



"""simple docstring"""




import copy
import os
from typing import Union

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


_lowerCAmelCase    : str							=							logging.get_logger(__name__)

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






class      UpperCAmelCase_  (    _UpperCamelCase						):
      __SCREAMING_SNAKE_CASE						:       int								=			'pix2struct_text_model'
      __SCREAMING_SNAKE_CASE						:       str								=			['past_key_values']
      __SCREAMING_SNAKE_CASE						:       int								=			{
          'hidden_size': 'hidden_size',
          'num_attention_heads': 'num_heads',
          'num_hidden_layers': 'num_layers',
      }



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

           _UpperCAmelCase      :     List[str]        =   eos_token_id
           _UpperCAmelCase      :     Optional[Any]        =   decoder_start_token_id

           # for backwards compatibility
           _UpperCAmelCase      :     Optional[Any]        =   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[Any]					,     A					:     Union[str, os.PathLike]					,     **A					:     Tuple						):
           cls._set_token_in_kwargs(A						)

           _UpperCAmelCase      :     Dict        =   cls.get_config_dict(A					,     **A						)

           # get the text config dict if we are loading from Pix2StructConfig
           if config_dict.get("model_type"						) == "pix2struct":
                _UpperCAmelCase      :     List[str]        =   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						):
      __SCREAMING_SNAKE_CASE						:       str								=			'pix2struct_vision_model'



      def __init__(					self					:     Tuple					,     A					:     Optional[Any]=7_6_8					,     A					:     str=7_6_8					,     A					:     Any=2_0_4_8					,     A					:     Optional[int]=6_4					,     A					:     Tuple=1_2					,     A					:     Tuple=1_2					,     A					:     List[str]="gelu_new"					,     A					:     Any=1e-6					,     A					:     Union[str, Any]=0.0					,     A					:     Dict=0.0					,     A					:     Optional[Any]=1e-10					,     A					:     List[str]=1.0					,     A					:     Any=4_0_9_6					,     A					:     Optional[int]=3_2					,     A					:     Optional[int]=1_2_8					,     **A					:     Optional[int]					,     ):
           super().__init__(**A						)

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



      @classmethod
      def 						snake_case_ (					cls					:     Optional[Any]					,     A					:     Union[str, os.PathLike]					,     **A					:     Any						):
           cls._set_token_in_kwargs(A						)

           _UpperCAmelCase      :     Optional[int]        =   cls.get_config_dict(A					,     **A						)

           # 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(A					,     **A						)







class      UpperCAmelCase_  (    _UpperCamelCase						):
      __SCREAMING_SNAKE_CASE						:       Optional[int]								=			'pix2struct'
      __SCREAMING_SNAKE_CASE						:       Dict								=			True



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

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

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

           _UpperCAmelCase      :     Tuple        =   PixaStructTextConfig(**A						)
           _UpperCAmelCase      :     Optional[Any]        =   PixaStructVisionConfig(**A						)

           _UpperCAmelCase      :     Any        =   self.text_config.decoder_start_token_id
           _UpperCAmelCase      :     Optional[Any]        =   self.text_config.pad_token_id
           _UpperCAmelCase      :     Any        =   self.text_config.eos_token_id

           _UpperCAmelCase      :     int        =   initializer_factor
           _UpperCAmelCase      :     Optional[int]        =   initializer_range

           _UpperCAmelCase      :     Tuple        =   self.initializer_range
           _UpperCAmelCase      :     Union[str, Any]        =   self.initializer_range

           _UpperCAmelCase      :     int        =   is_vqa



      @classmethod
      def 						snake_case_ (					cls					:     Optional[Any]					,     A					:     PixaStructTextConfig					,     A					:     PixaStructVisionConfig					,     **A					:     Union[str, Any]						):
           return cls(text_config=text_config.to_dict()					,     vision_config=vision_config.to_dict()					,     **A						)



