


"""simple docstring"""






import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
					from PIL import Image

					from transformers import CLIPSegProcessor, ViTImageProcessor






@require_vision
class      A(  unittest.TestCase		):

		"""simple docstring"""





		def   _UpperCamelCase( self   )      ->	List[Any]:

					"""simple docstring"""



					_UpperCamelCase					:Union[str, Any]					     =	tempfile.mkdtemp()

					# fmt: off
					_UpperCamelCase					:List[Any]					     =	['''l''', '''o''', '''w''', '''e''', '''r''', '''s''', '''t''', '''i''', '''d''', '''n''', '''lo''', '''l</w>''', '''w</w>''', '''r</w>''', '''t</w>''', '''low</w>''', '''er</w>''', '''lowest</w>''', '''newer</w>''', '''wider''', '''<unk>''', '''<|startoftext|>''', '''<|endoftext|>''']
					# fmt: on
					_UpperCamelCase					:List[Any]					     =	dict(zip(snake_case_					,				range(len(snake_case_   )   )   )   )
					_UpperCamelCase					:int					     =	['''#version: 0.2''', '''l o''', '''lo w</w>''', '''e r</w>''', '''''']
					_UpperCamelCase					:str					     =	{'''unk_token''': '''<unk>'''}

					_UpperCamelCase					:str					     =	os.path.join(self.tmpdirname					,				VOCAB_FILES_NAMES['''vocab_file''']   )
					_UpperCamelCase					:Union[str, Any]					     =	os.path.join(self.tmpdirname					,				VOCAB_FILES_NAMES['''merges_file''']   )
					with open(self.vocab_file					,				'''w'''					,				encoding='''utf-8'''   ) as fp:
								fp.write(json.dumps(snake_case_   ) + '''\n'''   )
					with open(self.merges_file					,				'''w'''					,				encoding='''utf-8'''   ) as fp:
								fp.write('''\n'''.join(snake_case_   )   )

					_UpperCamelCase					:Optional[int]					     =	{
					    '''do_resize''': True,
					    '''size''': 20,
					    '''do_center_crop''': True,
					    '''crop_size''': 18,
					    '''do_normalize''': True,
					    '''image_mean''': [0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3],
					    '''image_std''': [0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1],
					}
					_UpperCamelCase					:List[str]					     =	os.path.join(self.tmpdirname					,				snake_case_   )
					with open(self.image_processor_file					,				'''w'''					,				encoding='''utf-8'''   ) as fp:
								json.dump(snake_case_					,				snake_case_   )
		def   _UpperCamelCase( self					,				**SCREAMING_SNAKE_CASE__   )      ->	List[Any]:

					"""simple docstring"""



					return CLIPTokenizer.from_pretrained(self.tmpdirname					,				**snake_case_   )
		def   _UpperCamelCase( self					,				**SCREAMING_SNAKE_CASE__   )      ->	Any:

					"""simple docstring"""



					return CLIPTokenizerFast.from_pretrained(self.tmpdirname					,				**snake_case_   )
		def   _UpperCamelCase( self					,				**SCREAMING_SNAKE_CASE__   )      ->	str:

					"""simple docstring"""



					return ViTImageProcessor.from_pretrained(self.tmpdirname					,				**snake_case_   )
		def   _UpperCamelCase( self   )      ->	Optional[Any]:

					"""simple docstring"""



					shutil.rmtree(self.tmpdirname   )
		def   _UpperCamelCase( self   )      ->	int:

					"""simple docstring"""



					_UpperCamelCase					:str					     =	[np.random.randint(2_55					,				size=(3, 30, 4_00)					,				dtype=np.uinta   )]

					_UpperCamelCase					:Optional[int]					     =	[Image.fromarray(np.moveaxis(snake_case_					,				0					,				-1   )   ) for x in image_inputs]

					return image_inputs
		def   _UpperCamelCase( self   )      ->	Union[str, Any]:

					"""simple docstring"""



					_UpperCamelCase					:int					     =	self.get_tokenizer()
					_UpperCamelCase					:int					     =	self.get_rust_tokenizer()
					_UpperCamelCase					:List[str]					     =	self.get_image_processor()

					_UpperCamelCase					:str					     =	CLIPSegProcessor(tokenizer=snake_case_					,				image_processor=snake_case_   )
					processor_slow.save_pretrained(self.tmpdirname   )
					_UpperCamelCase					:Union[str, Any]					     =	CLIPSegProcessor.from_pretrained(self.tmpdirname					,				use_fast=snake_case_   )

