



import inspect
import unittest

import numpy as np

from tests.test_modeling_common import floats_tensor
from transformers import MaskaFormerConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
						import torch

						from transformers import MaskaFormerForUniversalSegmentation, MaskaFormerModel

						if is_vision_available():
												from transformers import MaskaFormerImageProcessor

if is_vision_available():
						from PIL import Image



class   UpperCAmelCase_    :







							def __init__(   self,			__a,			__a=2,			__a=True,			__a=False,			__a=10,			__a=3,			__a=32 * 8,			__a=32 * 8,			__a=4,			__a=64,			):

														'''simple docstring'''

														_lowerCAmelCase     :							Dict    =    parent
														_lowerCAmelCase     :							int    =    batch_size
														_lowerCAmelCase     :							Tuple    =    is_training
														_lowerCAmelCase     :							Tuple    =    use_auxiliary_loss
														_lowerCAmelCase     :							Dict    =    num_queries
														_lowerCAmelCase     :							List[Any]    =    num_channels
														_lowerCAmelCase     :							Union[str, Any]    =    min_size
														_lowerCAmelCase     :							Tuple    =    max_size
														_lowerCAmelCase     :							List[str]    =    num_labels
														_lowerCAmelCase     :							Tuple    =    hidden_dim
														_lowerCAmelCase     :							List[Any]    =    hidden_dim







							def    snake_case__      (   self):

														'''simple docstring'''

														_lowerCAmelCase     :							Tuple    =    floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size]).to(
														    __UpperCAmelCase)

														_lowerCAmelCase     :							Any    =    torch.ones([self.batch_size, self.min_size, self.max_size],			device=__UpperCAmelCase)

														_lowerCAmelCase     :							Optional[Any]    =    (
														    torch.rand([self.batch_size, self.num_labels, self.min_size, self.max_size],			device=__UpperCAmelCase) > 0.5
														).float()
														_lowerCAmelCase     :							Tuple    =    (torch.rand((self.batch_size, self.num_labels),			device=__UpperCAmelCase) > 0.5).long()

														_lowerCAmelCase     :							Optional[int]    =    self.get_config()
														return config, pixel_values, pixel_mask, mask_labels, class_labels







							def    snake_case__      (   self):

														'''simple docstring'''

														_lowerCAmelCase     :							int    =    MaskaFormerConfig(
														    hidden_size=self.hidden_dim,			)
														_lowerCAmelCase     :							Optional[Any]    =    self.num_queries
														_lowerCAmelCase     :							Dict    =    self.num_labels

														_lowerCAmelCase     :							Optional[Any]    =    [1, 1, 1, 1]
														_lowerCAmelCase     :							Union[str, Any]    =    self.num_channels

														_lowerCAmelCase     :							str    =    64
														_lowerCAmelCase     :							List[str]    =    128
														_lowerCAmelCase     :							str    =    self.hidden_dim
														_lowerCAmelCase     :							int    =    self.hidden_dim
														_lowerCAmelCase     :							str    =    self.hidden_dim
														return config







							def    snake_case__      (   self):

														'''simple docstring'''

														_lowerCAmelCase   ,    _lowerCAmelCase   ,    _lowerCAmelCase   ,    _lowerCAmelCase   ,    _lowerCAmelCase     :							int    =    self.prepare_config_and_inputs()
														_lowerCAmelCase     :							int    =    {"pixel_values": pixel_values, "pixel_mask": pixel_mask}
														return config, inputs_dict







							def    snake_case__      (   self,			__a,			__a):

														'''simple docstring'''

														_lowerCAmelCase     :							List[Any]    =    output.encoder_hidden_states
														_lowerCAmelCase     :							Union[str, Any]    =    output.pixel_decoder_hidden_states
														_lowerCAmelCase     :							str    =    output.transformer_decoder_hidden_states

														self.parent.assertTrue(len(__UpperCAmelCase),			len(config.backbone_config.depths))
														self.parent.assertTrue(len(__UpperCAmelCase),			len(config.backbone_config.depths))
														self.parent.assertTrue(len(__UpperCAmelCase),			config.decoder_layers)







							def    snake_case__      (   self,			__a,			__a,			__a,			__a=False):

														'''simple docstring'''

														with torch.no_grad():
																					_lowerCAmelCase     :							Optional[int]    =    MaskaFormerModel(config=__UpperCAmelCase)
																					model.to(__UpperCAmelCase)
																					model.eval()

