





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()
UpperCAmelCase						:   List[str]			  =     logging.get_logger(__name__)

UpperCAmelCase						:   List[str]			  =     '''Hello, World!'''
UpperCAmelCase						:   str			  =     '''en_XX'''







def  _SCREAMING_SNAKE_CASE				(   a					,						a					,						a  )      ->      Optional[int]:
					__A	:						Any       =				Path('data_bin'  )
					__A	:						Any       =				FairseqXmodModel.from_pretrained(
					    model_name_or_path=str(Path(a  ).parent  )					,						checkpoint_file=Path(a  ).name					,						_name='xmod_base'					,						arch='xmod_base'					,						task='multilingual_masked_lm'					,						data_name_or_path=str(a  )					,						bpe='sentencepiece'					,						sentencepiece_model=str(Path(a  ).parent / 'sentencepiece.bpe.model'  )					,						src_dict=str(data_dir / 'dict.txt'  )					,						)
					xmod.eval()  # disable dropout
					print(a  )

					__A	:						Dict       =				xmod.model.encoder.sentence_encoder
					__A	:						str       =				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=5_14					,						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	:						Tuple       =				xmod.model.classification_heads['mnli'].out_proj.weight.shape[0]

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

					__A	:						Any       =				XmodForSequenceClassification(a  ) if classification_head else XmodForMaskedLM(a  )
					model.eval()

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

					__A	:						Dict       =				xmod_sent_encoder.layernorm_embedding.weight
					__A	:						List[str]       =				xmod_sent_encoder.layernorm_embedding.bias

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

										# self attention
										__A	:						Union[str, Any]       =				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	:						Tuple       =				xmod_layer.self_attn.q_proj.weight
										__A	:						Optional[Any]       =				xmod_layer.self_attn.q_proj.bias
										__A	:						Optional[int]       =				xmod_layer.self_attn.k_proj.weight
										__A	:						str       =				xmod_layer.self_attn.k_proj.bias
										__A	:						int       =				xmod_layer.self_attn.v_proj.weight
										__A	:						Optional[Any]       =				xmod_layer.self_attn.v_proj.bias

										# self-attention output
										__A	:						Dict       =				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	:						int       =				xmod_layer.self_attn.out_proj.weight
										__A	:						Dict       =				xmod_layer.self_attn.out_proj.bias
										__A	:						List[str]       =				xmod_layer.self_attn_layer_norm.weight
										__A	:						str       =				xmod_layer.self_attn_layer_norm.bias

										# intermediate
										__A	:						Optional[Any]       =				layer.intermediate
										if intermediate.dense.weight.shape != xmod_layer.fca.weight.shape:
															raise AssertionError('Dimensions of intermediate weights do not match.'  )
										__A	:						Any       =				xmod_layer.fca.weight
										__A	:						Tuple       =				xmod_layer.fca.bias

										# output
										__A	:						List[Any]       =				layer.output
										if bert_output.dense.weight.shape != xmod_layer.fca.weight.shape:
															raise AssertionError('Dimensions of feed-forward weights do not match.'  )
										__A	:						str       =				xmod_layer.fca.weight
										__A	:						Tuple       =				xmod_layer.fca.bias
										__A	:						List[Any]       =				xmod_layer.final_layer_norm.weight
										__A	:						int       =				xmod_layer.final_layer_norm.bias
										if bert_output.adapter_layer_norm is not None:
															__A	:						List[str]       =				xmod_layer.adapter_layer_norm.weight
															__A	:						Dict       =				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	:						str       =				bert_output.adapter_modules[lang_code]
															__A	:						List[str]       =				xmod_layer.adapter_modules[lang_code]
															__A	:						List[str]       =				from_adapter.fca.weight
															__A	:						Tuple       =				from_adapter.fca.bias
															__A	:						Optional[int]       =				from_adapter.fca.weight
															__A	:						int       =				from_adapter.fca.bias

										# end of layer

					if xmod_sent_encoder.layer_norm is not None:
										__A	:						List[Any]       =				xmod_sent_encoder.layer_norm.weight
										__A	:						str       =				xmod_sent_encoder.layer_norm.bias

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

					# Let's check that we get the same results.
					__A	:						List[str]       =				xmod.encode(a  ).unsqueeze(0  )  # batch of size 1
					model.roberta.set_default_language(a  )

