"""simple docstring"""



import random
import unittest

import torch

from diffusers import IFSuperResolutionPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device

from ..pipeline_params import TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin

@skip_mps
class SCREAMING_SNAKE_CASE     (    _a   ,      _a   ,      unittest.TestCase	):







	"""simple docstring"""


	a_       :	Optional[Any]  			=IFSuperResolutionPipeline
	a_       :	Dict  			=TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'width', 'height'}
	a_       :	str  			=TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
	a_       :	Optional[Any]  			=PipelineTesterMixin.required_optional_params - {'latents'}


	def        _lowerCAmelCase				(   self		:      Optional[int]				)				->			Union[str, Any]:




				'''simple docstring'''






				return self._get_superresolution_dummy_components()


	def        _lowerCAmelCase				(   self		:      Optional[int]					,   _snake_case		:      List[Any]					,   _snake_case		:      Optional[int]=0				)				->			Union[str, Any]:




				'''simple docstring'''






				if str(_snake_case				).startswith('mps'				):
							a__     				=		torch.manual_seed(_snake_case				)
				else:
							a__     				=		torch.Generator(device=_snake_case				).manual_seed(_snake_case				)

				a__     				=		floats_tensor((1, 3, 32, 32)					,   rng=random.Random(_snake_case				)				).to(_snake_case				)

				a__     				=		{
				    'prompt': 'A painting of a squirrel eating a burger',
				    'image': image,
				    'generator': generator,
				    'num_inference_steps': 2,
				    'output_type': 'numpy',
				}

				return inputs


	@unittest.skipIf(
	    torch_device != 'cuda' or not is_xformers_available()					,   reason='XFormers attention is only available with CUDA and `xformers` installed'					,   )
	def        _lowerCAmelCase				(   self		:      List[str]				)				->			List[Any]:




				'''simple docstring'''






				self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3				)


	def        _lowerCAmelCase				(   self		:      Optional[int]				)				->			List[str]:




				'''simple docstring'''






				self._test_save_load_optional_components()


	@unittest.skipIf(torch_device != 'cuda'					,   reason='float16 requires CUDA'				)
	def        _lowerCAmelCase				(   self		:      Union[str, Any]				)				->			Tuple:




				'''simple docstring'''






				super().test_save_load_floataa(expected_max_diff=1E-1				)


	def        _lowerCAmelCase				(   self		:      Any				)				->			List[str]:




				'''simple docstring'''






				self._test_attention_slicing_forward_pass(expected_max_diff=1E-2				)


	def        _lowerCAmelCase				(   self		:      Dict				)				->			Tuple:




				'''simple docstring'''






				self._test_save_load_local()



	def        _lowerCAmelCase				(   self		:      Tuple				)				->			Tuple:




				'''simple docstring'''






				self._test_inference_batch_single_identical(
				    expected_max_diff=1E-2					,   )



"""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 RobertaConfig, RobertaForMaskedLM, RobertaForSequenceClassification
from transformers.models.bert.modeling_bert import (
    BertIntermediate,
    BertLayer,
    BertOutput,
    BertSelfAttention,
    BertSelfOutput,
)
from transformers.utils import logging


if version.parse(fairseq.__version__) < version.parse("0.9.0"):
     raise Exception("requires fairseq >= 0.9.0")


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

__magic_name__    				=					"Hello world! cécé herlolip"





def   _lowerCamelCase ( UpperCAmelCase__,UpperCAmelCase__,UpperCAmelCase__					)							->     Dict:


   '''simple docstring'''


   a__     				=		FairseqRobertaModel.from_pretrained(UpperCAmelCase__					)
   roberta.eval()  # disable dropout
   a__     				=		roberta.model.encoder.sentence_encoder
   a__     				=		RobertaConfig(
       vocab_size=roberta_sent_encoder.embed_tokens.num_embeddings,hidden_size=roberta.args.encoder_embed_dim,num_hidden_layers=roberta.args.encoder_layers,num_attention_heads=roberta.args.encoder_attention_heads,intermediate_size=roberta.args.encoder_ffn_embed_dim,max_position_embeddings=5_14,type_vocab_size=1,layer_norm_eps=1e-5,)
   if classification_head:
      a__     				=		roberta.model.classification_heads['mnli'].out_proj.weight.shape[0]
   print('Our BERT config:',UpperCAmelCase__					)

   a__     				=		RobertaForSequenceClassification(UpperCAmelCase__					) if classification_head else RobertaForMaskedLM(UpperCAmelCase__					)
   model.eval()

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

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

      # self attention
      a__     				=		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)					)
      )

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

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

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

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

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

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

   a__     				=		model(UpperCAmelCase__					)[0]
   if classification_head:
      a__     				=		roberta.model.classification_heads['mnli'](roberta.extract_features(UpperCAmelCase__					)					)
   else:
      a__     				=		roberta.model(UpperCAmelCase__					)[0]
   print(our_output.shape,their_output.shape					)
   a__     				=		torch.max(torch.abs(our_output - their_output					)					).item()
   print(f'''max_absolute_diff = {max_absolute_diff}'''					)  # ~ 1e-7
   a__     				=		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__":
     __magic_name__    				=					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."
     )
     __magic_name__    				=					parser.parse_args()
     convert_roberta_checkpoint_to_pytorch(
         args.roberta_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
     )


