



'''simple docstring'''
import copy
import os
from typing import Union

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


snake_case_					:      str    			=    logging.get_logger(__name__)

snake_case_					:      List[str]    			=    {
    'google/pix2struct-textcaps-base': (
        'https://huggingface.co/google/pix2struct-textcaps-base/resolve/main/config.json'
    ),
}






class 				lowercase__      (				lowercase  ):
	lowercase__    							=     """pix2struct_text_model"""
	lowercase__    							=     ["""past_key_values"""]
	lowercase__    							=     {
	    """hidden_size""": """hidden_size""",
	    """num_attention_heads""": """num_heads""",
	    """num_hidden_layers""": """num_layers""",
	}





	def __init__(					self   :							Optional[Any]					,lowerCamelCase__   :							List[Any]=50244					,lowerCamelCase__   :							List[str]=768					,lowerCamelCase__   :							Optional[int]=64					,lowerCamelCase__   :							Dict=2048					,lowerCamelCase__   :							Optional[int]=12					,lowerCamelCase__   :							int=12					,lowerCamelCase__   :							int=32					,lowerCamelCase__   :							List[str]=128					,lowerCamelCase__   :							Any=0.1					,lowerCamelCase__   :							List[Any]=1E-6					,lowerCamelCase__   :							Optional[Any]=1.0					,lowerCamelCase__   :							Optional[Any]="gelu_new"					,lowerCamelCase__   :							Optional[int]=0					,lowerCamelCase__   :							List[str]=False					,lowerCamelCase__   :							Dict=0					,lowerCamelCase__   :							int=1					,lowerCamelCase__   :							Union[str, Any]=False					,lowerCamelCase__   :							Tuple=True					,**lowerCamelCase__   :							List[Any]					,):







							'''simple docstring'''
							_UpperCamelCase   :						Optional[Any]												=  vocab_size
							_UpperCamelCase   :						Optional[int]												=  hidden_size
							_UpperCamelCase   :						List[str]												=  d_kv
							_UpperCamelCase   :						List[str]												=  d_ff
							_UpperCamelCase   :						List[str]												=  num_layers
							_UpperCamelCase   :						Union[str, Any]												=  num_heads
							_UpperCamelCase   :						str												=  relative_attention_num_buckets
							_UpperCamelCase   :						Any												=  relative_attention_max_distance
							_UpperCamelCase   :						Optional[Any]												=  dropout_rate
							_UpperCamelCase   :						Dict												=  layer_norm_epsilon
							_UpperCamelCase   :						List[str]												=  initializer_factor
							_UpperCamelCase   :						Any												=  use_cache

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

							# for backwards compatibility
							_UpperCamelCase   :						Optional[Any]												=  dense_act_fn

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





	@classmethod
	def       UpperCamelCase_    (					cls   :							Dict					,lowerCamelCase__   :							Union[str, os.PathLike]					,**lowerCamelCase__   :							Tuple					):







							'''simple docstring'''
							cls._set_token_in_kwargs(lowerCamelCase__					)

							_UpperCamelCase							,     _UpperCamelCase   :						Any												=  cls.get_config_dict(lowerCamelCase__					,**lowerCamelCase__					)

							# get the text config dict if we are loading from Pix2StructConfig
							if config_dict.get('model_type'					) == "pix2struct":
													_UpperCamelCase   :						Any												=  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(lowerCamelCase__					,**lowerCamelCase__					)






class 				lowercase__      (				lowercase  ):
	lowercase__    							=     """pix2struct_vision_model"""





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







							'''simple docstring'''
							super().__init__(**lowerCamelCase__					)

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





	@classmethod
	def       UpperCamelCase_    (					cls   :							Dict					,lowerCamelCase__   :							Union[str, os.PathLike]					,**lowerCamelCase__   :							Any					):







							'''simple docstring'''
							cls._set_token_in_kwargs(lowerCamelCase__					)

							_UpperCamelCase							,     _UpperCamelCase   :						List[Any]												=  cls.get_config_dict(lowerCamelCase__					,**lowerCamelCase__					)

