




import json
import os
import unittest

from transformers.models.fsmt.tokenization_fsmt import VOCAB_FILES_NAMES, FSMTTokenizer
from transformers.testing_utils import slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


# using a different tiny model than the one used for default params defined in init to ensure proper testing
__lowerCAmelCase		:    str	      =		'stas/tiny-wmt19-en-ru'




class    snake_case__							(_UpperCamelCase   ,	unittest.TestCase       ):
				"""simple docstring"""



				SCREAMING_SNAKE_CASE_	:							List[str]										= FSMTTokenizer
				SCREAMING_SNAKE_CASE_	:							Optional[int]										= False





				def 							__UpperCAmelCase							(				self    :					Union[str, Any]							) ->					int:
							super().setUp()

							# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
							a   =      [
							    "l",
							    "o",
							    "w",
							    "e",
							    "r",
							    "s",
							    "t",
							    "i",
							    "d",
							    "n",
							    "w</w>",
							    "r</w>",
							    "t</w>",
							    "lo",
							    "low",
							    "er</w>",
							    "low</w>",
							    "lowest</w>",
							    "newer</w>",
							    "wider</w>",
							    "<unk>",
							]
							a   =      dict(zip(__lowerCamelCase ,				range(len(__lowerCamelCase							)							)							)							)
							a   =      ["l o 123", "lo w 1456", "e r</w> 1789", ""]

							a   =      ["en", "ru"]
							a   =      {
							    "langs": self.langs,
							    "src_vocab_size": 10,
							    "tgt_vocab_size": 20,
							}

							a   =      os.path.join(self.tmpdirname ,				VOCAB_FILES_NAMES["src_vocab_file"]							)
							a   =      os.path.join(self.tmpdirname ,				VOCAB_FILES_NAMES["tgt_vocab_file"]							)
							a   =      os.path.join(self.tmpdirname ,				"tokenizer_config.json"							)
							a   =      os.path.join(self.tmpdirname ,				VOCAB_FILES_NAMES["merges_file"]							)
							with open(self.src_vocab_file ,				"w"							) as fp:
										fp.write(json.dumps(__lowerCamelCase							)							)
							with open(self.tgt_vocab_file ,				"w"							) as fp:
										fp.write(json.dumps(__lowerCamelCase							)							)
							with open(self.merges_file ,				"w"							) as fp:
										fp.write("\n".join(__lowerCamelCase							)							)
							with open(__lowerCamelCase ,				"w"							) as fp:
										fp.write(json.dumps(__lowerCamelCase							)							)





				@cached_property
				def 							__UpperCAmelCase							(				self    :					Union[str, Any]							) ->					Optional[Any]:
							return FSMTTokenizer.from_pretrained("facebook/wmt19-ru-en"							)





				@cached_property
				def 							__UpperCAmelCase							(				self    :					Dict							) ->					Optional[Any]:
							return FSMTTokenizer.from_pretrained("facebook/wmt19-en-ru"							)





				def 							__UpperCAmelCase							(				self    :					List[Any]							) ->					List[Any]:
							a   =      FSMTTokenizer.from_pretrained(__lowerCamelCase							)
							self.assertListEqual([tokenizer.src_lang, tokenizer.tgt_lang] ,				["en", "ru"]							)
							self.assertEqual(tokenizer.src_vocab_size ,				21							)
							self.assertEqual(tokenizer.tgt_vocab_size ,				21							)





				def 							__UpperCAmelCase							(				self    :					Any							) ->					int:
							a   =      FSMTTokenizer(self.langs ,				self.src_vocab_file ,				self.tgt_vocab_file ,				self.merges_file							)

							a   =      "lower"
							a   =      ["low", "er</w>"]
							a   =      tokenizer.tokenize(__lowerCamelCase							)
							self.assertListEqual(__lowerCamelCase ,				__lowerCamelCase							)

							a   =      tokens + ["<unk>"]
							a   =      [14, 15, 20]
							self.assertListEqual(tokenizer.convert_tokens_to_ids(__lowerCamelCase							) ,				__lowerCamelCase							)





