

import gc
import unittest

from diffusers import FlaxControlNetModel, FlaxStableDiffusionControlNetPipeline
from diffusers.utils import is_flax_available, load_image, 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			):






						"""simple docstring"""




						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


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


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						Any							=      FlaxControlNetModel.from_pretrained(
										    "lllyasviel/sd-controlnet-canny"  ,   from_pt=A_  ,   dtype=jnp.bfloataa		)
										_UpperCAmelCase    :						Tuple							=      FlaxStableDiffusionControlNetPipeline.from_pretrained(
										    "runwayml/stable-diffusion-v1-5"  ,   controlnet=A_  ,   from_pt=A_  ,   dtype=jnp.bfloataa		)
										_UpperCAmelCase    :						Optional[int]							=      controlnet_params

										_UpperCAmelCase    :						List[Any]							=      "bird"
										_UpperCAmelCase    :						List[Any]							=      jax.device_count()
										_UpperCAmelCase    :						str							=      pipe.prepare_text_inputs([prompts] * num_samples		)

										_UpperCAmelCase    :						Optional[int]							=      load_image(
										    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png"		)
										_UpperCAmelCase    :						Tuple							=      pipe.prepare_image_inputs([canny_image] * num_samples		)

										_UpperCAmelCase    :						str							=      jax.random.PRNGKey(0		)
										_UpperCAmelCase    :						Optional[int]							=      jax.random.split(A_  ,   jax.device_count()		)

										_UpperCAmelCase    :						str							=      replicate(A_		)
										_UpperCAmelCase    :						Tuple							=      shard(A_		)
										_UpperCAmelCase    :						Any							=      shard(A_		)

										_UpperCAmelCase    :						List[Any]							=      pipe(
										    prompt_ids=A_  ,   image=A_  ,   params=A_  ,   prng_seed=A_  ,   num_inference_steps=50  ,   jit=A_  ,   ).images
										assert images.shape == (jax.device_count(), 1, 768, 512, 3)

										_UpperCAmelCase    :						Optional[int]							=      images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]		)
										_UpperCAmelCase    :						Optional[int]							=      images[0, 253:256, 253:256, -1]

										_UpperCAmelCase    :						Dict							=      jnp.asarray(jax.device_get(image_slice.flatten()		)		)
										_UpperCAmelCase    :						str							=      jnp.array(
										    [0.16_79_69, 0.11_66_99, 0.08_15_43, 0.15_42_97, 0.13_28_12, 0.10_88_87, 0.16_99_22, 0.16_99_22, 0.20_50_78]		)
										print(f'output_slice: {output_slice}'		)
										assert jnp.abs(output_slice - expected_slice		).max() < 1e-2





						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						Optional[Any]							=      FlaxControlNetModel.from_pretrained(
										    "lllyasviel/sd-controlnet-openpose"  ,   from_pt=A_  ,   dtype=jnp.bfloataa		)
										_UpperCAmelCase    :						Dict							=      FlaxStableDiffusionControlNetPipeline.from_pretrained(
										    "runwayml/stable-diffusion-v1-5"  ,   controlnet=A_  ,   from_pt=A_  ,   dtype=jnp.bfloataa		)
										_UpperCAmelCase    :						str							=      controlnet_params

										_UpperCAmelCase    :						Dict							=      "Chef in the kitchen"
										_UpperCAmelCase    :						Union[str, Any]							=      jax.device_count()
										_UpperCAmelCase    :						List[Any]							=      pipe.prepare_text_inputs([prompts] * num_samples		)

										_UpperCAmelCase    :						Dict							=      load_image(
										    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/pose.png"		)
										_UpperCAmelCase    :						List[Any]							=      pipe.prepare_image_inputs([pose_image] * num_samples		)

										_UpperCAmelCase    :						Optional[int]							=      jax.random.PRNGKey(0		)
										_UpperCAmelCase    :						str							=      jax.random.split(A_  ,   jax.device_count()		)

										_UpperCAmelCase    :						Tuple							=      replicate(A_		)
										_UpperCAmelCase    :						List[str]							=      shard(A_		)
										_UpperCAmelCase    :						Tuple							=      shard(A_		)

