





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


lowercase    : List[str]    	=  [
    # 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     SCREAMING_SNAKE_CASE			(      lowerCAmelCase					):
       for pegasus_name, hf_name in PATTERNS:
              _UpperCamelCase              =						k.replace(lowerCAmelCase    ,       lowerCAmelCase					)
       return k


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


def     SCREAMING_SNAKE_CASE			(      lowerCAmelCase    ,       lowerCAmelCase					):
       # save tokenizer first
       _UpperCamelCase              =						Path(lowerCAmelCase					).parent.name
       _UpperCamelCase              =						task_specific_params[f'''summarization_{dataset}''']['''max_position_embeddings''']
       _UpperCamelCase              =						PegasusTokenizer.from_pretrained('''sshleifer/pegasus'''    ,       model_max_length=lowerCAmelCase					)
       assert tok.model_max_length == desired_max_model_length
       tok.save_pretrained(lowerCAmelCase					)

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


if __name__ == "__main__":
       lowercase    : Dict    	=  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.""")
       lowercase    : Optional[Any]    	=  parser.parse_args()
       if args.save_dir is None:
              lowercase    : str    	=  Path(args.tf_ckpt_path).parent.name
              lowercase    : Optional[int]    	=  os.path.join("""pegasus""", dataset)
       convert_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir)













import unittest

from transformers import (
    MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    TextaTextGenerationPipeline,
    pipeline,
)
from transformers.testing_utils import is_pipeline_test, require_tf, require_torch
from transformers.utils import is_torch_available

from .test_pipelines_common import ANY


if is_torch_available():
       import torch

@is_pipeline_test
class  __A(						unittest.TestCase ):
     __A	  =       MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
     __A	  =       TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING

     def 	_UpperCamelCase     (      self,       A,       A,       A      ):





            """simple docstring"""

            _UpperCamelCase              =						TextaTextGenerationPipeline(model=A,       tokenizer=A      )
            return generator, ["Something to write", "Something else"]

     def 	_UpperCamelCase     (      self,       A,       A      ):





            """simple docstring"""

            _UpperCamelCase              =						generator('''Something there'''      )
            self.assertEqual(A,       [{'''generated_text''': ANY(A      )}]      )
            # These are encoder decoder, they don't just append to incoming string
            self.assertFalse(outputs[0]['''generated_text'''].startswith('''Something there'''      )      )

            _UpperCamelCase              =						generator(['''This is great !''', '''Something else'''],       num_return_sequences=2,       do_sample=A      )
            self.assertEqual(
                A,       [
                    [{'''generated_text''': ANY(A      )}, {'''generated_text''': ANY(A      )}],
                    [{'''generated_text''': ANY(A      )}, {'''generated_text''': ANY(A      )}],
                ],       )

            _UpperCamelCase              =						generator(
                ['''This is great !''', '''Something else'''],       num_return_sequences=2,       batch_size=2,       do_sample=A      )
            self.assertEqual(
                A,       [
                    [{'''generated_text''': ANY(A      )}, {'''generated_text''': ANY(A      )}],
                    [{'''generated_text''': ANY(A      )}, {'''generated_text''': ANY(A      )}],
                ],       )

            with self.assertRaises(A      ):
                   generator(4      )

     @require_torch
     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            _UpperCamelCase              =						pipeline('''text2text-generation''',       model='''patrickvonplaten/t5-tiny-random''',       framework='''pt'''      )
            # do_sample=False necessary for reproducibility
            _UpperCamelCase              =						generator('''Something there''',       do_sample=A      )
            self.assertEqual(A,       [{'''generated_text''': ''''''}]      )

            _UpperCamelCase              =						3
            _UpperCamelCase              =						generator(
                '''Something there''',       num_return_sequences=A,       num_beams=A,       )
            _UpperCamelCase              =						[
                {'''generated_text''': '''Beide Beide Beide Beide Beide Beide Beide Beide Beide'''},
                {'''generated_text''': '''Beide Beide Beide Beide Beide Beide Beide Beide'''},
                {'''generated_text''': ''''''},
            ]
            self.assertEqual(A,       A      )

            _UpperCamelCase              =						generator('''This is a test''',       do_sample=A,       num_return_sequences=2,       return_tensors=A      )
            self.assertEqual(
                A,       [
                    {'''generated_token_ids''': ANY(torch.Tensor      )},
                    {'''generated_token_ids''': ANY(torch.Tensor      )},
                ],       )
            _UpperCamelCase              =						generator.model.config.eos_token_id
            _UpperCamelCase              =						'''<pad>'''
            _UpperCamelCase              =						generator(
                ['''This is a test''', '''This is a second test'''],       do_sample=A,       num_return_sequences=2,       batch_size=2,       return_tensors=A,       )
            self.assertEqual(
                A,       [
                    [
                        {'''generated_token_ids''': ANY(torch.Tensor      )},
                        {'''generated_token_ids''': ANY(torch.Tensor      )},
                    ],
                    [
                        {'''generated_token_ids''': ANY(torch.Tensor      )},
                        {'''generated_token_ids''': ANY(torch.Tensor      )},
                    ],
                ],       )



     @require_tf
     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            _UpperCamelCase              =						pipeline('''text2text-generation''',       model='''patrickvonplaten/t5-tiny-random''',       framework='''tf'''      )
            # do_sample=False necessary for reproducibility
            _UpperCamelCase              =						generator('''Something there''',       do_sample=A      )
            self.assertEqual(A,       [{'''generated_text''': ''''''}]      )






