


"""simple docstring"""


import unittest

import numpy as np

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

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_video_inputs


if is_torch_available():
	import torch

if is_vision_available():
	from PIL import Image

	from transformers import VivitImageProcessor
class 						_UpperCamelCase						(     unittest.TestCase	):
							'''simple docstring'''







							def __init__(  self					,     __a					,     __a=7					,     __a=3					,     __a=10					,     __a=18					,     __a=30					,     __a=4_00					,     __a=True					,     __a=None					,     __a=True					,     __a=[0.5, 0.5, 0.5]					,     __a=[0.5, 0.5, 0.5]					,     __a=None					,     ):
										__lowerCAmelCase       					=    size if size is not None else {"shortest_edge": 18}
										__lowerCAmelCase       					=    crop_size if crop_size is not None else {"height": 18, "width": 18}

										__lowerCAmelCase       					=    parent
										__lowerCAmelCase       					=    batch_size
										__lowerCAmelCase       					=    num_channels
										__lowerCAmelCase       					=    num_frames
										__lowerCAmelCase       					=    image_size
										__lowerCAmelCase       					=    min_resolution
										__lowerCAmelCase       					=    max_resolution
										__lowerCAmelCase       					=    do_resize
										__lowerCAmelCase       					=    size
										__lowerCAmelCase       					=    do_normalize
										__lowerCAmelCase       					=    image_mean
										__lowerCAmelCase       					=    image_std
										__lowerCAmelCase       					=    crop_size



							def     snake_case       (  self			):
										return {
										    "image_mean": self.image_mean,
										    "image_std": self.image_std,
										    "do_normalize": self.do_normalize,
										    "do_resize": self.do_resize,
										    "size": self.size,
										    "crop_size": self.crop_size,
										}
@require_torch
@require_vision
class 						_UpperCamelCase						(     lowerCAmelCase__ ,unittest.TestCase	):
							'''simple docstring'''







							__UpperCAmelCase		:					Tuple          =VivitImageProcessor if is_vision_available() else None

							def     snake_case       (  self			):
										__lowerCAmelCase       					=    VivitImageProcessingTester(self			)

							@property
							def     snake_case       (  self			):
										return self.image_processor_tester.prepare_image_processor_dict()

							def     snake_case       (  self			):
										__lowerCAmelCase       					=    self.image_processing_class(**self.image_processor_dict			)
										self.assertTrue(hasattr(__a					,     "image_mean"			)			)
										self.assertTrue(hasattr(__a					,     "image_std"			)			)
										self.assertTrue(hasattr(__a					,     "do_normalize"			)			)
										self.assertTrue(hasattr(__a					,     "do_resize"			)			)
										self.assertTrue(hasattr(__a					,     "do_center_crop"			)			)
										self.assertTrue(hasattr(__a					,     "size"			)			)

							def     snake_case       (  self			):
										__lowerCAmelCase       					=    self.image_processing_class.from_dict(self.image_processor_dict			)
										self.assertEqual(image_processor.size					,     {"shortest_edge": 18}			)
										self.assertEqual(image_processor.crop_size					,     {"height": 18, "width": 18}			)

										__lowerCAmelCase       					=    self.image_processing_class.from_dict(self.image_processor_dict					,     size=42					,     crop_size=84			)
										self.assertEqual(image_processor.size					,     {"shortest_edge": 42}			)
										self.assertEqual(image_processor.crop_size					,     {"height": 84, "width": 84}			)

							def     snake_case       (  self			):
										# Initialize image_processing
										__lowerCAmelCase       					=    self.image_processing_class(**self.image_processor_dict			)
										# create random PIL videos
										__lowerCAmelCase       					=    prepare_video_inputs(self.image_processor_tester					,     equal_resolution=__a			)
										for video in video_inputs:
													self.assertIsInstance(__a					,     __a			)
													self.assertIsInstance(video[0]					,     Image.Image			)

										# Test not batched input
										__lowerCAmelCase       					=    image_processing(video_inputs[0]					,     return_tensors="pt"			).pixel_values
										self.assertEqual(
										    encoded_videos.shape					,     (
										        1,
										        self.image_processor_tester.num_frames,
										        self.image_processor_tester.num_channels,
										        self.image_processor_tester.crop_size["height"],
										        self.image_processor_tester.crop_size["width"],
										    )					,     )

										# Test batched
										__lowerCAmelCase       					=    image_processing(__a					,     return_tensors="pt"			).pixel_values
										self.assertEqual(
										    encoded_videos.shape					,     (
										        self.image_processor_tester.batch_size,
										        self.image_processor_tester.num_frames,
										        self.image_processor_tester.num_channels,
										        self.image_processor_tester.crop_size["height"],
										        self.image_processor_tester.crop_size["width"],
										    )					,     )

							def     snake_case       (  self			):
										# Initialize image_processing
										__lowerCAmelCase       					=    self.image_processing_class(**self.image_processor_dict			)
										# create random numpy tensors
										__lowerCAmelCase       					=    prepare_video_inputs(self.image_processor_tester					,     equal_resolution=__a					,     numpify=__a			)
										for video in video_inputs:
													self.assertIsInstance(__a					,     __a			)
													self.assertIsInstance(video[0]					,     np.ndarray			)

