

'''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 		__SCREAMING_SNAKE_CASE       ( lowercase__	,       unittest.TestCase			):
				lowerCamelCase_							=						LEDTokenizer
				lowerCamelCase_							=						LEDTokenizerFast
				lowerCamelCase_							=						True







				def    lowerCamelCase_  (					self    :		int      ):
										'''simple docstring'''
										super().setUp()
										lowercase   :       Optional[int]								=[
										    '''l''',
										    '''o''',
										    '''w''',
										    '''e''',
										    '''r''',
										    '''s''',
										    '''t''',
										    '''i''',
										    '''d''',
										    '''n''',
										    '''\u0120''',
										    '''\u0120l''',
										    '''\u0120n''',
										    '''\u0120lo''',
										    '''\u0120low''',
										    '''er''',
										    '''\u0120lowest''',
										    '''\u0120newer''',
										    '''\u0120wider''',
										    '''<unk>''',
										]
										lowercase   :       Tuple								=dict(zip(UpperCAmelCase__		,				range(len(UpperCAmelCase__      )      )      )      )
										lowercase   :       Optional[int]								=['''#version: 0.2''', '''\u0120 l''', '''\u0120l o''', '''\u0120lo w''', '''e r''', '''''']
										lowercase   :       Union[str, Any]								={'''unk_token''': '''<unk>'''}

										lowercase   :       Dict								=os.path.join(self.tmpdirname		,				VOCAB_FILES_NAMES['''vocab_file''']      )
										lowercase   :       Dict								=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(UpperCAmelCase__      ) + '''\n'''      )
										with open(self.merges_file		,				'''w'''		,				encoding='''utf-8'''      ) as fp:
																fp.write('''\n'''.join(UpperCAmelCase__      )      )







				def    lowerCamelCase_  (					self    :		List[Any]		,				**UpperCAmelCase__    :		Dict      ):
										'''simple docstring'''
										kwargs.update(self.special_tokens_map      )
										return self.tokenizer_class.from_pretrained(self.tmpdirname		,				**UpperCAmelCase__      )







				def    lowerCamelCase_  (					self    :		Optional[int]		,				**UpperCAmelCase__    :		List[str]      ):
										'''simple docstring'''
										kwargs.update(self.special_tokens_map      )
										return self.rust_tokenizer_class.from_pretrained(self.tmpdirname		,				**UpperCAmelCase__      )







				def    lowerCamelCase_  (					self    :		Dict		,				UpperCAmelCase__    :		int      ):
										'''simple docstring'''
										return "lower newer", "lower newer"







				@cached_property
				def    lowerCamelCase_  (					self    :		Optional[Any]      ):
										'''simple docstring'''
										return LEDTokenizer.from_pretrained('''allenai/led-base-16384'''      )







				@cached_property
				def    lowerCamelCase_  (					self    :		Optional[Any]      ):
										'''simple docstring'''
										return LEDTokenizerFast.from_pretrained('''allenai/led-base-16384'''      )







				@require_torch
				def    lowerCamelCase_  (					self    :		Any      ):
										'''simple docstring'''
										lowercase   :       Union[str, Any]								=['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
										lowercase   :       Optional[int]								=[0, 250, 251, 17818, 13, 39186, 1938, 4, 2]

										for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																lowercase   :       Dict								=tokenizer(UpperCAmelCase__		,				max_length=len(UpperCAmelCase__      )		,				padding=UpperCAmelCase__		,				return_tensors='''pt'''      )
																self.assertIsInstance(UpperCAmelCase__		,				UpperCAmelCase__      )

																self.assertEqual((2, 9)		,				batch.input_ids.shape      )
																self.assertEqual((2, 9)		,				batch.attention_mask.shape      )
																lowercase   :       List[str]								=batch.input_ids.tolist()[0]
																self.assertListEqual(UpperCAmelCase__		,				UpperCAmelCase__      )







