





'''simple docstring'''

from __future__ import annotations

import copy
import tempfile
import unittest

from transformers import CONFIG_MAPPING, AutoConfig, BertConfig, GPTaConfig, TaConfig, TapasConfig, is_tf_available
from transformers.testing_utils import (
    DUMMY_UNKNOWN_IDENTIFIER,
    SMALL_MODEL_IDENTIFIER,
    RequestCounter,
    require_tensorflow_probability,
    require_tf,
    slow,
)

from ..bert.test_modeling_bert import BertModelTester


if is_tf_available():
   from transformers import (
       TFAutoModel,
       TFAutoModelForCausalLM,
       TFAutoModelForMaskedLM,
       TFAutoModelForPreTraining,
       TFAutoModelForQuestionAnswering,
       TFAutoModelForSeqaSeqLM,
       TFAutoModelForSequenceClassification,
       TFAutoModelForTableQuestionAnswering,
       TFAutoModelForTokenClassification,
       TFAutoModelWithLMHead,
       TFBertForMaskedLM,
       TFBertForPreTraining,
       TFBertForQuestionAnswering,
       TFBertForSequenceClassification,
       TFBertModel,
       TFFunnelBaseModel,
       TFFunnelModel,
       TFGPTaLMHeadModel,
       TFRobertaForMaskedLM,
       TFTaForConditionalGeneration,
       TFTapasForQuestionAnswering,
   )
   from transformers.models.auto.modeling_tf_auto import (
       TF_MODEL_FOR_CAUSAL_LM_MAPPING,
       TF_MODEL_FOR_MASKED_LM_MAPPING,
       TF_MODEL_FOR_PRETRAINING_MAPPING,
       TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
       TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
       TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
       TF_MODEL_MAPPING,
   )
   from transformers.models.bert.modeling_tf_bert import TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST
   from transformers.models.gpta.modeling_tf_gpta import TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST
   from transformers.models.ta.modeling_tf_ta import TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST
   from transformers.models.tapas.modeling_tf_tapas import TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST
class 							__lowerCAmelCase	(       __magic_name__					):






       """simple docstring"""
       _snake_case      :	List[str]									=       'new-model'


if is_tf_available():
   class 							__lowerCAmelCase	(       __magic_name__					):






          """simple docstring"""
          _snake_case      :	List[Any]									=       NewModelConfig
@require_tf
class 							__lowerCAmelCase	(       unittest.TestCase					):






       """simple docstring"""





       @slow
       def snake_case__		(				self  :						Any	)       ->	int:

            '''simple docstring'''




            _UpperCamelCase							       =			'''bert-base-cased'''
            _UpperCamelCase							       =			AutoConfig.from_pretrained(lowerCAmelCase__	)
            self.assertIsNotNone(lowerCAmelCase__	)
            self.assertIsInstance(lowerCAmelCase__			,					lowerCAmelCase__	)

            _UpperCamelCase							       =			TFAutoModel.from_pretrained(lowerCAmelCase__	)
            self.assertIsNotNone(lowerCAmelCase__	)
            self.assertIsInstance(lowerCAmelCase__			,					lowerCAmelCase__	)





       @slow
       def snake_case__		(				self  :						Dict	)       ->	Tuple:

            '''simple docstring'''




            _UpperCamelCase							       =			'''bert-base-cased'''
            _UpperCamelCase							       =			AutoConfig.from_pretrained(lowerCAmelCase__	)
            self.assertIsNotNone(lowerCAmelCase__	)
            self.assertIsInstance(lowerCAmelCase__			,					lowerCAmelCase__	)

            _UpperCamelCase							       =			TFAutoModelForPreTraining.from_pretrained(lowerCAmelCase__	)
            self.assertIsNotNone(lowerCAmelCase__	)
            self.assertIsInstance(lowerCAmelCase__			,					lowerCAmelCase__	)





       @slow
       def snake_case__		(				self  :						Optional[Any]	)       ->	Union[str, Any]:

            '''simple docstring'''




            for model_name in TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                 _UpperCamelCase							       =			AutoConfig.from_pretrained(lowerCAmelCase__	)
                 self.assertIsNotNone(lowerCAmelCase__	)
                 self.assertIsInstance(lowerCAmelCase__			,					lowerCAmelCase__	)

                 _UpperCamelCase							       =			TFAutoModelForCausalLM.from_pretrained(lowerCAmelCase__	)
                 _UpperCamelCase       ,							_UpperCamelCase							       =			TFAutoModelForCausalLM.from_pretrained(lowerCAmelCase__			,					output_loading_info=lowerCAmelCase__	)
                 self.assertIsNotNone(lowerCAmelCase__	)
                 self.assertIsInstance(lowerCAmelCase__			,					lowerCAmelCase__	)