										# Test not batched input
										__lowerCAmelCase       					=    image_processing(video_inputs[0]					,     return_tensors="pt"			).pixel_values
										self.assertEqual(
										    encoded_videos.shape					,     (
										        1,
										        self.image_processor_tester.num_frames,
										        self.image_processor_tester.num_channels,
										        self.image_processor_tester.crop_size["height"],
										        self.image_processor_tester.crop_size["width"],
										    )					,     )

										# Test batched
										__lowerCAmelCase       					=    image_processing(__a					,     return_tensors="pt"			).pixel_values
										self.assertEqual(
										    encoded_videos.shape					,     (
										        self.image_processor_tester.batch_size,
										        self.image_processor_tester.num_frames,
										        self.image_processor_tester.num_channels,
										        self.image_processor_tester.crop_size["height"],
										        self.image_processor_tester.crop_size["width"],
										    )					,     )



							def     snake_case       (  self			):
										# Initialize image_processing
										__lowerCAmelCase       					=    self.image_processing_class(**self.image_processor_dict			)
										# create random PyTorch tensors
										__lowerCAmelCase       					=    prepare_video_inputs(self.image_processor_tester					,     equal_resolution=__a					,     torchify=__a			)
										for video in video_inputs:
													self.assertIsInstance(__a					,     __a			)
													self.assertIsInstance(video[0]					,     torch.Tensor			)

										# Test not batched input
										__lowerCAmelCase       					=    image_processing(video_inputs[0]					,     return_tensors="pt"			).pixel_values
										self.assertEqual(
										    encoded_videos.shape					,     (
										        1,
										        self.image_processor_tester.num_frames,
										        self.image_processor_tester.num_channels,
										        self.image_processor_tester.crop_size["height"],
										        self.image_processor_tester.crop_size["width"],
										    )					,     )

										# Test batched
										__lowerCAmelCase       					=    image_processing(__a					,     return_tensors="pt"			).pixel_values
										self.assertEqual(
										    encoded_videos.shape					,     (
										        self.image_processor_tester.batch_size,
										        self.image_processor_tester.num_frames,
										        self.image_processor_tester.num_channels,
										        self.image_processor_tester.crop_size["height"],
										        self.image_processor_tester.crop_size["width"],
										    )					,     )








"""simple docstring"""


import unittest
from pathlib import Path
from tempfile import NamedTemporaryFile, TemporaryDirectory

from transformers import BertConfig, BertTokenizerFast, FeatureExtractionPipeline
from transformers.convert_graph_to_onnx import (
    convert,
    ensure_valid_input,
    generate_identified_filename,
    infer_shapes,
    quantize,
)
from transformers.testing_utils import require_tf, require_tokenizers, require_torch, slow
class 						_UpperCamelCase						:
							'''simple docstring'''







							def     snake_case       (  self					,     __a					,     __a					,     __a			):
										return None
class 						_UpperCamelCase						:
							'''simple docstring'''







							def     snake_case       (  self					,     __a					,     __a					,     __a					,     __a			):
										return None
class 						_UpperCamelCase						(     unittest.TestCase	):
							'''simple docstring'''







							__UpperCAmelCase		:					Tuple          =[
							    # (model_name, model_kwargs)
							    ("""bert-base-cased""", {}),
							    ("""gpt2""", {"""use_cache""": False}),  # We don't support exporting GPT2 past keys anymore
							]

							@require_tf
							@slow
							def     snake_case       (  self			):
										for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
													self._test_export(__a					,     "tf"					,     12					,     **__a			)

							@require_torch
							@slow
							def     snake_case       (  self			):
										for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
													self._test_export(__a					,     "pt"					,     12					,     **__a			)

							@require_torch
							@slow
							def     snake_case       (  self			):
										from transformers import BertModel

										__lowerCAmelCase       					=    ["[UNK]", "[SEP]", "[CLS]", "[PAD]", "[MASK]", "some", "other", "words"]
										with NamedTemporaryFile(mode="w+t"			) as vocab_file:
													vocab_file.write("\n".join(__a			)			)
													vocab_file.flush()
													__lowerCAmelCase       					=    BertTokenizerFast(vocab_file.name			)

										with TemporaryDirectory() as bert_save_dir:
													__lowerCAmelCase       					=    BertModel(BertConfig(vocab_size=len(__a			)			)			)
													model.save_pretrained(__a			)
													self._test_export(__a					,     "pt"					,     12					,     __a			)

							@require_tf
							@slow
							def     snake_case       (  self			):
										for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
													__lowerCAmelCase       					=    self._test_export(__a					,     "tf"					,     12					,     **__a			)
													__lowerCAmelCase       					=    quantize(Path(__a			)			)

													# Ensure the actual quantized model is not bigger than the original one
													if quantized_path.stat().st_size >= Path(__a			).stat().st_size:
																self.fail("Quantized model is bigger than initial ONNX model"			)