							# get the vision config dict if we are loading from Pix2StructConfig
							if config_dict.get('model_type'					) == "pix2struct":
													_UpperCamelCase   :						int												=  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(lowerCamelCase__					,**lowerCamelCase__					)






class 				lowercase__      (				lowercase  ):
	lowercase__    							=     """pix2struct"""
	lowercase__    							=     True





	def __init__(					self   :							int					,lowerCamelCase__   :							int=None					,lowerCamelCase__   :							str=None					,lowerCamelCase__   :							Tuple=1.0					,lowerCamelCase__   :							Tuple=0.0_2					,lowerCamelCase__   :							Tuple=False					,lowerCamelCase__   :							Dict=False					,lowerCamelCase__   :							List[str]=True					,**lowerCamelCase__   :							str					,):







							'''simple docstring'''
							super().__init__(tie_word_embeddings=lowerCamelCase__					,is_encoder_decoder=lowerCamelCase__					,**lowerCamelCase__					)

							if text_config is None:
													_UpperCamelCase   :						Tuple												=  {}
													logger.info('text_config is None. Initializing the Pix2StructTextConfig with default values.'					)

							if vision_config is None:
													_UpperCamelCase   :						Optional[int]												=  {}
													logger.info('vision_config is None. Initializing the Pix2StructVisionConfig with default values.'					)

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

							_UpperCamelCase   :						Union[str, Any]												=  self.text_config.decoder_start_token_id
							_UpperCamelCase   :						int												=  self.text_config.pad_token_id
							_UpperCamelCase   :						Tuple												=  self.text_config.eos_token_id

							_UpperCamelCase   :						Any												=  initializer_factor
							_UpperCamelCase   :						List[Any]												=  initializer_range

							_UpperCamelCase   :						str												=  self.initializer_range
							_UpperCamelCase   :						Optional[Any]												=  self.initializer_range

							_UpperCamelCase   :						int												=  is_vqa





	@classmethod
	def       UpperCamelCase_    (					cls   :							Optional[Any]					,lowerCamelCase__   :							PixaStructTextConfig					,lowerCamelCase__   :							PixaStructVisionConfig					,**lowerCamelCase__   :							List[str]					):







							'''simple docstring'''
							return cls(text_config=text_config.to_dict()					,vision_config=vision_config.to_dict()					,**lowerCamelCase__					)





	def       UpperCamelCase_    (					self   :							Dict					):







							'''simple docstring'''
							_UpperCamelCase   :						Optional[int]												=  copy.deepcopy(self.__dict__					)
							_UpperCamelCase   :						Optional[int]												=  self.text_config.to_dict()
							_UpperCamelCase   :						Tuple												=  self.vision_config.to_dict()
							_UpperCamelCase   :						int												=  self.__class__.model_type
							return output







'''simple docstring'''
import os
import tempfile
import unittest
import uuid
from pathlib import Path

from transformers.testing_utils import get_tests_dir, require_soundfile, require_torch, require_vision
from transformers.tools.agent_types import AgentAudio, AgentImage, AgentText
from transformers.utils import is_soundfile_availble, is_torch_available, is_vision_available


if is_torch_available():
					import torch

if is_soundfile_availble():
					import soundfile as sf

if is_vision_available():
					from PIL import Image






def A__       (	UpperCAmelCase_=""				):
						_UpperCamelCase   :						Any												=  tempfile.mkdtemp()
						return os.path.join(UpperCAmelCase_    ,    str(uuid.uuida()				) + suffix				)






@require_soundfile
@require_torch
class 				lowercase__      (				unittest.TestCase  ):





	def       UpperCamelCase_    (					self   :							int					):







							'''simple docstring'''
							_UpperCamelCase   :						List[str]												=  torch.rand(12					,dtype=torch.floataa					) - 0.5
							_UpperCamelCase   :						Optional[int]												=  AgentAudio(lowerCamelCase__					)
							_UpperCamelCase   :						List[str]												=  str(agent_type.to_string()					)

