



import argparse
import logging
import os
import sys

import numpy as np
import onnxruntime
import torch
from bart_onnx.generation_onnx import BARTBeamSearchGenerator
from bart_onnx.reduce_onnx_size import remove_dup_initializers

import transformers
from transformers import BartForConditionalGeneration, BartTokenizer


logging.basicConfig(
    format='%(asctime)s | %(levelname)s | %(name)s |  [%(filename)s:%(lineno)d] %(message)s',
    datefmt='%Y-%m-%d %H:%M:%S',
    level=os.environ.get('LOGLEVEL', 'INFO').upper(),
    stream=sys.stdout,
)

__snake_case    :		int  =logging.getLogger(__name__)

__snake_case    :		Dict  ={'facebook/bart-base': BartForConditionalGeneration}
__snake_case    :		Optional[int]  ={'facebook/bart-base': BartTokenizer}






def      lowerCAmelCase__		(   ):
				lowerCAmelCase__					:    Dict												=					argparse.ArgumentParser(description='''Export Bart model + Beam Search to ONNX graph.''')
				parser.add_argument(
				    '''--validation_file'''	,type=lowerCamelCase_	,default=lowerCamelCase_	,help='''A csv or a json file containing the validation data.''')
				parser.add_argument(
				    '''--max_length'''	,type=lowerCamelCase_	,default=5	,help='''The maximum total input sequence length after tokenization.'''	,)
				parser.add_argument(
				    '''--num_beams'''	,type=lowerCamelCase_	,default=lowerCamelCase_	,help=(
				        '''Number of beams to use for evaluation. This argument will be '''
				        '''passed to ``model.generate``, which is used during ``evaluate`` and ``predict``.'''
				    )	,)
				parser.add_argument(
				    '''--model_name_or_path'''	,type=lowerCamelCase_	,help='''Path to pretrained model or model identifier from huggingface.co/models.'''	,required=lowerCamelCase_	,)
				parser.add_argument(
				    '''--config_name'''	,type=lowerCamelCase_	,default=lowerCamelCase_	,help='''Pretrained config name or path if not the same as model_name'''	,)
				parser.add_argument(
				    '''--device'''	,type=lowerCamelCase_	,default='''cpu'''	,help='''Device where the model will be run'''	,)
				parser.add_argument('''--output_file_path'''	,type=lowerCamelCase_	,default=lowerCamelCase_	,help='''Where to store the final ONNX file.''')

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

				return args






def      lowerCAmelCase__		(   lowerCamelCase_		:		Optional[int]	,lowerCamelCase_		:		str="cpu"):
				lowerCAmelCase__					:    Optional[int]												=					model_dict[model_name].from_pretrained(lowerCamelCase_).to(lowerCamelCase_)
				lowerCAmelCase__					:    Dict												=					tokenizer_dict[model_name].from_pretrained(lowerCamelCase_)

				if model_name in ["facebook/bart-base"]:
								lowerCAmelCase__					:    Tuple												=					0
								lowerCAmelCase__					:    Optional[int]												=					None
								lowerCAmelCase__					:    int												=					0

				return huggingface_model, tokenizer






def      lowerCAmelCase__		(   lowerCamelCase_		:		Union[str, Any]	,lowerCamelCase_		:		Optional[Any]	,lowerCamelCase_		:		Dict	,lowerCamelCase_		:		int	,lowerCamelCase_		:		Any):
				model.eval()

				lowerCAmelCase__					:    Tuple												=					None
				lowerCAmelCase__					:    Optional[int]												=					torch.jit.script(BARTBeamSearchGenerator(lowerCamelCase_))

				with torch.no_grad():
								lowerCAmelCase__					:    Any												=					'''My friends are cool but they eat too many carbs.'''
								lowerCAmelCase__					:    List[Any]												=					tokenizer([ARTICLE_TO_SUMMARIZE]	,max_length=1024	,return_tensors='''pt''').to(model.device)

								lowerCAmelCase__					:    Tuple												=					model.generate(
								    inputs['''input_ids''']	,attention_mask=inputs['''attention_mask''']	,num_beams=lowerCamelCase_	,max_length=lowerCamelCase_	,early_stopping=lowerCamelCase_	,decoder_start_token_id=model.config.decoder_start_token_id	,)

								torch.onnx.export(
								    lowerCamelCase_	,(
								        inputs['''input_ids'''],
								        inputs['''attention_mask'''],
								        num_beams,
								        max_length,
								        model.config.decoder_start_token_id,
								    )	,lowerCamelCase_	,opset_version=14	,input_names=['''input_ids''', '''attention_mask''', '''num_beams''', '''max_length''', '''decoder_start_token_id''']	,output_names=['''output_ids''']	,dynamic_axes={
								        '''input_ids''': {0: '''batch''', 1: '''seq'''},
								        '''output_ids''': {0: '''batch''', 1: '''seq_out'''},
								    }	,example_outputs=lowerCamelCase_	,)

								logger.info('''Model exported to {}'''.format(lowerCamelCase_))

								lowerCAmelCase__					:    int												=					remove_dup_initializers(os.path.abspath(lowerCamelCase_))

								logger.info('''Deduplicated and optimized model written to {}'''.format(lowerCamelCase_))

								lowerCAmelCase__					:    List[str]												=					onnxruntime.InferenceSession(lowerCamelCase_)
								lowerCAmelCase__					:    Dict												=					ort_sess.run(
								    lowerCamelCase_	,{
								        '''input_ids''': inputs['''input_ids'''].cpu().numpy(),
								        '''attention_mask''': inputs['''attention_mask'''].cpu().numpy(),
								        '''num_beams''': np.array(lowerCamelCase_),
								        '''max_length''': np.array(lowerCamelCase_),
								        '''decoder_start_token_id''': np.array(model.config.decoder_start_token_id),
								    }	,)

								np.testing.assert_allclose(summary_ids.cpu().numpy()	,ort_out[0]	,rtol=1E-3	,atol=1E-3)

								logger.info('''Model outputs from torch and ONNX Runtime are similar.''')
								logger.info('''Success.''')






