



import gc
import random
import unittest

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

import diffusers
from diffusers import (
    AutoencoderKL,
    EulerDiscreteScheduler,
    StableDiffusionLatentUpscalePipeline,
    StableDiffusionPipeline,
    UNetaDConditionModel,
)
from diffusers.schedulers import KarrasDiffusionSchedulers
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 ..pipeline_params import TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()
def 							UpperCAmelCase__		(				_SCREAMING_SNAKE_CASE   :	Optional[int]				)->List[Any]:
       _lowerCAmelCase 			=  [tensor.shape for tensor in tensor_list]
       return all(shape == shapes[0] for shape in shapes[1:]				)





class 		UpperCAmelCase						(						snake_case_       ,snake_case_       ,snake_case_       ,unittest.TestCase				):
   SCREAMING_SNAKE_CASE__							=     StableDiffusionLatentUpscalePipeline
   SCREAMING_SNAKE_CASE__							=     TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {
       '''height''',
       '''width''',
       '''cross_attention_kwargs''',
       '''negative_prompt_embeds''',
       '''prompt_embeds''',
   }
   SCREAMING_SNAKE_CASE__							=     PipelineTesterMixin.required_optional_params - {'''num_images_per_prompt'''}
   SCREAMING_SNAKE_CASE__							=     TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
   SCREAMING_SNAKE_CASE__							=     frozenset(
       []				)  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
   SCREAMING_SNAKE_CASE__							=     frozenset([]				)

   SCREAMING_SNAKE_CASE__							=     True





   @property
   def 	__lowerCAmelCase							(	self			):
          _lowerCAmelCase 			=  1
          _lowerCAmelCase 			=  4
          _lowerCAmelCase 			=  (16, 16)

          _lowerCAmelCase 			=  floats_tensor((batch_size, num_channels) + sizes       ,     rng=random.Random(0			)			).to(_lowerCAmelCase			)
          return image





   def 	__lowerCAmelCase							(	self			):
          torch.manual_seed(0			)
          _lowerCAmelCase 			=  UNetaDConditionModel(
              act_fn='''gelu'''       ,     attention_head_dim=8       ,     norm_num_groups=_lowerCAmelCase       ,     block_out_channels=[32, 32, 64, 64]       ,     time_cond_proj_dim=160       ,     conv_in_kernel=1       ,     conv_out_kernel=1       ,     cross_attention_dim=32       ,     down_block_types=(
                  '''KDownBlock2D''',
                  '''KCrossAttnDownBlock2D''',
                  '''KCrossAttnDownBlock2D''',
                  '''KCrossAttnDownBlock2D''',
              )       ,     in_channels=8       ,     mid_block_type=_lowerCAmelCase       ,     only_cross_attention=_lowerCAmelCase       ,     out_channels=5       ,     resnet_time_scale_shift='''scale_shift'''       ,     time_embedding_type='''fourier'''       ,     timestep_post_act='''gelu'''       ,     up_block_types=('''KCrossAttnUpBlock2D''', '''KCrossAttnUpBlock2D''', '''KCrossAttnUpBlock2D''', '''KUpBlock2D''')       ,     )
          _lowerCAmelCase 			=  AutoencoderKL(
              block_out_channels=[32, 32, 64, 64]       ,     in_channels=3       ,     out_channels=3       ,     down_block_types=[
                  '''DownEncoderBlock2D''',
                  '''DownEncoderBlock2D''',
                  '''DownEncoderBlock2D''',
                  '''DownEncoderBlock2D''',
              ]       ,     up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''', '''UpDecoderBlock2D''', '''UpDecoderBlock2D''']       ,     latent_channels=4       ,     )
          _lowerCAmelCase 			=  EulerDiscreteScheduler(prediction_type='''sample'''			)
          _lowerCAmelCase 			=  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=1_000       ,     hidden_act='''quick_gelu'''       ,     projection_dim=512       ,     )
          _lowerCAmelCase 			=  CLIPTextModel(_lowerCAmelCase			)
          _lowerCAmelCase 			=  CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''			)

