjn_Arab""", """bjn_Latn""", """bod_Tibt""", """bos_Latn""", """bug_Latn""", """bul_Cyrl""", """cat_Latn""", """ceb_Latn""", """ces_Latn""", """cjk_Latn""", """ckb_Arab""", """crh_Latn""", """cym_Latn""", """dan_Latn""", """deu_Latn""", """dik_Latn""", """dyu_Latn""", """dzo_Tibt""", """ell_Grek""", """eng_Latn""", """epo_Latn""", """est_Latn""", """eus_Latn""", """ewe_Latn""", """fao_Latn""", """pes_Arab""", """fij_Latn""", """fin_Latn""", """fon_Latn""", """fra_Latn""", """fur_Latn""", """fuv_Latn""", """gla_Latn""", """gle_Latn""", """glg_Latn""", """grn_Latn""", """guj_Gujr""", """hat_Latn""", """hau_Latn""", """heb_Hebr""", """hin_Deva""", """hne_Deva""", """hrv_Latn""", """hun_Latn""", """hye_Armn""", """ibo_Latn""", """ilo_Latn""", """ind_Latn""", """isl_Latn""", """ita_Latn""", """jav_Latn""", """jpn_Jpan""", """kab_Latn""", """kac_Latn""", """kam_Latn""", """kan_Knda""", """kas_Arab""", """kas_Deva""", """kat_Geor""", """knc_Arab""", """knc_Latn""", """kaz_Cyrl""", """kbp_Latn""", """kea_Latn""", """khm_Khmr""", """kik_Latn""", """kin_Latn""", """kir_Cyrl""", """kmb_Latn""", """kon_Latn""", """kor_Hang""", """kmr_Latn""", """lao_Laoo""", """lvs_Latn""", """lij_Latn""", """lim_Latn""", """lin_Latn""", """lit_Latn""", """lmo_Latn""", """ltg_Latn""", """ltz_Latn""", """lua_Latn""", """lug_Latn""", """luo_Latn""", """lus_Latn""", """mag_Deva""", """mai_Deva""", """mal_Mlym""", """mar_Deva""", """min_Latn""", """mkd_Cyrl""", """plt_Latn""", """mlt_Latn""", """mni_Beng""", """khk_Cyrl""", """mos_Latn""", """mri_Latn""", """zsm_Latn""", """mya_Mymr""", """nld_Latn""", """nno_Latn""", """nob_Latn""", """npi_Deva""", """nso_Latn""", """nus_Latn""", """nya_Latn""", """oci_Latn""", """gaz_Latn""", """ory_Orya""", """pag_Latn""", """pan_Guru""", """pap_Latn""", """pol_Latn""", """por_Latn""", """prs_Arab""", """pbt_Arab""", """quy_Latn""", """ron_Latn""", """run_Latn""", """rus_Cyrl""", """sag_Latn""", """san_Deva""", """sat_Beng""", """scn_Latn""", """shn_Mymr""", """sin_Sinh""", """slk_Latn""", """slv_Latn""", """smo_Latn""", """sna_Latn""", """snd_Arab""", """som_Latn""", """sot_Latn""", """spa_Latn""", """als_Latn""", """srd_Latn""", """srp_Cyrl""", """ssw_Latn""", """sun_Latn""", """swe_Latn""", """swh_Latn""", """szl_Latn""", """tam_Taml""", """tat_Cyrl""", """tel_Telu""", """tgk_Cyrl""", """tgl_Latn""", """tha_Thai""", """tir_Ethi""", """taq_Latn""", """taq_Tfng""", """tpi_Latn""", """tsn_Latn""", """tso_Latn""", """tuk_Latn""", """tum_Latn""", """tur_Latn""", """twi_Latn""", """tzm_Tfng""", """uig_Arab""", """ukr_Cyrl""", """umb_Latn""", """urd_Arab""", """uzn_Latn""", """vec_Latn""", """vie_Latn""", """war_Latn""", """wol_Latn""", """xho_Latn""", """ydd_Hebr""", """yor_Latn""", """yue_Hant""", """zho_Hans""", """zho_Hant""", """zul_Latn"""]

class 		UpperCamelCase__			(				lowerCAmelCase_       ):



