


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()
snake_case			:      Any 							=   logging.get_logger(__name__)

snake_case			:      Optional[int] 							=   'Hello world! cécé herlolip'


def 				SCREAMING_SNAKE_CASE (     UpperCAmelCase__   ,UpperCAmelCase__   ,UpperCAmelCase__							)     ->   str:

				"""simple docstring"""






				_SCREAMING_SNAKE_CASE      ,      _SCREAMING_SNAKE_CASE      = os.path.split(UpperCAmelCase__							)
				_SCREAMING_SNAKE_CASE      = DataaVecTextModel.from_pretrained(
				    UpperCAmelCase__   ,checkpoint_file=UpperCAmelCase__							)
				dataavec.eval()  # disable dropout
				_SCREAMING_SNAKE_CASE      = dataavec.models[0]
				_SCREAMING_SNAKE_CASE      = dataavec_model.encoder.sentence_encoder
				_SCREAMING_SNAKE_CASE      = 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:
								_SCREAMING_SNAKE_CASE      = dataavec.model.classification_heads['mnli'].out_proj.weight.shape[0]
				print('Our BERT config:'   ,UpperCAmelCase__							)

				_SCREAMING_SNAKE_CASE      = DataaVecTextForSequenceClassification(UpperCAmelCase__							) if classification_head else DataaVecTextForMaskedLM(UpperCAmelCase__							)
				model.eval()

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

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

								# self attention
								_SCREAMING_SNAKE_CASE      = 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)							)}'''
								)

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

								# self-attention output
								_SCREAMING_SNAKE_CASE      = 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}'''
								_SCREAMING_SNAKE_CASE      = dataavec_layer.self_attn.out_proj.weight
								_SCREAMING_SNAKE_CASE      = dataavec_layer.self_attn.out_proj.bias
								_SCREAMING_SNAKE_CASE      = dataavec_layer.self_attn_layer_norm.weight
								_SCREAMING_SNAKE_CASE      = dataavec_layer.self_attn_layer_norm.bias

								# intermediate
								_SCREAMING_SNAKE_CASE      = 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}'''
								_SCREAMING_SNAKE_CASE      = dataavec_layer.fca.weight
								_SCREAMING_SNAKE_CASE      = dataavec_layer.fca.bias

								# output
								_SCREAMING_SNAKE_CASE      = 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}'''
								_SCREAMING_SNAKE_CASE      = dataavec_layer.fca.weight
								_SCREAMING_SNAKE_CASE      = dataavec_layer.fca.bias
								_SCREAMING_SNAKE_CASE      = dataavec_layer.final_layer_norm.weight
								_SCREAMING_SNAKE_CASE      = dataavec_layer.final_layer_norm.bias
								# end of layer

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

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

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

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


if __name__ == "__main__":
							snake_case			:      Union[str, Any] 							=   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.'
							)
							snake_case			:      Tuple 							=   parser.parse_args()
							convert_dataavec_checkpoint_to_pytorch(
							    args.checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
							)




import tempfile
import unittest

import torch

from diffusers import UNetaDConditionModel
from diffusers.training_utils import EMAModel
from diffusers.utils.testing_utils import enable_full_determinism, skip_mps, torch_device


enable_full_determinism()



class    __lowercase  (       unittest.TestCase							):

							"""simple docstring"""


							SCREAMING_SNAKE_CASE   :					str        =						"hf-internal-testing/tiny-stable-diffusion-pipe"
							SCREAMING_SNAKE_CASE   :					str        =						1
							SCREAMING_SNAKE_CASE   :					Optional[Any]        =						77
							SCREAMING_SNAKE_CASE   :					Dict        =						32
							SCREAMING_SNAKE_CASE   :					Optional[Any]        =						4
							SCREAMING_SNAKE_CASE   :					Optional[int]        =						64
							SCREAMING_SNAKE_CASE   :					Tuple        =						torch.manual_seed(0							)





							def 		__magic_name__			(    self				, A_=0.9999	)->	Optional[int]:
											_SCREAMING_SNAKE_CASE      = UNetaDConditionModel.from_pretrained(self.model_id				, subfolder='unet'	)
											_SCREAMING_SNAKE_CASE      = unet.to(A_	)
											_SCREAMING_SNAKE_CASE      = EMAModel(unet.parameters()				, decay=A_				, model_cls=A_				, model_config=unet.config	)
											return unet, ema_unet





							def 		__magic_name__			(    self	)->	Optional[int]:
											_SCREAMING_SNAKE_CASE      = torch.randn(
											    self.batch_size				, self.num_in_channels				, self.latent_height				, self.latent_width				, generator=self.generator	).to(A_	)
											_SCREAMING_SNAKE_CASE      = torch.randint(0				, 1000				, size=(self.batch_size,)				, generator=self.generator	).to(A_	)
											_SCREAMING_SNAKE_CASE      = torch.randn(
											    self.batch_size				, self.prompt_length				, self.text_encoder_hidden_dim				, generator=self.generator	).to(A_	)
											return noisy_latents, timesteps, encoder_hidden_states





							def 		__magic_name__			(    self				, A_	)->	str:
											_SCREAMING_SNAKE_CASE      = {}
											for k, param in unet.state_dict().items():
															_SCREAMING_SNAKE_CASE      = torch.randn_like(A_	) + (param * torch.randn_like(A_	))
															updated_state_dict.update({k: updated_param}	)
											unet.load_state_dict(A_	)
											return unet





							def 		__magic_name__			(    self	)->	Dict:
											_SCREAMING_SNAKE_CASE      ,      _SCREAMING_SNAKE_CASE      = self.get_models()
											# Take the first (hypothetical) EMA step.
											ema_unet.step(unet.parameters()	)
											assert ema_unet.optimization_step == 1