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








"""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 CLIPImageProcessor, CLIPProcessor






@require_vision
class      UpperCAmelCase_  (    unittest.TestCase						):



      def 						snake_case_ (					self					:     List[Any]						):
           _UpperCAmelCase      :     List[str]        =   tempfile.mkdtemp()

           # fmt: off
           _UpperCAmelCase      :     Union[str, 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(A					,     range(len(A						)						)						)						)
           _UpperCAmelCase      :     Union[str, Any]        =   ["#version: 0.2", "l o", "lo w</w>", "e r</w>", ""]
           _UpperCAmelCase      :     Optional[int]        =   {"unk_token": "<unk>"}

           _UpperCAmelCase      :     Optional[Any]        =   os.path.join(self.tmpdirname					,     VOCAB_FILES_NAMES["vocab_file"]						)
           _UpperCAmelCase      :     Optional[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(A						) + "\n"						)
           with open(self.merges_file					,     "w"					,     encoding="utf-8"						) as fp:
                fp.write("\n".join(A						)						)

           _UpperCAmelCase      :     List[str]        =   {
               "do_resize": True,
               "size": 2_0,
               "do_center_crop": True,
               "crop_size": 1_8,
               "do_normalize": True,
               "image_mean": [0.48_145_466, 0.4_578_275, 0.40_821_073],
               "image_std": [0.26_862_954, 0.26_130_258, 0.27_577_711],
           }
           _UpperCAmelCase      :     Any        =   os.path.join(self.tmpdirname					,     A						)
           with open(self.image_processor_file					,     "w"					,     encoding="utf-8"						) as fp:
                json.dump(A					,     A						)



      def 						snake_case_ (					self					:     List[Any]					,     **A					:     Union[str, Any]						):
           return CLIPTokenizer.from_pretrained(self.tmpdirname					,     **A						)



      def 						snake_case_ (					self					:     int					,     **A					:     Any						):
           return CLIPTokenizerFast.from_pretrained(self.tmpdirname					,     **A						)



      def 						snake_case_ (					self					:     List[str]					,     **A					:     Optional[Any]						):
           return CLIPImageProcessor.from_pretrained(self.tmpdirname					,     **A						)



      def 						snake_case_ (					self					:     Optional[int]						):
           shutil.rmtree(self.tmpdirname						)



      def 						snake_case_ (					self					:     str						):
           _UpperCAmelCase      :     int        =   [np.random.randint(2_5_5					,     size=(3, 3_0, 4_0_0)					,     dtype=np.uinta						)]

           _UpperCAmelCase      :     Dict        =   [Image.fromarray(np.moveaxis(A					,     0					,     -1						)						) for x in image_inputs]

           return image_inputs



      def 						snake_case_ (					self					:     List[str]						):
           _UpperCAmelCase      :     int        =   self.get_tokenizer()
           _UpperCAmelCase      :     Dict        =   self.get_rust_tokenizer()
           _UpperCAmelCase      :     int        =   self.get_image_processor()

           _UpperCAmelCase      :     List[Any]        =   CLIPProcessor(tokenizer=A					,     image_processor=A						)
           processor_slow.save_pretrained(self.tmpdirname						)
           _UpperCAmelCase      :     Optional[Any]        =   CLIPProcessor.from_pretrained(self.tmpdirname					,     use_fast=A						)

           _UpperCAmelCase      :     Optional[Any]        =   CLIPProcessor(tokenizer=A					,     image_processor=A						)
           processor_fast.save_pretrained(self.tmpdirname						)
           _UpperCAmelCase      :     Optional[int]        =   CLIPProcessor.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					,     A						)
           self.assertIsInstance(processor_fast.tokenizer					,     A						)

           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					,     A						)
           self.assertIsInstance(processor_fast.image_processor					,     A						)



      def 						snake_case_ (					self					:     List[str]						):
           _UpperCAmelCase      :     List[str]        =   CLIPProcessor(tokenizer=self.get_tokenizer()					,     image_processor=self.get_image_processor()						)
           processor.save_pretrained(self.tmpdirname						)

