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


__magic_name__								=logging.get_logger(__name__)

__magic_name__								='''▁'''

__magic_name__								={'''vocab_file''': '''sentencepiece.bpe.model'''}

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

__magic_name__								={
    '''facebook/nllb-200-distilled-600M''': 1024,
}

# fmt: off
__magic_name__								=['''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     _A   (    __UpperCamelCase							):
  SCREAMING_SNAKE_CASE_	:	List[Any]						=VOCAB_FILES_NAMES
  SCREAMING_SNAKE_CASE_	:	List[str]						=PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  SCREAMING_SNAKE_CASE_	:	Dict						=PRETRAINED_VOCAB_FILES_MAP
  SCREAMING_SNAKE_CASE_	:	List[str]						=["input_ids", "attention_mask"]

  SCREAMING_SNAKE_CASE_	:	List[int]						=[]
  SCREAMING_SNAKE_CASE_	:	List[int]						=[]






  def __init__(self   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_="<s>"   ,				SCREAMING_SNAKE_CASE_="</s>"   ,				SCREAMING_SNAKE_CASE_="</s>"   ,				SCREAMING_SNAKE_CASE_="<s>"   ,				SCREAMING_SNAKE_CASE_="<unk>"   ,				SCREAMING_SNAKE_CASE_="<pad>"   ,				SCREAMING_SNAKE_CASE_="<mask>"   ,				SCREAMING_SNAKE_CASE_=None   ,				SCREAMING_SNAKE_CASE_=None   ,				SCREAMING_SNAKE_CASE_=None   ,				SCREAMING_SNAKE_CASE_ = None   ,				SCREAMING_SNAKE_CASE_=None   ,				SCREAMING_SNAKE_CASE_=False   ,				**SCREAMING_SNAKE_CASE_   ,				)		->      str:





       '''simple docstring'''





       UpperCamelCase__										=      AddedToken(SCREAMING_SNAKE_CASE_   ,				lstrip=SCREAMING_SNAKE_CASE_   ,				rstrip=SCREAMING_SNAKE_CASE_	) if isinstance(SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_	) else mask_token

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

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

       UpperCamelCase__										=      spm.SentencePieceProcessor(**self.sp_model_kwargs	)
       self.sp_model.Load(str(SCREAMING_SNAKE_CASE_	)	)
       UpperCamelCase__										=      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
       UpperCamelCase__										=      {'''<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
       UpperCamelCase__										=      1

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

       self.fairseq_tokens_to_ids.update(self.lang_code_to_id	)
       UpperCamelCase__										=      {v: k for k, v in self.fairseq_tokens_to_ids.items()}
       UpperCamelCase__										=      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]	)

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






  def __getstate__(self	)		->      Any:





       '''simple docstring'''





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






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





       '''simple docstring'''





       UpperCamelCase__										=      d

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

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






  @property
  def 		_a					(self	)		->      Tuple:





       '''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 		_a					(self	)		->      str:





       '''simple docstring'''





       return self._src_lang






  @src_lang.setter
  def 		_a					(self   ,				SCREAMING_SNAKE_CASE_	)		->      None:





       '''simple docstring'''





       UpperCamelCase__										=      new_src_lang
       self.set_src_lang_special_tokens(self._src_lang	)






  def 		_a					(self   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_ = None   ,				SCREAMING_SNAKE_CASE_ = False	)		->      List[int]:





       '''simple docstring'''





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

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






  def 		_a					(self   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_ = 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 		_a					(self   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_ = None	)		->      List[int]:





       '''simple docstring'''





       UpperCamelCase__										=      [self.sep_token_id]
       UpperCamelCase__										=      [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 		_a					(self   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				**SCREAMING_SNAKE_CASE_	)		->      List[Any]:





       '''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'''	)
       UpperCamelCase__										=      src_lang
       UpperCamelCase__										=      self(SCREAMING_SNAKE_CASE_   ,				add_special_tokens=SCREAMING_SNAKE_CASE_   ,				return_tensors=SCREAMING_SNAKE_CASE_   ,				**SCREAMING_SNAKE_CASE_	)
       UpperCamelCase__										=      self.convert_tokens_to_ids(SCREAMING_SNAKE_CASE_	)
       UpperCamelCase__										=      tgt_lang_id
       return inputs






  def 		_a					(self	)		->      str:





       '''simple docstring'''





