

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						:Tuple            = 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 		__A  (      self       :							str  ):
            A_	     =							tempfile.mkdtemp()

            A_	     =							PixaStructImageProcessor()
            A_	     =							TaTokenizer.from_pretrained("t5-small"  )

            A_	     =							PixaStructProcessor(UpperCAmelCase	, UpperCAmelCase  )

            processor.save_pretrained(self.tmpdirname  )





      def 		__A  (      self       :							List[str]	, **UpperCAmelCase       :							Dict  ):
            return AutoProcessor.from_pretrained(self.tmpdirname	, **UpperCAmelCase  ).tokenizer





      def 		__A  (      self       :							Any	, **UpperCAmelCase       :							Optional[Any]  ):
            return AutoProcessor.from_pretrained(self.tmpdirname	, **UpperCAmelCase  ).image_processor





      def 		__A  (      self       :							Any  ):
            shutil.rmtree(self.tmpdirname  )





      def 		__A  (      self       :							int  ):
            A_	     =							[np.random.randint(255	, size=(3, 30, 400)	, dtype=np.uinta  )]

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

            return image_inputs





      def 		__A  (      self       :							str  ):
            A_	     =							PixaStructProcessor(tokenizer=self.get_tokenizer()	, image_processor=self.get_image_processor()  )
            processor.save_pretrained(self.tmpdirname  )

            A_	     =							self.get_tokenizer(bos_token="(BOS)"	, eos_token="(EOS)"  )
            A_	     =							self.get_image_processor(do_normalize=UpperCAmelCase	, padding_value=1.0  )

            A_	     =							PixaStructProcessor.from_pretrained(
                self.tmpdirname	, bos_token="(BOS)"	, eos_token="(EOS)"	, do_normalize=UpperCAmelCase	, padding_value=1.0  )

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

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





      def 		__A  (      self       :							Optional[int]  ):
            A_	     =							self.get_image_processor()
            A_	     =							self.get_tokenizer()

            A_	     =							PixaStructProcessor(tokenizer=UpperCAmelCase	, image_processor=UpperCAmelCase  )

            A_	     =							self.prepare_image_inputs()

            A_	     =							image_processor(UpperCAmelCase	, return_tensors="np"  )
            A_	     =							processor(images=UpperCAmelCase	, 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 		__A  (      self       :							Dict  ):
            A_	     =							self.get_image_processor()
            A_	     =							self.get_tokenizer()

            A_	     =							PixaStructProcessor(tokenizer=UpperCAmelCase	, image_processor=UpperCAmelCase  )

            A_	     =							"lower newer"

            A_	     =							processor(text=UpperCAmelCase  )

            A_	     =							tokenizer(UpperCAmelCase	, return_token_type_ids=UpperCAmelCase	, add_special_tokens=UpperCAmelCase  )

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





      def 		__A  (      self       :							Union[str, Any]  ):
            A_	     =							self.get_image_processor()
            A_	     =							self.get_tokenizer()

            A_	     =							PixaStructProcessor(tokenizer=UpperCAmelCase	, image_processor=UpperCAmelCase  )

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

            A_	     =							processor(text=UpperCAmelCase	, images=UpperCAmelCase  )

            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(UpperCAmelCase  ):
                  processor()





      def 		__A  (      self       :							Optional[int]  ):
            A_	     =							self.get_image_processor()
            A_	     =							self.get_tokenizer()

            A_	     =							PixaStructProcessor(tokenizer=UpperCAmelCase	, image_processor=UpperCAmelCase  )

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

            A_	     =							processor(text=UpperCAmelCase	, images=UpperCAmelCase  )

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

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





      def 		__A  (      self       :							Any  ):
            A_	     =							self.get_image_processor()
            A_	     =							self.get_tokenizer()

            A_	     =							PixaStructProcessor(tokenizer=UpperCAmelCase	, image_processor=UpperCAmelCase  )

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

            A_	     =							processor.batch_decode(UpperCAmelCase  )
            A_	     =							tokenizer.batch_decode(UpperCAmelCase  )

            self.assertListEqual(UpperCAmelCase	, UpperCAmelCase  )







      def 		__A  (      self       :							Dict  ):
            A_	     =							self.get_image_processor()
            A_	     =							self.get_tokenizer()

            A_	     =							PixaStructProcessor(tokenizer=UpperCAmelCase	, image_processor=UpperCAmelCase  )

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

            A_	     =							processor(text=UpperCAmelCase	, images=UpperCAmelCase  )

            # 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"]  )

            A_	     =							processor(text=UpperCAmelCase  )

            # 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 json
import os
import tempfile

import datasets
from datasets.arrow_writer import ArrowWriter
from datasets.features import ArrayaD
from utils import generate_examples, get_duration


__a						:Dict            = (30, 487)
__a						:int            = (36, 1024)
__a						:Tuple            = (100, 100)
__a						:Dict            = 100

__a						:List[str]            = datasets.Features(
    {'text': ArrayaD(SHAPE_TEST_1, dtype='float32'), 'image': ArrayaD(SHAPE_TEST_2, dtype='float32')}
)

__a      ,			__a						:str            = os.path.split(__file__)
__a						:Optional[Any]            = os.path.join(RESULTS_BASEPATH, 'results', RESULTS_FILENAME.replace('.py', '.json'))

@get_duration
def 			__snake_case	(   __UpperCamelCase  :   Optional[int]							,__UpperCamelCase  :   Union[str, Any]							,__UpperCamelCase  :   Dict						):



      """simple docstring"""

      with ArrowWriter(features=__UpperCamelCase							,path=os.path.join(__UpperCamelCase							,"beta.arrow"						)						) as writer:
            for key, record in dummy_data:
                  A_	     =							my_features.encode_example(__UpperCamelCase						)
                  writer.write(__UpperCamelCase						)
            A_     ,    A_	     =							writer.finalize()