																					_lowerCAmelCase     :							str    =    model(pixel_values=__UpperCAmelCase,			pixel_mask=__UpperCAmelCase)
																					_lowerCAmelCase     :							Optional[int]    =    model(__UpperCAmelCase,			output_hidden_states=__UpperCAmelCase)

														self.parent.assertEqual(
														    output.transformer_decoder_last_hidden_state.shape,			(self.batch_size, self.num_queries, self.hidden_dim),			)
														# let's ensure the other two hidden state exists
														self.parent.assertTrue(output.pixel_decoder_last_hidden_state is not None)
														self.parent.assertTrue(output.encoder_last_hidden_state is not None)

														if output_hidden_states:
																					self.check_output_hidden_state(__UpperCAmelCase,			__UpperCAmelCase)







							def    snake_case__      (   self,			__a,			__a,			__a,			__a,			__a):

														'''simple docstring'''

														_lowerCAmelCase     :							Dict    =    MaskaFormerForUniversalSegmentation(config=__UpperCAmelCase)
														model.to(__UpperCAmelCase)
														model.eval()

														def comm_check_on_output(__a):
																					# let's still check that all the required stuff is there
																					self.parent.assertTrue(result.transformer_decoder_last_hidden_state is not None)
																					self.parent.assertTrue(result.pixel_decoder_last_hidden_state is not None)
																					self.parent.assertTrue(result.encoder_last_hidden_state is not None)
																					# okay, now we need to check the logits shape
																					# due to the encoder compression, masks have a //4 spatial size
																					self.parent.assertEqual(
																					    result.masks_queries_logits.shape,			(self.batch_size, self.num_queries, self.min_size // 4, self.max_size // 4),			)
																					# + 1 for null class
																					self.parent.assertEqual(
																					    result.class_queries_logits.shape,			(self.batch_size, self.num_queries, self.num_labels + 1))

														with torch.no_grad():
																					_lowerCAmelCase     :							Optional[int]    =    model(pixel_values=__UpperCAmelCase,			pixel_mask=__UpperCAmelCase)
																					_lowerCAmelCase     :							List[str]    =    model(__UpperCAmelCase)

																					comm_check_on_output(__UpperCAmelCase)

																					_lowerCAmelCase     :							Dict    =    model(
																					    pixel_values=__UpperCAmelCase,			pixel_mask=__UpperCAmelCase,			mask_labels=__UpperCAmelCase,			class_labels=__UpperCAmelCase)

														comm_check_on_output(__UpperCAmelCase)

														self.parent.assertTrue(result.loss is not None)
														self.parent.assertEqual(result.loss.shape,			torch.Size([1]))



@require_torch
class   UpperCAmelCase_    (       __A   ,					__A   ,					unittest.TestCase):
							lowerCamelCase__   			=		(MaskaFormerModel, MaskaFormerForUniversalSegmentation) if is_torch_available() else ()
							lowerCamelCase__   			=		{'feature-extraction': MaskaFormerModel} if is_torch_available() else {}

							lowerCamelCase__   			=		False
							lowerCamelCase__   			=		False
							lowerCamelCase__   			=		False
							lowerCamelCase__   			=		False







							def    snake_case__      (   self):

														'''simple docstring'''

														_lowerCAmelCase     :							Optional[Any]    =    MaskaFormerModelTester(self)
														_lowerCAmelCase     :							Any    =    ConfigTester(self,			config_class=__UpperCAmelCase,			has_text_modality=__UpperCAmelCase)







							def    snake_case__      (   self):

														'''simple docstring'''

														self.config_tester.run_common_tests()







							def    snake_case__      (   self):

														'''simple docstring'''

														_lowerCAmelCase   ,    _lowerCAmelCase     :							int    =    self.model_tester.prepare_config_and_inputs_for_common()
														self.model_tester.create_and_check_maskaformer_model(__UpperCAmelCase,			**__UpperCAmelCase,			output_hidden_states=__UpperCAmelCase)







							def    snake_case__      (   self):

														'''simple docstring'''

														_lowerCAmelCase     :							Any    =    self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_maskaformer_instance_segmentation_head_model(*__UpperCAmelCase)







							@unittest.skip(reason="Mask2Former does not use inputs_embeds")
							def    snake_case__      (   self):