def      lowerCAmelCase__		(   ):
				lowerCAmelCase__					:    Any												=					parse_args()
				lowerCAmelCase__					:    Union[str, Any]												=					5
				lowerCAmelCase__					:    int												=					4

				# Make one log on every process with the configuration for debugging.
				logging.basicConfig(
				    format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s'''	,datefmt='''%m/%d/%Y %H:%M:%S'''	,level=logging.INFO	,)

				logger.setLevel(logging.INFO)
				transformers.utils.logging.set_verbosity_error()

				lowerCAmelCase__					:    Any												=					torch.device(args.device)

				lowerCAmelCase__					:    Dict												=					load_model_tokenizer(args.model_name_or_path	,lowerCamelCase_)

				if model.config.decoder_start_token_id is None:
								raise ValueError('''Make sure that `config.decoder_start_token_id` is correctly defined''')

				model.to(lowerCamelCase_)

				if args.max_length:
								lowerCAmelCase__					:    int												=					args.max_length

				if args.num_beams:
								lowerCAmelCase__					:    Optional[Any]												=					args.num_beams

				if args.output_file_path:
								lowerCAmelCase__					:    Dict												=					args.output_file_path
				else:
								lowerCAmelCase__					:    Union[str, Any]												=					'''BART.onnx'''

				logger.info('''Exporting model to ONNX''')
				export_and_validate_model(lowerCamelCase_	,lowerCamelCase_	,lowerCamelCase_	,lowerCamelCase_	,lowerCamelCase_)


if __name__ == "__main__":
							main()






import random
import unittest

import torch

from diffusers import IFInpaintingSuperResolutionPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device

from ..pipeline_params import (
    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin



@skip_mps
class 	lowerCamelCase__      (							lowerCamelCase__				,    lowerCamelCase__				,    unittest.TestCase):




		'''simple docstring'''



		snake_case_     =IFInpaintingSuperResolutionPipeline
		snake_case_     =TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {"""width""", """height"""}
		snake_case_     =TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS.union({"""original_image"""})
		snake_case_     =PipelineTesterMixin.required_optional_params - {"""latents"""}



		def 		lowerCAmelCase__				(self			)					->      Union[str, Any]:

						"""simple docstring"""






						return self._get_superresolution_dummy_components()



		def 		lowerCAmelCase__				(self		,__lowerCamelCase		,__lowerCamelCase=0			)					->      Dict:

						"""simple docstring"""






						if str(__lowerCamelCase			).startswith('''mps'''			):
										lowerCAmelCase__					:    Union[str, Any]												=					torch.manual_seed(__lowerCamelCase			)
						else:
										lowerCAmelCase__					:    int												=					torch.Generator(device=__lowerCamelCase			).manual_seed(__lowerCamelCase			)

						lowerCAmelCase__					:    str												=					floats_tensor((1, 3, 16, 16)		,rng=random.Random(__lowerCamelCase			)			).to(__lowerCamelCase			)
						lowerCAmelCase__					:    Optional[Any]												=					floats_tensor((1, 3, 32, 32)		,rng=random.Random(__lowerCamelCase			)			).to(__lowerCamelCase			)
						lowerCAmelCase__					:    Union[str, Any]												=					floats_tensor((1, 3, 32, 32)		,rng=random.Random(__lowerCamelCase			)			).to(__lowerCamelCase			)

						lowerCAmelCase__					:    Dict												=					{
						    '''prompt''': '''A painting of a squirrel eating a burger''',
						    '''image''': image,
						    '''original_image''': original_image,
						    '''mask_image''': mask_image,
						    '''generator''': generator,
						    '''num_inference_steps''': 2,
						    '''output_type''': '''numpy''',
						}

						return inputs



		@unittest.skipIf(
		    torch_device != '''cuda''' or not is_xformers_available()		,reason='''XFormers attention is only available with CUDA and `xformers` installed'''		,)
		def 		lowerCAmelCase__				(self			)					->      int:

						"""simple docstring"""






						self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3			)



		def 		lowerCAmelCase__				(self			)					->      int:

						"""simple docstring"""






						self._test_save_load_optional_components()



		@unittest.skipIf(torch_device != '''cuda'''		,reason='''float16 requires CUDA'''			)
		def 		lowerCAmelCase__				(self			)					->      str:

						"""simple docstring"""






						super().test_save_load_floataa(expected_max_diff=1e-1			)



		def 		lowerCAmelCase__				(self			)					->      Any:

						"""simple docstring"""






						self._test_attention_slicing_forward_pass(expected_max_diff=1e-2			)



		def 		lowerCAmelCase__				(self			)					->      Optional[Any]:

						"""simple docstring"""






						self._test_save_load_local()



		def 		lowerCAmelCase__				(self			)					->      Any:

						"""simple docstring"""






						self._test_inference_batch_single_identical(
						    expected_max_diff=1e-2		,)

