





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


snake_case								=							logging.get_logger(__name__)  # pylint: disable=invalid-name





class 					A_					(   UpperCAmelCase						):







       """simple docstring"""





       def __init__(     self						:						str    ,__A						:						AutoencoderKL    ,__A						:						CLIPTextModel    ,__A						:						CLIPTokenizer    ,__A						:						UNetaDConditionModel    ,__A						:						Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler]    ,__A						:						StableDiffusionSafetyChecker    ,__A						:						CLIPImageProcessor    ,)    ->       Dict:
         super().__init__()
         self.register_modules(
             vae=__A    ,text_encoder=__A    ,tokenizer=__A    ,unet=__A    ,scheduler=__A    ,safety_checker=__A    ,feature_extractor=__A    ,)







       def  __UpperCAmelCase			(     self						:						List[Any]    ,__A						:						Optional[Union[str, int]] = "auto"	)    ->       Union[str, Any]:
         if slice_size == "auto":
           # half the attention head size is usually a good trade-off between
           # speed and memory
           _lowercase						    =			self.unet.config.attention_head_dim // 2
         self.unet.set_attention_slice(__A	)







       def  __UpperCAmelCase			(     self						:						List[Any]	)    ->       Dict:
         self.enable_attention_slicing(__A	)







       @torch.no_grad()
       def __call__(     self						:						int    ,__A						:						Union[str, List[str]]    ,__A						:						int = 512    ,__A						:						int = 512    ,__A						:						int = 50    ,__A						:						float = 7.5    ,__A						:						Optional[Union[str, List[str]]] = None    ,__A						:						Optional[int] = 1    ,__A						:						float = 0.0    ,__A						:						Optional[torch.Generator] = None    ,__A						:						Optional[torch.FloatTensor] = None    ,__A						:						Optional[str] = "pil"    ,__A						:						bool = True    ,__A						:						Optional[Callable[[int, int, torch.FloatTensor], None]] = None    ,__A						:						int = 1    ,__A						:						Optional[torch.FloatTensor] = None    ,**__A						:						Dict    ,)    ->       Optional[Any]:

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

         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(__A    ,__A	) or callback_steps <= 0)
         ):
           raise ValueError(
               F"""`callback_steps` has to be a positive integer but is {callback_steps} of type"""
               F""" {type(__A	)}."""	)

         # get prompt text embeddings
         _lowercase						    =			self.tokenizer(
             __A    ,padding='max_length'    ,max_length=self.tokenizer.model_max_length    ,return_tensors='pt'    ,)
         _lowercase						    =			text_inputs.input_ids

         if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
           _lowercase						    =			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}"""	)
           _lowercase						    =			text_input_ids[:, : self.tokenizer.model_max_length]

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

         # duplicate text embeddings for each generation per prompt, using mps friendly method
         _lowercase    ,		_lowercase    ,		_lowercase						    =			text_embeddings.shape
         _lowercase						    =			text_embeddings.repeat(1    ,__A    ,1	)
         _lowercase						    =			text_embeddings.view(bs_embed * num_images_per_prompt    ,__A    ,-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.
         _lowercase						    =			guidance_scale > 1.0
         # get unconditional embeddings for classifier free guidance
         if do_classifier_free_guidance:
           _lowercase						    =			42
           if negative_prompt is None:
             _lowercase						    =			['']
           elif type(__A	) is not type(__A	):
             raise TypeError(
                 F"""`negative_prompt` should be the same type to `prompt`, but got {type(__A	)} !="""
                 F""" {type(__A	)}."""	)
           elif isinstance(__A    ,__A	):
             _lowercase						    =			[negative_prompt]
           elif batch_size != len(__A	):
             raise ValueError(
                 F"""`negative_prompt`: {negative_prompt} has batch size {len(__A	)}, but `prompt`:"""
                 F""" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"""
                 ' the batch size of `prompt`.'	)
           else:
             _lowercase						    =			negative_prompt

           _lowercase						    =			text_input_ids.shape[-1]
           _lowercase						    =			self.tokenizer(
               __A    ,padding='max_length'    ,max_length=__A    ,truncation=__A    ,return_tensors='pt'    ,)
           _lowercase						    =			self.text_encoder(uncond_input.input_ids.to(self.device	)	)[0]

           # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
           _lowercase						    =			uncond_embeddings.shape[1]
           _lowercase						    =			uncond_embeddings.repeat(__A    ,__A    ,1	)
           _lowercase						    =			uncond_embeddings.view(batch_size * num_images_per_prompt    ,__A    ,-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
           _lowercase						    =			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`.
         _lowercase						    =			(batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
         _lowercase						    =			(batch_size * num_images_per_prompt, self.unet.config.in_channels, 64, 64)
         _lowercase						    =			text_embeddings.dtype
         if latents is None:
           if self.device.type == "mps":
             # randn does not exist on mps
             _lowercase						    =			torch.randn(
                 __A    ,generator=__A    ,device='cpu'    ,dtype=__A	).to(self.device	)
             _lowercase						    =			torch.randn(__A    ,generator=__A    ,device='cpu'    ,dtype=__A	).to(
                 self.device	)
           else:
             _lowercase						    =			torch.randn(
                 __A    ,generator=__A    ,device=self.device    ,dtype=__A	)
             _lowercase						    =			torch.randn(__A    ,generator=__A    ,device=self.device    ,dtype=__A	)
         else:
           if latents_reference.shape != latents_shape:
             raise ValueError(F"""Unexpected latents shape, got {latents.shape}, expected {latents_shape}"""	)
           _lowercase						    =			latents_reference.to(self.device	)
           _lowercase						    =			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
         _lowercase						    =			(latents_shape[3] - latents_shape_reference[3]) // 2
         _lowercase						    =			(latents_shape[2] - latents_shape_reference[2]) // 2
         _lowercase						    =			latents_shape_reference[3] if dx >= 0 else latents_shape_reference[3] + 2 * dx
         _lowercase						    =			latents_shape_reference[2] if dy >= 0 else latents_shape_reference[2] + 2 * dy
         _lowercase						    =			0 if dx < 0 else dx
         _lowercase						    =			0 if dy < 0 else dy
         _lowercase						    =			max(-dx    ,0	)
         _lowercase						    =			max(-dy    ,0	)
         # import pdb
         # pdb.set_trace()
         _lowercase						    =			latents_reference[:, :, dy : dy + h, dx : dx + w]

         # set timesteps
         self.scheduler.set_timesteps(__A	)

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

         # scale the initial noise by the standard deviation required by the scheduler
         _lowercase						    =			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]
         _lowercase						    =			'eta' in set(inspect.signature(self.scheduler.step	).parameters.keys()	)
         _lowercase						    =			{}
         if accepts_eta:
           _lowercase						    =			eta

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

           # predict the noise residual
           _lowercase						    =			self.unet(__A    ,__A    ,encoder_hidden_states=__A	).sample

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

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

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

         _lowercase						    =			1 / 0.18215 * latents
         _lowercase						    =			self.vae.decode(__A	).sample

         _lowercase						    =			(image / 2 + 0.5).clamp(0    ,1	)

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

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

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

         if not return_dict:
           return (image, has_nsfw_concept)

         return StableDiffusionPipelineOutput(images=__A    ,nsfw_content_detected=__A	)

# Note: if you intend to run this script make sure you look under scripts/fsmt/
# to locate the appropriate script to do the work correctly. There is a set of scripts to:
# - download and prepare data and run the conversion script
# - perform eval to get the best hparam into the config
# - generate model_cards - useful if you have multiple models from the same paper

import argparse
import json
import os
import re
from collections import OrderedDict
from os.path import basename, dirname

import fairseq
import torch
from fairseq import hub_utils
from fairseq.data.dictionary import Dictionary

from transformers import FSMTConfig, FSMTForConditionalGeneration
from transformers.models.fsmt.tokenization_fsmt import VOCAB_FILES_NAMES
from transformers.tokenization_utils_base import TOKENIZER_CONFIG_FILE
from transformers.utils import WEIGHTS_NAME, logging


logging.set_verbosity_warning()

