


import argparse
from typing import Dict

import tensorflow as tf
import torch
from tqdm import tqdm

from transformers import BigBirdPegasusConfig, BigBirdPegasusForConditionalGeneration


snake_case							:       Tuple     			=      [
    # tf -> hf
    ('''/''', '''.'''),
    ('''layer_''', '''layers.'''),
    ('''kernel''', '''weight'''),
    ('''beta''', '''bias'''),
    ('''gamma''', '''weight'''),
    ('''pegasus''', '''model'''),
]
snake_case							:       List[Any]     			=      [
    ('''.output.dense''', '''.fc2'''),
    ('''intermediate.LayerNorm''', '''final_layer_norm'''),
    ('''intermediate.dense''', '''fc1'''),
]

snake_case							:       Optional[Any]     			=      (
    INIT_COMMON
    + [
        ('''attention.self.LayerNorm''', '''self_attn_layer_norm'''),
        ('''attention.output.dense''', '''self_attn.out_proj'''),
        ('''attention.self''', '''self_attn'''),
        ('''attention.encdec.LayerNorm''', '''encoder_attn_layer_norm'''),
        ('''attention.encdec_output.dense''', '''encoder_attn.out_proj'''),
        ('''attention.encdec''', '''encoder_attn'''),
        ('''key''', '''k_proj'''),
        ('''value''', '''v_proj'''),
        ('''query''', '''q_proj'''),
        ('''decoder.LayerNorm''', '''decoder.layernorm_embedding'''),
    ]
    + END_COMMON
)

snake_case							:       Any     			=      (
    INIT_COMMON
    + [
        ('''embeddings.word_embeddings''', '''shared.weight'''),
        ('''embeddings.position_embeddings''', '''embed_positions.weight'''),
        ('''attention.self.LayerNorm''', '''self_attn_layer_norm'''),
        ('''attention.output.dense''', '''self_attn.output'''),
        ('''attention.self''', '''self_attn.self'''),
        ('''encoder.LayerNorm''', '''encoder.layernorm_embedding'''),
    ]
    + END_COMMON
)

snake_case							:       Union[str, Any]     			=      [
    '''encdec/key/bias''',
    '''encdec/query/bias''',
    '''encdec/value/bias''',
    '''self/key/bias''',
    '''self/query/bias''',
    '''self/value/bias''',
    '''encdec_output/dense/bias''',
    '''attention/output/dense/bias''',
]






def 							__lowercase   ( __lowerCAmelCase							:				List[str]					,    __lowerCAmelCase							:				str   ):
							for tf_name, hf_name in patterns:
														a__             =     k.replace(__lowerCAmelCase					,    __lowerCAmelCase   )
							return k






def 							__lowercase   ( __lowerCAmelCase							:				dict					,    __lowerCAmelCase							:				dict   ):
							a__             =     BigBirdPegasusConfig(**__lowerCAmelCase   )
							a__             =     BigBirdPegasusForConditionalGeneration(__lowerCAmelCase   )
							a__             =     torch_model.state_dict()
							a__             =     {}

							# separating decoder weights
							a__             =     {k: tf_weights[k] for k in tf_weights if k.startswith('pegasus/decoder'   )}
							a__             =     {k: tf_weights[k] for k in tf_weights if not k.startswith('pegasus/decoder'   )}

							for k, v in tqdm(decoder_weights.items()					,    'tf -> hf conversion'   ):
														a__             =     [k.endswith(__lowerCAmelCase   ) for ending in KEYS_TO_IGNORE]
														if any(__lowerCAmelCase   ):
																					continue
														a__             =     DECODER_PATTERNS
														a__             =     rename_state_dict_key(__lowerCAmelCase					,    __lowerCAmelCase   )
														if new_k not in state_dict:
																					raise ValueError(F'could not find new key {new_k} in state dict. (converted from {k})'   )
														if any(True if i in k else False for i in ['dense', 'query', 'key', 'value']   ):
																					a__             =     v.T
														a__             =     torch.from_numpy(__lowerCAmelCase   )
														assert v.shape == state_dict[new_k].shape, F'{new_k}, {k}, {v.shape}, {state_dict[new_k].shape}'

							for k, v in tqdm(remaining_weights.items()					,    'tf -> hf conversion'   ):
														a__             =     [k.endswith(__lowerCAmelCase   ) for ending in KEYS_TO_IGNORE]
														if any(__lowerCAmelCase   ):
																					continue
														a__             =     REMAINING_PATTERNS
														a__             =     rename_state_dict_key(__lowerCAmelCase					,    __lowerCAmelCase   )
														if new_k not in state_dict and k != "pegasus/embeddings/position_embeddings":
																					raise ValueError(F'could not find new key {new_k} in state dict. (converted from {k})'   )
														if any(True if i in k else False for i in ['dense', 'query', 'key', 'value']   ):
																					a__             =     v.T
														a__             =     torch.from_numpy(__lowerCAmelCase   )
														if k != "pegasus/embeddings/position_embeddings":
																					assert v.shape == state_dict[new_k].shape, F'{new_k}, {k}, {v.shape}, {state_dict[new_k].shape}'

