
import copy
import os
from typing import Union

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


lowerCamelCase_						: Dict         =  logging.get_logger(__name__)

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

class        _UpperCamelCase				(					_snake_case  ):






			'''simple docstring'''

			__UpperCamelCase      :						Optional[int]					      = """pix2struct_text_model"""
			__UpperCamelCase      :						Optional[Any]					      = ["""past_key_values"""]
			__UpperCamelCase      :						List[Any]					      = {
			    """hidden_size""": """hidden_size""",
			    """num_attention_heads""": """num_heads""",
			    """num_hidden_layers""": """num_layers""",
			}

			def __init__(       self :       Optional[Any]						,	snake_case_ :       str=5_0244						,	snake_case_ :       List[Any]=768						,	snake_case_ :       Any=64						,	snake_case_ :       int=2048						,	snake_case_ :       int=12						,	snake_case_ :       List[Any]=12						,	snake_case_ :       Tuple=32						,	snake_case_ :       Optional[int]=128						,	snake_case_ :       int=0.1						,	snake_case_ :       Optional[int]=1e-6						,	snake_case_ :       List[Any]=1.0						,	snake_case_ :       List[str]="gelu_new"						,	snake_case_ :       List[str]=0						,	snake_case_ :       str=False						,	snake_case_ :       List[str]=0						,	snake_case_ :       str=1						,	snake_case_ :       List[str]=False						,	snake_case_ :       Dict=True						,	**snake_case_ :       Any						,	):
							UpperCamelCase_:							int	=						vocab_size
							UpperCamelCase_:							List[str]	=						hidden_size
							UpperCamelCase_:							int	=						d_kv
							UpperCamelCase_:							Any	=						d_ff
							UpperCamelCase_:							int	=						num_layers
							UpperCamelCase_:							List[str]	=						num_heads
							UpperCamelCase_:							Any	=						relative_attention_num_buckets
							UpperCamelCase_:							Union[str, Any]	=						relative_attention_max_distance
							UpperCamelCase_:							str	=						dropout_rate
							UpperCamelCase_:							str	=						layer_norm_epsilon
							UpperCamelCase_:							Optional[Any]	=						initializer_factor
							UpperCamelCase_:							Any	=						use_cache

							UpperCamelCase_:							Dict	=						eos_token_id
							UpperCamelCase_:							Dict	=						decoder_start_token_id

							# for backwards compatibility
							UpperCamelCase_:							List[str]	=						dense_act_fn

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




			@classmethod
			def       lowerCAmelCase__	(       cls :       Tuple						,	snake_case_ :       Optional[Any]						,	**snake_case_ :       List[str]      ):
							cls._set_token_in_kwargs(snake_case_      )

							UpperCamelCase_:							Dict	=						cls.get_config_dict(snake_case_						,	**snake_case_      )

							# get the text config dict if we are loading from Pix2StructConfig
							if config_dict.get("""model_type"""      ) == "pix2struct":
											UpperCamelCase_:							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(snake_case_						,	**snake_case_      )




class        _UpperCamelCase				(					_snake_case  ):






			'''simple docstring'''

			__UpperCamelCase      :						List[str]					      = """pix2struct_vision_model"""

			def __init__(       self :       Optional[Any]						,	snake_case_ :       int=768						,	snake_case_ :       Dict=768						,	snake_case_ :       List[Any]=2048						,	snake_case_ :       Any=64						,	snake_case_ :       Optional[Any]=12						,	snake_case_ :       Optional[int]=12						,	snake_case_ :       Tuple="gelu_new"						,	snake_case_ :       Optional[int]=1e-6						,	snake_case_ :       List[Any]=0.0						,	snake_case_ :       Dict=0.0						,	snake_case_ :       Optional[int]=1e-10						,	snake_case_ :       Dict=1.0						,	snake_case_ :       Any=4096						,	snake_case_ :       List[str]=32						,	snake_case_ :       List[str]=128						,	**snake_case_ :       List[str]						,	):
							super().__init__(**snake_case_      )

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




			@classmethod
			def       lowerCAmelCase__	(       cls :       Union[str, Any]						,	snake_case_ :       int						,	**snake_case_ :       Optional[Any]      ):
							cls._set_token_in_kwargs(snake_case_      )

