
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()

UpperCamelCase_					:						Tuple				= 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
UpperCamelCase_					:						Optional[Any]				= {
    # 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
UpperCamelCase_					:						Union[str, Any]				= {}
for m in ["wmt19-ru-en", "wmt19-en-ru", "wmt19-en-de", "wmt19-de-en"]:
		UpperCamelCase_					:						List[Any]				= '''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",
]:
		UpperCamelCase_					:						int				= '''allenai'''
def 							A_     (__a				):

					'''simple docstring'''





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

					'''simple docstring'''





					assert os.path.exists(__a				)
					os.makedirs(__a				,   exist_ok=__a				)
					print(f'Writing results to {pytorch_dump_folder_path}'				)

					# handle various types of models

					A_				 =						basename(__a				)
					A_				 =						dirname(__a				)

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

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

					A_				 =						args["source_lang"]
					A_				 =						args["target_lang"]

					A_				 =						dirname(__a				)
					A_				 =						basename(__a				)

					# dicts
					A_				 =						os.path.join(__a				,   f'dict.{src_lang}.txt'				)
					A_				 =						os.path.join(__a				,   f'dict.{tgt_lang}.txt'				)

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

					# 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
					A_				 =						True
					for k in src_vocab.keys():
										if not k.islower():
															A_				 =						False
															break

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

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

					# model config
					A_				 =						os.path.join(__a				,   "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"]}'

					A_				 =						{
					    "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
					A_				 =						5
					A_				 =						False
					if model_dir in best_score_hparams and "length_penalty" in best_score_hparams[model_dir]:
										A_				 =						best_score_hparams[model_dir]["length_penalty"]
					else:
										A_				 =						1.0

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

					# tokenizer config
					A_				 =						os.path.join(__a				,   __a				)

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

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

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

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

					# remove unneeded keys
					A_				 =						[
					    "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(__a				,   __a				)

					A_				 =						FSMTConfig.from_pretrained(__a				)
					A_				 =						FSMTForConditionalGeneration(__a				)

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

					# save
					A_				 =						os.path.join(__a				,   __a				)
					print(f'Generating {pytorch_weights_dump_path}'				)
					torch.save(__a				,   __a				)

					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__":
		UpperCamelCase_					:						Optional[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.'''
		)
		UpperCamelCase_					:						str				= parser.parse_args()
		convert_fsmt_checkpoint_to_pytorch(args.fsmt_checkpoint_path, args.pytorch_dump_folder_path)






"""simple docstring"""





import inspect
import unittest

from transformers import RegNetConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
		import torch
		from torch import nn

		from transformers import RegNetForImageClassification, RegNetModel
		from transformers.models.regnet.modeling_regnet import REGNET_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
		from PIL import Image

		from transformers import AutoImageProcessor



class __lowerCAmelCase				:




			"""simple docstring"""







			def __init__(				self						:					Optional[int]       ,   _snake_case						:					Any       ,   _snake_case						:					int=3       ,   _snake_case						:					List[Any]=32       ,   _snake_case						:					str=3       ,   _snake_case						:					Any=10       ,   _snake_case						:					List[Any]=[10, 20, 30, 40]       ,   _snake_case						:					Dict=[1, 1, 2, 1]       ,   _snake_case						:					Dict=True       ,   _snake_case						:					Any=True       ,   _snake_case						:					List[str]="relu"       ,   _snake_case						:					List[Any]=3       ,   _snake_case						:					List[str]=None       ,   )      -> int:







								"""simple docstring"""
								A_				 =						parent
								A_				 =						batch_size
								A_				 =						image_size
								A_				 =						num_channels
								A_				 =						embeddings_size
								A_				 =						hidden_sizes
								A_				 =						depths
								A_				 =						is_training
								A_				 =						use_labels
								A_				 =						hidden_act
								A_				 =						num_labels
								A_				 =						scope
								A_				 =						len(_snake_case	)




			def      lowerCamelCase__     (				self						:					Any	)      -> Optional[int]:







								"""simple docstring"""
								A_				 =						floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]	)

								A_				 =						None
								if self.use_labels:
													A_				 =						ids_tensor([self.batch_size]       ,   self.num_labels	)