				@slow
				def 							__UpperCAmelCase							(				self    :					Optional[int]							) ->					Any:
							a   =      self.tokenizer_ru_en

							a   =      tokenizer.encode("sequence builders" ,				add_special_tokens=__lowerCamelCase							)
							a   =      tokenizer.encode("multi-sequence build" ,				add_special_tokens=__lowerCamelCase							)

							a   =      tokenizer.build_inputs_with_special_tokens(__lowerCamelCase							)
							a   =      tokenizer.build_inputs_with_special_tokens(__lowerCamelCase ,				__lowerCamelCase							)

							assert encoded_sentence == text + [2]
							assert encoded_pair == text + [2] + text_a + [2]





				@slow
				def 							__UpperCAmelCase							(				self    :					Union[str, Any]							) ->					Optional[Any]:
							a   =      self.tokenizer_en_ru
							a   =      self.tokenizer_ru_en

							a   =      [
							    [
							        "Here's a little song I wrote. Don't worry, be happy.",
							        [24_70, 39, 11, 23_49, 72_22, 70, 59_79, 7, 84_50, 10_50, 1_31_60, 5, 26, 64_45, 7, 2],
							    ],
							    ["This is it. No more. I'm done!", [1_32, 21, 37, 7, 14_34, 86, 7, 70, 64_76, 13_05, 4_27, 2]],
							]

							# if data needs to be recreated or added, run:
							# import torch
							# model = torch.hub.load("pytorch/fairseq", "transformer.wmt19.en-ru", checkpoint_file="model4.pt", tokenizer="moses", bpe="fastbpe")
							# for src_text, _ in targets: print(f"""[\n"{src_text}",\n {model.encode(src_text).tolist()}\n],""")

							for src_text, tgt_input_ids in targets:
										a   =      tokenizer_enc.encode(__lowerCamelCase ,				return_tensors=__lowerCamelCase							)
										self.assertListEqual(__lowerCamelCase ,				__lowerCamelCase							)

										# and decode backward, using the reversed languages model
										a   =      tokenizer_dec.decode(__lowerCamelCase ,				skip_special_tokens=__lowerCamelCase							)
										self.assertEqual(__lowerCamelCase ,				__lowerCamelCase							)





				@slow
				def 							__UpperCAmelCase							(				self    :					List[Any]							) ->					Dict:
							a   =      FSMTTokenizer.from_pretrained("facebook/wmt19-ru-en" ,				do_lower_case=__lowerCamelCase							)
							a   =      tokenizer.tokenize("USA is United States of America"							)
							a   =      ["us", "a</w>", "is</w>", "un", "i", "ted</w>", "st", "ates</w>", "of</w>", "am", "er", "ica</w>"]
							self.assertListEqual(__lowerCamelCase ,				__lowerCamelCase							)





				@unittest.skip("FSMTConfig.__init__  requires non-optional args"							)
				def 							__UpperCAmelCase							(				self    :					str							) ->					Any:
							pass





				@unittest.skip("FSMTConfig.__init__  requires non-optional args"							)
				def 							__UpperCAmelCase							(				self    :					str							) ->					Union[str, Any]:
							pass












import argparse
import os
from functools import reduce

import fairseq
import torch
from datasets import load_dataset

from transformers import WavaVecaProcessor, logging
from transformers.models.dataavec.configuration_dataavec_audio import DataaVecAudioConfig

# Copied from https://github.com/pytorch/fairseq/blob/main/examples/data2vec/models/data2vec_audio.py
from transformers.models.dataavec.dataavec_audio import DataaVecAudioModel as Dummy  # noqa: F401
from transformers.models.dataavec.modeling_dataavec_audio import DataaVecAudioForCTC, DataaVecAudioModel


logging.set_verbosity_info()
__lowerCAmelCase		:    Dict	      =		logging.get_logger(__name__)

