'''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()
_A      :				str 				=    logging.get_logger(__name__)

_A      :				List[str] 				=    '''Hello world! cécé herlolip'''







def 					UpperCamelCase_						( snake_case_	:	List[Any]						,		snake_case_	:	Tuple						,		snake_case_	:	str				) ->					Optional[int]:






 '''simple docstring'''

 __lowerCAmelCase							,	__lowerCAmelCase    							=						os.path.split(SCREAMING_SNAKE_CASE_				)
 __lowerCAmelCase    							=						DataaVecTextModel.from_pretrained(
     SCREAMING_SNAKE_CASE_						,		checkpoint_file=SCREAMING_SNAKE_CASE_				)
 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=5_14						,		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:"""						,		SCREAMING_SNAKE_CASE_				)

 __lowerCAmelCase    							=						DataaVecTextForSequenceClassification(SCREAMING_SNAKE_CASE_				) if classification_head else DataaVecTextForMaskedLM(SCREAMING_SNAKE_CASE_				)
 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(SCREAMING_SNAKE_CASE_				).unsqueeze(0				)  # batch of size 1

 __lowerCAmelCase    							=						model(SCREAMING_SNAKE_CASE_				)[0]
 if classification_head:
  __lowerCAmelCase    							=						dataavec.model.classification_heads["""mnli"""](dataavec.extract_features(SCREAMING_SNAKE_CASE_				)				)
 else:
  __lowerCAmelCase    							=						dataavec_model(SCREAMING_SNAKE_CASE_				)[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(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__":
 _A      :				Tuple 				=    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      :				List[Any] 				=    parser.parse_args()
 convert_dataavec_checkpoint_to_pytorch(
     args.checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
 )





'''simple docstring'''

import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionDualGuidedPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device


_A      :				Any 				=    False







@nightly
@require_torch_gpu
class     _lowercase					(    unittest.TestCase      ):


 '''simple docstring'''






 def a (				self       :     Tuple							)  ->   Dict:
  # clean up the VRAM after each test
  super().tearDown()
  gc.collect()
  torch.cuda.empty_cache()
 def a (				self       :     Optional[int]							)  ->   Optional[Any]:
  __lowerCAmelCase    							=						VersatileDiffusionDualGuidedPipeline.from_pretrained("""shi-labs/versatile-diffusion"""							)
  # remove text_unet
  pipe.remove_unused_weights()
  pipe.to(SCREAMING_SNAKE_CASE__							)
  pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__							)

  __lowerCAmelCase    							=						load_image(
      """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg"""							)

  __lowerCAmelCase    							=						torch.manual_seed(0							)
  __lowerCAmelCase    							=						pipe(
      prompt="""first prompt"""				,	image=SCREAMING_SNAKE_CASE__				,	text_to_image_strength=0.7_5				,	generator=SCREAMING_SNAKE_CASE__				,	guidance_scale=7.5				,	num_inference_steps=2				,	output_type="""numpy"""				,	).images

  with tempfile.TemporaryDirectory() as tmpdirname:
   pipe.save_pretrained(SCREAMING_SNAKE_CASE__							)
   __lowerCAmelCase    							=						VersatileDiffusionDualGuidedPipeline.from_pretrained(SCREAMING_SNAKE_CASE__							)

  pipe.to(SCREAMING_SNAKE_CASE__							)
  pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__							)

  __lowerCAmelCase    							=						generator.manual_seed(0							)
  __lowerCAmelCase    							=						pipe(
      prompt="""first prompt"""				,	image=SCREAMING_SNAKE_CASE__				,	text_to_image_strength=0.7_5				,	generator=SCREAMING_SNAKE_CASE__				,	guidance_scale=7.5				,	num_inference_steps=2				,	output_type="""numpy"""				,	).images

  assert np.abs(image - new_image							).sum() < 1e-5, "Models don't have the same forward pass"





 def a (				self       :     str							)  ->   Tuple:
  __lowerCAmelCase    							=						VersatileDiffusionDualGuidedPipeline.from_pretrained("""shi-labs/versatile-diffusion"""							)
  pipe.remove_unused_weights()
  pipe.to(SCREAMING_SNAKE_CASE__							)
  pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__							)

  __lowerCAmelCase    							=						"""cyberpunk 2077"""
  __lowerCAmelCase    							=						load_image(
      """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg"""							)
  __lowerCAmelCase    							=						torch.manual_seed(0							)
  __lowerCAmelCase    							=						pipe(
      prompt=SCREAMING_SNAKE_CASE__				,	image=SCREAMING_SNAKE_CASE__				,	text_to_image_strength=0.7_5				,	generator=SCREAMING_SNAKE_CASE__				,	guidance_scale=7.5				,	num_inference_steps=50				,	output_type="""numpy"""				,	).images

  __lowerCAmelCase    							=						image[0, 2_53:2_56, 2_53:2_56, -1]

  assert image.shape == (1, 5_12, 5_12, 3)
  __lowerCAmelCase    							=						np.array([0.0_7_8_7, 0.0_8_4_9, 0.0_8_2_6, 0.0_8_1_2, 0.0_8_0_7, 0.0_7_9_5, 0.0_8_1_8, 0.0_7_9_8, 0.0_7_7_9]							)

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






