


'''simple docstring'''






import json
import os
import shutil
import tempfile
import unittest

from transformers import BatchEncoding, CanineTokenizer
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.tokenization_utils import AddedToken
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin
class 						__UpperCamelCase	(						lowercase__						,  unittest.TestCase							):
							lowercase			:		List[Any]   		=  CanineTokenizer
							lowercase			:		str   		=  False





							def 		a__   (  self		:Optional[Any]			):
									super().setUp()
									snake_case_		:							int			       =						CanineTokenizer()
									tokenizer.save_pretrained(self.tmpdirname			)





							@cached_property
							def 		a__   (  self		:Optional[int]			):
									return CanineTokenizer.from_pretrained("""google/canine-s"""			)





							def 		a__   (  self		:int  ,**_UpperCamelCase		:Dict			):
									snake_case_		:							Dict			       =						self.tokenizer_class.from_pretrained(self.tmpdirname  ,**_UpperCamelCase			)
									snake_case_		:							Optional[Any]			       =						1_0_2_4
									return tokenizer





							@require_torch
							def 		a__   (  self		:List[str]			):
									snake_case_		:							Union[str, Any]			       =						self.canine_tokenizer
									snake_case_		:							Optional[int]			       =						["""Life is like a box of chocolates.""", """You never know what you're gonna get."""]
									# fmt: off
									snake_case_		:							Dict			       =						[5_7_3_4_4, 7_6, 1_0_5, 1_0_2, 1_0_1, 3_2, 1_0_5, 1_1_5, 3_2, 1_0_8, 1_0_5, 1_0_7, 1_0_1, 3_2, 9_7, 3_2, 9_8, 1_1_1, 1_2_0, 3_2, 1_1_1, 1_0_2, 3_2, 9_9, 1_0_4, 1_1_1, 9_9, 1_1_1, 1_0_8, 9_7, 1_1_6, 1_0_1, 1_1_5, 4_6, 5_7_3_4_5, 0, 0, 0, 0]
									# fmt: on
									snake_case_		:							Any			       =						tokenizer(_UpperCamelCase  ,padding=_UpperCamelCase  ,return_tensors="""pt"""			)
									self.assertIsInstance(_UpperCamelCase  ,_UpperCamelCase			)

									snake_case_		:							Optional[Any]			       =						list(batch.input_ids.numpy()[0]			)

									self.assertListEqual(_UpperCamelCase  ,_UpperCamelCase			)

									self.assertEqual((2, 3_9)  ,batch.input_ids.shape			)
									self.assertEqual((2, 3_9)  ,batch.attention_mask.shape			)





							@require_torch
							def 		a__   (  self		:int			):
									snake_case_		:							Union[str, Any]			       =						self.canine_tokenizer
									snake_case_		:							Dict			       =						["""Once there was a man.""", """He wrote a test in HuggingFace Tranformers."""]
									snake_case_		:							int			       =						tokenizer(_UpperCamelCase  ,padding=_UpperCamelCase  ,return_tensors="""pt"""			)
									# check if input_ids, attention_mask and token_type_ids are returned
									self.assertIn("""input_ids"""  ,_UpperCamelCase			)
									self.assertIn("""attention_mask"""  ,_UpperCamelCase			)
									self.assertIn("""token_type_ids"""  ,_UpperCamelCase			)





							@require_torch
							def 		a__   (  self		:Tuple			):
									snake_case_		:							Union[str, Any]			       =						self.canine_tokenizer
									snake_case_		:							Tuple			       =						[
									    """What's the weater?""",
									    """It's about 25 degrees.""",
									]
									snake_case_		:							Optional[int]			       =						tokenizer(
									    text_target=_UpperCamelCase  ,max_length=3_2  ,padding="""max_length"""  ,truncation=_UpperCamelCase  ,return_tensors="""pt"""			)
									self.assertEqual(3_2  ,targets["""input_ids"""].shape[1]			)





							def 		a__   (  self		:List[str]			):
									# safety check on max_len default value so we are sure the test works
									snake_case_		:							Optional[int]			       =						self.get_tokenizers()
									for tokenizer in tokenizers:
											with self.subTest(F'''{tokenizer.__class__.__name__}'''			):
													self.assertNotEqual(tokenizer.model_max_length  ,4_2			)