							@require_torch
							@slow
							def     snake_case       (  self			):
										for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
													__lowerCAmelCase       					=    self._test_export(__a					,     "pt"					,     12					,     **__a			)
													__lowerCAmelCase       					=    quantize(__a			)

													# Ensure the actual quantized model is not bigger than the original one
													if quantized_path.stat().st_size >= Path(__a			).stat().st_size:
																self.fail("Quantized model is bigger than initial ONNX model"			)

							def     snake_case       (  self					,     __a					,     __a					,     __a					,     __a=None					,     **__a			):
										try:
													# Compute path
													with TemporaryDirectory() as tempdir:
																__lowerCAmelCase       					=    Path(__a			).joinpath("model.onnx"			)

													# Remove folder if exists
													if path.parent.exists():
																path.parent.rmdir()

													# Export
													convert(__a					,     __a					,     __a					,     __a					,     __a					,     **__a			)

													return path
										except Exception as e:
													self.fail(__a			)

							@require_torch
							@require_tokenizers
							@slow
							def     snake_case       (  self			):
										from transformers import BertModel

										__lowerCAmelCase       					=    BertModel(BertConfig.from_pretrained("lysandre/tiny-bert-random"			)			)
										__lowerCAmelCase       					=    BertTokenizerFast.from_pretrained("lysandre/tiny-bert-random"			)
										self._test_infer_dynamic_axis(__a					,     __a					,     "pt"			)

							@require_tf
							@require_tokenizers
							@slow
							def     snake_case       (  self			):
										from transformers import TFBertModel

										__lowerCAmelCase       					=    TFBertModel(BertConfig.from_pretrained("lysandre/tiny-bert-random"			)			)
										__lowerCAmelCase       					=    BertTokenizerFast.from_pretrained("lysandre/tiny-bert-random"			)
										self._test_infer_dynamic_axis(__a					,     __a					,     "tf"			)

							def     snake_case       (  self					,     __a					,     __a					,     __a			):
										__lowerCAmelCase       					=    FeatureExtractionPipeline(__a					,     __a			)

										__lowerCAmelCase       					=    ["input_ids", "token_type_ids", "attention_mask", "output_0", "output_1"]
										__lowerCAmelCase					,			__lowerCAmelCase					,			__lowerCAmelCase					,			__lowerCAmelCase       					=    infer_shapes(__a					,     __a			)

										# Assert all variables are present
										self.assertEqual(len(__a			)					,     len(__a			)			)
										self.assertTrue(all(var_name in shapes for var_name in variable_names			)			)
										self.assertSequenceEqual(variable_names[:3]					,     __a			)
										self.assertSequenceEqual(variable_names[3:]					,     __a			)

										# Assert inputs are {0: batch, 1: sequence}
										for var_name in ["input_ids", "token_type_ids", "attention_mask"]:
													self.assertDictEqual(shapes[var_name]					,     {0: "batch", 1: "sequence"}			)

										# Assert outputs are {0: batch, 1: sequence} and {0: batch}
										self.assertDictEqual(shapes["output_0"]					,     {0: "batch", 1: "sequence"}			)
										self.assertDictEqual(shapes["output_1"]					,     {0: "batch"}			)

							def     snake_case       (  self			):
										__lowerCAmelCase       					=    ["input_ids", "attention_mask", "token_type_ids"]
										__lowerCAmelCase       					=    {"input_ids": [1, 2, 3, 4], "attention_mask": [0, 0, 0, 0], "token_type_ids": [1, 1, 1, 1]}
										__lowerCAmelCase					,			__lowerCAmelCase       					=    ensure_valid_input(FuncContiguousArgs()					,     __a					,     __a			)

										# Should have exactly the same number of args (all are valid)
										self.assertEqual(len(__a			)					,     3			)

										# Should have exactly the same input names
										self.assertEqual(set(__a			)					,     set(__a			)			)

										# Parameter should be reordered according to their respective place in the function:
										# (input_ids, token_type_ids, attention_mask)
										self.assertEqual(__a					,     (tokens["input_ids"], tokens["token_type_ids"], tokens["attention_mask"])			)

										# Generated args are interleaved with another args (for instance parameter "past" in GPT2)
										__lowerCAmelCase					,			__lowerCAmelCase       					=    ensure_valid_input(FuncNonContiguousArgs()					,     __a					,     __a			)

										# Should have exactly the one arg (all before the one not provided "some_other_args")
										self.assertEqual(len(__a			)					,     1			)
										self.assertEqual(len(__a			)					,     1			)

										# Should have only "input_ids"
										self.assertEqual(inputs_args[0]					,     tokens["input_ids"]			)
										self.assertEqual(ordered_input_names[0]					,     "input_ids"			)



							def     snake_case       (  self			):
										__lowerCAmelCase       					=    generate_identified_filename(Path("/home/something/my_fake_model.onnx"			)					,     "-test"			)
										self.assertEqual("/home/something/my_fake_model-test.onnx"					,     generated.as_posix()			)




