






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					:		Optional[int]	  =						logging.get_logger(__name__)

__UpperCamelCase					:		int	  =						"""Hello world! cécé herlolip"""
def      a_ (      _A      ,   _A      ,   _A  )			->			int:







				"""simple docstring"""
				snake_case__									=     FairseqRobertaModel.from_pretrained(_A  )
				roberta.eval()  # disable dropout
				snake_case__									=     roberta.model.encoder.sentence_encoder
				snake_case__									=     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:
								snake_case__									=     roberta.model.classification_heads['mnli'].out_proj.weight.shape[0]

				print('Our RoBERTa config:'      ,   _A  )

				snake_case__									=     XLMRobertaXLForSequenceClassification(_A  ) if classification_head else XLMRobertaXLForMaskedLM(_A  )
				model.eval()

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

				snake_case__									=     roberta_sent_encoder.layer_norm.weight
				snake_case__									=     roberta_sent_encoder.layer_norm.bias

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

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

								# self attention
								snake_case__									=     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)  )
								)

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

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

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

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

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

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

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

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

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


if __name__ == "__main__":
							__UpperCamelCase					:		int	  =						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					:		int	  =						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 transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    StableDiffusionSAGPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()


class   __SCREAMING_SNAKE_CASE(	a_    ,  a_    ,  unittest.TestCase	):
							_UpperCAmelCase     =       StableDiffusionSAGPipeline
							_UpperCAmelCase     =       TEXT_TO_IMAGE_PARAMS
							_UpperCAmelCase     =       TEXT_TO_IMAGE_BATCH_PARAMS
							_UpperCAmelCase     =       TEXT_TO_IMAGE_IMAGE_PARAMS
							_UpperCAmelCase     =       TEXT_TO_IMAGE_IMAGE_PARAMS
							_UpperCAmelCase     =       False
							def lowerCAmelCase_ (     self:      int						)		->		Any:
											torch.manual_seed(0						)
											snake_case__									=     UNetaDConditionModel(
											    block_out_channels=(32, 64)       ,	layers_per_block=2       ,	sample_size=32       ,	in_channels=4       ,	out_channels=4       ,	down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')       ,	up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')       ,	cross_attention_dim=32       ,	)
											snake_case__									=     DDIMScheduler(
											    beta_start=0.00_085       ,	beta_end=0.012       ,	beta_schedule='scaled_linear'       ,	clip_sample=UpperCamelCase       ,	set_alpha_to_one=UpperCamelCase       ,	)
											torch.manual_seed(0						)
											snake_case__									=     AutoencoderKL(
											    block_out_channels=[32, 64]       ,	in_channels=3       ,	out_channels=3       ,	down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']       ,	up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']       ,	latent_channels=4       ,	)
											torch.manual_seed(0						)
											snake_case__									=     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=10_00       ,	)
											snake_case__									=     CLIPTextModel(UpperCamelCase						)
											snake_case__									=     CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'						)

											snake_case__									=     {
											    'unet': unet,
											    'scheduler': scheduler,
											    'vae': vae,
											    'text_encoder': text_encoder,
											    'tokenizer': tokenizer,
											    'safety_checker': None,
											    'feature_extractor': None,
											}
											return components
							def lowerCAmelCase_ (     self:      Optional[int]       ,	UpperCamelCase:      Dict       ,	UpperCamelCase:      Optional[int]=0						)		->		Union[str, Any]:
											if str(UpperCamelCase						).startswith('mps'						):
															snake_case__									=     torch.manual_seed(UpperCamelCase						)
											else:
															snake_case__									=     torch.Generator(device=UpperCamelCase						).manual_seed(UpperCamelCase						)
											snake_case__									=     {
											    'prompt': '.',
											    'generator': generator,
											    'num_inference_steps': 2,
											    'guidance_scale': 1.0,
											    'sag_scale': 1.0,
											    'output_type': 'numpy',
											}
											return inputs


							def lowerCAmelCase_ (     self:      Union[str, Any]						)		->		Dict:
											super().test_inference_batch_single_identical(expected_max_diff=3e-3						)


@slow
@require_torch_gpu
class   __SCREAMING_SNAKE_CASE(	unittest.TestCase	):
							def lowerCAmelCase_ (     self:      Optional[int]						)		->		Optional[int]:
											# clean up the VRAM after each test
											super().tearDown()
											gc.collect()
											torch.cuda.empty_cache()
							def lowerCAmelCase_ (     self:      Dict						)		->		Tuple:
											snake_case__									=     StableDiffusionSAGPipeline.from_pretrained('CompVis/stable-diffusion-v1-4'						)
											snake_case__									=     sag_pipe.to(UpperCamelCase						)
											sag_pipe.set_progress_bar_config(disable=UpperCamelCase						)

											snake_case__									=     '.'
											snake_case__									=     torch.manual_seed(0						)
											snake_case__									=     sag_pipe(
											    [prompt]       ,	generator=UpperCamelCase       ,	guidance_scale=7.5       ,	sag_scale=1.0       ,	num_inference_steps=20       ,	output_type='np'						)

											snake_case__									=     output.images

											snake_case__									=     image[0, -3:, -3:, -1]

											assert image.shape == (1, 5_12, 5_12, 3)
											snake_case__									=     np.array([0.1_568, 0.1_738, 0.1_695, 0.1_693, 0.1_507, 0.1_705, 0.1_547, 0.1_751, 0.1_949]						)

											assert np.abs(image_slice.flatten() - expected_slice						).max() < 5e-2
							def lowerCAmelCase_ (     self:      Union[str, Any]						)		->		List[Any]:
											snake_case__									=     StableDiffusionSAGPipeline.from_pretrained('stabilityai/stable-diffusion-2-1-base'						)
											snake_case__									=     sag_pipe.to(UpperCamelCase						)
											sag_pipe.set_progress_bar_config(disable=UpperCamelCase						)

											snake_case__									=     '.'
											snake_case__									=     torch.manual_seed(0						)
											snake_case__									=     sag_pipe(
											    [prompt]       ,	generator=UpperCamelCase       ,	guidance_scale=7.5       ,	sag_scale=1.0       ,	num_inference_steps=20       ,	output_type='np'						)

											snake_case__									=     output.images

											snake_case__									=     image[0, -3:, -3:, -1]

											assert image.shape == (1, 5_12, 5_12, 3)
											snake_case__									=     np.array([0.3_459, 0.2_876, 0.2_537, 0.3_002, 0.2_671, 0.2_160, 0.3_026, 0.2_262, 0.2_371]						)

											assert np.abs(image_slice.flatten() - expected_slice						).max() < 5e-2


							def lowerCAmelCase_ (     self:      Union[str, Any]						)		->		Optional[Any]:
											snake_case__									=     StableDiffusionSAGPipeline.from_pretrained('stabilityai/stable-diffusion-2-1-base'						)
											snake_case__									=     sag_pipe.to(UpperCamelCase						)
											sag_pipe.set_progress_bar_config(disable=UpperCamelCase						)

											snake_case__									=     '.'
											snake_case__									=     torch.manual_seed(0						)
											snake_case__									=     sag_pipe(
											    [prompt]       ,	width=7_68       ,	height=5_12       ,	generator=UpperCamelCase       ,	guidance_scale=7.5       ,	sag_scale=1.0       ,	num_inference_steps=20       ,	output_type='np'       ,	)

											snake_case__									=     output.images

											assert image.shape == (1, 5_12, 7_68, 3)




