





import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available


if is_torch_available():
   from transformers.pytorch_utils import is_torch_greater_or_equal_than_1_11
else:
   A__:  Union[str, Any]       					=					False

if is_vision_available():
   from PIL import Image

   from transformers import (
       AutoProcessor,
       PixaStructImageProcessor,
       PixaStructProcessor,
       PreTrainedTokenizerFast,
       TaTokenizer,
   )




@unittest.skipIf(
    not is_torch_greater_or_equal_than_1_11		,	reason="""`Pix2StructImageProcessor` requires `torch>=1.11.0`."""		,	)
@require_vision
@require_torch
class    _a  (  unittest.TestCase):

      """simple docstring"""


      def 	UpperCAmelCase_   (						self:	List[Any]			):
          '''simple docstring'''


          UpperCamelCase__:					Optional[int]												=      tempfile.mkdtemp()

          UpperCamelCase__:					Optional[int]												=      PixaStructImageProcessor()
          UpperCamelCase__:					Any												=      TaTokenizer.from_pretrained("t5-small"			)

          UpperCamelCase__:					Any												=      PixaStructProcessor(__lowerCamelCase					,  __lowerCamelCase			)

          processor.save_pretrained(self.tmpdirname			)






      def 	UpperCAmelCase_   (						self:	str					,  **__lowerCamelCase:	str			):
          '''simple docstring'''


          return AutoProcessor.from_pretrained(self.tmpdirname					,  **__lowerCamelCase			).tokenizer






      def 	UpperCAmelCase_   (						self:	Union[str, Any]					,  **__lowerCamelCase:	Optional[int]			):
          '''simple docstring'''


          return AutoProcessor.from_pretrained(self.tmpdirname					,  **__lowerCamelCase			).image_processor






      def 	UpperCAmelCase_   (						self:	str			):
          '''simple docstring'''


          shutil.rmtree(self.tmpdirname			)






      def 	UpperCAmelCase_   (						self:	List[str]			):
          '''simple docstring'''


          UpperCamelCase__:					Union[str, Any]												=      [np.random.randint(255					,  size=(3, 30, 400)					,  dtype=np.uinta			)]

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

          return image_inputs






      def 	UpperCAmelCase_   (						self:	Dict			):
          '''simple docstring'''


          UpperCamelCase__:					Union[str, Any]												=      PixaStructProcessor(tokenizer=self.get_tokenizer()					,  image_processor=self.get_image_processor()			)
          processor.save_pretrained(self.tmpdirname			)

          UpperCamelCase__:					int												=      self.get_tokenizer(bos_token="(BOS)"					,  eos_token="(EOS)"			)
          UpperCamelCase__:					str												=      self.get_image_processor(do_normalize=__lowerCamelCase					,  padding_value=1.0			)

          UpperCamelCase__:					int												=      PixaStructProcessor.from_pretrained(
              self.tmpdirname					,  bos_token="(BOS)"					,  eos_token="(EOS)"					,  do_normalize=__lowerCamelCase					,  padding_value=1.0			)

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

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






      def 	UpperCAmelCase_   (						self:	Dict			):
          '''simple docstring'''


          UpperCamelCase__:					Union[str, Any]												=      self.get_image_processor()
          UpperCamelCase__:					Union[str, Any]												=      self.get_tokenizer()

          UpperCamelCase__:					Tuple												=      PixaStructProcessor(tokenizer=__lowerCamelCase					,  image_processor=__lowerCamelCase			)

          UpperCamelCase__:					Optional[int]												=      self.prepare_image_inputs()

          UpperCamelCase__:					str												=      image_processor(__lowerCamelCase					,  return_tensors="np"			)
          UpperCamelCase__:					Dict												=      processor(images=__lowerCamelCase					,  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:	List[Any]			):
          '''simple docstring'''


          UpperCamelCase__:					Any												=      self.get_image_processor()
          UpperCamelCase__:					int												=      self.get_tokenizer()

          UpperCamelCase__:					Any												=      PixaStructProcessor(tokenizer=__lowerCamelCase					,  image_processor=__lowerCamelCase			)

