





'''simple docstring'''

import gc
import random
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, CycleDiffusionPipeline, DDIMScheduler, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps

from ..pipeline_params import (
    IMAGE_TO_IMAGE_IMAGE_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()






class        UpperCAmelCase     (	UpperCamelCase__      ,	UpperCamelCase__      ,	unittest.TestCase    ):
 __lowercase       		=    CycleDiffusionPipeline
 __lowercase       		=    TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {
     """negative_prompt""",
     """height""",
     """width""",
     """negative_prompt_embeds""",
 }
 __lowercase       		=    PipelineTesterMixin.required_optional_params - {"""latents"""}
 __lowercase       		=    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({"""source_prompt"""}    )
 __lowercase       		=    IMAGE_TO_IMAGE_IMAGE_PARAMS
 __lowercase       		=    IMAGE_TO_IMAGE_IMAGE_PARAMS
 def 					UpperCAmelCase_							(    self				:List[str]							)->							Dict:
     torch.manual_seed(0							)
     A__    =      UNetaDConditionModel(
         block_out_channels=(32, 64)						,	layers_per_block=2						,	sample_size=32						,	in_channels=4						,	out_channels=4						,	down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")						,	up_block_types=("CrossAttnUpBlock2D", "UpBlock2D")						,	cross_attention_dim=32						,	)
     A__    =      DDIMScheduler(
         beta_start=0.0_0_0_8_5						,	beta_end=0.0_1_2						,	beta_schedule="scaled_linear"						,	num_train_timesteps=10_00						,	clip_sample=lowerCamelCase_						,	set_alpha_to_one=lowerCamelCase_						,	)
     torch.manual_seed(0							)
     A__    =      AutoencoderKL(
         block_out_channels=[32, 64]						,	in_channels=3						,	out_channels=3						,	down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"]						,	up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"]						,	latent_channels=4						,	)
     torch.manual_seed(0							)
     A__    =      CLIPTextConfig(
         bos_token_id=0						,	eos_token_id=2						,	hidden_size=32						,	intermediate_size=37						,	layer_norm_eps=1E-05						,	num_attention_heads=4						,	num_hidden_layers=5						,	pad_token_id=1						,	vocab_size=10_00						,	)
     A__    =      CLIPTextModel(lowerCamelCase_							)
     A__    =      CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"							)

     A__    =      {
         "unet": unet,
         "scheduler": scheduler,
         "vae": vae,
         "text_encoder": text_encoder,
         "tokenizer": tokenizer,
         "safety_checker": None,
         "feature_extractor": None,
     }
     return components
 def 					UpperCAmelCase_							(    self				:Tuple						,	lowercase_				:Union[str, Any]						,	lowercase_				:Dict=0							)->							Any:
     A__    =      floats_tensor((1, 3, 32, 32)						,	rng=random.Random(lowerCamelCase_							)							).to(lowerCamelCase_							)
     A__    =      image / 2 + 0.5
     if str(lowerCamelCase_							).startswith("mps"							):
         A__    =      torch.manual_seed(lowerCamelCase_							)
     else:
         A__    =      torch.Generator(device=lowerCamelCase_							).manual_seed(lowerCamelCase_							)
     A__    =      {
         "prompt": "An astronaut riding an elephant",
         "source_prompt": "An astronaut riding a horse",
         "image": image,
         "generator": generator,
         "num_inference_steps": 2,
         "eta": 0.1,
         "strength": 0.8,
         "guidance_scale": 3,
         "source_guidance_scale": 1,
         "output_type": "numpy",
     }
     return inputs
 def 					UpperCAmelCase_							(    self				:Union[str, Any]							)->							Optional[int]:
     A__    =      "cpu"  # ensure determinism for the device-dependent torch.Generator

     A__    =      self.get_dummy_components()
     A__    =      CycleDiffusionPipeline(**lowerCamelCase_							)
     A__    =      pipe.to(lowerCamelCase_							)
     pipe.set_progress_bar_config(disable=lowerCamelCase_							)