					__A	:						Optional[Any]       =				model(a  )[0]
					if classification_head:
										__A	:						Optional[int]       =				xmod.model.classification_heads['mnli'](xmod.extract_features(a  )  )
					else:
										__A	:						Any       =				xmod.model(a					,						lang_id=[SAMPLE_LANGUAGE]  )[0]
					print(our_output.shape					,						their_output.shape  )
					__A	:						int       =				torch.max(torch.abs(our_output - their_output  )  ).item()
					print(F"""max_absolute_diff = {max_absolute_diff}"""  )  # ~ 1e-7
					__A	:						Union[str, Any]       =				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'  )

					Path(a  ).mkdir(parents=a					,						exist_ok=a  )
					print(F"""Saving model to {pytorch_dump_folder_path}"""  )
					model.save_pretrained(a  )


if __name__ == "__main__":
				UpperCAmelCase						:   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.'''
				)
				UpperCAmelCase						:   Any			  =     parser.parse_args()
				convert_xmod_checkpoint_to_pytorch(
				    args.xmod_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
				)











from typing import List, Optional, Union

import numpy as np
import PIL
import torch
from PIL import Image

from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)


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

UpperCAmelCase						:   Dict			  =     '''
    Examples:
        ```py
        >>> from diffusers import KandinskyV22Img2ImgPipeline, KandinskyV22PriorPipeline
        >>> from diffusers.utils import load_image
        >>> import torch

        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(
        ...     "kandinsky-community/kandinsky-2-2-prior", torch_dtype=torch.float16
        ... )
        >>> pipe_prior.to("cuda")

        >>> prompt = "A red cartoon frog, 4k"
        >>> image_emb, zero_image_emb = pipe_prior(prompt, return_dict=False)

        >>> pipe = KandinskyV22Img2ImgPipeline.from_pretrained(
        ...     "kandinsky-community/kandinsky-2-2-decoder", torch_dtype=torch.float16
        ... )
        >>> pipe.to("cuda")

        >>> init_image = load_image(
        ...     "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
        ...     "/kandinsky/frog.png"
        ... )

        >>> image = pipe(
        ...     image=init_image,
        ...     image_embeds=image_emb,
        ...     negative_image_embeds=zero_image_emb,
        ...     height=768,
        ...     width=768,
        ...     num_inference_steps=100,
        ...     strength=0.2,
        ... ).images

        >>> image[0].save("red_frog.png")
        ```
'''







def  _SCREAMING_SNAKE_CASE				(   a					,						a					,						a=8  )      ->      Tuple:
					__A	:						List[str]       =				height // scale_factor**2
					if height % scale_factor**2 != 0:
										new_height += 1
					__A	:						Optional[int]       =				width // scale_factor**2
					if width % scale_factor**2 != 0:
										new_width += 1
					return new_height * scale_factor, new_width * scale_factor







def  _SCREAMING_SNAKE_CASE				(   a					,						a=5_12					,						a=5_12  )      ->      int:
					__A	:						Optional[Any]       =				pil_image.resize((w, h)					,						resample=Image.BICUBIC					,						reducing_gap=1  )
					__A	:						Union[str, Any]       =				np.array(pil_image.convert('RGB'  )  )
					__A	:						Optional[int]       =				arr.astype(np.floataa  ) / 127.5 - 1
					__A	:						int       =				np.transpose(a					,						[2, 0, 1]  )
					__A	:						Tuple       =				torch.from_numpy(a  ).unsqueeze(0  )
					return image





class 						_A(					snake_case__   ):


				"""simple docstring"""


				def __init__(			self     ,					_A     ,					_A     ,					_A     ,					):
									super().__init__()

									self.register_modules(
									    unet=_A     ,					scheduler=_A     ,					movq=_A     ,					)
									__A	:						Tuple       =				2 ** (len(self.movq.config.block_out_channels     ) - 1)



				def 					UpperCAmelCase_		(			self     ,					_A     ,					_A     ,					_A     ):
									# get the original timestep using init_timestep
									__A	:						Optional[int]       =				min(int(num_inference_steps * strength     )     ,					_A     )