          UpperCamelCase__:					Any												=      "lower newer"

          UpperCamelCase__:					List[Any]												=      processor(text=__lowerCamelCase			)

          UpperCamelCase__:					List[str]												=      tokenizer(__lowerCamelCase					,  return_token_type_ids=__lowerCamelCase					,  add_special_tokens=__lowerCamelCase			)

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






      def 	UpperCAmelCase_   (						self:	Tuple			):
          '''simple docstring'''


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

          UpperCamelCase__:					Union[str, Any]												=      PixaStructProcessor(tokenizer=__lowerCamelCase					,  image_processor=__lowerCamelCase			)

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

          UpperCamelCase__:					str												=      processor(text=__lowerCamelCase					,  images=__lowerCamelCase			)

          self.assertListEqual(
              list(inputs.keys()			)					,  ["flattened_patches", "attention_mask", "decoder_attention_mask", "decoder_input_ids"]			)

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






      def 	UpperCAmelCase_   (						self:	int			):
          '''simple docstring'''


          UpperCamelCase__:					Tuple												=      self.get_image_processor()
          UpperCamelCase__:					Union[str, Any]												=      self.get_tokenizer()

          UpperCamelCase__:					Tuple												=      PixaStructProcessor(tokenizer=__lowerCamelCase					,  image_processor=__lowerCamelCase			)

          UpperCamelCase__:					str												=      "lower newer"
          UpperCamelCase__:					Tuple												=      self.prepare_image_inputs()

          UpperCamelCase__:					str												=      processor(text=__lowerCamelCase					,  images=__lowerCamelCase			)

          UpperCamelCase__:					List[Any]												=      [512, 1024, 2048, 4096]
          UpperCamelCase__:					Union[str, Any]												=      [770, 770, 770, 770]
          # with text
          for i, max_patch in enumerate(__lowerCamelCase			):
              UpperCamelCase__:					str												=      processor(text=__lowerCamelCase					,  images=__lowerCamelCase					,  max_patches=__lowerCamelCase			)
              self.assertEqual(inputs["flattened_patches"][0].shape[0]					,  __lowerCamelCase			)
              self.assertEqual(inputs["flattened_patches"][0].shape[1]					,  expected_hidden_size[i]			)

          # without text input
          for i, max_patch in enumerate(__lowerCamelCase			):
              UpperCamelCase__:					Optional[Any]												=      processor(images=__lowerCamelCase					,  max_patches=__lowerCamelCase			)
              self.assertEqual(inputs["flattened_patches"][0].shape[0]					,  __lowerCamelCase			)
              self.assertEqual(inputs["flattened_patches"][0].shape[1]					,  expected_hidden_size[i]			)






      def 	UpperCAmelCase_   (						self:	List[Any]			):
          '''simple docstring'''


          UpperCamelCase__:					Tuple												=      self.get_image_processor()
          UpperCamelCase__:					Any												=      self.get_tokenizer()

          UpperCamelCase__:					int												=      PixaStructProcessor(tokenizer=__lowerCamelCase					,  image_processor=__lowerCamelCase			)

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

          UpperCamelCase__:					Any												=      processor.batch_decode(__lowerCamelCase			)
          UpperCamelCase__:					Union[str, Any]												=      tokenizer.batch_decode(__lowerCamelCase			)

          self.assertListEqual(__lowerCamelCase					,  __lowerCamelCase			)






      def 	UpperCAmelCase_   (						self:	Union[str, Any]			):
          '''simple docstring'''


          UpperCamelCase__:					Dict												=      self.get_image_processor()
          UpperCamelCase__:					int												=      self.get_tokenizer()

          UpperCamelCase__:					Tuple												=      PixaStructProcessor(tokenizer=__lowerCamelCase					,  image_processor=__lowerCamelCase			)

          UpperCamelCase__:					List[Any]												=      "lower newer"
          UpperCamelCase__:					List[Any]												=      self.prepare_image_inputs()

          UpperCamelCase__:					Optional[int]												=      processor(text=__lowerCamelCase					,  images=__lowerCamelCase			)

