






"""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_       			=   logging.get_logger(__name__)

lowerCamelCase_       			=   'Hello world! cécé herlolip'

def  snake_case   (     A__			,A__			,A__		):
			UpperCAmelCase_  :				Optional[Any]      =      FairseqRobertaModel.from_pretrained(lowerCAmelCase_		)
			roberta.eval()  # disable dropout
			UpperCAmelCase_  :				Optional[int]      =      roberta.model.encoder.sentence_encoder
			UpperCAmelCase_  :				Optional[Any]      =      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_14			,type_vocab_size=1			,layer_norm_eps=1e-5			,)
			if classification_head:
						UpperCAmelCase_  :				Dict      =      roberta.model.classification_heads['''mnli'''].out_proj.weight.shape[0]

			print("Our RoBERTa config:"			,lowerCAmelCase_		)

			UpperCAmelCase_  :				List[str]      =      XLMRobertaXLForSequenceClassification(lowerCAmelCase_		) if classification_head else XLMRobertaXLForMaskedLM(lowerCAmelCase_		)
			model.eval()

			# Now let's copy all the weights.
			# Embeddings
			UpperCAmelCase_  :				int      =      roberta_sent_encoder.embed_tokens.weight
			UpperCAmelCase_  :				Union[str, 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_  :				List[Any]      =      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_  :				Optional[Any]      =      roberta_layer.self_attn_layer_norm.weight
						UpperCAmelCase_  :				List[str]      =      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_  :				int      =      roberta_layer.self_attn.q_proj.bias
						UpperCAmelCase_  :				Optional[int]      =      roberta_layer.self_attn.k_proj.weight
						UpperCAmelCase_  :				Any      =      roberta_layer.self_attn.k_proj.bias
						UpperCAmelCase_  :				List[Any]      =      roberta_layer.self_attn.v_proj.weight
						UpperCAmelCase_  :				Any      =      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_  :				Optional[Any]      =      roberta_layer.self_attn.out_proj.weight
						UpperCAmelCase_  :				Union[str, Any]      =      roberta_layer.self_attn.out_proj.bias

						# this one is final layer norm
						UpperCAmelCase_  :				str      =      roberta_layer.final_layer_norm.weight
						UpperCAmelCase_  :				Optional[Any]      =      roberta_layer.final_layer_norm.bias

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

						# output
						UpperCAmelCase_  :				BertOutput      =      layer.output
						assert bert_output.dense.weight.shape == roberta_layer.fca.weight.shape
						UpperCAmelCase_  :				Optional[int]      =      roberta_layer.fca.weight
						UpperCAmelCase_  :				Any      =      roberta_layer.fca.bias
						# end of layer

			if classification_head:
						UpperCAmelCase_  :				List[Any]      =      roberta.model.classification_heads['''mnli'''].dense.weight
						UpperCAmelCase_  :				Tuple      =      roberta.model.classification_heads['''mnli'''].dense.bias
						UpperCAmelCase_  :				int      =      roberta.model.classification_heads['''mnli'''].out_proj.weight
						UpperCAmelCase_  :				Any      =      roberta.model.classification_heads['''mnli'''].out_proj.bias
			else:
						# LM Head
						UpperCAmelCase_  :				Tuple      =      roberta.model.encoder.lm_head.dense.weight
						UpperCAmelCase_  :				Union[str, Any]      =      roberta.model.encoder.lm_head.dense.bias
						UpperCAmelCase_  :				int      =      roberta.model.encoder.lm_head.layer_norm.weight
						UpperCAmelCase_  :				Dict      =      roberta.model.encoder.lm_head.layer_norm.bias
						UpperCAmelCase_  :				int      =      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(lowerCAmelCase_		).unsqueeze(0		)  # batch of size 1

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

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


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
			)











"""simple docstring"""
import gc
import unittest

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

from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.utils import load_numpy, slow
from diffusers.utils.testing_utils import require_torch_gpu, torch_device

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference







class     UpperCamelCase_ (__A       ,		unittest.TestCase  ):
	__magic_name__			   =       ShapEPipeline
	__magic_name__			   =       ['''prompt''']
	__magic_name__			   =       ['''prompt''']
	__magic_name__			   =       [
	    '''num_images_per_prompt''',
	    '''num_inference_steps''',
	    '''generator''',
	    '''latents''',
	    '''guidance_scale''',
	    '''frame_size''',
	    '''output_type''',
	    '''return_dict''',
	]
	__magic_name__			   =       False





	@property
	def   _SCREAMING_SNAKE_CASE   (   self      :				Union[str, Any]		)		->    Dict:
				return 32





