


import ast
import os
import re
import shutil
import tempfile
import unittest
from unittest import mock

import torch

from accelerate.test_utils.examples import compare_against_test
from accelerate.test_utils.testing import TempDirTestCase, require_trackers, run_command, slow
from accelerate.utils import write_basic_config


# DataLoaders built from `test_samples/MRPC` for quick testing
# Should mock `{script_name}.get_dataloaders` via:
# @mock.patch("{script_name}.get_dataloaders", mocked_dataloaders)

A			:						str        =  [
    'cross_validation.py',
    'gradient_accumulation.py',
    'local_sgd.py',
    'multi_process_metrics.py',
    'memory.py',
    'automatic_gradient_accumulation.py',
    'fsdp_with_peak_mem_tracking.py',
    'deepspeed_with_config_support.py',
    'megatron_lm_gpt_pretraining.py',
]



class 				A       (			unittest.TestCase				):
					'''simple docstring'''






					def     lowerCamelCase__	(self       :		List[Any] ,   _UpperCAmelCase       :		str ,   _UpperCAmelCase       :		bool ,   _UpperCAmelCase       :		str = None ,   _UpperCAmelCase       :		list = None						)		->  List[Any]:




							"""simple docstring"""


							lowercase__	     =						None
							lowercase__	     =						os.path.abspath(os.path.join("""examples""" ,   """by_feature"""						)						)
							lowercase__	     =						os.path.abspath("""examples"""						)
							for item in os.listdir(_UpperCAmelCase						):
									if item not in EXCLUDE_EXAMPLES:
											lowercase__	     =						os.path.join(_UpperCAmelCase ,   _UpperCAmelCase						)
											if os.path.isfile(_UpperCAmelCase						) and ".py" in item_path:
													with self.subTest(
													    tested_script=_UpperCAmelCase ,   feature_script=_UpperCAmelCase ,   tested_section="""main()""" if parser_only else """training_function()""" ,   ):
															lowercase__	     =						compare_against_test(
															    os.path.join(_UpperCAmelCase ,   _UpperCAmelCase						) ,   _UpperCAmelCase ,   _UpperCAmelCase ,   _UpperCAmelCase						)
															lowercase__	     =						"""\n""".join(_UpperCAmelCase						)
															if special_strings is not None:
																	for string in special_strings:
																			lowercase__	     =						diff.replace(_UpperCAmelCase ,   """"""						)
															self.assertEqual(_UpperCAmelCase ,   """"""						)

					def     lowerCamelCase__	(self       :		Optional[int]						)		->  int:




							"""simple docstring"""


							self.one_complete_example("""complete_nlp_example.py""" ,   _UpperCAmelCase						)
							self.one_complete_example("""complete_nlp_example.py""" ,   _UpperCAmelCase						)


					def     lowerCamelCase__	(self       :		Tuple						)		->  Dict:




							"""simple docstring"""


							lowercase__	     =						os.path.abspath(os.path.join("""examples""" ,   """cv_example.py"""						)						)
							lowercase__	     =						[
							    """ """ * 16 + """{\n\n""",
							    """ """ * 20 + """\"accuracy\": eval_metric[\"accuracy\"],\n\n""",
							    """ """ * 20 + """\"f1\": eval_metric[\"f1\"],\n\n""",
							    """ """ * 20 + """\"train_loss\": total_loss.item() / len(train_dataloader),\n\n""",
							    """ """ * 20 + """\"epoch\": epoch,\n\n""",
							    """ """ * 16 + """},\n\n""",
							    """ """ * 16 + """step=epoch,\n""",
							    """ """ * 12,
							    """ """ * 8 + """for step, batch in enumerate(active_dataloader):\n""",
							]
							self.one_complete_example("""complete_cv_example.py""" ,   _UpperCAmelCase ,   _UpperCAmelCase ,   _UpperCAmelCase						)
							self.one_complete_example("""complete_cv_example.py""" ,   _UpperCAmelCase ,   _UpperCAmelCase ,   _UpperCAmelCase						)






