





import os
from pathlib import Path



def 				lowerCAmelCase_			(      SCREAMING_SNAKE_CASE       :							List[str]    ,						SCREAMING_SNAKE_CASE       :							Optional[Any]    ,						SCREAMING_SNAKE_CASE       :							Dict   ):


							'''simple docstring'''







							__lowerCamelCase      :      Dict         ={
							    '''en''': '''Machine learning is great, isn\'t it?''',
							    '''ru''': '''Машинное обучение - это здорово, не так ли?''',
							    '''de''': '''Maschinelles Lernen ist großartig, oder?''',
							}

							# BLUE scores as follows:
							# "pair": [fairseq, transformers]
							__lowerCamelCase      :      Dict         ={
							    '''ru-en''': ['''[41.3](http://matrix.statmt.org/matrix/output/1907?run_id=6937)''', '''39.20'''],
							    '''en-ru''': ['''[36.4](http://matrix.statmt.org/matrix/output/1914?run_id=6724)''', '''33.47'''],
							    '''en-de''': ['''[43.1](http://matrix.statmt.org/matrix/output/1909?run_id=6862)''', '''42.83'''],
							    '''de-en''': ['''[42.3](http://matrix.statmt.org/matrix/output/1902?run_id=6750)''', '''41.35'''],
							}
							__lowerCamelCase      :      str         =F'{src_lang}-{tgt_lang}'

							__lowerCamelCase      :      Dict         =F'\n---\nlanguage: \n- {src_lang}\n- {tgt_lang}\nthumbnail:\ntags:\n- translation\n- wmt19\n- facebook\nlicense: apache-2.0\ndatasets:\n- wmt19\nmetrics:\n- bleu\n---\n\n# FSMT\n\n## Model description\n\nThis is a ported version of [fairseq wmt19 transformer](https://github.com/pytorch/fairseq/blob/master/examples/wmt19/README.md) for {src_lang}-{tgt_lang}.\n\nFor more details, please see, [Facebook FAIR\'s WMT19 News Translation Task Submission](https://arxiv.org/abs/1907.06616).\n\nThe abbreviation FSMT stands for FairSeqMachineTranslation\n\nAll four models are available:\n\n* [wmt19-en-ru](https://huggingface.co/facebook/wmt19-en-ru)\n* [wmt19-ru-en](https://huggingface.co/facebook/wmt19-ru-en)\n* [wmt19-en-de](https://huggingface.co/facebook/wmt19-en-de)\n* [wmt19-de-en](https://huggingface.co/facebook/wmt19-de-en)\n\n## Intended uses & limitations\n\n#### How to use\n\n```python\nfrom transformers import FSMTForConditionalGeneration, FSMTTokenizer\nmname = "facebook/wmt19-{src_lang}-{tgt_lang}"\ntokenizer = FSMTTokenizer.from_pretrained(mname)\nmodel = FSMTForConditionalGeneration.from_pretrained(mname)\n\ninput = "{texts[src_lang]}"\ninput_ids = tokenizer.encode(input, return_tensors="pt")\noutputs = model.generate(input_ids)\ndecoded = tokenizer.decode(outputs[0], skip_special_tokens=True)\nprint(decoded) # {texts[tgt_lang]}\n\n```\n\n#### Limitations and bias\n\n- The original (and this ported model) doesn\'t seem to handle well inputs with repeated sub-phrases, [content gets truncated](https://discuss.huggingface.co/t/issues-with-translating-inputs-containing-repeated-phrases/981)\n\n## Training data\n\nPretrained weights were left identical to the original model released by fairseq. For more details, please, see the [paper](https://arxiv.org/abs/1907.06616).\n\n## Eval results\n\npair   | fairseq | transformers\n-------|---------|----------\n{pair}  | {scores[pair][0]} | {scores[pair][1]}\n\nThe score is slightly below the score reported by `fairseq`, since `transformers`` currently doesn\'t support:\n- model ensemble, therefore the best performing checkpoint was ported (``model4.pt``).\n- re-ranking\n\nThe score was calculated using this code:\n\n```bash\ngit clone https://github.com/huggingface/transformers\ncd transformers\nexport PAIR={pair}\nexport DATA_DIR=data/$PAIR\nexport SAVE_DIR=data/$PAIR\nexport BS=8\nexport NUM_BEAMS=15\nmkdir -p $DATA_DIR\nsacrebleu -t wmt19 -l $PAIR --echo src > $DATA_DIR/val.source\nsacrebleu -t wmt19 -l $PAIR --echo ref > $DATA_DIR/val.target\necho $PAIR\nPYTHONPATH="src:examples/seq2seq" python examples/seq2seq/run_eval.py facebook/wmt19-$PAIR $DATA_DIR/val.source $SAVE_DIR/test_translations.txt --reference_path $DATA_DIR/val.target --score_path $SAVE_DIR/test_bleu.json --bs $BS --task translation --num_beams $NUM_BEAMS\n```\nnote: fairseq reports using a beam of 50, so you should get a slightly higher score if re-run with `--num_beams 50`.\n\n## Data Sources\n\n- [training, etc.](http://www.statmt.org/wmt19/)\n- [test set](http://matrix.statmt.org/test_sets/newstest2019.tgz?1556572561)\n\n\n### BibTeX entry and citation info\n\n```bibtex\n@inproceedings{{...,\n  year={{2020}},\n  title={{Facebook FAIR\'s WMT19 News Translation Task Submission}},\n  author={{Ng, Nathan and Yee, Kyra and Baevski, Alexei and Ott, Myle and Auli, Michael and Edunov, Sergey}},\n  booktitle={{Proc. of WMT}},\n}}\n```\n\n\n## TODO\n\n- port model ensemble (fairseq uses 4 model checkpoints)\n\n'
							os.makedirs(SCREAMING_SNAKE_CASE    ,						exist_ok=SCREAMING_SNAKE_CASE   )
							__lowerCamelCase      :      Dict         =os.path.join(SCREAMING_SNAKE_CASE    ,						'''README.md'''   )
							print(F'Generating {path}'   )
							with open(SCREAMING_SNAKE_CASE    ,						'''w'''    ,						encoding='''utf-8'''   ) as f:
														f.write(SCREAMING_SNAKE_CASE   )


