

'''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			:






						"""simple docstring"""




						def       __magic_name__							(							self					:      Tuple			,			snake_case_					:      List[str]			,			snake_case_					:      Any			,			snake_case_					:      Optional[int]  ):



									'''simple docstring'''



									return None





class     a			:






						"""simple docstring"""




						def       __magic_name__							(							self					:      Tuple			,			snake_case_					:      Dict			,			snake_case_					:      Union[str, Any]			,			snake_case_					:      Dict			,			snake_case_					:      Tuple  ):



									'''simple docstring'''



									return None





class     a			(				unittest.TestCase       ):






						"""simple docstring"""




						__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       __magic_name__							(							self					:      Union[str, Any]  ):



									'''simple docstring'''



									for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
												self._test_export(lowercase_			,			'''tf'''			,			1_2			,			**lowercase_  )







						@require_torch
						@slow
						def       __magic_name__							(							self					:      Optional[int]  ):



									'''simple docstring'''



									for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
												self._test_export(lowercase_			,			'''pt'''			,			1_2			,			**lowercase_  )







						@require_torch
						@slow
						def       __magic_name__							(							self					:      Optional[int]  ):



									'''simple docstring'''



									from transformers import BertModel

									snake_case__						:       int       						=  ['''[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()
												snake_case__						:       List[Any]       						=  BertTokenizerFast(vocab_file.name  )

									with TemporaryDirectory() as bert_save_dir:
												snake_case__						:       int       						=  BertModel(BertConfig(vocab_size=len(lowercase_  )  )  )
												model.save_pretrained(lowercase_  )
												self._test_export(lowercase_			,			'''pt'''			,			1_2			,			lowercase_  )







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



									'''simple docstring'''



									for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
												snake_case__						:       List[Any]       						=  self._test_export(lowercase_			,			'''tf'''			,			1_2			,			**lowercase_  )
												snake_case__						:       Optional[int]       						=  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       __magic_name__							(							self					:      Dict  ):



									'''simple docstring'''



									for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
												snake_case__						:       Optional[int]       						=  self._test_export(lowercase_			,			'''pt'''			,			1_2			,			**lowercase_  )
												snake_case__						:       int       						=  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       __magic_name__							(							self					:      Optional[int]			,			snake_case_					:      Optional[Any]			,			snake_case_					:      Tuple			,			snake_case_					:      Optional[int]			,			snake_case_					:      str=None			,			**snake_case_					:      List[str]  ):



									'''simple docstring'''



									try:
												# Compute path
												with TemporaryDirectory() as tempdir:
															snake_case__						:       int       						=  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       __magic_name__							(							self					:      Dict  ):



									'''simple docstring'''



									from transformers import BertModel

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







						@require_tf
						@require_tokenizers
						@slow
						def       __magic_name__							(							self					:      str  ):



									'''simple docstring'''



									from transformers import TFBertModel

									snake_case__						:       Tuple       						=  TFBertModel(BertConfig.from_pretrained('''lysandre/tiny-bert-random'''  )  )
									snake_case__						:       Union[str, Any]       						=  BertTokenizerFast.from_pretrained('''lysandre/tiny-bert-random'''  )
									self._test_infer_dynamic_axis(lowercase_			,			lowercase_			,			'''tf'''  )







						def       __magic_name__							(							self					:      Any			,			snake_case_					:      List[str]			,			snake_case_					:      str			,			snake_case_					:      List[str]  ):



									'''simple docstring'''



									snake_case__						:       Optional[Any]       						=  FeatureExtractionPipeline(lowercase_			,			lowercase_  )

									snake_case__						:       Union[str, Any]       						=  ['''input_ids''', '''token_type_ids''', '''attention_mask''', '''output_0''', '''output_1''']
									snake_case__						,       snake_case__						,       snake_case__						,       snake_case__						:       int       						=  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       __magic_name__							(							self					:      Tuple  ):



									'''simple docstring'''



									snake_case__						:       str       						=  ['''input_ids''', '''attention_mask''', '''token_type_ids''']
									snake_case__						:       Tuple       						=  {'''input_ids''': [1, 2, 3, 4], '''attention_mask''': [0, 0, 0, 0], '''token_type_ids''': [1, 1, 1, 1]}
									snake_case__						,       snake_case__						:       Dict       						=  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)
									snake_case__						,       snake_case__						:       int       						=  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       __magic_name__							(							self					:      List[Any]  ):



									'''simple docstring'''



									snake_case__						:       Tuple       						=  generate_identified_filename(Path('''/home/something/my_fake_model.onnx'''  )			,			'''-test'''  )
									self.assertEqual('''/home/something/my_fake_model-test.onnx'''			,			generated.as_posix()  )






'''simple docstring'''


import argparse

from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection

from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline


if __name__ == "__main__":
					lowerCAmelCase__						:       Dict										=						argparse.ArgumentParser()

					parser.add_argument("""--dump_path""", default=None, type=str, required=True, help="""Path to the output model.""")

					parser.add_argument(
					    """--txt2img_unclip""",
					    default="""kakaobrain/karlo-v1-alpha""",
					    type=str,
					    required=False,
					    help="""The pretrained txt2img unclip.""",
					)

					lowerCAmelCase__						:       List[Any]										=						parser.parse_args()

					lowerCAmelCase__						:       Optional[Any]										=						UnCLIPPipeline.from_pretrained(args.txtaimg_unclip)

					lowerCAmelCase__						:       Optional[int]										=						CLIPImageProcessor()
					lowerCAmelCase__						:       str										=						CLIPVisionModelWithProjection.from_pretrained("""openai/clip-vit-large-patch14""")

					lowerCAmelCase__						:       Dict										=						UnCLIPImageVariationPipeline(
					    decoder=txtaimg.decoder,
					    text_encoder=txtaimg.text_encoder,
					    tokenizer=txtaimg.tokenizer,
					    text_proj=txtaimg.text_proj,
					    feature_extractor=feature_extractor,
					    image_encoder=image_encoder,
					    super_res_first=txtaimg.super_res_first,
					    super_res_last=txtaimg.super_res_last,
					    decoder_scheduler=txtaimg.decoder_scheduler,
					    super_res_scheduler=txtaimg.super_res_scheduler,
					)

					imgaimg.save_pretrained(args.dump_path)



