
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    a_ (			_a					,			unittest.TestCase    ):
			a	:			Union[str, Any]					   =							LEDTokenizer
			a	:			str					   =							LEDTokenizerFast
			a	:			List[Any]					   =							True






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

									_lowercase					       =					os.path.join(self.tmpdirname				,			VOCAB_FILES_NAMES["""vocab_file"""]  )
									_lowercase					       =					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    UpperCamelCase_				(      self				,			**__UpperCamelCase  ):
									kwargs.update(self.special_tokens_map  )
									return self.tokenizer_class.from_pretrained(self.tmpdirname				,			**__UpperCamelCase  )






			def    UpperCamelCase_				(      self				,			**__UpperCamelCase  ):
									kwargs.update(self.special_tokens_map  )
									return self.rust_tokenizer_class.from_pretrained(self.tmpdirname				,			**__UpperCamelCase  )






			def    UpperCamelCase_				(      self				,			__UpperCamelCase  ):
									return "lower newer", "lower newer"






			@cached_property
			def    UpperCamelCase_				(      self  ):
									return LEDTokenizer.from_pretrained("""allenai/led-base-16384"""  )






			@cached_property
			def    UpperCamelCase_				(      self  ):
									return LEDTokenizerFast.from_pretrained("""allenai/led-base-16384"""  )






			@require_torch
			def    UpperCamelCase_				(      self  ):
									_lowercase					       =					["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
									_lowercase					       =					[0, 250, 251, 17_818, 13, 39_186, 1_938, 4, 2]

									for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
															_lowercase					       =					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					       =					batch.input_ids.tolist()[0]
															self.assertListEqual(__UpperCamelCase				,			__UpperCamelCase  )






			@require_torch
			def    UpperCamelCase_				(      self  ):
									_lowercase					       =					["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
									for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
															_lowercase					       =					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    UpperCamelCase_				(      self  ):
									_lowercase					       =					[
									    """Summary of the text.""",
									    """Another summary.""",
									]
									for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
															_lowercase					       =					tokenizer(text_target=__UpperCamelCase				,			max_length=32				,			padding="""max_length"""				,			return_tensors="""pt"""  )
															self.assertEqual(32				,			targets["""input_ids"""].shape[1]  )






			@require_torch
			def    UpperCamelCase_				(      self  ):
									for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
															_lowercase					       =					tokenizer(
															    ["""I am a small frog""" * 1_024, """I am a small frog"""]				,			padding=__UpperCamelCase				,			truncation=__UpperCamelCase				,			return_tensors="""pt"""  )
															self.assertIsInstance(__UpperCamelCase				,			__UpperCamelCase  )
															self.assertEqual(batch.input_ids.shape				,			(2, 5_122)  )






			@require_torch
			def    UpperCamelCase_				(      self  ):
									_lowercase					       =					["""A long paragraph for summarization."""]
									_lowercase					       =					[
									    """Summary of the text.""",
									]
									for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
															_lowercase					       =					tokenizer(__UpperCamelCase				,			return_tensors="""pt"""  )
															_lowercase					       =					tokenizer(text_target=__UpperCamelCase				,			return_tensors="""pt"""  )
															_lowercase					       =					inputs["""input_ids"""]
															_lowercase					       =					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    UpperCamelCase_				(      self  ):
									for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
															_lowercase					       =					["""Summary of the text.""", """Another summary."""]
															_lowercase					       =					[[0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, -1, -1]]

