


# 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()

lowerCamelCase__							:						List[Any]							     =   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
lowerCamelCase__							:						int							     =   {
    # fairseq:
    """wmt19-ru-en""": {"""length_penalty""": 1.1},
    """wmt19-en-ru""": {"""length_penalty""": 1.1_5},
    """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
lowerCamelCase__							:						List[str]							     =   {}
for m in ["wmt19-ru-en", "wmt19-en-ru", "wmt19-en-de", "wmt19-de-en"]:
   lowerCamelCase__							:						Optional[int]							     =   """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",
]:
   lowerCamelCase__							:						int							     =   """allenai"""







def SCREAMING_SNAKE_CASE	(			__lowerCAmelCase				)    ->				Union[str, Any]:
  # (1) remove word breaking symbol, (2) add word ending symbol where the word is not broken up,
  # e.g.: d = {'le@@': 5, 'tt@@': 6, 'er': 7} => {'le': 5, 'tt': 6, 'er</w>': 7}
  snake_case__						= dict((re.sub(r'''@@$'''							,   ''''''							,   __lowerCAmelCase				), v) if k.endswith('''@@'''				) else (re.sub(r'''$'''							,   '''</w>'''							,   __lowerCAmelCase				), v) for k, v in d.items()				)
  snake_case__						= '''<s> <pad> </s> <unk>'''.split()
  # restore the special tokens
  for k in keep_keys:
    del da[F"""{k}</w>"""]
    snake_case__						= d[k]  # restore
  return da







def SCREAMING_SNAKE_CASE	(			__lowerCAmelCase							,   __lowerCAmelCase				)    ->				Any:
  # prep
  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

  snake_case__						= basename(__lowerCAmelCase				)
  snake_case__						= dirname(__lowerCAmelCase				)

  snake_case__						= fairseq.model_parallel.models.transformer.ModelParallelTransformerModel
  snake_case__						= cls.hub_models()
  snake_case__						= {'''bpe''': '''fastbpe''', '''tokenizer''': '''moses'''}
  snake_case__						= '''.'''
  # 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}"""				)
  snake_case__						= hub_utils.from_pretrained(
      __lowerCAmelCase							,   __lowerCAmelCase							,   __lowerCAmelCase							,   archive_map=__lowerCAmelCase							,   **__lowerCAmelCase				)

  snake_case__						= vars(chkpt['''args''']['''model''']				)

  snake_case__						= args['''source_lang''']
  snake_case__						= args['''target_lang''']

  snake_case__						= dirname(__lowerCAmelCase				)
  snake_case__						= basename(__lowerCAmelCase				)

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

  snake_case__						= Dictionary.load(__lowerCAmelCase				)
  snake_case__						= rewrite_dict_keys(src_dict.indices				)
  snake_case__						= len(__lowerCAmelCase				)
  snake_case__						= 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
  snake_case__						= True
  for k in src_vocab.keys():
    if not k.islower():
      snake_case__						= False
      break

  snake_case__						= Dictionary.load(__lowerCAmelCase				)
  snake_case__						= rewrite_dict_keys(tgt_dict.indices				)
  snake_case__						= len(__lowerCAmelCase				)
  snake_case__						= 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)
  snake_case__						= os.path.join(__lowerCAmelCase							,   VOCAB_FILES_NAMES['''merges_file''']				)
  for fn in ["bpecodes", "code"]:  # older fairseq called the merges file "code"
    snake_case__						= os.path.join(__lowerCAmelCase							,   __lowerCAmelCase				)
    if os.path.exists(__lowerCAmelCase				):
      break
  with open(__lowerCAmelCase							,   encoding='''utf-8'''				) as fin:
    snake_case__						= fin.read()
  snake_case__						= 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
  snake_case__						= 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']}"""

  snake_case__						= {
      '''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
  snake_case__						= 5
  snake_case__						= False
  if model_dir in best_score_hparams and "length_penalty" in best_score_hparams[model_dir]:
    snake_case__						= best_score_hparams[model_dir]['''length_penalty''']
  else:
    snake_case__						= 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
  snake_case__						= os.path.join(__lowerCAmelCase							,   __lowerCAmelCase				)

  snake_case__						= {
      '''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
  snake_case__						= chkpt['''models'''][0]
  snake_case__						= model.state_dict()

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

  # remove unneeded keys
  snake_case__						= [
      '''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				)

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

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

  # save
  snake_case__						= 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__":
   lowerCamelCase__							:						Union[str, Any]							     =   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."""
   )
   lowerCamelCase__							:						Dict							     =   parser.parse_args()
   convert_fsmt_checkpoint_to_pytorch(args.fsmt_checkpoint_path, args.pytorch_dump_folder_path)





import gc
import random
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

import diffusers
from diffusers import (
    AutoencoderKL,
    EulerDiscreteScheduler,
    StableDiffusionLatentUpscalePipeline,
    StableDiffusionPipeline,
    UNetaDConditionModel,
)
from diffusers.schedulers import KarrasDiffusionSchedulers
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()







def SCREAMING_SNAKE_CASE	(			__lowerCAmelCase				)    ->				List[Any]:
  snake_case__						= [tensor.shape for tensor in tensor_list]
  return all(shape == shapes[0] for shape in shapes[1:]				)
class 		__magic_name__						(snake_case_   ,snake_case_   ,snake_case_   ,unittest.TestCase				):