									__A	:						Dict       =				max(num_inference_steps - init_timestep     ,					0     )
									__A	:						Tuple       =				self.scheduler.timesteps[t_start:]

									return timesteps, num_inference_steps - t_start



				def 					UpperCAmelCase_		(			self     ,					_A     ,					_A     ,					_A     ,					_A     ,					_A     ,					_A     ,					_A=None     ):
									if not isinstance(_A     ,					(torch.Tensor, PIL.Image.Image, list)     ):
														raise ValueError(
														    F"""`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(_A     )}"""     )

									__A	:						Union[str, Any]       =				image.to(device=_A     ,					dtype=_A     )

									__A	:						Optional[Any]       =				batch_size * num_images_per_prompt

									if image.shape[1] == 4:
														__A	:						int       =				image

									else:
														if isinstance(_A     ,					_A     ) and len(_A     ) != batch_size:
																			raise ValueError(
																			    F"""You have passed a list of generators of length {len(_A     )}, but requested an effective batch"""
																			    F""" size of {batch_size}. Make sure the batch size matches the length of the generators."""     )

														elif isinstance(_A     ,					_A     ):
																			__A	:						str       =				[
																			    self.movq.encode(image[i : i + 1]     ).latent_dist.sample(generator[i]     ) for i in range(_A     )
																			]
																			__A	:						str       =				torch.cat(_A     ,					dim=0     )
														else:
																			__A	:						List[str]       =				self.movq.encode(_A     ).latent_dist.sample(_A     )

														__A	:						Tuple       =				self.movq.config.scaling_factor * init_latents

									__A	:						Optional[int]       =				torch.cat([init_latents]     ,					dim=0     )

									__A	:						Union[str, Any]       =				init_latents.shape
									__A	:						List[str]       =				randn_tensor(_A     ,					generator=_A     ,					device=_A     ,					dtype=_A     )

									# get latents
									__A	:						Optional[Any]       =				self.scheduler.add_noise(_A     ,					_A     ,					_A     )

									__A	:						Optional[int]       =				init_latents

									return latents



				def 					UpperCAmelCase_		(			self     ,					_A=0     ):
									if is_accelerate_available():
														from accelerate import cpu_offload
									else:
														raise ImportError('Please install accelerate via `pip install accelerate`'     )

									__A	:						Optional[int]       =				torch.device(F"""cuda:{gpu_id}"""     )

									__A	:						Union[str, Any]       =				[
									    self.unet,
									    self.movq,
									]
									for cpu_offloaded_model in models:
														if cpu_offloaded_model is not None:
																			cpu_offload(_A     ,					_A     )



				def 					UpperCAmelCase_		(			self     ,					_A=0     ):
									if is_accelerate_available() and is_accelerate_version('>='     ,					'0.17.0.dev0'     ):
														from accelerate import cpu_offload_with_hook
									else:
														raise ImportError('`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.'     )

									__A	:						List[Any]       =				torch.device(F"""cuda:{gpu_id}"""     )

									if self.device.type != "cpu":
														self.to('cpu'     ,					silence_dtype_warnings=_A     )
														torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

									__A	:						int       =				None
									for cpu_offloaded_model in [self.unet, self.movq]:
														__A    ,				__A	:						Optional[int]       =				cpu_offload_with_hook(_A     ,					_A     ,					prev_module_hook=_A     )

									# We'll offload the last model manually.
									__A	:						List[str]       =				hook



				@property
				# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
				def 					UpperCAmelCase_		(			self     ):
									if not hasattr(self.unet     ,					'_hf_hook'     ):
														return self.device
									for module in self.unet.modules():
														if (
														    hasattr(_A     ,					'_hf_hook'     )
														    and hasattr(module._hf_hook     ,					'execution_device'     )
														    and module._hf_hook.execution_device is not None
														):
																			return torch.device(module._hf_hook.execution_device     )
									return self.device





				@torch.no_grad()
				@replace_example_docstring(_A     )
				def __call__(			self     ,					_A     ,					_A     ,					_A     ,					_A = 512     ,					_A = 512     ,					_A = 100     ,					_A = 4.0     ,					_A = 0.3     ,					_A = 1     ,					_A = None     ,					_A = "pil"     ,					_A = True     ,					):
									__A	:						List[Any]       =				self._execution_device

