






import argparse
import os
from pathlib import Path

import fairseq
import torch
from packaging import version
from torch import nn

from transformers import (
    BartConfig,
    BartForConditionalGeneration,
    BartForSequenceClassification,
    BartModel,
    BartTokenizer,
)
from transformers.utils import logging


snake_case    				=		["""bart.large""", """bart.large.mnli""", """bart.large.cnn""", """bart_xsum/model.pt"""]
snake_case    				=		{"""bart.large""": BartModel, """bart.large.mnli""": BartForSequenceClassification}
if version.parse(fairseq.__version__) < version.parse("""0.9.0"""):
   raise Exception("""requires fairseq >= 0.9.0""")


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

snake_case    				=		""" Hello world! cécé herlolip"""

snake_case    				=		[
    ("""model.classification_heads.mnli.dense.weight""", """classification_head.dense.weight"""),
    ("""model.classification_heads.mnli.dense.bias""", """classification_head.dense.bias"""),
    ("""model.classification_heads.mnli.out_proj.weight""", """classification_head.out_proj.weight"""),
    ("""model.classification_heads.mnli.out_proj.bias""", """classification_head.out_proj.bias"""),
]


def   lowerCamelCase__			(     lowercase				):

  """simple docstring"""






  SCREAMING_SNAKE_CASE       :      str							=    [
      "encoder.version",
      "decoder.version",
      "model.encoder.version",
      "model.decoder.version",
      "_float_tensor",
  ]
  for k in ignore_keys:
    state_dict.pop(lowercase						,     lowercase				)


def   lowerCamelCase__			(     lowercase						,     lowercase						,     lowercase				):

  """simple docstring"""






  SCREAMING_SNAKE_CASE       :      Dict							=    dct.pop(lowercase				)
  SCREAMING_SNAKE_CASE       :      Optional[Any]							=    val


def   lowerCamelCase__			(     lowercase				):

  """simple docstring"""






  SCREAMING_SNAKE_CASE       :      str							=    torch.load(lowercase						,     map_location="cpu"				)
  SCREAMING_SNAKE_CASE       :      Optional[Any]							=    torch.hub.load("pytorch/fairseq"						,     "bart.large.cnn"				).eval()
  hub_interface.model.load_state_dict(sd["model"]				)
  return hub_interface


def   lowerCamelCase__			(     lowercase				):

  """simple docstring"""






  SCREAMING_SNAKE_CASE						,  SCREAMING_SNAKE_CASE       :      List[str]							=    emb.weight.shape
  SCREAMING_SNAKE_CASE       :      Dict							=    nn.Linear(lowercase						,     lowercase						,     bias=lowercase				)
  SCREAMING_SNAKE_CASE       :      Optional[int]							=    emb.weight.data
  return lin_layer


@torch.no_grad()
def   lowerCamelCase__			(     lowercase						,     lowercase						,     lowercase=None				):

  """simple docstring"""






  if not os.path.exists(lowercase				):
    SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    torch.hub.load("pytorch/fairseq"						,     lowercase				).eval()
  else:
    SCREAMING_SNAKE_CASE       :      Dict							=    load_xsum_checkpoint(lowercase				)

  bart.model.upgrade_state_dict(bart.model.state_dict()				)
  if hf_checkpoint_name is None:
    SCREAMING_SNAKE_CASE       :      Optional[int]							=    checkpoint_path.replace("."						,     "-"				)
  SCREAMING_SNAKE_CASE       :      str							=    BartConfig.from_pretrained(lowercase				)
  SCREAMING_SNAKE_CASE       :      Tuple							=    bart.encode(lowercase				).unsqueeze(0				)
  SCREAMING_SNAKE_CASE       :      Any							=    BartTokenizer.from_pretrained(lowercase				).encode(lowercase						,     return_tensors="pt"				).unsqueeze(0				)
  if not torch.eq(lowercase						,     lowercase				).all():
    raise ValueError(
        F'''converted tokenizer and pretrained tokenizer returned different output: {tokens} != {tokensa}'''				)

  if checkpoint_path == "bart.large.mnli":
    SCREAMING_SNAKE_CASE       :      Any							=    bart.state_dict()
    remove_ignore_keys_(lowercase				)
    SCREAMING_SNAKE_CASE       :      int							=    state_dict["model.decoder.embed_tokens.weight"]
    for src, dest in mnli_rename_keys:
      rename_key(lowercase						,     lowercase						,     lowercase				)
    SCREAMING_SNAKE_CASE       :      List[Any]							=    BartForSequenceClassification(lowercase				).eval()
    model.load_state_dict(lowercase				)
    SCREAMING_SNAKE_CASE       :      Any							=    bart.predict("mnli"						,     lowercase						,     return_logits=lowercase				)
    SCREAMING_SNAKE_CASE       :      Tuple							=    model(lowercase				)[0]  # logits
  else:  # no classification heads to worry about
    SCREAMING_SNAKE_CASE       :      Dict							=    bart.model.state_dict()
    remove_ignore_keys_(lowercase				)
    SCREAMING_SNAKE_CASE       :      Optional[Any]							=    state_dict["decoder.embed_tokens.weight"]
    SCREAMING_SNAKE_CASE       :      Optional[Any]							=    bart.extract_features(lowercase				)
    if hf_checkpoint_name == "facebook/bart-large":
      SCREAMING_SNAKE_CASE       :      int							=    BartModel(lowercase				).eval()
      model.load_state_dict(lowercase				)
      SCREAMING_SNAKE_CASE       :      Optional[Any]							=    model(lowercase				).model[0]
    else:
      SCREAMING_SNAKE_CASE       :      Tuple							=    BartForConditionalGeneration(lowercase				).eval()  # an existing summarization ckpt
      model.model.load_state_dict(lowercase				)
      if hasattr(lowercase						,     "lm_head"				):
        SCREAMING_SNAKE_CASE       :      Dict							=    make_linear_from_emb(model.model.shared				)
      SCREAMING_SNAKE_CASE       :      Tuple							=    model.model(lowercase				)[0]

