



"""simple docstring"""





import json
import os
import tempfile

import datasets
from utils import generate_example_dataset, get_duration


lowerCamelCase  			=		50_000
lowerCamelCase  			=		5_000

lowerCamelCase			,		lowerCamelCase  			=		os.path.split(__file__)
lowerCamelCase  			=		os.path.join(RESULTS_BASEPATH, """results""", RESULTS_FILENAME.replace(""".py""", """.json"""))
@get_duration
def   a__      ( lowerCAmelCase__      ,     lowerCAmelCase__ ):
      for i in range(lowerCAmelCase__ ):
            UpperCAmelCase_     					=				dataset[i]
@get_duration
def   a__      ( lowerCAmelCase__      ,     lowerCAmelCase__      ,     lowerCAmelCase__ ):
      for i in range(0      ,     len(lowerCAmelCase__ )      ,     lowerCAmelCase__ ):
            UpperCAmelCase_     					=				dataset[i : i + batch_size]
@get_duration
def   a__      ( lowerCAmelCase__      ,     lowerCAmelCase__      ,     lowerCAmelCase__ ):
      with dataset.formatted_as(type=lowerCAmelCase__ ):
            for i in range(lowerCAmelCase__ ):
                  UpperCAmelCase_     					=				dataset[i]
@get_duration
def   a__      ( lowerCAmelCase__      ,     lowerCAmelCase__      ,     lowerCAmelCase__      ,     lowerCAmelCase__ ):
      with dataset.formatted_as(type=lowerCAmelCase__ ):
            for i in range(0      ,     lowerCAmelCase__      ,     lowerCAmelCase__ ):
                  UpperCAmelCase_     					=				dataset[i : i + batch_size]
def   a__      ( ):
      UpperCAmelCase_     					=				{"num examples": SPEED_TEST_N_EXAMPLES}
      UpperCAmelCase_     					=				[
          (read, {"length": SMALL_TEST}),
          (read, {"length": SPEED_TEST_N_EXAMPLES}),
          (read_batch, {"length": SPEED_TEST_N_EXAMPLES, "batch_size": 10}),
          (read_batch, {"length": SPEED_TEST_N_EXAMPLES, "batch_size": 100}),
          (read_batch, {"length": SPEED_TEST_N_EXAMPLES, "batch_size": 1000}),
          (read_formatted, {"type": "numpy", "length": SMALL_TEST}),
          (read_formatted, {"type": "pandas", "length": SMALL_TEST}),
          (read_formatted, {"type": "torch", "length": SMALL_TEST}),
          (read_formatted, {"type": "tensorflow", "length": SMALL_TEST}),
          (read_formatted_batch, {"type": "numpy", "length": SMALL_TEST, "batch_size": 10}),
          (read_formatted_batch, {"type": "numpy", "length": SMALL_TEST, "batch_size": 1000}),
      ]

      UpperCAmelCase_     					=				[
          (read, {"length": SMALL_TEST}),
          (read, {"length": SPEED_TEST_N_EXAMPLES}),
          (read_batch, {"length": SPEED_TEST_N_EXAMPLES, "batch_size": 10}),
          (read_batch, {"length": SPEED_TEST_N_EXAMPLES, "batch_size": 100}),
          (read_batch, {"length": SPEED_TEST_N_EXAMPLES, "batch_size": 1000}),
          (read_formatted, {"type": "numpy", "length": SMALL_TEST}),
          (read_formatted_batch, {"type": "numpy", "length": SMALL_TEST, "batch_size": 10}),
          (read_formatted_batch, {"type": "numpy", "length": SMALL_TEST, "batch_size": 1000}),
      ]
      with tempfile.TemporaryDirectory() as tmp_dir:
            print("generating dataset" )
            UpperCAmelCase_     					=				datasets.Features(
                {"list": datasets.Sequence(datasets.Value("float32" ) ), "numbers": datasets.Value("float32" )} )
            UpperCAmelCase_     					=				generate_example_dataset(
                os.path.join(lowerCAmelCase__      ,     "dataset.arrow" )      ,     lowerCAmelCase__      ,     num_examples=lowerCAmelCase__      ,     seq_shapes={"list": (100,)}      ,     )
            print("first set of iterations" )
            for func, kwargs in functions:
                  print(func.__name__      ,     str(lowerCAmelCase__ ) )
                  UpperCAmelCase_     					=				func(lowerCAmelCase__      ,     **lowerCAmelCase__ )

            print("shuffling dataset" )
            UpperCAmelCase_     					=				dataset.shuffle()
            print("Second set of iterations (after shuffling" )
            for func, kwargs in functions_shuffled:
                  print("shuffled "      ,     func.__name__      ,     str(lowerCAmelCase__ ) )
                  UpperCAmelCase_     					=				func(
                      lowerCAmelCase__      ,     **lowerCAmelCase__ )

