import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    PNDMScheduler,
    StableDiffusionLDMaDPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import nightly, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS


enable_full_determinism()







class __snake_case       (		unittest.TestCase	):
   lowerCAmelCase_														=							StableDiffusionLDMaDPipeline
   lowerCAmelCase_														=							TEXT_TO_IMAGE_PARAMS
   lowerCAmelCase_														=							TEXT_TO_IMAGE_BATCH_PARAMS
   lowerCAmelCase_														=							TEXT_TO_IMAGE_IMAGE_PARAMS




   def 			__a		(					self    :       str   ):



     """simple docstring"""






     torch.manual_seed(0   )
     SCREAMING_SNAKE_CASE__    							=   UNetaDConditionModel(
         block_out_channels=(32, 64) ,			layers_per_block=2 ,			sample_size=32 ,			in_channels=4 ,			out_channels=4 ,			down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""") ,			up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""") ,			cross_attention_dim=32 ,			)
     SCREAMING_SNAKE_CASE__    							=   DDIMScheduler(
         beta_start=0.0_00_85 ,			beta_end=0.0_12 ,			beta_schedule="""scaled_linear""" ,			clip_sample=_lowercase ,			set_alpha_to_one=_lowercase ,			)
     torch.manual_seed(0   )
     SCREAMING_SNAKE_CASE__    							=   AutoencoderKL(
         block_out_channels=[32, 64] ,			in_channels=6 ,			out_channels=6 ,			down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""] ,			up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""] ,			latent_channels=4 ,			)
     torch.manual_seed(0   )
     SCREAMING_SNAKE_CASE__    							=   CLIPTextConfig(
         bos_token_id=0 ,			eos_token_id=2 ,			hidden_size=32 ,			intermediate_size=37 ,			layer_norm_eps=1E-05 ,			num_attention_heads=4 ,			num_hidden_layers=5 ,			pad_token_id=1 ,			vocab_size=10_00 ,			)
     SCREAMING_SNAKE_CASE__    							=   CLIPTextModel(_lowercase   )
     SCREAMING_SNAKE_CASE__    							=   CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""   )

     SCREAMING_SNAKE_CASE__    							=   {
         """unet""": unet,
         """scheduler""": scheduler,
         """vae""": vae,
         """text_encoder""": text_encoder,
         """tokenizer""": tokenizer,
         """safety_checker""": None,
         """feature_extractor""": None,
     }
     return components




   def 			__a		(					self    :       Any ,			_lowercase    :       Union[str, Any] ,			_lowercase    :       List[str]=0   ):



     """simple docstring"""






     if str(_lowercase   ).startswith("""mps"""   ):
       SCREAMING_SNAKE_CASE__    							=   torch.manual_seed(_lowercase   )
     else:
       SCREAMING_SNAKE_CASE__    							=   torch.Generator(device=_lowercase   ).manual_seed(_lowercase   )
     SCREAMING_SNAKE_CASE__    							=   {
         """prompt""": """A painting of a squirrel eating a burger""",
         """generator""": generator,
         """num_inference_steps""": 2,
         """guidance_scale""": 6.0,
         """output_type""": """numpy""",
     }
     return inputs




   def 			__a		(					self    :       List[Any]   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   """cpu"""  # ensure determinism for the device-dependent torch.Generator

     SCREAMING_SNAKE_CASE__    							=   self.get_dummy_components()
     SCREAMING_SNAKE_CASE__    							=   StableDiffusionLDMaDPipeline(**_lowercase   )
     SCREAMING_SNAKE_CASE__    							=   ldmad_pipe.to(_lowercase   )
     ldmad_pipe.set_progress_bar_config(disable=_lowercase   )

     SCREAMING_SNAKE_CASE__    							=   self.get_dummy_inputs(_lowercase   )
     SCREAMING_SNAKE_CASE__    							=   ldmad_pipe(**_lowercase   )
     SCREAMING_SNAKE_CASE__							,  SCREAMING_SNAKE_CASE__    							=   output.rgb, output.depth

     SCREAMING_SNAKE_CASE__    							=   rgb[0, -3:, -3:, -1]
     SCREAMING_SNAKE_CASE__    							=   depth[0, -3:, -1]

     assert rgb.shape == (1, 64, 64, 3)
     assert depth.shape == (1, 64, 64)