	@property
	def   _SCREAMING_SNAKE_CASE   (   self      :				List[str]		)		->    Tuple:
				return 32





	@property
	def   _SCREAMING_SNAKE_CASE   (   self      :				List[Any]		)		->    Optional[int]:
				return self.time_input_dim * 4





	@property
	def   _SCREAMING_SNAKE_CASE   (   self      :				Union[str, Any]		)		->    List[Any]:
				return 8





	@property
	def   _SCREAMING_SNAKE_CASE   (   self      :				Dict		)		->    int:
				UpperCAmelCase_  :				str      =      CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"		)
				return tokenizer





	@property
	def   _SCREAMING_SNAKE_CASE   (   self      :				Union[str, Any]		)		->    List[str]:
				torch.manual_seed(0		)
				UpperCAmelCase_  :				Dict      =      CLIPTextConfig(
				    bos_token_id=0			,			eos_token_id=2			,			hidden_size=self.text_embedder_hidden_size			,			projection_dim=self.text_embedder_hidden_size			,			intermediate_size=37			,			layer_norm_eps=1e-05			,			num_attention_heads=4			,			num_hidden_layers=5			,			pad_token_id=1			,			vocab_size=1_000			,			)
				return CLIPTextModelWithProjection(lowerCAmelCase_		)





	@property
	def   _SCREAMING_SNAKE_CASE   (   self      :				Dict		)		->    Optional[Any]:
				torch.manual_seed(0		)

				UpperCAmelCase_  :				str      =      {
				    "num_attention_heads": 2,
				    "attention_head_dim": 16,
				    "embedding_dim": self.time_input_dim,
				    "num_embeddings": 32,
				    "embedding_proj_dim": self.text_embedder_hidden_size,
				    "time_embed_dim": self.time_embed_dim,
				    "num_layers": 1,
				    "clip_embed_dim": self.time_input_dim * 2,
				    "additional_embeddings": 0,
				    "time_embed_act_fn": "gelu",
				    "norm_in_type": "layer",
				    "encoder_hid_proj_type": None,
				    "added_emb_type": None,
				}

				UpperCAmelCase_  :				List[Any]      =      PriorTransformer(**lowerCAmelCase_		)
				return model





	@property
	def   _SCREAMING_SNAKE_CASE   (   self      :				Optional[Any]		)		->    int:
				torch.manual_seed(0		)

				UpperCAmelCase_  :				Any      =      {
				    "param_shapes": (
				        (self.renderer_dim, 93),
				        (self.renderer_dim, 8),
				        (self.renderer_dim, 8),
				        (self.renderer_dim, 8),
				    ),
				    "d_latent": self.time_input_dim,
				    "d_hidden": self.renderer_dim,
				    "n_output": 12,
				    "background": (
				        0.1,
				        0.1,
				        0.1,
				    ),
				}
				UpperCAmelCase_  :				Tuple      =      ShapERenderer(**lowerCAmelCase_		)
				return model





	def   _SCREAMING_SNAKE_CASE   (   self      :				List[str]		)		->    Tuple:
				UpperCAmelCase_  :				str      =      self.dummy_prior
				UpperCAmelCase_  :				Dict      =      self.dummy_text_encoder
				UpperCAmelCase_  :				Any      =      self.dummy_tokenizer
				UpperCAmelCase_  :				int      =      self.dummy_renderer

				UpperCAmelCase_  :				Optional[int]      =      HeunDiscreteScheduler(
				    beta_schedule="exp"			,			num_train_timesteps=1_024			,			prediction_type="sample"			,			use_karras_sigmas=lowerCAmelCase_			,			clip_sample=lowerCAmelCase_			,			clip_sample_range=1.0			,			)
				UpperCAmelCase_  :				str      =      {
				    "prior": prior,
				    "text_encoder": text_encoder,
				    "tokenizer": tokenizer,
				    "renderer": renderer,
				    "scheduler": scheduler,
				}

				return components





	def   _SCREAMING_SNAKE_CASE   (   self      :				Any			,			lowerCAmelCase_      :				int			,			lowerCAmelCase_      :				Any=0		)		->    Union[str, Any]:
				if str(lowerCAmelCase_		).startswith("mps"		):
							UpperCAmelCase_  :				str      =      torch.manual_seed(lowerCAmelCase_		)
				else:
							UpperCAmelCase_  :				Union[str, Any]      =      torch.Generator(device=lowerCAmelCase_		).manual_seed(lowerCAmelCase_		)
				UpperCAmelCase_  :				List[Any]      =      {
				    "prompt": "horse",
				    "generator": generator,
				    "num_inference_steps": 1,
				    "frame_size": 32,
				    "output_type": "np",
				}
				return inputs





	def   _SCREAMING_SNAKE_CASE   (   self      :				str		)		->    List[Any]:
				UpperCAmelCase_  :				Any      =      "cpu"