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






  def 		_a					(self   ,				SCREAMING_SNAKE_CASE_	)		->      List[str]:





       '''simple docstring'''





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






  def 		_a					(self   ,				SCREAMING_SNAKE_CASE_	)		->      Optional[int]:





       '''simple docstring'''





       if token in self.fairseq_tokens_to_ids:
            return self.fairseq_tokens_to_ids[token]
       UpperCamelCase__										=      self.sp_model.PieceToId(SCREAMING_SNAKE_CASE_	)

       # 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 		_a					(self   ,				SCREAMING_SNAKE_CASE_	)		->      str:





       '''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 		_a					(self   ,				SCREAMING_SNAKE_CASE_	)		->      Optional[Any]:





       '''simple docstring'''





       UpperCamelCase__										=      ''''''.join(SCREAMING_SNAKE_CASE_	).replace(SCREAMING_SNAKE_CASE_   ,				''' '''	).strip()
       return out_string






  def 		_a					(self   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_ = None	)		->      Tuple[str]:





       '''simple docstring'''





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

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

       return (out_vocab_file,)






  def 		_a					(self   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_ = "eng_Latn"   ,				SCREAMING_SNAKE_CASE_ = None   ,				SCREAMING_SNAKE_CASE_ = "fra_Latn"   ,				**SCREAMING_SNAKE_CASE_   ,				)		->      BatchEncoding:





       '''simple docstring'''





       UpperCamelCase__										=      src_lang
       UpperCamelCase__										=      tgt_lang
       return super().prepare_seqaseq_batch(SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				**SCREAMING_SNAKE_CASE_	)






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





       '''simple docstring'''





       return self.set_src_lang_special_tokens(self.src_lang	)






  def 		_a					(self	)		->      Dict:





       '''simple docstring'''





       return self.set_tgt_lang_special_tokens(self.tgt_lang	)






  def 		_a					(self   ,				SCREAMING_SNAKE_CASE_	)		->      None:





       '''simple docstring'''





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






  def 		_a					(self   ,				SCREAMING_SNAKE_CASE_	)		->      None:





       '''simple docstring'''





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




import collections
import tempfile
import unittest

import numpy as np

from transformers.testing_utils import (
    is_pt_flax_cross_test,
    require_flax,
    require_torch,
    require_vision,
    slow,
    torch_device,
)
from transformers.utils import is_flax_available, is_torch_available, is_vision_available

from ...test_modeling_flax_common import floats_tensor, ids_tensor, random_attention_mask
from ..bert.test_modeling_flax_bert import FlaxBertModelTester
from ..clip.test_modeling_flax_clip import FlaxCLIPVisionModelTester
from ..vit.test_modeling_flax_vit import FlaxViTModelTester


if is_flax_available():
     from transformers import (
         FlaxBertModel,
         FlaxCLIPVisionModel,
         FlaxVisionTextDualEncoderModel,
         FlaxViTModel,
         VisionTextDualEncoderConfig,
         VisionTextDualEncoderProcessor,
     )
     from transformers.modeling_flax_pytorch_utils import (
         convert_pytorch_state_dict_to_flax,
         load_flax_weights_in_pytorch_model,
     )


if is_torch_available():
     import torch

     from transformers import VisionTextDualEncoderModel

if is_vision_available():
     from PIL import Image







def  __UpperCamelCase    (						A							):
     if isinstance(A ,							collections.abc.Iterable							):
          return x
     return (x, x)



@require_flax
class     _A   :






  def 		_a					(self   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_	)		->      Tuple:





       '''simple docstring'''





       pass






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





       '''simple docstring'''





       pass






  def 		_a					(self	)		->      Tuple:





       '''simple docstring'''





       pass






  def 		_a					(self   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_	)		->      Any:





       '''simple docstring'''





       UpperCamelCase__										=      np.abs((a - b)	).max()
       self.assertLessEqual(SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				F"Difference between torch and flax is {diff} (>= {tol})."	)






  def 		_a					(self   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_=None   ,				**SCREAMING_SNAKE_CASE_	)		->      Dict:





       '''simple docstring'''





       UpperCamelCase__										=      VisionTextDualEncoderConfig.from_vision_text_configs(SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_	)

       UpperCamelCase__										=      FlaxVisionTextDualEncoderModel(SCREAMING_SNAKE_CASE_	)

