


"""simple docstring"""


from __future__ import annotations

import inspect
import unittest

from transformers import ViTConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
	import tensorflow as tf

	from transformers import TFViTForImageClassification, TFViTModel


if is_vision_available():
	from PIL import Image

	from transformers import ViTImageProcessor
class 						_UpperCamelCase						:
							'''simple docstring'''







							def __init__(  self					,     __a					,     __a=13					,     __a=30					,     __a=2					,     __a=3					,     __a=True					,     __a=True					,     __a=32					,     __a=2					,     __a=4					,     __a=37					,     __a="gelu"					,     __a=0.1					,     __a=0.1					,     __a=10					,     __a=0.0_2					,     __a=3					,     __a=None					,     ):
										__lowerCAmelCase       					=    parent
										__lowerCAmelCase       					=    batch_size
										__lowerCAmelCase       					=    image_size
										__lowerCAmelCase       					=    patch_size
										__lowerCAmelCase       					=    num_channels
										__lowerCAmelCase       					=    is_training
										__lowerCAmelCase       					=    use_labels
										__lowerCAmelCase       					=    hidden_size
										__lowerCAmelCase       					=    num_hidden_layers
										__lowerCAmelCase       					=    num_attention_heads
										__lowerCAmelCase       					=    intermediate_size
										__lowerCAmelCase       					=    hidden_act
										__lowerCAmelCase       					=    hidden_dropout_prob
										__lowerCAmelCase       					=    attention_probs_dropout_prob
										__lowerCAmelCase       					=    type_sequence_label_size
										__lowerCAmelCase       					=    initializer_range
										__lowerCAmelCase       					=    scope

										# in ViT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
										__lowerCAmelCase       					=    (image_size // patch_size) ** 2
										__lowerCAmelCase       					=    num_patches + 1

							def     snake_case       (  self			):
										__lowerCAmelCase       					=    floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]			)

										__lowerCAmelCase       					=    None
										if self.use_labels:
													__lowerCAmelCase       					=    ids_tensor([self.batch_size]					,     self.type_sequence_label_size			)

										__lowerCAmelCase       					=    self.get_config()

										return config, pixel_values, labels

							def     snake_case       (  self			):
										return ViTConfig(
										    image_size=self.image_size					,     patch_size=self.patch_size					,     num_channels=self.num_channels					,     hidden_size=self.hidden_size					,     num_hidden_layers=self.num_hidden_layers					,     num_attention_heads=self.num_attention_heads					,     intermediate_size=self.intermediate_size					,     hidden_act=self.hidden_act					,     hidden_dropout_prob=self.hidden_dropout_prob					,     attention_probs_dropout_prob=self.attention_probs_dropout_prob					,     is_decoder=_a					,     initializer_range=self.initializer_range					,     )

							def     snake_case       (  self					,     __a					,     __a					,     __a			):
										__lowerCAmelCase       					=    TFViTModel(config=_a			)
										__lowerCAmelCase       					=    model(_a					,     training=_a			)
										self.parent.assertEqual(result.last_hidden_state.shape					,     (self.batch_size, self.seq_length, self.hidden_size)			)

										# Test with an image with different size than the one specified in config.
										__lowerCAmelCase       					=    self.image_size // 2
										__lowerCAmelCase       					=    pixel_values[:, :, :image_size, :image_size]
										__lowerCAmelCase       					=    model(_a					,     interpolate_pos_encoding=_a					,     training=_a			)
										__lowerCAmelCase       					=    (image_size // self.patch_size) ** 2 + 1
										self.parent.assertEqual(result.last_hidden_state.shape					,     (self.batch_size, seq_length, self.hidden_size)			)

							def     snake_case       (  self					,     __a					,     __a					,     __a			):
										__lowerCAmelCase       					=    self.type_sequence_label_size
										__lowerCAmelCase       					=    TFViTForImageClassification(_a			)
										__lowerCAmelCase       					=    model(_a					,     labels=_a					,     training=_a			)
										self.parent.assertEqual(result.logits.shape					,     (self.batch_size, self.type_sequence_label_size)			)

										# Test with an image with different size than the one specified in config.
										__lowerCAmelCase       					=    self.image_size // 2
										__lowerCAmelCase       					=    pixel_values[:, :, :image_size, :image_size]
										__lowerCAmelCase       					=    model(_a					,     interpolate_pos_encoding=_a					,     training=_a			)
										self.parent.assertEqual(result.logits.shape					,     (self.batch_size, self.type_sequence_label_size)			)