					_UpperCamelCase					:Any					     =	CLIPSegProcessor(tokenizer=snake_case_					,				image_processor=snake_case_   )
					processor_fast.save_pretrained(self.tmpdirname   )
					_UpperCamelCase					:Optional[Any]					     =	CLIPSegProcessor.from_pretrained(self.tmpdirname   )

					self.assertEqual(processor_slow.tokenizer.get_vocab()					,				tokenizer_slow.get_vocab()   )
					self.assertEqual(processor_fast.tokenizer.get_vocab()					,				tokenizer_fast.get_vocab()   )
					self.assertEqual(tokenizer_slow.get_vocab()					,				tokenizer_fast.get_vocab()   )
					self.assertIsInstance(processor_slow.tokenizer					,				snake_case_   )
					self.assertIsInstance(processor_fast.tokenizer					,				snake_case_   )

					self.assertEqual(processor_slow.image_processor.to_json_string()					,				image_processor.to_json_string()   )
					self.assertEqual(processor_fast.image_processor.to_json_string()					,				image_processor.to_json_string()   )
					self.assertIsInstance(processor_slow.image_processor					,				snake_case_   )
					self.assertIsInstance(processor_fast.image_processor					,				snake_case_   )
		def   _UpperCamelCase( self   )      ->	int:

					"""simple docstring"""



					_UpperCamelCase					:Optional[int]					     =	CLIPSegProcessor(tokenizer=self.get_tokenizer()					,				image_processor=self.get_image_processor()   )
					processor.save_pretrained(self.tmpdirname   )

					_UpperCamelCase					:List[str]					     =	self.get_tokenizer(bos_token='''(BOS)'''					,				eos_token='''(EOS)'''   )
					_UpperCamelCase					:Optional[Any]					     =	self.get_image_processor(do_normalize=snake_case_					,				padding_value=1.0   )

					_UpperCamelCase					:str					     =	CLIPSegProcessor.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.image_processor.to_json_string()					,				image_processor_add_kwargs.to_json_string()   )
					self.assertIsInstance(processor.image_processor					,				snake_case_   )
		def   _UpperCamelCase( self   )      ->	Dict:

					"""simple docstring"""



					_UpperCamelCase					:str					     =	self.get_image_processor()
					_UpperCamelCase					:int					     =	self.get_tokenizer()

					_UpperCamelCase					:Dict					     =	CLIPSegProcessor(tokenizer=snake_case_					,				image_processor=snake_case_   )

					_UpperCamelCase					:Any					     =	self.prepare_image_inputs()

					_UpperCamelCase					:Any					     =	image_processor(snake_case_					,				return_tensors='''np'''   )
					_UpperCamelCase					:Union[str, Any]					     =	processor(images=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   _UpperCamelCase( self   )      ->	Optional[Any]:

					"""simple docstring"""



					_UpperCamelCase					:Tuple					     =	self.get_image_processor()
					_UpperCamelCase					:Optional[Any]					     =	self.get_tokenizer()

					_UpperCamelCase					:int					     =	CLIPSegProcessor(tokenizer=snake_case_					,				image_processor=snake_case_   )

					_UpperCamelCase					:Union[str, Any]					     =	'''lower newer'''

					_UpperCamelCase					:List[Any]					     =	processor(text=snake_case_   )

					_UpperCamelCase					:Dict					     =	tokenizer(snake_case_   )

					for key in encoded_tok.keys():
								self.assertListEqual(encoded_tok[key]					,				encoded_processor[key]   )
		def   _UpperCamelCase( self   )      ->	int:

					"""simple docstring"""



					_UpperCamelCase					:Optional[int]					     =	self.get_image_processor()
					_UpperCamelCase					:Optional[Any]					     =	self.get_tokenizer()

					_UpperCamelCase					:int					     =	CLIPSegProcessor(tokenizer=snake_case_					,				image_processor=snake_case_   )

					_UpperCamelCase					:Union[str, Any]					     =	'''lower newer'''
					_UpperCamelCase					:Optional[int]					     =	self.prepare_image_inputs()

					_UpperCamelCase					:Optional[Any]					     =	processor(text=snake_case_					,				images=snake_case_   )

					self.assertListEqual(list(inputs.keys()   )					,				['''input_ids''', '''attention_mask''', '''pixel_values''']   )

					# test if it raises when no input is passed
					with pytest.raises(snake_case_   ):
								processor()
		def   _UpperCamelCase( self   )      ->	Union[str, Any]:

					"""simple docstring"""



					_UpperCamelCase					:List[str]					     =	self.get_image_processor()
					_UpperCamelCase					:Tuple					     =	self.get_tokenizer()

					_UpperCamelCase					:List[str]					     =	CLIPSegProcessor(tokenizer=snake_case_					,				image_processor=snake_case_   )

					_UpperCamelCase					:Optional[Any]					     =	self.prepare_image_inputs()
					_UpperCamelCase					:Any					     =	self.prepare_image_inputs()

					_UpperCamelCase					:Dict					     =	processor(images=snake_case_					,				visual_prompt=snake_case_   )

					self.assertListEqual(list(inputs.keys()   )					,				['''pixel_values''', '''conditional_pixel_values''']   )

