






'''simple docstring'''


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()
SCREAMING_SNAKE_CASE_				      =							logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_				      =							"Hello world! cécé herlolip"






def   lowerCAmelCase__			(					SCREAMING_SNAKE_CASE__       ,		SCREAMING_SNAKE_CASE__       ,		SCREAMING_SNAKE_CASE__		):
 __a    :	List[Any]									=						os.path.split(SCREAMING_SNAKE_CASE__		)
 __a    :	Optional[int]									=						DataaVecTextModel.from_pretrained(
     SCREAMING_SNAKE_CASE__       ,		checkpoint_file=SCREAMING_SNAKE_CASE__		)
 dataavec.eval()  # disable dropout
 __a    :	Optional[Any]									=						dataavec.models[0]
 __a    :	int									=						dataavec_model.encoder.sentence_encoder
 __a    :	Optional[int]									=						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:
  __a    :	Union[str, Any]									=						dataavec.model.classification_heads['mnli'].out_proj.weight.shape[0]
 print('Our BERT config:'       ,		SCREAMING_SNAKE_CASE__		)

 __a    :	Optional[int]									=						DataaVecTextForSequenceClassification(SCREAMING_SNAKE_CASE__		) if classification_head else DataaVecTextForMaskedLM(SCREAMING_SNAKE_CASE__		)
 model.eval()

 # Now let's copy all the weights.
 # Embeddings
 __a    :	str									=						dataavec_sent_encoder.embed_tokens.weight
 __a    :	List[Any]									=						dataavec_sent_encoder.embed_positions.weight
 __a    :	List[str]									=						torch.zeros_like(
     model.dataavec_text.embeddings.token_type_embeddings.weight		)  # just zero them out b/c data2vec doesn't use them.
 __a    :	str									=						dataavec_sent_encoder.layernorm_embedding.weight
 __a    :	Union[str, Any]									=						dataavec_sent_encoder.layernorm_embedding.bias

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

  # self attention
  __a    :	BertSelfAttention									=						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)		)}'''
  )

  __a    :	Tuple									=						dataavec_layer.self_attn.q_proj.weight
  __a    :	str									=						dataavec_layer.self_attn.q_proj.bias
  __a    :	Dict									=						dataavec_layer.self_attn.k_proj.weight
  __a    :	Union[str, Any]									=						dataavec_layer.self_attn.k_proj.bias
  __a    :	str									=						dataavec_layer.self_attn.v_proj.weight
  __a    :	Tuple									=						dataavec_layer.self_attn.v_proj.bias

  # self-attention output
  __a    :	BertSelfOutput									=						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}'''
  __a    :	Tuple									=						dataavec_layer.self_attn.out_proj.weight
  __a    :	List[str]									=						dataavec_layer.self_attn.out_proj.bias
  __a    :	List[Any]									=						dataavec_layer.self_attn_layer_norm.weight
  __a    :	Optional[Any]									=						dataavec_layer.self_attn_layer_norm.bias

  # intermediate
  __a    :	BertIntermediate									=						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}'''
  __a    :	str									=						dataavec_layer.fca.weight
  __a    :	int									=						dataavec_layer.fca.bias

  # output
  __a    :	BertOutput									=						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}'''
  __a    :	Union[str, Any]									=						dataavec_layer.fca.weight
  __a    :	str									=						dataavec_layer.fca.bias
  __a    :	Dict									=						dataavec_layer.final_layer_norm.weight
  __a    :	List[Any]									=						dataavec_layer.final_layer_norm.bias
  # end of layer

 if classification_head:
  __a    :	Any									=						dataavec.model.classification_heads['mnli'].dense.weight
  __a    :	Any									=						dataavec.model.classification_heads['mnli'].dense.bias
  __a    :	Dict									=						dataavec.model.classification_heads['mnli'].out_proj.weight
  __a    :	Any									=						dataavec.model.classification_heads['mnli'].out_proj.bias
 else:
  # LM Head
  __a    :	Dict									=						dataavec_model.encoder.lm_head.dense.weight
  __a    :	List[str]									=						dataavec_model.encoder.lm_head.dense.bias
  __a    :	Optional[int]									=						dataavec_model.encoder.lm_head.layer_norm.weight
  __a    :	Dict									=						dataavec_model.encoder.lm_head.layer_norm.bias
  __a    :	Union[str, Any]									=						dataavec_model.encoder.lm_head.weight
  __a    :	Dict									=						dataavec_model.encoder.lm_head.bias

