


'''simple docstring'''


import json
import os
import unittest

from transformers import BatchEncoding, LEDTokenizer, LEDTokenizerFast
from transformers.models.led.tokenization_led import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin






@require_tokenizers
class 	lowerCAmelCase__			(			UpperCAmelCase__    ,       unittest.TestCase	):
			lowerCAmelCase :	Any          =     LEDTokenizer
			lowerCAmelCase :	int          =     LEDTokenizerFast
			lowerCAmelCase :	Dict          =     True




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



					'''simple docstring'''







					super().setUp()
					_UpperCAmelCase		:       Any								=      [
					    "l",
					    "o",
					    "w",
					    "e",
					    "r",
					    "s",
					    "t",
					    "i",
					    "d",
					    "n",
					    "\u0120",
					    "\u0120l",
					    "\u0120n",
					    "\u0120lo",
					    "\u0120low",
					    "er",
					    "\u0120lowest",
					    "\u0120newer",
					    "\u0120wider",
					    "<unk>",
					]
					_UpperCAmelCase		:       Tuple								=      dict(zip(lowerCamelCase__	,  range(len(lowerCamelCase__ ) ) ) )
					_UpperCAmelCase		:       Tuple								=      ["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
					_UpperCAmelCase		:       Optional[int]								=      {"unk_token": "<unk>"}

					_UpperCAmelCase		:       Tuple								=      os.path.join(self.tmpdirname	,  VOCAB_FILES_NAMES["vocab_file"] )
					_UpperCAmelCase		:       str								=      os.path.join(self.tmpdirname	,  VOCAB_FILES_NAMES["merges_file"] )
					with open(self.vocab_file	,  "w"	,  encoding="utf-8" ) as fp:
							fp.write(json.dumps(lowerCamelCase__ ) + "\n" )
					with open(self.merges_file	,  "w"	,  encoding="utf-8" ) as fp:
							fp.write("\n".join(lowerCamelCase__ ) )




			def 							lowerCAmelCase__    (		self  :     Dict	,  **lowerCamelCase__  :     Optional[Any] )   ->Any:



					'''simple docstring'''







					kwargs.update(self.special_tokens_map )
					return self.tokenizer_class.from_pretrained(self.tmpdirname	,  **lowerCamelCase__ )




			def 							lowerCAmelCase__    (		self  :     Dict	,  **lowerCamelCase__  :     int )   ->Optional[Any]:



					'''simple docstring'''







					kwargs.update(self.special_tokens_map )
					return self.rust_tokenizer_class.from_pretrained(self.tmpdirname	,  **lowerCamelCase__ )




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



					'''simple docstring'''







					return "lower newer", "lower newer"




			@cached_property
			def 							lowerCAmelCase__    (		self  :     Optional[Any] )   ->str:



					'''simple docstring'''







					return LEDTokenizer.from_pretrained("allenai/led-base-16384" )




			@cached_property
			def 							lowerCAmelCase__    (		self  :     Tuple )   ->Any:



					'''simple docstring'''







					return LEDTokenizerFast.from_pretrained("allenai/led-base-16384" )




			@require_torch
			def 							lowerCAmelCase__    (		self  :     Any )   ->str:



					'''simple docstring'''







					_UpperCAmelCase		:       int								=      ["A long paragraph for summarization.", "Another paragraph for summarization."]
					_UpperCAmelCase		:       Dict								=      [0, 2_50, 2_51, 1_78_18, 13, 3_91_86, 19_38, 4, 2]

					for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
							_UpperCAmelCase		:       Any								=      tokenizer(lowerCamelCase__	,  max_length=len(lowerCamelCase__ )	,  padding=lowerCamelCase__	,  return_tensors="pt" )
							self.assertIsInstance(lowerCamelCase__	,  lowerCamelCase__ )

							self.assertEqual((2, 9)	,  batch.input_ids.shape )
							self.assertEqual((2, 9)	,  batch.attention_mask.shape )
							_UpperCAmelCase		:       List[Any]								=      batch.input_ids.tolist()[0]
							self.assertListEqual(lowerCamelCase__	,  lowerCamelCase__ )




			@require_torch
			def 							lowerCAmelCase__    (		self  :     Tuple )   ->Union[str, Any]:



					'''simple docstring'''