								A_				 =						self.get_config()

								return config, pixel_values, labels




			def      lowerCamelCase__     (				self						:					List[str]	)      -> Union[str, Any]:







								"""simple docstring"""
								return RegNetConfig(
								    num_channels=self.num_channels       ,   embeddings_size=self.embeddings_size       ,   hidden_sizes=self.hidden_sizes       ,   depths=self.depths       ,   hidden_act=self.hidden_act       ,   num_labels=self.num_labels       ,   )




			def      lowerCamelCase__     (				self						:					Union[str, Any]       ,   _snake_case						:					str       ,   _snake_case						:					Tuple       ,   _snake_case						:					Tuple	)      -> List[str]:







								"""simple docstring"""
								A_				 =						RegNetModel(config=_snake_case	)
								model.to(_snake_case	)
								model.eval()
								A_				 =						model(_snake_case	)
								# expected last hidden states: B, C, H // 32, W // 32
								self.parent.assertEqual(
								    result.last_hidden_state.shape       ,   (self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)       ,   )




			def      lowerCamelCase__     (				self						:					Dict       ,   _snake_case						:					Tuple       ,   _snake_case						:					Optional[Any]       ,   _snake_case						:					Optional[Any]	)      -> Optional[Any]:







								"""simple docstring"""
								A_				 =						self.num_labels
								A_				 =						RegNetForImageClassification(_snake_case	)
								model.to(_snake_case	)
								model.eval()
								A_				 =						model(_snake_case       ,   labels=_snake_case	)
								self.parent.assertEqual(result.logits.shape       ,   (self.batch_size, self.num_labels)	)






			def      lowerCamelCase__     (				self						:					int	)      -> Optional[Any]:







								"""simple docstring"""
								A_				 =						self.prepare_config_and_inputs()
								A_ ,    A_ ,    A_				 =						config_and_inputs
								A_				 =						{"pixel_values": pixel_values}
								return config, inputs_dict





@require_torch
class __lowerCAmelCase				(					_lowercase     ,				_lowercase     ,				unittest.TestCase      ):




			"""simple docstring"""







			snake_case	      =				(RegNetModel, RegNetForImageClassification) if is_torch_available() else ()
			snake_case	      =				(
			    {"feature-extraction": RegNetModel, "image-classification": RegNetForImageClassification}
			    if is_torch_available()
			    else {}
			)

			snake_case	      =				False
			snake_case	      =				False
			snake_case	      =				False
			snake_case	      =				False




			def      lowerCamelCase__     (				self						:					str	)      -> str:







								"""simple docstring"""
								A_				 =						RegNetModelTester(self	)
								A_				 =						ConfigTester(self       ,   config_class=_snake_case       ,   has_text_modality=_snake_case	)




			def      lowerCamelCase__     (				self						:					Tuple	)      -> List[Any]:







								"""simple docstring"""
								self.create_and_test_config_common_properties()
								self.config_tester.create_and_test_config_to_json_string()
								self.config_tester.create_and_test_config_to_json_file()
								self.config_tester.create_and_test_config_from_and_save_pretrained()
								self.config_tester.create_and_test_config_with_num_labels()
								self.config_tester.check_config_can_be_init_without_params()
								self.config_tester.check_config_arguments_init()




			def      lowerCamelCase__     (				self						:					int	)      -> List[Any]:







								"""simple docstring"""
								return




			@unittest.skip(reason="RegNet does not use inputs_embeds"	)
			def      lowerCamelCase__     (				self						:					int	)      -> Optional[Any]:







								"""simple docstring"""
								pass




			@unittest.skip(reason="RegNet does not support input and output embeddings"	)
			def      lowerCamelCase__     (				self						:					int	)      -> Union[str, Any]:







								"""simple docstring"""
								pass




			def      lowerCamelCase__     (				self						:					Optional[int]	)      -> List[Any]:







								"""simple docstring"""
								A_ ,    A_				 =						self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
													A_				 =						model_class(_snake_case	)
													A_				 =						inspect.signature(model.forward	)
													# signature.parameters is an OrderedDict => so arg_names order is deterministic
													A_				 =						[*signature.parameters.keys()]