       @slow
       def snake_case__		(				self  :						str	)       ->	str:

            '''simple docstring'''




            for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                 _UpperCamelCase							       =			AutoConfig.from_pretrained(lowerCAmelCase__	)
                 self.assertIsNotNone(lowerCAmelCase__	)
                 self.assertIsInstance(lowerCAmelCase__			,					lowerCAmelCase__	)

                 _UpperCamelCase							       =			TFAutoModelWithLMHead.from_pretrained(lowerCAmelCase__	)
                 self.assertIsNotNone(lowerCAmelCase__	)
                 self.assertIsInstance(lowerCAmelCase__			,					lowerCAmelCase__	)





       @slow
       def snake_case__		(				self  :						int	)       ->	Optional[int]:

            '''simple docstring'''




            for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                 _UpperCamelCase							       =			AutoConfig.from_pretrained(lowerCAmelCase__	)
                 self.assertIsNotNone(lowerCAmelCase__	)
                 self.assertIsInstance(lowerCAmelCase__			,					lowerCAmelCase__	)

                 _UpperCamelCase							       =			TFAutoModelForMaskedLM.from_pretrained(lowerCAmelCase__	)
                 _UpperCamelCase       ,							_UpperCamelCase							       =			TFAutoModelForMaskedLM.from_pretrained(lowerCAmelCase__			,					output_loading_info=lowerCAmelCase__	)
                 self.assertIsNotNone(lowerCAmelCase__	)
                 self.assertIsInstance(lowerCAmelCase__			,					lowerCAmelCase__	)





       @slow
       def snake_case__		(				self  :						Union[str, Any]	)       ->	Union[str, Any]:

            '''simple docstring'''




            for model_name in TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                 _UpperCamelCase							       =			AutoConfig.from_pretrained(lowerCAmelCase__	)
                 self.assertIsNotNone(lowerCAmelCase__	)
                 self.assertIsInstance(lowerCAmelCase__			,					lowerCAmelCase__	)

                 _UpperCamelCase							       =			TFAutoModelForSeqaSeqLM.from_pretrained(lowerCAmelCase__	)
                 _UpperCamelCase       ,							_UpperCamelCase							       =			TFAutoModelForSeqaSeqLM.from_pretrained(lowerCAmelCase__			,					output_loading_info=lowerCAmelCase__	)
                 self.assertIsNotNone(lowerCAmelCase__	)
                 self.assertIsInstance(lowerCAmelCase__			,					lowerCAmelCase__	)





       @slow
       def snake_case__		(				self  :						Optional[int]	)       ->	Optional[Any]:

            '''simple docstring'''




            for model_name in ["bert-base-uncased"]:
                 _UpperCamelCase							       =			AutoConfig.from_pretrained(lowerCAmelCase__	)
                 self.assertIsNotNone(lowerCAmelCase__	)
                 self.assertIsInstance(lowerCAmelCase__			,					lowerCAmelCase__	)

                 _UpperCamelCase							       =			TFAutoModelForSequenceClassification.from_pretrained(lowerCAmelCase__	)
                 self.assertIsNotNone(lowerCAmelCase__	)
                 self.assertIsInstance(lowerCAmelCase__			,					lowerCAmelCase__	)





       @slow
       def snake_case__		(				self  :						List[str]	)       ->	int:

            '''simple docstring'''




            for model_name in ["bert-base-uncased"]:
                 _UpperCamelCase							       =			AutoConfig.from_pretrained(lowerCAmelCase__	)
                 self.assertIsNotNone(lowerCAmelCase__	)
                 self.assertIsInstance(lowerCAmelCase__			,					lowerCAmelCase__	)

                 _UpperCamelCase							       =			TFAutoModelForQuestionAnswering.from_pretrained(lowerCAmelCase__	)
                 self.assertIsNotNone(lowerCAmelCase__	)
                 self.assertIsInstance(lowerCAmelCase__			,					lowerCAmelCase__	)





       @slow
       @require_tensorflow_probability
       def snake_case__		(				self  :						int	)       ->	Union[str, Any]:

            '''simple docstring'''




            for model_name in TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST[5:6]:
                 _UpperCamelCase							       =			AutoConfig.from_pretrained(lowerCAmelCase__	)
                 self.assertIsNotNone(lowerCAmelCase__	)
                 self.assertIsInstance(lowerCAmelCase__			,					lowerCAmelCase__	)

                 _UpperCamelCase							       =			TFAutoModelForTableQuestionAnswering.from_pretrained(lowerCAmelCase__	)
                 _UpperCamelCase       ,							_UpperCamelCase							       =			TFAutoModelForTableQuestionAnswering.from_pretrained(
                     lowerCAmelCase__			,					output_loading_info=lowerCAmelCase__	)
                 self.assertIsNotNone(lowerCAmelCase__	)
                 self.assertIsInstance(lowerCAmelCase__			,					lowerCAmelCase__	)





       def snake_case__		(				self  :						List[Any]	)       ->	List[Any]:

            '''simple docstring'''




            _UpperCamelCase							       =			TFAutoModelWithLMHead.from_pretrained(lowerCAmelCase__	)
            self.assertIsInstance(lowerCAmelCase__			,					lowerCAmelCase__	)
            self.assertEqual(model.num_parameters()			,					14410	)
            self.assertEqual(model.num_parameters(only_trainable=lowerCAmelCase__	)			,					14410	)





       def snake_case__		(				self  :						Dict	)       ->	Union[str, Any]:

            '''simple docstring'''




            _UpperCamelCase							       =			TFAutoModelWithLMHead.from_pretrained(lowerCAmelCase__	)
            self.assertIsInstance(lowerCAmelCase__			,					lowerCAmelCase__	)
            self.assertEqual(model.num_parameters()			,					14410	)
            self.assertEqual(model.num_parameters(only_trainable=lowerCAmelCase__	)			,					14410	)





       def snake_case__		(				self  :						Any	)       ->	Optional[Any]:

            '''simple docstring'''




            _UpperCamelCase							       =			TFAutoModel.from_pretrained('''sgugger/funnel-random-tiny'''	)
            self.assertIsInstance(lowerCAmelCase__			,					lowerCAmelCase__	)

            _UpperCamelCase							       =			copy.deepcopy(model.config	)
            _UpperCamelCase							       =			['''FunnelBaseModel''']
            _UpperCamelCase							       =			TFAutoModel.from_config(lowerCAmelCase__	)
            self.assertIsInstance(lowerCAmelCase__			,					lowerCAmelCase__	)

            with tempfile.TemporaryDirectory() as tmp_dir:
                 model.save_pretrained(lowerCAmelCase__	)
                 _UpperCamelCase							       =			TFAutoModel.from_pretrained(lowerCAmelCase__	)
                 self.assertIsInstance(lowerCAmelCase__			,					lowerCAmelCase__	)





       def snake_case__		(				self  :						Optional[Any]	)       ->	str:

            '''simple docstring'''




            try:
                 AutoConfig.register('''new-model'''			,					lowerCAmelCase__	)

                 _UpperCamelCase							       =			[
                     TFAutoModel,
                     TFAutoModelForCausalLM,
                     TFAutoModelForMaskedLM,
                     TFAutoModelForPreTraining,
                     TFAutoModelForQuestionAnswering,
                     TFAutoModelForSequenceClassification,
                     TFAutoModelForTokenClassification,
                 ]

                 for auto_class in auto_classes:
                      with self.subTest(auto_class.__name__	):
                           # Wrong config class will raise an error
                           with self.assertRaises(lowerCAmelCase__	):
                                auto_class.register(lowerCAmelCase__			,					lowerCAmelCase__	)
                           auto_class.register(lowerCAmelCase__			,					lowerCAmelCase__	)
                           # Trying to register something existing in the Transformers library will raise an error
                           with self.assertRaises(lowerCAmelCase__	):
                                auto_class.register(lowerCAmelCase__			,					lowerCAmelCase__	)

                           # Now that the config is registered, it can be used as any other config with the auto-API
                           _UpperCamelCase							       =			BertModelTester(self	).get_config()
                           _UpperCamelCase							       =			NewModelConfig(**tiny_config.to_dict()	)
                           _UpperCamelCase							       =			auto_class.from_config(lowerCAmelCase__	)
                           self.assertIsInstance(lowerCAmelCase__			,					lowerCAmelCase__	)

                           with tempfile.TemporaryDirectory() as tmp_dir:
                                model.save_pretrained(lowerCAmelCase__	)
                                _UpperCamelCase							       =			auto_class.from_pretrained(lowerCAmelCase__	)
                                self.assertIsInstance(lowerCAmelCase__			,					lowerCAmelCase__	)

            finally:
                 if "new-model" in CONFIG_MAPPING._extra_content:
                      del CONFIG_MAPPING._extra_content["new-model"]
                 for mapping in (
                     TF_MODEL_MAPPING,
                     TF_MODEL_FOR_PRETRAINING_MAPPING,
                     TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
                     TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
                     TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
                     TF_MODEL_FOR_CAUSAL_LM_MAPPING,
                     TF_MODEL_FOR_MASKED_LM_MAPPING,
                 ):
                      if NewModelConfig in mapping._extra_content:
                           del mapping._extra_content[NewModelConfig]





       def snake_case__		(				self  :						Union[str, Any]	)       ->	Dict:

            '''simple docstring'''




            with self.assertRaisesRegex(
                lowerCAmelCase__			,					'''bert-base is not a local folder and is not a valid model identifier'''	):
                 _UpperCamelCase							       =			TFAutoModel.from_pretrained('''bert-base'''	)





       def snake_case__		(				self  :						Dict	)       ->	Union[str, Any]:

            '''simple docstring'''




            with self.assertRaisesRegex(
                lowerCAmelCase__			,					r'''aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)'''	):
                 _UpperCamelCase							       =			TFAutoModel.from_pretrained(lowerCAmelCase__			,					revision='''aaaaaa'''	)





       def snake_case__		(				self  :						Optional[Any]	)       ->	Optional[int]:

            '''simple docstring'''




            with self.assertRaisesRegex(
                lowerCAmelCase__			,					'''hf-internal-testing/config-no-model does not appear to have a file named pytorch_model.bin'''			,					):
                 _UpperCamelCase							       =			TFAutoModel.from_pretrained('''hf-internal-testing/config-no-model'''	)





       def snake_case__		(				self  :						Tuple	)       ->	Optional[int]:

            '''simple docstring'''




            with self.assertRaisesRegex(lowerCAmelCase__			,					'''Use `from_pt=True` to load this model'''	):
                 _UpperCamelCase							       =			TFAutoModel.from_pretrained('''hf-internal-testing/tiny-bert-pt-only'''	)







       def snake_case__		(				self  :						Optional[Any]	)       ->	str:

            '''simple docstring'''




            _UpperCamelCase							       =			TFAutoModel.from_pretrained('''hf-internal-testing/tiny-random-bert'''	)
            with RequestCounter() as counter:
                 _UpperCamelCase							       =			TFAutoModel.from_pretrained('''hf-internal-testing/tiny-random-bert'''	)
                 self.assertEqual(counter.get_request_count			,					0	)
                 self.assertEqual(counter.head_request_count			,					1	)
                 self.assertEqual(counter.other_request_count			,					0	)

            # With a sharded checkpoint
            _UpperCamelCase							       =			TFAutoModel.from_pretrained('''ArthurZ/tiny-random-bert-sharded'''	)
            with RequestCounter() as counter:
                 _UpperCamelCase							       =			TFAutoModel.from_pretrained('''ArthurZ/tiny-random-bert-sharded'''	)
                 self.assertEqual(counter.get_request_count			,					0	)
                 self.assertEqual(counter.head_request_count			,					1	)
                 self.assertEqual(counter.other_request_count			,					0	)










'''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


lowercase__		:     Dict			=							[
    # 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__							(							lowercase   :	List[str] )						->						List[Any]:





     """simple docstring"""

     for pegasus_name, hf_name in PATTERNS:
          _UpperCamelCase							       =			k.replace(lowercase,   lowercase )
     return k







def    a__							(							lowercase   :	dict,   lowercase   :	dict )						->						PegasusForConditionalGeneration:





     """simple docstring"""

     _UpperCamelCase							       =			DEFAULTS.copy()
     cfg_kwargs.update(lowercase )
     _UpperCamelCase							       =			PegasusConfig(**lowercase )
     _UpperCamelCase							       =			PegasusForConditionalGeneration(lowercase )
     _UpperCamelCase							       =			torch_model.model.state_dict()
     _UpperCamelCase							       =			{}
     for k, v in tf_weights.items():
          _UpperCamelCase							       =			rename_state_dict_key(lowercase )
          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(lowercase,   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(lowercase ) for k, v in sd.items() if k.endswith('''bias''' ) and k not in mapping}
     mapping.update(**lowercase )
     _UpperCamelCase       ,							_UpperCamelCase							       =			torch_model.model.load_state_dict(lowercase,   strict=lowercase )
     _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    a__							(							lowercase   :	List[Any]="./ckpt/aeslc/model.ckpt-32000" )						->						Dict:





     """simple docstring"""

     _UpperCamelCase							       =			tf.train.list_variables(lowercase )
     _UpperCamelCase							       =			{}
     _UpperCamelCase							       =			['''Adafactor''', '''global_step''']
     for name, shape in tqdm(lowercase,   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(lowercase,   lowercase )
          _UpperCamelCase							       =			array
     return tf_weights







def    a__							(							lowercase   :	str,   lowercase   :	str )						->						Optional[int]:





     """simple docstring"""

     _UpperCamelCase							       =			Path(lowercase ).parent.name
     _UpperCamelCase							       =			task_specific_params[F"""summarization_{dataset}"""]['''max_position_embeddings''']
     _UpperCamelCase							       =			PegasusTokenizer.from_pretrained('''sshleifer/pegasus''',   model_max_length=lowercase )
     assert tok.model_max_length == desired_max_model_length
     tok.save_pretrained(lowercase )

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


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