					_UpperCAmelCase		:       Union[str, Any]								=      ["A long paragraph for summarization.", "Another paragraph for summarization."]
					for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
							_UpperCAmelCase		:       Optional[Any]								=      tokenizer(lowerCamelCase__	,  padding=lowerCamelCase__	,  return_tensors="pt" )
							self.assertIn("input_ids"	,  lowerCamelCase__ )
							self.assertIn("attention_mask"	,  lowerCamelCase__ )
							self.assertNotIn("labels"	,  lowerCamelCase__ )
							self.assertNotIn("decoder_attention_mask"	,  lowerCamelCase__ )




			@require_torch
			def 							lowerCAmelCase__    (		self  :     Tuple )   ->List[Any]:



					'''simple docstring'''







					_UpperCAmelCase		:       str								=      [
					    "Summary of the text.",
					    "Another summary.",
					]
					for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
							_UpperCAmelCase		:       Dict								=      tokenizer(text_target=lowerCamelCase__	,  max_length=32	,  padding="max_length"	,  return_tensors="pt" )
							self.assertEqual(32	,  targets["input_ids"].shape[1] )




			@require_torch
			def 							lowerCAmelCase__    (		self  :     Optional[int] )   ->Tuple:



					'''simple docstring'''







					for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
							_UpperCAmelCase		:       Union[str, Any]								=      tokenizer(
							    ["I am a small frog" * 10_24, "I am a small frog"]	,  padding=lowerCamelCase__	,  truncation=lowerCamelCase__	,  return_tensors="pt" )
							self.assertIsInstance(lowerCamelCase__	,  lowerCamelCase__ )
							self.assertEqual(batch.input_ids.shape	,  (2, 51_22) )




			@require_torch
			def 							lowerCAmelCase__    (		self  :     Union[str, Any] )   ->List[Any]:



					'''simple docstring'''







					_UpperCAmelCase		:       Tuple								=      ["A long paragraph for summarization."]
					_UpperCAmelCase		:       List[str]								=      [
					    "Summary of the text.",
					]
					for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
							_UpperCAmelCase		:       List[str]								=      tokenizer(lowerCamelCase__	,  return_tensors="pt" )
							_UpperCAmelCase		:       Tuple								=      tokenizer(text_target=lowerCamelCase__	,  return_tensors="pt" )
							_UpperCAmelCase		:       str								=      inputs["input_ids"]
							_UpperCAmelCase		:       Optional[int]								=      targets["input_ids"]
							self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item() )
							self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item() )
							self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item() )
							self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item() )




			@require_torch
			def 							lowerCAmelCase__    (		self  :     Optional[Any] )   ->Tuple:



					'''simple docstring'''







					for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
							_UpperCAmelCase		:       List[Any]								=      ["Summary of the text.", "Another summary."]
							_UpperCAmelCase		:       Dict								=      [[0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, -1, -1]]

							_UpperCAmelCase		:       List[str]								=      tokenizer(lowerCamelCase__	,  padding=lowerCamelCase__ )
							_UpperCAmelCase		:       List[Any]								=      [[0] * len(lowerCamelCase__ ) for x in encoded_output["input_ids"]]
							_UpperCAmelCase		:       Tuple								=      tokenizer.pad(lowerCamelCase__ )
							self.assertSequenceEqual(outputs["global_attention_mask"]	,  lowerCamelCase__ )




			def 							lowerCAmelCase__    (		self  :     Any )   ->Any:



					'''simple docstring'''







					pass






			def 							lowerCAmelCase__    (		self  :     Tuple )   ->Union[str, Any]:



					'''simple docstring'''







					for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
							with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})""" ):
									_UpperCAmelCase		:       Any								=      self.rust_tokenizer_class.from_pretrained(lowerCamelCase__	,  **lowerCamelCase__ )
									_UpperCAmelCase		:       Tuple								=      self.tokenizer_class.from_pretrained(lowerCamelCase__	,  **lowerCamelCase__ )
									_UpperCAmelCase		:       Dict								=      "A, <mask> AllenNLP sentence."
									_UpperCAmelCase		:       List[str]								=      tokenizer_r.encode_plus(lowerCamelCase__	,  add_special_tokens=lowerCamelCase__	,  return_token_type_ids=lowerCamelCase__ )
									_UpperCAmelCase		:       List[Any]								=      tokenizer_p.encode_plus(lowerCamelCase__	,  add_special_tokens=lowerCamelCase__	,  return_token_type_ids=lowerCamelCase__ )
									self.assertEqual(sum(tokens_r["token_type_ids"] )	,  sum(tokens_p["token_type_ids"] ) )
									self.assertEqual(
									    sum(tokens_r["attention_mask"] ) / len(tokens_r["attention_mask"] )	,  sum(tokens_p["attention_mask"] ) / len(tokens_p["attention_mask"] )	,  )