       UpperCamelCase__										=      model(input_ids=SCREAMING_SNAKE_CASE_   ,				pixel_values=SCREAMING_SNAKE_CASE_   ,				attention_mask=SCREAMING_SNAKE_CASE_	)

       self.assertEqual(output['''text_embeds'''].shape   ,				(input_ids.shape[0], config.projection_dim)	)
       self.assertEqual(output['''image_embeds'''].shape   ,				(pixel_values.shape[0], config.projection_dim)	)






  def 		_a					(self   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_=None   ,				**SCREAMING_SNAKE_CASE_	)		->      int:





       '''simple docstring'''





       UpperCamelCase__						,       UpperCamelCase__										=      self.get_vision_text_model(SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_	)
       UpperCamelCase__										=      {'''vision_model''': vision_model, '''text_model''': text_model}
       UpperCamelCase__										=      FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(**SCREAMING_SNAKE_CASE_	)

       UpperCamelCase__										=      model(input_ids=SCREAMING_SNAKE_CASE_   ,				pixel_values=SCREAMING_SNAKE_CASE_   ,				attention_mask=SCREAMING_SNAKE_CASE_	)

       self.assertEqual(output['''text_embeds'''].shape   ,				(input_ids.shape[0], model.config.projection_dim)	)
       self.assertEqual(output['''image_embeds'''].shape   ,				(pixel_values.shape[0], model.config.projection_dim)	)






  def 		_a					(self   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_=None   ,				**SCREAMING_SNAKE_CASE_	)		->      List[Any]:





       '''simple docstring'''





       UpperCamelCase__						,       UpperCamelCase__										=      self.get_vision_text_model(SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_	)
       UpperCamelCase__										=      {'''vision_model''': vision_model, '''text_model''': text_model}
       UpperCamelCase__										=      FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(**SCREAMING_SNAKE_CASE_	)

       UpperCamelCase__										=      model(input_ids=SCREAMING_SNAKE_CASE_   ,				pixel_values=SCREAMING_SNAKE_CASE_   ,				attention_mask=SCREAMING_SNAKE_CASE_	)
       UpperCamelCase__										=      output[0]

       with tempfile.TemporaryDirectory() as tmpdirname:
            model.save_pretrained(SCREAMING_SNAKE_CASE_	)
            UpperCamelCase__										=      FlaxVisionTextDualEncoderModel.from_pretrained(SCREAMING_SNAKE_CASE_	)

            UpperCamelCase__										=      model(input_ids=SCREAMING_SNAKE_CASE_   ,				pixel_values=SCREAMING_SNAKE_CASE_   ,				attention_mask=SCREAMING_SNAKE_CASE_	)
            UpperCamelCase__										=      after_output[0]
            UpperCamelCase__										=      np.amax(np.abs(out_a - out_a	)	)
            self.assertLessEqual(SCREAMING_SNAKE_CASE_   ,				1E-3	)






  def 		_a					(self   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_=None   ,				**SCREAMING_SNAKE_CASE_	)		->      Any:





       '''simple docstring'''





       UpperCamelCase__						,       UpperCamelCase__										=      self.get_vision_text_model(SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_	)
       UpperCamelCase__										=      {'''vision_model''': vision_model, '''text_model''': text_model}
       UpperCamelCase__										=      FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(**SCREAMING_SNAKE_CASE_	)

       UpperCamelCase__										=      model(
           input_ids=SCREAMING_SNAKE_CASE_   ,				pixel_values=SCREAMING_SNAKE_CASE_   ,				attention_mask=SCREAMING_SNAKE_CASE_   ,				output_attentions=SCREAMING_SNAKE_CASE_	)

       UpperCamelCase__										=      output.vision_model_output.attentions
       self.assertEqual(len(SCREAMING_SNAKE_CASE_	)   ,				vision_config.num_hidden_layers	)

       # in ViT, the seq_len equals the number of patches + 1 (we add 1 for the [CLS] token)
       UpperCamelCase__										=      to_atuple(vision_model.config.image_size	)
       UpperCamelCase__										=      to_atuple(vision_model.config.patch_size	)
       UpperCamelCase__										=      (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
       UpperCamelCase__										=      num_patches + 1
       self.assertEqual(vision_attentions[0].shape[-3:]   ,				(vision_config.num_attention_heads, seq_len, seq_len)	)