     SCREAMING_SNAKE_CASE__    							=   np.array(
         [0.37_33_81_76, 0.7_02_47, 0.74_20_31_93, 0.51_64_36_04, 0.58_25_67_93, 0.60_93_21_36, 0.4_18_10_95, 0.48_35_58_77, 0.46_53_52_62]   )
     SCREAMING_SNAKE_CASE__    							=   np.array([1_03.4_67_27, 85.81_20_04, 87.84_92_36]   )

     assert np.abs(image_slice_rgb.flatten() - expected_slice_rgb   ).max() < 1E-2
     assert np.abs(image_slice_depth.flatten() - expected_slice_depth   ).max() < 1E-2




   def 			__a		(					self    :       Any   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   self.get_dummy_components()
     SCREAMING_SNAKE_CASE__    							=   StableDiffusionLDMaDPipeline(**_lowercase   )
     SCREAMING_SNAKE_CASE__    							=   ldmad_pipe.to(_lowercase   )
     ldmad_pipe.set_progress_bar_config(disable=_lowercase   )

     SCREAMING_SNAKE_CASE__    							=   self.get_dummy_inputs(_lowercase   )
     SCREAMING_SNAKE_CASE__    							=   3 * [inputs["""prompt"""]]

     # forward
     SCREAMING_SNAKE_CASE__    							=   ldmad_pipe(**_lowercase   )
     SCREAMING_SNAKE_CASE__							,  SCREAMING_SNAKE_CASE__    							=   output.rgb, output.depth
     SCREAMING_SNAKE_CASE__    							=   rgb_slice_a[0, -3:, -3:, -1]
     SCREAMING_SNAKE_CASE__    							=   depth_slice_a[0, -3:, -1]

     SCREAMING_SNAKE_CASE__    							=   self.get_dummy_inputs(_lowercase   )
     SCREAMING_SNAKE_CASE__    							=   3 * [inputs.pop("""prompt"""   )]

     SCREAMING_SNAKE_CASE__    							=   ldmad_pipe.tokenizer(
         _lowercase ,			padding="""max_length""" ,			max_length=ldmad_pipe.tokenizer.model_max_length ,			truncation=_lowercase ,			return_tensors="""pt""" ,			)
     SCREAMING_SNAKE_CASE__    							=   text_inputs["""input_ids"""].to(_lowercase   )

     SCREAMING_SNAKE_CASE__    							=   ldmad_pipe.text_encoder(_lowercase   )[0]

     SCREAMING_SNAKE_CASE__    							=   prompt_embeds

     # forward
     SCREAMING_SNAKE_CASE__    							=   ldmad_pipe(**_lowercase   )
     SCREAMING_SNAKE_CASE__							,  SCREAMING_SNAKE_CASE__    							=   output.rgb, output.depth
     SCREAMING_SNAKE_CASE__    							=   rgb_slice_a[0, -3:, -3:, -1]
     SCREAMING_SNAKE_CASE__    							=   depth_slice_a[0, -3:, -1]

     assert np.abs(rgb_slice_a.flatten() - rgb_slice_a.flatten()   ).max() < 1E-4
     assert np.abs(depth_slice_a.flatten() - depth_slice_a.flatten()   ).max() < 1E-4




   def 			__a		(					self    :       List[str]   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   """cpu"""  # ensure determinism for the device-dependent torch.Generator
     SCREAMING_SNAKE_CASE__    							=   self.get_dummy_components()
     SCREAMING_SNAKE_CASE__    							=   PNDMScheduler(skip_prk_steps=_lowercase   )
     SCREAMING_SNAKE_CASE__    							=   StableDiffusionLDMaDPipeline(**_lowercase   )
     SCREAMING_SNAKE_CASE__    							=   ldmad_pipe.to(_lowercase   )
     ldmad_pipe.set_progress_bar_config(disable=_lowercase   )

     SCREAMING_SNAKE_CASE__    							=   self.get_dummy_inputs(_lowercase   )
     SCREAMING_SNAKE_CASE__    							=   """french fries"""
     SCREAMING_SNAKE_CASE__    							=   ldmad_pipe(**_lowercase ,			negative_prompt=_lowercase   )

