


'''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	(    SCREAMING_SNAKE_CASE						,			unittest.TestCase):


				'''simple docstring'''


				_snake_case						       =				ShapEPipeline
				_snake_case						       =				['''prompt''']
				_snake_case						       =				['''prompt''']
				_snake_case						       =				[
				    '''num_images_per_prompt''',
				    '''num_inference_steps''',
				    '''generator''',
				    '''latents''',
				    '''guidance_scale''',
				    '''frame_size''',
				    '''output_type''',
				    '''return_dict''',
				]
				_snake_case						       =				False

				@property
				def 	a__		(     self							)    ->  Optional[int]:
					return 3_2

				@property
				def 	a__		(     self							)    ->  Tuple:
					return 3_2

				@property
				def 	a__		(     self							)    ->  Union[str, Any]:
					return self.time_input_dim * 4

				@property
				def 	a__		(     self							)    ->  Optional[Any]:
					return 8

				@property
				def 	a__		(     self							)    ->  Dict:
					lowercase						:		Optional[Any]   					=		CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"							)
					return tokenizer

				@property
				def 	a__		(     self							)    ->  Optional[Any]:
					torch.manual_seed(0							)
					lowercase						:		Any   					=		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=3_7							,      layer_norm_eps=1e-0_5							,      num_attention_heads=4							,      num_hidden_layers=5							,      pad_token_id=1							,      vocab_size=1_0_0_0							,      )
					return CLIPTextModelWithProjection(a_							)

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

					lowercase						:		List[str]   					=		{
					    "num_attention_heads": 2,
					    "attention_head_dim": 1_6,
					    "embedding_dim": self.time_input_dim,
					    "num_embeddings": 3_2,
					    "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,
					}

					lowercase						:		str   					=		PriorTransformer(**a_							)
					return model

				@property
				def 	a__		(     self							)    ->  Union[str, Any]:
					torch.manual_seed(0							)

					lowercase						:		int   					=		{
					    "param_shapes": (
					        (self.renderer_dim, 9_3),
					        (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": 1_2,
					    "background": (
					        0.1,
					        0.1,
					        0.1,
					    ),
					}
					lowercase						:		Optional[Any]   					=		ShapERenderer(**a_							)
					return model

				def 	a__		(     self							)    ->  Optional[Any]:
					lowercase						:		List[str]   					=		self.dummy_prior
					lowercase						:		Optional[Any]   					=		self.dummy_text_encoder
					lowercase						:		List[str]   					=		self.dummy_tokenizer
					lowercase						:		Tuple   					=		self.dummy_renderer

					lowercase						:		List[Any]   					=		HeunDiscreteScheduler(
					    beta_schedule="exp"							,      num_train_timesteps=1_0_2_4							,      prediction_type="sample"							,      use_karras_sigmas=a_							,      clip_sample=a_							,      clip_sample_range=1.0							,      )
					lowercase						:		Tuple   					=		{
					    "prior": prior,
					    "text_encoder": text_encoder,
					    "tokenizer": tokenizer,
					    "renderer": renderer,
					    "scheduler": scheduler,
					}

					return components

				def 	a__		(     self							,      a_							,      a_=0							)    ->  Dict:
					if str(a_							).startswith("mps"							):
						lowercase						:		Union[str, Any]   					=		torch.manual_seed(a_							)
					else:
						lowercase						:		Optional[Any]   					=		torch.Generator(device=a_							).manual_seed(a_							)
					lowercase						:		Dict   					=		{
					    "prompt": "horse",
					    "generator": generator,
					    "num_inference_steps": 1,
					    "frame_size": 3_2,
					    "output_type": "np",
					}
					return inputs

				def 	a__		(     self							)    ->  Dict:
					lowercase						:		Optional[Any]   					=		"cpu"

					lowercase						:		Optional[int]   					=		self.get_dummy_components()

					lowercase						:		Optional[Any]   					=		self.pipeline_class(**a_							)
					lowercase						:		int   					=		pipe.to(a_							)

					pipe.set_progress_bar_config(disable=a_							)

					lowercase						:		Optional[int]   					=		pipe(**self.get_dummy_inputs(a_							)							)
					lowercase						:		Dict   					=		output.images[0]
					lowercase						:		List[Any]   					=		image[0, -3:, -3:, -1]

					assert image.shape == (2_0, 3_2, 3_2, 3)

					lowercase						:		str   					=		np.array(
					    [
					        0.00_03_92_16,
					        0.00_03_92_16,
					        0.00_03_92_16,
					        0.00_03_92_16,
					        0.00_03_92_16,
					        0.00_03_92_16,
					        0.00_03_92_16,
					        0.00_03_92_16,
					        0.00_03_92_16,
					    ]							)

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

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

				def 	a__		(     self							)    ->  Optional[int]:
					lowercase						:		List[Any]   					=		torch_device == "cpu"
					lowercase						:		Any   					=		True

