


import gc
import unittest

import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DDPMScheduler,
    PriorTransformer,
    StableUnCLIPPipeline,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.stable_unclip_image_normalizer import StableUnCLIPImageNormalizer
from diffusers.utils.testing_utils import enable_full_determinism, load_numpy, require_torch_gpu, slow, torch_device

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import (
    PipelineKarrasSchedulerTesterMixin,
    PipelineLatentTesterMixin,
    PipelineTesterMixin,
    assert_mean_pixel_difference,
)


enable_full_determinism()





class 					_a							(				SCREAMING_SNAKE_CASE_     ,						SCREAMING_SNAKE_CASE_     ,						SCREAMING_SNAKE_CASE_     ,						unittest.TestCase):
	_a  :  Optional[int]          =							StableUnCLIPPipeline
	_a  :  Optional[Any]          =							TEXT_TO_IMAGE_PARAMS
	_a  :  Optional[int]          =							TEXT_TO_IMAGE_BATCH_PARAMS
	_a  :  str          =							TEXT_TO_IMAGE_IMAGE_PARAMS
	_a  :  Tuple          =							TEXT_TO_IMAGE_IMAGE_PARAMS

	# TODO(will) Expected attn_bias.stride(1) == 0 to be true, but got false
	_a  :  Optional[Any]          =							False




	def 							UpperCAmelCase__(					self						:		List[str]		)->       Any:
			lowerCAmelCase__   :			Any         =					32
			lowerCAmelCase__   :			str         =					embedder_hidden_size

			# prior components

			torch.manual_seed(0		)
			lowerCAmelCase__   :			List[Any]         =					CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''		)

			torch.manual_seed(0		)
			lowerCAmelCase__   :			str         =					CLIPTextModelWithProjection(
			    CLIPTextConfig(
			        bos_token_id=0		,					eos_token_id=2		,					hidden_size=a__		,					projection_dim=a__		,					intermediate_size=37		,					layer_norm_eps=1E-05		,					num_attention_heads=4		,					num_hidden_layers=5		,					pad_token_id=1		,					vocab_size=1000		,					)		)

			torch.manual_seed(0		)
			lowerCAmelCase__   :			List[Any]         =					PriorTransformer(
			    num_attention_heads=2		,					attention_head_dim=12		,					embedding_dim=a__		,					num_layers=1		,					)

			torch.manual_seed(0		)
			lowerCAmelCase__   :			Optional[Any]         =					DDPMScheduler(
			    variance_type='''fixed_small_log'''		,					prediction_type='''sample'''		,					num_train_timesteps=1000		,					clip_sample=a__		,					clip_sample_range=5.0		,					beta_schedule='''squaredcos_cap_v2'''		,					)

			# regular denoising components

			torch.manual_seed(0		)
			lowerCAmelCase__   :			Any         =					StableUnCLIPImageNormalizer(embedding_dim=a__		)
			lowerCAmelCase__   :			int         =					DDPMScheduler(beta_schedule='''squaredcos_cap_v2'''		)

			torch.manual_seed(0		)
			lowerCAmelCase__   :			Union[str, Any]         =					CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''		)

			torch.manual_seed(0		)
			lowerCAmelCase__   :			List[Any]         =					CLIPTextModel(
			    CLIPTextConfig(
			        bos_token_id=0		,					eos_token_id=2		,					hidden_size=a__		,					projection_dim=32		,					intermediate_size=37		,					layer_norm_eps=1E-05		,					num_attention_heads=4		,					num_hidden_layers=5		,					pad_token_id=1		,					vocab_size=1000		,					)		)

			torch.manual_seed(0		)
			lowerCAmelCase__   :			Tuple         =					UNetaDConditionModel(
			    sample_size=32		,					in_channels=4		,					out_channels=4		,					down_block_types=('''CrossAttnDownBlock2D''', '''DownBlock2D''')		,					up_block_types=('''UpBlock2D''', '''CrossAttnUpBlock2D''')		,					block_out_channels=(32, 64)		,					attention_head_dim=(2, 4)		,					class_embed_type='''projection'''		,					projection_class_embeddings_input_dim=embedder_projection_dim * 2		,					cross_attention_dim=a__		,					layers_per_block=1		,					upcast_attention=a__		,					use_linear_projection=a__		,					)

			torch.manual_seed(0		)
			lowerCAmelCase__   :			List[Any]         =					DDIMScheduler(
			    beta_schedule='''scaled_linear'''		,					beta_start=0.0_0085		,					beta_end=0.012		,					prediction_type='''v_prediction'''		,					set_alpha_to_one=a__		,					steps_offset=1		,					)