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


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








"""simple docstring"""


import unittest

from transformers import MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING, is_vision_available
from transformers.pipelines import pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_vision_available():
	from PIL import Image
else:
	class     _UpperCamelCase    :
								'''simple docstring'''







								@staticmethod
								def      snake_case							(	*__a       ,  **__a      ):
											pass
@is_pipeline_test
@require_torch
@require_vision
class     _UpperCamelCase    (  unittest.TestCase						):
							'''simple docstring'''







							__UpperCAmelCase		:       str							 =MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING

							def      snake_case							(	self       ,  __a       ,  __a       ,  __a      ):
										__lowerCAmelCase							       =     pipeline("visual-question-answering"       ,  model="hf-internal-testing/tiny-vilt-random-vqa"      )
										__lowerCAmelCase							       =     [
										    {
										        "image": Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"      ),
										        "question": "How many cats are there?",
										    },
										    {
										        "image": "./tests/fixtures/tests_samples/COCO/000000039769.png",
										        "question": "How many cats are there?",
										    },
										]
										return vqa_pipeline, examples

							def      snake_case							(	self       ,  __a       ,  __a      ):
										__lowerCAmelCase							       =     vqa_pipeline(__a       ,  top_k=1      )
										self.assertEqual(
										    __a       ,  [
										        [{"score": ANY(__a      ), "answer": ANY(__a      )}],
										        [{"score": ANY(__a      ), "answer": ANY(__a      )}],
										    ]       ,  )

							@require_torch
							def      snake_case							(	self      ):
										__lowerCAmelCase							       =     pipeline("visual-question-answering"       ,  model="hf-internal-testing/tiny-vilt-random-vqa"      )
										__lowerCAmelCase							       =     "./tests/fixtures/tests_samples/COCO/000000039769.png"
										__lowerCAmelCase							       =     "How many cats are there?"

										__lowerCAmelCase							       =     vqa_pipeline(image=__a       ,  question="How many cats are there?"       ,  top_k=2      )
										self.assertEqual(
										    __a       ,  [{"score": ANY(__a      ), "answer": ANY(__a      )}, {"score": ANY(__a      ), "answer": ANY(__a      )}]      )

										__lowerCAmelCase							       =     vqa_pipeline({"image": image, "question": question}       ,  top_k=2      )
										self.assertEqual(
										    __a       ,  [{"score": ANY(__a      ), "answer": ANY(__a      )}, {"score": ANY(__a      ), "answer": ANY(__a      )}]      )

							@slow
							@require_torch
							def      snake_case							(	self      ):
										__lowerCAmelCase							       =     pipeline("visual-question-answering"       ,  model="dandelin/vilt-b32-finetuned-vqa"      )
										__lowerCAmelCase							       =     "./tests/fixtures/tests_samples/COCO/000000039769.png"
										__lowerCAmelCase							       =     "How many cats are there?"

										__lowerCAmelCase							       =     vqa_pipeline(image=__a       ,  question=__a       ,  top_k=2      )
										self.assertEqual(
										    nested_simplify(__a       ,  decimals=4      )       ,  [{"score": 0.8_7_9_9, "answer": "2"}, {"score": 0.2_9_6, "answer": "1"}]      )

										__lowerCAmelCase							       =     vqa_pipeline({"image": image, "question": question}       ,  top_k=2      )
										self.assertEqual(
										    nested_simplify(__a       ,  decimals=4      )       ,  [{"score": 0.8_7_9_9, "answer": "2"}, {"score": 0.2_9_6, "answer": "1"}]      )

										__lowerCAmelCase							       =     vqa_pipeline(
										    [{"image": image, "question": question}, {"image": image, "question": question}]       ,  top_k=2      )
										self.assertEqual(
										    nested_simplify(__a       ,  decimals=4      )       ,  [[{"score": 0.8_7_9_9, "answer": "2"}, {"score": 0.2_9_6, "answer": "1"}]] * 2       ,  )



							@require_tf
							@unittest.skip("Visual question answering not implemented in TF"      )
							def      snake_case							(	self      ):
										pass