     '''simple docstring'''



     __lowercase				:							Dict	   =       StableDiffusionLatentUpscalePipeline
     __lowercase				:							List[str]	   =       TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {
         'height',
         'width',
         'cross_attention_kwargs',
         'negative_prompt_embeds',
         'prompt_embeds',
     }
     __lowercase				:							List[Any]	   =       PipelineTesterMixin.required_optional_params - {'num_images_per_prompt'}
     __lowercase				:							Any	   =       TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
     __lowercase				:							int	   =       frozenset(
         []				)  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
     __lowercase				:							List[Any]	   =       frozenset([]				)

     __lowercase				:							Any	   =       True

     @property
     def 	SCREAMING_SNAKE_CASE__						(					self:List[str]   ):
       snake_case__						= 1
       snake_case__						= 4
       snake_case__						= (16, 16)

       snake_case__						= floats_tensor((batch_size, num_channels) + sizes     ,		rng=random.Random(0   )   ).to(_a   )
       return image

     def 	SCREAMING_SNAKE_CASE__						(					self:Union[str, Any]   ):
       torch.manual_seed(0   )
       snake_case__						= UNetaDConditionModel(
           act_fn='''gelu'''     ,		attention_head_dim=8     ,		norm_num_groups=_a     ,		block_out_channels=[32, 32, 64, 64]     ,		time_cond_proj_dim=1_60     ,		conv_in_kernel=1     ,		conv_out_kernel=1     ,		cross_attention_dim=32     ,		down_block_types=(
               '''KDownBlock2D''',
               '''KCrossAttnDownBlock2D''',
               '''KCrossAttnDownBlock2D''',
               '''KCrossAttnDownBlock2D''',
           )     ,		in_channels=8     ,		mid_block_type=_a     ,		only_cross_attention=_a     ,		out_channels=5     ,		resnet_time_scale_shift='''scale_shift'''     ,		time_embedding_type='''fourier'''     ,		timestep_post_act='''gelu'''     ,		up_block_types=('''KCrossAttnUpBlock2D''', '''KCrossAttnUpBlock2D''', '''KCrossAttnUpBlock2D''', '''KUpBlock2D''')     ,		)
       snake_case__						= AutoencoderKL(
           block_out_channels=[32, 32, 64, 64]     ,		in_channels=3     ,		out_channels=3     ,		down_block_types=[
               '''DownEncoderBlock2D''',
               '''DownEncoderBlock2D''',
               '''DownEncoderBlock2D''',
               '''DownEncoderBlock2D''',
           ]     ,		up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''', '''UpDecoderBlock2D''', '''UpDecoderBlock2D''']     ,		latent_channels=4     ,		)
       snake_case__						= EulerDiscreteScheduler(prediction_type='''sample'''   )
       snake_case__						= CLIPTextConfig(
           bos_token_id=0     ,		eos_token_id=2     ,		hidden_size=32     ,		intermediate_size=37     ,		layer_norm_eps=1e-05     ,		num_attention_heads=4     ,		num_hidden_layers=5     ,		pad_token_id=1     ,		vocab_size=10_00     ,		hidden_act='''quick_gelu'''     ,		projection_dim=5_12     ,		)
       snake_case__						= CLIPTextModel(_a   )
       snake_case__						= CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''   )

       snake_case__						= {
           '''unet''': model.eval(),
           '''vae''': vae.eval(),
           '''scheduler''': scheduler,
           '''text_encoder''': text_encoder,
           '''tokenizer''': tokenizer,
       }

       return components

     def 	SCREAMING_SNAKE_CASE__						(					self:List[Any]     ,		_a:Optional[Any]     ,		_a:List[str]=0   ):
       if str(_a   ).startswith('''mps'''   ):
         snake_case__						= torch.manual_seed(_a   )
       else:
         snake_case__						= torch.Generator(device=_a   ).manual_seed(_a   )
       snake_case__						= {
           '''prompt''': '''A painting of a squirrel eating a burger''',
           '''image''': self.dummy_image.cpu(),
           '''generator''': generator,
           '''num_inference_steps''': 2,
           '''output_type''': '''numpy''',
       }
       return inputs

     def 	SCREAMING_SNAKE_CASE__						(					self:str   ):
       snake_case__						= '''cpu'''

       snake_case__						= self.get_dummy_components()
       snake_case__						= self.pipeline_class(**_a   )
       pipe.to(_a   )
       pipe.set_progress_bar_config(disable=_a   )

       snake_case__						= self.get_dummy_inputs(_a   )
       snake_case__						= pipe(**_a   ).images
       snake_case__						= image[0, -3:, -3:, -1]