          _lowerCAmelCase 			=  {
              '''unet''': model.eval(),
              '''vae''': vae.eval(),
              '''scheduler''': scheduler,
              '''text_encoder''': text_encoder,
              '''tokenizer''': tokenizer,
          }

          return components





   def 	__lowerCAmelCase							(	self       ,     _lowerCAmelCase       ,     _lowerCAmelCase=0			):
          if str(_lowerCAmelCase			).startswith('''mps'''			):
                 _lowerCAmelCase 			=  torch.manual_seed(_lowerCAmelCase			)
          else:
                 _lowerCAmelCase 			=  torch.Generator(device=_lowerCAmelCase			).manual_seed(_lowerCAmelCase			)
          _lowerCAmelCase 			=  {
              '''prompt''': '''A painting of a squirrel eating a burger''',
              '''image''': self.dummy_image.cpu(),
              '''generator''': generator,
              '''num_inference_steps''': 2,
              '''output_type''': '''numpy''',
          }
          return inputs





   def 	__lowerCAmelCase							(	self			):
          _lowerCAmelCase 			=  '''cpu'''

          _lowerCAmelCase 			=  self.get_dummy_components()
          _lowerCAmelCase 			=  self.pipeline_class(**_lowerCAmelCase			)
          pipe.to(_lowerCAmelCase			)
          pipe.set_progress_bar_config(disable=_lowerCAmelCase			)

          _lowerCAmelCase 			=  self.get_dummy_inputs(_lowerCAmelCase			)
          _lowerCAmelCase 			=  pipe(**_lowerCAmelCase			).images
          _lowerCAmelCase 			=  image[0, -3:, -3:, -1]

          self.assertEqual(image.shape       ,     (1, 256, 256, 3)			)
          _lowerCAmelCase 			=  np.array(
              [0.47_222_412, 0.41_921_633, 0.44_717_434, 0.46_874_192, 0.42_588_258, 0.46_150_726, 0.4_677_534, 0.45_583_832, 0.48_579_055]			)
          _lowerCAmelCase 			=  np.abs(image_slice.flatten() - expected_slice			).max()
          self.assertLessEqual(_lowerCAmelCase       ,     1E-3			)





   def 	__lowerCAmelCase							(	self			):
          super().test_attention_slicing_forward_pass(expected_max_diff=7E-3			)





   def 	__lowerCAmelCase							(	self			):
          super().test_cpu_offload_forward_pass(expected_max_diff=3E-3			)





   def 	__lowerCAmelCase							(	self			):
          super().test_dict_tuple_outputs_equivalent(expected_max_difference=3E-3			)





   def 	__lowerCAmelCase							(	self			):
          super().test_inference_batch_single_identical(expected_max_diff=7E-3			)





   def 	__lowerCAmelCase							(	self			):
          super().test_pt_np_pil_outputs_equivalent(expected_max_diff=3E-3			)





   def 	__lowerCAmelCase							(	self			):
          super().test_save_load_local(expected_max_difference=3E-3			)





   def 	__lowerCAmelCase							(	self			):
          super().test_save_load_optional_components(expected_max_difference=3E-3			)





   def 	__lowerCAmelCase							(	self			):
          _lowerCAmelCase 			=  [
              '''DDIMScheduler''',
              '''DDPMScheduler''',
              '''PNDMScheduler''',
              '''HeunDiscreteScheduler''',
              '''EulerAncestralDiscreteScheduler''',
              '''KDPM2DiscreteScheduler''',
              '''KDPM2AncestralDiscreteScheduler''',
              '''DPMSolverSDEScheduler''',
          ]
          _lowerCAmelCase 			=  self.get_dummy_components()
          _lowerCAmelCase 			=  self.pipeline_class(**_lowerCAmelCase			)

          # make sure that PNDM does not need warm-up
          pipe.scheduler.register_to_config(skip_prk_steps=_lowerCAmelCase			)

          pipe.to(_lowerCAmelCase			)
          pipe.set_progress_bar_config(disable=_lowerCAmelCase			)
          _lowerCAmelCase 			=  self.get_dummy_inputs(_lowerCAmelCase			)
          _lowerCAmelCase 			=  2

          _lowerCAmelCase 			=  []
          for scheduler_enum in KarrasDiffusionSchedulers:
                 if scheduler_enum.name in skip_schedulers:
                        # no sigma schedulers are not supported
                        # no schedulers
                        continue

                 _lowerCAmelCase 			=  getattr(_lowerCAmelCase       ,     scheduler_enum.name			)
                 _lowerCAmelCase 			=  scheduler_cls.from_config(pipe.scheduler.config			)
                 _lowerCAmelCase 			=  pipe(**_lowerCAmelCase			)[0]
                 outputs.append(_lowerCAmelCase			)

          assert check_same_shape(_lowerCAmelCase			)






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





   def 	__lowerCAmelCase							(	self			):
          super().tearDown()
          gc.collect()
          torch.cuda.empty_cache()





   def 	__lowerCAmelCase							(	self			):
          _lowerCAmelCase 			=  torch.manual_seed(33			)