				UpperCAmelCase_  :				Union[str, Any]      =      self.get_dummy_components()

				UpperCAmelCase_  :				Tuple      =      self.pipeline_class(**lowerCAmelCase_		)
				UpperCAmelCase_  :				Union[str, Any]      =      pipe.to(lowerCAmelCase_		)

				pipe.set_progress_bar_config(disable=lowerCAmelCase_		)

				UpperCAmelCase_  :				int      =      pipe(**self.get_dummy_inputs(lowerCAmelCase_		)		)
				UpperCAmelCase_  :				Dict      =      output.images[0]
				UpperCAmelCase_  :				Any      =      image[0, -3:, -3:, -1]

				assert image.shape == (20, 32, 32, 3)

				UpperCAmelCase_  :				Optional[int]      =      np.array(
				    [
				        0.0_0_0_3_9_2_1_6,
				        0.0_0_0_3_9_2_1_6,
				        0.0_0_0_3_9_2_1_6,
				        0.0_0_0_3_9_2_1_6,
				        0.0_0_0_3_9_2_1_6,
				        0.0_0_0_3_9_2_1_6,
				        0.0_0_0_3_9_2_1_6,
				        0.0_0_0_3_9_2_1_6,
				        0.0_0_0_3_9_2_1_6,
				    ]		)

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





	def   _SCREAMING_SNAKE_CASE   (   self      :				List[str]		)		->    Optional[Any]:
				# NOTE: Larger batch sizes cause this test to timeout, only test on smaller batches
				self._test_inference_batch_consistent(batch_sizes=[1, 2]		)





	def   _SCREAMING_SNAKE_CASE   (   self      :				int		)		->    List[str]:
				UpperCAmelCase_  :				Optional[Any]      =      torch_device == "cpu"
				UpperCAmelCase_  :				Dict      =      True

				self._test_inference_batch_single_identical(
				    batch_size=2			,			test_max_difference=lowerCAmelCase_			,			relax_max_difference=lowerCAmelCase_			,			)





	def   _SCREAMING_SNAKE_CASE   (   self      :				Any		)		->    List[str]:
				UpperCAmelCase_  :				int      =      self.get_dummy_components()
				UpperCAmelCase_  :				Tuple      =      self.pipeline_class(**lowerCAmelCase_		)
				UpperCAmelCase_  :				List[str]      =      pipe.to(lowerCAmelCase_		)
				pipe.set_progress_bar_config(disable=lowerCAmelCase_		)

				UpperCAmelCase_  :				List[str]      =      1
				UpperCAmelCase_  :				List[Any]      =      2

				UpperCAmelCase_  :				int      =      self.get_dummy_inputs(lowerCAmelCase_		)

				for key in inputs.keys():
							if key in self.batch_params:
										UpperCAmelCase_  :				int      =      batch_size * [inputs[key]]

				UpperCAmelCase_  :				int      =      pipe(**lowerCAmelCase_			,			num_images_per_prompt=lowerCAmelCase_		)[0]

				assert images.shape[0] == batch_size * num_images_per_prompt







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





	def   _SCREAMING_SNAKE_CASE   (   self      :				Union[str, Any]		)		->    Any:
				# clean up the VRAM after each test
				super().tearDown()
				gc.collect()
				torch.cuda.empty_cache()





	def   _SCREAMING_SNAKE_CASE   (   self      :				str		)		->    Union[str, Any]:
				UpperCAmelCase_  :				Any      =      load_numpy(
				    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
				    "/shap_e/test_shap_e_np_out.npy"		)
				UpperCAmelCase_  :				str      =      ShapEPipeline.from_pretrained("openai/shap-e"		)
				UpperCAmelCase_  :				Union[str, Any]      =      pipe.to(lowerCAmelCase_		)
				pipe.set_progress_bar_config(disable=lowerCAmelCase_		)

				UpperCAmelCase_  :				Union[str, Any]      =      torch.Generator(device=lowerCAmelCase_		).manual_seed(0		)

				UpperCAmelCase_  :				Optional[int]      =      pipe(
				    "a shark"			,			generator=lowerCAmelCase_			,			guidance_scale=1_5.0			,			num_inference_steps=64			,			frame_size=64			,			output_type="np"			,			).images[0]

				assert images.shape == (20, 64, 64, 3)

				assert_mean_pixel_difference(lowerCAmelCase_			,			lowerCAmelCase_		)