# make sure we are under the root of the project
_UpperCamelCase							=      Path(__file__).resolve().parent.parent.parent
_UpperCamelCase							=      repo_dir / 'model_cards'

for model_name in ["wmt19-ru-en", "wmt19-en-ru", "wmt19-en-de", "wmt19-de-en"]:
							_UpperCamelCase			,    _UpperCamelCase			,    _UpperCamelCase							=      model_name.split('-')
							_UpperCamelCase							=      model_cards_dir / 'facebook' / model_name
							write_model_card(model_card_dir, src_lang=src_lang, tgt_lang=tgt_lang)











"""simple docstring"""





from pickle import UnpicklingError

import jax
import jax.numpy as jnp
import numpy as np
from flax.serialization import from_bytes
from flax.traverse_util import flatten_dict

from ..utils import logging


_UpperCamelCase							=      logging.get_logger(__name__)



def 				lowerCAmelCase_			(      SCREAMING_SNAKE_CASE       :							str    ,						SCREAMING_SNAKE_CASE       :							str   ):


							'''simple docstring'''







							try:
														with open(SCREAMING_SNAKE_CASE    ,						'''rb'''   ) as flax_state_f:
																					__lowerCamelCase      :      int         =from_bytes(SCREAMING_SNAKE_CASE    ,						flax_state_f.read()   )
							except UnpicklingError as e:
														try:
																					with open(SCREAMING_SNAKE_CASE   ) as f:
																												if f.read().startswith('''version'''   ):
																																			raise OSError(
																																			    '''You seem to have cloned a repository without having git-lfs installed. Please'''
																																			    ''' install git-lfs and run `git lfs install` followed by `git lfs pull` in the'''
																																			    ''' folder you cloned.'''   )
																												else:
																																			raise ValueError from e
														except (UnicodeDecodeError, ValueError):
																					raise EnvironmentError(F'Unable to convert {model_file} to Flax deserializable object. '   )

							return load_flax_weights_in_pytorch_model(SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE   )



def 				lowerCAmelCase_			(      SCREAMING_SNAKE_CASE       :							int    ,						SCREAMING_SNAKE_CASE       :							Union[str, Any]   ):


							'''simple docstring'''







							try:
														import torch  # noqa: F401
							except ImportError:
														logger.error(
														    '''Loading Flax weights in PyTorch requires both PyTorch and Flax to be installed. Please see'''
														    ''' https://pytorch.org/ and https://flax.readthedocs.io/en/latest/installation.html for installation'''
														    ''' instructions.'''   )
														raise

							# check if we have bf16 weights
							__lowerCamelCase      :      str         =flatten_dict(jax.tree_util.tree_map(lambda SCREAMING_SNAKE_CASE   : x.dtype == jnp.bfloataa    ,						SCREAMING_SNAKE_CASE   )   ).values()
							if any(SCREAMING_SNAKE_CASE   ):
														# convert all weights to fp32 if they are bf16 since torch.from_numpy can-not handle bf16

														# and bf16 is not fully supported in PT yet.
														logger.warning(
														    '''Found ``bfloat16`` weights in Flax model. Casting all ``bfloat16`` weights to ``float32`` '''
														    '''before loading those in PyTorch model.'''   )
														__lowerCamelCase      :      List[str]         =jax.tree_util.tree_map(
														    lambda SCREAMING_SNAKE_CASE   : params.astype(np.floataa   ) if params.dtype == jnp.bfloataa else params    ,						SCREAMING_SNAKE_CASE   )

