





import os
import shutil
import tempfile
import unittest

import numpy as np

from transformers import AutoTokenizer, BarkProcessor
from transformers.testing_utils import require_torch, slow




@require_torch
class   UpperCamelCase_       (     unittest.TestCase    ):


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



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__			=      """ylacombe/bark-small"""
									SCREAMING_SNAKE_CASE__			=      tempfile.mkdtemp()
									SCREAMING_SNAKE_CASE__			=      """en_speaker_1"""
									SCREAMING_SNAKE_CASE__			=      """This is a test string"""
									SCREAMING_SNAKE_CASE__			=      """speaker_embeddings_path.json"""
									SCREAMING_SNAKE_CASE__			=      """speaker_embeddings"""


							def      _snake_case  (							self    :List[str]							,				**__A    :List[str]		)   ->   List[str]:



									"""simple docstring"""



									return AutoTokenizer.from_pretrained(self.checkpoint							,				**__A		)


							def      _snake_case  (							self    :List[str]		)   ->   Tuple:



									"""simple docstring"""



									shutil.rmtree(self.tmpdirname		)


							def      _snake_case  (							self    :List[str]		)   ->   Optional[int]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__			=      self.get_tokenizer()

									SCREAMING_SNAKE_CASE__			=      BarkProcessor(tokenizer=__A		)

									processor.save_pretrained(self.tmpdirname		)
									SCREAMING_SNAKE_CASE__			=      BarkProcessor.from_pretrained(self.tmpdirname		)

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


							@slow
							def      _snake_case  (							self    :Any		)   ->   str:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__			=      BarkProcessor.from_pretrained(
									    pretrained_processor_name_or_path=self.checkpoint							,				speaker_embeddings_dict_path=self.speaker_embeddings_dict_path							,				)
									processor.save_pretrained(
									    self.tmpdirname							,				speaker_embeddings_dict_path=self.speaker_embeddings_dict_path							,				speaker_embeddings_directory=self.speaker_embeddings_directory							,				)

									SCREAMING_SNAKE_CASE__			=      self.get_tokenizer(bos_token="""(BOS)"""							,				eos_token="""(EOS)"""		)

									SCREAMING_SNAKE_CASE__			=      BarkProcessor.from_pretrained(
									    self.tmpdirname							,				self.speaker_embeddings_dict_path							,				bos_token="""(BOS)"""							,				eos_token="""(EOS)"""							,				)

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


							def      _snake_case  (							self    :Dict		)   ->   List[str]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__			=      BarkProcessor.from_pretrained(
									    pretrained_processor_name_or_path=self.checkpoint							,				speaker_embeddings_dict_path=self.speaker_embeddings_dict_path							,				)

									SCREAMING_SNAKE_CASE__			=      35
									SCREAMING_SNAKE_CASE__			=      2
									SCREAMING_SNAKE_CASE__			=      8

									SCREAMING_SNAKE_CASE__			=      {
									    """semantic_prompt""": np.ones(__A		),
									    """coarse_prompt""": np.ones((nb_codebooks_coarse, seq_len)		),
									    """fine_prompt""": np.ones((nb_codebooks_total, seq_len)		),
									}

									# test providing already loaded voice_preset
									SCREAMING_SNAKE_CASE__			=      processor(text=self.input_string							,				voice_preset=__A		)

									SCREAMING_SNAKE_CASE__			=      inputs["""history_prompt"""]
									for key in voice_preset:
											self.assertListEqual(voice_preset[key].tolist()							,				processed_voice_preset.get(__A							,				np.array([]		)		).tolist()		)

									# test loading voice preset from npz file
									SCREAMING_SNAKE_CASE__			=      os.path.join(self.tmpdirname							,				"""file.npz"""		)
									np.savez(__A							,				**__A		)
									SCREAMING_SNAKE_CASE__			=      processor(text=self.input_string							,				voice_preset=__A		)
									SCREAMING_SNAKE_CASE__			=      inputs["""history_prompt"""]

									for key in voice_preset:
											self.assertListEqual(voice_preset[key].tolist()							,				processed_voice_preset.get(__A							,				np.array([]		)		).tolist()		)

									# test loading voice preset from the hub
									SCREAMING_SNAKE_CASE__			=      processor(text=self.input_string							,				voice_preset=self.voice_preset		)


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



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__			=      self.get_tokenizer()

									SCREAMING_SNAKE_CASE__			=      BarkProcessor(tokenizer=__A		)

									SCREAMING_SNAKE_CASE__			=      processor(text=self.input_string		)

									SCREAMING_SNAKE_CASE__			=      tokenizer(
									    self.input_string							,				padding="""max_length"""							,				max_length=256							,				add_special_tokens=__A							,				return_attention_mask=__A							,				return_token_type_ids=__A							,				)

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






import copy
import os
from typing import Union

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


_lowerCamelCase   				=			logging.get_logger(__name__)