__lowerCAmelCase		:    Tuple	      =		{
    'post_extract_proj': 'feature_projection.projection',
    'models.0.layer_norm': 'feature_projection.layer_norm',
    'self_attn.k_proj': 'encoder.layers.*.attention.k_proj',
    'self_attn.v_proj': 'encoder.layers.*.attention.v_proj',
    'self_attn.q_proj': 'encoder.layers.*.attention.q_proj',
    'self_attn.out_proj': 'encoder.layers.*.attention.out_proj',
    'self_attn_layer_norm': 'encoder.layers.*.layer_norm',
    'fc1': 'encoder.layers.*.feed_forward.intermediate_dense',
    'fc2': 'encoder.layers.*.feed_forward.output_dense',
    'final_layer_norm': 'encoder.layers.*.final_layer_norm',
    'encoder.layer_norm': 'encoder.layer_norm',
    'w2v_model.layer_norm': 'feature_projection.layer_norm',
    'w2v_encoder.proj': 'lm_head',
    'mask_emb': 'masked_spec_embed',
}
__lowerCAmelCase		:    List[Any]	      =		[
    'lm_head',
]




def       __magic_name__    (			A		: int,  A		: List[Any],  A		: Tuple,  A		: Dict,  A		: List[str]       ):






			'''simple docstring'''







			for attribute in key.split("."       ):
						a   =      getattr(A,  A       )

			if weight_type is not None:
						a   =      getattr(A,  A       ).shape
			else:
						a   =      hf_pointer.shape

			if hf_shape != value.shape:
						raise ValueError(
						    F"""Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be"""
						    F""" {value.shape} for {full_name}"""       )

			if weight_type == "weight":
						a   =      value
			elif weight_type == "weight_g":
						a   =      value
			elif weight_type == "weight_v":
						a   =      value
			elif weight_type == "bias":
						a   =      value
			else:
						a   =      value

			logger.info(F"""{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}."""       )




def       __magic_name__    (			A		: Optional[Any],  A		: int,  A		: Dict       ):






			'''simple docstring'''







			a   =      []
			a   =      fairseq_model.state_dict()

			if not is_headless:
						a   =      hf_model.dataavec_audio.feature_extractor
						a   =      hf_model.dataavec_audio.encoder.pos_conv_embed

			else:
						a   =      hf_model.feature_extractor
						a   =      hf_model.encoder.pos_conv_embed

			for name, value in fairseq_dict.items():
						a   =      False
						if "conv_layers" in name:
									load_conv_layer(
									    A,  A,  A,  A,  )
									a   =      True
						elif "pos_conv" in name:
									load_pos_conv_layer(
									    A,  A,  A,  A,  )
									a   =      True
						else:
									for key, mapped_key in MAPPING.items():
												if not is_headless:
															a   =      "data2vec_audio." + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
												if key in name or key.split("w2v_model."       )[-1] == name.split("."       )[0]:
															a   =      True
															if "*" in mapped_key:
																		a   =      name.split(A       )[0].split("."       )[-2]
																		a   =      mapped_key.replace("*",  A       )
															if "weight_g" in name:
																		a   =      "weight_g"
															elif "weight_v" in name:
																		a   =      "weight_v"
															elif "bias" in name:
																		a   =      "bias"
															elif "weight" in name:
																		# TODO: don't match quantizer.weight_proj
																		a   =      "weight"
															else:
																		a   =      None
															set_recursively(A,  A,  A,  A,  A       )
												continue
						if not is_used:
									unused_weights.append(A       )

			logger.warning(F"""Unused weights: {unused_weights}"""       )




def       __magic_name__    (			A		: str,  A		: Dict       ):






			'''simple docstring'''







			a   =      path.split("."       )
			return reduce(A,  A,  A       )




def       __magic_name__    (			A		: Optional[int],  A		: Tuple,  A		: List[str],  A		: Optional[int]       ):






			'''simple docstring'''







			a   =      access_by_string(A,  A       )
			a   =      hf_weight.data

			if fsq_value.shape != hf_value.shape:
						raise ValueError(F"""{full_name} has size {fsq_value.shape}, but {hf_value.shape} was found."""       )
			a   =      fsq_value
			logger.info(F"""{full_name} was correctly initialized from {hf_weight_path}."""       )




def       __magic_name__    (			A		: Optional[Any],  A		: List[str],  A		: Optional[Any],  A		: Optional[int]       ):






			'''simple docstring'''







			a   =      full_name.split("conv_layers."       )[-1]
			a   =      name.split("."       )
			a   =      int(items[0]       )
			a   =      int(items[1]       )

			a   =      name.split("."       )[-1]
			if type_id == 0:
						a   =      "conv"
			elif type_id == 2:
						a   =      "layer_norm"
			else:
						unused_weights.append(A       )
						return

			set_weights(A,  A,  A,  F"""conv_layers.{layer_id}.{layer_type}.{weight_type}"""       )




def       __magic_name__    (			A		: int,  A		: Dict,  A		: List[Any],  A		: Tuple       ):






