
"""simple docstring"""




import unittest

import numpy as np

from transformers import BlipTextConfig
from transformers.testing_utils import require_torch, slow, torch_device
from transformers.utils import is_torch_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask


if is_torch_available():
	import torch

	from transformers import BlipTextModel
	from transformers.models.blip.modeling_blip import BLIP_PRETRAINED_MODEL_ARCHIVE_LIST




class      UpperCamelCase	:

				"""simple docstring"""



				def __init__(				self		,	lowercase__		,	lowercase__=12		,	lowercase__=7		,	lowercase__=True		,	lowercase__=True		,	lowercase__=True		,	lowercase__=99		,	lowercase__=32		,	lowercase__=32		,	lowercase__=5		,	lowercase__=4		,	lowercase__=37		,	lowercase__=0.1		,	lowercase__=0.1		,	lowercase__=512		,	lowercase__=0.02		,	lowercase__=0		,	lowercase__=None		,	)    ->    Dict:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE				=	parent
									SCREAMING_SNAKE_CASE				=	batch_size
									SCREAMING_SNAKE_CASE				=	seq_length
									SCREAMING_SNAKE_CASE				=	is_training
									SCREAMING_SNAKE_CASE				=	use_input_mask
									SCREAMING_SNAKE_CASE				=	use_labels
									SCREAMING_SNAKE_CASE				=	vocab_size
									SCREAMING_SNAKE_CASE				=	hidden_size
									SCREAMING_SNAKE_CASE				=	projection_dim
									SCREAMING_SNAKE_CASE				=	num_hidden_layers
									SCREAMING_SNAKE_CASE				=	num_attention_heads
									SCREAMING_SNAKE_CASE				=	intermediate_size
									SCREAMING_SNAKE_CASE				=	dropout
									SCREAMING_SNAKE_CASE				=	attention_dropout
									SCREAMING_SNAKE_CASE				=	max_position_embeddings
									SCREAMING_SNAKE_CASE				=	initializer_range
									SCREAMING_SNAKE_CASE				=	scope
									SCREAMING_SNAKE_CASE				=	bos_token_id


				def 							A					(				self			)    ->    List[str]:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE				=	ids_tensor([self.batch_size, self.seq_length]		,	self.vocab_size			)

									SCREAMING_SNAKE_CASE				=	None
									if self.use_input_mask:
														SCREAMING_SNAKE_CASE				=	random_attention_mask([self.batch_size, self.seq_length]			)

									if input_mask is not None:
														SCREAMING_SNAKE_CASE	,    SCREAMING_SNAKE_CASE				=	input_mask.shape
														SCREAMING_SNAKE_CASE				=	np.random.randint(1		,	seq_length - 1		,	size=(batch_size,)			)
														for batch_idx, start_index in enumerate(lowercase__			):
																			SCREAMING_SNAKE_CASE				=	1
																			SCREAMING_SNAKE_CASE				=	0

									SCREAMING_SNAKE_CASE				=	self.get_config()

									return config, input_ids, input_mask


				def 							A					(				self			)    ->    Union[str, Any]:
									"""simple docstring"""







									return BlipTextConfig(
									    vocab_size=self.vocab_size		,	hidden_size=self.hidden_size		,	projection_dim=self.projection_dim		,	num_hidden_layers=self.num_hidden_layers		,	num_attention_heads=self.num_attention_heads		,	intermediate_size=self.intermediate_size		,	dropout=self.dropout		,	attention_dropout=self.attention_dropout		,	max_position_embeddings=self.max_position_embeddings		,	initializer_range=self.initializer_range		,	bos_token_id=self.bos_token_id		,	)


				def 							A					(				self		,	lowercase__		,	lowercase__		,	lowercase__			)    ->    Any:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE				=	BlipTextModel(config=lowercase__			)
									model.to(lowercase__			)
									model.eval()
									with torch.no_grad():
														SCREAMING_SNAKE_CASE				=	model(lowercase__		,	attention_mask=lowercase__			)
														SCREAMING_SNAKE_CASE				=	model(lowercase__			)
									self.parent.assertEqual(result.last_hidden_state.shape		,	(self.batch_size, self.seq_length, self.hidden_size)			)
									self.parent.assertEqual(result.pooler_output.shape		,	(self.batch_size, self.hidden_size)			)