        # Now let's start the test
									snake_case_		:							List[Any]			       =						self.get_tokenizers()
									for tokenizer in tokenizers:
											with self.subTest(F'''{tokenizer.__class__.__name__}'''			):
													# Isolate this from the other tests because we save additional tokens/etc
													snake_case_		:							Dict			       =						tempfile.mkdtemp()

													snake_case_		:							Optional[int]			       =						""" He is very happy, UNwant\u00E9d,running"""
													snake_case_		:							Optional[Any]			       =						tokenizer.encode(_UpperCamelCase  ,add_special_tokens=_UpperCamelCase			)
													tokenizer.save_pretrained(_UpperCamelCase			)

													snake_case_		:							Union[str, Any]			       =						tokenizer.__class__.from_pretrained(_UpperCamelCase			)
													snake_case_		:							Dict			       =						after_tokenizer.encode(_UpperCamelCase  ,add_special_tokens=_UpperCamelCase			)
													self.assertListEqual(_UpperCamelCase  ,_UpperCamelCase			)

													shutil.rmtree(_UpperCamelCase			)

									snake_case_		:							Optional[int]			       =						self.get_tokenizers(model_max_length=4_2			)
									for tokenizer in tokenizers:
											with self.subTest(F'''{tokenizer.__class__.__name__}'''			):
													# Isolate this from the other tests because we save additional tokens/etc
													snake_case_		:							List[Any]			       =						tempfile.mkdtemp()

													snake_case_		:							List[str]			       =						""" He is very happy, UNwant\u00E9d,running"""

													snake_case_		:							Any			       =						tokenizer.additional_special_tokens

													# We can add a new special token for Canine as follows:
													snake_case_		:							List[Any]			       =						chr(0xE_0_0_7			)
													additional_special_tokens.append(_UpperCamelCase			)
													tokenizer.add_special_tokens({"""additional_special_tokens""": additional_special_tokens}			)
													snake_case_		:							Tuple			       =						tokenizer.encode(_UpperCamelCase  ,add_special_tokens=_UpperCamelCase			)
													tokenizer.save_pretrained(_UpperCamelCase			)

													snake_case_		:							Optional[Any]			       =						tokenizer.__class__.from_pretrained(_UpperCamelCase			)
													snake_case_		:							Tuple			       =						after_tokenizer.encode(_UpperCamelCase  ,add_special_tokens=_UpperCamelCase			)
													self.assertListEqual(_UpperCamelCase  ,_UpperCamelCase			)
													self.assertIn(_UpperCamelCase  ,after_tokenizer.additional_special_tokens			)
													self.assertEqual(after_tokenizer.model_max_length  ,4_2			)

													snake_case_		:							str			       =						tokenizer.__class__.from_pretrained(_UpperCamelCase  ,model_max_length=4_3			)
													self.assertEqual(tokenizer.model_max_length  ,4_3			)

													shutil.rmtree(_UpperCamelCase			)





							def 		a__   (  self		:Union[str, Any]			):
									snake_case_		:							Dict			       =						self.get_tokenizers(do_lower_case=_UpperCamelCase			)
									for tokenizer in tokenizers:
											with self.subTest(F'''{tokenizer.__class__.__name__}'''			):
													snake_case_		:							List[str]			       =						self.get_clean_sequence(_UpperCamelCase			)