													A_				 =						["pixel_values"]
													self.assertListEqual(arg_names[:1]       ,   _snake_case	)




			def      lowerCamelCase__     (				self						:					Any	)      -> int:







								"""simple docstring"""
								A_				 =						self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*_snake_case	)




			def      lowerCamelCase__     (				self						:					List[str]	)      -> int:







								"""simple docstring"""
								A_ ,    A_				 =						self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
													A_				 =						model_class(config=_snake_case	)
													for name, module in model.named_modules():
																		if isinstance(_snake_case       ,   (nn.BatchNormad, nn.GroupNorm)	):
																							self.assertTrue(
																							    torch.all(module.weight == 1	)       ,   msg=F'Parameter {name} of model {model_class} seems not properly initialized'       ,   )
																							self.assertTrue(
																							    torch.all(module.bias == 0	)       ,   msg=F'Parameter {name} of model {model_class} seems not properly initialized'       ,   )




			def      lowerCamelCase__     (				self						:					List[Any]	)      -> List[str]:







								"""simple docstring"""
								def check_hidden_states_output(_snake_case						:					Tuple       ,   _snake_case						:					Optional[int]       ,   _snake_case						:					Optional[int]	):
													A_				 =						model_class(_snake_case	)
													model.to(_snake_case	)
													model.eval()

													with torch.no_grad():
																		A_				 =						model(**self._prepare_for_class(_snake_case       ,   _snake_case	)	)

													A_				 =						outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

													A_				 =						self.model_tester.num_stages
													self.assertEqual(len(_snake_case	)       ,   expected_num_stages + 1	)

													# RegNet's feature maps are of shape (batch_size, num_channels, height, width)
													self.assertListEqual(
													    list(hidden_states[0].shape[-2:]	)       ,   [self.model_tester.image_size // 2, self.model_tester.image_size // 2]       ,   )

								A_ ,    A_				 =						self.model_tester.prepare_config_and_inputs_for_common()
								A_				 =						["basic", "bottleneck"]
								for model_class in self.all_model_classes:
													for layer_type in layers_type:
																		A_				 =						layer_type
																		A_				 =						True
																		check_hidden_states_output(_snake_case       ,   _snake_case       ,   _snake_case	)

																		# check that output_hidden_states also work using config
																		del inputs_dict["output_hidden_states"]
																		A_				 =						True

																		check_hidden_states_output(_snake_case       ,   _snake_case       ,   _snake_case	)




			def      lowerCamelCase__     (				self						:					str	)      -> List[str]:







								"""simple docstring"""
								A_				 =						self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_image_classification(*_snake_case	)






			@slow
			def      lowerCamelCase__     (				self						:					Optional[Any]	)      -> int:







								"""simple docstring"""
								for model_name in REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
													A_				 =						RegNetModel.from_pretrained(_snake_case	)
													self.assertIsNotNone(_snake_case	)





def 							A_     ():

					'''simple docstring'''





					A_				 =						Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"				)
					return image




@require_torch
@require_vision
class __lowerCAmelCase				(					unittest.TestCase      ):




			"""simple docstring"""







			@cached_property
			def      lowerCamelCase__     (				self						:					Union[str, Any]	)      -> int:







								"""simple docstring"""
								return (
								    AutoImageProcessor.from_pretrained(REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]	)
								    if is_vision_available()
								    else None
								)






			@slow
			def      lowerCamelCase__     (				self						:					str	)      -> List[Any]:







								"""simple docstring"""
								A_				 =						RegNetForImageClassification.from_pretrained(REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]	).to(_snake_case	)

								A_				 =						self.default_image_processor
								A_				 =						prepare_img()
								A_				 =						image_processor(images=_snake_case       ,   return_tensors="pt"	).to(_snake_case	)

								# forward pass
								with torch.no_grad():
													A_				 =						model(**_snake_case	)

								# verify the logits
								A_				 =						torch.Size((1, 1_000)	)
								self.assertEqual(outputs.logits.shape       ,   _snake_case	)

								A_				 =						torch.tensor([-0.4_1_8_0, -1.5_0_5_1, -3.4_8_3_6]	).to(_snake_case	)

								self.assertTrue(torch.allclose(outputs.logits[0, :3]       ,   _snake_case       ,   atol=1e-4	)	)