											# Take two more.
											for _ in range(2	):
															ema_unet.step(unet.parameters()	)
											assert ema_unet.optimization_step == 3





							def 		__magic_name__			(    self	)->	Optional[int]:
											_SCREAMING_SNAKE_CASE      ,      _SCREAMING_SNAKE_CASE      = self.get_models()
											# Since the `unet` is not being updated (i.e., backprop'd)
											# there won't be any difference between the `params` of `unet`
											# and `ema_unet` even if we call `ema_unet.step(unet.parameters())`.
											ema_unet.step(unet.parameters()	)
											_SCREAMING_SNAKE_CASE      = list(unet.parameters()	)
											for s_param, param in zip(ema_unet.shadow_params				, A_	):
															assert torch.allclose(A_				, A_	)

											# The above holds true even if we call `ema.step()` multiple times since
											# `unet` params are still not being updated.
											for _ in range(4	):
															ema_unet.step(unet.parameters()	)
											for s_param, param in zip(ema_unet.shadow_params				, A_	):
															assert torch.allclose(A_				, A_	)





							def 		__magic_name__			(    self	)->	Union[str, Any]:
											_SCREAMING_SNAKE_CASE      ,      _SCREAMING_SNAKE_CASE      = self.get_models()
											# Here we simulate the parameter updates for `unet`. Since there might
											# be some parameters which are initialized to zero we take extra care to
											# initialize their values to something non-zero before the multiplication.
											_SCREAMING_SNAKE_CASE      = self.simulate_backprop(A_	)

											# Take the EMA step.
											ema_unet.step(unet_pseudo_updated_step_one.parameters()	)

											# Now the EMA'd parameters won't be equal to the original model parameters.
											_SCREAMING_SNAKE_CASE      = list(unet_pseudo_updated_step_one.parameters()	)
											for s_param, param in zip(ema_unet.shadow_params				, A_	):
															assert ~torch.allclose(A_				, A_	)

											# Ensure this is the case when we take multiple EMA steps.
											for _ in range(4	):
															ema_unet.step(unet.parameters()	)
											for s_param, param in zip(ema_unet.shadow_params				, A_	):
															assert ~torch.allclose(A_				, A_	)





							def 		__magic_name__			(    self	)->	Optional[int]:
											# If we call EMA step after a backpropagation consecutively for two times,
											# the shadow params from those two steps should be different.
											_SCREAMING_SNAKE_CASE      ,      _SCREAMING_SNAKE_CASE      = self.get_models()

											# First backprop + EMA
											_SCREAMING_SNAKE_CASE      = self.simulate_backprop(A_	)
											ema_unet.step(unet_step_one.parameters()	)
											_SCREAMING_SNAKE_CASE      = ema_unet.shadow_params

											# Second backprop + EMA
											_SCREAMING_SNAKE_CASE      = self.simulate_backprop(A_	)
											ema_unet.step(unet_step_two.parameters()	)
											_SCREAMING_SNAKE_CASE      = ema_unet.shadow_params

											for step_one, step_two in zip(A_				, A_	):
															assert ~torch.allclose(A_				, A_	)





							def 		__magic_name__			(    self	)->	Dict:
											# If there's no decay even if there are backprops, EMA steps
											# won't take any effect i.e., the shadow params would remain the
											# same.
											_SCREAMING_SNAKE_CASE      ,      _SCREAMING_SNAKE_CASE      = self.get_models(decay=0.0	)
											_SCREAMING_SNAKE_CASE      = self.simulate_backprop(A_	)
											ema_unet.step(unet_step_one.parameters()	)
											_SCREAMING_SNAKE_CASE      = ema_unet.shadow_params

											_SCREAMING_SNAKE_CASE      = self.simulate_backprop(A_	)
											ema_unet.step(unet_step_two.parameters()	)
											_SCREAMING_SNAKE_CASE      = ema_unet.shadow_params

											for step_one, step_two in zip(A_				, A_	):
															assert torch.allclose(A_				, A_	)





							@skip_mps
							def 		__magic_name__			(    self	)->	Optional[Any]:
											_SCREAMING_SNAKE_CASE      ,      _SCREAMING_SNAKE_CASE      = self.get_models()
											_SCREAMING_SNAKE_CASE      ,      _SCREAMING_SNAKE_CASE      ,      _SCREAMING_SNAKE_CASE      = self.get_dummy_inputs()

											with tempfile.TemporaryDirectory() as tmpdir:
															ema_unet.save_pretrained(A_	)
															_SCREAMING_SNAKE_CASE      = UNetaDConditionModel.from_pretrained(A_				, model_cls=A_	)
															_SCREAMING_SNAKE_CASE      = loaded_unet.to(unet.device	)

											# Since no EMA step has been performed the outputs should match.
											_SCREAMING_SNAKE_CASE      = unet(A_				, A_				, A_	).sample
											_SCREAMING_SNAKE_CASE      = loaded_unet(A_				, A_				, A_	).sample

											assert torch.allclose(A_				, A_				, atol=1e-4	)
