
'''simple docstring'''




import gc
import random
import unittest

import torch

from diffusers import (
    IFImgaImgPipeline,
    IFImgaImgSuperResolutionPipeline,
    IFInpaintingPipeline,
    IFInpaintingSuperResolutionPipeline,
    IFPipeline,
    IFSuperResolutionPipeline,
)
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import floats_tensor, load_numpy, require_torch_gpu, skip_mps, slow, torch_device

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
from . import IFPipelineTesterMixin





@skip_mps
class  A				(							__snake_case    ,			__snake_case    ,			unittest.TestCase						):
     __magic_name__						=		IFPipeline
     __magic_name__						=		TEXT_TO_IMAGE_PARAMS - {'''width''', '''height''', '''latents'''}
     __magic_name__						=		TEXT_TO_IMAGE_BATCH_PARAMS
     __magic_name__						=		PipelineTesterMixin.required_optional_params - {'''latents'''}





     def 				__lowerCAmelCase							(							self	) ->				List[str]:






         """simple docstring"""






         return self._get_dummy_components()





     def 				__lowerCAmelCase							(							self						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE=0	) ->				Optional[int]:






         """simple docstring"""






         if str(SCREAMING_SNAKE_CASE	).startswith('''mps'''	):
             A       :   str					=						torch.manual_seed(SCREAMING_SNAKE_CASE	)
         else:
             A       :   List[Any]					=						torch.Generator(device=SCREAMING_SNAKE_CASE	).manual_seed(SCREAMING_SNAKE_CASE	)

         A       :   Any					=						{
             '''prompt''': '''A painting of a squirrel eating a burger''',
             '''generator''': generator,
             '''num_inference_steps''': 2,
             '''output_type''': '''numpy''',
         }

         return inputs





     def 				__lowerCAmelCase							(							self	) ->				Tuple:






         """simple docstring"""






         self._test_save_load_optional_components()





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






         """simple docstring"""






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





     def 				__lowerCAmelCase							(							self	) ->				Tuple:






         """simple docstring"""






         self._test_attention_slicing_forward_pass(expected_max_diff=1e-2	)





     def 				__lowerCAmelCase							(							self	) ->				Optional[int]:






         """simple docstring"""






         self._test_save_load_local()





     def 				__lowerCAmelCase							(							self	) ->				Optional[int]:






         """simple docstring"""






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





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






         """simple docstring"""






         self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3	)





@slow
@require_torch_gpu
class  A				(							unittest.TestCase						):





     def 				__lowerCAmelCase							(							self	) ->				Tuple:






         """simple docstring"""






         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()





     def 				__lowerCAmelCase							(							self	) ->				str:






         """simple docstring"""






         A       :   Optional[int]					=						IFPipeline.from_pretrained('''DeepFloyd/IF-I-XL-v1.0'''						,			variant='''fp16'''						,			torch_dtype=torch.floataa	)

         A       :   str					=						IFSuperResolutionPipeline.from_pretrained(
             '''DeepFloyd/IF-II-L-v1.0'''						,			variant='''fp16'''						,			torch_dtype=torch.floataa						,			text_encoder=SCREAMING_SNAKE_CASE						,			tokenizer=SCREAMING_SNAKE_CASE	)

         # pre compute text embeddings and remove T5 to save memory

         pipe_a.text_encoder.to('''cuda'''	)

         A,  A       :   Optional[Any]					=						pipe_a.encode_prompt('''anime turtle'''						,			device='''cuda'''	)

         del pipe_a.tokenizer
         del pipe_a.text_encoder
         gc.collect()

         A       :   List[Any]					=						None
         A       :   List[Any]					=						None

         pipe_a.enable_model_cpu_offload()
         pipe_a.enable_model_cpu_offload()

         pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()	)
         pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()	)

         self._test_if(SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE	)

         pipe_a.remove_all_hooks()
         pipe_a.remove_all_hooks()

         # img2img

         A       :   Optional[Any]					=						IFImgaImgPipeline(**pipe_a.components	)
         A       :   Union[str, Any]					=						IFImgaImgSuperResolutionPipeline(**pipe_a.components	)

         pipe_a.enable_model_cpu_offload()
         pipe_a.enable_model_cpu_offload()

         pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()	)
         pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()	)

         self._test_if_imgaimg(SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE	)

         pipe_a.remove_all_hooks()
         pipe_a.remove_all_hooks()

         # inpainting

         A       :   str					=						IFInpaintingPipeline(**pipe_a.components	)
         A       :   Any					=						IFInpaintingSuperResolutionPipeline(**pipe_a.components	)

         pipe_a.enable_model_cpu_offload()
         pipe_a.enable_model_cpu_offload()

         pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()	)
         pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()	)

         self._test_if_inpainting(SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE	)





     def 				__lowerCAmelCase							(							self						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE	) ->				str:






         """simple docstring"""






         _start_torch_memory_measurement()