     '''simple docstring'''

     __snake_case   :     Dict		     =      VOCAB_FILES_NAMES
     __snake_case   :     Any		     =      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     __snake_case   :     Any		     =      PRETRAINED_VOCAB_FILES_MAP
     __snake_case   :     str		     =      ["input_ids", "attention_mask"]

     __snake_case   :     List[int]		     =      []
     __snake_case   :     List[int]		     =      []






     def __init__(							self						:   Tuple						,lowerCamelCase__						:   Any						,lowerCamelCase__						:   str="<s>"						,lowerCamelCase__						:   Union[str, Any]="</s>"						,lowerCamelCase__						:   int="</s>"						,lowerCamelCase__						:   Any="<s>"						,lowerCamelCase__						:   str="<unk>"						,lowerCamelCase__						:   Optional[Any]="<pad>"						,lowerCamelCase__						:   List[Any]="<mask>"						,lowerCamelCase__						:   Optional[Any]=None						,lowerCamelCase__						:   int=None						,lowerCamelCase__						:   List[str]=None						,lowerCamelCase__						:   Optional[Dict[str, Any]] = None						,lowerCamelCase__						:   List[str]=None						,lowerCamelCase__						:   Dict=False						,**lowerCamelCase__						:   Union[str, Any]						,)					->		Optional[int]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						AddedToken(lowerCamelCase__						,lstrip=lowerCamelCase__						,rstrip=lowerCamelCase__	) if isinstance(lowerCamelCase__						,lowerCamelCase__	) else mask_token

           SCREAMING_SNAKE_CASE		=						{} if sp_model_kwargs is None else sp_model_kwargs
           SCREAMING_SNAKE_CASE		=						legacy_behaviour

           super().__init__(
               bos_token=lowerCamelCase__						,eos_token=lowerCamelCase__						,unk_token=lowerCamelCase__						,sep_token=lowerCamelCase__						,cls_token=lowerCamelCase__						,pad_token=lowerCamelCase__						,mask_token=lowerCamelCase__						,tokenizer_file=lowerCamelCase__						,src_lang=lowerCamelCase__						,tgt_lang=lowerCamelCase__						,additional_special_tokens=lowerCamelCase__						,sp_model_kwargs=self.sp_model_kwargs						,legacy_behaviour=lowerCamelCase__						,**lowerCamelCase__						,)

           SCREAMING_SNAKE_CASE		=						spm.SentencePieceProcessor(**self.sp_model_kwargs	)
           self.sp_model.Load(str(lowerCamelCase__	)	)
           SCREAMING_SNAKE_CASE		=						vocab_file
           # Original fairseq vocab and spm vocab must be "aligned":
           # Vocab    |    0    |    1    |   2    |    3    |  4   |  5   |  6   |   7  |   8  |  9
           # -------- | ------- | ------- | ------ | ------- | ---- | ---- | ---- | ---- | ---- | ----
           # fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | 'an' | '▁n' | '▁m' | '▁t' | '▁k' | '▁a'
           # spm      | '<unk>' | '<s>'   | '</s>' | 'an'    | '▁n' | '▁m' | '▁t' | '▁k' | '▁a' | '▁s'

           # Mimic fairseq token-to-id alignment for the first 4 token
           SCREAMING_SNAKE_CASE		=						{"""<s>""": 0, """<pad>""": 1, """</s>""": 2, """<unk>""": 3}