				@require_torch
				def    lowerCamelCase_  (					self    :		List[Any]      ):
										'''simple docstring'''
										lowercase   :       str								=['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
										for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																lowercase   :       Any								=tokenizer(UpperCAmelCase__		,				padding=UpperCAmelCase__		,				return_tensors='''pt'''      )
																self.assertIn('''input_ids'''		,				UpperCAmelCase__      )
																self.assertIn('''attention_mask'''		,				UpperCAmelCase__      )
																self.assertNotIn('''labels'''		,				UpperCAmelCase__      )
																self.assertNotIn('''decoder_attention_mask'''		,				UpperCAmelCase__      )







				@require_torch
				def    lowerCamelCase_  (					self    :		Union[str, Any]      ):
										'''simple docstring'''
										lowercase   :       Optional[int]								=[
										    '''Summary of the text.''',
										    '''Another summary.''',
										]
										for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																lowercase   :       str								=tokenizer(text_target=UpperCAmelCase__		,				max_length=32		,				padding='''max_length'''		,				return_tensors='''pt'''      )
																self.assertEqual(32		,				targets['''input_ids'''].shape[1]      )







				@require_torch
				def    lowerCamelCase_  (					self    :		Optional[int]      ):
										'''simple docstring'''
										for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																lowercase   :       Tuple								=tokenizer(
																    ['''I am a small frog''' * 1024, '''I am a small frog''']		,				padding=UpperCAmelCase__		,				truncation=UpperCAmelCase__		,				return_tensors='''pt'''      )
																self.assertIsInstance(UpperCAmelCase__		,				UpperCAmelCase__      )
																self.assertEqual(batch.input_ids.shape		,				(2, 5122)      )







				@require_torch
				def    lowerCamelCase_  (					self    :		int      ):
										'''simple docstring'''
										lowercase   :       Optional[int]								=['''A long paragraph for summarization.''']
										lowercase   :       Any								=[
										    '''Summary of the text.''',
										]
										for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																lowercase   :       List[Any]								=tokenizer(UpperCAmelCase__		,				return_tensors='''pt'''      )
																lowercase   :       Optional[Any]								=tokenizer(text_target=UpperCAmelCase__		,				return_tensors='''pt'''      )
																lowercase   :       Any								=inputs['''input_ids''']
																lowercase   :       List[Any]								=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    :		Union[str, Any]      ):
										'''simple docstring'''
										for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																lowercase   :       Any								=['''Summary of the text.''', '''Another summary.''']
																lowercase   :       Optional[int]								=[[0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, -1, -1]]

																lowercase   :       List[Any]								=tokenizer(UpperCAmelCase__		,				padding=UpperCAmelCase__      )
																lowercase   :       Any								=[[0] * len(UpperCAmelCase__      ) for x in encoded_output['''input_ids''']]
																lowercase   :       Tuple								=tokenizer.pad(UpperCAmelCase__      )
																self.assertSequenceEqual(outputs['''global_attention_mask''']		,				UpperCAmelCase__      )







				def    lowerCamelCase_  (					self    :		Optional[Any]      ):
										'''simple docstring'''
										pass







				def    lowerCamelCase_  (					self    :		int      ):
										'''simple docstring'''
										for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
																with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''      ):
																						lowercase   :       List[Any]								=self.rust_tokenizer_class.from_pretrained(UpperCAmelCase__		,				**UpperCAmelCase__      )
																						lowercase   :       str								=self.tokenizer_class.from_pretrained(UpperCAmelCase__		,				**UpperCAmelCase__      )
																						lowercase   :       Optional[Any]								='''A, <mask> AllenNLP sentence.'''
																						lowercase   :       List[Any]								=tokenizer_r.encode_plus(UpperCAmelCase__		,				add_special_tokens=UpperCAmelCase__		,				return_token_type_ids=UpperCAmelCase__      )
																						lowercase   :       Optional[Any]								=tokenizer_p.encode_plus(UpperCAmelCase__		,				add_special_tokens=UpperCAmelCase__		,				return_token_type_ids=UpperCAmelCase__      )
																						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''']      )		,				)

																						lowercase   :       Optional[int]								=tokenizer_r.convert_ids_to_tokens(tokens_r['''input_ids''']      )
																						lowercase   :       str								=tokenizer_p.convert_ids_to_tokens(tokens_p['''input_ids''']      )
																						self.assertSequenceEqual(tokens_p['''input_ids''']		,				[0, 250, 6, 50264, 3823, 487, 21992, 3645, 4, 2]      )
																						self.assertSequenceEqual(tokens_r['''input_ids''']		,				[0, 250, 6, 50264, 3823, 487, 21992, 3645, 4, 2]      )