     SCREAMING_SNAKE_CASE__							,  SCREAMING_SNAKE_CASE__    							=   output.rgb, output.depth
     SCREAMING_SNAKE_CASE__    							=   rgb[0, -3:, -3:, -1]
     SCREAMING_SNAKE_CASE__    							=   depth[0, -3:, -1]

     assert rgb.shape == (1, 64, 64, 3)
     assert depth.shape == (1, 64, 64)

     SCREAMING_SNAKE_CASE__    							=   np.array(
         [0.3_70_44, 0.71_81_15_03, 0.7_22_32_51, 0.48_60_36_75, 0.5_63_83_91, 0.6_36_49_48, 0.42_83_37_04, 0.4_90_13_15, 0.47_92_62_17]   )
     SCREAMING_SNAKE_CASE__    							=   np.array([1_07.8_47_38, 84.6_28_02, 89.96_21_35]   )
     assert np.abs(rgb_slice.flatten() - expected_slice_rgb   ).max() < 1E-2
     assert np.abs(depth_slice.flatten() - expected_slice_depth   ).max() < 1E-2







@slow
@require_torch_gpu
class __snake_case       (		unittest.TestCase	):




   def 			__a		(					self    :       Any   ):



     """simple docstring"""






     super().tearDown()
     gc.collect()
     torch.cuda.empty_cache()




   def 			__a		(					self    :       List[Any] ,			_lowercase    :       List[Any] ,			_lowercase    :       Union[str, Any]="cpu" ,			_lowercase    :       Any=torch.floataa ,			_lowercase    :       str=0   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   torch.Generator(device=_lowercase   ).manual_seed(_lowercase   )
     SCREAMING_SNAKE_CASE__    							=   np.random.RandomState(_lowercase   ).standard_normal((1, 4, 64, 64)   )
     SCREAMING_SNAKE_CASE__    							=   torch.from_numpy(_lowercase   ).to(device=_lowercase ,			dtype=_lowercase   )
     SCREAMING_SNAKE_CASE__    							=   {
         """prompt""": """a photograph of an astronaut riding a horse""",
         """latents""": latents,
         """generator""": generator,
         """num_inference_steps""": 3,
         """guidance_scale""": 7.5,
         """output_type""": """numpy""",
     }
     return inputs




   def 			__a		(					self    :       Tuple   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   StableDiffusionLDMaDPipeline.from_pretrained("""Intel/ldm3d"""   )
     SCREAMING_SNAKE_CASE__    							=   ldmad_pipe.to(_lowercase   )
     ldmad_pipe.set_progress_bar_config(disable=_lowercase   )

     SCREAMING_SNAKE_CASE__    							=   self.get_inputs(_lowercase   )
     SCREAMING_SNAKE_CASE__    							=   ldmad_pipe(**_lowercase   )
     SCREAMING_SNAKE_CASE__							,  SCREAMING_SNAKE_CASE__    							=   output.rgb, output.depth
     SCREAMING_SNAKE_CASE__    							=   rgb[0, -3:, -3:, -1].flatten()
     SCREAMING_SNAKE_CASE__    							=   rgb[0, -3:, -1].flatten()

     assert rgb.shape == (1, 5_12, 5_12, 3)
     assert depth.shape == (1, 5_12, 5_12)

     SCREAMING_SNAKE_CASE__    							=   np.array(
         [0.53_80_54_65, 0.56_70_73_05, 0.5_48_65_15, 0.57_01_22_36, 0.5_81_45_11, 0.56_25_34_87, 0.54_84_30_14, 0.55_09_22_63, 0.6_45_97_06]   )
     SCREAMING_SNAKE_CASE__    							=   np.array(
         [0.9_26_37_81, 0.6_67_86_72, 0.5_48_65_15, 0.92_20_21_45, 0.67_83_11_35, 0.56_25_34_87, 0.9_24_16_94, 0.7_55_14_78, 0.6_45_97_06]   )
     assert np.abs(rgb_slice - expected_slice_rgb   ).max() < 3E-3
     assert np.abs(depth_slice - expected_slice_depth   ).max() < 3E-3