_A  =	2

# based on the results of a search on a range of `num_beams`, `length_penalty` and `early_stopping`
# values against wmt19 test data to obtain the best BLEU scores, we will use the following defaults:
#
# * `num_beams`: 5 (higher scores better, but requires more memory/is slower, can be adjusted by users)
# * `early_stopping`: `False` consistently scored better
# * `length_penalty` varied, so will assign the best one depending on the model
_A  =	{
    # fairseq:
    "wmt19-ru-en": {"length_penalty": 1.1},
    "wmt19-en-ru": {"length_penalty": 1.15},
    "wmt19-en-de": {"length_penalty": 1.0},
    "wmt19-de-en": {"length_penalty": 1.1},
    # allenai:
    "wmt16-en-de-dist-12-1": {"length_penalty": 0.6},
    "wmt16-en-de-dist-6-1": {"length_penalty": 0.6},
    "wmt16-en-de-12-1": {"length_penalty": 0.8},
    "wmt19-de-en-6-6-base": {"length_penalty": 0.6},
    "wmt19-de-en-6-6-big": {"length_penalty": 0.6},
}

# this remaps the different models to their organization names
_A  =	{}
for m in ["wmt19-ru-en", "wmt19-en-ru", "wmt19-en-de", "wmt19-de-en"]:
			_A  =	"facebook"
for m in [
    "wmt16-en-de-dist-12-1",
    "wmt16-en-de-dist-6-1",
    "wmt16-en-de-12-1",
    "wmt19-de-en-6-6-base",
    "wmt19-de-en-6-6-big",
]:
			_A  =	"allenai"






def   lowerCamelCase__							(							__lowerCAmelCase    :    Dict							):




			"""simple docstring"""


			lowerCAmelCase_         =      dict((re.sub(r"@@$"       ,				""       ,				__lowerCAmelCase							), v) if k.endswith("@@"							) else (re.sub(r"$"       ,				"</w>"       ,				__lowerCAmelCase							), v) for k, v in d.items()							)
			lowerCAmelCase_         =      "<s> <pad> </s> <unk>".split()
			# restore the special tokens
			for k in keep_keys:
						del da[F"""{k}</w>"""]
						lowerCAmelCase_         =      d[k]  # restore
			return da






def   lowerCamelCase__							(							__lowerCAmelCase    :    List[str]       ,				__lowerCAmelCase    :    Optional[Any]							):




			"""simple docstring"""


			assert os.path.exists(__lowerCAmelCase							)
			os.makedirs(__lowerCAmelCase       ,				exist_ok=__lowerCAmelCase							)
			print(F"""Writing results to {pytorch_dump_folder_path}"""							)

			# handle various types of models

			lowerCAmelCase_         =      basename(__lowerCAmelCase							)
			lowerCAmelCase_         =      dirname(__lowerCAmelCase							)

			lowerCAmelCase_         =      fairseq.model_parallel.models.transformer.ModelParallelTransformerModel
			lowerCAmelCase_         =      cls.hub_models()
			lowerCAmelCase_         =      {"bpe": "fastbpe", "tokenizer": "moses"}
			lowerCAmelCase_         =      "."
			# note: since the model dump is old, fairseq has upgraded its model some
			# time later, and it does a whole lot of rewrites and splits on the saved
			# weights, therefore we can't use torch.load() directly on the model file.
			# see: upgrade_state_dict(state_dict) in fairseq_model.py
			print(F"""using checkpoint {checkpoint_file}"""							)
			lowerCAmelCase_         =      hub_utils.from_pretrained(
			    __lowerCAmelCase       ,				__lowerCAmelCase       ,				__lowerCAmelCase       ,				archive_map=__lowerCAmelCase       ,				**__lowerCAmelCase							)

			lowerCAmelCase_         =      vars(chkpt["args"]["model"]							)

			lowerCAmelCase_         =      args["source_lang"]
			lowerCAmelCase_         =      args["target_lang"]

			lowerCAmelCase_         =      dirname(__lowerCAmelCase							)
			lowerCAmelCase_         =      basename(__lowerCAmelCase							)

