

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     						=      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	):



      def 		UpperCamelCase_						(     self :							List[str]						)       ->	List[str]:
        _snake_case		=				tempfile.mkdtemp()

        _snake_case		=				PixaStructImageProcessor()
        _snake_case		=				TaTokenizer.from_pretrained('''t5-small'''						)

        _snake_case		=				PixaStructProcessor(__SCREAMING_SNAKE_CASE     ,   __SCREAMING_SNAKE_CASE						)

        processor.save_pretrained(self.tmpdirname						)



      def 		UpperCamelCase_						(     self :							Optional[int]     ,   **A__ :							Dict						)       ->	Optional[int]:
        return AutoProcessor.from_pretrained(self.tmpdirname     ,   **__SCREAMING_SNAKE_CASE						).tokenizer



      def 		UpperCamelCase_						(     self :							List[str]     ,   **A__ :							Union[str, Any]						)       ->	Union[str, Any]:
        return AutoProcessor.from_pretrained(self.tmpdirname     ,   **__SCREAMING_SNAKE_CASE						).image_processor



      def 		UpperCamelCase_						(     self :							Optional[Any]						)       ->	int:
        shutil.rmtree(self.tmpdirname						)



      def 		UpperCamelCase_						(     self :							Dict						)       ->	Union[str, Any]:
        _snake_case		=				[np.random.randint(255     ,   size=(3, 30, 400)     ,   dtype=np.uinta						)]

        _snake_case		=				[Image.fromarray(np.moveaxis(__SCREAMING_SNAKE_CASE     ,   0     ,   -1						)						) for x in image_inputs]

        return image_inputs



      def 		UpperCamelCase_						(     self :							int						)       ->	Optional[Any]:
        _snake_case		=				PixaStructProcessor(tokenizer=self.get_tokenizer()     ,   image_processor=self.get_image_processor()						)
        processor.save_pretrained(self.tmpdirname						)

        _snake_case		=				self.get_tokenizer(bos_token='''(BOS)'''     ,   eos_token='''(EOS)'''						)
        _snake_case		=				self.get_image_processor(do_normalize=__SCREAMING_SNAKE_CASE     ,   padding_value=1.0						)

        _snake_case		=				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 :							List[Any]						)       ->	Dict:
        _snake_case		=				self.get_image_processor()
        _snake_case		=				self.get_tokenizer()

        _snake_case		=				PixaStructProcessor(tokenizer=__SCREAMING_SNAKE_CASE     ,   image_processor=__SCREAMING_SNAKE_CASE						)

        _snake_case		=				self.prepare_image_inputs()

        _snake_case		=				image_processor(__SCREAMING_SNAKE_CASE     ,   return_tensors='''np'''						)
        _snake_case		=				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 :							Tuple						)       ->	List[Any]:
        _snake_case		=				self.get_image_processor()
        _snake_case		=				self.get_tokenizer()

        _snake_case		=				PixaStructProcessor(tokenizer=__SCREAMING_SNAKE_CASE     ,   image_processor=__SCREAMING_SNAKE_CASE						)

        _snake_case		=				"lower newer"

        _snake_case		=				processor(text=__SCREAMING_SNAKE_CASE						)

        _snake_case		=				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 :							str						)       ->	Optional[int]:
        _snake_case		=				self.get_image_processor()
        _snake_case		=				self.get_tokenizer()

        _snake_case		=				PixaStructProcessor(tokenizer=__SCREAMING_SNAKE_CASE     ,   image_processor=__SCREAMING_SNAKE_CASE						)

        _snake_case		=				"lower newer"
        _snake_case		=				self.prepare_image_inputs()

        _snake_case		=				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 :							Union[str, Any]						)       ->	List[Any]:
        _snake_case		=				self.get_image_processor()
        _snake_case		=				self.get_tokenizer()

        _snake_case		=				PixaStructProcessor(tokenizer=__SCREAMING_SNAKE_CASE     ,   image_processor=__SCREAMING_SNAKE_CASE						)

        _snake_case		=				"lower newer"
        _snake_case		=				self.prepare_image_inputs()

        _snake_case		=				processor(text=__SCREAMING_SNAKE_CASE     ,   images=__SCREAMING_SNAKE_CASE						)

        _snake_case		=				[512, 1024, 2048, 4096]
        _snake_case		=				[770, 770, 770, 770]
        # with text
        for i, max_patch in enumerate(__SCREAMING_SNAKE_CASE						):
          _snake_case		=				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						):
          _snake_case		=				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 :							str						)       ->	Optional[Any]:
        _snake_case		=				self.get_image_processor()
        _snake_case		=				self.get_tokenizer()

        _snake_case		=				PixaStructProcessor(tokenizer=__SCREAMING_SNAKE_CASE     ,   image_processor=__SCREAMING_SNAKE_CASE						)

        _snake_case		=				[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

        _snake_case		=				processor.batch_decode(__SCREAMING_SNAKE_CASE						)
        _snake_case		=				tokenizer.batch_decode(__SCREAMING_SNAKE_CASE						)

        self.assertListEqual(__SCREAMING_SNAKE_CASE     ,   __SCREAMING_SNAKE_CASE						)







      def 		UpperCamelCase_						(     self :							int						)       ->	Tuple:
        _snake_case		=				self.get_image_processor()
        _snake_case		=				self.get_tokenizer()

        _snake_case		=				PixaStructProcessor(tokenizer=__SCREAMING_SNAKE_CASE     ,   image_processor=__SCREAMING_SNAKE_CASE						)

        _snake_case		=				"lower newer"
        _snake_case		=				self.prepare_image_inputs()

        _snake_case		=				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''']						)

        _snake_case		=				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_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__UpperCAmelCase      			=  {
    """configuration_switch_transformers""": [
        """SWITCH_TRANSFORMERS_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """SwitchTransformersConfig""",
        """SwitchTransformersOnnxConfig""",
    ]
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __UpperCAmelCase      			=  [
         """SWITCH_TRANSFORMERS_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """SwitchTransformersEncoderModel""",
         """SwitchTransformersForConditionalGeneration""",
         """SwitchTransformersModel""",
         """SwitchTransformersPreTrainedModel""",
         """SwitchTransformersTop1Router""",
         """SwitchTransformersSparseMLP""",
     ]


if TYPE_CHECKING:
     from .configuration_switch_transformers import (
         SWITCH_TRANSFORMERS_PRETRAINED_CONFIG_ARCHIVE_MAP,
         SwitchTransformersConfig,
         SwitchTransformersOnnxConfig,
     )

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_switch_transformers import (
              SWITCH_TRANSFORMERS_PRETRAINED_MODEL_ARCHIVE_LIST,
              SwitchTransformersEncoderModel,
              SwitchTransformersForConditionalGeneration,
              SwitchTransformersModel,
              SwitchTransformersPreTrainedModel,
              SwitchTransformersSparseMLP,
              SwitchTransformersTopaRouter,
          )


else:
     import sys

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