							a__             =     mapping['model.embed_positions.weight']
							a__             =     mapping.pop('model.embed_positions.weight'   )
							a__      ,					a__             =     torch_model.load_state_dict(__lowerCAmelCase					,    strict=__lowerCAmelCase   )
							a__             =     [
							    k
							    for k in missing
							    if k
							    not in [
							        'final_logits_bias',
							        'model.encoder.embed_tokens.weight',
							        'model.decoder.embed_tokens.weight',
							        'lm_head.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 							__lowercase   ( __lowerCAmelCase							:				Optional[int]   ):
							a__             =     tf.train.list_variables(__lowerCAmelCase   )
							a__             =     {}
							a__             =     ['global_step']
							for name, shape in tqdm(__lowerCAmelCase					,    desc='converting tf checkpoint to dict'   ):
														a__             =     any(pat in name for pat in ignore_name   )
														if skip_key:
																					continue
														a__             =     tf.train.load_variable(__lowerCAmelCase					,    __lowerCAmelCase   )
														a__             =     array
							return tf_weights






def 							__lowercase   ( __lowerCAmelCase							:				str					,    __lowerCAmelCase							:				str					,    __lowerCAmelCase							:				dict   ):
							a__             =     get_tf_weights_as_numpy(__lowerCAmelCase   )
							a__             =     convert_bigbird_pegasus(__lowerCAmelCase					,    __lowerCAmelCase   )
							torch_model.save_pretrained(__lowerCAmelCase   )


if __name__ == "__main__":
						snake_case							:       Dict     			=      argparse.ArgumentParser()
						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.''')
						snake_case							:       Dict     			=      parser.parse_args()
						snake_case							:       int     			=      {}
						convert_bigbird_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir, config_update=config_update)








import json
import os
import subprocess
import unittest
from ast import literal_eval

import pytest
from parameterized import parameterized_class

from . import is_sagemaker_available


if is_sagemaker_available():
						from sagemaker import Session, TrainingJobAnalytics
						from sagemaker.huggingface import HuggingFace







@pytest.mark.skipif(
    literal_eval(os.getenv('''TEST_SAGEMAKER'''	,						'''False'''      )      ) is not True	,						reason='''Skipping test because should only be run when releasing minor transformers version'''	,						)
@pytest.mark.usefixtures('''sm_env'''      )
@parameterized_class(
    [
        {
            '''framework''': '''pytorch''',
            '''script''': '''run_glue.py''',
            '''model_name_or_path''': '''distilbert-base-cased''',
            '''instance_type''': '''ml.g4dn.xlarge''',
            '''results''': {'''train_runtime''': 6_5_0, '''eval_accuracy''': 0.6, '''eval_loss''': 0.9},
        },
        {
            '''framework''': '''tensorflow''',
            '''script''': '''run_tf.py''',
            '''model_name_or_path''': '''distilbert-base-cased''',
            '''instance_type''': '''ml.g4dn.xlarge''',
            '''results''': {'''train_runtime''': 6_0_0, '''eval_accuracy''': 0.3, '''eval_loss''': 0.9},
        },
    ]      )
class        snake_case_   (unittest.TestCase      ):
					def  lowerCamelCase__(      self   :Dict      )					->    Union[str, Any]:
												if self.framework == "pytorch":
																			subprocess.run(
																			    F'cp ./examples/pytorch/text-classification/run_glue.py {self.env.test_path}/run_glue.py'.split()			,encoding='utf-8'			,check=__snake_case			,)
												assert hasattr(self			,'env'      )
					def  lowerCamelCase__(      self   :List[Any]			,__snake_case   :Optional[Any]=1      )					->    int:
												# creates estimator
												return HuggingFace(
												    entry_point=self.script			,source_dir=self.env.test_path			,role=self.env.role			,image_uri=self.env.image_uri			,base_job_name=F'{self.env.base_job_name}-single'			,instance_count=__snake_case			,instance_type=self.instance_type			,debugger_hook_config=__snake_case			,hyperparameters={**self.env.hyperparameters, 'model_name_or_path': self.model_name_or_path}			,metric_definitions=self.env.metric_definitions			,py_version='py36'			,)
					def  lowerCamelCase__(      self   :Dict			,__snake_case   :str      )					->    Optional[int]:
												TrainingJobAnalytics(__snake_case      ).export_csv(F'{self.env.test_path}/{job_name}_metrics.csv'      )



					def  lowerCamelCase__(      self   :Tuple      )					->    Union[str, Any]:
												# create estimator
												a__             =     self.create_estimator()

												# run training
												estimator.fit()

												# result dataframe
												a__             =     TrainingJobAnalytics(estimator.latest_training_job.name      ).dataframe()

												# extract kpis
												a__             =     list(result_metrics_df[result_metrics_df.metric_name == 'eval_accuracy']['value']      )
												a__             =     list(result_metrics_df[result_metrics_df.metric_name == 'eval_loss']['value']      )
												# get train time from SageMaker job, this includes starting, preprocessing, stopping
												a__             =     (
												    Session().describe_training_job(estimator.latest_training_job.name      ).get('TrainingTimeInSeconds'			,99_99_99      )
												)

												# assert kpis
												assert train_runtime <= self.results["train_runtime"]
												assert all(t >= self.results['eval_accuracy'] for t in eval_accuracy      )
												assert all(t <= self.results['eval_loss'] for t in eval_loss      )

												# dump tests result into json file to share in PR
												with open(F'{estimator.latest_training_job.name}.json'			,'w'      ) as outfile:
																			json.dump({'train_time': train_runtime, 'eval_accuracy': eval_accuracy, 'eval_loss': eval_loss}			,__snake_case      )