															_lowercase					       =					tokenizer(__UpperCamelCase				,			padding=__UpperCamelCase  )
															_lowercase					       =					[[0] * len(__UpperCamelCase  ) for x in encoded_output["""input_ids"""]]
															_lowercase					       =					tokenizer.pad(__UpperCamelCase  )
															self.assertSequenceEqual(outputs["""global_attention_mask"""]				,			__UpperCamelCase  )






			def    UpperCamelCase_				(      self  ):
									pass






			def    UpperCamelCase_				(      self  ):
									for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
															with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""  ):
																					_lowercase					       =					self.rust_tokenizer_class.from_pretrained(__UpperCamelCase				,			**__UpperCamelCase  )
																					_lowercase					       =					self.tokenizer_class.from_pretrained(__UpperCamelCase				,			**__UpperCamelCase  )
																					_lowercase					       =					"""A, <mask> AllenNLP sentence."""
																					_lowercase					       =					tokenizer_r.encode_plus(__UpperCamelCase				,			add_special_tokens=__UpperCamelCase				,			return_token_type_ids=__UpperCamelCase  )
																					_lowercase					       =					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					       =					tokenizer_r.convert_ids_to_tokens(tokens_r["""input_ids"""]  )
																					_lowercase					       =					tokenizer_p.convert_ids_to_tokens(tokens_p["""input_ids"""]  )
																					self.assertSequenceEqual(tokens_p["""input_ids"""]				,			[0, 250, 6, 50_264, 3_823, 487, 21_992, 3_645, 4, 2]  )
																					self.assertSequenceEqual(tokens_r["""input_ids"""]				,			[0, 250, 6, 50_264, 3_823, 487, 21_992, 3_645, 4, 2]  )

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

import argparse
import os
from pathlib import Path
from typing import Dict

import tensorflow as tf
import torch
from tqdm import tqdm

from transformers import PegasusConfig, PegasusForConditionalGeneration, PegasusTokenizer
from transformers.models.pegasus.configuration_pegasus import DEFAULTS, task_specific_params


A		: Any								=     [
    # replace left string with right string to get the relevant state_dict key (identical state dict to bart)
    ['''memory_attention''', '''encoder_attn'''],
    ['''attention''', '''attn'''],
    ['''/''', '''.'''],
    ['''.LayerNorm.gamma''', '''_layer_norm.weight'''],
    ['''.LayerNorm.beta''', '''_layer_norm.bias'''],
    ['''r.layer_''', '''r.layers.'''],
    ['''output_proj''', '''out_proj'''],
    ['''ffn.dense_1.''', '''fc2.'''],
    ['''ffn.dense.''', '''fc1.'''],
    ['''ffn_layer_norm''', '''final_layer_norm'''],
    ['''kernel''', '''weight'''],
    ['''encoder_layer_norm.''', '''encoder.layer_norm.'''],
    ['''decoder_layer_norm.''', '''decoder.layer_norm.'''],
    ['''embeddings.weights''', '''shared.weight'''],
]




def 	UpperCamelCase__  (     SCREAMING_SNAKE_CASE_	:	Union[str, Any]       )  ->     List[Any]:
						for pegasus_name, hf_name in PATTERNS:
												_lowercase					       =					k.replace(SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_       )
						return k




def 	UpperCamelCase__  (     SCREAMING_SNAKE_CASE_	:	dict		,   SCREAMING_SNAKE_CASE_	:	dict       )  ->     PegasusForConditionalGeneration:
						_lowercase					       =					DEFAULTS.copy()
						cfg_kwargs.update(SCREAMING_SNAKE_CASE_       )
						_lowercase					       =					PegasusConfig(**SCREAMING_SNAKE_CASE_       )
						_lowercase					       =					PegasusForConditionalGeneration(SCREAMING_SNAKE_CASE_       )
						_lowercase					       =					torch_model.model.state_dict()
						_lowercase					       =					{}
						for k, v in tf_weights.items():
												_lowercase					       =					rename_state_dict_key(SCREAMING_SNAKE_CASE_       )
												if new_k not in sd:
																		raise ValueError(f"""could not find new key {new_k} in state dict. (converted from {k})"""       )