			'''simple docstring'''







			a   =      full_name.split("pos_conv."       )[-1]
			a   =      name.split("."       )
			a   =      int(items[0]       )
			a   =      int(items[1]       )

			a   =      name.split("."       )[-1]
			if type_id != 0:
						unused_weights.append(A       )
						return
			else:
						a   =      "conv"

			set_weights(A,  A,  A,  F"""layers.{layer_id}.{layer_type}.{weight_type}"""       )




@torch.no_grad()
def       __magic_name__    (			A		: str,  A		: Dict,  A		: int=None,  A		: Tuple=None,  A		: List[str]=True       ):






			'''simple docstring'''







			if config_path is not None:
						a   =      DataaVecAudioConfig.from_pretrained(A       )
			else:
						a   =      DataaVecAudioConfig()

			if not is_finetuned:
						# Modify final_proj layer name
						a   =      DataaVecAudioModel(A       )
						a   =      os.path.dirname(A       )

						a   =      torch.load(A       )
						a   =      state_dict["model"].pop("final_proj.0.weight"       )
						a   =      state_dict["model"].pop("final_proj.0.bias"       )
						a   =      os.path.join(A,  "converted.pt"       )
						torch.save(A,  A       )
			else:
						a   =      DataaVecAudioForCTC(A       )
						a   =      checkpoint_path

			def load_dataavec(A		: Optional[Any]       ):
						a   ,		a   ,		a   =      fairseq.checkpoint_utils.load_model_ensemble_and_task([path]       )
						return model[0].eval()

			a   =      load_dataavec(A       )

			recursively_load_weights(A,  A,  not is_finetuned       )

			a   =      WavaVecaProcessor.from_pretrained("facebook/wav2vec2-large-lv60"       )

			a   =      load_dataset("patrickvonplaten/librispeech_asr_dummy",  "clean",  split="validation"       )
			a   =      [x["array"] for x in ds[:4]["audio"]]

			a   =      processor(A,  return_tensors="pt",  padding=A       )

			a   =      inputs.input_values
			a   =      inputs.attention_mask
			#    input_values = inputs.input_values[:, :-1]
			#    attention_mask = inputs.attention_mask[:, :-1]

			hf_wavavec.eval()
			model.eval()
			if is_finetuned:
						a   =      model(source=A,  padding_mask=(1 - attention_mask),  mask=A,  features_only=A       )[
						    "encoder_out"
						].transpose(0,  1       )
						a   =      hf_wavavec(A,  attention_mask=A       )["logits"]

						a   =      torch.argmax(A,  dim=-1       )
						a   =      processor.batch_decode(A       )

						print(F"""Expected Output: {ds[:4]["text"]}, Pred: {output_string}"""       )
			else:
						a   =      model(source=A,  padding_mask=(1 - attention_mask),  mask=A,  features_only=A       )[
						    "layer_results"
						][-1][0].transpose(0,  1       )
						a   =      hf_wavavec(A,  attention_mask=A       )["last_hidden_state"]

			print(our_output.shape,  their_output.shape       )
			a   =      torch.max(torch.abs(our_output - their_output       )       ).item()
			print(F"""max_absolute_diff = {max_absolute_diff}"""       )  # ~ 1e-7
			a   =      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"       )

			hf_wavavec.save_pretrained(A       )

			if is_finetuned:
						processor.save_pretrained(A       )
			else:
						processor.feature_extractor.save_pretrained(A       )


if __name__ == "__main__":
		__lowerCAmelCase		:    List[str]	      =		argparse.ArgumentParser()
		parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
		parser.add_argument('--checkpoint_path', default=None, type=str, help='Path to fairseq checkpoint')
		parser.add_argument('--dict_path', default=None, type=str, help='Path to dict of fine-tuned model')
		parser.add_argument('--config_path', default=None, type=str, help='Path to hf config.json of model to convert')
		parser.add_argument(
		    '--not_finetuned', action='store_true', help='Whether the model to convert is a fine-tuned model or not'
		)
		__lowerCAmelCase		:    int	      =		parser.parse_args()
		convert_wavaveca_checkpoint(
		    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
		)






