
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  =logging.get_logger(__name__)

lowerCamelCase  ="Hello world! cécé herlolip"


def 				SCREAMING_SNAKE_CASE_   (      UpperCamelCase__			,		UpperCamelCase__			,		UpperCamelCase__		):
      UpperCamelCase__     : Union[str, Any]											=					FairseqRobertaModel.from_pretrained(UpperCamelCase__		)
      roberta.eval()  # disable dropout
      UpperCamelCase__     : Union[str, Any]											=					roberta.model.encoder.sentence_encoder
      UpperCamelCase__     : List[str]											=					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:
            UpperCamelCase__     : Any											=					roberta.model.classification_heads['''mnli'''].out_proj.weight.shape[0]

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

      UpperCamelCase__     : Union[str, Any]											=					XLMRobertaXLForSequenceClassification(UpperCamelCase__		) if classification_head else XLMRobertaXLForMaskedLM(UpperCamelCase__		)
      model.eval()

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

      UpperCamelCase__     : int											=					roberta_sent_encoder.layer_norm.weight
      UpperCamelCase__     : List[Any]											=					roberta_sent_encoder.layer_norm.bias

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

            UpperCamelCase__     : RobertaAttention											=					layer.attention
            UpperCamelCase__     : List[Any]											=					roberta_layer.self_attn_layer_norm.weight
            UpperCamelCase__     : Optional[Any]											=					roberta_layer.self_attn_layer_norm.bias

            # self attention
            UpperCamelCase__     : BertSelfAttention											=					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__     : Optional[Any]											=					roberta_layer.self_attn.q_proj.weight
            UpperCamelCase__     : Optional[Any]											=					roberta_layer.self_attn.q_proj.bias
            UpperCamelCase__     : Union[str, Any]											=					roberta_layer.self_attn.k_proj.weight
            UpperCamelCase__     : List[str]											=					roberta_layer.self_attn.k_proj.bias
            UpperCamelCase__     : Dict											=					roberta_layer.self_attn.v_proj.weight
            UpperCamelCase__     : Tuple											=					roberta_layer.self_attn.v_proj.bias

            # self-attention output
            UpperCamelCase__     : BertSelfOutput											=					layer.attention.output
            assert self_output.dense.weight.shape == roberta_layer.self_attn.out_proj.weight.shape
            UpperCamelCase__     : int											=					roberta_layer.self_attn.out_proj.weight
            UpperCamelCase__     : Optional[Any]											=					roberta_layer.self_attn.out_proj.bias

            # this one is final layer norm
            UpperCamelCase__     : Union[str, Any]											=					roberta_layer.final_layer_norm.weight
            UpperCamelCase__     : Optional[int]											=					roberta_layer.final_layer_norm.bias

            # intermediate
            UpperCamelCase__     : BertIntermediate											=					layer.intermediate
            assert intermediate.dense.weight.shape == roberta_layer.fca.weight.shape
            UpperCamelCase__     : List[Any]											=					roberta_layer.fca.weight
            UpperCamelCase__     : Any											=					roberta_layer.fca.bias

            # output
            UpperCamelCase__     : BertOutput											=					layer.output
            assert bert_output.dense.weight.shape == roberta_layer.fca.weight.shape
            UpperCamelCase__     : Any											=					roberta_layer.fca.weight
            UpperCamelCase__     : Any											=					roberta_layer.fca.bias
            # end of layer

      if classification_head:
            UpperCamelCase__     : str											=					roberta.model.classification_heads['''mnli'''].dense.weight
            UpperCamelCase__     : int											=					roberta.model.classification_heads['''mnli'''].dense.bias
            UpperCamelCase__     : str											=					roberta.model.classification_heads['''mnli'''].out_proj.weight
            UpperCamelCase__     : Tuple											=					roberta.model.classification_heads['''mnli'''].out_proj.bias
      else:
            # LM Head
            UpperCamelCase__     : Tuple											=					roberta.model.encoder.lm_head.dense.weight
            UpperCamelCase__     : Tuple											=					roberta.model.encoder.lm_head.dense.bias
            UpperCamelCase__     : Any											=					roberta.model.encoder.lm_head.layer_norm.weight
            UpperCamelCase__     : List[str]											=					roberta.model.encoder.lm_head.layer_norm.bias
            UpperCamelCase__     : Any											=					roberta.model.encoder.lm_head.weight
            UpperCamelCase__     : Union[str, Any]											=					roberta.model.encoder.lm_head.bias