           _UpperCAmelCase      :     Any        =   self.get_tokenizer(bos_token="(BOS)"					,     eos_token="(EOS)"						)
           _UpperCAmelCase      :     Any        =   self.get_image_processor(do_normalize=A					,     padding_value=1.0						)

           _UpperCAmelCase      :     Any        =   CLIPProcessor.from_pretrained(
               self.tmpdirname					,     bos_token="(BOS)"					,     eos_token="(EOS)"					,     do_normalize=A					,     padding_value=1.0						)

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

           self.assertEqual(processor.image_processor.to_json_string()					,     image_processor_add_kwargs.to_json_string()						)
           self.assertIsInstance(processor.image_processor					,     A						)



      def 						snake_case_ (					self					:     List[Any]						):
           _UpperCAmelCase      :     str        =   self.get_image_processor()
           _UpperCAmelCase      :     List[str]        =   self.get_tokenizer()

           _UpperCAmelCase      :     Any        =   CLIPProcessor(tokenizer=A					,     image_processor=A						)

           _UpperCAmelCase      :     Dict        =   self.prepare_image_inputs()

           _UpperCAmelCase      :     Optional[int]        =   image_processor(A					,     return_tensors="np"						)
           _UpperCAmelCase      :     Any        =   processor(images=A					,     return_tensors="np"						)

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



      def 						snake_case_ (					self					:     str						):
           _UpperCAmelCase      :     Tuple        =   self.get_image_processor()
           _UpperCAmelCase      :     int        =   self.get_tokenizer()

           _UpperCAmelCase      :     List[str]        =   CLIPProcessor(tokenizer=A					,     image_processor=A						)

           _UpperCAmelCase      :     Optional[int]        =   "lower newer"

           _UpperCAmelCase      :     Union[str, Any]        =   processor(text=A						)

           _UpperCAmelCase      :     Optional[int]        =   tokenizer(A						)

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



      def 						snake_case_ (					self					:     List[str]						):
           _UpperCAmelCase      :     Union[str, Any]        =   self.get_image_processor()
           _UpperCAmelCase      :     Tuple        =   self.get_tokenizer()

           _UpperCAmelCase      :     str        =   CLIPProcessor(tokenizer=A					,     image_processor=A						)

           _UpperCAmelCase      :     Tuple        =   "lower newer"
           _UpperCAmelCase      :     Union[str, Any]        =   self.prepare_image_inputs()

           _UpperCAmelCase      :     str        =   processor(text=A					,     images=A						)

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

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



      def 						snake_case_ (					self					:     int						):
           _UpperCAmelCase      :     List[str]        =   self.get_image_processor()
           _UpperCAmelCase      :     Dict        =   self.get_tokenizer()

           _UpperCAmelCase      :     List[Any]        =   CLIPProcessor(tokenizer=A					,     image_processor=A						)

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

           _UpperCAmelCase      :     List[str]        =   processor.batch_decode(A						)
           _UpperCAmelCase      :     int        =   tokenizer.batch_decode(A						)

           self.assertListEqual(A					,     A						)



      def 						snake_case_ (					self					:     Optional[int]						):
           _UpperCAmelCase      :     Optional[Any]        =   self.get_image_processor()
           _UpperCAmelCase      :     int        =   self.get_tokenizer()

           _UpperCAmelCase      :     int        =   CLIPProcessor(tokenizer=A					,     image_processor=A						)

           _UpperCAmelCase      :     str        =   "lower newer"
           _UpperCAmelCase      :     int        =   self.prepare_image_inputs()

           _UpperCAmelCase      :     Optional[Any]        =   processor(text=A					,     images=A						)

           self.assertListEqual(list(inputs.keys()						)					,     processor.model_input_names						)