          _lowerCAmelCase 			=  StableDiffusionPipeline.from_pretrained('''CompVis/stable-diffusion-v1-4'''       ,     torch_dtype=torch.floataa			)
          pipe.to('''cuda'''			)

          _lowerCAmelCase 			=  StableDiffusionLatentUpscalePipeline.from_pretrained(
              '''stabilityai/sd-x2-latent-upscaler'''       ,     torch_dtype=torch.floataa			)
          upscaler.to('''cuda'''			)

          _lowerCAmelCase 			=  '''a photo of an astronaut high resolution, unreal engine, ultra realistic'''

          _lowerCAmelCase 			=  pipe(_lowerCAmelCase       ,     generator=_lowerCAmelCase       ,     output_type='''latent'''			).images

          _lowerCAmelCase 			=  upscaler(
              prompt=_lowerCAmelCase       ,     image=_lowerCAmelCase       ,     num_inference_steps=20       ,     guidance_scale=0       ,     generator=_lowerCAmelCase       ,     output_type='''np'''       ,     ).images[0]

          _lowerCAmelCase 			=  load_numpy(
              '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/latent-upscaler/astronaut_1024.npy'''			)
          assert np.abs((expected_image - image).mean()			) < 5E-2





   def 	__lowerCAmelCase							(	self			):
          _lowerCAmelCase 			=  torch.manual_seed(33			)

          _lowerCAmelCase 			=  StableDiffusionLatentUpscalePipeline.from_pretrained(
              '''stabilityai/sd-x2-latent-upscaler'''       ,     torch_dtype=torch.floataa			)
          upscaler.to('''cuda'''			)

          _lowerCAmelCase 			=  '''the temple of fire by Ross Tran and Gerardo Dottori, oil on canvas'''

          _lowerCAmelCase 			=  load_image(
              '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/latent-upscaler/fire_temple_512.png'''			)

          _lowerCAmelCase 			=  upscaler(
              prompt=_lowerCAmelCase       ,     image=_lowerCAmelCase       ,     num_inference_steps=20       ,     guidance_scale=0       ,     generator=_lowerCAmelCase       ,     output_type='''np'''       ,     ).images[0]

          _lowerCAmelCase 			=  load_numpy(
              '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/latent-upscaler/fire_temple_1024.npy'''			)
          assert np.abs((expected_image - image).max()			) < 5E-2




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()
UpperCAmelCase_									=							logging.get_logger(__name__)

UpperCAmelCase_									=							"Hello world! cécé herlolip"
def 							UpperCAmelCase__		(				_SCREAMING_SNAKE_CASE   :	str     ,				_SCREAMING_SNAKE_CASE   :	str     ,				_SCREAMING_SNAKE_CASE   :	bool				)->List[Any]:
      _lowerCAmelCase 			=  FairseqRobertaModel.from_pretrained(_SCREAMING_SNAKE_CASE				)
      roberta.eval()  # disable dropout
      _lowerCAmelCase 			=  roberta.model.encoder.sentence_encoder
      _lowerCAmelCase 			=  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=5_1_4     ,				type_vocab_size=1     ,				layer_norm_eps=1e-5     ,				)
      if classification_head:
            _lowerCAmelCase 			=  roberta.model.classification_heads['''mnli'''].out_proj.weight.shape[0]

      print('''Our RoBERTa config:'''     ,				_SCREAMING_SNAKE_CASE				)

      _lowerCAmelCase 			=  XLMRobertaXLForSequenceClassification(_SCREAMING_SNAKE_CASE				) if classification_head else XLMRobertaXLForMaskedLM(_SCREAMING_SNAKE_CASE				)
      model.eval()

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

      _lowerCAmelCase 			=  roberta_sent_encoder.layer_norm.weight
      _lowerCAmelCase 			=  roberta_sent_encoder.layer_norm.bias

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

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

            # self attention
            _lowerCAmelCase 			=  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)				)
            )

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

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

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

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

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

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

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

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

      pathlib.Path(_SCREAMING_SNAKE_CASE				).mkdir(parents=_SCREAMING_SNAKE_CASE     ,				exist_ok=_SCREAMING_SNAKE_CASE				)
      print(f'''Saving model to {pytorch_dump_folder_path}'''				)
      model.save_pretrained(_SCREAMING_SNAKE_CASE				)


if __name__ == "__main__":
    UpperCAmelCase_									=							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."
    )
    UpperCAmelCase_									=							parser.parse_args()
    convert_xlm_roberta_xl_checkpoint_to_pytorch(
        args.roberta_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
    )