      # Let's check that we get the same results.
      UpperCamelCase__     : torch.Tensor											=					roberta.encode(UpperCamelCase__		).unsqueeze(0		)  # batch of size 1

      UpperCamelCase__     : List[Any]											=					model(UpperCamelCase__		)[0]
      if classification_head:
            UpperCamelCase__     : List[str]											=					roberta.model.classification_heads['''mnli'''](roberta.extract_features(UpperCamelCase__		)		)
      else:
            UpperCamelCase__     : List[str]											=					roberta.model(UpperCamelCase__		)[0]
      print(our_output.shape			,		their_output.shape		)
      UpperCamelCase__     : Dict											=					torch.max(torch.abs(our_output - their_output		)		).item()
      print(f'''max_absolute_diff = {max_absolute_diff}'''		)  # ~ 1e-7
      UpperCamelCase__     : List[str]											=					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  =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  =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 unittest

import numpy as np
import torch

from diffusers import DanceDiffusionPipeline, IPNDMScheduler, UNetaDModel
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps

from ..pipeline_params import UNCONDITIONAL_AUDIO_GENERATION_BATCH_PARAMS, UNCONDITIONAL_AUDIO_GENERATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()




class 					_lowerCamelCase			(   UpperCamelCase_						,				unittest.TestCase			):





     """simple docstring"""







     SCREAMING_SNAKE_CASE_		 =	DanceDiffusionPipeline
     SCREAMING_SNAKE_CASE_		 =	UNCONDITIONAL_AUDIO_GENERATION_PARAMS
     SCREAMING_SNAKE_CASE_		 =	PipelineTesterMixin.required_optional_params - {
         '''callback''',
         '''latents''',
         '''callback_steps''',
         '''output_type''',
         '''num_images_per_prompt''',
     }
     SCREAMING_SNAKE_CASE_		 =	UNCONDITIONAL_AUDIO_GENERATION_BATCH_PARAMS
     SCREAMING_SNAKE_CASE_		 =	False
     SCREAMING_SNAKE_CASE_		 =	False






     def  __SCREAMING_SNAKE_CASE      (				self	)	-> Any:




           """simple docstring"""







           torch.manual_seed(0	)
           UpperCamelCase__     : List[str]											=					UNetaDModel(
               block_out_channels=(3_2, 3_2, 6_4)    ,    extra_in_channels=1_6    ,    sample_size=5_1_2    ,    sample_rate=1_6_0_0_0    ,    in_channels=2    ,    out_channels=2    ,    flip_sin_to_cos=__SCREAMING_SNAKE_CASE    ,    use_timestep_embedding=__SCREAMING_SNAKE_CASE    ,    time_embedding_type='''fourier'''    ,    mid_block_type='''UNetMidBlock1D'''    ,    down_block_types=('''DownBlock1DNoSkip''', '''DownBlock1D''', '''AttnDownBlock1D''')    ,    up_block_types=('''AttnUpBlock1D''', '''UpBlock1D''', '''UpBlock1DNoSkip''')    ,    )
           UpperCamelCase__     : Union[str, Any]											=					IPNDMScheduler()

           UpperCamelCase__     : List[str]											=					{
               '''unet''': unet,
               '''scheduler''': scheduler,
           }
           return components






     def  __SCREAMING_SNAKE_CASE      (				self    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE=0	)	-> Any:




           """simple docstring"""







           if str(__SCREAMING_SNAKE_CASE	).startswith('''mps'''	):
                 UpperCamelCase__     : Optional[Any]											=					torch.manual_seed(__SCREAMING_SNAKE_CASE	)
           else:
                 UpperCamelCase__     : Optional[Any]											=					torch.Generator(device=__SCREAMING_SNAKE_CASE	).manual_seed(__SCREAMING_SNAKE_CASE	)
           UpperCamelCase__     : int											=					{
               '''batch_size''': 1,
               '''generator''': generator,
               '''num_inference_steps''': 4,
           }
           return inputs






     def  __SCREAMING_SNAKE_CASE      (				self	)	-> List[Any]:




           """simple docstring"""







           UpperCamelCase__     : str											=					'''cpu'''  # ensure determinism for the device-dependent torch.Generator
           UpperCamelCase__     : Dict											=					self.get_dummy_components()
           UpperCamelCase__     : str											=					DanceDiffusionPipeline(**__SCREAMING_SNAKE_CASE	)
           UpperCamelCase__     : List[Any]											=					pipe.to(__SCREAMING_SNAKE_CASE	)
           pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE	)