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

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

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


if __name__ == "__main__":
  SCREAMING_SNAKE_CASE_				      =							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."
  )
  SCREAMING_SNAKE_CASE_				      =							parser.parse_args()
  convert_dataavec_checkpoint_to_pytorch(
      args.checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
  )












'''simple docstring'''


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 							lowerCAmelCase					(							unittest.TestCase   ):







   """simple docstring"""







   @property
   def    __magic_name__    ( self			)    ->   Any:
    torch.manual_seed(0			)
    __a    :	Optional[Any]									=						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			)    ->   Tuple:
    __a    :	Union[str, Any]									=						'cpu'
    __a    :	Tuple									=						self.dummy_uncond_unet
    __a    :	str									=						DDPMScheduler()

    __a    :	int									=						DDPMPipeline(unet=_A   ,   scheduler=_A			)
    ddpm.to(_A			)
    ddpm.set_progress_bar_config(disable=_A			)

    __a    :	str									=						torch.Generator(device=_A			).manual_seed(0			)
    __a    :	int									=						ddpm(generator=_A   ,   num_inference_steps=2   ,   output_type='numpy'			).images

    __a    :	Union[str, Any]									=						torch.Generator(device=_A			).manual_seed(0			)
    __a    :	Optional[int]									=						ddpm(generator=_A   ,   num_inference_steps=2   ,   output_type='numpy'   ,   return_dict=_A			)[0]

    __a    :	List[str]									=						image[0, -3:, -3:, -1]
    __a    :	List[str]									=						image_from_tuple[0, -3:, -3:, -1]

    assert image.shape == (1, 32, 32, 3)
    __a    :	List[Any]									=						np.array(
        [9.9_5_6E-0_1, 5.7_8_5E-0_1, 4.6_7_5E-0_1, 9.9_3_0E-0_1, 0.0, 1.000, 1.1_9_9E-0_3, 2.6_4_8E-0_4, 5.1_0_1E-0_4]			)

    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			)    ->   Tuple:
    __a    :	Optional[Any]									=						self.dummy_uncond_unet
    __a    :	List[str]									=						DDPMScheduler(prediction_type='sample'			)

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

    __a    :	Optional[Any]									=						torch.manual_seed(0			)
    __a    :	int									=						ddpm(generator=_A   ,   num_inference_steps=2   ,   output_type='numpy'			).images

    __a    :	Union[str, Any]									=						torch.manual_seed(0			)
    __a    :	int									=						ddpm(generator=_A   ,   num_inference_steps=2   ,   output_type='numpy'			)[0]

    __a    :	Dict									=						image[0, -3:, -3:, -1]
    __a    :	int									=						image_eps[0, -3:, -3:, -1]

    assert image.shape == (1, 32, 32, 3)
    __a    :	int									=						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 							lowerCAmelCase					(							unittest.TestCase   ):







   """simple docstring"""







   def    __magic_name__    ( self			)    ->   List[str]:
    __a    :	Union[str, Any]									=						'google/ddpm-cifar10-32'

    __a    :	str									=						UNetaDModel.from_pretrained(_A			)
    __a    :	List[str]									=						DDPMScheduler.from_pretrained(_A			)

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

    __a    :	int									=						torch.manual_seed(0			)
    __a    :	Optional[Any]									=						ddpm(generator=_A   ,   output_type='numpy'			).images

    __a    :	int									=						image[0, -3:, -3:, -1]

    assert image.shape == (1, 32, 32, 3)
    __a    :	Optional[Any]									=						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