													# a special token for Canine can be defined as follows:
													snake_case_		:							Optional[int]			       =						0xE_0_0_5
													snake_case_		:							int			       =						chr(_UpperCamelCase			)

													tokenizer.add_special_tokens({"""cls_token""": special_token}			)
													snake_case_		:							str			       =						tokenizer.encode(_UpperCamelCase  ,add_special_tokens=_UpperCamelCase			)
													self.assertEqual(len(_UpperCamelCase			)  ,1			)

													snake_case_		:							List[str]			       =						tokenizer.decode(ids + encoded_special_token  ,clean_up_tokenization_spaces=_UpperCamelCase			)
													snake_case_		:							Any			       =						tokenizer.encode(_UpperCamelCase  ,add_special_tokens=_UpperCamelCase			)

													snake_case_		:							List[str]			       =						tokenizer.encode(_UpperCamelCase  ,add_special_tokens=_UpperCamelCase			)
													snake_case_		:							Optional[Any]			       =						tokenizer.encode(_UpperCamelCase  ,add_special_tokens=_UpperCamelCase			)
													self.assertEqual(_UpperCamelCase  ,input_encoded + special_token_id			)

													snake_case_		:							Optional[Any]			       =						tokenizer.decode(_UpperCamelCase  ,skip_special_tokens=_UpperCamelCase			)
													self.assertTrue(special_token not in decoded			)





							def 		a__   (  self		:Any			):
									snake_case_		:							str			       =						self.get_tokenizers(do_lower_case=_UpperCamelCase			)
									for tokenizer in tokenizers:
											with self.subTest(F'''{tokenizer.__class__.__name__}'''			):
													snake_case_		:							Optional[Any]			       =						chr(0xE_0_0_5			)
													snake_case_		:							List[str]			       =						chr(0xE_0_0_6			)

													# `add_tokens` method stores special tokens only in `tokenizer.unique_no_split_tokens`. (in tokenization_utils.py)
													tokenizer.add_tokens([SPECIAL_TOKEN_1]  ,special_tokens=_UpperCamelCase			)
													# `add_special_tokens` method stores special tokens in `tokenizer.additional_special_tokens`,
													# which also occur in `tokenizer.all_special_tokens`. (in tokenization_utils_base.py)
													tokenizer.add_special_tokens({"""additional_special_tokens""": [SPECIAL_TOKEN_2]}			)

													snake_case_		:							Optional[int]			       =						tokenizer.tokenize(_UpperCamelCase			)
													snake_case_		:							int			       =						tokenizer.tokenize(_UpperCamelCase			)

													self.assertEqual(len(_UpperCamelCase			)  ,1			)
													self.assertEqual(len(_UpperCamelCase			)  ,1			)
													self.assertEqual(token_a[0]  ,_UpperCamelCase			)
													self.assertEqual(token_a[0]  ,_UpperCamelCase			)





							@require_tokenizers
							def 		a__   (  self		:int			):
									snake_case_		:							Tuple			       =						self.get_tokenizers(do_lower_case=_UpperCamelCase			)
									for tokenizer in tokenizers:
											with self.subTest(F'''{tokenizer.__class__.__name__}'''			):
													# a special token for Canine can be defined as follows:
													snake_case_		:							Tuple			       =						0xE_0_0_6
													snake_case_		:							str			       =						chr(_UpperCamelCase			)

													snake_case_		:							List[Any]			       =						AddedToken(_UpperCamelCase  ,lstrip=_UpperCamelCase			)
													tokenizer.add_special_tokens({"""additional_special_tokens""": [new_token]}			)

													with tempfile.TemporaryDirectory() as tmp_dir_name:
															tokenizer.save_pretrained(_UpperCamelCase			)
															tokenizer.from_pretrained(_UpperCamelCase			)





							def 		a__   (  self		:int			):
									snake_case_		:							List[Any]			       =						[]
									if self.test_slow_tokenizer:
											tokenizer_list.append((self.tokenizer_class, self.get_tokenizer())			)

									if self.test_rust_tokenizer:
											tokenizer_list.append((self.rust_tokenizer_class, self.get_rust_tokenizer())			)