         A       :   Any					=						torch.Generator(device='''cpu'''	).manual_seed(0	)
         A       :   Dict					=						pipe_a(
             prompt_embeds=SCREAMING_SNAKE_CASE						,			negative_prompt_embeds=SCREAMING_SNAKE_CASE						,			num_inference_steps=2						,			generator=SCREAMING_SNAKE_CASE						,			output_type='''np'''						,			)

         A       :   Any					=						output.images[0]

         assert image.shape == (64, 64, 3)

         A       :   Union[str, Any]					=						torch.cuda.max_memory_allocated()
         assert mem_bytes < 13 * 10**9

         A       :   Union[str, Any]					=						load_numpy(
             '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if.npy'''	)
         assert_mean_pixel_difference(SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE	)

         # pipeline 2

         _start_torch_memory_measurement()

         A       :   str					=						torch.Generator(device='''cpu'''	).manual_seed(0	)

         A       :   Tuple					=						floats_tensor((1, 3, 64, 64)						,			rng=random.Random(0	)	).to(SCREAMING_SNAKE_CASE	)

         A       :   List[str]					=						pipe_a(
             prompt_embeds=SCREAMING_SNAKE_CASE						,			negative_prompt_embeds=SCREAMING_SNAKE_CASE						,			image=SCREAMING_SNAKE_CASE						,			generator=SCREAMING_SNAKE_CASE						,			num_inference_steps=2						,			output_type='''np'''						,			)

         A       :   List[str]					=						output.images[0]

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

         A       :   Tuple					=						torch.cuda.max_memory_allocated()
         assert mem_bytes < 4 * 10**9

         A       :   int					=						load_numpy(
             '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_superresolution_stage_II.npy'''	)
         assert_mean_pixel_difference(SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE	)





     def 				__lowerCAmelCase							(							self						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE	) ->				List[str]:






         """simple docstring"""






         _start_torch_memory_measurement()

         A       :   Optional[Any]					=						floats_tensor((1, 3, 64, 64)						,			rng=random.Random(0	)	).to(SCREAMING_SNAKE_CASE	)

         A       :   Optional[Any]					=						torch.Generator(device='''cpu'''	).manual_seed(0	)

         A       :   Any					=						pipe_a(
             prompt_embeds=SCREAMING_SNAKE_CASE						,			negative_prompt_embeds=SCREAMING_SNAKE_CASE						,			image=SCREAMING_SNAKE_CASE						,			num_inference_steps=2						,			generator=SCREAMING_SNAKE_CASE						,			output_type='''np'''						,			)

         A       :   List[str]					=						output.images[0]

         assert image.shape == (64, 64, 3)

         A       :   Any					=						torch.cuda.max_memory_allocated()
         assert mem_bytes < 10 * 10**9

         A       :   Union[str, Any]					=						load_numpy(
             '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_img2img.npy'''	)
         assert_mean_pixel_difference(SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE	)

         # pipeline 2

         _start_torch_memory_measurement()

         A       :   Any					=						torch.Generator(device='''cpu'''	).manual_seed(0	)

         A       :   Union[str, Any]					=						floats_tensor((1, 3, 256, 256)						,			rng=random.Random(0	)	).to(SCREAMING_SNAKE_CASE	)
         A       :   str					=						floats_tensor((1, 3, 64, 64)						,			rng=random.Random(0	)	).to(SCREAMING_SNAKE_CASE	)

         A       :   Tuple					=						pipe_a(
             prompt_embeds=SCREAMING_SNAKE_CASE						,			negative_prompt_embeds=SCREAMING_SNAKE_CASE						,			image=SCREAMING_SNAKE_CASE						,			original_image=SCREAMING_SNAKE_CASE						,			generator=SCREAMING_SNAKE_CASE						,			num_inference_steps=2						,			output_type='''np'''						,			)

         A       :   Any					=						output.images[0]

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

         A       :   Union[str, Any]					=						torch.cuda.max_memory_allocated()
         assert mem_bytes < 4 * 10**9

         A       :   List[str]					=						load_numpy(
             '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_img2img_superresolution_stage_II.npy'''	)
         assert_mean_pixel_difference(SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE	)





     def 				__lowerCAmelCase							(							self						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE	) ->				Optional[Any]:






         """simple docstring"""






         _start_torch_memory_measurement()

         A       :   List[str]					=						floats_tensor((1, 3, 64, 64)						,			rng=random.Random(0	)	).to(SCREAMING_SNAKE_CASE	)
         A       :   Dict					=						floats_tensor((1, 3, 64, 64)						,			rng=random.Random(1	)	).to(SCREAMING_SNAKE_CASE	)

         A       :   Optional[Any]					=						torch.Generator(device='''cpu'''	).manual_seed(0	)
         A       :   List[Any]					=						pipe_a(
             prompt_embeds=SCREAMING_SNAKE_CASE						,			negative_prompt_embeds=SCREAMING_SNAKE_CASE						,			image=SCREAMING_SNAKE_CASE						,			mask_image=SCREAMING_SNAKE_CASE						,			num_inference_steps=2						,			generator=SCREAMING_SNAKE_CASE						,			output_type='''np'''						,			)