			# dicts
			lowerCAmelCase_         =      os.path.join(__lowerCAmelCase       ,				F"""dict.{src_lang}.txt"""							)
			lowerCAmelCase_         =      os.path.join(__lowerCAmelCase       ,				F"""dict.{tgt_lang}.txt"""							)

			lowerCAmelCase_         =      Dictionary.load(__lowerCAmelCase							)
			lowerCAmelCase_         =      rewrite_dict_keys(src_dict.indices							)
			lowerCAmelCase_         =      len(__lowerCAmelCase							)
			lowerCAmelCase_         =      os.path.join(__lowerCAmelCase       ,				"vocab-src.json"							)
			print(F"""Generating {src_vocab_file} of {src_vocab_size} of {src_lang} records"""							)
			with open(__lowerCAmelCase       ,				"w"       ,				encoding="utf-8"							) as f:
						f.write(json.dumps(__lowerCAmelCase       ,				ensure_ascii=__lowerCAmelCase       ,				indent=__lowerCAmelCase							)							)

			# detect whether this is a do_lower_case situation, which can be derived by checking whether we
			# have at least one uppercase letter in the source vocab
			lowerCAmelCase_         =      True
			for k in src_vocab.keys():
						if not k.islower():
									lowerCAmelCase_         =      False
									break

			lowerCAmelCase_         =      Dictionary.load(__lowerCAmelCase							)
			lowerCAmelCase_         =      rewrite_dict_keys(tgt_dict.indices							)
			lowerCAmelCase_         =      len(__lowerCAmelCase							)
			lowerCAmelCase_         =      os.path.join(__lowerCAmelCase       ,				"vocab-tgt.json"							)
			print(F"""Generating {tgt_vocab_file} of {tgt_vocab_size} of {tgt_lang} records"""							)
			with open(__lowerCAmelCase       ,				"w"       ,				encoding="utf-8"							) as f:
						f.write(json.dumps(__lowerCAmelCase       ,				ensure_ascii=__lowerCAmelCase       ,				indent=__lowerCAmelCase							)							)

			# merges_file (bpecodes)
			lowerCAmelCase_         =      os.path.join(__lowerCAmelCase       ,				VOCAB_FILES_NAMES["merges_file"]							)
			for fn in ["bpecodes", "code"]:  # older fairseq called the merges file "code"
						lowerCAmelCase_         =      os.path.join(__lowerCAmelCase       ,				__lowerCAmelCase							)
						if os.path.exists(__lowerCAmelCase							):
									break
			with open(__lowerCAmelCase       ,				encoding="utf-8"							) as fin:
						lowerCAmelCase_         =      fin.read()
			lowerCAmelCase_         =      re.sub(r" \d+$"       ,				""       ,				__lowerCAmelCase       ,				0       ,				re.M							)  # remove frequency number
			print(F"""Generating {merges_file}"""							)
			with open(__lowerCAmelCase       ,				"w"       ,				encoding="utf-8"							) as fout:
						fout.write(__lowerCAmelCase							)

			# model config
			lowerCAmelCase_         =      os.path.join(__lowerCAmelCase       ,				"config.json"							)

			# validate bpe/tokenizer config, as currently it's hardcoded to moses+fastbpe -
			# may have to modify the tokenizer if a different type is used by a future model
			assert args["bpe"] == "fastbpe", F"""need to extend tokenizer to support bpe={args['bpe']}"""
			assert args["tokenizer"] == "moses", F"""need to extend tokenizer to support bpe={args['tokenizer']}"""

			lowerCAmelCase_         =      {
			    "architectures": ["FSMTForConditionalGeneration"],
			    "model_type": "fsmt",
			    "activation_dropout": args["activation_dropout"],
			    "activation_function": "relu",
			    "attention_dropout": args["attention_dropout"],
			    "d_model": args["decoder_embed_dim"],
			    "dropout": args["dropout"],
			    "init_std": 0.02,
			    "max_position_embeddings": args["max_source_positions"],
			    "num_hidden_layers": args["encoder_layers"],
			    "src_vocab_size": src_vocab_size,
			    "tgt_vocab_size": tgt_vocab_size,
			    "langs": [src_lang, tgt_lang],
			    "encoder_attention_heads": args["encoder_attention_heads"],
			    "encoder_ffn_dim": args["encoder_ffn_embed_dim"],
			    "encoder_layerdrop": args["encoder_layerdrop"],
			    "encoder_layers": args["encoder_layers"],
			    "decoder_attention_heads": args["decoder_attention_heads"],
			    "decoder_ffn_dim": args["decoder_ffn_embed_dim"],
			    "decoder_layerdrop": args["decoder_layerdrop"],
			    "decoder_layers": args["decoder_layers"],
			    "bos_token_id": 0,
			    "pad_token_id": 1,
			    "eos_token_id": 2,
			    "is_encoder_decoder": True,
			    "scale_embedding": not args["no_scale_embedding"],
			    "tie_word_embeddings": args["share_all_embeddings"],
			}