									for tokenizer_class, tokenizer_utils in tokenizer_list:
											with tempfile.TemporaryDirectory() as tmp_dir:
													tokenizer_utils.save_pretrained(_UpperCamelCase			)

													with open(os.path.join(_UpperCamelCase  ,"""special_tokens_map.json"""			)  ,encoding="""utf-8"""			) as json_file:
															snake_case_		:							Dict			       =						json.load(_UpperCamelCase			)

													with open(os.path.join(_UpperCamelCase  ,"""tokenizer_config.json"""			)  ,encoding="""utf-8"""			) as json_file:
															snake_case_		:							Optional[int]			       =						json.load(_UpperCamelCase			)

													# a special token for Canine can be defined as follows:
													snake_case_		:							int			       =						0xE_0_0_6
													snake_case_		:							List[str]			       =						chr(_UpperCamelCase			)

													snake_case_		:							Union[str, Any]			       =						[new_token_a]
													snake_case_		:							List[str]			       =						[new_token_a]

													with open(os.path.join(_UpperCamelCase  ,"""special_tokens_map.json"""			)  ,"""w"""  ,encoding="""utf-8"""			) as outfile:
															json.dump(_UpperCamelCase  ,_UpperCamelCase			)
													with open(os.path.join(_UpperCamelCase  ,"""tokenizer_config.json"""			)  ,"""w"""  ,encoding="""utf-8"""			) as outfile:
															json.dump(_UpperCamelCase  ,_UpperCamelCase			)

													# the following checks allow us to verify that our test works as expected, i.e. that the tokenizer takes
													# into account the new value of additional_special_tokens given in the "tokenizer_config.json" and
													# "special_tokens_map.json" files
													snake_case_		:							List[str]			       =						tokenizer_class.from_pretrained(_UpperCamelCase  ,extra_ids=0			)
													self.assertIn(_UpperCamelCase  ,tokenizer_without_change_in_init.additional_special_tokens			)
													# self.assertIn("an_additional_special_token",tokenizer_without_change_in_init.get_vocab()) # ByT5Tokenization no vocab
													self.assertEqual(
													    [new_token_a]  ,tokenizer_without_change_in_init.convert_ids_to_tokens(
													        tokenizer_without_change_in_init.convert_tokens_to_ids([new_token_a]			)			)  ,)

													snake_case_		:							Dict			       =						0xE_0_0_7
													snake_case_		:							Optional[int]			       =						chr(_UpperCamelCase			)
													# Now we test that we can change the value of additional_special_tokens in the from_pretrained
													snake_case_		:							Optional[int]			       =						[AddedToken(_UpperCamelCase  ,lstrip=_UpperCamelCase			)]
													snake_case_		:							List[Any]			       =						tokenizer_class.from_pretrained(
													    _UpperCamelCase  ,additional_special_tokens=_UpperCamelCase  ,extra_ids=0			)

													self.assertIn(_UpperCamelCase  ,tokenizer.additional_special_tokens			)
													# self.assertIn(new_token_2,tokenizer.get_vocab()) # ByT5Tokenization no vocab
													self.assertEqual(
													    [new_token_a]  ,tokenizer.convert_ids_to_tokens(tokenizer.convert_tokens_to_ids([new_token_a]			)			)			)





							@require_tokenizers
							def 		a__   (  self		:List[str]			):
									snake_case_		:							Union[str, Any]			       =						self.get_tokenizers(do_lower_case=_UpperCamelCase			)
									for tokenizer in tokenizers:
											with self.subTest(F'''{tokenizer.__class__.__name__}'''			):
													snake_case_		:							Any			       =						"""hello world"""
													if self.space_between_special_tokens:
															snake_case_		:							int			       =						"""[CLS] hello world [SEP]"""
													else:
															snake_case_		:							List[str]			       =						input
													snake_case_		:							Any			       =						tokenizer.encode(_UpperCamelCase  ,add_special_tokens=_UpperCamelCase			)
													snake_case_		:							Union[str, Any]			       =						tokenizer.decode(_UpperCamelCase  ,spaces_between_special_tokens=self.space_between_special_tokens			)
													self.assertIn(_UpperCamelCase  ,[output, output.lower()]			)





