import unittest

import numpy as np
import torch

from diffusers import DDPMPipeline, DDPMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device


enable_full_determinism()







class       __snake_case (  unittest.TestCase ):
 @property
 def 						UpperCAmelCase__							(	self    )						->				int:
       '''simple docstring'''





       torch.manual_seed(0    )
       UpperCAmelCase     :			Dict     	=UNetaDModel(
           block_out_channels=(32, 64)		, layers_per_block=2		, sample_size=32		, in_channels=3		, out_channels=3		, down_block_types=('''DownBlock2D''', '''AttnDownBlock2D''')		, up_block_types=('''AttnUpBlock2D''', '''UpBlock2D''')		, )
       return model
 def 						UpperCAmelCase__							(	self    )						->				str:
       '''simple docstring'''





       UpperCAmelCase     :			Optional[int]     	='''cpu'''
       UpperCAmelCase     :			Optional[int]     	=self.dummy_uncond_unet
       UpperCAmelCase     :			List[Any]     	=DDPMScheduler()

       UpperCAmelCase     :			List[str]     	=DDPMPipeline(unet=_lowerCAmelCase		, scheduler=_lowerCAmelCase    )
       ddpm.to(_lowerCAmelCase    )
       ddpm.set_progress_bar_config(disable=_lowerCAmelCase    )

       UpperCAmelCase     :			List[str]     	=torch.Generator(device=_lowerCAmelCase    ).manual_seed(0    )
       UpperCAmelCase     :			Dict     	=ddpm(generator=_lowerCAmelCase		, num_inference_steps=2		, output_type='''numpy'''    ).images

       UpperCAmelCase     :			Dict     	=torch.Generator(device=_lowerCAmelCase    ).manual_seed(0    )
       UpperCAmelCase     :			Optional[int]     	=ddpm(generator=_lowerCAmelCase		, num_inference_steps=2		, output_type='''numpy'''		, return_dict=_lowerCAmelCase    )[0]

       UpperCAmelCase     :			Tuple     	=image[0, -3:, -3:, -1]
       UpperCAmelCase     :			Optional[Any]     	=image_from_tuple[0, -3:, -3:, -1]

       assert image.shape == (1, 32, 32, 3)
       UpperCAmelCase     :			List[Any]     	=np.array(
           [9.9_56e-01, 5.7_85e-01, 4.6_75e-01, 9.9_30e-01, 0.0, 1.000, 1.1_99e-03, 2.6_48e-04, 5.1_01e-04]    )

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






 def 						UpperCAmelCase__							(	self    )						->				Any:
       '''simple docstring'''





       UpperCAmelCase     :			Optional[int]     	=self.dummy_uncond_unet
       UpperCAmelCase     :			str     	=DDPMScheduler(prediction_type='''sample'''    )

       UpperCAmelCase     :			int     	=DDPMPipeline(unet=_lowerCAmelCase		, scheduler=_lowerCAmelCase    )
       ddpm.to(_lowerCAmelCase    )
       ddpm.set_progress_bar_config(disable=_lowerCAmelCase    )

       UpperCAmelCase     :			Union[str, Any]     	=torch.manual_seed(0    )
       UpperCAmelCase     :			str     	=ddpm(generator=_lowerCAmelCase		, num_inference_steps=2		, output_type='''numpy'''    ).images

       UpperCAmelCase     :			Optional[Any]     	=torch.manual_seed(0    )
       UpperCAmelCase     :			Union[str, Any]     	=ddpm(generator=_lowerCAmelCase		, num_inference_steps=2		, output_type='''numpy'''    )[0]

       UpperCAmelCase     :			str     	=image[0, -3:, -3:, -1]
       UpperCAmelCase     :			Optional[Any]     	=image_eps[0, -3:, -3:, -1]

       assert image.shape == (1, 32, 32, 3)
       UpperCAmelCase     :			Optional[Any]     	=1e-2 if torch_device != '''mps''' else 3e-2
       assert np.abs(image_slice.flatten() - image_eps_slice.flatten()    ).max() < tolerance







@slow
@require_torch_gpu
class       __snake_case (  unittest.TestCase ):
 def 						UpperCAmelCase__							(	self    )						->				Any:
       '''simple docstring'''





       UpperCAmelCase     :			Any     	='''google/ddpm-cifar10-32'''

       UpperCAmelCase     :			List[Any]     	=UNetaDModel.from_pretrained(_lowerCAmelCase    )
       UpperCAmelCase     :			str     	=DDPMScheduler.from_pretrained(_lowerCAmelCase    )

       UpperCAmelCase     :			List[Any]     	=DDPMPipeline(unet=_lowerCAmelCase		, scheduler=_lowerCAmelCase    )
       ddpm.to(_lowerCAmelCase    )
       ddpm.set_progress_bar_config(disable=_lowerCAmelCase    )

       UpperCAmelCase     :			Tuple     	=torch.manual_seed(0    )
       UpperCAmelCase     :			List[str]     	=ddpm(generator=_lowerCAmelCase		, output_type='''numpy'''    ).images

       UpperCAmelCase     :			List[Any]     	=image[0, -3:, -3:, -1]

       assert image.shape == (1, 32, 32, 3)
       UpperCAmelCase     :			str     	=np.array([0.4200, 0.3588, 0.1939, 0.3847, 0.3382, 0.2647, 0.4155, 0.3582, 0.3385]    )
       assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1e-2







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()
A_        =  logging.get_logger(__name__)

A_        =  "Hello world! cécé herlolip"





def  __UpperCamelCase   (     a,  a,  a)			->Any:
					lowerCamelCase__      =							FairseqRobertaModel.from_pretrained(a)
					roberta.eval()  # disable dropout
					lowerCamelCase__      =							roberta.model.encoder.sentence_encoder
					lowerCamelCase__      =							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=514,  type_vocab_size=1,  layer_norm_eps=1E-5,  )
					if classification_head:
										lowerCamelCase__      =							roberta.model.classification_heads["mnli"].out_proj.weight.shape[0]
					print("Our BERT config:",  a)

					lowerCamelCase__      =							RobertaForSequenceClassification(a) if classification_head else RobertaForMaskedLM(a)
					model.eval()

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

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

										# self attention
										lowerCamelCase__      =							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))
										)

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

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

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

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

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

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

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