       UpperCamelCase__										=      output.text_model_output.attentions
       self.assertEqual(len(SCREAMING_SNAKE_CASE_	)   ,				text_config.num_hidden_layers	)

       self.assertEqual(
           text_attentions[0].shape[-3:]   ,				(text_config.num_attention_heads, input_ids.shape[-1], input_ids.shape[-1])   ,				)






  def 		_a					(self   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_	)		->      List[str]:





       '''simple docstring'''





       pt_model.to(SCREAMING_SNAKE_CASE_	)
       pt_model.eval()

       # prepare inputs
       UpperCamelCase__										=      inputs_dict
       UpperCamelCase__										=      {k: torch.tensor(v.tolist()	) for k, v in flax_inputs.items()}

       with torch.no_grad():
            UpperCamelCase__										=      pt_model(**SCREAMING_SNAKE_CASE_	).to_tuple()

       UpperCamelCase__										=      fx_model(**SCREAMING_SNAKE_CASE_	).to_tuple()
       self.assertEqual(len(SCREAMING_SNAKE_CASE_	)   ,				len(SCREAMING_SNAKE_CASE_	)   ,				'''Output lengths differ between Flax and PyTorch'''	)
       for fx_output, pt_output in zip(fx_outputs[:4]   ,				pt_outputs[:4]	):
            self.assert_almost_equals(SCREAMING_SNAKE_CASE_   ,				pt_output.numpy()   ,				4E-2	)

       # PT -> Flax
       with tempfile.TemporaryDirectory() as tmpdirname:
            pt_model.save_pretrained(SCREAMING_SNAKE_CASE_	)
            UpperCamelCase__										=      FlaxVisionTextDualEncoderModel.from_pretrained(SCREAMING_SNAKE_CASE_   ,				from_pt=SCREAMING_SNAKE_CASE_	)

       UpperCamelCase__										=      fx_model_loaded(**SCREAMING_SNAKE_CASE_	).to_tuple()
       self.assertEqual(len(SCREAMING_SNAKE_CASE_	)   ,				len(SCREAMING_SNAKE_CASE_	)   ,				'''Output lengths differ between Flax and PyTorch'''	)
       for fx_output_loaded, pt_output in zip(fx_outputs_loaded[:4]   ,				pt_outputs[:4]	):
            self.assert_almost_equals(SCREAMING_SNAKE_CASE_   ,				pt_output.numpy()   ,				4E-2	)

       # Flax -> PT
       with tempfile.TemporaryDirectory() as tmpdirname:
            fx_model.save_pretrained(SCREAMING_SNAKE_CASE_	)
            UpperCamelCase__										=      VisionTextDualEncoderModel.from_pretrained(SCREAMING_SNAKE_CASE_   ,				from_flax=SCREAMING_SNAKE_CASE_	)

       pt_model_loaded.to(SCREAMING_SNAKE_CASE_	)
       pt_model_loaded.eval()

       with torch.no_grad():
            UpperCamelCase__										=      pt_model_loaded(**SCREAMING_SNAKE_CASE_	).to_tuple()

       self.assertEqual(len(SCREAMING_SNAKE_CASE_	)   ,				len(SCREAMING_SNAKE_CASE_	)   ,				'''Output lengths differ between Flax and PyTorch'''	)
       for fx_output, pt_output_loaded in zip(fx_outputs[:4]   ,				pt_outputs_loaded[:4]	):
            self.assert_almost_equals(SCREAMING_SNAKE_CASE_   ,				pt_output_loaded.numpy()   ,				4E-2	)






  def 		_a					(self   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_	)		->      Tuple:





       '''simple docstring'''





       UpperCamelCase__										=      VisionTextDualEncoderConfig.from_vision_text_configs(SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_	)

       UpperCamelCase__										=      VisionTextDualEncoderModel(SCREAMING_SNAKE_CASE_	)
       UpperCamelCase__										=      FlaxVisionTextDualEncoderModel(SCREAMING_SNAKE_CASE_	)

       UpperCamelCase__										=      convert_pytorch_state_dict_to_flax(pt_model.state_dict()   ,				SCREAMING_SNAKE_CASE_	)
       UpperCamelCase__										=      fx_state

       self.check_pt_flax_equivalence(SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_	)






  def 		_a					(self   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_	)		->      str:





       '''simple docstring'''





       UpperCamelCase__										=      VisionTextDualEncoderConfig.from_vision_text_configs(SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_	)

