



"""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,
)

_UpperCAmelCase     =			logging.getLogger(__name__)

_UpperCAmelCase     =			{"""facebook/bart-base""": BartForConditionalGeneration}
_UpperCAmelCase     =			{"""facebook/bart-base""": BartTokenizer}





def      __magic_name__				(							):
   SCREAMING_SNAKE_CASE_: int     			=argparse.ArgumentParser(description="""Export Bart model + Beam Search to ONNX graph.""" )
   parser.add_argument(
       """--validation_file"""							,			type=lowercase							,			default=lowercase							,			help="""A csv or a json file containing the validation data.""" )
   parser.add_argument(
       """--max_length"""							,			type=lowercase							,			default=5							,			help="""The maximum total input sequence length after tokenization."""							,			)
   parser.add_argument(
       """--num_beams"""							,			type=lowercase							,			default=lowercase							,			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=lowercase							,			help="""Path to pretrained model or model identifier from huggingface.co/models."""							,			required=lowercase							,			)
   parser.add_argument(
       """--config_name"""							,			type=lowercase							,			default=lowercase							,			help="""Pretrained config name or path if not the same as model_name"""							,			)
   parser.add_argument(
       """--device"""							,			type=lowercase							,			default="""cpu"""							,			help="""Device where the model will be run"""							,			)
   parser.add_argument("""--output_file_path"""							,			type=lowercase							,			default=lowercase							,			help="""Where to store the final ONNX file.""" )

   SCREAMING_SNAKE_CASE_: str     			=parser.parse_args()

   return args





def      __magic_name__				(							lowercase							,			lowercase="cpu" ):
   SCREAMING_SNAKE_CASE_: Optional[int]     			=model_dict[model_name].from_pretrained(lowercase ).to(lowercase )
   SCREAMING_SNAKE_CASE_: List[str]     			=tokenizer_dict[model_name].from_pretrained(lowercase )

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

   return huggingface_model, tokenizer





def      __magic_name__				(							lowercase							,			lowercase							,			lowercase							,			lowercase							,			lowercase ):
   model.eval()

   SCREAMING_SNAKE_CASE_: Optional[int]     			=None
   SCREAMING_SNAKE_CASE_: int     			=torch.jit.script(BARTBeamSearchGenerator(lowercase ) )

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

      SCREAMING_SNAKE_CASE_: Union[str, Any]     			=model.generate(
          inputs["""input_ids"""]							,			attention_mask=inputs["""attention_mask"""]							,			num_beams=lowercase							,			max_length=lowercase							,			early_stopping=lowercase							,			decoder_start_token_id=model.config.decoder_start_token_id							,			)

      torch.onnx.export(
          lowercase							,			(
              inputs["""input_ids"""],
              inputs["""attention_mask"""],
              num_beams,
              max_length,
              model.config.decoder_start_token_id,
          )							,			lowercase							,			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=lowercase							,			)

      logger.info("""Model exported to {}""".format(lowercase ) )

      SCREAMING_SNAKE_CASE_: Tuple     			=remove_dup_initializers(os.path.abspath(lowercase ) )

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

      SCREAMING_SNAKE_CASE_: Any     			=onnxruntime.InferenceSession(lowercase )
      SCREAMING_SNAKE_CASE_: List[Any]     			=ort_sess.run(
          lowercase							,			{
              """input_ids""": inputs["""input_ids"""].cpu().numpy(),
              """attention_mask""": inputs["""attention_mask"""].cpu().numpy(),
              """num_beams""": np.array(lowercase ),
              """max_length""": np.array(lowercase ),
              """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      __magic_name__				(							):
   SCREAMING_SNAKE_CASE_: Tuple     			=parse_args()
   SCREAMING_SNAKE_CASE_: Optional[int]     			=5
   SCREAMING_SNAKE_CASE_: List[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()

   SCREAMING_SNAKE_CASE_: Dict     			=torch.device(args.device )

   SCREAMING_SNAKE_CASE_			,		SCREAMING_SNAKE_CASE_: Optional[int]     			=load_model_tokenizer(args.model_name_or_path							,			lowercase )

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

   model.to(lowercase )

   if args.max_length:
      SCREAMING_SNAKE_CASE_: str     			=args.max_length

   if args.num_beams:
      SCREAMING_SNAKE_CASE_: List[str]     			=args.num_beams

   if args.output_file_path:
      SCREAMING_SNAKE_CASE_: List[Any]     			=args.output_file_path
   else:
      SCREAMING_SNAKE_CASE_: int     			="""BART.onnx"""

   logger.info("""Exporting model to ONNX""" )
   export_and_validate_model(lowercase							,			lowercase							,			lowercase							,			lowercase							,			lowercase )


if __name__ == "__main__":
   main()








"""simple docstring"""
import gc
import unittest

from diffusers import FlaxDPMSolverMultistepScheduler, FlaxStableDiffusionPipeline
from diffusers.utils import is_flax_available, slow
from diffusers.utils.testing_utils import require_flax


if is_flax_available():
   import jax
   import jax.numpy as jnp
   from flax.jax_utils import replicate
   from flax.training.common_utils import shard
@slow
@require_flax
class 					a	(  unittest.TestCase       ):



  def 	lowerCamelCase__       (						self :					Dict		)     ->					str:


     '''simple docstring'''




     super().tearDown()
     gc.collect()



  def 	lowerCamelCase__       (						self :					Optional[Any]		)     ->					int:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_			,		SCREAMING_SNAKE_CASE_: Dict     			=FlaxStableDiffusionPipeline.from_pretrained(
         """stabilityai/stable-diffusion-2"""       ,		revision="""bf16"""       ,		dtype=jnp.bfloataa       ,		)

     SCREAMING_SNAKE_CASE_: Union[str, Any]     			="""A painting of a squirrel eating a burger"""
     SCREAMING_SNAKE_CASE_: Any     			=jax.device_count()
     SCREAMING_SNAKE_CASE_: Dict     			=num_samples * [prompt]
     SCREAMING_SNAKE_CASE_: List[Any]     			=sd_pipe.prepare_inputs(lowerCAmelCase		)

     SCREAMING_SNAKE_CASE_: Union[str, Any]     			=replicate(lowerCAmelCase		)
     SCREAMING_SNAKE_CASE_: Optional[int]     			=shard(lowerCAmelCase		)

     SCREAMING_SNAKE_CASE_: Dict     			=jax.random.PRNGKey(0		)
     SCREAMING_SNAKE_CASE_: Union[str, Any]     			=jax.random.split(lowerCAmelCase       ,		jax.device_count()		)

     SCREAMING_SNAKE_CASE_: Tuple     			=sd_pipe(lowerCAmelCase       ,		lowerCAmelCase       ,		lowerCAmelCase       ,		num_inference_steps=25       ,		jit=lowerCAmelCase		)[0]
     assert images.shape == (jax.device_count(), 1, 768, 768, 3)

     SCREAMING_SNAKE_CASE_: Dict     			=images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]		)
     SCREAMING_SNAKE_CASE_: Dict     			=images[0, 253:256, 253:256, -1]

     SCREAMING_SNAKE_CASE_: Optional[int]     			=jnp.asarray(jax.device_get(image_slice.flatten()		)		)
     SCREAMING_SNAKE_CASE_: Optional[int]     			=jnp.array([0.4_2_3_8, 0.4_4_1_4, 0.4_3_9_5, 0.4_4_5_3, 0.4_6_2_9, 0.4_5_9_0, 0.4_5_3_1, 0.4_5_5_0_8, 0.4_5_1_2]		)
     print(f'''output_slice: {output_slice}'''		)
     assert jnp.abs(output_slice - expected_slice		).max() < 1E-2





  def 	lowerCamelCase__       (						self :					List[str]		)     ->					Dict:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: int     			="""stabilityai/stable-diffusion-2"""
     SCREAMING_SNAKE_CASE_			,		SCREAMING_SNAKE_CASE_: Optional[int]     			=FlaxDPMSolverMultistepScheduler.from_pretrained(lowerCAmelCase       ,		subfolder="""scheduler"""		)
     SCREAMING_SNAKE_CASE_			,		SCREAMING_SNAKE_CASE_: Optional[int]     			=FlaxStableDiffusionPipeline.from_pretrained(
         lowerCAmelCase       ,		scheduler=lowerCAmelCase       ,		revision="""bf16"""       ,		dtype=jnp.bfloataa       ,		)
     SCREAMING_SNAKE_CASE_: Optional[int]     			=scheduler_params

     SCREAMING_SNAKE_CASE_: Tuple     			="""A painting of a squirrel eating a burger"""
     SCREAMING_SNAKE_CASE_: Union[str, Any]     			=jax.device_count()
     SCREAMING_SNAKE_CASE_: Optional[Any]     			=num_samples * [prompt]
     SCREAMING_SNAKE_CASE_: List[Any]     			=sd_pipe.prepare_inputs(lowerCAmelCase		)

     SCREAMING_SNAKE_CASE_: Tuple     			=replicate(lowerCAmelCase		)
     SCREAMING_SNAKE_CASE_: Optional[int]     			=shard(lowerCAmelCase		)

     SCREAMING_SNAKE_CASE_: Any     			=jax.random.PRNGKey(0		)
     SCREAMING_SNAKE_CASE_: Any     			=jax.random.split(lowerCAmelCase       ,		jax.device_count()		)

     SCREAMING_SNAKE_CASE_: Tuple     			=sd_pipe(lowerCAmelCase       ,		lowerCAmelCase       ,		lowerCAmelCase       ,		num_inference_steps=25       ,		jit=lowerCAmelCase		)[0]
     assert images.shape == (jax.device_count(), 1, 768, 768, 3)

     SCREAMING_SNAKE_CASE_: str     			=images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]		)
     SCREAMING_SNAKE_CASE_: Any     			=images[0, 253:256, 253:256, -1]

     SCREAMING_SNAKE_CASE_: Optional[Any]     			=jnp.asarray(jax.device_get(image_slice.flatten()		)		)
     SCREAMING_SNAKE_CASE_: Optional[int]     			=jnp.array([0.4_3_3_6, 0.4_2_9_6_9, 0.4_4_5_3, 0.4_1_9_9, 0.4_2_9_7, 0.4_5_3_1, 0.4_4_3_4, 0.4_4_3_4, 0.4_2_9_7]		)
     print(f'''output_slice: {output_slice}'''		)
     assert jnp.abs(output_slice - expected_slice		).max() < 1E-2



