






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 			A	(						self       ,					__lowerCAmelCase       ,					__lowerCAmelCase       ,					__lowerCAmelCase     ):
								"""simple docstring"""
								return None







class 						lowerCamelCase_				:







				def 			A	(						self       ,					__lowerCAmelCase       ,					__lowerCAmelCase       ,					__lowerCAmelCase       ,					__lowerCAmelCase     ):
								"""simple docstring"""
								return None







class 						lowerCamelCase_				(      unittest.TestCase ):
				a__					       =  [
				    # (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     ):
								"""simple docstring"""
								for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
												self._test_export(__lowerCAmelCase       ,					'''tf'''       ,					1_2       ,					**__lowerCAmelCase     )







				@require_torch
				@slow
				def 			A	(						self     ):
								"""simple docstring"""
								for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
												self._test_export(__lowerCAmelCase       ,					'''pt'''       ,					1_2       ,					**__lowerCAmelCase     )







				@require_torch
				@slow
				def 			A	(						self     ):
								"""simple docstring"""
								from transformers import BertModel

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

								with TemporaryDirectory() as bert_save_dir:
												__magic_name__		:List[str]        =      BertModel(BertConfig(vocab_size=len(__lowerCAmelCase     )     )     )
												model.save_pretrained(__lowerCAmelCase     )
												self._test_export(__lowerCAmelCase       ,					'''pt'''       ,					1_2       ,					__lowerCAmelCase     )







				@require_tf
				@slow
				def 			A	(						self     ):
								"""simple docstring"""
								for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
												__magic_name__		:Union[str, Any]        =      self._test_export(__lowerCAmelCase       ,					'''tf'''       ,					1_2       ,					**__lowerCAmelCase     )
												__magic_name__		:Dict        =      quantize(Path(__lowerCAmelCase     )     )

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







				@require_torch
				@slow
				def 			A	(						self     ):
								"""simple docstring"""
								for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
												__magic_name__		:Any        =      self._test_export(__lowerCAmelCase       ,					'''pt'''       ,					1_2       ,					**__lowerCAmelCase     )
												__magic_name__		:Dict        =      quantize(__lowerCAmelCase     )

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







				def 			A	(						self       ,					__lowerCAmelCase       ,					__lowerCAmelCase       ,					__lowerCAmelCase       ,					__lowerCAmelCase=None       ,					**__lowerCAmelCase     ):
								"""simple docstring"""
								try:
												# Compute path
												with TemporaryDirectory() as tempdir:
																__magic_name__		:str        =      Path(__lowerCAmelCase     ).joinpath('''model.onnx'''     )

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

												# Export
												convert(__lowerCAmelCase       ,					__lowerCAmelCase       ,					__lowerCAmelCase       ,					__lowerCAmelCase       ,					__lowerCAmelCase       ,					**__lowerCAmelCase     )

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







				@require_torch
				@require_tokenizers
				@slow
				def 			A	(						self     ):
								"""simple docstring"""
								from transformers import BertModel

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







				@require_tf
				@require_tokenizers
				@slow
				def 			A	(						self     ):
								"""simple docstring"""
								from transformers import TFBertModel

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







				def 			A	(						self       ,					__lowerCAmelCase       ,					__lowerCAmelCase       ,					__lowerCAmelCase     ):
								"""simple docstring"""
								__magic_name__		:Any        =      FeatureExtractionPipeline(__lowerCAmelCase       ,					__lowerCAmelCase     )

								__magic_name__		:Dict        =      ['''input_ids''', '''token_type_ids''', '''attention_mask''', '''output_0''', '''output_1''']
								__magic_name__     ,				__magic_name__     ,				__magic_name__     ,				__magic_name__		:Tuple        =      infer_shapes(__lowerCAmelCase       ,					__lowerCAmelCase     )

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

								# 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     ):
								"""simple docstring"""
								__magic_name__		:Tuple        =      ['''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__		:List[Any]        =      ensure_valid_input(FuncContiguousArgs()       ,					__lowerCAmelCase       ,					__lowerCAmelCase     )

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

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

								# Parameter should be reordered according to their respective place in the function:
								# (input_ids, token_type_ids, attention_mask)
								self.assertEqual(__lowerCAmelCase       ,					(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__		:Any        =      ensure_valid_input(FuncNonContiguousArgs()       ,					__lowerCAmelCase       ,					__lowerCAmelCase     )

								# Should have exactly the one arg (all before the one not provided "some_other_args")
								self.assertEqual(len(__lowerCAmelCase     )       ,					1     )
								self.assertEqual(len(__lowerCAmelCase     )       ,					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     ):
								"""simple docstring"""
								__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 argparse
import math
import os

import torch
from neural_compressor.utils.pytorch import load
from PIL import Image
from transformers import CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, StableDiffusionPipeline, UNetaDConditionModel

def      __lowercase					( ):