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








'''simple docstring'''





import argparse

import torch

from transformers import GPTaLMHeadModel, RobertaForMaskedLM


if __name__ == "__main__":
							UpperCamelCase_							=					argparse.ArgumentParser(
							    description=(
							        """Extraction some layers of the full RobertaForMaskedLM or GPT2LMHeadModel for Transfer Learned"""
							        """ Distillation"""
							    )
							)
							parser.add_argument("""--model_type""", default="""roberta""", choices=["""roberta""", """gpt2"""])
							parser.add_argument("""--model_name""", default="""roberta-large""", type=str)
							parser.add_argument("""--dump_checkpoint""", default="""serialization_dir/tf_roberta_048131723.pth""", type=str)
							parser.add_argument("""--vocab_transform""", action="""store_true""")
							UpperCamelCase_							=					parser.parse_args()

							if args.model_type == "roberta":
														UpperCamelCase_							=					RobertaForMaskedLM.from_pretrained(args.model_name)
														UpperCamelCase_							=					"""roberta"""
							elif args.model_type == "gpt2":
														UpperCamelCase_							=					GPTaLMHeadModel.from_pretrained(args.model_name)
														UpperCamelCase_							=					"""transformer"""

							UpperCamelCase_							=					model.state_dict()
							UpperCamelCase_							=					{}

							# Embeddings #
							if args.model_type == "gpt2":
														for param_name in ["wte.weight", "wpe.weight"]:
																					UpperCamelCase_							=					state_dict[f'''{prefix}.{param_name}''']
							else:
														for w in ["word_embeddings", "position_embeddings", "token_type_embeddings"]:
																					UpperCamelCase_							=					f'''{prefix}.embeddings.{w}.weight'''
																					UpperCamelCase_							=					state_dict[param_name]
														for w in ["weight", "bias"]:
																					UpperCamelCase_							=					f'''{prefix}.embeddings.LayerNorm.{w}'''
																					UpperCamelCase_							=					state_dict[param_name]

    # Transformer Blocks #
							UpperCamelCase_							=					0
							for teacher_idx in [0, 2, 4, 7, 9, 11]:
														if args.model_type == "gpt2":
																					for layer in ["ln_1", "attn.c_attn", "attn.c_proj", "ln_2", "mlp.c_fc", "mlp.c_proj"]:
																												for w in ["weight", "bias"]:
																																			UpperCamelCase_							=					state_dict[
																																			    f'''{prefix}.h.{teacher_idx}.{layer}.{w}'''
																																			]
																					UpperCamelCase_							=					state_dict[f'''{prefix}.h.{teacher_idx}.attn.bias''']
														else:
																					for layer in [
																					    "attention.self.query",
																					    "attention.self.key",
																					    "attention.self.value",
																					    "attention.output.dense",
																					    "attention.output.LayerNorm",
																					    "intermediate.dense",
																					    "output.dense",
																					    "output.LayerNorm",
																					]:
																												for w in ["weight", "bias"]:
																																			UpperCamelCase_							=					state_dict[
																																			    f'''{prefix}.encoder.layer.{teacher_idx}.{layer}.{w}'''
																																			]
														std_idx += 1

							# Language Modeling Head ###s
							if args.model_type == "roberta":
														for layer in ["lm_head.decoder.weight", "lm_head.bias"]:
																					UpperCamelCase_							=					state_dict[f'''{layer}''']
														if args.vocab_transform:
																					for w in ["weight", "bias"]:
																												UpperCamelCase_							=					state_dict[f'''lm_head.dense.{w}''']
																												UpperCamelCase_							=					state_dict[f'''lm_head.layer_norm.{w}''']
							elif args.model_type == "gpt2":
														for w in ["weight", "bias"]:
																					UpperCamelCase_							=					state_dict[f'''{prefix}.ln_f.{w}''']
														UpperCamelCase_							=					state_dict["""lm_head.weight"""]

							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)





