
"""simple docstring"""


import copy
import os
from typing import Union

from ...configuration_utils import PretrainedConfig
from ...utils import logging


SCREAMING_SNAKE_CASE      =     logging.get_logger(__name__)

SCREAMING_SNAKE_CASE      =     {
    """google/pix2struct-textcaps-base""": (
        """https://huggingface.co/google/pix2struct-textcaps-base/resolve/main/config.json"""
    ),
}



class   __a  (      _lowerCAmelCase	):
       UpperCamelCase_		:					Optional[Any]   =			'''pix2struct_text_model'''
       UpperCamelCase_		:					int   =			['''past_key_values''']
       UpperCamelCase_		:					Tuple   =			{
           '''hidden_size''': '''hidden_size''',
           '''num_attention_heads''': '''num_heads''',
           '''num_hidden_layers''': '''num_layers''',
       }






       def __init__(  self :							Dict       ,      UpperCAmelCase_ :							int=50_244       ,      UpperCAmelCase_ :							Optional[int]=768       ,      UpperCAmelCase_ :							Any=64       ,      UpperCAmelCase_ :							Optional[Any]=2_048       ,      UpperCAmelCase_ :							List[str]=12       ,      UpperCAmelCase_ :							Dict=12       ,      UpperCAmelCase_ :							Optional[Any]=32       ,      UpperCAmelCase_ :							Tuple=128       ,      UpperCAmelCase_ :							Dict=0.1       ,      UpperCAmelCase_ :							Tuple=1e-6       ,      UpperCAmelCase_ :							Any=1.0       ,      UpperCAmelCase_ :							List[str]="gelu_new"       ,      UpperCAmelCase_ :							List[Any]=0       ,      UpperCAmelCase_ :							Any=False       ,      UpperCAmelCase_ :							Any=0       ,      UpperCAmelCase_ :							Dict=1       ,      UpperCAmelCase_ :							List[Any]=False       ,      UpperCAmelCase_ :							Optional[int]=True       ,      **UpperCAmelCase_ :							List[Any]       ,      )->  Tuple:







              """simple docstring"""
              UpperCamelCase					 =		vocab_size
              UpperCamelCase					 =		hidden_size
              UpperCamelCase					 =		d_kv
              UpperCamelCase					 =		d_ff
              UpperCamelCase					 =		num_layers
              UpperCamelCase					 =		num_heads
              UpperCamelCase					 =		relative_attention_num_buckets
              UpperCamelCase					 =		relative_attention_max_distance
              UpperCamelCase					 =		dropout_rate
              UpperCamelCase					 =		layer_norm_epsilon
              UpperCamelCase					 =		initializer_factor
              UpperCamelCase					 =		use_cache

              UpperCamelCase					 =		eos_token_id
              UpperCamelCase					 =		decoder_start_token_id

              # for backwards compatibility
              UpperCamelCase					 =		dense_act_fn

              super().__init__(
                  pad_token_id=UpperCAmelCase_       ,      eos_token_id=UpperCAmelCase_       ,      decoder_start_token_id=UpperCAmelCase_       ,      tie_word_embeddings=UpperCAmelCase_       ,      is_decoder=UpperCAmelCase_       ,      **UpperCAmelCase_       ,      )






       @classmethod
       def 	_SCREAMING_SNAKE_CASE       (  cls :							str       ,      UpperCAmelCase_ :							Union[str, os.PathLike]       ,      **UpperCAmelCase_ :							int			)->  "PretrainedConfig":







              """simple docstring"""
              cls._set_token_in_kwargs(UpperCAmelCase_			)

              UpperCamelCase ,       UpperCamelCase					 =		cls.get_config_dict(UpperCAmelCase_       ,      **UpperCAmelCase_			)