           UpperCamelCase__     : int											=					self.get_dummy_inputs(__SCREAMING_SNAKE_CASE	)
           UpperCamelCase__     : Dict											=					pipe(**__SCREAMING_SNAKE_CASE	)
           UpperCamelCase__     : List[str]											=					output.audios

           UpperCamelCase__     : Dict											=					audio[0, -3:, -3:]

           assert audio.shape == (1, 2, components["unet"].sample_size)
           UpperCamelCase__     : List[Any]											=					np.array([-0.7265, 1.0000, -0.8388, 0.1175, 0.9498, -1.0000]	)
           assert np.abs(audio_slice.flatten() - expected_slice	).max() < 1e-2






     @skip_mps
     def  __SCREAMING_SNAKE_CASE      (				self	)	-> List[Any]:




           """simple docstring"""







           return super().test_save_load_local()






     @skip_mps
     def  __SCREAMING_SNAKE_CASE      (				self	)	-> Tuple:




           """simple docstring"""







           return super().test_dict_tuple_outputs_equivalent(expected_max_difference=3e-3	)






     @skip_mps
     def  __SCREAMING_SNAKE_CASE      (				self	)	-> str:




           """simple docstring"""







           return super().test_save_load_optional_components()






     @skip_mps
     def  __SCREAMING_SNAKE_CASE      (				self	)	-> Optional[int]:




           """simple docstring"""







           return super().test_attention_slicing_forward_pass()






     def  __SCREAMING_SNAKE_CASE      (				self	)	-> Tuple:




           """simple docstring"""







           super().test_inference_batch_single_identical(expected_max_diff=3e-3	)




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





     """simple docstring"""







     def  __SCREAMING_SNAKE_CASE      (				self	)	-> int:




           """simple docstring"""







           super().tearDown()
           gc.collect()
           torch.cuda.empty_cache()






     def  __SCREAMING_SNAKE_CASE      (				self	)	-> Union[str, Any]:




           """simple docstring"""







           UpperCamelCase__     : Any											=					torch_device

           UpperCamelCase__     : Any											=					DanceDiffusionPipeline.from_pretrained('''harmonai/maestro-150k'''	)
           UpperCamelCase__     : int											=					pipe.to(__SCREAMING_SNAKE_CASE	)
           pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE	)

           UpperCamelCase__     : int											=					torch.manual_seed(0	)
           UpperCamelCase__     : Optional[Any]											=					pipe(generator=__SCREAMING_SNAKE_CASE    ,    num_inference_steps=1_0_0    ,    audio_length_in_s=4.096	)
           UpperCamelCase__     : str											=					output.audios

           UpperCamelCase__     : List[Any]											=					audio[0, -3:, -3:]

           assert audio.shape == (1, 2, pipe.unet.sample_size)
           UpperCamelCase__     : Tuple											=					np.array([-0.0192, -0.0231, -0.0318, -0.0059, 0.0002, -0.0020]	)

           assert np.abs(audio_slice.flatten() - expected_slice	).max() < 1e-2






     def  __SCREAMING_SNAKE_CASE      (				self	)	-> Union[str, Any]:




           """simple docstring"""







           UpperCamelCase__     : Any											=					torch_device

           UpperCamelCase__     : Union[str, Any]											=					DanceDiffusionPipeline.from_pretrained('''harmonai/maestro-150k'''    ,    torch_dtype=torch.floataa	)
           UpperCamelCase__     : Tuple											=					pipe.to(__SCREAMING_SNAKE_CASE	)
           pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE	)

           UpperCamelCase__     : int											=					torch.manual_seed(0	)
           UpperCamelCase__     : Union[str, Any]											=					pipe(generator=__SCREAMING_SNAKE_CASE    ,    num_inference_steps=1_0_0    ,    audio_length_in_s=4.096	)
           UpperCamelCase__     : List[Any]											=					output.audios

           UpperCamelCase__     : List[Any]											=					audio[0, -3:, -3:]

           assert audio.shape == (1, 2, pipe.unet.sample_size)
           UpperCamelCase__     : Optional[Any]											=					np.array([-0.0367, -0.0488, -0.0771, -0.0525, -0.0444, -0.0341]	)

           assert np.abs(audio_slice.flatten() - expected_slice	).max() < 1e-2