														'''simple docstring'''

														pass







							@unittest.skip(reason="Mask2Former does not have a get_input_embeddings method")
							def    snake_case__      (   self):

														'''simple docstring'''

														pass







							@unittest.skip(reason="Mask2Former is not a generative model")
							def    snake_case__      (   self):

														'''simple docstring'''

														pass







							@unittest.skip(reason="Mask2Former does not use token embeddings")
							def    snake_case__      (   self):

														'''simple docstring'''

														pass







							@require_torch_multi_gpu
							@unittest.skip(
							    reason="Mask2Former has some layers using `add_module` which doesn\'t work well with `nn.DataParallel`")
							def    snake_case__      (   self):

														'''simple docstring'''

														pass







							@unittest.skip("Will be fixed soon by reducing the size of the model used for common tests.")
							def    snake_case__      (   self):

														'''simple docstring'''

														pass







							def    snake_case__      (   self):

														'''simple docstring'''

														_lowerCAmelCase   ,    _lowerCAmelCase     :							Tuple    =    self.model_tester.prepare_config_and_inputs_for_common()

														for model_class in self.all_model_classes:
																					_lowerCAmelCase     :							List[Any]    =    model_class(__UpperCAmelCase)
																					_lowerCAmelCase     :							Any    =    inspect.signature(model.forward)
																					# signature.parameters is an OrderedDict => so arg_names order is deterministic
																					_lowerCAmelCase     :							Union[str, Any]    =    [*signature.parameters.keys()]

																					_lowerCAmelCase     :							str    =    ["pixel_values"]
																					self.assertListEqual(arg_names[:1],			__UpperCAmelCase)







							@slow
							def    snake_case__      (   self):

														'''simple docstring'''

														for model_name in ["facebook/mask2former-swin-small-coco-instance"]:
																					_lowerCAmelCase     :							Union[str, Any]    =    MaskaFormerModel.from_pretrained(__UpperCAmelCase)
																					self.assertIsNotNone(__UpperCAmelCase)







							def    snake_case__      (   self):

														'''simple docstring'''

														_lowerCAmelCase     :							Optional[int]    =    (self.model_tester.min_size,) * 2
														_lowerCAmelCase     :							int    =    {
														    "pixel_values": torch.randn((2, 3, *size),			device=__UpperCAmelCase),
														    "mask_labels": torch.randn((2, 10, *size),			device=__UpperCAmelCase),
														    "class_labels": torch.zeros(2,			10,			device=__UpperCAmelCase).long(),
														}
														_lowerCAmelCase     :							Optional[int]    =    self.model_tester.get_config()

														_lowerCAmelCase     :							Optional[Any]    =    MaskaFormerForUniversalSegmentation(__UpperCAmelCase).to(__UpperCAmelCase)
														_lowerCAmelCase     :							Tuple    =    model(**__UpperCAmelCase)
														self.assertTrue(outputs.loss is not None)







							def    snake_case__      (   self):

														'''simple docstring'''

														_lowerCAmelCase   ,    _lowerCAmelCase     :							Union[str, Any]    =    self.model_tester.prepare_config_and_inputs_for_common()
														self.model_tester.create_and_check_maskaformer_model(__UpperCAmelCase,			**__UpperCAmelCase,			output_hidden_states=__UpperCAmelCase)







							def    snake_case__      (   self):

														'''simple docstring'''

														_lowerCAmelCase   ,    _lowerCAmelCase     :							int    =    self.model_tester.prepare_config_and_inputs_for_common()

														for model_class in self.all_model_classes:
																					_lowerCAmelCase     :							Optional[Any]    =    model_class(__UpperCAmelCase).to(__UpperCAmelCase)
																					_lowerCAmelCase     :							Dict    =    model(**__UpperCAmelCase,			output_attentions=__UpperCAmelCase)
																					self.assertTrue(outputs.attentions is not None)







							def    snake_case__      (   self):

														'''simple docstring'''

														if not self.model_tester.is_training:
																					return

														_lowerCAmelCase     :							Optional[int]    =    self.all_model_classes[1]
														_lowerCAmelCase   ,    _lowerCAmelCase   ,    _lowerCAmelCase   ,    _lowerCAmelCase   ,    _lowerCAmelCase     :							Union[str, Any]    =    self.model_tester.prepare_config_and_inputs()

