






"""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       __A		:

					def   lowerCamelCase__   (			self  :      List[str]     ,		__snake_case  :      List[str]     ,		__snake_case  :      Any     ,		__snake_case  :      Any					)			->							Tuple:
											return None



class       __A		:

					def   lowerCamelCase__   (			self  :      Optional[Any]     ,		__snake_case  :      Union[str, Any]     ,		__snake_case  :      Union[str, Any]     ,		__snake_case  :      int     ,		__snake_case  :      str					)			->							str:
											return None



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

					@require_tf
					@slow
					def   lowerCamelCase__   (			self  :      Union[str, Any]					)			->							Dict:
											for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
																	self._test_export(__snake_case     ,		"""tf"""     ,		1_2     ,		**__snake_case					)

					@require_torch
					@slow
					def   lowerCamelCase__   (			self  :      List[str]					)			->							int:
											for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
																	self._test_export(__snake_case     ,		"""pt"""     ,		1_2     ,		**__snake_case					)

					@require_torch
					@slow
					def   lowerCamelCase__   (			self  :      Optional[int]					)			->							List[str]:
											from transformers import BertModel

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

											with TemporaryDirectory() as bert_save_dir:
																	__magic_name__:	Optional[int]							=       BertModel(BertConfig(vocab_size=len(__snake_case					)					)					)
																	model.save_pretrained(__snake_case					)
																	self._test_export(__snake_case     ,		"""pt"""     ,		1_2     ,		__snake_case					)

					@require_tf
					@slow
					def   lowerCamelCase__   (			self  :      Tuple					)			->							List[str]:
											for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
																	__magic_name__:	int							=       self._test_export(__snake_case     ,		"""tf"""     ,		1_2     ,		**__snake_case					)
																	__magic_name__:	List[str]							=       quantize(Path(__snake_case					)					)

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

					@require_torch
					@slow
					def   lowerCamelCase__   (			self  :      List[Any]					)			->							List[Any]:
											for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
																	__magic_name__:	Dict							=       self._test_export(__snake_case     ,		"""pt"""     ,		1_2     ,		**__snake_case					)
																	__magic_name__:	Tuple							=       quantize(__snake_case					)

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

					def   lowerCamelCase__   (			self  :      Tuple     ,		__snake_case  :      Optional[Any]     ,		__snake_case  :      Optional[Any]     ,		__snake_case  :      Any     ,		__snake_case  :      Tuple=None     ,		**__snake_case  :      int					)			->							Dict:
											try:
																	# Compute path
																	with TemporaryDirectory() as tempdir:
																							__magic_name__:	Tuple							=       Path(__snake_case					).joinpath("""model.onnx"""					)

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

																	# Export
																	convert(__snake_case     ,		__snake_case     ,		__snake_case     ,		__snake_case     ,		__snake_case     ,		**__snake_case					)

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

					@require_torch
					@require_tokenizers
					@slow
					def   lowerCamelCase__   (			self  :      List[str]					)			->							Dict:
											from transformers import BertModel

											__magic_name__:	str							=       BertModel(BertConfig.from_pretrained("""lysandre/tiny-bert-random"""					)					)
											__magic_name__:	int							=       BertTokenizerFast.from_pretrained("""lysandre/tiny-bert-random"""					)
											self._test_infer_dynamic_axis(__snake_case     ,		__snake_case     ,		"""pt"""					)

					@require_tf
					@require_tokenizers
					@slow
					def   lowerCamelCase__   (			self  :      Union[str, Any]					)			->							List[str]:
											from transformers import TFBertModel

											__magic_name__:	str							=       TFBertModel(BertConfig.from_pretrained("""lysandre/tiny-bert-random"""					)					)
											__magic_name__:	List[Any]							=       BertTokenizerFast.from_pretrained("""lysandre/tiny-bert-random"""					)
											self._test_infer_dynamic_axis(__snake_case     ,		__snake_case     ,		"""tf"""					)

					def   lowerCamelCase__   (			self  :      str     ,		__snake_case  :      Union[str, Any]     ,		__snake_case  :      int     ,		__snake_case  :      int					)			->							int:
											__magic_name__:	List[Any]							=       FeatureExtractionPipeline(__snake_case     ,		__snake_case					)

