



"""simple docstring"""





import inspect
from typing import Callable, List, Optional, Union

import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer

from diffusers import DiffusionPipeline
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
from diffusers.utils import logging


__lowerCAmelCase							:       List[str]          =       logging.get_logger(__name__)  # pylint: disable=invalid-name



class  _lowerCAmelCase     (					SCREAMING_SNAKE_CASE__		):





		"""simple docstring"""






		def __init__(       self				,     _lowercase				,     _lowercase				,     _lowercase				,     _lowercase				,     _lowercase				,     _lowercase				,     _lowercase				,     )			->    int:







								'''simple docstring'''

								super().__init__()
								self.register_modules(
								    vae=_lowercase				,     text_encoder=_lowercase				,     tokenizer=_lowercase				,     unet=_lowercase				,     scheduler=_lowercase				,     safety_checker=_lowercase				,     feature_extractor=_lowercase				,     )





		def  UpperCAmelCase__				(       self				,     _lowercase = "auto" )			->    List[str]:







								'''simple docstring'''

								if slice_size == "auto":
														# half the attention head size is usually a good trade-off between
														# speed and memory
														snake_case_ :			List[str]            =      self.unet.config.attention_head_dim // 2
								self.unet.set_attention_slice(_lowercase )





		def  UpperCAmelCase__				(       self )			->    Optional[Any]:







								'''simple docstring'''

								self.enable_attention_slicing(_lowercase )





		@torch.no_grad()
		def __call__(       self				,     _lowercase				,     _lowercase = 5_1_2				,     _lowercase = 5_1_2				,     _lowercase = 5_0				,     _lowercase = 7.5				,     _lowercase = None				,     _lowercase = 1				,     _lowercase = 0.0				,     _lowercase = None				,     _lowercase = None				,     _lowercase = "pil"				,     _lowercase = True				,     _lowercase = None				,     _lowercase = 1				,     _lowercase = None				,     **_lowercase				,     )			->    Optional[Any]:







								'''simple docstring'''

								if isinstance(_lowercase				,     _lowercase ):
														snake_case_ :			str            =      1
								elif isinstance(_lowercase				,     _lowercase ):
														snake_case_ :			int            =      len(_lowercase )
								else:
														raise ValueError(f'`prompt` has to be of type `str` or `list` but is {type(_lowercase )}' )

								if height % 8 != 0 or width % 8 != 0:
														raise ValueError(f'`height` and `width` have to be divisible by 8 but are {height} and {width}.' )

								if (callback_steps is None) or (
								    callback_steps is not None and (not isinstance(_lowercase				,     _lowercase ) or callback_steps <= 0)
								):
														raise ValueError(
														    f'`callback_steps` has to be a positive integer but is {callback_steps} of type'
														    f' {type(_lowercase )}.' )

								# get prompt text embeddings
								snake_case_ :			List[Any]            =      self.tokenizer(
								    _lowercase				,     padding="""max_length"""				,     max_length=self.tokenizer.model_max_length				,     return_tensors="""pt"""				,     )
								snake_case_ :			Dict            =      text_inputs.input_ids

								if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
														snake_case_ :			Any            =      self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :] )
														logger.warning(
														    """The following part of your input was truncated because CLIP can only handle sequences up to"""
														    f' {self.tokenizer.model_max_length} tokens: {removed_text}' )
														snake_case_ :			Tuple            =      text_input_ids[:, : self.tokenizer.model_max_length]

								if text_embeddings is None:
														snake_case_ :			Tuple            =      self.text_encoder(text_input_ids.to(self.device ) )[0]

								# duplicate text embeddings for each generation per prompt, using mps friendly method
								snake_case_      ,		snake_case_      ,		snake_case_ :			List[str]            =      text_embeddings.shape
								snake_case_ :			Union[str, Any]            =      text_embeddings.repeat(1				,     _lowercase				,     1 )
								snake_case_ :			Union[str, Any]            =      text_embeddings.view(bs_embed * num_images_per_prompt				,     _lowercase				,     -1 )

								# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
								# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
								# corresponds to doing no classifier free guidance.
								snake_case_ :			Optional[Any]            =      guidance_scale > 1.0
								# get unconditional embeddings for classifier free guidance
								if do_classifier_free_guidance:
														snake_case_ :			List[str]
														if negative_prompt is None:
																				snake_case_ :			Optional[int]            =      [""""""]
														elif type(_lowercase ) is not type(_lowercase ):
																				raise TypeError(
																				    f'`negative_prompt` should be the same type to `prompt`, but got {type(_lowercase )} !='
																				    f' {type(_lowercase )}.' )
														elif isinstance(_lowercase				,     _lowercase ):
																				snake_case_ :			List[Any]            =      [negative_prompt]
														elif batch_size != len(_lowercase ):
																				raise ValueError(
																				    f'`negative_prompt`: {negative_prompt} has batch size {len(_lowercase )}, but `prompt`:'
																				    f' {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches'
																				    """ the batch size of `prompt`.""" )
														else:
																				snake_case_ :			Any            =      negative_prompt

														snake_case_ :			Tuple            =      text_input_ids.shape[-1]
														snake_case_ :			Tuple            =      self.tokenizer(
														    _lowercase				,     padding="""max_length"""				,     max_length=_lowercase				,     truncation=_lowercase				,     return_tensors="""pt"""				,     )
														snake_case_ :			Any            =      self.text_encoder(uncond_input.input_ids.to(self.device ) )[0]

														# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
														snake_case_ :			Any            =      uncond_embeddings.shape[1]
														snake_case_ :			List[Any]            =      uncond_embeddings.repeat(_lowercase				,     _lowercase				,     1 )
														snake_case_ :			Any            =      uncond_embeddings.view(batch_size * num_images_per_prompt				,     _lowercase				,     -1 )

														# For classifier free guidance, we need to do two forward passes.
														# Here we concatenate the unconditional and text embeddings into a single batch
														# to avoid doing two forward passes
														snake_case_ :			List[str]            =      torch.cat([uncond_embeddings, text_embeddings] )

								# get the initial random noise unless the user supplied it

								# Unlike in other pipelines, latents need to be generated in the target device
								# for 1-to-1 results reproducibility with the CompVis implementation.
								# However this currently doesn't work in `mps`.
								snake_case_ :			List[Any]            =      (batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
								snake_case_ :			Optional[int]            =      (batch_size * num_images_per_prompt, self.unet.config.in_channels, 6_4, 6_4)
								snake_case_ :			List[Any]            =      text_embeddings.dtype
								if latents is None:
														if self.device.type == "mps":
																				# randn does not exist on mps
																				snake_case_ :			int            =      torch.randn(
																				    _lowercase				,     generator=_lowercase				,     device="""cpu"""				,     dtype=_lowercase ).to(self.device )
																				snake_case_ :			Any            =      torch.randn(_lowercase				,     generator=_lowercase				,     device="""cpu"""				,     dtype=_lowercase ).to(
																				    self.device )
														else:
																				snake_case_ :			Union[str, Any]            =      torch.randn(
																				    _lowercase				,     generator=_lowercase				,     device=self.device				,     dtype=_lowercase )
																				snake_case_ :			List[Any]            =      torch.randn(_lowercase				,     generator=_lowercase				,     device=self.device				,     dtype=_lowercase )
								else:
														if latents_reference.shape != latents_shape:
																				raise ValueError(f'Unexpected latents shape, got {latents.shape}, expected {latents_shape}' )
														snake_case_ :			str            =      latents_reference.to(self.device )
														snake_case_ :			List[Any]            =      latents.to(self.device )

								# This is the key part of the pipeline where we
								# try to ensure that the generated images w/ the same seed
								# but different sizes actually result in similar images
								snake_case_ :			int            =      (latents_shape[3] - latents_shape_reference[3]) // 2
								snake_case_ :			Tuple            =      (latents_shape[2] - latents_shape_reference[2]) // 2
								snake_case_ :			int            =      latents_shape_reference[3] if dx >= 0 else latents_shape_reference[3] + 2 * dx
								snake_case_ :			List[str]            =      latents_shape_reference[2] if dy >= 0 else latents_shape_reference[2] + 2 * dy
								snake_case_ :			Union[str, Any]            =      0 if dx < 0 else dx
								snake_case_ :			List[str]            =      0 if dy < 0 else dy
								snake_case_ :			Union[str, Any]            =      max(-dx				,     0 )
								snake_case_ :			Dict            =      max(-dy				,     0 )
								# import pdb
								# pdb.set_trace()
								snake_case_ :			Union[str, Any]            =      latents_reference[:, :, dy : dy + h, dx : dx + w]

								# set timesteps
								self.scheduler.set_timesteps(_lowercase )

								# Some schedulers like PNDM have timesteps as arrays
								# It's more optimized to move all timesteps to correct device beforehand
								snake_case_ :			Dict            =      self.scheduler.timesteps.to(self.device )

								# scale the initial noise by the standard deviation required by the scheduler
								snake_case_ :			str            =      latents * self.scheduler.init_noise_sigma

								# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
								# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
								# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
								# and should be between [0, 1]
								snake_case_ :			Optional[Any]            =      """eta""" in set(inspect.signature(self.scheduler.step ).parameters.keys() )
								snake_case_ :			Optional[Any]            =      {}
								if accepts_eta:
														snake_case_ :			Tuple            =      eta

								for i, t in enumerate(self.progress_bar(_lowercase ) ):
														# expand the latents if we are doing classifier free guidance
														snake_case_ :			Optional[Any]            =      torch.cat([latents] * 2 ) if do_classifier_free_guidance else latents
														snake_case_ :			Union[str, Any]            =      self.scheduler.scale_model_input(_lowercase				,     _lowercase )