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


				def 	a__		(     self							)    ->  Optional[int]:
					lowercase						:		Optional[int]   					=		self.get_dummy_components()
					lowercase						:		str   					=		self.pipeline_class(**a_							)
					lowercase						:		Dict   					=		pipe.to(a_							)
					pipe.set_progress_bar_config(disable=a_							)

					lowercase						:		Tuple   					=		1
					lowercase						:		str   					=		2

					lowercase						:		Optional[Any]   					=		self.get_dummy_inputs(a_							)

					for key in inputs.keys():
						if key in self.batch_params:
							lowercase						:		List[Any]   					=		batch_size * [inputs[key]]

					lowercase						:		Any   					=		pipe(**a_							,      num_images_per_prompt=a_							)[0]

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




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


				'''simple docstring'''


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


				def 	a__		(     self							)    ->  Any:
					lowercase						:		int   					=		load_numpy(
					    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
					    "/shap_e/test_shap_e_np_out.npy"							)
					lowercase						:		Dict   					=		ShapEPipeline.from_pretrained("openai/shap-e"							)
					lowercase						:		Dict   					=		pipe.to(a_							)
					pipe.set_progress_bar_config(disable=a_							)

					lowercase						:		List[Any]   					=		torch.Generator(device=a_							).manual_seed(0							)

					lowercase						:		Tuple   					=		pipe(
					    "a shark"							,      generator=a_							,      guidance_scale=15.0							,      num_inference_steps=6_4							,      frame_size=6_4							,      output_type="np"							,      ).images[0]

					assert images.shape == (2_0, 6_4, 6_4, 3)

					assert_mean_pixel_difference(a_							,      a_							)






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

lowerCAmelCase   : Tuple             =				"""Hello world! cécé herlolip"""




def 	_A  (       A							,A							,A							)  ->				str:
	lowercase						:		Optional[Any]   					=		FairseqRobertaModel.from_pretrained(A							)
	roberta.eval()  # disable dropout
	lowercase						:		int   					=		roberta.model.encoder.sentence_encoder
	lowercase						:		Tuple   					=		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:
		lowercase						:		Tuple   					=		roberta.model.classification_heads["mnli"].out_proj.weight.shape[0]

	print("Our RoBERTa config:"							,A							)

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

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

	lowercase						:		Dict   					=		roberta_sent_encoder.layer_norm.weight
	lowercase						:		str   					=		roberta_sent_encoder.layer_norm.bias

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

		lowercase						:		RobertaAttention   					=		layer.attention
		lowercase						:		str   					=		roberta_layer.self_attn_layer_norm.weight
		lowercase						:		List[str]   					=		roberta_layer.self_attn_layer_norm.bias

		# self attention
		lowercase						:		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)							)
		)

		lowercase						:		str   					=		roberta_layer.self_attn.q_proj.weight
		lowercase						:		List[str]   					=		roberta_layer.self_attn.q_proj.bias
		lowercase						:		Union[str, Any]   					=		roberta_layer.self_attn.k_proj.weight
		lowercase						:		List[Any]   					=		roberta_layer.self_attn.k_proj.bias
		lowercase						:		Dict   					=		roberta_layer.self_attn.v_proj.weight
		lowercase						:		Tuple   					=		roberta_layer.self_attn.v_proj.bias

		# self-attention output
		lowercase						:		BertSelfOutput   					=		layer.attention.output
		assert self_output.dense.weight.shape == roberta_layer.self_attn.out_proj.weight.shape
		lowercase						:		Tuple   					=		roberta_layer.self_attn.out_proj.weight
		lowercase						:		str   					=		roberta_layer.self_attn.out_proj.bias

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

		# intermediate
		lowercase						:		BertIntermediate   					=		layer.intermediate
		assert intermediate.dense.weight.shape == roberta_layer.fca.weight.shape
		lowercase						:		Optional[int]   					=		roberta_layer.fca.weight
		lowercase						:		Dict   					=		roberta_layer.fca.bias

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

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

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

	lowercase						:		Tuple   					=		model(A							)[0]
	if classification_head:
		lowercase						:		Union[str, Any]   					=		roberta.model.classification_heads["mnli"](roberta.extract_features(A							)							)
	else:
		lowercase						:		Optional[Any]   					=		roberta.model(A							)[0]
	print(our_output.shape							,their_output.shape							)
	lowercase						:		Optional[Any]   					=		torch.max(torch.abs(our_output - their_output							)							).item()
	print(F'''max_absolute_diff = {max_absolute_diff}'''							)  # ~ 1e-7
	lowercase						:		Dict   					=		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__":
					lowerCAmelCase   : Union[str, Any]             =				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   : Optional[Any]             =				parser.parse_args()
					convert_xlm_roberta_xl_checkpoint_to_pytorch(
					    args.roberta_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
					)