           # The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
           SCREAMING_SNAKE_CASE		=						1

           SCREAMING_SNAKE_CASE		=						len(self.sp_model	)
           SCREAMING_SNAKE_CASE		=						{
               code: self.sp_model_size + i + self.fairseq_offset for i, code in enumerate(lowerCamelCase__	)
           }
           SCREAMING_SNAKE_CASE		=						{v: k for k, v in self.lang_code_to_id.items()}
           SCREAMING_SNAKE_CASE		=						len(self.sp_model	) + len(self.lang_code_to_id	) + self.fairseq_offset

           self.fairseq_tokens_to_ids.update(self.lang_code_to_id	)
           SCREAMING_SNAKE_CASE		=						{v: k for k, v in self.fairseq_tokens_to_ids.items()}
           SCREAMING_SNAKE_CASE		=						list(self.lang_code_to_id.keys()	)

           if additional_special_tokens is not None:
                 # Only add those special tokens if they are not already there.
                 self._additional_special_tokens.extend(
                     [t for t in additional_special_tokens if t not in self._additional_special_tokens]	)

           SCREAMING_SNAKE_CASE		=						src_lang if src_lang is not None else """eng_Latn"""
           SCREAMING_SNAKE_CASE		=						self.lang_code_to_id[self._src_lang]
           SCREAMING_SNAKE_CASE		=						tgt_lang
           self.set_src_lang_special_tokens(self._src_lang	)






     def __getstate__(							self						:   str	)					->		List[str]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						self.__dict__.copy()
           SCREAMING_SNAKE_CASE		=						None
           SCREAMING_SNAKE_CASE		=						self.sp_model.serialized_model_proto()
           return state






     def __setstate__(							self						:   List[str]						,lowerCamelCase__						:   Any	)					->		str:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						d

           # for backward compatibility
           if not hasattr(self						,"""sp_model_kwargs"""	):
                 SCREAMING_SNAKE_CASE		=						{}

           SCREAMING_SNAKE_CASE		=						spm.SentencePieceProcessor(**self.sp_model_kwargs	)
           self.sp_model.LoadFromSerializedProto(self.sp_model_proto	)






     @property
     def     SCREAMING_SNAKE_CASE__  (							self						:   Dict	)					->		List[Any]:




           '''simple docstring'''


           return len(self.sp_model	) + len(self.lang_code_to_id	) + self.fairseq_offset + 1  # Plus 1 for the mask token






     @property
     def     SCREAMING_SNAKE_CASE__  (							self						:   Optional[int]	)					->		str:




           '''simple docstring'''


           return self._src_lang






     @src_lang.setter
     def     SCREAMING_SNAKE_CASE__  (							self						:   str						,lowerCamelCase__						:   str	)					->		None:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						new_src_lang
           self.set_src_lang_special_tokens(self._src_lang	)






     def     SCREAMING_SNAKE_CASE__  (							self						:   Dict						,lowerCamelCase__						:   List[int]						,lowerCamelCase__						:   Optional[List[int]] = None						,lowerCamelCase__						:   bool = False	)					->		List[int]:




           '''simple docstring'''


           if already_has_special_tokens:
                 return super().get_special_tokens_mask(
                     token_ids_a=lowerCamelCase__						,token_ids_a=lowerCamelCase__						,already_has_special_tokens=lowerCamelCase__	)

           SCREAMING_SNAKE_CASE		=						[1] * len(self.prefix_tokens	)
           SCREAMING_SNAKE_CASE		=						[1] * len(self.suffix_tokens	)
           if token_ids_a is None:
                 return prefix_ones + ([0] * len(lowerCamelCase__	)) + suffix_ones
           return prefix_ones + ([0] * len(lowerCamelCase__	)) + ([0] * len(lowerCamelCase__	)) + suffix_ones






     def     SCREAMING_SNAKE_CASE__  (							self						:   Optional[Any]						,lowerCamelCase__						:   List[int]						,lowerCamelCase__						:   Optional[List[int]] = None	)					->		List[int]:




