'''simple docstring'''

import unittest

import numpy as np

from diffusers import LMSDiscreteScheduler, OnnxStableDiffusionInpaintPipeline
from diffusers.utils.testing_utils import (
    is_onnx_available,
    load_image,
    nightly,
    require_onnxruntime,
    require_torch_gpu,
)

from ..test_pipelines_onnx_common import OnnxPipelineTesterMixin


if is_onnx_available():
 import onnxruntime as ort







class 				_lowercase     (				UpperCAmelCase__  ,							unittest.TestCase			):


 '''simple docstring'''






 pass







@nightly
@require_onnxruntime
@require_torch_gpu
class 				_lowercase     (				unittest.TestCase			):


 '''simple docstring'''






 @property
 def a					(      self   :	List[Any]							)  ->    Optional[int]:
  return (
      "CUDAExecutionProvider",
      {
          "gpu_mem_limit": "15000000000",  # 15GB
          "arena_extend_strategy": "kSameAsRequested",
      },
  )
 @property
 def a					(      self   :	Union[str, Any]							)  ->    Any:
  __lowerCAmelCase											=						ort.SessionOptions()
  __lowerCAmelCase											=						False
  return options
 def a					(      self   :	Union[str, Any]							)  ->    List[str]:
  __lowerCAmelCase											=						load_image(
      """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
      """/in_paint/overture-creations-5sI6fQgYIuo.png"""							)
  __lowerCAmelCase											=						load_image(
      """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
      """/in_paint/overture-creations-5sI6fQgYIuo_mask.png"""							)
  __lowerCAmelCase											=						OnnxStableDiffusionInpaintPipeline.from_pretrained(
      """runwayml/stable-diffusion-inpainting"""      ,       revision="""onnx"""      ,       safety_checker=SCREAMING_SNAKE_CASE__      ,       feature_extractor=SCREAMING_SNAKE_CASE__      ,       provider=self.gpu_provider      ,       sess_options=self.gpu_options      ,       )
  pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__							)

  __lowerCAmelCase											=						"""A red cat sitting on a park bench"""

  __lowerCAmelCase											=						np.random.RandomState(0							)
  __lowerCAmelCase											=						pipe(
      prompt=SCREAMING_SNAKE_CASE__      ,       image=SCREAMING_SNAKE_CASE__      ,       mask_image=SCREAMING_SNAKE_CASE__      ,       guidance_scale=7.5      ,       num_inference_steps=10      ,       generator=SCREAMING_SNAKE_CASE__      ,       output_type="""np"""      ,       )
  __lowerCAmelCase											=						output.images
  __lowerCAmelCase											=						images[0, 2_55:2_58, 2_55:2_58, -1]

  assert images.shape == (1, 5_12, 5_12, 3)
  __lowerCAmelCase											=						np.array([0.2_5_1_4, 0.3_0_0_7, 0.3_5_1_7, 0.1_7_9_0, 0.2_3_8_2, 0.3_1_6_7, 0.1_9_4_4, 0.2_2_7_3, 0.2_4_6_4]							)

  assert np.abs(image_slice.flatten() - expected_slice							).max() < 1e-3





 def a					(      self   :	Optional[Any]							)  ->    Dict:
  __lowerCAmelCase											=						load_image(
      """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
      """/in_paint/overture-creations-5sI6fQgYIuo.png"""							)
  __lowerCAmelCase											=						load_image(
      """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
      """/in_paint/overture-creations-5sI6fQgYIuo_mask.png"""							)
  __lowerCAmelCase											=						LMSDiscreteScheduler.from_pretrained(
      """runwayml/stable-diffusion-inpainting"""      ,       subfolder="""scheduler"""      ,       revision="""onnx"""							)
  __lowerCAmelCase											=						OnnxStableDiffusionInpaintPipeline.from_pretrained(
      """runwayml/stable-diffusion-inpainting"""      ,       revision="""onnx"""      ,       scheduler=SCREAMING_SNAKE_CASE__      ,       safety_checker=SCREAMING_SNAKE_CASE__      ,       feature_extractor=SCREAMING_SNAKE_CASE__      ,       provider=self.gpu_provider      ,       sess_options=self.gpu_options      ,       )
  pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__							)

  __lowerCAmelCase											=						"""A red cat sitting on a park bench"""

  __lowerCAmelCase											=						np.random.RandomState(0							)
  __lowerCAmelCase											=						pipe(
      prompt=SCREAMING_SNAKE_CASE__      ,       image=SCREAMING_SNAKE_CASE__      ,       mask_image=SCREAMING_SNAKE_CASE__      ,       guidance_scale=7.5      ,       num_inference_steps=20      ,       generator=SCREAMING_SNAKE_CASE__      ,       output_type="""np"""      ,       )
  __lowerCAmelCase											=						output.images
  __lowerCAmelCase											=						images[0, 2_55:2_58, 2_55:2_58, -1]

  assert images.shape == (1, 5_12, 5_12, 3)
  __lowerCAmelCase											=						np.array([0.0_0_8_6, 0.0_0_7_7, 0.0_0_8_3, 0.0_0_9_3, 0.0_1_0_7, 0.0_1_3_9, 0.0_0_9_4, 0.0_0_9_7, 0.0_1_2_5]							)

  assert np.abs(image_slice.flatten() - expected_slice							).max() < 1e-3







'''simple docstring'''

import argparse

import torch

from transformers import BlenderbotConfig, BlenderbotForConditionalGeneration
from transformers.utils import logging


logging.set_verbosity_info()
_A			:      str					      =				logging.get_logger(__name__)