			# good hparam defaults to start with
			lowerCAmelCase_         =      5
			lowerCAmelCase_         =      False
			if model_dir in best_score_hparams and "length_penalty" in best_score_hparams[model_dir]:
						lowerCAmelCase_         =      best_score_hparams[model_dir]["length_penalty"]
			else:
						lowerCAmelCase_         =      1.0

			print(F"""Generating {fsmt_model_config_file}"""							)
			with open(__lowerCAmelCase       ,				"w"       ,				encoding="utf-8"							) as f:
						f.write(json.dumps(__lowerCAmelCase       ,				ensure_ascii=__lowerCAmelCase       ,				indent=__lowerCAmelCase							)							)

			# tokenizer config
			lowerCAmelCase_         =      os.path.join(__lowerCAmelCase       ,				__lowerCAmelCase							)

			lowerCAmelCase_         =      {
			    "langs": [src_lang, tgt_lang],
			    "model_max_length": 1024,
			    "do_lower_case": do_lower_case,
			}

			print(F"""Generating {fsmt_tokenizer_config_file}"""							)
			with open(__lowerCAmelCase       ,				"w"       ,				encoding="utf-8"							) as f:
						f.write(json.dumps(__lowerCAmelCase       ,				ensure_ascii=__lowerCAmelCase       ,				indent=__lowerCAmelCase							)							)

			# model
			lowerCAmelCase_         =      chkpt["models"][0]
			lowerCAmelCase_         =      model.state_dict()

			# rename keys to start with 'model.'
			lowerCAmelCase_         =      OrderedDict(("model." + k, v) for k, v in model_state_dict.items()							)

			# remove unneeded keys
			lowerCAmelCase_         =      [
			    "model.model",
			    "model.encoder.version",
			    "model.decoder.version",
			    "model.encoder_embed_tokens.weight",
			    "model.decoder_embed_tokens.weight",
			    "model.encoder.embed_positions._float_tensor",
			    "model.decoder.embed_positions._float_tensor",
			]
			for k in ignore_keys:
						model_state_dict.pop(__lowerCAmelCase       ,				__lowerCAmelCase							)

			lowerCAmelCase_         =      FSMTConfig.from_pretrained(__lowerCAmelCase							)
			lowerCAmelCase_         =      FSMTForConditionalGeneration(__lowerCAmelCase							)

			# check that it loads ok
			model_new.load_state_dict(__lowerCAmelCase       ,				strict=__lowerCAmelCase							)

			# save
			lowerCAmelCase_         =      os.path.join(__lowerCAmelCase       ,				__lowerCAmelCase							)
			print(F"""Generating {pytorch_weights_dump_path}"""							)
			torch.save(__lowerCAmelCase       ,				__lowerCAmelCase							)

			print("Conversion is done!"							)
			print("\nLast step is to upload the files to s3"							)
			print(F"""cd {data_root}"""							)
			print(F"""transformers-cli upload {model_dir}"""							)


if __name__ == "__main__":
			_A  =	argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    "--fsmt_checkpoint_path",
			    default=None,
			    type=str,
			    required=True,
			    help=(
			        "Path to the official PyTorch checkpoint file which is expected to reside in the dump dir with dicts,"
			        " bpecodes, etc."
			    ),
			)
			parser.add_argument(
			    "--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
			)
			_A  =	parser.parse_args()
			convert_fsmt_checkpoint_to_pytorch(args.fsmt_checkpoint_path, args.pytorch_dump_folder_path)