							def 		a__   (  self		:Dict			):
									snake_case_		:							Optional[Any]			       =						self.get_tokenizers()
									for tokenizer in tokenizers:
											with self.subTest(F'''{tokenizer.__class__.__name__}'''			):
													snake_case_		:							Tuple			       =						[
													    """bos_token""",
													    """eos_token""",
													    """unk_token""",
													    """sep_token""",
													    """pad_token""",
													    """cls_token""",
													    """mask_token""",
													]

									snake_case_		:							Tuple			       =						"""a"""
									snake_case_		:							Optional[Any]			       =						ord(_UpperCamelCase			)

									for attr in attributes_list:
											setattr(_UpperCamelCase  ,attr + """_id"""  ,_UpperCamelCase			)
											self.assertEqual(getattr(_UpperCamelCase  ,_UpperCamelCase			)  ,_UpperCamelCase			)
											self.assertEqual(getattr(_UpperCamelCase  ,attr + """_id"""			)  ,_UpperCamelCase			)

											setattr(_UpperCamelCase  ,attr + """_id"""  ,_UpperCamelCase			)
											self.assertEqual(getattr(_UpperCamelCase  ,_UpperCamelCase			)  ,_UpperCamelCase			)
											self.assertEqual(getattr(_UpperCamelCase  ,attr + """_id"""			)  ,_UpperCamelCase			)

									setattr(_UpperCamelCase  ,"""additional_special_tokens_ids"""  ,[]			)
									self.assertListEqual(getattr(_UpperCamelCase  ,"""additional_special_tokens"""			)  ,[]			)
									self.assertListEqual(getattr(_UpperCamelCase  ,"""additional_special_tokens_ids"""			)  ,[]			)

									snake_case_		:							Optional[int]			       =						0xE_0_0_6
									snake_case_		:							Union[str, Any]			       =						chr(_UpperCamelCase			)
									setattr(_UpperCamelCase  ,"""additional_special_tokens_ids"""  ,[additional_special_token_id]			)
									self.assertListEqual(getattr(_UpperCamelCase  ,"""additional_special_tokens"""			)  ,[additional_special_token]			)
									self.assertListEqual(getattr(_UpperCamelCase  ,"""additional_special_tokens_ids"""			)  ,[additional_special_token_id]			)





							def 		a__   (  self		:Dict			):
									pass





							def 		a__   (  self		:Tuple			):
									pass





							def 		a__   (  self		:Tuple			):
									pass





							def 		a__   (  self		:Any			):
									pass





							def 		a__   (  self		:List[Any]			):
									pass





							def 		a__   (  self		:Optional[Any]			):
									pass





							def 		a__   (  self		:Dict			):
									pass





							def 		a__   (  self		:Optional[int]			):
									pass



'''simple docstring'''






import copy
import os
from typing import Union

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


__A	:	str     =     logging.get_logger(__name__)

__A	:	Dict     =     {
    'google/pix2struct-textcaps-base': (
        'https://huggingface.co/google/pix2struct-textcaps-base/resolve/main/config.json'
    ),
}
class 						__UpperCamelCase	(						lowercase__							):
							lowercase			:		Tuple   		=  'pix2struct_text_model'
							lowercase			:		int   		=  ['past_key_values']
							lowercase			:		int   		=  {
							    'hidden_size': 'hidden_size',
							    'num_attention_heads': 'num_heads',
							    'num_hidden_layers': 'num_layers',
							}