														_lowerCAmelCase     :							List[str]    =    model_class(__UpperCAmelCase)
														model.to(__UpperCAmelCase)
														model.train()

														_lowerCAmelCase     :							List[Any]    =    model(__UpperCAmelCase,			mask_labels=__UpperCAmelCase,			class_labels=__UpperCAmelCase).loss
														loss.backward()







							def    snake_case__      (   self):

														'''simple docstring'''

														_lowerCAmelCase     :							List[Any]    =    self.all_model_classes[1]
														_lowerCAmelCase   ,    _lowerCAmelCase   ,    _lowerCAmelCase   ,    _lowerCAmelCase   ,    _lowerCAmelCase     :							int    =    self.model_tester.prepare_config_and_inputs()
														_lowerCAmelCase     :							List[str]    =    True
														_lowerCAmelCase     :							Tuple    =    True

														_lowerCAmelCase     :							List[Any]    =    model_class(__UpperCAmelCase).to(__UpperCAmelCase)
														model.train()

														_lowerCAmelCase     :							List[str]    =    model(__UpperCAmelCase,			mask_labels=__UpperCAmelCase,			class_labels=__UpperCAmelCase)

														_lowerCAmelCase     :							Optional[int]    =    outputs.encoder_hidden_states[0]
														encoder_hidden_states.retain_grad()

														_lowerCAmelCase     :							Tuple    =    outputs.pixel_decoder_hidden_states[0]
														pixel_decoder_hidden_states.retain_grad()

														_lowerCAmelCase     :							List[str]    =    outputs.transformer_decoder_hidden_states[0]
														transformer_decoder_hidden_states.retain_grad()

														_lowerCAmelCase     :							Optional[Any]    =    outputs.attentions[0]
														attentions.retain_grad()

														outputs.loss.backward(retain_graph=__UpperCAmelCase)

														self.assertIsNotNone(encoder_hidden_states.grad)
														self.assertIsNotNone(pixel_decoder_hidden_states.grad)
														self.assertIsNotNone(transformer_decoder_hidden_states.grad)
														self.assertIsNotNone(attentions.grad)



_snake_case		=  1e-4






def 				A				(					):





							'''simple docstring'''




							_lowerCAmelCase     :							Optional[Any]    =    Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"  )
							return image



@require_vision
@slow
class   UpperCAmelCase_    (       unittest.TestCase):







							@cached_property
							def    snake_case__      (   self):

														'''simple docstring'''

														return "facebook/mask2former-swin-small-coco-instance"







							@cached_property
							def    snake_case__      (   self):

														'''simple docstring'''

														return MaskaFormerImageProcessor.from_pretrained(self.model_checkpoints) if is_vision_available() else None







							def    snake_case__      (   self):

														'''simple docstring'''

														_lowerCAmelCase     :							List[str]    =    MaskaFormerModel.from_pretrained(self.model_checkpoints).to(__UpperCAmelCase)
														_lowerCAmelCase     :							Dict    =    self.default_image_processor
														_lowerCAmelCase     :							List[str]    =    prepare_img()
														_lowerCAmelCase     :							Union[str, Any]    =    image_processor(__UpperCAmelCase,			return_tensors="pt").to(__UpperCAmelCase)
														_lowerCAmelCase     :							str    =    inputs["pixel_values"].shape
														# check size is divisible by 32
														self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0)
														# check size
														self.assertEqual(__UpperCAmelCase,			(1, 3, 384, 384))

														with torch.no_grad():
																					_lowerCAmelCase     :							List[str]    =    model(**__UpperCAmelCase)

														_lowerCAmelCase     :							List[str]    =    torch.tensor(
														    [[-0.2_790, -1.0_717, -1.1_668], [-0.5_128, -0.3_128, -0.4_987], [-0.5_832, 0.1_971, -0.0_197]]).to(__UpperCAmelCase)
														self.assertTrue(
														    torch.allclose(
														        outputs.encoder_last_hidden_state[0, 0, :3, :3],			__UpperCAmelCase,			atol=__UpperCAmelCase))

														_lowerCAmelCase     :							List[str]    =    torch.tensor(
														    [[0.8_973, 1.1_847, 1.1_776], [1.1_934, 1.5_040, 1.5_128], [1.1_153, 1.4_486, 1.4_951]]).to(__UpperCAmelCase)
														self.assertTrue(
														    torch.allclose(
														        outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3],			__UpperCAmelCase,			atol=__UpperCAmelCase))