									__A	:						Optional[Any]       =				guidance_scale > 1.0

									if isinstance(_A     ,					_A     ):
														__A	:						Optional[Any]       =				torch.cat(_A     ,					dim=0     )
									__A	:						Tuple       =				image_embeds.shape[0]
									if isinstance(_A     ,					_A     ):
														__A	:						List[Any]       =				torch.cat(_A     ,					dim=0     )

									if do_classifier_free_guidance:
														__A	:						Union[str, Any]       =				image_embeds.repeat_interleave(_A     ,					dim=0     )
														__A	:						Optional[int]       =				negative_image_embeds.repeat_interleave(_A     ,					dim=0     )

									__A	:						List[str]       =				torch.cat([negative_image_embeds, image_embeds]     ,					dim=0     ).to(dtype=self.unet.dtype     ,					device=_A     )

									if not isinstance(_A     ,					_A     ):
														__A	:						List[Any]       =				[image]
									if not all(isinstance(_A     ,					(PIL.Image.Image, torch.Tensor)     ) for i in image     ):
														raise ValueError(
														    F"""Input is in incorrect format: {[type(_A     ) for i in image]}. Currently, we only support  PIL image and pytorch tensor"""     )

									__A	:						Dict       =				torch.cat([prepare_image(_A     ,					_A     ,					_A     ) for i in image]     ,					dim=0     )
									__A	:						Any       =				image.to(dtype=image_embeds.dtype     ,					device=_A     )

									__A	:						Tuple       =				self.movq.encode(_A     )['latents']
									__A	:						int       =				latents.repeat_interleave(_A     ,					dim=0     )
									self.scheduler.set_timesteps(_A     ,					device=_A     )
									__A    ,				__A	:						int       =				self.get_timesteps(_A     ,					_A     ,					_A     )
									__A	:						Union[str, Any]       =				timesteps[:1].repeat(batch_size * num_images_per_prompt     )
									__A    ,				__A	:						Any       =				downscale_height_and_width(_A     ,					_A     ,					self.movq_scale_factor     )
									__A	:						Tuple       =				self.prepare_latents(
									    _A     ,					_A     ,					_A     ,					_A     ,					image_embeds.dtype     ,					_A     ,					_A     )
									for i, t in enumerate(self.progress_bar(_A     )     ):
														# expand the latents if we are doing classifier free guidance
														__A	:						Optional[int]       =				torch.cat([latents] * 2     ) if do_classifier_free_guidance else latents

														__A	:						Dict       =				{'image_embeds': image_embeds}
														__A	:						List[str]       =				self.unet(
														    sample=_A     ,					timestep=_A     ,					encoder_hidden_states=_A     ,					added_cond_kwargs=_A     ,					return_dict=_A     ,					)[0]

														if do_classifier_free_guidance:
																			__A    ,				__A	:						Dict       =				noise_pred.split(latents.shape[1]     ,					dim=1     )
																			__A    ,				__A	:						Optional[Any]       =				noise_pred.chunk(2     )
																			__A    ,				__A	:						List[str]       =				variance_pred.chunk(2     )
																			__A	:						str       =				noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
																			__A	:						List[str]       =				torch.cat([noise_pred, variance_pred_text]     ,					dim=1     )

														if not (
														    hasattr(self.scheduler.config     ,					'variance_type'     )
														    and self.scheduler.config.variance_type in ["learned", "learned_range"]
														):
																			__A    ,				__A	:						Optional[Any]       =				noise_pred.split(latents.shape[1]     ,					dim=1     )

														# compute the previous noisy sample x_t -> x_t-1
														__A	:						List[str]       =				self.scheduler.step(
														    _A     ,					_A     ,					_A     ,					generator=_A     ,					)[0]

									# post-processing
									__A	:						List[Any]       =				self.movq.decode(_A     ,					force_not_quantize=_A     )['sample']

									if output_type not in ["pt", "np", "pil"]:
														raise ValueError(F"""Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}"""     )

									if output_type in ["np", "pil"]:
														__A	:						List[str]       =				image * 0.5 + 0.5
														__A	:						List[str]       =				image.clamp(0     ,					1     )
														__A	:						Optional[Any]       =				image.cpu().permute(0     ,					2     ,					3     ,					1     ).float().numpy()

									if output_type == "pil":
														__A	:						Any       =				self.numpy_to_pil(_A     )

									if not return_dict:
														return (image,)

									return ImagePipelineOutput(images=_A     )