     A__    =      self.get_dummy_inputs(lowerCamelCase_							)
     A__    =      pipe(**lowerCamelCase_							)
     A__    =      output.images

     A__    =      images[0, -3:, -3:, -1]

     assert images.shape == (1, 32, 32, 3)
     A__    =      np.array([0.4_4_5_9, 0.4_9_4_3, 0.4_5_4_4, 0.6_6_4_3, 0.5_4_7_4, 0.4_3_2_7, 0.5_7_0_1, 0.5_9_5_9, 0.5_1_7_9]							)

     assert np.abs(image_slice.flatten() - expected_slice							).max() < 1E-2
 @unittest.skipIf(torch_device != "cuda"						,	"This test requires a GPU"							)
 def 					UpperCAmelCase_							(    self				:Optional[Any]							)->							str:
     A__    =      self.get_dummy_components()
     for name, module in components.items():
         if hasattr(lowerCamelCase_						,	"half"							):
             A__    =      module.half()
     A__    =      CycleDiffusionPipeline(**lowerCamelCase_							)
     A__    =      pipe.to(lowerCamelCase_							)
     pipe.set_progress_bar_config(disable=lowerCamelCase_							)

     A__    =      self.get_dummy_inputs(lowerCamelCase_							)
     A__    =      pipe(**lowerCamelCase_							)
     A__    =      output.images

     A__    =      images[0, -3:, -3:, -1]

     assert images.shape == (1, 32, 32, 3)
     A__    =      np.array([0.3_5_0_6, 0.4_5_4_3, 0.4_4_6, 0.4_5_7_5, 0.5_1_9_5, 0.4_1_5_5, 0.5_2_7_3, 0.5_1_8, 0.4_1_1_6]							)

     assert np.abs(image_slice.flatten() - expected_slice							).max() < 1E-2
 @skip_mps
 def 					UpperCAmelCase_							(    self				:Any							)->							str:
     return super().test_save_load_local()
 @unittest.skip("non-deterministic pipeline"							)
 def 					UpperCAmelCase_							(    self				:Any							)->							str:
     return super().test_inference_batch_single_identical()
 @skip_mps
 def 					UpperCAmelCase_							(    self				:Union[str, Any]							)->							List[Any]:
     return super().test_dict_tuple_outputs_equivalent()
 @skip_mps
 def 					UpperCAmelCase_							(    self				:Tuple							)->							Union[str, Any]:
     return super().test_save_load_optional_components()



 @skip_mps
 def 					UpperCAmelCase_							(    self				:List[str]							)->							List[str]:
     return super().test_attention_slicing_forward_pass()






@slow
@require_torch_gpu
class        UpperCAmelCase     (	unittest.TestCase    ):
 def 					UpperCAmelCase_							(    self				:List[str]							)->							Union[str, Any]:
     super().tearDown()
     gc.collect()
     torch.cuda.empty_cache()
 def 					UpperCAmelCase_							(    self				:int							)->							Union[str, Any]:
     A__    =      load_image(
         "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
         "/cycle-diffusion/black_colored_car.png"							)
     A__    =      load_numpy(
         "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car_fp16.npy"							)
     A__    =      init_image.resize((5_12, 5_12)							)

     A__    =      "CompVis/stable-diffusion-v1-4"
     A__    =      DDIMScheduler.from_pretrained(lowerCamelCase_						,	subfolder="scheduler"							)
     A__    =      CycleDiffusionPipeline.from_pretrained(
         lowerCamelCase_						,	scheduler=lowerCamelCase_						,	safety_checker=lowerCamelCase_						,	torch_dtype=torch.floataa						,	revision="fp16"							)

     pipe.to(lowerCamelCase_							)
     pipe.set_progress_bar_config(disable=lowerCamelCase_							)
     pipe.enable_attention_slicing()

     A__    =      "A black colored car"
     A__    =      "A blue colored car"