@get_duration
def 			__snake_case	(   __UpperCamelCase  :   int							,__UpperCamelCase  :   Any						):



      """simple docstring"""

      A_	     =							datasets.Dataset.from_file(
          filename=os.path.join(__UpperCamelCase							,"beta.arrow"						)							,info=datasets.DatasetInfo(features=__UpperCamelCase						)						)
      for _ in dataset:
            pass

@get_duration
def 			__snake_case	(   __UpperCamelCase  :   Optional[Any]							,__UpperCamelCase  :   List[str]						):



      """simple docstring"""

      A_	     =							datasets.Dataset.from_file(
          filename=os.path.join(__UpperCamelCase							,"beta.arrow"						)							,info=datasets.DatasetInfo(features=__UpperCamelCase						)						)
      dataset.set_format("numpy"						)
      for _ in dataset:
            pass

@get_duration
def 			__snake_case	(   __UpperCamelCase  :   List[str]							,__UpperCamelCase  :   Tuple						):



      """simple docstring"""

      A_	     =							10
      A_	     =							datasets.Dataset.from_file(
          filename=os.path.join(__UpperCamelCase							,"beta.arrow"						)							,info=datasets.DatasetInfo(features=__UpperCamelCase						)						)
      for i in range(0							,len(__UpperCamelCase						)							,__UpperCamelCase						):
            A_	     =							dataset[i : i + batch_size]

@get_duration
def 			__snake_case	(   __UpperCamelCase  :   int							,__UpperCamelCase  :   List[Any]						):



      """simple docstring"""

      A_	     =							10
      A_	     =							datasets.Dataset.from_file(
          filename=os.path.join(__UpperCamelCase							,"beta.arrow"						)							,info=datasets.DatasetInfo(features=__UpperCamelCase						)						)
      dataset.set_format("numpy"						)
      for i in range(0							,len(__UpperCamelCase						)							,__UpperCamelCase						):
            A_	     =							dataset[i : i + batch_size]

@get_duration
def 			__snake_case	(   __UpperCamelCase  :   int							,__UpperCamelCase  :   Tuple						):



      """simple docstring"""

      A_	     =							datasets.Dataset.from_file(
          filename=os.path.join(__UpperCamelCase							,"beta.arrow"						)							,info=datasets.DatasetInfo(features=__UpperCamelCase						)						)
      for col in feats:
            A_	     =							dataset[col]

@get_duration
def 			__snake_case	(   __UpperCamelCase  :   Optional[int]							,__UpperCamelCase  :   Optional[Any]						):



      """simple docstring"""

      A_	     =							datasets.Dataset.from_file(
          filename=os.path.join(__UpperCamelCase							,"beta.arrow"						)							,info=datasets.DatasetInfo(features=__UpperCamelCase						)						)
      dataset.set_format("numpy"						)
      for col in feats:
            A_	     =							dataset[col]

def 			__snake_case	(   ):



      """simple docstring"""

      A_	     =							{}
      A_	     =							(
          read_unformated,
          read_formatted_as_numpy,
          read_batch_unformated,
          read_batch_formatted_as_numpy,
          read_col_unformated,
          read_col_formatted_as_numpy,
      )
      with tempfile.TemporaryDirectory() as tmp_dir:
            A_	     =							datasets.Features({"image": ArrayaD(__UpperCamelCase							,dtype="float32"						)}						)
            A_	     =							generate_examples(features=__UpperCamelCase							,num_examples=__UpperCamelCase						)
            A_	     =							write(__UpperCamelCase							,__UpperCamelCase							,__UpperCamelCase						)
            for read_func in read_functions:
                  A_	     =							read_func(__UpperCamelCase							,__UpperCamelCase						)

      with tempfile.TemporaryDirectory() as tmp_dir:
            # don't use fixed length for fair comparison
            # feats = datasets.Features(
            #     {"image": datasets.Sequence(datasets.Sequence(datasets.Value("float32"), SPEED_TEST_SHAPE[1]), SPEED_TEST_SHAPE[0])}
            # )
            A_	     =							datasets.Features({"image": datasets.Sequence(datasets.Sequence(datasets.Value("float32"						)						)						)}						)
            A_	     =							generate_examples(
                features=__UpperCamelCase							,num_examples=__UpperCamelCase							,seq_shapes={"image": SPEED_TEST_SHAPE}						)
            A_	     =							write(__UpperCamelCase							,__UpperCamelCase							,__UpperCamelCase						)
            for read_func in read_functions:
                  A_	     =							read_func(__UpperCamelCase							,__UpperCamelCase						)

      with tempfile.TemporaryDirectory() as tmp_dir:
            # don't use fixed length for fair comparison
            # feats = datasets.Features(
            #     {"image": datasets.Sequence(datasets.Value("float32"), SPEED_TEST_SHAPE[0] * SPEED_TEST_SHAPE[1])}
            # )
            A_	     =							datasets.Features({"image": datasets.Sequence(datasets.Value("float32"						)						)}						)
            A_	     =							generate_examples(
                features=__UpperCamelCase							,num_examples=__UpperCamelCase							,seq_shapes={"image": [SPEED_TEST_SHAPE[0] * SPEED_TEST_SHAPE[1]]}							,)
            A_	     =							write(__UpperCamelCase							,__UpperCamelCase							,__UpperCamelCase						)
            for read_func in read_functions:
                  A_	     =							read_func(__UpperCamelCase							,__UpperCamelCase						)

      with open(__UpperCamelCase							,"wb"						) as f:
            f.write(json.dumps(__UpperCamelCase						).encode("utf-8"						)						)


if __name__ == "__main__":  # useful to run the profiler
      benchmark_array_xd()