       UpperCamelCase__										=      VisionTextDualEncoderModel(SCREAMING_SNAKE_CASE_	)
       UpperCamelCase__										=      FlaxVisionTextDualEncoderModel(SCREAMING_SNAKE_CASE_	)

       UpperCamelCase__										=      load_flax_weights_in_pytorch_model(SCREAMING_SNAKE_CASE_   ,				fx_model.params	)

       self.check_pt_flax_equivalence(SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_	)






  def 		_a					(self	)		->      str:





       '''simple docstring'''





       UpperCamelCase__										=      self.prepare_config_and_inputs()
       self.check_model_from_pretrained_configs(**SCREAMING_SNAKE_CASE_	)






  def 		_a					(self	)		->      str:





       '''simple docstring'''





       UpperCamelCase__										=      self.prepare_config_and_inputs()
       self.check_vision_text_dual_encoder_from_pretrained(**SCREAMING_SNAKE_CASE_	)






  def 		_a					(self	)		->      Dict:





       '''simple docstring'''





       UpperCamelCase__										=      self.prepare_config_and_inputs()
       self.check_save_load(**SCREAMING_SNAKE_CASE_	)






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





       '''simple docstring'''





       UpperCamelCase__										=      self.prepare_config_and_inputs()
       self.check_vision_text_output_attention(**SCREAMING_SNAKE_CASE_	)






  @is_pt_flax_cross_test
  def 		_a					(self	)		->      str:





       '''simple docstring'''





       UpperCamelCase__										=      self.prepare_config_and_inputs()
       UpperCamelCase__										=      config_inputs_dict.pop('''vision_config'''	)
       UpperCamelCase__										=      config_inputs_dict.pop('''text_config'''	)

       UpperCamelCase__										=      config_inputs_dict

       self.check_equivalence_pt_to_flax(SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_	)
       self.check_equivalence_flax_to_pt(SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_	)






  @slow
  def 		_a					(self	)		->      Tuple:





       '''simple docstring'''





       UpperCamelCase__						,       UpperCamelCase__										=      self.get_pretrained_model_and_inputs()

       UpperCamelCase__										=      model_a(**SCREAMING_SNAKE_CASE_	)
       UpperCamelCase__										=      outputs[0]

       with tempfile.TemporaryDirectory() as tmp_dirname:
            model_a.save_pretrained(SCREAMING_SNAKE_CASE_	)
            UpperCamelCase__										=      FlaxVisionTextDualEncoderModel.from_pretrained(SCREAMING_SNAKE_CASE_	)

            UpperCamelCase__										=      model_a(**SCREAMING_SNAKE_CASE_	)
            UpperCamelCase__										=      after_outputs[0]
            UpperCamelCase__										=      np.amax(np.abs(out_a - out_a	)	)
            self.assertLessEqual(SCREAMING_SNAKE_CASE_   ,				1E-5	)



@require_flax
class     _A   (    __UpperCamelCase	, unittest.TestCase							):






  def 		_a					(self	)		->      Any:





       '''simple docstring'''





       UpperCamelCase__										=      FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(
           '''hf-internal-testing/tiny-random-vit'''   ,				'''hf-internal-testing/tiny-bert'''   ,				vision_from_pt=SCREAMING_SNAKE_CASE_   ,				text_from_pt=SCREAMING_SNAKE_CASE_   ,				)
       UpperCamelCase__										=      13
       UpperCamelCase__										=      floats_tensor(
           [
               batch_size,
               model.config.vision_config.num_channels,
               model.config.vision_config.image_size,
               model.config.vision_config.image_size,
           ]	)
       UpperCamelCase__										=      ids_tensor([batch_size, 4]   ,				model.config.text_config.vocab_size	)
       UpperCamelCase__										=      random_attention_mask([batch_size, 4]	)
       UpperCamelCase__										=      {'''pixel_values''': pixel_values, '''input_ids''': input_ids, '''attention_mask''': attention_mask}

       return model, inputs






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





       '''simple docstring'''





       UpperCamelCase__										=      FlaxViTModel(SCREAMING_SNAKE_CASE_	)
       UpperCamelCase__										=      FlaxBertModel(SCREAMING_SNAKE_CASE_	)
       return vision_model, text_model






  def 		_a					(self	)		->      Optional[Any]:





       '''simple docstring'''





       UpperCamelCase__										=      FlaxViTModelTester(self	)
       UpperCamelCase__										=      FlaxBertModelTester(self	)
       UpperCamelCase__										=      vit_model_tester.prepare_config_and_inputs()
       UpperCamelCase__										=      bert_model_tester.prepare_config_and_inputs()