     A__    =      torch.manual_seed(0							)
     A__    =      pipe(
         prompt=lowerCamelCase_						,	source_prompt=lowerCamelCase_						,	image=lowerCamelCase_						,	num_inference_steps=1_00						,	eta=0.1						,	strength=0.8_5						,	guidance_scale=3						,	source_guidance_scale=1						,	generator=lowerCamelCase_						,	output_type="np"						,	)
     A__    =      output.images

     # the values aren't exactly equal, but the images look the same visually
     assert np.abs(image - expected_image							).max() < 5E-1



 def 					UpperCAmelCase_							(    self				:List[Any]							)->							Dict:
     A__    =      load_image(
         "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
         "/cycle-diffusion/black_colored_car.png"							)
     A__    =      load_numpy(
         "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car.npy"							)
     A__    =      init_image.resize((5_12, 5_12)							)

     A__    =      "CompVis/stable-diffusion-v1-4"
     A__    =      DDIMScheduler.from_pretrained(lowerCamelCase_						,	subfolder="scheduler"							)
     A__    =      CycleDiffusionPipeline.from_pretrained(lowerCamelCase_						,	scheduler=lowerCamelCase_						,	safety_checker=lowerCamelCase_							)

     pipe.to(lowerCamelCase_							)
     pipe.set_progress_bar_config(disable=lowerCamelCase_							)
     pipe.enable_attention_slicing()

     A__    =      "A black colored car"
     A__    =      "A blue colored car"

     A__    =      torch.manual_seed(0							)
     A__    =      pipe(
         prompt=lowerCamelCase_						,	source_prompt=lowerCamelCase_						,	image=lowerCamelCase_						,	num_inference_steps=1_00						,	eta=0.1						,	strength=0.8_5						,	guidance_scale=3						,	source_guidance_scale=1						,	generator=lowerCamelCase_						,	output_type="np"						,	)
     A__    =      output.images

     assert np.abs(image - expected_image							).max() < 2E-2






'''simple docstring'''

import argparse

import torch

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


logging.set_verbosity_info()
__lowerCAmelCase							:     Union[str, Any]		     =logging.get_logger(__name__)

__lowerCAmelCase							:     Optional[int]		     =[
    ["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    (							_lowerCamelCase : int	):
    if k == "embeddings.weight":
        return "shared.weight"

    for parlai_name, hf_name in PATTERNS:
        A__    =      k.replace(_lowerCamelCase						,	_lowerCamelCase	)

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





def 						UpperCamelCase    (							_lowerCamelCase : int	):
    A__    =      [
        "model.encoder.layernorm_embedding.weight",
        "model.encoder.layernorm_embedding.bias",
        "model.decoder.layernorm_embedding.weight",
        "model.decoder.layernorm_embedding.bias",
    ]
    for k in keys:
        A__    =      sd.pop(_lowerCamelCase	)
        A__    =      k.replace("layernorm_embedding"						,	"layer_norm"	)
        assert new_k not in sd
        A__    =      v


__lowerCAmelCase							:     Optional[int]		     =["START"]





@torch.no_grad()
def 						UpperCamelCase    (							_lowerCamelCase : Dict						,	_lowerCamelCase : Optional[Any]						,	_lowerCamelCase : Optional[Any]	):
    A__    =      torch.load(_lowerCamelCase						,	map_location="cpu"	)
    A__    =      model["model"]
    A__    =      BlenderbotConfig.from_json_file(_lowerCamelCase	)
    A__    =      BlenderbotForConditionalGeneration(_lowerCamelCase	)
    A__    =      m.model.state_dict().keys()
    A__    =      []
    A__    =      {}
    for k, v in sd.items():
        if k in IGNORE_KEYS:
            continue

        A__    =      rename_state_dict_key(_lowerCamelCase	)
        if new_k not in valid_keys:
            failures.append([k, new_k]	)
        else:
            A__    =      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__":
  __lowerCAmelCase							:     Union[str, 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"
  )
  __lowerCAmelCase							:     Union[str, Any]		     =parser.parse_args()
  convert_parlai_checkpoint(args.src_path, args.save_dir, args.hf_config_json)



