





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  __lowerCAmelCase	:






			def       lowerCamelCase  (self ,							__magic_name__ ,							__magic_name__ ,							__magic_name__   )					->       int:


							'''simple docstring'''



							return None




class  __lowerCAmelCase	:






			def       lowerCamelCase  (self ,							__magic_name__ ,							__magic_name__ ,							__magic_name__ ,							__magic_name__   )					->       Optional[int]:


							'''simple docstring'''



							return None




class  __lowerCAmelCase	(      unittest.TestCase					):
			lowerCamelCase_					:					Optional[int]	      =				[
			    # (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]:


							'''simple docstring'''



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






			@require_torch
			@slow
			def       lowerCamelCase  (self   )					->       List[Any]:


							'''simple docstring'''



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






			@require_torch
			@slow
			def       lowerCamelCase  (self   )					->       Optional[Any]:


							'''simple docstring'''



							from transformers import BertModel

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

							with TemporaryDirectory() as bert_save_dir:
											snake_case_						:   List[Any]           =  BertModel(BertConfig(vocab_size=len(__magic_name__   )   )   )
											model.save_pretrained(__magic_name__   )
											self._test_export(__magic_name__ ,							'''pt''' ,							12 ,							__magic_name__   )






			@require_tf
			@slow
			def       lowerCamelCase  (self   )					->       str:


							'''simple docstring'''



							for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
											snake_case_						:   Optional[Any]           =  self._test_export(__magic_name__ ,							'''tf''' ,							12 ,							**__magic_name__   )
											snake_case_						:   str           =  quantize(Path(__magic_name__   )   )

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






			@require_torch
			@slow
			def       lowerCamelCase  (self   )					->       List[Any]:


							'''simple docstring'''



							for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
											snake_case_						:   str           =  self._test_export(__magic_name__ ,							'''pt''' ,							12 ,							**__magic_name__   )
											snake_case_						:   int           =  quantize(__magic_name__   )

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






			def       lowerCamelCase  (self ,							__magic_name__ ,							__magic_name__ ,							__magic_name__ ,							__magic_name__=None ,							**__magic_name__   )					->       List[str]:


							'''simple docstring'''



							try:
											# Compute path
											with TemporaryDirectory() as tempdir:
															snake_case_						:   int           =  Path(__magic_name__   ).joinpath('''model.onnx'''   )

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

											# Export
											convert(__magic_name__ ,							__magic_name__ ,							__magic_name__ ,							__magic_name__ ,							__magic_name__ ,							**__magic_name__   )

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






			@require_torch
			@require_tokenizers
			@slow
			def       lowerCamelCase  (self   )					->       Optional[int]:


							'''simple docstring'''



							from transformers import BertModel

							snake_case_						:   Optional[Any]           =  BertModel(BertConfig.from_pretrained('''lysandre/tiny-bert-random'''   )   )
							snake_case_						:   Union[str, Any]           =  BertTokenizerFast.from_pretrained('''lysandre/tiny-bert-random'''   )
							self._test_infer_dynamic_axis(__magic_name__ ,							__magic_name__ ,							'''pt'''   )






			@require_tf
			@require_tokenizers
			@slow
			def       lowerCamelCase  (self   )					->       Union[str, Any]:


							'''simple docstring'''



							from transformers import TFBertModel

							snake_case_						:   Any           =  TFBertModel(BertConfig.from_pretrained('''lysandre/tiny-bert-random'''   )   )
							snake_case_						:   str           =  BertTokenizerFast.from_pretrained('''lysandre/tiny-bert-random'''   )
							self._test_infer_dynamic_axis(__magic_name__ ,							__magic_name__ ,							'''tf'''   )






			def       lowerCamelCase  (self ,							__magic_name__ ,							__magic_name__ ,							__magic_name__   )					->       List[Any]:


							'''simple docstring'''



							snake_case_						:   str           =  FeatureExtractionPipeline(__magic_name__ ,							__magic_name__   )

							snake_case_						:   Dict           =  ['''input_ids''', '''token_type_ids''', '''attention_mask''', '''output_0''', '''output_1''']
							snake_case_      ,							snake_case_      ,							snake_case_      ,							snake_case_						:   Union[str, Any]           =  infer_shapes(__magic_name__ ,							__magic_name__   )

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

							# 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   )					->       List[str]:


							'''simple docstring'''



							snake_case_						:   Optional[Any]           =  ['''input_ids''', '''attention_mask''', '''token_type_ids''']
							snake_case_						:   Any           =  {'''input_ids''': [1, 2, 3, 4], '''attention_mask''': [0, 0, 0, 0], '''token_type_ids''': [1, 1, 1, 1]}
							snake_case_      ,							snake_case_						:   Optional[Any]           =  ensure_valid_input(FuncContiguousArgs() ,							__magic_name__ ,							__magic_name__   )

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

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

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

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

							# Should have exactly the one arg (all before the one not provided "some_other_args")
							self.assertEqual(len(__magic_name__   ) ,							1   )
							self.assertEqual(len(__magic_name__   ) ,							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   )					->       str:


							'''simple docstring'''



							snake_case_						:   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 PNDMPipeline, PNDMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()



class  __lowerCAmelCase	(      unittest.TestCase					):






			@property
			def       lowerCamelCase  (self   )					->       Union[str, Any]:


							'''simple docstring'''



							torch.manual_seed(0   )
							snake_case_						:   int           =  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       lowerCamelCase  (self   )					->       Dict:


							'''simple docstring'''



							snake_case_						:   Any           =  self.dummy_uncond_unet
							snake_case_						:   Optional[Any]           =  PNDMScheduler()

							snake_case_						:   Optional[Any]           =  PNDMPipeline(unet=__magic_name__ ,							scheduler=__magic_name__   )
							pndm.to(__magic_name__   )
							pndm.set_progress_bar_config(disable=__magic_name__   )

							snake_case_						:   str           =  torch.manual_seed(0   )
							snake_case_						:   Dict           =  pndm(generator=__magic_name__ ,							num_inference_steps=20 ,							output_type='''numpy'''   ).images

							snake_case_						:   str           =  torch.manual_seed(0   )
							snake_case_						:   str           =  pndm(generator=__magic_name__ ,							num_inference_steps=20 ,							output_type='''numpy''' ,							return_dict=__magic_name__   )[0]

							snake_case_						:   Any           =  image[0, -3:, -3:, -1]
							snake_case_						:   str           =  image_from_tuple[0, -3:, -3:, -1]

							assert image.shape == (1, 32, 32, 3)
							snake_case_						:   str           =  np.array([1.0, 1.0, 0.0, 1.0, 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  __lowerCAmelCase	(      unittest.TestCase					):






			def       lowerCamelCase  (self   )					->       Any:


							'''simple docstring'''



							snake_case_						:   Tuple           =  '''google/ddpm-cifar10-32'''

							snake_case_						:   Tuple           =  UNetaDModel.from_pretrained(__magic_name__   )
							snake_case_						:   Optional[Any]           =  PNDMScheduler()

							snake_case_						:   Any           =  PNDMPipeline(unet=__magic_name__ ,							scheduler=__magic_name__   )
							pndm.to(__magic_name__   )
							pndm.set_progress_bar_config(disable=__magic_name__   )
							snake_case_						:   int           =  torch.manual_seed(0   )
							snake_case_						:   Tuple           =  pndm(generator=__magic_name__ ,							output_type='''numpy'''   ).images

							snake_case_						:   List[str]           =  image[0, -3:, -3:, -1]

							assert image.shape == (1, 32, 32, 3)
							snake_case_						:   str           =  np.array([0.1_564, 0.14_645, 0.1_406, 0.14_715, 0.12_425, 0.14_045, 0.13_115, 0.12_175, 0.125]   )

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