							UpperCamelCase_:							str	=						cls.get_config_dict(snake_case_						,	**snake_case_      )

							# get the vision config dict if we are loading from Pix2StructConfig
							if config_dict.get("""model_type"""      ) == "pix2struct":
											UpperCamelCase_:							Optional[Any]	=						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(snake_case_						,	**snake_case_      )




class        _UpperCamelCase				(					_snake_case  ):






			'''simple docstring'''

			__UpperCamelCase      :						List[Any]					      = """pix2struct"""
			__UpperCamelCase      :						str					      = True

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

							if text_config is None:
											UpperCamelCase_:							List[str]	=						{}
											logger.info("""text_config is None. Initializing the Pix2StructTextConfig with default values."""      )

							if vision_config is None:
											UpperCamelCase_:							Optional[Any]	=						{}
											logger.info("""vision_config is None. Initializing the Pix2StructVisionConfig with default values."""      )

							UpperCamelCase_:							Tuple	=						PixaStructTextConfig(**snake_case_      )
							UpperCamelCase_:							Optional[int]	=						PixaStructVisionConfig(**snake_case_      )

							UpperCamelCase_:							List[str]	=						self.text_config.decoder_start_token_id
							UpperCamelCase_:							List[str]	=						self.text_config.pad_token_id
							UpperCamelCase_:							Dict	=						self.text_config.eos_token_id

							UpperCamelCase_:							Union[str, Any]	=						initializer_factor
							UpperCamelCase_:							str	=						initializer_range

							UpperCamelCase_:							Any	=						self.initializer_range
							UpperCamelCase_:							Optional[int]	=						self.initializer_range

							UpperCamelCase_:							List[str]	=						is_vqa

			@classmethod
			def       lowerCAmelCase__	(       cls :       Optional[Any]						,	snake_case_ :       Dict						,	snake_case_ :       Optional[Any]						,	**snake_case_ :       int      ):
							return cls(text_config=text_config.to_dict()						,	vision_config=vision_config.to_dict()						,	**snake_case_      )




			def       lowerCAmelCase__	(       self :       str      ):
							UpperCamelCase_:							List[str]	=						copy.deepcopy(self.__dict__      )
							UpperCamelCase_:							str	=						self.text_config.to_dict()
							UpperCamelCase_:							Optional[int]	=						self.vision_config.to_dict()
							UpperCamelCase_:							Optional[int]	=						self.__class__.model_type
							return output








import shutil
import tempfile
import unittest

from transformers import ClapFeatureExtractor, ClapProcessor, RobertaTokenizer, RobertaTokenizerFast
from transformers.testing_utils import require_sentencepiece, require_torchaudio

from .test_feature_extraction_clap import floats_list

@require_torchaudio
@require_sentencepiece
class        _UpperCamelCase				(					unittest.TestCase  ):






			'''simple docstring'''

			def       lowerCAmelCase__	(       self :       List[str]      ):
							UpperCamelCase_:							Union[str, Any]	=						"""laion/clap-htsat-unfused"""
							UpperCamelCase_:							List[str]	=						tempfile.mkdtemp()

			def       lowerCAmelCase__	(       self :       Tuple						,	**snake_case_ :       Optional[Any]      ):
							return RobertaTokenizer.from_pretrained(self.checkpoint						,	**snake_case_      )

			def       lowerCAmelCase__	(       self :       str						,	**snake_case_ :       Any      ):
							return ClapFeatureExtractor.from_pretrained(self.checkpoint						,	**snake_case_      )

			def       lowerCAmelCase__	(       self :       Tuple      ):
							shutil.rmtree(self.tmpdirname      )

			def       lowerCAmelCase__	(       self :       str      ):
							UpperCamelCase_:							Union[str, Any]	=						self.get_tokenizer()
							UpperCamelCase_:							int	=						self.get_feature_extractor()