       UpperCamelCase__						,       UpperCamelCase__										=      vision_config_and_inputs

       UpperCamelCase__						,       UpperCamelCase__						,       UpperCamelCase__						,       UpperCamelCase__										=      text_config_and_inputs

       # make sure that cross attention layers are added
       return {
           "text_config": text_config,
           "vision_config": vision_config,
           "pixel_values": pixel_values,
           "attention_mask": attention_mask,
           "input_ids": input_ids,
           "token_type_ids": token_type_ids,
       }



@require_torch
class     _A   (    __UpperCamelCase	, unittest.TestCase							):






  def 		_a					(self	)		->      Dict:





       '''simple docstring'''





       UpperCamelCase__										=      FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(
           '''hf-internal-testing/tiny-random-clip'''   ,				'''hf-internal-testing/tiny-bert'''   ,				vision_from_pt=SCREAMING_SNAKE_CASE_   ,				text_from_pt=SCREAMING_SNAKE_CASE_   ,				)
       UpperCamelCase__										=      13
       UpperCamelCase__										=      floats_tensor(
           [
               batch_size,
               model.config.vision_config.num_channels,
               model.config.vision_config.image_size,
               model.config.vision_config.image_size,
           ]	)
       UpperCamelCase__										=      ids_tensor([batch_size, 4]   ,				model.config.text_config.vocab_size	)
       UpperCamelCase__										=      random_attention_mask([batch_size, 4]	)
       UpperCamelCase__										=      {'''pixel_values''': pixel_values, '''input_ids''': input_ids, '''attention_mask''': attention_mask}

       return model, inputs






  def 		_a					(self   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_	)		->      int:





       '''simple docstring'''





       UpperCamelCase__										=      FlaxCLIPVisionModel(SCREAMING_SNAKE_CASE_	)
       UpperCamelCase__										=      FlaxBertModel(SCREAMING_SNAKE_CASE_	)
       return vision_model, text_model






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





       '''simple docstring'''





       UpperCamelCase__										=      FlaxCLIPVisionModelTester(self	)
       UpperCamelCase__										=      FlaxBertModelTester(self	)
       UpperCamelCase__										=      clip_model_tester.prepare_config_and_inputs()
       UpperCamelCase__										=      bert_model_tester.prepare_config_and_inputs()

       UpperCamelCase__						,       UpperCamelCase__										=      vision_config_and_inputs

       UpperCamelCase__						,       UpperCamelCase__						,       UpperCamelCase__						,       UpperCamelCase__										=      text_config_and_inputs

       # make sure that cross attention layers are added
       return {
           "text_config": text_config,
           "vision_config": vision_config,
           "pixel_values": pixel_values,
           "attention_mask": attention_mask,
           "input_ids": input_ids,
           "token_type_ids": token_type_ids,
       }



@require_flax
@require_vision
class     _A   (    unittest.TestCase							):






  @slow
  def 		_a					(self	)		->      int:





       '''simple docstring'''





       UpperCamelCase__										=      FlaxVisionTextDualEncoderModel.from_pretrained('''clip-italian/clip-italian'''   ,				logit_scale_init_value=1.0	)
       UpperCamelCase__										=      VisionTextDualEncoderProcessor.from_pretrained('''clip-italian/clip-italian'''	)

       UpperCamelCase__										=      Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''	)
       UpperCamelCase__										=      processor(
           text=['''una foto di un gatto''', '''una foto di un cane''']   ,				images=SCREAMING_SNAKE_CASE_   ,				padding=SCREAMING_SNAKE_CASE_   ,				return_tensors='''np'''	)

       UpperCamelCase__										=      model(**SCREAMING_SNAKE_CASE_	)

       # verify the logits
       self.assertEqual(outputs.logits_per_image.shape   ,				(inputs.pixel_values.shape[0], inputs.input_ids.shape[0])	)
       self.assertEqual(
           outputs.logits_per_text.shape   ,				(inputs.input_ids.shape[0], inputs.pixel_values.shape[0])   ,				)

       UpperCamelCase__										=      np.array([[1.2284727, 0.3104122]]	)

       self.assertTrue(np.allclose(outputs.logits_per_image   ,				SCREAMING_SNAKE_CASE_   ,				atol=1E-3	)	)



