



'''simple docstring'''


import argparse
import pathlib

import fairseq
import torch
from fairseq.models.roberta import RobertaModel as FairseqRobertaModel
from fairseq.modules import TransformerSentenceEncoderLayer
from packaging import version

from transformers import XLMRobertaConfig, XLMRobertaXLForMaskedLM, XLMRobertaXLForSequenceClassification
from transformers.models.bert.modeling_bert import (
    BertIntermediate,
    BertLayer,
    BertOutput,
    BertSelfAttention,
    BertSelfOutput,
)
from transformers.models.roberta.modeling_roberta import RobertaAttention
from transformers.utils import logging


if version.parse(fairseq.__version__) < version.parse("1.0.0a"):
   raise Exception("requires fairseq >= 1.0.0a")

logging.set_verbosity_info()
_lowerCamelCase	:       Tuple      	=	logging.get_logger(__name__)

_lowerCamelCase	:       Dict      	=	"Hello world! cécé herlolip"







def 				__lowerCamelCase				(   A__    ,						A__    ,						A__    )       ->	Optional[Any]:






   """simple docstring"""
   UpperCamelCase					=  FairseqRobertaModel.from_pretrained(__UpperCamelCase    )
   roberta.eval()  # disable dropout
   UpperCamelCase					=  roberta.model.encoder.sentence_encoder
   UpperCamelCase					=  XLMRobertaConfig(
       vocab_size=roberta_sent_encoder.embed_tokens.num_embeddings    ,						hidden_size=roberta.cfg.model.encoder_embed_dim    ,						num_hidden_layers=roberta.cfg.model.encoder_layers    ,						num_attention_heads=roberta.cfg.model.encoder_attention_heads    ,						intermediate_size=roberta.cfg.model.encoder_ffn_embed_dim    ,						max_position_embeddings=514    ,						type_vocab_size=1    ,						layer_norm_eps=1e-5    ,						)
   if classification_head:
      UpperCamelCase					=  roberta.model.classification_heads['mnli'].out_proj.weight.shape[0]

   print('Our RoBERTa config:'    ,						__UpperCamelCase    )

   UpperCamelCase					=  XLMRobertaXLForSequenceClassification(__UpperCamelCase    ) if classification_head else XLMRobertaXLForMaskedLM(__UpperCamelCase    )
   model.eval()

   # Now let's copy all the weights.
   # Embeddings
   UpperCamelCase					=  roberta_sent_encoder.embed_tokens.weight
   UpperCamelCase					=  roberta_sent_encoder.embed_positions.weight
   UpperCamelCase					=  torch.zeros_like(
       model.roberta.embeddings.token_type_embeddings.weight    )  # just zero them out b/c RoBERTa doesn't use them.

   UpperCamelCase					=  roberta_sent_encoder.layer_norm.weight
   UpperCamelCase					=  roberta_sent_encoder.layer_norm.bias

   for i in range(config.num_hidden_layers    ):
      # Encoder: start of layer
      UpperCamelCase					=  model.roberta.encoder.layer[i]
      UpperCamelCase					=  roberta_sent_encoder.layers[i]

      UpperCamelCase					=  layer.attention
      UpperCamelCase					=  roberta_layer.self_attn_layer_norm.weight
      UpperCamelCase					=  roberta_layer.self_attn_layer_norm.bias

      # self attention
      UpperCamelCase					=  layer.attention.self
      assert (
          roberta_layer.self_attn.k_proj.weight.data.shape
          == roberta_layer.self_attn.q_proj.weight.data.shape
          == roberta_layer.self_attn.v_proj.weight.data.shape
          == torch.Size((config.hidden_size, config.hidden_size)    )
      )

      UpperCamelCase					=  roberta_layer.self_attn.q_proj.weight
      UpperCamelCase					=  roberta_layer.self_attn.q_proj.bias
      UpperCamelCase					=  roberta_layer.self_attn.k_proj.weight
      UpperCamelCase					=  roberta_layer.self_attn.k_proj.bias
      UpperCamelCase					=  roberta_layer.self_attn.v_proj.weight
      UpperCamelCase					=  roberta_layer.self_attn.v_proj.bias

      # self-attention output
      UpperCamelCase					=  layer.attention.output
      assert self_output.dense.weight.shape == roberta_layer.self_attn.out_proj.weight.shape
      UpperCamelCase					=  roberta_layer.self_attn.out_proj.weight
      UpperCamelCase					=  roberta_layer.self_attn.out_proj.bias

