


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:
     __UpperCAmelCase      			=  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 							lowercase__(						unittest.TestCase			):






      '''simple docstring'''







      def    UpperCAmelCase							(		self)					->						List[str]:







         """simple docstring"""







         UpperCamelCase__ :			List[Any] 						=tempfile.mkdtemp()

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

         UpperCamelCase__ :			Any 						=PixaStructProcessor(__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE)

         processor.save_pretrained(self.tmpdirname)




      def    UpperCAmelCase							(		self   ,			**__SCREAMING_SNAKE_CASE)					->						Optional[int]:







         """simple docstring"""







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




      def    UpperCAmelCase							(		self   ,			**__SCREAMING_SNAKE_CASE)					->						Union[str, Any]:







         """simple docstring"""







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




      def    UpperCAmelCase							(		self)					->						int:







         """simple docstring"""







         shutil.rmtree(self.tmpdirname)




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







         """simple docstring"""







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

         UpperCamelCase__ :			Union[str, Any] 						=[Image.fromarray(np.moveaxis(__SCREAMING_SNAKE_CASE   ,			0   ,			-1)) for x in image_inputs]

         return image_inputs




      def    UpperCAmelCase							(		self)					->						Optional[Any]:







         """simple docstring"""







         UpperCamelCase__ :			Tuple 						=PixaStructProcessor(tokenizer=self.get_tokenizer()   ,			image_processor=self.get_image_processor())
         processor.save_pretrained(self.tmpdirname)

         UpperCamelCase__ :			Dict 						=self.get_tokenizer(bos_token="(BOS)"   ,			eos_token="(EOS)")
         UpperCamelCase__ :			List[str] 						=self.get_image_processor(do_normalize=__SCREAMING_SNAKE_CASE   ,			padding_value=1.0)

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

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

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




      def    UpperCAmelCase							(		self)					->						Dict:







         """simple docstring"""







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

         UpperCamelCase__ :			List[Any] 						=PixaStructProcessor(tokenizer=__SCREAMING_SNAKE_CASE   ,			image_processor=__SCREAMING_SNAKE_CASE)

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

         UpperCamelCase__ :			List[str] 						=image_processor(__SCREAMING_SNAKE_CASE   ,			return_tensors="np")
         UpperCamelCase__ :			Optional[int] 						=processor(images=__SCREAMING_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)					->						List[Any]:







         """simple docstring"""







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

         UpperCamelCase__ :			List[str] 						=PixaStructProcessor(tokenizer=__SCREAMING_SNAKE_CASE   ,			image_processor=__SCREAMING_SNAKE_CASE)

         UpperCamelCase__ :			int 						="lower newer"

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

         UpperCamelCase__ :			Dict 						=tokenizer(__SCREAMING_SNAKE_CASE   ,			return_token_type_ids=__SCREAMING_SNAKE_CASE   ,			add_special_tokens=__SCREAMING_SNAKE_CASE)

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




      def    UpperCAmelCase							(		self)					->						Optional[int]:







         """simple docstring"""







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

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

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

         UpperCamelCase__ :			Tuple 						=processor(text=__SCREAMING_SNAKE_CASE   ,			images=__SCREAMING_SNAKE_CASE)

         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(__SCREAMING_SNAKE_CASE):
            processor()




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







         """simple docstring"""







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

         UpperCamelCase__ :			List[str] 						=PixaStructProcessor(tokenizer=__SCREAMING_SNAKE_CASE   ,			image_processor=__SCREAMING_SNAKE_CASE)

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

         UpperCamelCase__ :			Tuple 						=processor(text=__SCREAMING_SNAKE_CASE   ,			images=__SCREAMING_SNAKE_CASE)

         UpperCamelCase__ :			int 						=[5_12, 10_24, 20_48, 40_96]
         UpperCamelCase__ :			int 						=[7_70, 7_70, 7_70, 7_70]
         # with text
         for i, max_patch in enumerate(__SCREAMING_SNAKE_CASE):
            UpperCamelCase__ :			Tuple 						=processor(text=__SCREAMING_SNAKE_CASE   ,			images=__SCREAMING_SNAKE_CASE   ,			max_patches=__SCREAMING_SNAKE_CASE)
            self.assertEqual(inputs["flattened_patches"][0].shape[0]   ,			__SCREAMING_SNAKE_CASE)
            self.assertEqual(inputs["flattened_patches"][0].shape[1]   ,			expected_hidden_size[i])

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




      def    UpperCAmelCase							(		self)					->						Optional[Any]:







         """simple docstring"""







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

         UpperCamelCase__ :			Dict 						=PixaStructProcessor(tokenizer=__SCREAMING_SNAKE_CASE   ,			image_processor=__SCREAMING_SNAKE_CASE)

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

         UpperCamelCase__ :			Dict 						=processor.batch_decode(__SCREAMING_SNAKE_CASE)
         UpperCamelCase__ :			Optional[int] 						=tokenizer.batch_decode(__SCREAMING_SNAKE_CASE)

         self.assertListEqual(__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE)




      def    UpperCAmelCase							(		self)					->						Tuple:







         """simple docstring"""







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

         UpperCamelCase__ :			str 						=PixaStructProcessor(tokenizer=__SCREAMING_SNAKE_CASE   ,			image_processor=__SCREAMING_SNAKE_CASE)

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

         UpperCamelCase__ :			List[str] 						=processor(text=__SCREAMING_SNAKE_CASE   ,			images=__SCREAMING_SNAKE_CASE)

         # 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__ :			Dict 						=processor(text=__SCREAMING_SNAKE_CASE)

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





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__UpperCAmelCase      			=  {"""configuration_sew_d""": ["""SEW_D_PRETRAINED_CONFIG_ARCHIVE_MAP""", """SEWDConfig"""]}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __UpperCAmelCase      			=  [
         """SEW_D_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """SEWDForCTC""",
         """SEWDForSequenceClassification""",
         """SEWDModel""",
         """SEWDPreTrainedModel""",
     ]

if TYPE_CHECKING:
     from .configuration_sew_d import SEW_D_PRETRAINED_CONFIG_ARCHIVE_MAP, SEWDConfig

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_sew_d import (
              SEW_D_PRETRAINED_MODEL_ARCHIVE_LIST,
              SEWDForCTC,
              SEWDForSequenceClassification,
              SEWDModel,
              SEWDPreTrainedModel,
          )


else:
     import sys

     __UpperCAmelCase      			=  _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