										_UpperCAmelCase    :						Union[str, Any]							=      pipe(
										    prompt_ids=A_  ,   image=A_  ,   params=A_  ,   prng_seed=A_  ,   num_inference_steps=50  ,   jit=A_  ,   ).images
										assert images.shape == (jax.device_count(), 1, 768, 512, 3)

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

										_UpperCAmelCase    :						Optional[int]							=      jnp.asarray(jax.device_get(image_slice.flatten()		)		)
										_UpperCAmelCase    :						Tuple							=      jnp.array(
										    [[0.27_14_84, 0.26_17_19, 0.27_53_91, 0.27_73_44, 0.27_92_97, 0.29_10_16, 0.29_49_22, 0.30_27_34, 0.30_27_34]]		)
										print(f'output_slice: {output_slice}'		)
										assert jnp.abs(output_slice - expected_slice		).max() < 1e-2





import argparse

import torch

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


logging.set_verbosity_info()
SCREAMING_SNAKE_CASE_            = logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_            = [
    ['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:					List[str]	)				->    Tuple:
				if k == "embeddings.weight":
								return "shared.weight"

				for parlai_name, hf_name in PATTERNS:
								_UpperCAmelCase    :						Optional[int]							=      k.replace(lowerCAmelCase					, lowerCAmelCase	)

				if k.startswith("encoder"	):
								_UpperCAmelCase    :						Tuple							=      k.replace(".attn"					, ".self_attn"	)
								_UpperCAmelCase    :						str							=      k.replace("norm1"					, "self_attn_layer_norm"	)
								_UpperCAmelCase    :						Optional[Any]							=      k.replace("norm2"					, "final_layer_norm"	)
				elif k.startswith("decoder"	):
								_UpperCAmelCase    :						Union[str, Any]							=      k.replace("norm1"					, "self_attn_layer_norm"	)
								_UpperCAmelCase    :						Tuple							=      k.replace("norm2"					, "encoder_attn_layer_norm"	)
								_UpperCAmelCase    :						Any							=      k.replace("norm3"					, "final_layer_norm"	)
				return k

def 							__SCREAMING_SNAKE_CASE							(			lowerCAmelCase:					Optional[int]	)				->    List[Any]:
				_UpperCAmelCase    :						int							=      [
				    "model.encoder.layernorm_embedding.weight",
				    "model.encoder.layernorm_embedding.bias",
				    "model.decoder.layernorm_embedding.weight",
				    "model.decoder.layernorm_embedding.bias",
				]
				for k in keys:
								_UpperCAmelCase    :						Dict							=      sd.pop(lowerCAmelCase	)
								_UpperCAmelCase    :						str							=      k.replace("layernorm_embedding"					, "layer_norm"	)
								assert new_k not in sd
								_UpperCAmelCase    :						Dict							=      v


SCREAMING_SNAKE_CASE_            = ['START']

@torch.no_grad()
def 							__SCREAMING_SNAKE_CASE							(			lowerCAmelCase:					Optional[Any]					, lowerCAmelCase:					int					, lowerCAmelCase:					int	)				->    int:
				_UpperCAmelCase    :						Union[str, Any]							=      torch.load(lowerCAmelCase					, map_location="cpu"	)
				_UpperCAmelCase    :						List[str]							=      model["model"]
				_UpperCAmelCase    :						List[str]							=      BlenderbotConfig.from_json_file(lowerCAmelCase	)
				_UpperCAmelCase    :						str							=      BlenderbotForConditionalGeneration(lowerCAmelCase	)
				_UpperCAmelCase    :						List[str]							=      m.model.state_dict().keys()
				_UpperCAmelCase    :						List[Any]							=      []
				_UpperCAmelCase    :						List[str]							=      {}
				for k, v in sd.items():
								if k in IGNORE_KEYS:
												continue

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


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE_            = 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'
					)
					SCREAMING_SNAKE_CASE_            = parser.parse_args()
					convert_parlai_checkpoint(args.src_path, args.save_dir, args.hf_config_json)