				def 							A					(				self			)    ->    Optional[Any]:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE				=	self.prepare_config_and_inputs()
									SCREAMING_SNAKE_CASE	,    SCREAMING_SNAKE_CASE	,    SCREAMING_SNAKE_CASE				=	config_and_inputs
									SCREAMING_SNAKE_CASE				=	{'input_ids': input_ids, 'attention_mask': input_mask}
									return config, inputs_dict






@require_torch
class      UpperCamelCase	(       __magic_name__  ,      unittest.TestCase      ):

				"""simple docstring"""



				UpperCAmelCase_				:      Dict						  =				(BlipTextModel,) if is_torch_available() else ()
				UpperCAmelCase_				:      Dict						  =				False
				UpperCAmelCase_				:      Dict						  =				False
				UpperCAmelCase_				:      Optional[Any]						  =				False


				def 							A					(				self			)    ->    List[str]:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE				=	BlipTextModelTester(self			)
									SCREAMING_SNAKE_CASE				=	ConfigTester(self		,	config_class=lowercase__		,	hidden_size=37			)


				def 							A					(				self			)    ->    List[Any]:
									"""simple docstring"""







									self.config_tester.run_common_tests()


				def 							A					(				self			)    ->    Tuple:
									"""simple docstring"""







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


				def 							A					(				self			)    ->    str:
									"""simple docstring"""







									pass


				def 							A					(				self			)    ->    Optional[Any]:
									"""simple docstring"""







									pass


				@unittest.skip(reason='Blip does not use inputs_embeds'			)
				def 							A					(				self			)    ->    int:
									"""simple docstring"""







									pass


				@unittest.skip(reason='BlipTextModel has no base class and is not available in MODEL_MAPPING'			)
				def 							A					(				self			)    ->    Optional[int]:
									"""simple docstring"""







									pass


				@unittest.skip(reason='BlipTextModel has no base class and is not available in MODEL_MAPPING'			)
				def 							A					(				self			)    ->    str:
									"""simple docstring"""







									pass


				@slow
				def 							A					(				self			)    ->    Union[str, Any]:
									"""simple docstring"""







									for model_name in BLIP_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
														SCREAMING_SNAKE_CASE				=	BlipTextModel.from_pretrained(lowercase__			)
														self.assertIsNotNone(lowercase__			)




				def 							A					(				self			)    ->    Optional[Any]:
									"""simple docstring"""







									super().test_pt_tf_model_equivalence(allow_missing_keys=lowercase__			)







"""simple docstring"""


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

from tokenizers import processors

from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
  from .tokenization_mbartaa import MBartaaTokenizer
else:
  lowerCamelCase__      =      None


lowerCamelCase__      =      logging.get_logger(__name__)

lowerCamelCase__      =      {"vocab_file": "sentencepiece.bpe.model", "tokenizer_file": "tokenizer.json"}

lowerCamelCase__      =      {
    "vocab_file": {
        "facebook/mbart-large-50-one-to-many-mmt": (
            "https://huggingface.co/facebook/mbart-large-50-one-to-many-mmt/resolve/main/sentencepiece.bpe.model"
        ),
    },
    "tokenizer_file": {
        "facebook/mbart-large-50-one-to-many-mmt": (
            "https://huggingface.co/facebook/mbart-large-50-one-to-many-mmt/resolve/main/tokenizer.json"
        ),
    },
}

lowerCamelCase__      =      {
    "facebook/mbart-large-50-one-to-many-mmt": 1024,
}