           '''simple docstring'''


           if token_ids_a is None:
                 return self.prefix_tokens + token_ids_a + self.suffix_tokens
           # We don't expect to process pairs, but leave the pair logic for API consistency
           return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens






     def     SCREAMING_SNAKE_CASE__  (							self						:   int						,lowerCamelCase__						:   List[int]						,lowerCamelCase__						:   Optional[List[int]] = None	)					->		List[int]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						[self.sep_token_id]
           SCREAMING_SNAKE_CASE		=						[self.cls_token_id]

           if token_ids_a is None:
                 return len(cls + token_ids_a + sep	) * [0]
           return len(cls + token_ids_a + sep + sep + token_ids_a + sep	) * [0]






     def     SCREAMING_SNAKE_CASE__  (							self						:   List[Any]						,lowerCamelCase__						:   Any						,lowerCamelCase__						:   str						,lowerCamelCase__						:   Optional[str]						,lowerCamelCase__						:   Optional[str]						,**lowerCamelCase__						:   List[str]	)					->		int:




           '''simple docstring'''


           if src_lang is None or tgt_lang is None:
                 raise ValueError("""Translation requires a `src_lang` and a `tgt_lang` for this model"""	)
           SCREAMING_SNAKE_CASE		=						src_lang
           SCREAMING_SNAKE_CASE		=						self(lowerCamelCase__						,add_special_tokens=lowerCamelCase__						,return_tensors=lowerCamelCase__						,**lowerCamelCase__	)
           SCREAMING_SNAKE_CASE		=						self.convert_tokens_to_ids(lowerCamelCase__	)
           SCREAMING_SNAKE_CASE		=						tgt_lang_id
           return inputs






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




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						{self.convert_ids_to_tokens(lowerCamelCase__	): i for i in range(self.vocab_size	)}
           vocab.update(self.added_tokens_encoder	)
           return vocab






     def     SCREAMING_SNAKE_CASE__  (							self						:   Any						,lowerCamelCase__						:   str	)					->		List[str]:




           '''simple docstring'''


           return self.sp_model.encode(lowerCamelCase__						,out_type=lowerCamelCase__	)






     def     SCREAMING_SNAKE_CASE__  (							self						:   Tuple						,lowerCamelCase__						:   Dict	)					->		List[Any]:




           '''simple docstring'''


           if token in self.fairseq_tokens_to_ids:
                 return self.fairseq_tokens_to_ids[token]
           SCREAMING_SNAKE_CASE		=						self.sp_model.PieceToId(lowerCamelCase__	)

           # Need to return unknown token if the SP model returned 0
           return spm_id + self.fairseq_offset if spm_id else self.unk_token_id






     def     SCREAMING_SNAKE_CASE__  (							self						:   Tuple						,lowerCamelCase__						:   int	)					->		Tuple:




           '''simple docstring'''


           if index in self.fairseq_ids_to_tokens:
                 return self.fairseq_ids_to_tokens[index]
           return self.sp_model.IdToPiece(index - self.fairseq_offset	)






     def     SCREAMING_SNAKE_CASE__  (							self						:   List[Any]						,lowerCamelCase__						:   Tuple	)					->		Optional[Any]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						"""""".join(lowerCamelCase__	).replace(lowerCamelCase__						,""" """	).strip()
           return out_string






     def     SCREAMING_SNAKE_CASE__  (							self						:   List[Any]						,lowerCamelCase__						:   str						,lowerCamelCase__						:   Optional[str] = None	)					->		Tuple[str]:




           '''simple docstring'''


           if not os.path.isdir(lowerCamelCase__	):
                 logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""	)
                 return
           SCREAMING_SNAKE_CASE		=						os.path.join(
               lowerCamelCase__						,(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]	)

           if os.path.abspath(self.vocab_file	) != os.path.abspath(lowerCamelCase__	) and os.path.isfile(self.vocab_file	):
                 copyfile(self.vocab_file						,lowerCamelCase__	)
           elif not os.path.isfile(self.vocab_file	):
                 with open(lowerCamelCase__						,"""wb"""	) as fi:
                       SCREAMING_SNAKE_CASE		=						self.sp_model.serialized_model_proto()
                       fi.write(lowerCamelCase__	)

           return (out_vocab_file,)