          # For now the processor supports only ["flattened_patches", "input_ids", "attention_mask", "decoder_attention_mask"]
          self.assertListEqual(
              list(inputs.keys()			)					,  ["flattened_patches", "attention_mask", "decoder_attention_mask", "decoder_input_ids"]			)

          UpperCamelCase__:					Optional[Any]												=      processor(text=__lowerCamelCase			)

          # For now the processor supports only ["flattened_patches", "input_ids", "attention_mask", "decoder_attention_mask"]
          self.assertListEqual(list(inputs.keys()			)					,  ["input_ids", "attention_mask"]			)








import inspect

from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax


if is_flax_available():
   import jax




@require_flax
class    _a  :

      """simple docstring"""


      def 	UpperCAmelCase_   (						self:	int			):
          '''simple docstring'''


          UpperCamelCase__							,				UpperCamelCase__:					Optional[int]												=      self.prepare_init_args_and_inputs_for_common()

          UpperCamelCase__:					Tuple												=      self.model_class(**__lowerCamelCase			)
          UpperCamelCase__:					Union[str, Any]												=      model.init(inputs_dict["prng_key"]					,  inputs_dict["sample"]			)
          jax.lax.stop_gradient(__lowerCamelCase			)

          UpperCamelCase__:					int												=      model.apply(__lowerCamelCase					,  inputs_dict["sample"]			)

          if isinstance(__lowerCamelCase					,  __lowerCamelCase			):
              UpperCamelCase__:					str												=      output.sample

          self.assertIsNotNone(__lowerCamelCase			)
          UpperCamelCase__:					Union[str, Any]												=      inputs_dict["sample"].shape
          self.assertEqual(output.shape					,  __lowerCamelCase					,  "Input and output shapes do not match"			)






      def 	UpperCAmelCase_   (						self:	Union[str, Any]			):
          '''simple docstring'''


          UpperCamelCase__							,				UpperCamelCase__:					List[str]												=      self.prepare_init_args_and_inputs_for_common()

          UpperCamelCase__:					List[Any]												=      16
          UpperCamelCase__:					int												=      (16, 32)

          UpperCamelCase__:					Tuple												=      self.model_class(**__lowerCamelCase			)
          UpperCamelCase__:					Union[str, Any]												=      model.init(inputs_dict["prng_key"]					,  inputs_dict["sample"]			)
          jax.lax.stop_gradient(__lowerCamelCase			)

          UpperCamelCase__:					Union[str, Any]												=      model.apply(__lowerCamelCase					,  inputs_dict["sample"]			)

          if isinstance(__lowerCamelCase					,  __lowerCamelCase			):
              UpperCamelCase__:					Dict												=      output.sample

          self.assertIsNotNone(__lowerCamelCase			)
          UpperCamelCase__:					Optional[Any]												=      inputs_dict["sample"].shape
          self.assertEqual(output.shape					,  __lowerCamelCase					,  "Input and output shapes do not match"			)






      def 	UpperCAmelCase_   (						self:	Tuple			):
          '''simple docstring'''


          UpperCamelCase__:					List[str]												=      "kwargs" in inspect.signature(self.model_class.__init__			).parameters
          UpperCamelCase__:					Any												=      len(self.model_class._deprecated_kwargs			) > 0

          if has_kwarg_in_model_class and not has_deprecated_kwarg:
              raise ValueError(
                  F"{self.model_class} has `**kwargs` in its __init__ method but has not defined any deprecated kwargs"
                  " under the `_deprecated_kwargs` class attribute. Make sure to either remove `**kwargs` if there are"
                  " no deprecated arguments or add the deprecated argument with `_deprecated_kwargs ="
                  " [<deprecated_argument>]`"			)

          if not has_kwarg_in_model_class and has_deprecated_kwarg:
              raise ValueError(
                  F"{self.model_class} doesn't have `**kwargs` in its __init__ method but has defined deprecated kwargs"
                  " under the `_deprecated_kwargs` class attribute. Make sure to either add the `**kwargs` argument to"
                  F" {self.model_class}.__init__ if there are deprecated arguments or remove the deprecated argument"
                  " from `_deprecated_kwargs = [<deprecated_argument>]`"			)