              # get the text config dict if we are loading from Pix2StructConfig
              if config_dict.get("model_type"			) == "pix2struct":
                     UpperCamelCase					 =		config_dict["text_config"]

              if "model_type" in config_dict and hasattr(cls       ,      "model_type"			) and config_dict["model_type"] != cls.model_type:
                     logger.warning(
                         f"You are using a model of type {config_dict['model_type']} to instantiate a model of type "
                         f"{cls.model_type}. This is not supported for all configurations of models and can yield errors."			)

              return cls.from_dict(UpperCAmelCase_       ,      **UpperCAmelCase_			)





class   __a  (      _lowerCAmelCase	):
       UpperCamelCase_		:					Union[str, Any]   =			'''pix2struct_vision_model'''






       def __init__(  self :							Tuple       ,      UpperCAmelCase_ :							str=768       ,      UpperCAmelCase_ :							Optional[Any]=768       ,      UpperCAmelCase_ :							List[str]=2_048       ,      UpperCAmelCase_ :							Union[str, Any]=64       ,      UpperCAmelCase_ :							Dict=12       ,      UpperCAmelCase_ :							Tuple=12       ,      UpperCAmelCase_ :							str="gelu_new"       ,      UpperCAmelCase_ :							List[Any]=1e-6       ,      UpperCAmelCase_ :							Tuple=0.0       ,      UpperCAmelCase_ :							Union[str, Any]=0.0       ,      UpperCAmelCase_ :							List[str]=1e-10       ,      UpperCAmelCase_ :							Dict=1.0       ,      UpperCAmelCase_ :							int=4_096       ,      UpperCAmelCase_ :							Any=32       ,      UpperCAmelCase_ :							List[Any]=128       ,      **UpperCAmelCase_ :							List[Any]       ,      )->  Optional[Any]:







              """simple docstring"""
              super().__init__(**UpperCAmelCase_			)

              UpperCamelCase					 =		hidden_size
              UpperCamelCase					 =		patch_embed_hidden_size
              UpperCamelCase					 =		d_ff
              UpperCamelCase					 =		dropout_rate
              UpperCamelCase					 =		num_hidden_layers
              UpperCamelCase					 =		num_attention_heads
              UpperCamelCase					 =		initializer_range
              UpperCamelCase					 =		initializer_factor
              UpperCamelCase					 =		attention_dropout
              UpperCamelCase					 =		layer_norm_eps
              UpperCamelCase					 =		dense_act_fn
              UpperCamelCase					 =		seq_len
              UpperCamelCase					 =		relative_attention_num_buckets
              UpperCamelCase					 =		relative_attention_max_distance
              UpperCamelCase					 =		d_kv






       @classmethod
       def 	_SCREAMING_SNAKE_CASE       (  cls :							Optional[Any]       ,      UpperCAmelCase_ :							Union[str, os.PathLike]       ,      **UpperCAmelCase_ :							Optional[int]			)->  "PretrainedConfig":







              """simple docstring"""
              cls._set_token_in_kwargs(UpperCAmelCase_			)

              UpperCamelCase ,       UpperCamelCase					 =		cls.get_config_dict(UpperCAmelCase_       ,      **UpperCAmelCase_			)

              # get the vision config dict if we are loading from Pix2StructConfig
              if config_dict.get("model_type"			) == "pix2struct":
                     UpperCamelCase					 =		config_dict["vision_config"]

              if "model_type" in config_dict and hasattr(cls       ,      "model_type"			) and config_dict["model_type"] != cls.model_type:
                     logger.warning(
                         f"You are using a model of type {config_dict['model_type']} to instantiate a model of type "
                         f"{cls.model_type}. This is not supported for all configurations of models and can yield errors."			)

              return cls.from_dict(UpperCAmelCase_       ,      **UpperCAmelCase_			)





class   __a  (      _lowerCAmelCase	):
       UpperCamelCase_		:					Dict   =			'''pix2struct'''
       UpperCamelCase_		:					int   =			True






       def __init__(  self :							Optional[Any]       ,      UpperCAmelCase_ :							Union[str, Any]=None       ,      UpperCAmelCase_ :							Optional[Any]=None       ,      UpperCAmelCase_ :							int=1.0       ,      UpperCAmelCase_ :							List[str]=0.02       ,      UpperCAmelCase_ :							List[str]=False       ,      UpperCAmelCase_ :							Tuple=False       ,      UpperCAmelCase_ :							List[Any]=True       ,      **UpperCAmelCase_ :							Optional[Any]       ,      )->  int:







              """simple docstring"""
              super().__init__(tie_word_embeddings=UpperCAmelCase_       ,      is_encoder_decoder=UpperCAmelCase_       ,      **UpperCAmelCase_			)

              if text_config is None:
                     UpperCamelCase					 =		{}
                     logger.info("text_config is None. Initializing the Pix2StructTextConfig with default values."			)

              if vision_config is None:
                     UpperCamelCase					 =		{}
                     logger.info("vision_config is None. Initializing the Pix2StructVisionConfig with default values."			)

              UpperCamelCase					 =		PixaStructTextConfig(**UpperCAmelCase_			)
              UpperCamelCase					 =		PixaStructVisionConfig(**UpperCAmelCase_			)

              UpperCamelCase					 =		self.text_config.decoder_start_token_id
              UpperCamelCase					 =		self.text_config.pad_token_id
              UpperCamelCase					 =		self.text_config.eos_token_id

              UpperCamelCase					 =		initializer_factor
              UpperCamelCase					 =		initializer_range

              UpperCamelCase					 =		self.initializer_range
              UpperCamelCase					 =		self.initializer_range

              UpperCamelCase					 =		is_vqa






       @classmethod
       def 	_SCREAMING_SNAKE_CASE       (  cls :							Optional[Any]       ,      UpperCAmelCase_ :							PixaStructTextConfig       ,      UpperCAmelCase_ :							PixaStructVisionConfig       ,      **UpperCAmelCase_ :							int			)->  int:







              """simple docstring"""
              return cls(text_config=text_config.to_dict()       ,      vision_config=vision_config.to_dict()       ,      **UpperCAmelCase_			)






       def 	_SCREAMING_SNAKE_CASE       (  self :							Tuple			)->  str:







              """simple docstring"""
              UpperCamelCase					 =		copy.deepcopy(self.__dict__			)
              UpperCamelCase					 =		self.text_config.to_dict()
              UpperCamelCase					 =		self.vision_config.to_dict()
              UpperCamelCase					 =		self.__class__.model_type
              return output







"""simple docstring"""


import unittest

from transformers import BarthezTokenizer, BarthezTokenizerFast, BatchEncoding
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow

from ...test_tokenization_common import TokenizerTesterMixin



@require_tokenizers
@require_sentencepiece
@slow  # see https://github.com/huggingface/transformers/issues/11457
class   __a  (      _lowerCAmelCase    ,     unittest.TestCase	):
       UpperCamelCase_		:					Any   =			BarthezTokenizer
       UpperCamelCase_		:					Tuple   =			BarthezTokenizerFast
       UpperCamelCase_		:					str   =			True
       UpperCamelCase_		:					Dict   =			True






       def 	_SCREAMING_SNAKE_CASE       (  self :							List[Any]			)->  int:







              """simple docstring"""
              super().setUp()

              UpperCamelCase					 =		BarthezTokenizerFast.from_pretrained("moussaKam/mbarthez"			)
              tokenizer.save_pretrained(self.tmpdirname			)
              tokenizer.save_pretrained(self.tmpdirname       ,      legacy_format=UpperCAmelCase_			)
              UpperCamelCase					 =		tokenizer






       def 	_SCREAMING_SNAKE_CASE       (  self :							Dict			)->  Optional[Any]:







              """simple docstring"""
              UpperCamelCase					 =		"<pad>"
              UpperCamelCase					 =		1

              self.assertEqual(self.get_tokenizer()._convert_token_to_id(UpperCAmelCase_			)       ,      UpperCAmelCase_			)
              self.assertEqual(self.get_tokenizer()._convert_id_to_token(UpperCAmelCase_			)       ,      UpperCAmelCase_			)






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







              """simple docstring"""
              UpperCamelCase					 =		list(self.get_tokenizer().get_vocab().keys()			)

              self.assertEqual(vocab_keys[0]       ,      "<s>"			)
              self.assertEqual(vocab_keys[1]       ,      "<pad>"			)
              self.assertEqual(vocab_keys[-1]       ,      "<mask>"			)
              self.assertEqual(len(UpperCAmelCase_			)       ,      101_122			)






       def 	_SCREAMING_SNAKE_CASE       (  self :							List[Any]			)->  List[str]:







              """simple docstring"""
              self.assertEqual(self.get_tokenizer().vocab_size       ,      101_122			)






       @require_torch
       def 	_SCREAMING_SNAKE_CASE       (  self :							Union[str, Any]			)->  int:







              """simple docstring"""
              UpperCamelCase					 =		["A long paragraph for summarization.", "Another paragraph for summarization."]
              UpperCamelCase					 =		[0, 57, 3_018, 70_307, 91, 2]

              UpperCamelCase					 =		self.tokenizer(
                  UpperCAmelCase_       ,      max_length=len(UpperCAmelCase_			)       ,      padding=UpperCAmelCase_       ,      truncation=UpperCAmelCase_       ,      return_tensors="pt"			)
              self.assertIsInstance(UpperCAmelCase_       ,      UpperCAmelCase_			)

              self.assertEqual((2, 6)       ,      batch.input_ids.shape			)
              self.assertEqual((2, 6)       ,      batch.attention_mask.shape			)
              UpperCamelCase					 =		batch.input_ids.tolist()[0]
              self.assertListEqual(UpperCAmelCase_       ,      UpperCAmelCase_			)






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







              """simple docstring"""
              if not self.test_rust_tokenizer:
                     return

              UpperCamelCase					 =		self.get_tokenizer()
              UpperCamelCase					 =		self.get_rust_tokenizer()

              UpperCamelCase					 =		"I was born in 92000, and this is falsé."

              UpperCamelCase					 =		tokenizer.tokenize(UpperCAmelCase_			)
              UpperCamelCase					 =		rust_tokenizer.tokenize(UpperCAmelCase_			)
              self.assertListEqual(UpperCAmelCase_       ,      UpperCAmelCase_			)

              UpperCamelCase					 =		tokenizer.encode(UpperCAmelCase_       ,      add_special_tokens=UpperCAmelCase_			)
              UpperCamelCase					 =		rust_tokenizer.encode(UpperCAmelCase_       ,      add_special_tokens=UpperCAmelCase_			)
              self.assertListEqual(UpperCAmelCase_       ,      UpperCAmelCase_			)

              UpperCamelCase					 =		self.get_rust_tokenizer()
              UpperCamelCase					 =		tokenizer.encode(UpperCAmelCase_			)
              UpperCamelCase					 =		rust_tokenizer.encode(UpperCAmelCase_			)
              self.assertListEqual(UpperCAmelCase_       ,      UpperCAmelCase_			)






       @slow
       def 	_SCREAMING_SNAKE_CASE       (  self :							int			)->  Union[str, Any]:







              """simple docstring"""
              # fmt: off
              UpperCamelCase					 =		{"input_ids": [[0, 490, 14_328, 4_507, 354, 47, 43_669, 95, 25, 78_117, 20_215, 19_779, 190, 22, 400, 4, 35_343, 80_310, 603, 86, 24_937, 105, 33_438, 94_762, 196, 39_642, 7, 15, 15_933, 173, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 10_534, 87, 25, 66, 3_358, 196, 55_289, 8, 82_961, 81, 2_204, 75_203, 7, 15, 763, 12_956, 216, 178, 14_328, 9_595, 1_377, 69_693, 7, 448, 71_021, 196, 18_106, 1_437, 13_974, 108, 9_083, 4, 49_315, 7, 39, 86, 1_326, 2_793, 46_333, 4, 448, 196, 74_588, 7, 49_315, 7, 39, 21, 822, 38_470, 74, 21, 66_723, 62_480, 8, 22_050, 5, 2]], "attention_mask": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}  # noqa: E501
              # fmt: on

              # moussaKam/mbarthez is a french model. So we also use french texts.
              UpperCamelCase					 =		[
                  "Le transformeur est un modèle d'apprentissage profond introduit en 2017, "
                  "utilisé principalement dans le domaine du traitement automatique des langues (TAL).",
                  "À l'instar des réseaux de neurones récurrents (RNN), les transformeurs sont conçus "
                  "pour gérer des données séquentielles, telles que le langage naturel, pour des tâches "
                  "telles que la traduction et la synthèse de texte.",
              ]

              self.tokenizer_integration_test_util(
                  expected_encoding=UpperCAmelCase_       ,      model_name="moussaKam/mbarthez"       ,      revision="c2e4ecbca5e3cd2c37fe1ac285ca4fbdf1366fb6"       ,      sequences=UpperCAmelCase_       ,      )