      # this one is final layer norm
      UpperCamelCase					=  roberta_layer.final_layer_norm.weight
      UpperCamelCase					=  roberta_layer.final_layer_norm.bias

      # intermediate
      UpperCamelCase					=  layer.intermediate
      assert intermediate.dense.weight.shape == roberta_layer.fca.weight.shape
      UpperCamelCase					=  roberta_layer.fca.weight
      UpperCamelCase					=  roberta_layer.fca.bias

      # output
      UpperCamelCase					=  layer.output
      assert bert_output.dense.weight.shape == roberta_layer.fca.weight.shape
      UpperCamelCase					=  roberta_layer.fca.weight
      UpperCamelCase					=  roberta_layer.fca.bias
      # end of layer

   if classification_head:
      UpperCamelCase					=  roberta.model.classification_heads['mnli'].dense.weight
      UpperCamelCase					=  roberta.model.classification_heads['mnli'].dense.bias
      UpperCamelCase					=  roberta.model.classification_heads['mnli'].out_proj.weight
      UpperCamelCase					=  roberta.model.classification_heads['mnli'].out_proj.bias
   else:
      # LM Head
      UpperCamelCase					=  roberta.model.encoder.lm_head.dense.weight
      UpperCamelCase					=  roberta.model.encoder.lm_head.dense.bias
      UpperCamelCase					=  roberta.model.encoder.lm_head.layer_norm.weight
      UpperCamelCase					=  roberta.model.encoder.lm_head.layer_norm.bias
      UpperCamelCase					=  roberta.model.encoder.lm_head.weight
      UpperCamelCase					=  roberta.model.encoder.lm_head.bias

   # Let's check that we get the same results.
   UpperCamelCase					=  roberta.encode(__UpperCamelCase    ).unsqueeze(0    )  # batch of size 1

   UpperCamelCase					=  model(__UpperCamelCase    )[0]
   if classification_head:
      UpperCamelCase					=  roberta.model.classification_heads['mnli'](roberta.extract_features(__UpperCamelCase    )    )
   else:
      UpperCamelCase					=  roberta.model(__UpperCamelCase    )[0]
   print(our_output.shape    ,						their_output.shape    )
   UpperCamelCase					=  torch.max(torch.abs(our_output - their_output    )    ).item()
   print(F"""max_absolute_diff = {max_absolute_diff}"""    )  # ~ 1e-7
   UpperCamelCase					=  torch.allclose(__UpperCamelCase    ,						__UpperCamelCase    ,						atol=1e-3    )
   print('Do both models output the same tensors?'    ,						'🔥' if success else '💩'    )
   if not success:
      raise Exception('Something went wRoNg'    )

   pathlib.Path(__UpperCamelCase    ).mkdir(parents=__UpperCamelCase    ,						exist_ok=__UpperCamelCase    )
   print(F"""Saving model to {pytorch_dump_folder_path}"""    )
   model.save_pretrained(__UpperCamelCase    )


if __name__ == "__main__":
   _lowerCamelCase	:       Tuple      	=	argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       "--roberta_checkpoint_path", default=None, type=str, required=True, help="Path the official PyTorch dump."
   )
   parser.add_argument(
       "--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
   )
   parser.add_argument(
       "--classification_head", action="store_true", help="Whether to convert a final classification head."
   )
   _lowerCamelCase	:       List[Any]      	=	parser.parse_args()
   convert_xlm_roberta_xl_checkpoint_to_pytorch(
       args.roberta_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
   )


import gc
import random
import unittest

import numpy as np
import torch

from diffusers import (
    DDIMScheduler,
    KandinskyVaaControlnetPipeline,
    KandinskyVaaPriorPipeline,
    UNetaDConditionModel,
    VQModel,
)
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

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()


class      lowerCamelCase    (SCREAMING_SNAKE_CASE__     ,					unittest.TestCase    ):
  """simple docstring"""







  lowerCamelCase__   =  KandinskyVaaControlnetPipeline
  lowerCamelCase__   =  ['''image_embeds''', '''negative_image_embeds''', '''hint''']
  lowerCamelCase__   =  ['''image_embeds''', '''negative_image_embeds''', '''hint''']
  lowerCamelCase__   =  [
      '''generator''',
      '''height''',
      '''width''',
      '''latents''',
      '''guidance_scale''',
      '''num_inference_steps''',
      '''return_dict''',
      '''guidance_scale''',
      '''num_images_per_prompt''',
      '''output_type''',
      '''return_dict''',
  ]
  lowerCamelCase__   =  False