# fmt: off
lowerCamelCase__      =      ["ar_AR", "cs_CZ", "de_DE", "en_XX", "es_XX", "et_EE", "fi_FI", "fr_XX", "gu_IN", "hi_IN", "it_IT", "ja_XX", "kk_KZ", "ko_KR", "lt_LT", "lv_LV", "my_MM", "ne_NP", "nl_XX", "ro_RO", "ru_RU", "si_LK", "tr_TR", "vi_VN", "zh_CN", "af_ZA", "az_AZ", "bn_IN", "fa_IR", "he_IL", "hr_HR", "id_ID", "ka_GE", "km_KH", "mk_MK", "ml_IN", "mn_MN", "mr_IN", "pl_PL", "ps_AF", "pt_XX", "sv_SE", "sw_KE", "ta_IN", "te_IN", "th_TH", "tl_XX", "uk_UA", "ur_PK", "xh_ZA", "gl_ES", "sl_SI"]



class    __SCREAMING_SNAKE_CASE			(  _UpperCamelCase      ):







  '''simple docstring'''


  SCREAMING_SNAKE_CASE__							:Tuple		   =    VOCAB_FILES_NAMES
  SCREAMING_SNAKE_CASE__							:List[Any]		   =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  SCREAMING_SNAKE_CASE__							:Union[str, Any]		   =    PRETRAINED_VOCAB_FILES_MAP
  SCREAMING_SNAKE_CASE__							:int		   =    ["input_ids", "attention_mask"]
  SCREAMING_SNAKE_CASE__							:Dict		   =    MBartaaTokenizer

  SCREAMING_SNAKE_CASE__							:List[int]		   =    []
  SCREAMING_SNAKE_CASE__							:List[int]		   =    []

  def __init__(						self      :				str    ,      __a      :				int=None    ,      __a      :				List[str]=None    ,      __a      :				Optional[Any]=None    ,      __a      :				List[str]=None    ,      __a      :				Tuple="</s>"    ,      __a      :				Union[str, Any]="</s>"    ,      __a      :				str="<s>"    ,      __a      :				Any="<unk>"    ,      __a      :				List[str]="<pad>"    ,      __a      :				Optional[Any]="<mask>"    ,      **__a      :				Any    ,      )						-> Union[str, Any]:
        # Mask token behave like a normal word, i.e. include the space before it
        _UpperCamelCase					:		Dict  =				AddedToken(__a    ,      lstrip=__a    ,      rstrip=__a		) if isinstance(__a    ,      __a		) else mask_token

        _UpperCamelCase					:		str  =				kwargs.get("additional_special_tokens"    ,      []		)
        kwargs["additional_special_tokens"] += [
            code for code in FAIRSEQ_LANGUAGE_CODES if code not in kwargs["additional_special_tokens"]
        ]

        super().__init__(
            __a    ,      src_lang=__a    ,      tgt_lang=__a    ,      tokenizer_file=__a    ,      eos_token=__a    ,      sep_token=__a    ,      cls_token=__a    ,      unk_token=__a    ,      pad_token=__a    ,      mask_token=__a    ,      **__a    ,      )

        _UpperCamelCase					:		Optional[int]  =				vocab_file
        _UpperCamelCase					:		int  =				False if not self.vocab_file else True

        _UpperCamelCase					:		int  =				{
            lang_code: self.convert_tokens_to_ids(__a		) for lang_code in FAIRSEQ_LANGUAGE_CODES
        }

        _UpperCamelCase					:		List[str]  =				src_lang if src_lang is not None else "en_XX"
        _UpperCamelCase					:		Dict  =				tgt_lang
        _UpperCamelCase					:		int  =				self.lang_code_to_id[self._src_lang]
        self.set_src_lang_special_tokens(self._src_lang		)

  @property
  def       __SCREAMING_SNAKE_CASE (						self      :				Optional[int]		)						-> str:
        return self._src_lang