							def __init__(  self		:int  ,_UpperCamelCase		:List[str]=5_0_2_4_4  ,_UpperCamelCase		:Optional[Any]=7_6_8  ,_UpperCamelCase		:Dict=6_4  ,_UpperCamelCase		:Dict=2_0_4_8  ,_UpperCamelCase		:Optional[int]=1_2  ,_UpperCamelCase		:Union[str, Any]=1_2  ,_UpperCamelCase		:List[str]=3_2  ,_UpperCamelCase		:Union[str, Any]=1_2_8  ,_UpperCamelCase		:Tuple=0.1  ,_UpperCamelCase		:List[str]=1E-6  ,_UpperCamelCase		:List[Any]=1.0  ,_UpperCamelCase		:Optional[int]="gelu_new"  ,_UpperCamelCase		:Dict=0  ,_UpperCamelCase		:List[str]=False  ,_UpperCamelCase		:Optional[int]=0  ,_UpperCamelCase		:Dict=1  ,_UpperCamelCase		:List[Any]=False  ,_UpperCamelCase		:Tuple=True  ,**_UpperCamelCase		:List[Any]  ,):
									snake_case_		:							List[str]			       =						vocab_size
									snake_case_		:							Any			       =						hidden_size
									snake_case_		:							Any			       =						d_kv
									snake_case_		:							List[Any]			       =						d_ff
									snake_case_		:							Union[str, Any]			       =						num_layers
									snake_case_		:							Union[str, Any]			       =						num_heads
									snake_case_		:							str			       =						relative_attention_num_buckets
									snake_case_		:							Optional[int]			       =						relative_attention_max_distance
									snake_case_		:							Tuple			       =						dropout_rate
									snake_case_		:							Tuple			       =						layer_norm_epsilon
									snake_case_		:							Any			       =						initializer_factor
									snake_case_		:							List[Any]			       =						use_cache

									snake_case_		:							Optional[int]			       =						eos_token_id
									snake_case_		:							List[Any]			       =						decoder_start_token_id

									# for backwards compatibility
									snake_case_		:							List[str]			       =						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 		a__   (  cls		:Optional[int]  ,_UpperCamelCase		:Union[str, os.PathLike]  ,**_UpperCamelCase		:Optional[int]			):
									cls._set_token_in_kwargs(_UpperCamelCase			)

									snake_case_       ,      snake_case_		:							Optional[Any]			       =						cls.get_config_dict(_UpperCamelCase  ,**_UpperCamelCase			)

									# get the text config dict if we are loading from Pix2StructConfig
									if config_dict.get("""model_type"""			) == "pix2struct":
											snake_case_		:							Optional[int]			       =						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 						__UpperCamelCase	(						lowercase__							):
							lowercase			:		Dict   		=  'pix2struct_vision_model'





							def __init__(  self		:List[str]  ,_UpperCamelCase		:Any=7_6_8  ,_UpperCamelCase		:List[str]=7_6_8  ,_UpperCamelCase		:List[Any]=2_0_4_8  ,_UpperCamelCase		:Union[str, Any]=6_4  ,_UpperCamelCase		:int=1_2  ,_UpperCamelCase		:int=1_2  ,_UpperCamelCase		:Any="gelu_new"  ,_UpperCamelCase		:Optional[int]=1E-6  ,_UpperCamelCase		:List[Any]=0.0  ,_UpperCamelCase		:Union[str, Any]=0.0  ,_UpperCamelCase		:int=1E-1_0  ,_UpperCamelCase		:str=1.0  ,_UpperCamelCase		:Optional[int]=4_0_9_6  ,_UpperCamelCase		:str=3_2  ,_UpperCamelCase		:Union[str, Any]=1_2_8  ,**_UpperCamelCase		:Union[str, Any]  ,):
									super().__init__(**_UpperCamelCase			)

