



"""simple docstring"""
import argparse

import torch

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


logging.set_verbosity_info()
UpperCAmelCase    :		List[Any]      =			logging.get_logger(__name__)

UpperCAmelCase    :		List[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       _SCREAMING_SNAKE_CASE    (__lowerCAmelCase				)			->    Any:







  '''simple docstring'''


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

  for parlai_name, hf_name in PATTERNS:
    lowercase_             =			k.replace(UpperCamelCase__			,	UpperCamelCase__				)

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


def       _SCREAMING_SNAKE_CASE    (__lowerCAmelCase				)			->    str:







  '''simple docstring'''


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


UpperCAmelCase    :		List[str]      =			["START"]


@torch.no_grad()
def       _SCREAMING_SNAKE_CASE    (__lowerCAmelCase			,	__lowerCAmelCase			,	__lowerCAmelCase				)			->    str:







  '''simple docstring'''


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

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


if __name__ == "__main__":
       UpperCAmelCase    :		Tuple      =			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"
       )
       UpperCAmelCase    :		int      =			parser.parse_args()
       convert_parlai_checkpoint(args.src_path, args.save_dir, args.hf_config_json)








import gc
import unittest

import numpy as np
import torch

from diffusers import StableDiffusionKDiffusionPipeline
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()




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


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



									"""simple docstring"""



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


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



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   StableDiffusionKDiffusionPipeline.from_pretrained("""CompVis/stable-diffusion-v1-4"""		)
									SCREAMING_SNAKE_CASE__								=   sd_pipe.to(__A		)
									sd_pipe.set_progress_bar_config(disable=__A		)

									sd_pipe.set_scheduler("""sample_euler"""		)

									SCREAMING_SNAKE_CASE__								=   """A painting of a squirrel eating a burger"""
									SCREAMING_SNAKE_CASE__								=   torch.manual_seed(0		)
									SCREAMING_SNAKE_CASE__								=   sd_pipe([prompt]				,					generator=__A				,					guidance_scale=9.0				,					num_inference_steps=20				,					output_type="""np"""		)

									SCREAMING_SNAKE_CASE__								=   output.images

									SCREAMING_SNAKE_CASE__								=   image[0, -3:, -3:, -1]

									assert image.shape == (1, 512, 512, 3)
									SCREAMING_SNAKE_CASE__								=   np.array([0.0_4_4_7, 0.0_4_9_2, 0.0_4_6_8, 0.0_4_0_8, 0.0_3_8_3, 0.0_4_0_8, 0.0_3_5_4, 0.0_3_8_0, 0.0_3_3_9]		)

									assert np.abs(image_slice.flatten() - expected_slice		).max() < 1E-2


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



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   StableDiffusionKDiffusionPipeline.from_pretrained("""stabilityai/stable-diffusion-2-1-base"""		)
									SCREAMING_SNAKE_CASE__								=   sd_pipe.to(__A		)
									sd_pipe.set_progress_bar_config(disable=__A		)

									sd_pipe.set_scheduler("""sample_euler"""		)

									SCREAMING_SNAKE_CASE__								=   """A painting of a squirrel eating a burger"""
									SCREAMING_SNAKE_CASE__								=   torch.manual_seed(0		)
									SCREAMING_SNAKE_CASE__								=   sd_pipe([prompt]				,					generator=__A				,					guidance_scale=9.0				,					num_inference_steps=20				,					output_type="""np"""		)

									SCREAMING_SNAKE_CASE__								=   output.images

									SCREAMING_SNAKE_CASE__								=   image[0, -3:, -3:, -1]

									assert image.shape == (1, 512, 512, 3)
									SCREAMING_SNAKE_CASE__								=   np.array([0.1_2_3_7, 0.1_3_2_0, 0.1_4_3_8, 0.1_3_5_9, 0.1_3_9_0, 0.1_1_3_2, 0.1_2_7_7, 0.1_1_7_5, 0.1_1_1_2]		)

									assert np.abs(image_slice.flatten() - expected_slice		).max() < 5E-1


							def   _snake_case	(				self     :Tuple		) -> Optional[Any]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   StableDiffusionKDiffusionPipeline.from_pretrained("""stabilityai/stable-diffusion-2-1-base"""		)
									SCREAMING_SNAKE_CASE__								=   sd_pipe.to(__A		)
									sd_pipe.set_progress_bar_config(disable=__A		)

									sd_pipe.set_scheduler("""sample_dpmpp_2m"""		)

									SCREAMING_SNAKE_CASE__								=   """A painting of a squirrel eating a burger"""
									SCREAMING_SNAKE_CASE__								=   torch.manual_seed(0		)
									SCREAMING_SNAKE_CASE__								=   sd_pipe(
									    [prompt]				,					generator=__A				,					guidance_scale=7.5				,					num_inference_steps=15				,					output_type="""np"""				,					use_karras_sigmas=__A				,					)

									SCREAMING_SNAKE_CASE__								=   output.images

									SCREAMING_SNAKE_CASE__								=   image[0, -3:, -3:, -1]

									assert image.shape == (1, 512, 512, 3)
									SCREAMING_SNAKE_CASE__								=   np.array(
									    [0.1_1_3_8_1_6_8_9, 0.1_2_1_1_2_9_2_1, 0.1_3_8_9_4_5_7, 0.1_2_5_4_9_6_0_6, 0.1_2_4_4_9_6_4, 0.1_0_8_3_1_5_1_7, 0.1_1_5_6_2_8_6_6, 0.1_0_8_6_7_8_1_6, 0.1_0_4_9_9_0_4_8]		)

									assert np.abs(image_slice.flatten() - expected_slice		).max() < 1E-2