							__lowerCamelCase      :      int         =''''''

							__lowerCamelCase      :      List[str]         =flatten_dict(SCREAMING_SNAKE_CASE    ,						sep='''.'''   )
							__lowerCamelCase      :      List[str]         =pt_model.state_dict()

							# keep track of unexpected & missing keys
							__lowerCamelCase      :      Union[str, Any]         =[]
							__lowerCamelCase      :      int         =set(pt_model_dict.keys()   )

							for flax_key_tuple, flax_tensor in flax_state_dict.items():
														__lowerCamelCase      :      Tuple         =flax_key_tuple.split('''.'''   )

														if flax_key_tuple_array[-1] == "kernel" and flax_tensor.ndim == 4:
																					__lowerCamelCase      :      Optional[int]         =flax_key_tuple_array[:-1] + ['''weight''']
																					__lowerCamelCase      :      Union[str, Any]         =jnp.transpose(SCREAMING_SNAKE_CASE    ,						(3, 2, 0, 1)   )
														elif flax_key_tuple_array[-1] == "kernel":
																					__lowerCamelCase      :      Optional[Any]         =flax_key_tuple_array[:-1] + ['''weight''']
																					__lowerCamelCase      :      Dict         =flax_tensor.T
														elif flax_key_tuple_array[-1] == "scale":
																					__lowerCamelCase      :      str         =flax_key_tuple_array[:-1] + ['''weight''']

														if "time_embedding" not in flax_key_tuple_array:
																					for i, flax_key_tuple_string in enumerate(SCREAMING_SNAKE_CASE   ):
																												__lowerCamelCase      :      Union[str, Any]         =(
																												    flax_key_tuple_string.replace('''_0'''    ,						'''.0'''   )
																												    .replace('''_1'''    ,						'''.1'''   )
																												    .replace('''_2'''    ,						'''.2'''   )
																												    .replace('''_3'''    ,						'''.3'''   )
																												    .replace('''_4'''    ,						'''.4'''   )
																												    .replace('''_5'''    ,						'''.5'''   )
																												    .replace('''_6'''    ,						'''.6'''   )
																												    .replace('''_7'''    ,						'''.7'''   )
																												    .replace('''_8'''    ,						'''.8'''   )
																												    .replace('''_9'''    ,						'''.9'''   )
																												)

														__lowerCamelCase      :      Optional[int]         ='''.'''.join(SCREAMING_SNAKE_CASE   )

														if flax_key in pt_model_dict:
																					if flax_tensor.shape != pt_model_dict[flax_key].shape:
																												raise ValueError(
																												    F'Flax checkpoint seems to be incorrect. Weight {flax_key_tuple} was expected '
																												    F'to be of shape {pt_model_dict[flax_key].shape}, but is {flax_tensor.shape}.'   )
																					else:
																												# add weight to pytorch dict
																												__lowerCamelCase      :      Tuple         =np.asarray(SCREAMING_SNAKE_CASE   ) if not isinstance(SCREAMING_SNAKE_CASE    ,						np.ndarray   ) else flax_tensor
																												__lowerCamelCase      :      Any         =torch.from_numpy(SCREAMING_SNAKE_CASE   )
																												# remove from missing keys
																												missing_keys.remove(SCREAMING_SNAKE_CASE   )
														else:
																					# weight is not expected by PyTorch model
																					unexpected_keys.append(SCREAMING_SNAKE_CASE   )

							pt_model.load_state_dict(SCREAMING_SNAKE_CASE   )

							# re-transform missing_keys to list
							__lowerCamelCase      :      Optional[Any]         =list(SCREAMING_SNAKE_CASE   )

							if len(SCREAMING_SNAKE_CASE   ) > 0:
														logger.warning(
														    '''Some weights of the Flax model were not used when initializing the PyTorch model'''
														    F' {pt_model.__class__.__name__}: {unexpected_keys}\n- This IS expected if you are initializing'
														    F' {pt_model.__class__.__name__} from a Flax model trained on another task or with another architecture'
														    ''' (e.g. initializing a BertForSequenceClassification model from a FlaxBertForPreTraining model).\n- This'''
														    F' IS NOT expected if you are initializing {pt_model.__class__.__name__} from a Flax model that you expect'
														    ''' to be exactly identical (e.g. initializing a BertForSequenceClassification model from a'''
														    ''' FlaxBertForSequenceClassification model).'''   )
							if len(SCREAMING_SNAKE_CASE   ) > 0:
														logger.warning(
														    F'Some weights of {pt_model.__class__.__name__} were not initialized from the Flax model and are newly'
														    F' initialized: {missing_keys}\nYou should probably TRAIN this model on a down-stream task to be able to'
														    ''' use it for predictions and inference.'''   )

							return pt_model