  @src_lang.setter
  def       __SCREAMING_SNAKE_CASE (						self      :				Union[str, Any]    ,      __a      :				str		)						-> None:
        _UpperCamelCase					:		int  =				new_src_lang
        self.set_src_lang_special_tokens(self._src_lang		)

  def       __SCREAMING_SNAKE_CASE (						self      :				Optional[int]    ,      __a      :				List[int]    ,      __a      :				Optional[List[int]] = None		)						-> List[int]:
        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      :				Any    ,      __a      :				List[str]    ,      __a      :				str = "en_XX"    ,      __a      :				Optional[List[str]] = None    ,      __a      :				str = "ro_RO"    ,      **__a      :				Tuple    ,      )						-> BatchEncoding:
        _UpperCamelCase					:		List[Any]  =				src_lang
        _UpperCamelCase					:		Union[str, Any]  =				tgt_lang
        return super().prepare_seqaseq_batch(__a    ,      __a    ,      **__a		)

  def       __SCREAMING_SNAKE_CASE (						self      :				List[str]		)						-> int:
        return self.set_src_lang_special_tokens(self.src_lang		)

  def       __SCREAMING_SNAKE_CASE (						self      :				Tuple		)						-> Tuple:
        return self.set_tgt_lang_special_tokens(self.tgt_lang		)

  def       __SCREAMING_SNAKE_CASE (						self      :				List[str]    ,      __a      :				str		)						-> None:
        _UpperCamelCase					:		List[Any]  =				self.convert_tokens_to_ids(__a		)
        _UpperCamelCase					:		Union[str, Any]  =				[self.cur_lang_code_id]
        _UpperCamelCase					:		List[Any]  =				[self.eos_token_id]

        _UpperCamelCase					:		Union[str, Any]  =				self.convert_ids_to_tokens(self.prefix_tokens		)
        _UpperCamelCase					:		Union[str, Any]  =				self.convert_ids_to_tokens(self.suffix_tokens		)

        _UpperCamelCase					:		Optional[Any]  =				processors.TemplateProcessing(
            single=prefix_tokens_str + ["$A"] + suffix_tokens_str    ,      pair=prefix_tokens_str + ["$A", "$B"] + suffix_tokens_str    ,      special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str    ,      self.prefix_tokens + self.suffix_tokens		)		)    ,      )

  def       __SCREAMING_SNAKE_CASE (						self      :				Dict    ,      __a      :				str		)						-> None:
        _UpperCamelCase					:		Tuple  =				self.convert_tokens_to_ids(__a		)
        _UpperCamelCase					:		Union[str, Any]  =				[self.cur_lang_code_id]
        _UpperCamelCase					:		Optional[Any]  =				[self.eos_token_id]

        _UpperCamelCase					:		int  =				self.convert_ids_to_tokens(self.prefix_tokens		)
        _UpperCamelCase					:		Any  =				self.convert_ids_to_tokens(self.suffix_tokens		)

        _UpperCamelCase					:		Tuple  =				processors.TemplateProcessing(
            single=prefix_tokens_str + ["$A"] + suffix_tokens_str    ,      pair=prefix_tokens_str + ["$A", "$B"] + suffix_tokens_str    ,      special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str    ,      self.prefix_tokens + self.suffix_tokens		)		)    ,      )

  def       __SCREAMING_SNAKE_CASE (						self      :				Dict    ,      __a      :				Optional[int]    ,      __a      :				str    ,      __a      :				Optional[str]    ,      __a      :				Optional[str]    ,      **__a      :				int		)						-> List[str]:
        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					:		List[str]  =				src_lang
        _UpperCamelCase					:		List[str]  =				self(__a    ,      add_special_tokens=__a    ,      return_tensors=__a    ,      **__a		)
        _UpperCamelCase					:		str  =				self.convert_tokens_to_ids(__a		)
        _UpperCamelCase					:		str  =				tgt_lang_id
        return inputs


  def       __SCREAMING_SNAKE_CASE (						self      :				Union[str, Any]    ,      __a      :				str    ,      __a      :				Optional[str] = None		)						-> Tuple[str]:
        if not self.can_save_slow_tokenizer:
              raise ValueError(
                  "Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
                  "tokenizer."		)

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

        if os.path.abspath(self.vocab_file		) != os.path.abspath(__a		):
              copyfile(self.vocab_file    ,      __a		)

        return (out_vocab_file,)