														_lowerCAmelCase     :							Optional[Any]    =    torch.tensor(
														    [[2.1_152, 1.7_000, -0.8_603], [1.5_808, 1.8_004, -0.9_353], [1.6_043, 1.7_495, -0.5_999]]).to(__UpperCAmelCase)
														self.assertTrue(
														    torch.allclose(
														        outputs.transformer_decoder_last_hidden_state[0, :3, :3],			__UpperCAmelCase,			atol=__UpperCAmelCase))







							def    snake_case__      (   self):

														'''simple docstring'''

														_lowerCAmelCase     :							int    =    MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints).to(__UpperCAmelCase).eval()
														_lowerCAmelCase     :							List[Any]    =    self.default_image_processor
														_lowerCAmelCase     :							List[Any]    =    prepare_img()
														_lowerCAmelCase     :							List[str]    =    image_processor(__UpperCAmelCase,			return_tensors="pt").to(__UpperCAmelCase)
														_lowerCAmelCase     :							Optional[int]    =    inputs["pixel_values"].shape
														# check size is divisible by 32
														self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0)
														# check size
														self.assertEqual(__UpperCAmelCase,			(1, 3, 384, 384))

														with torch.no_grad():
																					_lowerCAmelCase     :							List[str]    =    model(**__UpperCAmelCase)
														# masks_queries_logits
														_lowerCAmelCase     :							Optional[Any]    =    outputs.masks_queries_logits
														self.assertEqual(
														    masks_queries_logits.shape,			(1, model.config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4))
														_lowerCAmelCase     :							str    =    [
														    [-8.7_839, -9.0_056, -8.8_121],
														    [-7.4_104, -7.0_313, -6.5_401],
														    [-6.6_105, -6.3_427, -6.4_675],
														]
														_lowerCAmelCase     :							Optional[int]    =    torch.tensor(__UpperCAmelCase).to(__UpperCAmelCase)
														self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3],			__UpperCAmelCase,			atol=__UpperCAmelCase))
														# class_queries_logits
														_lowerCAmelCase     :							List[Any]    =    outputs.class_queries_logits
														self.assertEqual(class_queries_logits.shape,			(1, model.config.num_queries, model.config.num_labels + 1))
														_lowerCAmelCase     :							str    =    torch.tensor(
														    [
														        [1.8_324, -8.0_835, -4.1_922],
														        [0.8_450, -9.0_050, -3.6_053],
														        [0.3_045, -7.7_293, -3.0_275],
														    ]).to(__UpperCAmelCase)
														self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3],			__UpperCAmelCase,			atol=__UpperCAmelCase))







							def    snake_case__      (   self):

														'''simple docstring'''

														_lowerCAmelCase     :							int    =    MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints).to(__UpperCAmelCase).eval()
														_lowerCAmelCase     :							Any    =    self.default_image_processor

														_lowerCAmelCase     :							List[Any]    =    image_processor(
														    [np.zeros((3, 800, 1333)), np.zeros((3, 800, 1333))],			segmentation_maps=[np.zeros((384, 384)).astype(np.floataa), np.zeros((384, 384)).astype(np.floataa)],			return_tensors="pt",			)

														_lowerCAmelCase     :							Dict    =    inputs["pixel_values"].to(__UpperCAmelCase)
														_lowerCAmelCase     :							Tuple    =    [el.to(__UpperCAmelCase) for el in inputs["mask_labels"]]
														_lowerCAmelCase     :							Any    =    [el.to(__UpperCAmelCase) for el in inputs["class_labels"]]

														with torch.no_grad():
																					_lowerCAmelCase     :							int    =    model(**__UpperCAmelCase)

														self.assertTrue(outputs.loss is not None)

"""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__:





						def  snake_case__		(				self						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase   )    -> int:
													return None






class 			UpperCamelCase__:





						def  snake_case__		(				self						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase   )    -> Tuple:
													return None






class 			UpperCamelCase__(	unittest.TestCase       ):
						lowerCAmelCase__     :  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   )    -> int:
													for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
																				self._test_export(__UpperCAmelCase						,'tf'						,12						,**__UpperCAmelCase   )