@nightly
@require_torch_gpu
class __snake_case       (		unittest.TestCase	):




   def 			__a		(					self    :       List[str]   ):



     """simple docstring"""






     super().tearDown()
     gc.collect()
     torch.cuda.empty_cache()




   def 			__a		(					self    :       Optional[Any] ,			_lowercase    :       Optional[Any] ,			_lowercase    :       Any="cpu" ,			_lowercase    :       Tuple=torch.floataa ,			_lowercase    :       Any=0   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   torch.Generator(device=_lowercase   ).manual_seed(_lowercase   )
     SCREAMING_SNAKE_CASE__    							=   np.random.RandomState(_lowercase   ).standard_normal((1, 4, 64, 64)   )
     SCREAMING_SNAKE_CASE__    							=   torch.from_numpy(_lowercase   ).to(device=_lowercase ,			dtype=_lowercase   )
     SCREAMING_SNAKE_CASE__    							=   {
         """prompt""": """a photograph of an astronaut riding a horse""",
         """latents""": latents,
         """generator""": generator,
         """num_inference_steps""": 50,
         """guidance_scale""": 7.5,
         """output_type""": """numpy""",
     }
     return inputs




   def 			__a		(					self    :       Any   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   StableDiffusionLDMaDPipeline.from_pretrained("""Intel/ldm3d"""   ).to(_lowercase   )
     ldmad_pipe.set_progress_bar_config(disable=_lowercase   )

     SCREAMING_SNAKE_CASE__    							=   self.get_inputs(_lowercase   )
     SCREAMING_SNAKE_CASE__    							=   ldmad_pipe(**_lowercase   )
     SCREAMING_SNAKE_CASE__							,  SCREAMING_SNAKE_CASE__    							=   output.rgb, output.depth

     SCREAMING_SNAKE_CASE__    							=   0.49_55_86
     SCREAMING_SNAKE_CASE__    							=   0.33_79_55_15
     SCREAMING_SNAKE_CASE__    							=   1_12.4_85_18
     SCREAMING_SNAKE_CASE__    							=   98.48_97_46
     assert np.abs(expected_rgb_mean - rgb.mean()   ) < 1E-3
     assert np.abs(expected_rgb_std - rgb.std()   ) < 1E-3
     assert np.abs(expected_depth_mean - depth.mean()   ) < 1E-3
     assert np.abs(expected_depth_std - depth.std()   ) < 1E-3




   def 			__a		(					self    :       Tuple   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   StableDiffusionLDMaDPipeline.from_pretrained("""Intel/ldm3d-4c"""   ).to(_lowercase   )
     ldmad_pipe.set_progress_bar_config(disable=_lowercase   )

     SCREAMING_SNAKE_CASE__    							=   self.get_inputs(_lowercase   )
     SCREAMING_SNAKE_CASE__    							=   ldmad_pipe(**_lowercase   )
     SCREAMING_SNAKE_CASE__							,  SCREAMING_SNAKE_CASE__    							=   output.rgb, output.depth

     SCREAMING_SNAKE_CASE__    							=   0.4_19_41_27
     SCREAMING_SNAKE_CASE__    							=   0.35_37_55_86
     SCREAMING_SNAKE_CASE__    							=   0.5_63_85_02
     SCREAMING_SNAKE_CASE__    							=   0.34_68_61_03

     assert rgb.shape == (1, 5_12, 5_12, 3)
     assert depth.shape == (1, 5_12, 5_12, 1)
     assert np.abs(expected_rgb_mean - rgb.mean()   ) < 1E-3
     assert np.abs(expected_rgb_std - rgb.std()   ) < 1E-3
     assert np.abs(expected_depth_mean - depth.mean()   ) < 1E-3
     assert np.abs(expected_depth_std - depth.std()   ) < 1E-3

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 __snake_case       :




   def 			__a		(					self    :       str ,			_lowercase    :       str ,			_lowercase    :       Dict ,			_lowercase    :       List[Any]   ):



     """simple docstring"""






     return None







class __snake_case       :




   def 			__a		(					self    :       List[str] ,			_lowercase    :       List[str] ,			_lowercase    :       str ,			_lowercase    :       List[Any] ,			_lowercase    :       str   ):



