





import argparse
import os
import pathlib

import fairseq
import torch
from fairseq.modules import TransformerSentenceEncoderLayer
from packaging import version

from transformers import (
    DataaVecTextConfig,
    DataaVecTextForMaskedLM,
    DataaVecTextForSequenceClassification,
    DataaVecTextModel,
)
from transformers.models.bert.modeling_bert import (
    BertIntermediate,
    BertLayer,
    BertOutput,
    BertSelfAttention,
    BertSelfOutput,
)

# IMPORTANT: In order for this script to run, please make sure to download the dictionary: `dict.txt` from wget https://dl.fbaipublicfiles.com/fairseq/models/roberta.large.tar.gz
# File copied from https://github.com/pytorch/fairseq/blob/main/examples/data2vec/models/data2vec_text.py
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)			->Tuple:
					lowerCamelCase__     ,		lowerCamelCase__      =							os.path.split(a)
					lowerCamelCase__      =							DataaVecTextModel.from_pretrained(
					    a,  checkpoint_file=a)
					dataavec.eval()  # disable dropout
					lowerCamelCase__      =							dataavec.models[0]
					lowerCamelCase__      =							dataavec_model.encoder.sentence_encoder
					lowerCamelCase__      =							DataaVecTextConfig(
					    vocab_size=dataavec_sent_encoder.embed_tokens.num_embeddings,  hidden_size=dataavec_model.args.encoder_embed_dim,  num_hidden_layers=dataavec_model.args.encoder_layers,  num_attention_heads=dataavec_model.args.encoder_attention_heads,  intermediate_size=dataavec_model.args.encoder_ffn_embed_dim,  max_position_embeddings=514,  type_vocab_size=1,  layer_norm_eps=1E-5,  )
					if classification_head:
										lowerCamelCase__      =							dataavec.model.classification_heads["mnli"].out_proj.weight.shape[0]
					print("Our BERT config:",  a)

					lowerCamelCase__      =							DataaVecTextForSequenceClassification(a) if classification_head else DataaVecTextForMaskedLM(a)
					model.eval()

					# Now let's copy all the weights.
					# Embeddings
					lowerCamelCase__      =							dataavec_sent_encoder.embed_tokens.weight
					lowerCamelCase__      =							dataavec_sent_encoder.embed_positions.weight
					lowerCamelCase__      =							torch.zeros_like(
					    model.dataavec_text.embeddings.token_type_embeddings.weight)  # just zero them out b/c data2vec doesn't use them.
					lowerCamelCase__      =							dataavec_sent_encoder.layernorm_embedding.weight
					lowerCamelCase__      =							dataavec_sent_encoder.layernorm_embedding.bias

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

										# self attention
										lowerCamelCase__      =							layer.attention.self
										assert dataavec_layer.self_attn.k_proj.weight.data.shape == torch.Size(
										    (config.hidden_size, config.hidden_size)), (
										    "Shape for data2vec_layer.self_attn.k_proj.weight.data should be"
										    f" {torch.Size((config.hidden_size, config.hidden_size))}"
										)
										assert dataavec_layer.self_attn.q_proj.weight.data.shape == torch.Size(
										    (config.hidden_size, config.hidden_size)), (
										    "Shape for data2vec_layer.self_attn.q_proj.weight.data should be"
										    f" {torch.Size((config.hidden_size, config.hidden_size))}"
										)
										assert dataavec_layer.self_attn.v_proj.weight.data.shape == torch.Size(
										    (config.hidden_size, config.hidden_size)), (
										    "Shape for data2vec_layer.self_attn.v_proj.weight.data should be"
										    f" {torch.Size((config.hidden_size, config.hidden_size))}"
										)

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

										# self-attention output
										lowerCamelCase__      =							layer.attention.output
										assert (
										    self_output.dense.weight.shape == dataavec_layer.self_attn.out_proj.weight.shape
										), f"Shape for self_output.dense.weight should be {dataavec_layer.self_attn.out_proj.weight.shape}"
										lowerCamelCase__      =							dataavec_layer.self_attn.out_proj.weight
										lowerCamelCase__      =							dataavec_layer.self_attn.out_proj.bias
										lowerCamelCase__      =							dataavec_layer.self_attn_layer_norm.weight
										lowerCamelCase__      =							dataavec_layer.self_attn_layer_norm.bias

										# intermediate
										lowerCamelCase__      =							layer.intermediate
										assert (
										    intermediate.dense.weight.shape == dataavec_layer.fca.weight.shape
										), f"Shape for intermediate.dense.weight should be {dataavec_layer.fca.weight.shape}"
										lowerCamelCase__      =							dataavec_layer.fca.weight
										lowerCamelCase__      =							dataavec_layer.fca.bias

										# output
										lowerCamelCase__      =							layer.output
										assert (
										    bert_output.dense.weight.shape == dataavec_layer.fca.weight.shape
										), f"Shape for bert_output.dense.weight should be {dataavec_layer.fca.weight.shape}"
										lowerCamelCase__      =							dataavec_layer.fca.weight
										lowerCamelCase__      =							dataavec_layer.fca.bias
										lowerCamelCase__      =							dataavec_layer.final_layer_norm.weight
										lowerCamelCase__      =							dataavec_layer.final_layer_norm.bias
										# end of layer

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

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

					lowerCamelCase__      =							model(a)[0]
					if classification_head:
										lowerCamelCase__      =							dataavec.model.classification_heads["mnli"](dataavec.extract_features(a))
					else:
										lowerCamelCase__      =							dataavec_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(
						    "--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_dataavec_checkpoint_to_pytorch(
						    args.checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
						)








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 SCREAMING_SNAKE_CASE_			(       unittest.TestCase ):





					"""simple docstring"""


					@property
					def        __magic_name__			(			self						):
										torch.manual_seed(0						)
										lowerCamelCase__      =							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        __magic_name__			(			self						):
										lowerCamelCase__      =							"cpu"
										lowerCamelCase__      =							self.dummy_uncond_unet
										lowerCamelCase__      =							DDPMScheduler()

										lowerCamelCase__      =							DDPMPipeline(unet=_lowerCAmelCase						,	scheduler=_lowerCAmelCase						)
										ddpm.to(_lowerCAmelCase						)
										ddpm.set_progress_bar_config(disable=_lowerCAmelCase						)

										lowerCamelCase__      =							torch.Generator(device=_lowerCAmelCase						).manual_seed(0						)
										lowerCamelCase__      =							ddpm(generator=_lowerCAmelCase						,	num_inference_steps=2						,	output_type="numpy"						).images

										lowerCamelCase__      =							torch.Generator(device=_lowerCAmelCase						).manual_seed(0						)
										lowerCamelCase__      =							ddpm(generator=_lowerCAmelCase						,	num_inference_steps=2						,	output_type="numpy"						,	return_dict=_lowerCAmelCase						)[0]

										lowerCamelCase__      =							image[0, -3:, -3:, -1]
										lowerCamelCase__      =							image_from_tuple[0, -3:, -3:, -1]

										assert image.shape == (1, 32, 32, 3)
										lowerCamelCase__      =							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        __magic_name__			(			self						):
										lowerCamelCase__      =							self.dummy_uncond_unet
										lowerCamelCase__      =							DDPMScheduler(prediction_type="sample"						)

										lowerCamelCase__      =							DDPMPipeline(unet=_lowerCAmelCase						,	scheduler=_lowerCAmelCase						)
										ddpm.to(_lowerCAmelCase						)
										ddpm.set_progress_bar_config(disable=_lowerCAmelCase						)

										lowerCamelCase__      =							torch.manual_seed(0						)
										lowerCamelCase__      =							ddpm(generator=_lowerCAmelCase						,	num_inference_steps=2						,	output_type="numpy"						).images

										lowerCamelCase__      =							torch.manual_seed(0						)
										lowerCamelCase__      =							ddpm(generator=_lowerCAmelCase						,	num_inference_steps=2						,	output_type="numpy"						)[0]

										lowerCamelCase__      =							image[0, -3:, -3:, -1]
										lowerCamelCase__      =							image_eps[0, -3:, -3:, -1]

										assert image.shape == (1, 32, 32, 3)
										lowerCamelCase__      =							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 SCREAMING_SNAKE_CASE_			(       unittest.TestCase ):





					"""simple docstring"""


					def        __magic_name__			(			self						):
										lowerCamelCase__      =							"google/ddpm-cifar10-32"

										lowerCamelCase__      =							UNetaDModel.from_pretrained(_lowerCAmelCase						)
										lowerCamelCase__      =							DDPMScheduler.from_pretrained(_lowerCAmelCase						)

										lowerCamelCase__      =							DDPMPipeline(unet=_lowerCAmelCase						,	scheduler=_lowerCAmelCase						)
										ddpm.to(_lowerCAmelCase						)
										ddpm.set_progress_bar_config(disable=_lowerCAmelCase						)

										lowerCamelCase__      =							torch.manual_seed(0						)
										lowerCamelCase__      =							ddpm(generator=_lowerCAmelCase						,	output_type="numpy"						).images

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

										assert image.shape == (1, 32, 32, 3)
										lowerCamelCase__      =							np.array([0.4_200, 0.3_588, 0.1_939, 0.3_847, 0.3_382, 0.2_647, 0.4_155, 0.3_582, 0.3_385]						)
										assert np.abs(image_slice.flatten() - expected_slice						).max() < 1E-2