  @property
  def       __A   (							self       :				str      )     ->		Optional[int]:
        return 32







  @property
  def       __A   (							self       :				Optional[Any]      )     ->		Any:
        return 32







  @property
  def       __A   (							self       :				Any      )     ->		List[Any]:
        return self.time_input_dim







  @property
  def       __A   (							self       :				Tuple      )     ->		int:
        return self.time_input_dim * 4







  @property
  def       __A   (							self       :				Optional[int]      )     ->		Tuple:
        return 100







  @property
  def       __A   (							self       :				int      )     ->		Tuple:
        torch.manual_seed(0      )

        SCREAMING_SNAKE_CASE_      =     {
            "in_channels": 8,
            # Out channels is double in channels because predicts mean and variance
            "out_channels": 8,
            "addition_embed_type": "image_hint",
            "down_block_types": ("ResnetDownsampleBlock2D", "SimpleCrossAttnDownBlock2D"),
            "up_block_types": ("SimpleCrossAttnUpBlock2D", "ResnetUpsampleBlock2D"),
            "mid_block_type": "UNetMidBlock2DSimpleCrossAttn",
            "block_out_channels": (self.block_out_channels_a, self.block_out_channels_a * 2),
            "layers_per_block": 1,
            "encoder_hid_dim": self.text_embedder_hidden_size,
            "encoder_hid_dim_type": "image_proj",
            "cross_attention_dim": self.cross_attention_dim,
            "attention_head_dim": 4,
            "resnet_time_scale_shift": "scale_shift",
            "class_embed_type": None,
        }

        SCREAMING_SNAKE_CASE_      =     UNetaDConditionModel(**__magic_name__      )
        return model







  @property
  def       __A   (							self       :				Optional[int]      )     ->		str:
        return {
            "block_out_channels": [32, 32, 64, 64],
            "down_block_types": [
                "DownEncoderBlock2D",
                "DownEncoderBlock2D",
                "DownEncoderBlock2D",
                "AttnDownEncoderBlock2D",
            ],
            "in_channels": 3,
            "latent_channels": 4,
            "layers_per_block": 1,
            "norm_num_groups": 8,
            "norm_type": "spatial",
            "num_vq_embeddings": 12,
            "out_channels": 3,
            "up_block_types": ["AttnUpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D"],
            "vq_embed_dim": 4,
        }







  @property
  def       __A   (							self       :				List[str]      )     ->		Dict:
        torch.manual_seed(0      )
        SCREAMING_SNAKE_CASE_      =     VQModel(**self.dummy_movq_kwargs      )
        return model







  def       __A   (							self       :				Dict      )     ->		Optional[int]:
        SCREAMING_SNAKE_CASE_      =     self.dummy_unet
        SCREAMING_SNAKE_CASE_      =     self.dummy_movq

        SCREAMING_SNAKE_CASE_      =     DDIMScheduler(
            num_train_timesteps=1_000     ,   beta_schedule="linear"     ,   beta_start=0.0_0085     ,   beta_end=0.012     ,   clip_sample=__magic_name__     ,   set_alpha_to_one=__magic_name__     ,   steps_offset=1     ,   prediction_type="epsilon"     ,   thresholding=__magic_name__     ,   )

        SCREAMING_SNAKE_CASE_      =     {
            "unet": unet,
            "scheduler": scheduler,
            "movq": movq,
        }
        return components







  def       __A   (							self       :				int     ,   __magic_name__       :				Any     ,   __magic_name__       :				Optional[int]=0      )     ->		List[str]:
        SCREAMING_SNAKE_CASE_      =     floats_tensor((1, self.text_embedder_hidden_size)     ,   rng=random.Random(__magic_name__      )      ).to(__magic_name__      )
        SCREAMING_SNAKE_CASE_      =     floats_tensor((1, self.text_embedder_hidden_size)     ,   rng=random.Random(seed + 1      )      ).to(
            __magic_name__      )

        # create hint
        SCREAMING_SNAKE_CASE_      =     floats_tensor((1, 3, 64, 64)     ,   rng=random.Random(__magic_name__      )      ).to(__magic_name__      )

        if str(__magic_name__      ).startswith("mps"      ):
              SCREAMING_SNAKE_CASE_      =     torch.manual_seed(__magic_name__      )
        else:
              SCREAMING_SNAKE_CASE_      =     torch.Generator(device=__magic_name__      ).manual_seed(__magic_name__      )
        SCREAMING_SNAKE_CASE_      =     {
            "image_embeds": image_embeds,
            "negative_image_embeds": negative_image_embeds,
            "hint": hint,
            "generator": generator,
            "height": 64,
            "width": 64,
            "guidance_scale": 4.0,
            "num_inference_steps": 2,
            "output_type": "np",
        }
        return inputs







  def       __A   (							self       :				Optional[int]      )     ->		Any:
        SCREAMING_SNAKE_CASE_      =     "cpu"