_A			:      str					      =				[
    ['''attention''', '''attn'''],
    ['''encoder_attention''', '''encoder_attn'''],
    ['''q_lin''', '''q_proj'''],
    ['''k_lin''', '''k_proj'''],
    ['''v_lin''', '''v_proj'''],
    ['''out_lin''', '''out_proj'''],
    ['''norm_embeddings''', '''layernorm_embedding'''],
    ['''position_embeddings''', '''embed_positions'''],
    ['''embeddings''', '''embed_tokens'''],
    ['''ffn.lin''', '''fc'''],
]







def      UpperCamelCase_						(					snake_case_       :       Tuple      )					->     Optional[int]:






 '''simple docstring'''

 if k == "embeddings.weight":
  return "shared.weight"

 for parlai_name, hf_name in PATTERNS:
  __lowerCAmelCase											=						k.replace(snake_case_						,		snake_case_      )

 if k.startswith("""encoder"""      ):
  __lowerCAmelCase											=						k.replace(""".attn"""						,		""".self_attn"""      )
  __lowerCAmelCase											=						k.replace("""norm1"""						,		"""self_attn_layer_norm"""      )
  __lowerCAmelCase											=						k.replace("""norm2"""						,		"""final_layer_norm"""      )
 elif k.startswith("""decoder"""      ):
  __lowerCAmelCase											=						k.replace("""norm1"""						,		"""self_attn_layer_norm"""      )
  __lowerCAmelCase											=						k.replace("""norm2"""						,		"""encoder_attn_layer_norm"""      )
  __lowerCAmelCase											=						k.replace("""norm3"""						,		"""final_layer_norm"""      )
 return k







def      UpperCamelCase_						(					snake_case_       :       List[str]      )					->     Dict:






 '''simple docstring'''

 __lowerCAmelCase											=						[
     """model.encoder.layernorm_embedding.weight""",
     """model.encoder.layernorm_embedding.bias""",
     """model.decoder.layernorm_embedding.weight""",
     """model.decoder.layernorm_embedding.bias""",
 ]
 for k in keys:
  __lowerCAmelCase											=						sd.pop(snake_case_      )
  __lowerCAmelCase											=						k.replace("""layernorm_embedding"""						,		"""layer_norm"""      )
  assert new_k not in sd
  __lowerCAmelCase											=						v


_A			:      int					      =				['''START''']







@torch.no_grad()
def      UpperCamelCase_						(					snake_case_       :       List[Any]						,		snake_case_       :       Any						,		snake_case_       :       Optional[int]      )					->     Optional[Any]:






 '''simple docstring'''

 __lowerCAmelCase											=						torch.load(snake_case_						,		map_location="""cpu"""      )
 __lowerCAmelCase											=						model["""model"""]
 __lowerCAmelCase											=						BlenderbotConfig.from_json_file(snake_case_      )
 __lowerCAmelCase											=						BlenderbotForConditionalGeneration(snake_case_      )
 __lowerCAmelCase											=						m.model.state_dict().keys()
 __lowerCAmelCase											=						[]
 __lowerCAmelCase											=						{}
 for k, v in sd.items():
  if k in IGNORE_KEYS:
   continue

  __lowerCAmelCase											=						rename_state_dict_key(snake_case_      )
  if new_k not in valid_keys:
   failures.append([k, new_k]      )
  else:
   __lowerCAmelCase											=						v
 if cfg.normalize_before:  # Blenderbot-3B checkpoints. Rename layernorm_embedding -> layer_norm
  rename_layernorm_keys(snake_case_      )
 m.model.load_state_dict(snake_case_						,		strict=snake_case_      )
 m.half()
 m.save_pretrained(snake_case_      )


if __name__ == "__main__":
 _A			:      Optional[Any]					      =				argparse.ArgumentParser()
 # Required parameters
 parser.add_argument('''--src_path''', type=str, help='''like blenderbot-model.bin''')
 parser.add_argument('''--save_dir''', default='''hf_blenderbot''', type=str, help='''Where to save converted model.''')
 parser.add_argument(
     '''--hf_config_json''', default='''blenderbot-3b-config.json''', type=str, help='''Path to config to use'''
 )
 _A			:      Tuple					      =				parser.parse_args()
 convert_parlai_checkpoint(args.src_path, args.save_dir, args.hf_config_json)