					# test if it raises when no input is passed
					with pytest.raises(snake_case_   ):
								processor()





		def   _UpperCamelCase( self   )      ->	Dict:

					"""simple docstring"""



					_UpperCamelCase					:List[str]					     =	self.get_image_processor()
					_UpperCamelCase					:int					     =	self.get_tokenizer()

					_UpperCamelCase					:str					     =	CLIPSegProcessor(tokenizer=snake_case_					,				image_processor=snake_case_   )

					_UpperCamelCase					:int					     =	[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

					_UpperCamelCase					:Any					     =	processor.batch_decode(snake_case_   )
					_UpperCamelCase					:Tuple					     =	tokenizer.batch_decode(snake_case_   )

					self.assertListEqual(snake_case_					,				snake_case_   )





import copy
import os
from typing import Union

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


a			:			Optional[int]												=					logging.get_logger(__name__)

a			:			Dict												=					{
    '''google/pix2struct-textcaps-base''': (
        '''https://huggingface.co/google/pix2struct-textcaps-base/resolve/main/config.json'''
    ),
}
class 						lowerCamelCase_							(					lowerCAmelCase__					):



					'''simple docstring'''







					__UpperCAmelCase							  =					"pix2struct_text_model"
					__UpperCAmelCase							  =					["past_key_values"]
					__UpperCAmelCase							  =					{
					    "hidden_size": "hidden_size",
					    "num_attention_heads": "num_heads",
					    "num_hidden_layers": "num_layers",
					}






					def __init__(					self			,							snake_case_=5_0_2_4_4			,							snake_case_=7_6_8			,							snake_case_=6_4			,							snake_case_=2_0_4_8			,							snake_case_=1_2			,							snake_case_=1_2			,							snake_case_=3_2			,							snake_case_=1_2_8			,							snake_case_=0.1			,							snake_case_=1e-6			,							snake_case_=1.0			,							snake_case_="gelu_new"			,							snake_case_=0			,							snake_case_=False			,							snake_case_=0			,							snake_case_=1			,							snake_case_=False			,							snake_case_=True			,							**snake_case_			,							) ->      Optional[int]:







								'''simple docstring'''

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

								__lowercase			       = eos_token_id
								__lowercase			       = decoder_start_token_id

								# for backwards compatibility
								__lowercase			       = 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 A   (					cls			,							snake_case_			,							**snake_case_    ) ->      "PretrainedConfig":







								'''simple docstring'''

								cls._set_token_in_kwargs(snake_case_    )

								__lowercase       ,			__lowercase			       = 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":
											__lowercase			       = 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 						lowerCamelCase_							(					lowerCAmelCase__					):



					'''simple docstring'''







					__UpperCAmelCase							  =					"pix2struct_vision_model"






					def __init__(					self			,							snake_case_=7_6_8			,							snake_case_=7_6_8			,							snake_case_=2_0_4_8			,							snake_case_=6_4			,							snake_case_=1_2			,							snake_case_=1_2			,							snake_case_="gelu_new"			,							snake_case_=1e-6			,							snake_case_=0.0			,							snake_case_=0.0			,							snake_case_=1e-1_0			,							snake_case_=1.0			,							snake_case_=4_0_9_6			,							snake_case_=3_2			,							snake_case_=1_2_8			,							**snake_case_			,							) ->      Union[str, Any]:







								'''simple docstring'''

								super().__init__(**snake_case_    )

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






					@classmethod
					def A   (					cls			,							snake_case_			,							**snake_case_    ) ->      "PretrainedConfig":







								'''simple docstring'''

								cls._set_token_in_kwargs(snake_case_    )

								__lowercase       ,			__lowercase			       = 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":
											__lowercase			       = 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 						lowerCamelCase_							(					lowerCAmelCase__					):



					'''simple docstring'''







					__UpperCAmelCase							  =					"pix2struct"
					__UpperCAmelCase							  =					True






					def __init__(					self			,							snake_case_=None			,							snake_case_=None			,							snake_case_=1.0			,							snake_case_=0.0_2			,							snake_case_=False			,							snake_case_=False			,							snake_case_=True			,							**snake_case_			,							) ->      Optional[int]:







								'''simple docstring'''

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

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

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

								__lowercase			       = PixaStructTextConfig(**snake_case_    )
								__lowercase			       = PixaStructVisionConfig(**snake_case_    )

								__lowercase			       = self.text_config.decoder_start_token_id
								__lowercase			       = self.text_config.pad_token_id
								__lowercase			       = self.text_config.eos_token_id

								__lowercase			       = initializer_factor
								__lowercase			       = initializer_range

								__lowercase			       = self.initializer_range
								__lowercase			       = self.initializer_range

								__lowercase			       = is_vqa






					@classmethod
					def A   (					cls			,							snake_case_			,							snake_case_			,							**snake_case_    ) ->      List[str]:







								'''simple docstring'''

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






					def A   (					self    ) ->      Tuple:







								'''simple docstring'''

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