     def     SCREAMING_SNAKE_CASE__  (							self						:   Any						,lowerCamelCase__						:   List[str]						,lowerCamelCase__						:   str = "eng_Latn"						,lowerCamelCase__						:   Optional[List[str]] = None						,lowerCamelCase__						:   str = "fra_Latn"						,**lowerCamelCase__						:   List[str]						,)					->		BatchEncoding:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						src_lang
           SCREAMING_SNAKE_CASE		=						tgt_lang
           return super().prepare_seqaseq_batch(lowerCamelCase__						,lowerCamelCase__						,**lowerCamelCase__	)






     def     SCREAMING_SNAKE_CASE__  (							self						:   Tuple	)					->		Union[str, Any]:




           '''simple docstring'''


           return self.set_src_lang_special_tokens(self.src_lang	)






     def     SCREAMING_SNAKE_CASE__  (							self						:   List[Any]	)					->		Optional[Any]:




           '''simple docstring'''


           return self.set_tgt_lang_special_tokens(self.tgt_lang	)






     def     SCREAMING_SNAKE_CASE__  (							self						:   List[Any]						,lowerCamelCase__						:   Any	)					->		None:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						self.lang_code_to_id[src_lang]
           if self.legacy_behaviour:
                 SCREAMING_SNAKE_CASE		=						[]
                 SCREAMING_SNAKE_CASE		=						[self.eos_token_id, self.cur_lang_code]
           else:
                 SCREAMING_SNAKE_CASE		=						[self.cur_lang_code]
                 SCREAMING_SNAKE_CASE		=						[self.eos_token_id]






     def     SCREAMING_SNAKE_CASE__  (							self						:   Tuple						,lowerCamelCase__						:   str	)					->		None:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						self.lang_code_to_id[lang]
           if self.legacy_behaviour:
                 SCREAMING_SNAKE_CASE		=						[]
                 SCREAMING_SNAKE_CASE		=						[self.eos_token_id, self.cur_lang_code]
           else:
                 SCREAMING_SNAKE_CASE		=						[self.cur_lang_code]
                 SCREAMING_SNAKE_CASE		=						[self.eos_token_id]









import inspect
import unittest

from transformers import RegNetConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from transformers.utils import cached_property, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor


if is_flax_available():
      import jax
      import jax.numpy as jnp

      from transformers.models.regnet.modeling_flax_regnet import FlaxRegNetForImageClassification, FlaxRegNetModel

if is_vision_available():
      from PIL import Image

      from transformers import AutoImageProcessor

class 		UpperCamelCase__			(				unittest.TestCase       ):



     '''simple docstring'''

     def __init__(							self						:   Optional[int]						,lowerCamelCase__						:   List[str]						,lowerCamelCase__						:   Optional[Any]=3						,lowerCamelCase__						:   List[str]=32						,lowerCamelCase__						:   List[Any]=3						,lowerCamelCase__						:   str=10						,lowerCamelCase__						:   Any=[10, 20, 30, 40]						,lowerCamelCase__						:   Optional[Any]=[1, 1, 2, 1]						,lowerCamelCase__						:   Union[str, Any]=True						,lowerCamelCase__						:   int=True						,lowerCamelCase__						:   Tuple="relu"						,lowerCamelCase__						:   Dict=3						,lowerCamelCase__						:   Optional[int]=None						,)					->		Optional[int]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						parent
           SCREAMING_SNAKE_CASE		=						batch_size
           SCREAMING_SNAKE_CASE		=						image_size
           SCREAMING_SNAKE_CASE		=						num_channels
           SCREAMING_SNAKE_CASE		=						embeddings_size
           SCREAMING_SNAKE_CASE		=						hidden_sizes
           SCREAMING_SNAKE_CASE		=						depths
           SCREAMING_SNAKE_CASE		=						is_training
           SCREAMING_SNAKE_CASE		=						use_labels
           SCREAMING_SNAKE_CASE		=						hidden_act
           SCREAMING_SNAKE_CASE		=						num_labels
           SCREAMING_SNAKE_CASE		=						scope
           SCREAMING_SNAKE_CASE		=						len(lowerCamelCase__	)






     def     SCREAMING_SNAKE_CASE__  (							self						:   Tuple	)					->		Tuple:




           '''simple docstring'''


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

           SCREAMING_SNAKE_CASE		=						self.get_config()

           return config, pixel_values






     def     SCREAMING_SNAKE_CASE__  (							self						:   str	)					->		Optional[int]:




           '''simple docstring'''


           return RegNetConfig(
               num_channels=self.num_channels						,embeddings_size=self.embeddings_size						,hidden_sizes=self.hidden_sizes						,depths=self.depths						,hidden_act=self.hidden_act						,num_labels=self.num_labels						,image_size=self.image_size						,)






     def     SCREAMING_SNAKE_CASE__  (							self						:   Union[str, Any]						,lowerCamelCase__						:   int						,lowerCamelCase__						:   Union[str, Any]	)					->		Any:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						FlaxRegNetModel(config=lowerCamelCase__	)
           SCREAMING_SNAKE_CASE		=						model(lowerCamelCase__	)

           # Output shape (b, c, h, w)
           self.parent.assertEqual(
               result.last_hidden_state.shape						,(self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)						,)






     def     SCREAMING_SNAKE_CASE__  (							self						:   Union[str, Any]						,lowerCamelCase__						:   Optional[int]						,lowerCamelCase__						:   Optional[Any]	)					->		Tuple:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						self.num_labels
           SCREAMING_SNAKE_CASE		=						FlaxRegNetForImageClassification(config=lowerCamelCase__	)
           SCREAMING_SNAKE_CASE		=						model(lowerCamelCase__	)
           self.parent.assertEqual(result.logits.shape						,(self.batch_size, self.num_labels)	)






     def     SCREAMING_SNAKE_CASE__  (							self						:   str	)					->		int:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						self.prepare_config_and_inputs()
           SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE		=						config_and_inputs
           SCREAMING_SNAKE_CASE		=						{"""pixel_values""": pixel_values}
           return config, inputs_dict

@require_flax
class 		UpperCamelCase__			(				lowerCAmelCase_							,				unittest.TestCase       ):



     '''simple docstring'''

     __snake_case   :     Union[str, Any]		     =      (FlaxRegNetModel, FlaxRegNetForImageClassification) if is_flax_available() else ()

     __snake_case   :     Tuple		     =      False
     __snake_case   :     int		     =      False
     __snake_case   :     Tuple		     =      False






     def     SCREAMING_SNAKE_CASE__  (							self						:   Union[str, Any]	)					->		None:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						FlaxRegNetModelTester(self	)
           SCREAMING_SNAKE_CASE		=						ConfigTester(self						,config_class=lowerCamelCase__						,has_text_modality=lowerCamelCase__	)






     def     SCREAMING_SNAKE_CASE__  (							self						:   Any	)					->		Optional[int]:




           '''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     SCREAMING_SNAKE_CASE__  (							self						:   int	)					->		List[str]:




           '''simple docstring'''


           return






     def     SCREAMING_SNAKE_CASE__  (							self						:   Tuple	)					->		List[Any]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*lowerCamelCase__	)






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




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_image_classification(*lowerCamelCase__	)






     @unittest.skip(reason="""RegNet does not use inputs_embeds"""	)
     def     SCREAMING_SNAKE_CASE__  (							self						:   str	)					->		Any:




           '''simple docstring'''


           pass






     @unittest.skip(reason="""RegNet does not support input and output embeddings"""	)
     def     SCREAMING_SNAKE_CASE__  (							self						:   str	)					->		Any:




           '''simple docstring'''


           pass






     def     SCREAMING_SNAKE_CASE__  (							self						:   Union[str, Any]	)					->		Any:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE		=						self.model_tester.prepare_config_and_inputs_for_common()