						@require_torch
						@slow
						def  snake_case__		(				self   )    -> int:
													for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
																				self._test_export(__UpperCAmelCase						,'pt'						,12						,**__UpperCAmelCase   )





						@require_torch
						@slow
						def  snake_case__		(				self   )    -> Optional[Any]:
													from transformers import BertModel

													A__		   =							['[UNK]', '[SEP]', '[CLS]', '[PAD]', '[MASK]', 'some', 'other', 'words']
													with NamedTemporaryFile(mode='w+t'   ) as vocab_file:
																				vocab_file.write('\n'.join(__UpperCAmelCase   )   )
																				vocab_file.flush()
																				A__		   =							BertTokenizerFast(vocab_file.name   )

													with TemporaryDirectory() as bert_save_dir:
																				A__		   =							BertModel(BertConfig(vocab_size=len(__UpperCAmelCase   )   )   )
																				model.save_pretrained(__UpperCAmelCase   )
																				self._test_export(__UpperCAmelCase						,'pt'						,12						,__UpperCAmelCase   )





						@require_tf
						@slow
						def  snake_case__		(				self   )    -> Optional[Any]:
													for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
																				A__		   =							self._test_export(__UpperCAmelCase						,'tf'						,12						,**__UpperCAmelCase   )
																				A__		   =							quantize(Path(__UpperCAmelCase   )   )

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





						@require_torch
						@slow
						def  snake_case__		(				self   )    -> str:
													for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
																				A__		   =							self._test_export(__UpperCAmelCase						,'pt'						,12						,**__UpperCAmelCase   )
																				A__		   =							quantize(__UpperCAmelCase   )

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





						def  snake_case__		(				self						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase=None						,**__UpperCAmelCase   )    -> Union[str, Any]:
													try:
																				# Compute path
																				with TemporaryDirectory() as tempdir:
																											A__		   =							Path(__UpperCAmelCase   ).joinpath('model.onnx'   )

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

																				# Export
																				convert(__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase						,**__UpperCAmelCase   )

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





						@require_torch
						@require_tokenizers
						@slow
						def  snake_case__		(				self   )    -> Optional[Any]:
													from transformers import BertModel

													A__		   =							BertModel(BertConfig.from_pretrained('lysandre/tiny-bert-random'   )   )
													A__		   =							BertTokenizerFast.from_pretrained('lysandre/tiny-bert-random'   )
													self._test_infer_dynamic_axis(__UpperCAmelCase						,__UpperCAmelCase						,'pt'   )





						@require_tf
						@require_tokenizers
						@slow
						def  snake_case__		(				self   )    -> Optional[Any]:
													from transformers import TFBertModel

													A__		   =							TFBertModel(BertConfig.from_pretrained('lysandre/tiny-bert-random'   )   )
													A__		   =							BertTokenizerFast.from_pretrained('lysandre/tiny-bert-random'   )
													self._test_infer_dynamic_axis(__UpperCAmelCase						,__UpperCAmelCase						,'tf'   )





						def  snake_case__		(				self						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase   )    -> str:
													A__		   =							FeatureExtractionPipeline(__UpperCAmelCase						,__UpperCAmelCase   )

													A__		   =							['input_ids', 'token_type_ids', 'attention_mask', 'output_0', 'output_1']
													A__   ,     A__   ,     A__   ,     A__		   =							infer_shapes(__UpperCAmelCase						,__UpperCAmelCase   )

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

													# 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   )    -> Union[str, Any]:
													A__		   =							['input_ids', 'attention_mask', 'token_type_ids']
													A__		   =							{'input_ids': [1, 2, 3, 4], 'attention_mask': [0, 0, 0, 0], 'token_type_ids': [1, 1, 1, 1]}
													A__   ,     A__		   =							ensure_valid_input(FuncContiguousArgs()						,__UpperCAmelCase						,__UpperCAmelCase   )

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

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

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

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

													# Should have exactly the one arg (all before the one not provided "some_other_args")
													self.assertEqual(len(__UpperCAmelCase   )						,1   )
													self.assertEqual(len(__UpperCAmelCase   )						,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   )    -> Union[str, Any]:
													A__		   =							generate_identified_filename(Path('/home/something/my_fake_model.onnx'   )						,'-test'   )
													self.assertEqual('/home/something/my_fake_model-test.onnx'						,generated.as_posix()   )