							# Ensure that the tensor and the agent_type's tensor are the same
							self.assertTrue(torch.allclose(lowerCamelCase__					,agent_type.to_raw()					,atol=1E-4					)					)

							del agent_type

							# Ensure the path remains even after the object deletion
							self.assertTrue(os.path.exists(lowerCamelCase__					)					)

							# Ensure that the file contains the same value as the original tensor
							_UpperCamelCase							,     _UpperCamelCase   :						Union[str, Any]												=  sf.read(lowerCamelCase__					)
							self.assertTrue(torch.allclose(lowerCamelCase__					,torch.tensor(lowerCamelCase__					)					,atol=1E-4					)					)





	def       UpperCamelCase_    (					self   :							Dict					):







							'''simple docstring'''
							_UpperCamelCase   :						Optional[Any]												=  torch.rand(12					,dtype=torch.floataa					) - 0.5
							_UpperCamelCase   :						Any												=  get_new_path(suffix='.wav'					)
							sf.write(lowerCamelCase__					,lowerCamelCase__					,16000					)

							_UpperCamelCase   :						List[Any]												=  AgentAudio(lowerCamelCase__					)

							self.assertTrue(torch.allclose(lowerCamelCase__					,agent_type.to_raw()					,atol=1E-4					)					)
							self.assertEqual(agent_type.to_string()					,lowerCamelCase__					)






@require_vision
@require_torch
class 				lowercase__      (				unittest.TestCase  ):





	def       UpperCamelCase_    (					self   :							List[Any]					):







							'''simple docstring'''
							_UpperCamelCase   :						int												=  torch.randint(0					,256					,(64, 64, 3)					)
							_UpperCamelCase   :						Optional[Any]												=  AgentImage(lowerCamelCase__					)
							_UpperCamelCase   :						List[Any]												=  str(agent_type.to_string()					)

							# Ensure that the tensor and the agent_type's tensor are the same
							self.assertTrue(torch.allclose(lowerCamelCase__					,agent_type._tensor					,atol=1E-4					)					)

							self.assertIsInstance(agent_type.to_raw()					,Image.Image					)

							# Ensure the path remains even after the object deletion
							del agent_type
							self.assertTrue(os.path.exists(lowerCamelCase__					)					)





	def       UpperCamelCase_    (					self   :							Dict					):







							'''simple docstring'''
							_UpperCamelCase   :						str												=  Path(get_tests_dir('fixtures/tests_samples/COCO'					)					) / '000000039769.png'
							_UpperCamelCase   :						Tuple												=  Image.open(lowerCamelCase__					)
							_UpperCamelCase   :						Optional[Any]												=  AgentImage(lowerCamelCase__					)

							self.assertTrue(path.samefile(agent_type.to_string()					)					)
							self.assertTrue(image == agent_type.to_raw()					)

							# Ensure the path remains even after the object deletion
							del agent_type
							self.assertTrue(os.path.exists(lowerCamelCase__					)					)





	def       UpperCamelCase_    (					self   :							Optional[int]					):







							'''simple docstring'''
							_UpperCamelCase   :						List[Any]												=  Path(get_tests_dir('fixtures/tests_samples/COCO'					)					) / '000000039769.png'
							_UpperCamelCase   :						Union[str, Any]												=  Image.open(lowerCamelCase__					)
							_UpperCamelCase   :						List[Any]												=  AgentImage(lowerCamelCase__					)

							self.assertFalse(path.samefile(agent_type.to_string()					)					)
							self.assertTrue(image == agent_type.to_raw()					)

							# Ensure the path remains even after the object deletion
							del agent_type
							self.assertTrue(os.path.exists(lowerCamelCase__					)					)






class 				lowercase__      (				unittest.TestCase  ):





	def       UpperCamelCase_    (					self   :							Any					):







							'''simple docstring'''
							_UpperCamelCase   :						List[Any]												=  'Hey!'
							_UpperCamelCase   :						Optional[int]												=  AgentText(lowerCamelCase__					)

							self.assertEqual(lowerCamelCase__					,agent_type.to_string()					)
							self.assertEqual(lowerCamelCase__					,agent_type.to_raw()					)
							self.assertEqual(lowerCamelCase__					,lowerCamelCase__					)