									_UpperCAmelCase		:       int								=      tokenizer_r.convert_ids_to_tokens(tokens_r["input_ids"] )
									_UpperCAmelCase		:       List[str]								=      tokenizer_p.convert_ids_to_tokens(tokens_p["input_ids"] )
									self.assertSequenceEqual(tokens_p["input_ids"]	,  [0, 2_50, 6, 5_02_64, 38_23, 4_87, 2_19_92, 36_45, 4, 2] )
									self.assertSequenceEqual(tokens_r["input_ids"]	,  [0, 2_50, 6, 5_02_64, 38_23, 4_87, 2_19_92, 36_45, 4, 2] )

									self.assertSequenceEqual(
									    lowerCamelCase__	,  ["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"] )
									self.assertSequenceEqual(
									    lowerCamelCase__	,  ["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"] )







'''simple docstring'''


import argparse

import torch

from transformers import BertForMaskedLM


if __name__ == "__main__":
						lowerCamelCase__    	=     argparse.ArgumentParser(
						    description=(
						        'Extraction some layers of the full BertForMaskedLM or RObertaForMaskedLM for Transfer Learned'
						        ' Distillation'
						    )
						)
						parser.add_argument('--model_type', default='bert', choices=['bert'])
						parser.add_argument('--model_name', default='bert-base-uncased', type=str)
						parser.add_argument('--dump_checkpoint', default='serialization_dir/tf_bert-base-uncased_0247911.pth', type=str)
						parser.add_argument('--vocab_transform', action='store_true')
						lowerCamelCase__    	=     parser.parse_args()

						if args.model_type == "bert":
												lowerCamelCase__    	=     BertForMaskedLM.from_pretrained(args.model_name)
												lowerCamelCase__    	=     'bert'
						else:
												raise ValueError('args.model_type should be "bert".')

						lowerCamelCase__    	=     model.state_dict()
						lowerCamelCase__    	=     {}

						for w in ["word_embeddings", "position_embeddings"]:
												lowerCamelCase__    	=     state_dict[F'''{prefix}.embeddings.{w}.weight''']
						for w in ["weight", "bias"]:
												lowerCamelCase__    	=     state_dict[F'''{prefix}.embeddings.LayerNorm.{w}''']

						lowerCamelCase__    	=     0
						for teacher_idx in [0, 2, 4, 7, 9, 11]:
												for w in ["weight", "bias"]:
																		lowerCamelCase__    	=     state_dict[
																		    F'''{prefix}.encoder.layer.{teacher_idx}.attention.self.query.{w}'''
																		]
																		lowerCamelCase__    	=     state_dict[
																		    F'''{prefix}.encoder.layer.{teacher_idx}.attention.self.key.{w}'''
																		]
																		lowerCamelCase__    	=     state_dict[
																		    F'''{prefix}.encoder.layer.{teacher_idx}.attention.self.value.{w}'''
																		]

																		lowerCamelCase__    	=     state_dict[
																		    F'''{prefix}.encoder.layer.{teacher_idx}.attention.output.dense.{w}'''
																		]
																		lowerCamelCase__    	=     state_dict[
																		    F'''{prefix}.encoder.layer.{teacher_idx}.attention.output.LayerNorm.{w}'''
																		]

																		lowerCamelCase__    	=     state_dict[
																		    F'''{prefix}.encoder.layer.{teacher_idx}.intermediate.dense.{w}'''
																		]
																		lowerCamelCase__    	=     state_dict[
																		    F'''{prefix}.encoder.layer.{teacher_idx}.output.dense.{w}'''
																		]
																		lowerCamelCase__    	=     state_dict[
																		    F'''{prefix}.encoder.layer.{teacher_idx}.output.LayerNorm.{w}'''
																		]
												std_idx += 1

						lowerCamelCase__    	=     state_dict['cls.predictions.decoder.weight']
						lowerCamelCase__    	=     state_dict['cls.predictions.bias']
						if args.vocab_transform:
												for w in ["weight", "bias"]:
																		lowerCamelCase__    	=     state_dict[F'''cls.predictions.transform.dense.{w}''']
																		lowerCamelCase__    	=     state_dict[F'''cls.predictions.transform.LayerNorm.{w}''']

						print(F'''N layers selected for distillation: {std_idx}''')
						print(F'''Number of params transferred for distillation: {len(compressed_sd.keys())}''')

						print(F'''Save transferred checkpoint to {args.dump_checkpoint}.''')
						torch.save(compressed_sd, args.dump_checkpoint)