														# predict the noise residual
														snake_case_ :			List[Any]            =      self.unet(_lowercase				,     _lowercase				,     encoder_hidden_states=_lowercase ).sample

														# perform guidance
														if do_classifier_free_guidance:
																				snake_case_      ,		snake_case_ :			Tuple            =      noise_pred.chunk(2 )
																				snake_case_ :			int            =      noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

														# compute the previous noisy sample x_t -> x_t-1
														snake_case_ :			Tuple            =      self.scheduler.step(_lowercase				,     _lowercase				,     _lowercase				,     **_lowercase ).prev_sample

														# call the callback, if provided
														if callback is not None and i % callback_steps == 0:
																				callback(_lowercase				,     _lowercase				,     _lowercase )

								snake_case_ :			Optional[Any]            =      1 / 0.1_8215 * latents
								snake_case_ :			Any            =      self.vae.decode(_lowercase ).sample

								snake_case_ :			Dict            =      (image / 2 + 0.5).clamp(0				,     1 )

								# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
								snake_case_ :			Union[str, Any]            =      image.cpu().permute(0				,     2				,     3				,     1 ).float().numpy()

								if self.safety_checker is not None:
														snake_case_ :			Any            =      self.feature_extractor(self.numpy_to_pil(_lowercase )				,     return_tensors="""pt""" ).to(
														    self.device )
														snake_case_      ,		snake_case_ :			str            =      self.safety_checker(
														    images=_lowercase				,     clip_input=safety_checker_input.pixel_values.to(text_embeddings.dtype ) )
								else:
														snake_case_ :			str            =      None

								if output_type == "pil":
														snake_case_ :			int            =      self.numpy_to_pil(_lowercase )

								if not return_dict:
														return (image, has_nsfw_concept)

								return StableDiffusionPipelineOutput(images=_lowercase				,     nsfw_content_detected=_lowercase )








import argparse
from pathlib import Path

import fairseq
import torch
from fairseq.models.xmod import XMODModel as FairseqXmodModel
from packaging import version

from transformers import XmodConfig, XmodForMaskedLM, XmodForSequenceClassification
from transformers.utils import logging


if version.parse(fairseq.__version__) < version.parse('0.12.2'):
      raise Exception('requires fairseq >= 0.12.2')
if version.parse(fairseq.__version__) > version.parse('2'):
      raise Exception('requires fairseq < v2')

logging.set_verbosity_info()
a__      :     Any         =		logging.get_logger(__name__)

a__      :     Tuple         =		'Hello, World!'
a__      :     Any         =		'en_XX'

def      UpperCAmelCase_    (    _UpperCAmelCase							:str  ,						_UpperCAmelCase							:str  ,						_UpperCAmelCase							:bool				)	->				Optional[Any]:
     '''simple docstring'''


     A_          =       Path('''data_bin'''				)
     A_          =       FairseqXmodModel.from_pretrained(
         model_name_or_path=str(Path(_UpperCAmelCase				).parent				)  ,						checkpoint_file=Path(_UpperCAmelCase				).name  ,						_name='''xmod_base'''  ,						arch='''xmod_base'''  ,						task='''multilingual_masked_lm'''  ,						data_name_or_path=str(_UpperCAmelCase				)  ,						bpe='''sentencepiece'''  ,						sentencepiece_model=str(Path(_UpperCAmelCase				).parent / '''sentencepiece.bpe.model'''				)  ,						src_dict=str(data_dir / '''dict.txt'''				)  ,						)
     xmod.eval()  # disable dropout
     print(_UpperCAmelCase				)

     A_          =       xmod.model.encoder.sentence_encoder
     A_          =       XmodConfig(
         vocab_size=xmod_sent_encoder.embed_tokens.num_embeddings  ,						hidden_size=xmod.cfg.model.encoder_embed_dim  ,						num_hidden_layers=xmod.cfg.model.encoder_layers  ,						num_attention_heads=xmod.cfg.model.encoder_attention_heads  ,						intermediate_size=xmod.cfg.model.encoder_ffn_embed_dim  ,						max_position_embeddings=514  ,						type_vocab_size=1  ,						layer_norm_eps=1e-5  ,						pre_norm=xmod.cfg.model.encoder_normalize_before  ,						adapter_reduction_factor=getattr(xmod.cfg.model  ,						'''bottleneck'''  ,						2				)  ,						adapter_layer_norm=xmod.cfg.model.adapter_layer_norm  ,						adapter_reuse_layer_norm=xmod.cfg.model.adapter_reuse_layer_norm  ,						ln_before_adapter=xmod.cfg.model.ln_before_adapter  ,						languages=xmod.cfg.model.languages  ,						)
     if classification_head:
          A_          =       xmod.model.classification_heads['''mnli'''].out_proj.weight.shape[0]

     print('''Our X-MOD config:'''  ,						_UpperCAmelCase				)