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

                 SCREAMING_SNAKE_CASE		=						["""pixel_values"""]
                 self.assertListEqual(arg_names[:1]						,lowerCamelCase__	)






     def     SCREAMING_SNAKE_CASE__  (							self						:   Optional[Any]	)					->		int:




           '''simple docstring'''


           def check_hidden_states_output(lowerCamelCase__						:   Tuple						,lowerCamelCase__						:   Union[str, Any]						,lowerCamelCase__						:   Optional[int]	):
                 SCREAMING_SNAKE_CASE		=						model_class(lowerCamelCase__	)

                 SCREAMING_SNAKE_CASE		=						model(**self._prepare_for_class(lowerCamelCase__						,lowerCamelCase__	)	)

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

                 SCREAMING_SNAKE_CASE		=						self.model_tester.num_stages
                 self.assertEqual(len(lowerCamelCase__	)						,expected_num_stages + 1	)

           SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE		=						self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
                 SCREAMING_SNAKE_CASE		=						True
                 check_hidden_states_output(lowerCamelCase__						,lowerCamelCase__						,lowerCamelCase__	)

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

                 check_hidden_states_output(lowerCamelCase__						,lowerCamelCase__						,lowerCamelCase__	)






     def     SCREAMING_SNAKE_CASE__  (							self						:   Any	)					->		List[Any]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE		=						self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
                 with self.subTest(model_class.__name__	):
                       SCREAMING_SNAKE_CASE		=						self._prepare_for_class(lowerCamelCase__						,lowerCamelCase__	)
                       SCREAMING_SNAKE_CASE		=						model_class(lowerCamelCase__	)

                       @jax.jit
                       def model_jitted(lowerCamelCase__						:   Dict						,**lowerCamelCase__						:   Optional[Any]	):
                             return model(pixel_values=lowerCamelCase__						,**lowerCamelCase__	)

                       with self.subTest("""JIT Enabled"""	):
                             SCREAMING_SNAKE_CASE		=						model_jitted(**lowerCamelCase__	).to_tuple()

                       with self.subTest("""JIT Disabled"""	):
                             with jax.disable_jit():
                                   SCREAMING_SNAKE_CASE		=						model_jitted(**lowerCamelCase__	).to_tuple()

                       self.assertEqual(len(lowerCamelCase__	)						,len(lowerCamelCase__	)	)
                       for jitted_output, output in zip(lowerCamelCase__						,lowerCamelCase__	):
                             self.assertEqual(jitted_output.shape						,output.shape	)



def        __lowercase       (				)			->       Union[str, Any]:


      '''simple docstring'''


      SCREAMING_SNAKE_CASE		=						Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""     )
      return image



@require_flax
class 		UpperCamelCase__			(				unittest.TestCase       ):



     '''simple docstring'''

     @cached_property
     def     SCREAMING_SNAKE_CASE__  (							self						:   int	)					->		str:




           '''simple docstring'''


           return AutoImageProcessor.from_pretrained("""facebook/regnet-y-040"""	) if is_vision_available() else None






     @slow
     def     SCREAMING_SNAKE_CASE__  (							self						:   Optional[int]	)					->		List[str]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						FlaxRegNetForImageClassification.from_pretrained("""facebook/regnet-y-040"""	)

           SCREAMING_SNAKE_CASE		=						self.default_image_processor
           SCREAMING_SNAKE_CASE		=						prepare_img()
           SCREAMING_SNAKE_CASE		=						image_processor(images=lowerCamelCase__						,return_tensors="""np"""	)

           SCREAMING_SNAKE_CASE		=						model(**lowerCamelCase__	)

           # verify the logits
           SCREAMING_SNAKE_CASE		=						(1, 1000)
           self.assertEqual(outputs.logits.shape						,lowerCamelCase__	)

           SCREAMING_SNAKE_CASE		=						jnp.array([-0.4180, -1.5051, -3.4836]	)

           self.assertTrue(jnp.allclose(outputs.logits[0, :3]						,lowerCamelCase__						,atol=1e-4	)	)