       self.assertEqual(image.shape     ,		(1, 2_56, 2_56, 3)   )
       snake_case__						= np.array(
           [0.47222412, 0.41921633, 0.44717434, 0.46874192, 0.42588258, 0.46150726, 0.4677534, 0.45583832, 0.48579055]   )
       snake_case__						= np.abs(image_slice.flatten() - expected_slice   ).max()
       self.assertLessEqual(_a     ,		1e-3   )

     def 	SCREAMING_SNAKE_CASE__						(					self:Union[str, Any]   ):
       super().test_attention_slicing_forward_pass(expected_max_diff=7e-3   )

     def 	SCREAMING_SNAKE_CASE__						(					self:List[Any]   ):
       super().test_cpu_offload_forward_pass(expected_max_diff=3e-3   )

     def 	SCREAMING_SNAKE_CASE__						(					self:str   ):
       super().test_dict_tuple_outputs_equivalent(expected_max_difference=3e-3   )

     def 	SCREAMING_SNAKE_CASE__						(					self:Any   ):
       super().test_inference_batch_single_identical(expected_max_diff=7e-3   )

     def 	SCREAMING_SNAKE_CASE__						(					self:Tuple   ):
       super().test_pt_np_pil_outputs_equivalent(expected_max_diff=3e-3   )

     def 	SCREAMING_SNAKE_CASE__						(					self:Dict   ):
       super().test_save_load_local(expected_max_difference=3e-3   )

     def 	SCREAMING_SNAKE_CASE__						(					self:str   ):
       super().test_save_load_optional_components(expected_max_difference=3e-3   )







     def 	SCREAMING_SNAKE_CASE__						(					self:Any   ):
       snake_case__						= [
           '''DDIMScheduler''',
           '''DDPMScheduler''',
           '''PNDMScheduler''',
           '''HeunDiscreteScheduler''',
           '''EulerAncestralDiscreteScheduler''',
           '''KDPM2DiscreteScheduler''',
           '''KDPM2AncestralDiscreteScheduler''',
           '''DPMSolverSDEScheduler''',
       ]
       snake_case__						= self.get_dummy_components()
       snake_case__						= self.pipeline_class(**_a   )

       # make sure that PNDM does not need warm-up
       pipe.scheduler.register_to_config(skip_prk_steps=_a   )

       pipe.to(_a   )
       pipe.set_progress_bar_config(disable=_a   )
       snake_case__						= self.get_dummy_inputs(_a   )
       snake_case__						= 2

       snake_case__						= []
       for scheduler_enum in KarrasDiffusionSchedulers:
         if scheduler_enum.name in skip_schedulers:
           # no sigma schedulers are not supported
           # no schedulers
           continue

         snake_case__						= getattr(_a     ,		scheduler_enum.name   )
         snake_case__						= scheduler_cls.from_config(pipe.scheduler.config   )
         snake_case__						= pipe(**_a   )[0]
         outputs.append(_a   )

       assert check_same_shape(_a   )




@require_torch_gpu
@slow
class 		__magic_name__						(unittest.TestCase				):





     '''simple docstring'''



     def 	SCREAMING_SNAKE_CASE__						(					self:Optional[Any]   ):
       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()

     def 	SCREAMING_SNAKE_CASE__						(					self:str   ):
       snake_case__						= torch.manual_seed(33   )

       snake_case__						= StableDiffusionPipeline.from_pretrained('''CompVis/stable-diffusion-v1-4'''     ,		torch_dtype=torch.floataa   )
       pipe.to('''cuda'''   )

       snake_case__						= StableDiffusionLatentUpscalePipeline.from_pretrained(
           '''stabilityai/sd-x2-latent-upscaler'''     ,		torch_dtype=torch.floataa   )
       upscaler.to('''cuda'''   )

       snake_case__						= '''a photo of an astronaut high resolution, unreal engine, ultra realistic'''

       snake_case__						= pipe(_a     ,		generator=_a     ,		output_type='''latent'''   ).images

       snake_case__						= upscaler(
           prompt=_a     ,		image=_a     ,		num_inference_steps=20     ,		guidance_scale=0     ,		generator=_a     ,		output_type='''np'''     ,		).images[0]

       snake_case__						= load_numpy(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/latent-upscaler/astronaut_1024.npy'''   )
       assert np.abs((expected_image - image).mean()   ) < 5e-2







     def 	SCREAMING_SNAKE_CASE__						(					self:Optional[int]   ):
       snake_case__						= torch.manual_seed(33   )

       snake_case__						= StableDiffusionLatentUpscalePipeline.from_pretrained(
           '''stabilityai/sd-x2-latent-upscaler'''     ,		torch_dtype=torch.floataa   )
       upscaler.to('''cuda'''   )

       snake_case__						= '''the temple of fire by Ross Tran and Gerardo Dottori, oil on canvas'''

       snake_case__						= load_image(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/latent-upscaler/fire_temple_512.png'''   )

       snake_case__						= upscaler(
           prompt=_a     ,		image=_a     ,		num_inference_steps=20     ,		guidance_scale=0     ,		generator=_a     ,		output_type='''np'''     ,		).images[0]

       snake_case__						= load_numpy(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/latent-upscaler/fire_temple_1024.npy'''   )
       assert np.abs((expected_image - image).max()   ) < 5e-2