				"""simple docstring"""






				__magic_name__		:Optional[int]        =      argparse.ArgumentParser()
				parser.add_argument(
				    '''-m''',  '''--pretrained_model_name_or_path''',  type=snake_case,  default=snake_case,  required=snake_case,  help='''Path to pretrained model or model identifier from huggingface.co/models.''',  )
				parser.add_argument(
				    '''-c''',  '''--caption''',  type=snake_case,  default='''robotic cat with wings''',  help='''Text used to generate images.''',  )
				parser.add_argument(
				    '''-n''',  '''--images_num''',  type=snake_case,  default=4,  help='''How much images to generate.''',  )
				parser.add_argument(
				    '''-s''',  '''--seed''',  type=snake_case,  default=4_2,  help='''Seed for random process.''',  )
				parser.add_argument(
				    '''-ci''',  '''--cuda_id''',  type=snake_case,  default=0,  help='''cuda_id.''',  )
				__magic_name__		:Optional[Any]        =      parser.parse_args()
				return args

def      __lowercase					( snake_case,  snake_case,  snake_case ):







				"""simple docstring"""






				if not len(snake_case ) == rows * cols:
								raise ValueError('''The specified number of rows and columns are not correct.''' )

				__magic_name__     ,				__magic_name__		:Tuple        =      imgs[0].size
				__magic_name__		:List[Any]        =      Image.new('''RGB''',  size=(cols * w, rows * h) )
				__magic_name__     ,				__magic_name__		:List[Any]        =      grid.size

				for i, img in enumerate(snake_case ):
								grid.paste(snake_case,  box=(i % cols * w, i // cols * h) )
				return grid

def      __lowercase					( snake_case,  snake_case="robotic cat with wings",  snake_case=7.5,  snake_case=5_0,  snake_case=1,  snake_case=4_2,  ):







				"""simple docstring"""






				__magic_name__		:List[Any]        =      torch.Generator(pipeline.device ).manual_seed(snake_case )
				__magic_name__		:str        =      pipeline(
				    snake_case,  guidance_scale=snake_case,  num_inference_steps=snake_case,  generator=snake_case,  num_images_per_prompt=snake_case,  ).images
				__magic_name__		:Tuple        =      int(math.sqrt(snake_case ) )
				__magic_name__		:Union[str, Any]        =      image_grid(snake_case,  rows=_rows,  cols=num_images_per_prompt // _rows )
				return grid, images


SCREAMING_SNAKE_CASE__      :			Any							=		parse_args()
# Load models and create wrapper for stable diffusion
SCREAMING_SNAKE_CASE__      :			int							=		CLIPTokenizer.from_pretrained(args.pretrained_model_name_or_path, subfolder="""tokenizer""")
SCREAMING_SNAKE_CASE__      :			str							=		CLIPTextModel.from_pretrained(args.pretrained_model_name_or_path, subfolder="""text_encoder""")
SCREAMING_SNAKE_CASE__      :			Optional[Any]							=		AutoencoderKL.from_pretrained(args.pretrained_model_name_or_path, subfolder="""vae""")
SCREAMING_SNAKE_CASE__      :			int							=		UNetaDConditionModel.from_pretrained(args.pretrained_model_name_or_path, subfolder="""unet""")

SCREAMING_SNAKE_CASE__      :			Union[str, Any]							=		StableDiffusionPipeline.from_pretrained(
    args.pretrained_model_name_or_path, text_encoder=text_encoder, vae=vae, unet=unet, tokenizer=tokenizer
)
SCREAMING_SNAKE_CASE__      :			str							=		lambda images, clip_input: (images, False)
if os.path.exists(os.path.join(args.pretrained_model_name_or_path, """best_model.pt""")):
					SCREAMING_SNAKE_CASE__      :			str							=		load(args.pretrained_model_name_or_path, model=unet)
					unet.eval()
					setattr(pipeline, """unet""", unet)
else:
					SCREAMING_SNAKE_CASE__      :			Dict							=		unet.to(torch.device("""cuda""", args.cuda_id))
SCREAMING_SNAKE_CASE__      :			Any							=		pipeline.to(unet.device)
SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__      :			Dict							=		generate_images(pipeline, prompt=args.caption, num_images_per_prompt=args.images_num, seed=args.seed)
grid.save(os.path.join(args.pretrained_model_name_or_path, """{}.png""".format("""_""".join(args.caption.split()))))
SCREAMING_SNAKE_CASE__      :			int							=		os.path.join(args.pretrained_model_name_or_path, """_""".join(args.caption.split()))
os.makedirs(dirname, exist_ok=True)
for idx, image in enumerate(images):
					image.save(os.path.join(dirname, """{}.png""".format(idx + 1)))