     """simple docstring"""






     return None







class __snake_case       (		unittest.TestCase	):
   lowerCAmelCase_														=							[
       # (model_name, model_kwargs)
       ("bert-base-cased", {}),
       ("gpt2", {"use_cache": False}),  # We don't support exporting GPT2 past keys anymore
   ]




   @require_tf
   @slow
   def 			__a		(					self    :       List[Any]   ):



     """simple docstring"""






     for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
       self._test_export(_lowercase ,			"""tf""" ,			12 ,			**_lowercase   )




   @require_torch
   @slow
   def 			__a		(					self    :       Optional[Any]   ):



     """simple docstring"""






     for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
       self._test_export(_lowercase ,			"""pt""" ,			12 ,			**_lowercase   )




   @require_torch
   @slow
   def 			__a		(					self    :       Dict   ):



     """simple docstring"""






     from transformers import BertModel

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

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




   @require_tf
   @slow
   def 			__a		(					self    :       Dict   ):



     """simple docstring"""






     for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
       SCREAMING_SNAKE_CASE__    							=   self._test_export(_lowercase ,			"""tf""" ,			12 ,			**_lowercase   )
       SCREAMING_SNAKE_CASE__    							=   quantize(Path(_lowercase   )   )

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




   @require_torch
   @slow
   def 			__a		(					self    :       Any   ):



     """simple docstring"""






     for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
       SCREAMING_SNAKE_CASE__    							=   self._test_export(_lowercase ,			"""pt""" ,			12 ,			**_lowercase   )
       SCREAMING_SNAKE_CASE__    							=   quantize(_lowercase   )

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




   def 			__a		(					self    :       Union[str, Any] ,			_lowercase    :       List[str] ,			_lowercase    :       Tuple ,			_lowercase    :       Optional[Any] ,			_lowercase    :       str=None ,			**_lowercase    :       List[str]   ):



     """simple docstring"""






     try:
       # Compute path
       with TemporaryDirectory() as tempdir:
         SCREAMING_SNAKE_CASE__    							=   Path(_lowercase   ).joinpath("""model.onnx"""   )

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

       # Export
       convert(_lowercase ,			_lowercase ,			_lowercase ,			_lowercase ,			_lowercase ,			**_lowercase   )

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




   @require_torch
   @require_tokenizers
   @slow
   def 			__a		(					self    :       List[str]   ):



     """simple docstring"""






     from transformers import BertModel

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




   @require_tf
   @require_tokenizers
   @slow
   def 			__a		(					self    :       Dict   ):



     """simple docstring"""






     from transformers import TFBertModel

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




   def 			__a		(					self    :       List[Any] ,			_lowercase    :       List[Any] ,			_lowercase    :       Optional[int] ,			_lowercase    :       Dict   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   FeatureExtractionPipeline(_lowercase ,			_lowercase   )

     SCREAMING_SNAKE_CASE__    							=   ["""input_ids""", """token_type_ids""", """attention_mask""", """output_0""", """output_1"""]
     SCREAMING_SNAKE_CASE__							,  SCREAMING_SNAKE_CASE__							,  SCREAMING_SNAKE_CASE__							,  SCREAMING_SNAKE_CASE__    							=   infer_shapes(_lowercase ,			_lowercase   )

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

     # 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 			__a		(					self    :       Dict   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   ["""input_ids""", """attention_mask""", """token_type_ids"""]
     SCREAMING_SNAKE_CASE__    							=   {"""input_ids""": [1, 2, 3, 4], """attention_mask""": [0, 0, 0, 0], """token_type_ids""": [1, 1, 1, 1]}
     SCREAMING_SNAKE_CASE__							,  SCREAMING_SNAKE_CASE__    							=   ensure_valid_input(FuncContiguousArgs() ,			_lowercase ,			_lowercase   )

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

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

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

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

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

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




   def 			__a		(					self    :       List[str]   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   generate_identified_filename(Path("""/home/something/my_fake_model.onnx"""   ) ,			"""-test"""   )
     self.assertEqual("""/home/something/my_fake_model-test.onnx""" ,			generated.as_posix()   )