												if "dense" in k or "proj" in new_k:
																		_lowercase					       =					v.T
												_lowercase					       =					torch.tensor(SCREAMING_SNAKE_CASE_		,   dtype=sd[new_k].dtype       )
												assert v.shape == sd[new_k].shape, f"""{new_k}, {k}, {v.shape}, {sd[new_k].shape}"""
						# make sure embedding.padding_idx is respected
						_lowercase					       =					torch.zeros_like(mapping["""shared.weight"""][cfg.pad_token_id + 1]       )
						_lowercase					       =					mapping["""shared.weight"""]
						_lowercase					       =					mapping["""shared.weight"""]
						_lowercase					       =					{k: torch.zeros_like(SCREAMING_SNAKE_CASE_       ) for k, v in sd.items() if k.endswith("""bias"""       ) and k not in mapping}
						mapping.update(**SCREAMING_SNAKE_CASE_       )
						_lowercase       ,						_lowercase					       =					torch_model.model.load_state_dict(SCREAMING_SNAKE_CASE_		,   strict=SCREAMING_SNAKE_CASE_       )
						_lowercase					       =					[
						    k for k in missing if k not in ["""encoder.embed_positions.weight""", """decoder.embed_positions.weight"""]
						]
						assert unexpected_missing == [], f"""no matches found for the following torch keys {unexpected_missing}"""
						assert extra == [], f"""no matches found for the following tf keys {extra}"""
						return torch_model




def 	UpperCamelCase__  (     SCREAMING_SNAKE_CASE_	:	Optional[Any]="./ckpt/aeslc/model.ckpt-32000"       )  ->     Dict:
						_lowercase					       =					tf.train.list_variables(SCREAMING_SNAKE_CASE_       )
						_lowercase					       =					{}
						_lowercase					       =					["""Adafactor""", """global_step"""]
						for name, shape in tqdm(SCREAMING_SNAKE_CASE_		,   desc="""converting tf checkpoint to dict"""       ):
												_lowercase					       =					any(pat in name for pat in ignore_name       )
												if skip_key:
																		continue
												_lowercase					       =					tf.train.load_variable(SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_       )
												_lowercase					       =					array
						return tf_weights




def 	UpperCamelCase__  (     SCREAMING_SNAKE_CASE_	:	str		,   SCREAMING_SNAKE_CASE_	:	str       )  ->     str:
						# save tokenizer first
						_lowercase					       =					Path(SCREAMING_SNAKE_CASE_       ).parent.name
						_lowercase					       =					task_specific_params[f"""summarization_{dataset}"""]["""max_position_embeddings"""]
						_lowercase					       =					PegasusTokenizer.from_pretrained("""sshleifer/pegasus"""		,   model_max_length=SCREAMING_SNAKE_CASE_       )
						assert tok.model_max_length == desired_max_model_length
						tok.save_pretrained(SCREAMING_SNAKE_CASE_       )

						# convert model
						_lowercase					       =					get_tf_weights_as_numpy(SCREAMING_SNAKE_CASE_       )
						_lowercase					       =					task_specific_params[f"""summarization_{dataset}"""]
						if dataset == "large":
												_lowercase					       =					task_specific_params
						_lowercase					       =					convert_pegasus(SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_       )
						torch_model.save_pretrained(SCREAMING_SNAKE_CASE_       )
						_lowercase					       =					torch_model.state_dict()
						sd.pop("""model.decoder.embed_positions.weight"""       )
						sd.pop("""model.encoder.embed_positions.weight"""       )
						torch.save(SCREAMING_SNAKE_CASE_		,   Path(SCREAMING_SNAKE_CASE_       ) / """pytorch_model.bin"""       )


if __name__ == "__main__":
		A		: List[str]								=     argparse.ArgumentParser()
		# Required parameters
		parser.add_argument('''tf_ckpt_path''', type=str, help='''passed to tf.train.list_variables''')
		parser.add_argument('''save_dir''', default=None, type=str, help='''Path to the output PyTorch model.''')
		A		: str								=     parser.parse_args()
		if args.save_dir is None:
				A		: Tuple								=     Path(args.tf_ckpt_path).parent.name
				A		: List[Any]								=     os.path.join('''pegasus''', dataset)
		convert_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir)