"""simple docstring"""



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


_UpperCamelCase								=		[
    # 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  _A(		lowerCAmelCase							):


							for pegasus_name, hf_name in PATTERNS:
														A__       :     List[Any]								=			k.replace(snake_case__			,  snake_case__							)
							return k

def  _A(		lowerCAmelCase			,  lowerCAmelCase							):
							A__       :     List[Any]								=			DEFAULTS.copy()
							cfg_kwargs.update(snake_case__							)
							A__       :     Optional[Any]								=			PegasusConfig(**snake_case__							)
							A__       :     List[Any]								=			PegasusForConditionalGeneration(snake_case__							)
							A__       :     List[Any]								=			torch_model.model.state_dict()
							A__       :     Dict								=			{}
							for k, v in tf_weights.items():
														A__       :     Any								=			rename_state_dict_key(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:
																					A__       :     Tuple								=			v.T
														A__       :     Optional[Any]								=			torch.tensor(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
							A__       :     Tuple								=			torch.zeros_like(mapping["""shared.weight"""][cfg.pad_token_id + 1]							)
							A__       :     int								=			mapping["""shared.weight"""]
							A__       :     List[Any]								=			mapping["""shared.weight"""]
							A__       :     Optional[Any]								=			{k: torch.zeros_like(snake_case__							) for k, v in sd.items() if k.endswith("""bias"""							) and k not in mapping}
							mapping.update(**snake_case__							)
							A__				,							A__       :     Any								=			torch_model.model.load_state_dict(snake_case__			,  strict=snake_case__							)
							A__       :     List[str]								=			[
							    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  _A(		lowerCAmelCase="./ckpt/aeslc/model.ckpt-32000"							):
							A__       :     List[str]								=			tf.train.list_variables(snake_case__							)
							A__       :     List[str]								=			{}
							A__       :     Tuple								=			["""Adafactor""", """global_step"""]
							for name, shape in tqdm(snake_case__			,  desc="""converting tf checkpoint to dict"""							):
														A__       :     Any								=			any(pat in name for pat in ignore_name							)
														if skip_key:
																					continue
														A__       :     List[str]								=			tf.train.load_variable(snake_case__			,  snake_case__							)
														A__       :     Tuple								=			array
							return tf_weights

def  _A(		lowerCAmelCase			,  lowerCAmelCase							):
							A__       :     Tuple								=			Path(snake_case__							).parent.name
							A__       :     Any								=			task_specific_params[F'''summarization_{dataset}''']["""max_position_embeddings"""]
							A__       :     Dict								=			PegasusTokenizer.from_pretrained("""sshleifer/pegasus"""			,  model_max_length=snake_case__							)
							assert tok.model_max_length == desired_max_model_length
							tok.save_pretrained(snake_case__							)

							# convert model
							A__       :     List[str]								=			get_tf_weights_as_numpy(snake_case__							)
							A__       :     Any								=			task_specific_params[F'''summarization_{dataset}''']
							if dataset == "large":
														A__       :     Optional[Any]								=			task_specific_params
							A__       :     str								=			convert_pegasus(snake_case__			,  snake_case__							)
							torch_model.save_pretrained(snake_case__							)
							A__       :     int								=			torch_model.state_dict()
							sd.pop("""model.decoder.embed_positions.weight"""							)
							sd.pop("""model.encoder.embed_positions.weight"""							)
							torch.save(snake_case__			,  Path(snake_case__							) / """pytorch_model.bin"""							)


if __name__ == "__main__":
				_UpperCamelCase								=		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.")
				_UpperCamelCase								=		parser.parse_args()
				if args.save_dir is None:
								_UpperCamelCase								=		Path(args.tf_ckpt_path).parent.name
								_UpperCamelCase								=		os.path.join("pegasus", dataset)
				convert_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir)








"""simple docstring"""




import unittest
from pathlib import Path
from tempfile import TemporaryDirectory

from transformers import AutoConfig, TFGPTaLMHeadModel, is_keras_nlp_available, is_tf_available
from transformers.models.gpta.tokenization_gpta import GPTaTokenizer
from transformers.testing_utils import require_keras_nlp, require_tf, slow


if is_tf_available():
						import tensorflow as tf

if is_keras_nlp_available():
						from transformers.models.gpta import TFGPTaTokenizer


A_				:      Union[str, Any]           =			["gpt2"]
A_				:      Optional[int]           =			"gpt2"

if is_tf_available():





						class  lowerCamelCase    (tf.Module      ):

									def __init__(       self					:    Dict     ,					__UpperCAmelCase					:    str							) ->     Any:
												super().__init__()
												SCREAMING_SNAKE_CASE__										=   tokenizer
												SCREAMING_SNAKE_CASE__										=   AutoConfig.from_pretrained(__UpperCAmelCase							)
												SCREAMING_SNAKE_CASE__										=   TFGPTaLMHeadModel.from_config(__UpperCAmelCase							)





									@tf.function(input_signature=(tf.TensorSpec((None,)     ,					tf.string     ,					name="""text"""							),)							)
									def        SCREAMING_SNAKE_CASE					(       self					:    Tuple     ,					__UpperCAmelCase					:    Tuple							) ->     List[Any]:
												SCREAMING_SNAKE_CASE__										=   self.tokenizer(__UpperCAmelCase							)
												SCREAMING_SNAKE_CASE__										=   tokenized["""input_ids"""].to_tensor()

												SCREAMING_SNAKE_CASE__										=   tf.cast(input_ids_dense > 0     ,					tf.intaa							)
												# input_mask = tf.reshape(input_mask, [-1, MAX_SEQ_LEN])

												SCREAMING_SNAKE_CASE__										=   self.model(input_ids=__UpperCAmelCase     ,					attention_mask=__UpperCAmelCase							)["""logits"""]

												return outputs





@require_tf
@require_keras_nlp
class  lowerCamelCase    (unittest.TestCase      ):

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

						SCREAMING_SNAKE_CASE__										=   [GPTaTokenizer.from_pretrained(__UpperCAmelCase							) for checkpoint in (TOKENIZER_CHECKPOINTS)]
						SCREAMING_SNAKE_CASE__										=   [TFGPTaTokenizer.from_pretrained(__UpperCAmelCase							) for checkpoint in TOKENIZER_CHECKPOINTS]
						assert len(self.tokenizers							) == len(self.tf_tokenizers							)

						SCREAMING_SNAKE_CASE__										=   [
						    """This is a straightforward English test sentence.""",
						    """This one has some weird characters\rto\nsee\r\nif  those\u00E9break things.""",
						    """Now we're going to add some Chinese: 一 二 三 一二三""",
						    """And some much more rare Chinese: 齉 堃 齉堃""",
						    """Je vais aussi écrire en français pour tester les accents""",
						    """Classical Irish also has some unusual characters, so in they go: Gaelaċ, ꝼ""",
						]
						SCREAMING_SNAKE_CASE__										=   list(zip(self.test_sentences     ,					self.test_sentences[::-1]							)							)

			def        SCREAMING_SNAKE_CASE					(       self					:    Tuple							) ->     str:
						for tokenizer, tf_tokenizer in zip(self.tokenizers     ,					self.tf_tokenizers							):
									for test_inputs in self.test_sentences:
												SCREAMING_SNAKE_CASE__										=   tokenizer([test_inputs]     ,					return_tensors="""tf"""							)
												SCREAMING_SNAKE_CASE__										=   tf_tokenizer([test_inputs]							)

												for key in python_outputs.keys():
															# convert them to numpy to avoid messing with ragged tensors
															SCREAMING_SNAKE_CASE__										=   python_outputs[key].numpy()
															SCREAMING_SNAKE_CASE__										=   tf_outputs[key].numpy()

															self.assertTrue(tf.reduce_all(python_outputs_values.shape == tf_outputs_values.shape							)							)
															self.assertTrue(tf.reduce_all(tf.cast(__UpperCAmelCase     ,					tf.intaa							) == tf_outputs_values							)							)

			@slow
			def        SCREAMING_SNAKE_CASE					(       self					:    Union[str, Any]							) ->     str:
						for tf_tokenizer in self.tf_tokenizers:
									SCREAMING_SNAKE_CASE__										=   tf.function(__UpperCAmelCase							)
									for test_inputs in self.test_sentences:
												SCREAMING_SNAKE_CASE__										=   tf.constant(__UpperCAmelCase							)
												SCREAMING_SNAKE_CASE__										=   compiled_tokenizer(__UpperCAmelCase							)
												SCREAMING_SNAKE_CASE__										=   tf_tokenizer(__UpperCAmelCase							)

												for key in eager_outputs.keys():
															self.assertTrue(tf.reduce_all(eager_outputs[key] == compiled_outputs[key]							)							)

			@slow
			def        SCREAMING_SNAKE_CASE					(       self					:    List[str]							) ->     List[str]:
						for tf_tokenizer in self.tf_tokenizers:
									SCREAMING_SNAKE_CASE__										=   ModelToSave(tokenizer=__UpperCAmelCase							)
									SCREAMING_SNAKE_CASE__										=   tf.convert_to_tensor([self.test_sentences[0]]							)
									SCREAMING_SNAKE_CASE__										=   model.serving(__UpperCAmelCase							)  # Build model with some sample inputs
									with TemporaryDirectory() as tempdir:
												SCREAMING_SNAKE_CASE__										=   Path(__UpperCAmelCase							) / """saved.model"""
												tf.saved_model.save(__UpperCAmelCase     ,					__UpperCAmelCase     ,					signatures={"""serving_default""": model.serving}							)
												SCREAMING_SNAKE_CASE__										=   tf.saved_model.load(__UpperCAmelCase							)
									SCREAMING_SNAKE_CASE__										=   loaded_model.signatures["""serving_default"""](__UpperCAmelCase							)["""output_0"""]
									# We may see small differences because the loaded model is compiled, so we need an epsilon for the test
									self.assertTrue(tf.reduce_all(out == loaded_output							)							)

			@slow
			def        SCREAMING_SNAKE_CASE					(       self					:    str							) ->     List[Any]:
						for tf_tokenizer in self.tf_tokenizers:
									SCREAMING_SNAKE_CASE__										=   tf.convert_to_tensor([self.test_sentences[0]]							)
									SCREAMING_SNAKE_CASE__										=   tf_tokenizer(__UpperCAmelCase							)  # Build model with some sample inputs

									SCREAMING_SNAKE_CASE__										=   tf_tokenizer.get_config()
									SCREAMING_SNAKE_CASE__										=   TFGPTaTokenizer.from_config(__UpperCAmelCase							)
									SCREAMING_SNAKE_CASE__										=   model_from_config(__UpperCAmelCase							)

									for key in from_config_output.keys():
												self.assertTrue(tf.reduce_all(from_config_output[key] == out[key]							)							)





			@slow
			def        SCREAMING_SNAKE_CASE					(       self					:    int							) ->     Optional[int]:
						for tf_tokenizer in self.tf_tokenizers:
									# for the test to run
									SCREAMING_SNAKE_CASE__										=   1_2_3_1_2_3

									for max_length in [3, 5, 1_0_2_4]:
												SCREAMING_SNAKE_CASE__										=   tf.convert_to_tensor([self.test_sentences[0]]							)
												SCREAMING_SNAKE_CASE__										=   tf_tokenizer(__UpperCAmelCase     ,					max_length=__UpperCAmelCase							)

												SCREAMING_SNAKE_CASE__										=   out["""input_ids"""].numpy().shape[1]

												assert out_length == max_length