							UpperCamelCase_:							List[Any]	=						ClapProcessor(tokenizer=snake_case_						,	feature_extractor=snake_case_      )

							processor.save_pretrained(self.tmpdirname      )
							UpperCamelCase_:							Any	=						ClapProcessor.from_pretrained(self.tmpdirname      )

							self.assertEqual(processor.tokenizer.get_vocab()						,	tokenizer.get_vocab()      )
							self.assertIsInstance(processor.tokenizer						,	snake_case_      )

							self.assertEqual(processor.feature_extractor.to_json_string()						,	feature_extractor.to_json_string()      )
							self.assertIsInstance(processor.feature_extractor						,	snake_case_      )

			def       lowerCAmelCase__	(       self :       Optional[Any]      ):
							UpperCamelCase_:							Any	=						ClapProcessor(tokenizer=self.get_tokenizer()						,	feature_extractor=self.get_feature_extractor()      )
							processor.save_pretrained(self.tmpdirname      )

							UpperCamelCase_:							Optional[int]	=						self.get_tokenizer(bos_token="""(BOS)"""						,	eos_token="""(EOS)"""      )
							UpperCamelCase_:							Dict	=						self.get_feature_extractor(do_normalize=snake_case_						,	padding_value=1.0      )

							UpperCamelCase_:							List[str]	=						ClapProcessor.from_pretrained(
							    self.tmpdirname						,	bos_token="""(BOS)"""						,	eos_token="""(EOS)"""						,	do_normalize=snake_case_						,	padding_value=1.0      )

							self.assertEqual(processor.tokenizer.get_vocab()						,	tokenizer_add_kwargs.get_vocab()      )
							self.assertIsInstance(processor.tokenizer						,	snake_case_      )

							self.assertEqual(processor.feature_extractor.to_json_string()						,	feature_extractor_add_kwargs.to_json_string()      )
							self.assertIsInstance(processor.feature_extractor						,	snake_case_      )

			def       lowerCAmelCase__	(       self :       Tuple      ):
							UpperCamelCase_:							int	=						self.get_feature_extractor()
							UpperCamelCase_:							Optional[Any]	=						self.get_tokenizer()

							UpperCamelCase_:							Dict	=						ClapProcessor(tokenizer=snake_case_						,	feature_extractor=snake_case_      )

							UpperCamelCase_:							Optional[Any]	=						floats_list((3, 1000)      )

							UpperCamelCase_:							List[str]	=						feature_extractor(snake_case_						,	return_tensors="""np"""      )
							UpperCamelCase_:							int	=						processor(audios=snake_case_						,	return_tensors="""np"""      )

							for key in input_feat_extract.keys():
											self.assertAlmostEqual(input_feat_extract[key].sum()						,	input_processor[key].sum()						,	delta=1e-2      )

			def       lowerCAmelCase__	(       self :       Union[str, Any]      ):
							UpperCamelCase_:							List[Any]	=						self.get_feature_extractor()
							UpperCamelCase_:							List[str]	=						self.get_tokenizer()

							UpperCamelCase_:							List[Any]	=						ClapProcessor(tokenizer=snake_case_						,	feature_extractor=snake_case_      )

							UpperCamelCase_:							Dict	=						"""This is a test string"""

							UpperCamelCase_:							Tuple	=						processor(text=snake_case_      )

							UpperCamelCase_:							Optional[int]	=						tokenizer(snake_case_      )

							for key in encoded_tok.keys():
											self.assertListEqual(encoded_tok[key]						,	encoded_processor[key]      )

			def       lowerCAmelCase__	(       self :       Any      ):
							UpperCamelCase_:							List[str]	=						self.get_feature_extractor()
							UpperCamelCase_:							Any	=						self.get_tokenizer()

							UpperCamelCase_:							Optional[Any]	=						ClapProcessor(tokenizer=snake_case_						,	feature_extractor=snake_case_      )

							UpperCamelCase_:							str	=						[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

							UpperCamelCase_:							Tuple	=						processor.batch_decode(snake_case_      )
							UpperCamelCase_:							str	=						tokenizer.batch_decode(snake_case_      )

							self.assertListEqual(snake_case_						,	snake_case_      )




			def       lowerCAmelCase__	(       self :       List[str]      ):
							UpperCamelCase_:							Any	=						self.get_feature_extractor()
							UpperCamelCase_:							str	=						self.get_tokenizer()

							UpperCamelCase_:							Optional[Any]	=						ClapProcessor(tokenizer=snake_case_						,	feature_extractor=snake_case_      )

							self.assertListEqual(
							    processor.model_input_names[2:]						,	feature_extractor.model_input_names						,	msg="""`processor` and `feature_extractor` model input names do not match"""						,	)