									snake_case_		:							str			       =						hidden_size
									snake_case_		:							Tuple			       =						patch_embed_hidden_size
									snake_case_		:							Optional[int]			       =						d_ff
									snake_case_		:							Dict			       =						dropout_rate
									snake_case_		:							Optional[int]			       =						num_hidden_layers
									snake_case_		:							Dict			       =						num_attention_heads
									snake_case_		:							Union[str, Any]			       =						initializer_range
									snake_case_		:							Optional[Any]			       =						initializer_factor
									snake_case_		:							List[str]			       =						attention_dropout
									snake_case_		:							List[str]			       =						layer_norm_eps
									snake_case_		:							List[str]			       =						dense_act_fn
									snake_case_		:							int			       =						seq_len
									snake_case_		:							str			       =						relative_attention_num_buckets
									snake_case_		:							Tuple			       =						relative_attention_max_distance
									snake_case_		:							List[str]			       =						d_kv





							@classmethod
							def 		a__   (  cls		:Optional[Any]  ,_UpperCamelCase		:Union[str, os.PathLike]  ,**_UpperCamelCase		:List[str]			):
									cls._set_token_in_kwargs(_UpperCamelCase			)

									snake_case_       ,      snake_case_		:							Tuple			       =						cls.get_config_dict(_UpperCamelCase  ,**_UpperCamelCase			)

									# get the vision config dict if we are loading from Pix2StructConfig
									if config_dict.get("""model_type"""			) == "pix2struct":
											snake_case_		:							Dict			       =						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 						__UpperCamelCase	(						lowercase__							):
							lowercase			:		Union[str, Any]   		=  'pix2struct'
							lowercase			:		int   		=  True





							def __init__(  self		:int  ,_UpperCamelCase		:Tuple=None  ,_UpperCamelCase		:Optional[int]=None  ,_UpperCamelCase		:Dict=1.0  ,_UpperCamelCase		:Optional[int]=0.02  ,_UpperCamelCase		:Tuple=False  ,_UpperCamelCase		:List[str]=False  ,_UpperCamelCase		:Dict=True  ,**_UpperCamelCase		:Union[str, Any]  ,):
									super().__init__(tie_word_embeddings=_UpperCamelCase  ,is_encoder_decoder=_UpperCamelCase  ,**_UpperCamelCase			)

									if text_config is None:
											snake_case_		:							Optional[int]			       =						{}
											logger.info("""text_config is None. Initializing the Pix2StructTextConfig with default values."""			)

									if vision_config is None:
											snake_case_		:							List[str]			       =						{}
											logger.info("""vision_config is None. Initializing the Pix2StructVisionConfig with default values."""			)

									snake_case_		:							Optional[int]			       =						PixaStructTextConfig(**_UpperCamelCase			)
									snake_case_		:							List[str]			       =						PixaStructVisionConfig(**_UpperCamelCase			)

									snake_case_		:							Any			       =						self.text_config.decoder_start_token_id
									snake_case_		:							List[Any]			       =						self.text_config.pad_token_id
									snake_case_		:							Optional[int]			       =						self.text_config.eos_token_id

									snake_case_		:							Optional[int]			       =						initializer_factor
									snake_case_		:							List[str]			       =						initializer_range

									snake_case_		:							Union[str, Any]			       =						self.initializer_range
									snake_case_		:							str			       =						self.initializer_range

									snake_case_		:							Dict			       =						is_vqa





							@classmethod
							def 		a__   (  cls		:Any  ,_UpperCamelCase		:PixaStructTextConfig  ,_UpperCamelCase		:PixaStructVisionConfig  ,**_UpperCamelCase		:Any			):
									return cls(text_config=text_config.to_dict()  ,vision_config=vision_config.to_dict()  ,**_UpperCamelCase			)





							def 		a__   (  self		:Optional[int]			):
									snake_case_		:							int			       =						copy.deepcopy(self.__dict__			)
									snake_case_		:							str			       =						self.text_config.to_dict()
									snake_case_		:							Tuple			       =						self.vision_config.to_dict()
									snake_case_		:							Any			       =						self.__class__.model_type
									return output