										# test greyscale images
										__lowerCAmelCase       					=    1
										__lowerCAmelCase       					=    TFViTForImageClassification(_a			)
										__lowerCAmelCase       					=    floats_tensor([self.batch_size, 1, self.image_size, self.image_size]			)
										__lowerCAmelCase       					=    model(_a			)
										self.parent.assertEqual(result.logits.shape					,     (self.batch_size, self.type_sequence_label_size)			)



							def     snake_case       (  self			):
										__lowerCAmelCase       					=    self.prepare_config_and_inputs()
										__lowerCAmelCase       					=    config_and_inputs
										__lowerCAmelCase       					=    {"""pixel_values""": pixel_values}
										return config, inputs_dict
@require_tf
class 						_UpperCamelCase						(     UpperCamelCase__ ,UpperCamelCase__ ,unittest.TestCase	):
							'''simple docstring'''







							__UpperCAmelCase		:					Tuple          =(TFViTModel, TFViTForImageClassification) if is_tf_available() else ()
							__UpperCAmelCase		:					Union[str, Any]          =(
							    {"""feature-extraction""": TFViTModel, """image-classification""": TFViTForImageClassification}
							    if is_tf_available()
							    else {}
							)

							__UpperCAmelCase		:					Tuple          =False
							__UpperCAmelCase		:					Any          =False
							__UpperCAmelCase		:					Tuple          =False

							def     snake_case       (  self			):
										__lowerCAmelCase       					=    TFViTModelTester(self			)
										__lowerCAmelCase       					=    ConfigTester(self					,     config_class=_a					,     has_text_modality=_a					,     hidden_size=37			)

							def     snake_case       (  self			):
										self.config_tester.run_common_tests()

							@unittest.skip(reason="ViT does not use inputs_embeds"			)
							def     snake_case       (  self			):
										pass

							@unittest.skip(reason="ViT does not use inputs_embeds"			)
							def     snake_case       (  self			):
										pass

							def     snake_case       (  self			):
										__lowerCAmelCase       					=    self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
													__lowerCAmelCase       					=    model_class(_a			)
													self.assertIsInstance(model.get_input_embeddings()					,     (tf.keras.layers.Layer)			)
													__lowerCAmelCase       					=    model.get_output_embeddings()
													self.assertTrue(x is None or isinstance(_a					,     tf.keras.layers.Layer			)			)

							def     snake_case       (  self			):
										__lowerCAmelCase       					=    self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
													__lowerCAmelCase       					=    model_class(_a			)
													__lowerCAmelCase       					=    inspect.signature(model.call			)
													# signature.parameters is an OrderedDict => so arg_names order is deterministic
													__lowerCAmelCase       					=    [*signature.parameters.keys()]

													__lowerCAmelCase       					=    ["""pixel_values"""]
													self.assertListEqual(arg_names[:1]					,     _a			)

							def     snake_case       (  self			):
										__lowerCAmelCase       					=    self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*_a			)

							def     snake_case       (  self			):
										__lowerCAmelCase       					=    self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_image_classification(*_a			)



							@slow
							def     snake_case       (  self			):
										__lowerCAmelCase       					=    TFViTModel.from_pretrained("google/vit-base-patch16-224"			)
										self.assertIsNotNone(_a			)


def _lowerCamelCase			(	):

			'''simple docstring'''




			__lowerCAmelCase       					=    Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"   )
			return image


@require_tf
@require_vision
class 						_UpperCamelCase						(     unittest.TestCase	):
							'''simple docstring'''







							@cached_property
							def     snake_case       (  self			):
										return ViTImageProcessor.from_pretrained("google/vit-base-patch16-224"			) if is_vision_available() else None



							@slow
							def     snake_case       (  self			):
										__lowerCAmelCase       					=    TFViTForImageClassification.from_pretrained("google/vit-base-patch16-224"			)

										__lowerCAmelCase       					=    self.default_image_processor
										__lowerCAmelCase       					=    prepare_img()
										__lowerCAmelCase       					=    image_processor(images=_a					,     return_tensors="tf"			)

										# forward pass
										__lowerCAmelCase       					=    model(**_a			)

										# verify the logits
										__lowerCAmelCase       					=    tf.TensorShape((1, 10_00)			)
										self.assertEqual(outputs.logits.shape					,     _a			)

										__lowerCAmelCase       					=    tf.constant([-0.2_7_4_4, 0.8_2_1_5, -0.0_8_3_6]			)

										tf.debugging.assert_near(outputs.logits[0, :3]					,     _a					,     atol=1e-4			)








"""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()			)