        SCREAMING_SNAKE_CASE_      =     self.get_dummy_components()

        SCREAMING_SNAKE_CASE_      =     self.pipeline_class(**__magic_name__      )
        SCREAMING_SNAKE_CASE_      =     pipe.to(__magic_name__      )

        pipe.set_progress_bar_config(disable=__magic_name__      )

        SCREAMING_SNAKE_CASE_      =     pipe(**self.get_dummy_inputs(__magic_name__      )      )
        SCREAMING_SNAKE_CASE_      =     output.images

        SCREAMING_SNAKE_CASE_      =     pipe(
            **self.get_dummy_inputs(__magic_name__      )     ,   return_dict=__magic_name__     ,   )[0]

        SCREAMING_SNAKE_CASE_      =     image[0, -3:, -3:, -1]
        SCREAMING_SNAKE_CASE_      =     image_from_tuple[0, -3:, -3:, -1]

        assert image.shape == (1, 64, 64, 3)

        SCREAMING_SNAKE_CASE_      =     np.array(
            [0.695_9826, 0.86_8279, 0.755_8092, 0.6876_9467, 0.8580_5804, 0.6597_7496, 0.4488_5302, 0.595_9111, 0.425_1595]      )

        assert (
            np.abs(image_slice.flatten() - expected_slice      ).max() < 1e-2
        ), F''' expected_slice {expected_slice}, but got {image_slice.flatten()}'''

        assert (
            np.abs(image_from_tuple_slice.flatten() - expected_slice      ).max() < 1e-2
        ), F''' expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}'''



@slow
@require_torch_gpu
class      lowerCamelCase    (unittest.TestCase    ):
  """simple docstring"""







  def       __A   (							self       :				Any      )     ->		Tuple:
        # clean up the VRAM after each test
        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()







  def       __A   (							self       :				List[Any]      )     ->		Union[str, Any]:
        SCREAMING_SNAKE_CASE_      =     load_numpy(
            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
            "/kandinskyv22/kandinskyv22_controlnet_robotcat_fp16.npy"      )

        SCREAMING_SNAKE_CASE_      =     load_image(
            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
            "/kandinskyv22/hint_image_cat.png"      )
        SCREAMING_SNAKE_CASE_      =     torch.from_numpy(np.array(__magic_name__      )      ).float() / 255.0
        SCREAMING_SNAKE_CASE_      =     hint.permute(2     ,   0     ,   1      ).unsqueeze(0      )

        SCREAMING_SNAKE_CASE_      =     KandinskyVaaPriorPipeline.from_pretrained(
            "kandinsky-community/kandinsky-2-2-prior"     ,   torch_dtype=torch.floataa      )
        pipe_prior.to(__magic_name__      )

        SCREAMING_SNAKE_CASE_      =     KandinskyVaaControlnetPipeline.from_pretrained(
            "kandinsky-community/kandinsky-2-2-controlnet-depth"     ,   torch_dtype=torch.floataa      )
        SCREAMING_SNAKE_CASE_      =     pipeline.to(__magic_name__      )
        pipeline.set_progress_bar_config(disable=__magic_name__      )

        SCREAMING_SNAKE_CASE_      =     "A robot, 4k photo"

        SCREAMING_SNAKE_CASE_      =     torch.Generator(device="cuda"      ).manual_seed(0      )
        SCREAMING_SNAKE_CASE_   ,       SCREAMING_SNAKE_CASE_      =     pipe_prior(
            __magic_name__     ,   generator=__magic_name__     ,   num_inference_steps=5     ,   negative_prompt=""     ,   ).to_tuple()

        SCREAMING_SNAKE_CASE_      =     torch.Generator(device="cuda"      ).manual_seed(0      )
        SCREAMING_SNAKE_CASE_      =     pipeline(
            image_embeds=__magic_name__     ,   negative_image_embeds=__magic_name__     ,   hint=__magic_name__     ,   generator=__magic_name__     ,   num_inference_steps=100     ,   output_type="np"     ,   )

        SCREAMING_SNAKE_CASE_      =     output.images[0]

        assert image.shape == (512, 512, 3)

        assert_mean_pixel_difference(__magic_name__     ,   __magic_name__      )