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




class   UpperCamelCase_       (     UpperCamelCase__    ):
							lowerCamelCase_           =					"pix2struct_text_model"
							lowerCamelCase_           =					["past_key_values"]
							lowerCamelCase_           =					{
							    "hidden_size": "hidden_size",
							    "num_attention_heads": "num_heads",
							    "num_hidden_layers": "num_layers",
							}


							def __init__(							self    :Union[str, Any]							,				__A    :Any=5_0244							,				__A    :Optional[Any]=768							,				__A    :Tuple=64							,				__A    :List[str]=2048							,				__A    :int=12							,				__A    :str=12							,				__A    :Any=32							,				__A    :Tuple=128							,				__A    :int=0.1							,				__A    :str=1E-6							,				__A    :Optional[Any]=1.0							,				__A    :Union[str, Any]="gelu_new"							,				__A    :Any=0							,				__A    :List[str]=False							,				__A    :Optional[Any]=0							,				__A    :int=1							,				__A    :Optional[int]=False							,				__A    :Optional[Any]=True							,				**__A    :List[Any]							,				)   ->   Any:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__			=      vocab_size
									SCREAMING_SNAKE_CASE__			=      hidden_size
									SCREAMING_SNAKE_CASE__			=      d_kv
									SCREAMING_SNAKE_CASE__			=      d_ff
									SCREAMING_SNAKE_CASE__			=      num_layers
									SCREAMING_SNAKE_CASE__			=      num_heads
									SCREAMING_SNAKE_CASE__			=      relative_attention_num_buckets
									SCREAMING_SNAKE_CASE__			=      relative_attention_max_distance
									SCREAMING_SNAKE_CASE__			=      dropout_rate
									SCREAMING_SNAKE_CASE__			=      layer_norm_epsilon
									SCREAMING_SNAKE_CASE__			=      initializer_factor
									SCREAMING_SNAKE_CASE__			=      use_cache

									SCREAMING_SNAKE_CASE__			=      eos_token_id
									SCREAMING_SNAKE_CASE__			=      decoder_start_token_id

									# for backwards compatibility
									SCREAMING_SNAKE_CASE__			=      dense_act_fn

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


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



									"""simple docstring"""



									cls._set_token_in_kwargs(__A		)

									SCREAMING_SNAKE_CASE__	,						SCREAMING_SNAKE_CASE__			=      cls.get_config_dict(__A							,				**__A		)

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




class   UpperCamelCase_       (     UpperCamelCase__    ):
							lowerCamelCase_           =					"pix2struct_vision_model"


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



									"""simple docstring"""



									super().__init__(**__A		)

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


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



									"""simple docstring"""



									cls._set_token_in_kwargs(__A		)

									SCREAMING_SNAKE_CASE__	,						SCREAMING_SNAKE_CASE__			=      cls.get_config_dict(__A							,				**__A		)

									# get the vision config dict if we are loading from Pix2StructConfig
									if config_dict.get("""model_type"""		) == "pix2struct":
											SCREAMING_SNAKE_CASE__			=      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(__A							,				**__A		)




class   UpperCamelCase_       (     UpperCamelCase__    ):
							lowerCamelCase_           =					"pix2struct"
							lowerCamelCase_           =					True


							def __init__(							self    :str							,				__A    :Optional[Any]=None							,				__A    :List[str]=None							,				__A    :Optional[Any]=1.0							,				__A    :Optional[Any]=0.0_2							,				__A    :Any=False							,				__A    :Tuple=False							,				__A    :Any=True							,				**__A    :Dict							,				)   ->   Union[str, Any]:



									"""simple docstring"""



									super().__init__(tie_word_embeddings=__A							,				is_encoder_decoder=__A							,				**__A		)

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

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

									SCREAMING_SNAKE_CASE__			=      PixaStructTextConfig(**__A		)
									SCREAMING_SNAKE_CASE__			=      PixaStructVisionConfig(**__A		)

									SCREAMING_SNAKE_CASE__			=      self.text_config.decoder_start_token_id
									SCREAMING_SNAKE_CASE__			=      self.text_config.pad_token_id
									SCREAMING_SNAKE_CASE__			=      self.text_config.eos_token_id

									SCREAMING_SNAKE_CASE__			=      initializer_factor
									SCREAMING_SNAKE_CASE__			=      initializer_range

									SCREAMING_SNAKE_CASE__			=      self.initializer_range
									SCREAMING_SNAKE_CASE__			=      self.initializer_range

									SCREAMING_SNAKE_CASE__			=      is_vqa


							@classmethod
							def      _snake_case  (							cls    :Union[str, Any]							,				__A    :PixaStructTextConfig							,				__A    :PixaStructVisionConfig							,				**__A    :Optional[int]		)   ->   Optional[Any]:



									"""simple docstring"""



									return cls(text_config=text_config.to_dict()							,				vision_config=vision_config.to_dict()							,				**__A		)


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



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__			=      copy.deepcopy(self.__dict__		)
									SCREAMING_SNAKE_CASE__			=      self.text_config.to_dict()
									SCREAMING_SNAKE_CASE__			=      self.vision_config.to_dict()
									SCREAMING_SNAKE_CASE__			=      self.__class__.model_type
									return output