    # Check results
  if fairseq_output.shape != new_model_outputs.shape:
    raise ValueError(
        F'''`fairseq_output` shape and `new_model_output` shape are different: {fairseq_output.shape=}, {new_model_outputs.shape}'''				)
  if (fairseq_output != new_model_outputs).any().item():
    raise ValueError("Some values in `fairseq_output` are different from `new_model_outputs`"				)
  Path(lowercase				).mkdir(exist_ok=lowercase				)
  model.save_pretrained(lowercase				)


if __name__ == "__main__":
   snake_case    				=		argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       """fairseq_path""", type=str, help="""bart.large, bart.large.cnn or a path to a model.pt on local filesystem."""
   )
   parser.add_argument("""pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
   parser.add_argument(
       """--hf_config""", default=None, type=str, help="""Which huggingface architecture to use: bart-large-xsum"""
   )
   snake_case    				=		parser.parse_args()
   convert_bart_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, hf_checkpoint_name=args.hf_config)














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  SCREAMING_SNAKE_CASE			(			unittest.TestCase					):
     '''simple docstring'''


     def 				_A       (				self       :	List[Any]      ):
       # clean up the VRAM after each test
       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()


     def 				_A       (				self       :	str      ):
       SCREAMING_SNAKE_CASE       :      List[str]							=    StableDiffusionKDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4"      )
       SCREAMING_SNAKE_CASE       :      int							=    sd_pipe.to(UpperCAmelCase_      )
       sd_pipe.set_progress_bar_config(disable=UpperCAmelCase_      )

       sd_pipe.set_scheduler("sample_euler"      )

       SCREAMING_SNAKE_CASE       :      str							=    "A painting of a squirrel eating a burger"
       SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    torch.manual_seed(0      )
       SCREAMING_SNAKE_CASE       :      Any							=    sd_pipe([prompt]					, generator=UpperCAmelCase_					, guidance_scale=9.0					, num_inference_steps=20					, output_type="np"      )

       SCREAMING_SNAKE_CASE       :      List[str]							=    output.images

       SCREAMING_SNAKE_CASE       :      str							=    image[0, -3:, -3:, -1]

       assert image.shape == (1, 512, 512, 3)
       SCREAMING_SNAKE_CASE       :      int							=    np.array([0.0_447, 0.0_492, 0.0_468, 0.0_408, 0.0_383, 0.0_408, 0.0_354, 0.0_380, 0.0_339]      )

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


     def 				_A       (				self       :	List[Any]      ):
       SCREAMING_SNAKE_CASE       :      str							=    StableDiffusionKDiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2-1-base"      )
       SCREAMING_SNAKE_CASE       :      str							=    sd_pipe.to(UpperCAmelCase_      )
       sd_pipe.set_progress_bar_config(disable=UpperCAmelCase_      )

       sd_pipe.set_scheduler("sample_euler"      )

       SCREAMING_SNAKE_CASE       :      List[str]							=    "A painting of a squirrel eating a burger"
       SCREAMING_SNAKE_CASE       :      Dict							=    torch.manual_seed(0      )
       SCREAMING_SNAKE_CASE       :      Optional[int]							=    sd_pipe([prompt]					, generator=UpperCAmelCase_					, guidance_scale=9.0					, num_inference_steps=20					, output_type="np"      )

       SCREAMING_SNAKE_CASE       :      Optional[int]							=    output.images

       SCREAMING_SNAKE_CASE       :      Dict							=    image[0, -3:, -3:, -1]

       assert image.shape == (1, 512, 512, 3)
       SCREAMING_SNAKE_CASE       :      int							=    np.array([0.1_237, 0.1_320, 0.1_438, 0.1_359, 0.1_390, 0.1_132, 0.1_277, 0.1_175, 0.1_112]      )

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






     def 				_A       (				self       :	Optional[int]      ):
       SCREAMING_SNAKE_CASE       :      List[str]							=    StableDiffusionKDiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2-1-base"      )
       SCREAMING_SNAKE_CASE       :      Any							=    sd_pipe.to(UpperCAmelCase_      )
       sd_pipe.set_progress_bar_config(disable=UpperCAmelCase_      )

       sd_pipe.set_scheduler("sample_dpmpp_2m"      )

       SCREAMING_SNAKE_CASE       :      List[str]							=    "A painting of a squirrel eating a burger"
       SCREAMING_SNAKE_CASE       :      Any							=    torch.manual_seed(0      )
       SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    sd_pipe(
           [prompt]					, generator=UpperCAmelCase_					, guidance_scale=7.5					, num_inference_steps=15					, output_type="np"					, use_karras_sigmas=UpperCAmelCase_					, )

       SCREAMING_SNAKE_CASE       :      Optional[Any]							=    output.images

       SCREAMING_SNAKE_CASE       :      List[Any]							=    image[0, -3:, -3:, -1]

       assert image.shape == (1, 512, 512, 3)
       SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    np.array(
           [0.11_381_689, 0.12_112_921, 0.1_389_457, 0.12_549_606, 0.1_244_964, 0.10_831_517, 0.11_562_866, 0.10_867_816, 0.10_499_048]      )

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