     A_          =       XmodForSequenceClassification(_UpperCAmelCase				) if classification_head else XmodForMaskedLM(_UpperCAmelCase				)
     model.eval()

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

     A_          =       xmod_sent_encoder.layernorm_embedding.weight
     A_          =       xmod_sent_encoder.layernorm_embedding.bias

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

          # self attention
          A_          =       layer.attention.self
          if not (
              xmod_layer.self_attn.k_proj.weight.data.shape
              == xmod_layer.self_attn.q_proj.weight.data.shape
              == xmod_layer.self_attn.v_proj.weight.data.shape
              == torch.Size((config.hidden_size, config.hidden_size)				)
          ):
               raise AssertionError('''Dimensions of self-attention weights do not match.'''				)

          A_          =       xmod_layer.self_attn.q_proj.weight
          A_          =       xmod_layer.self_attn.q_proj.bias
          A_          =       xmod_layer.self_attn.k_proj.weight
          A_          =       xmod_layer.self_attn.k_proj.bias
          A_          =       xmod_layer.self_attn.v_proj.weight
          A_          =       xmod_layer.self_attn.v_proj.bias

          # self-attention output
          A_          =       layer.attention.output
          if self_output.dense.weight.shape != xmod_layer.self_attn.out_proj.weight.shape:
               raise AssertionError('''Dimensions of self-attention output weights do not match.'''				)
          A_          =       xmod_layer.self_attn.out_proj.weight
          A_          =       xmod_layer.self_attn.out_proj.bias
          A_          =       xmod_layer.self_attn_layer_norm.weight
          A_          =       xmod_layer.self_attn_layer_norm.bias

          # intermediate
          A_          =       layer.intermediate
          if intermediate.dense.weight.shape != xmod_layer.fca.weight.shape:
               raise AssertionError('''Dimensions of intermediate weights do not match.'''				)
          A_          =       xmod_layer.fca.weight
          A_          =       xmod_layer.fca.bias

          # output
          A_          =       layer.output
          if bert_output.dense.weight.shape != xmod_layer.fca.weight.shape:
               raise AssertionError('''Dimensions of feed-forward weights do not match.'''				)
          A_          =       xmod_layer.fca.weight
          A_          =       xmod_layer.fca.bias
          A_          =       xmod_layer.final_layer_norm.weight
          A_          =       xmod_layer.final_layer_norm.bias
          if bert_output.adapter_layer_norm is not None:
               A_          =       xmod_layer.adapter_layer_norm.weight
               A_          =       xmod_layer.adapter_layer_norm.bias

          if sorted(bert_output.adapter_modules.keys()				) != sorted(xmod_layer.adapter_modules.keys()				):
               raise AssertionError('''Lists of language adapters do not match.'''				)
          for lang_code, adapter in xmod_layer.adapter_modules.items():
               A_          =       bert_output.adapter_modules[lang_code]
               A_          =       xmod_layer.adapter_modules[lang_code]
               A_          =       from_adapter.fca.weight
               A_          =       from_adapter.fca.bias
               A_          =       from_adapter.fca.weight
               A_          =       from_adapter.fca.bias

          # end of layer

     if xmod_sent_encoder.layer_norm is not None:
          A_          =       xmod_sent_encoder.layer_norm.weight
          A_          =       xmod_sent_encoder.layer_norm.bias

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

     # Let's check that we get the same results.
     A_          =       xmod.encode(_UpperCAmelCase				).unsqueeze(0				)  # batch of size 1
     model.roberta.set_default_language(_UpperCAmelCase				)

     A_          =       model(_UpperCAmelCase				)[0]
     if classification_head:
          A_          =       xmod.model.classification_heads['''mnli'''](xmod.extract_features(_UpperCAmelCase				)				)
     else:
          A_          =       xmod.model(_UpperCAmelCase  ,						lang_id=[SAMPLE_LANGUAGE]				)[0]
     print(our_output.shape  ,						their_output.shape				)
     A_          =       torch.max(torch.abs(our_output - their_output				)				).item()
     print(f'max_absolute_diff = {max_absolute_diff}'				)  # ~ 1e-7
     A_          =       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'''				)

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


if __name__ == "__main__":
      a__      :     List[str]         =		argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          '--xmod_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__      :     Tuple         =		parser.parse_args()
      convert_xmod_checkpoint_to_pytorch(
          args.xmod_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
      )