											__magic_name__:	Optional[int]							=       ["""input_ids""", """token_type_ids""", """attention_mask""", """output_0""", """output_1"""]
											__magic_name__,			__magic_name__,			__magic_name__,			__magic_name__:	List[str]							=       infer_shapes(__snake_case     ,		__snake_case					)

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

											# 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   lowerCamelCase__   (			self  :      int					)			->							List[str]:
											__magic_name__:	int							=       ["""input_ids""", """attention_mask""", """token_type_ids"""]
											__magic_name__:	str							=       {"""input_ids""": [1, 2, 3, 4], """attention_mask""": [0, 0, 0, 0], """token_type_ids""": [1, 1, 1, 1]}
											__magic_name__,			__magic_name__:	int							=       ensure_valid_input(FuncContiguousArgs()     ,		__snake_case     ,		__snake_case					)

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

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

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

											# Generated args are interleaved with another args (for instance parameter "past" in GPT2)
											__magic_name__,			__magic_name__:	List[Any]							=       ensure_valid_input(FuncNonContiguousArgs()     ,		__snake_case     ,		__snake_case					)

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

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






					def   lowerCamelCase__   (			self  :      Union[str, Any]					)			->							List[str]:
											__magic_name__:	Optional[int]							=       generate_identified_filename(Path("""/home/something/my_fake_model.onnx"""					)     ,		"""-test"""					)
											self.assertEqual("""/home/something/my_fake_model-test.onnx"""     ,		generated.as_posix()					)











import unittest

import numpy as np
import torch

from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()






class 					SCREAMING_SNAKE_CASE_  (       unittest.TestCase ):
       """simple docstring"""


       @property
       def 				UpperCamelCase__						(				self	:Optional[int]):



          """simple docstring"""

          torch.manual_seed(0)
          _lowercase              =UNetaDModel(
              block_out_channels=(32, 64),      layers_per_block=2,      sample_size=32,      in_channels=3,      out_channels=3,      down_block_types=('DownBlock2D', 'AttnDownBlock2D'),      up_block_types=('AttnUpBlock2D', 'UpBlock2D'),      )
          return model





       def 				UpperCamelCase__						(				self	:str):



          """simple docstring"""

          _lowercase              =self.dummy_uncond_unet
          _lowercase              =ScoreSdeVeScheduler()

          _lowercase              =ScoreSdeVePipeline(unet=snake_case,      scheduler=snake_case)
          sde_ve.to(snake_case)
          sde_ve.set_progress_bar_config(disable=snake_case)

          _lowercase              =torch.manual_seed(0)
          _lowercase              =sde_ve(num_inference_steps=2,      output_type='numpy',      generator=snake_case).images

          _lowercase              =torch.manual_seed(0)
          _lowercase              =sde_ve(num_inference_steps=2,      output_type='numpy',      generator=snake_case,      return_dict=snake_case)[
              0
          ]

          _lowercase              =image[0, -3:, -3:, -1]
          _lowercase              =image_from_tuple[0, -3:, -3:, -1]

          assert image.shape == (1, 32, 32, 3)
          _lowercase              =np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0])

          assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
          assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1e-2






@slow
@require_torch
class 					SCREAMING_SNAKE_CASE_  (       unittest.TestCase ):
       """simple docstring"""


       def 				UpperCamelCase__						(				self	:int):



          """simple docstring"""

          _lowercase              ='google/ncsnpp-church-256'
          _lowercase              =UNetaDModel.from_pretrained(snake_case)

          _lowercase              =ScoreSdeVeScheduler.from_pretrained(snake_case)

          _lowercase              =ScoreSdeVePipeline(unet=snake_case,      scheduler=snake_case)
          sde_ve.to(snake_case)
          sde_ve.set_progress_bar_config(disable=snake_case)

          _lowercase              =torch.manual_seed(0)
          _lowercase              =sde_ve(num_inference_steps=10,      output_type='numpy',      generator=snake_case).images

          _lowercase              =image[0, -3:, -3:, -1]

          assert image.shape == (1, 256, 256, 3)

          _lowercase              =np.array([0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0])

          assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2