			torch.manual_seed(0		)
			lowerCAmelCase__   :			str         =					AutoencoderKL()

			lowerCAmelCase__   :			Any         =					{
			    # prior components
			    """prior_tokenizer""": prior_tokenizer,
			    """prior_text_encoder""": prior_text_encoder,
			    """prior""": prior,
			    """prior_scheduler""": prior_scheduler,
			    # image noising components
			    """image_normalizer""": image_normalizer,
			    """image_noising_scheduler""": image_noising_scheduler,
			    # regular denoising components
			    """tokenizer""": tokenizer,
			    """text_encoder""": text_encoder,
			    """unet""": unet,
			    """scheduler""": scheduler,
			    """vae""": vae,
			}

			return components




	def 							UpperCAmelCase__(					self						:		List[Any]		,					_SCREAMING_SNAKE_CASE						:		Dict		,					_SCREAMING_SNAKE_CASE						:		List[str]=0		)->       Tuple:
			if str(a__		).startswith('''mps'''		):
					lowerCAmelCase__   :			Optional[int]         =					torch.manual_seed(a__		)
			else:
					lowerCAmelCase__   :			Tuple         =					torch.Generator(device=a__		).manual_seed(a__		)
			lowerCAmelCase__   :			List[Any]         =					{
			    """prompt""": """A painting of a squirrel eating a burger""",
			    """generator""": generator,
			    """num_inference_steps""": 2,
			    """prior_num_inference_steps""": 2,
			    """output_type""": """numpy""",
			}
			return inputs




	def 							UpperCAmelCase__(					self						:		str		)->       Union[str, Any]:
			lowerCAmelCase__   :			Optional[Any]         =					torch_device == """cpu"""

			self._test_attention_slicing_forward_pass(test_max_difference=a__		)




	def 							UpperCAmelCase__(					self						:		Union[str, Any]		)->       List[str]:
			lowerCAmelCase__   :			Union[str, Any]         =					torch_device in ["""cpu""", """mps"""]

			self._test_inference_batch_single_identical(test_max_difference=a__		)





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




	def 							UpperCAmelCase__(					self						:		List[Any]		)->       List[Any]:
			# clean up the VRAM after each test
			super().tearDown()
			gc.collect()
			torch.cuda.empty_cache()




	def 							UpperCAmelCase__(					self						:		int		)->       List[Any]:
			lowerCAmelCase__   :			Optional[int]         =					load_numpy(
			    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_l_anime_turtle_fp16.npy'''		)

			lowerCAmelCase__   :			Optional[Any]         =					StableUnCLIPPipeline.from_pretrained('''fusing/stable-unclip-2-1-l'''		,					torch_dtype=torch.floataa		)
			pipe.to(a__		)
			pipe.set_progress_bar_config(disable=a__		)
			# stable unclip will oom when integration tests are run on a V100,
			# so turn on memory savings
			pipe.enable_attention_slicing()
			pipe.enable_sequential_cpu_offload()

			lowerCAmelCase__   :			str         =					torch.Generator(device='''cpu'''		).manual_seed(0		)
			lowerCAmelCase__   :			Tuple         =					pipe('''anime turle'''		,					generator=a__		,					output_type='''np'''		)

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

			assert image.shape == (768, 768, 3)

			assert_mean_pixel_difference(a__		,					a__		)




	def 							UpperCAmelCase__(					self						:		str		)->       Any:
			torch.cuda.empty_cache()
			torch.cuda.reset_max_memory_allocated()
			torch.cuda.reset_peak_memory_stats()

			lowerCAmelCase__   :			Optional[int]         =					StableUnCLIPPipeline.from_pretrained('''fusing/stable-unclip-2-1-l'''		,					torch_dtype=torch.floataa		)
			lowerCAmelCase__   :			int         =					pipe.to(a__		)
			pipe.set_progress_bar_config(disable=a__		)
			pipe.enable_attention_slicing()
			pipe.enable_sequential_cpu_offload()

			lowerCAmelCase__   :			Any         =					pipe(
			    '''anime turtle'''		,					prior_num_inference_steps=2		,					num_inference_steps=2		,					output_type='''np'''		,					)

			lowerCAmelCase__   :			int         =					torch.cuda.max_memory_allocated()
			# make sure that less than 7 GB is allocated
			assert mem_bytes < 7 * 10**9





"""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()