         A       :   Any					=						output.images[0]

         assert image.shape == (64, 64, 3)

         A       :   Optional[Any]					=						torch.cuda.max_memory_allocated()
         assert mem_bytes < 10 * 10**9

         A       :   int					=						load_numpy(
             '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_inpainting.npy'''	)
         assert_mean_pixel_difference(SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE	)

         # pipeline 2

         _start_torch_memory_measurement()

         A       :   int					=						torch.Generator(device='''cpu'''	).manual_seed(0	)

         A       :   Union[str, Any]					=						floats_tensor((1, 3, 64, 64)						,			rng=random.Random(0	)	).to(SCREAMING_SNAKE_CASE	)
         A       :   Any					=						floats_tensor((1, 3, 256, 256)						,			rng=random.Random(0	)	).to(SCREAMING_SNAKE_CASE	)
         A       :   Optional[int]					=						floats_tensor((1, 3, 256, 256)						,			rng=random.Random(1	)	).to(SCREAMING_SNAKE_CASE	)

         A       :   Dict					=						pipe_a(
             prompt_embeds=SCREAMING_SNAKE_CASE						,			negative_prompt_embeds=SCREAMING_SNAKE_CASE						,			image=SCREAMING_SNAKE_CASE						,			mask_image=SCREAMING_SNAKE_CASE						,			original_image=SCREAMING_SNAKE_CASE						,			generator=SCREAMING_SNAKE_CASE						,			num_inference_steps=2						,			output_type='''np'''						,			)

         A       :   Optional[int]					=						output.images[0]

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

         A       :   List[Any]					=						torch.cuda.max_memory_allocated()
         assert mem_bytes < 4 * 10**9

         A       :   Any					=						load_numpy(
             '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_inpainting_superresolution_stage_II.npy'''	)
         assert_mean_pixel_difference(SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE	)







def 		lowerCAmelCase_				(   ):




    '''simple docstring'''



    torch.cuda.empty_cache()
    torch.cuda.reset_max_memory_allocated()
    torch.cuda.reset_peak_memory_stats()





"""simple docstring"""




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,
)

_a      :		Tuple           =   logging.getLogger(__name__)

_a      :		Any           =   {'facebook/bart-base': BartForConditionalGeneration}
_a      :		List[str]           =   {'facebook/bart-base': BartTokenizer}





def 							SCREAMING_SNAKE_CASE     (			)       ->      int:
  _lowerCAmelCase						:       int  			=     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						:       Optional[Any]  			=     parser.parse_args()

  return args





def 							SCREAMING_SNAKE_CASE     (			_lowerCamelCase						:       List[Any]			,_lowerCamelCase						:       Union[str, Any]="cpu"	)       ->      str:
  _lowerCAmelCase						:       List[str]  			=     model_dict[model_name].from_pretrained(_lowerCamelCase	).to(_lowerCamelCase	)
  _lowerCAmelCase						:       Optional[Any]  			=     tokenizer_dict[model_name].from_pretrained(_lowerCamelCase	)

  if model_name in ["facebook/bart-base"]:
    _lowerCAmelCase						:       List[Any]  			=     0
    _lowerCAmelCase						:       str  			=     None
    _lowerCAmelCase						:       List[str]  			=     0

  return huggingface_model, tokenizer





def 							SCREAMING_SNAKE_CASE     (			_lowerCamelCase						:       Union[str, Any]			,_lowerCamelCase						:       List[str]			,_lowerCamelCase						:       int			,_lowerCamelCase						:       List[Any]			,_lowerCamelCase						:       List[str]	)       ->      Tuple:
  model.eval()

  _lowerCAmelCase						:       str  			=     None
  _lowerCAmelCase						:       int  			=     torch.jit.script(BARTBeamSearchGenerator(_lowerCamelCase	)	)

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

    _lowerCAmelCase						:       Any  			=     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						:       List[str]  			=     remove_dup_initializers(os.path.abspath(_lowerCamelCase	)	)

    logger.info("""Deduplicated and optimized model written to {}""".format(_lowerCamelCase	)	)

    _lowerCAmelCase						:       str  			=     onnxruntime.InferenceSession(_lowerCamelCase	)
    _lowerCAmelCase						:       Union[str, Any]  			=     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 							SCREAMING_SNAKE_CASE     (			)       ->      Any:
  _lowerCAmelCase						:       Any  			=     parse_args()
  _lowerCAmelCase						:       List[Any]  			=     5
  _lowerCAmelCase						:       str  			=     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						:       Optional[Any]  			=     torch.device(args.device	)

  _lowerCAmelCase			,	_lowerCAmelCase						:       List[str]  			=     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						:       Dict  			=     args.max_length

  if args.num_beams:
    _lowerCAmelCase						:       Dict  			=     args.num_beams

  if args.output_file_path:
    _lowerCAmelCase						:       Any  			=     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()