@mock.patch.dict(os.environ					,    {'''TESTING_MOCKED_DATALOADERS''': '''1'''}				)
class 				A       (			UpperCAmelCase__				):
					'''simple docstring'''






					A__     =					False

					@classmethod
					def     lowerCamelCase__	(cls       :		Tuple						)		->  int:




							"""simple docstring"""


							super().setUpClass()
							lowercase__	     =						tempfile.mkdtemp()
							lowercase__	     =						os.path.join(cls._tmpdir ,   """default_config.yml"""						)

							write_basic_config(save_location=cls.configPath						)
							lowercase__	     =						["""accelerate""", """launch""", """--config_file""", cls.configPath]

					@classmethod
					def     lowerCamelCase__	(cls       :		List[Any]						)		->  int:




							"""simple docstring"""


							super().tearDownClass()
							shutil.rmtree(cls._tmpdir						)

					def     lowerCamelCase__	(self       :		str						)		->  Optional[int]:




							"""simple docstring"""


							lowercase__	     =						f'''
        examples/by_feature/checkpointing.py
        --checkpointing_steps epoch
        --output_dir {self.tmpdir}
        '''.split()
							run_command(self._launch_args + testargs						)
							self.assertTrue(os.path.exists(os.path.join(self.tmpdir ,   """epoch_0"""						)						)						)

					def     lowerCamelCase__	(self       :		List[Any]						)		->  Dict:




							"""simple docstring"""


							lowercase__	     =						f'''
        examples/by_feature/checkpointing.py
        --checkpointing_steps 1
        --output_dir {self.tmpdir}
        '''.split()
							lowercase__	     =						run_command(self._launch_args + testargs						)
							self.assertTrue(os.path.exists(os.path.join(self.tmpdir ,   """step_2"""						)						)						)

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




							"""simple docstring"""


							lowercase__	     =						f'''
        examples/by_feature/checkpointing.py
        --resume_from_checkpoint {os.path.join(self.tmpdir ,   "epoch_0"						)}
        '''.split()
							lowercase__	     =						run_command(self._launch_args + testargs ,   return_stdout=_UpperCAmelCase						)
							self.assertNotIn("""epoch 0:""" ,   _UpperCAmelCase						)
							self.assertIn("""epoch 1:""" ,   _UpperCAmelCase						)

					def     lowerCamelCase__	(self       :		List[Any]						)		->  Tuple:




							"""simple docstring"""


							lowercase__	     =						f'''
        examples/by_feature/checkpointing.py
        --resume_from_checkpoint {os.path.join(self.tmpdir ,   "step_2"						)}
        '''.split()
							lowercase__	     =						run_command(self._launch_args + testargs ,   return_stdout=_UpperCAmelCase						)
							if torch.cuda.is_available():
									lowercase__	     =						torch.cuda.device_count()
							else:
									lowercase__	     =						1
							if num_processes > 1:
									self.assertNotIn("""epoch 0:""" ,   _UpperCAmelCase						)
									self.assertIn("""epoch 1:""" ,   _UpperCAmelCase						)
							else:
									self.assertIn("""epoch 0:""" ,   _UpperCAmelCase						)
									self.assertIn("""epoch 1:""" ,   _UpperCAmelCase						)

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




							"""simple docstring"""


							lowercase__	     =						"""
        examples/by_feature/cross_validation.py
        --num_folds 2
        """.split()
							with mock.patch.dict(os.environ ,   {"""TESTING_MOCKED_DATALOADERS""": """0"""}						):
									lowercase__	     =						run_command(self._launch_args + testargs ,   return_stdout=_UpperCAmelCase						)
									lowercase__	     =						re.findall("""({.+})""" ,   _UpperCAmelCase						)
									lowercase__	     =						[r for r in results if """accuracy""" in r][-1]
									lowercase__	     =						ast.literal_eval(_UpperCAmelCase						)
									self.assertGreaterEqual(results["""accuracy"""] ,   0.75						)

					def     lowerCamelCase__	(self       :		List[Any]						)		->  int:




							"""simple docstring"""


							lowercase__	     =						["""examples/by_feature/multi_process_metrics.py"""]
							run_command(self._launch_args + testargs						)

					@require_trackers
					@mock.patch.dict(os.environ ,   {"""WANDB_MODE""": """offline"""}						)
					def     lowerCamelCase__	(self       :		List[str]						)		->  str:




							"""simple docstring"""


							with tempfile.TemporaryDirectory() as tmpdir:
									lowercase__	     =						f'''
            examples/by_feature/tracking.py
            --with_tracking
            --project_dir {tmpdir}
            '''.split()
									run_command(self._launch_args + testargs						)
									self.assertTrue(os.path.exists(os.path.join(_UpperCAmelCase ,   """tracking"""						)						)						)

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




							"""simple docstring"""


							lowercase__	     =						["""examples/by_feature/gradient_accumulation.py"""]
							run_command(self._launch_args + testargs						)


					def     lowerCamelCase__	(self       :		List[Any]						)		->  Optional[Any]:




							"""simple docstring"""


							lowercase__	     =						["""examples/by_feature/local_sgd.py"""]
							run_command(self._launch_args + testargs						)



"""simple docstring"""
import argparse
import pickle

import numpy as np
import torch
from torch import nn

from transformers import ReformerConfig, ReformerModelWithLMHead
from transformers.utils import logging


logging.set_verbosity_info()

def  lowercase     (_lowerCAmelCase ,    _lowerCAmelCase ,    _lowerCAmelCase=None						):
						# set parameter of one layer
						assert torch_layer.weight.shape == weight.shape, f"""{torch_layer} layer.weight does not match"""
						__lowerCAmelCase     =  nn.Parameter(_lowerCAmelCase						)
						if bias is not None:
												assert torch_layer.bias.shape == bias.shape, f"""{torch_layer} layer.bias does not match"""
												__lowerCAmelCase     =  nn.Parameter(_lowerCAmelCase						)

def  lowercase     (_lowerCAmelCase ,    _lowerCAmelCase ,    _lowerCAmelCase						):
						# set torch weights for 1-to-1 comparison
						__lowerCAmelCase     =  np.asarray(weights[0]						)
						__lowerCAmelCase     =  np.asarray(weights[1]						)
						__lowerCAmelCase     =  np.asarray(weights[2]						)

						set_param(
						    torch_layer.self_attention.query_key ,    torch.tensor(_lowerCAmelCase						).transpose(1 ,    2						).contiguous().view(-1 ,    _lowerCAmelCase						) ,    )
						set_param(
						    torch_layer.self_attention.value ,    torch.tensor(_lowerCAmelCase						).transpose(1 ,    2						).contiguous().view(-1 ,    _lowerCAmelCase						) ,    )
						set_param(
						    torch_layer.output.dense ,    torch.tensor(_lowerCAmelCase						).view(-1 ,    _lowerCAmelCase						).contiguous().transpose(0 ,    1						) ,    )

def  lowercase     (_lowerCAmelCase ,    _lowerCAmelCase ,    _lowerCAmelCase						):
						# set torch weights for 1-to-1 comparison
						__lowerCAmelCase     =  np.asarray(weights[0]						)
						__lowerCAmelCase     =  np.asarray(weights[1]						)
						__lowerCAmelCase     =  np.asarray(weights[2]						)
						__lowerCAmelCase     =  np.asarray(weights[3]						)

						set_param(
						    torch_layer.self_attention.query ,    torch.tensor(_lowerCAmelCase						).transpose(1 ,    2						).contiguous().view(-1 ,    _lowerCAmelCase						) ,    )
						set_param(
						    torch_layer.self_attention.key ,    torch.tensor(_lowerCAmelCase						).transpose(1 ,    2						).contiguous().view(-1 ,    _lowerCAmelCase						) ,    )
						set_param(
						    torch_layer.self_attention.value ,    torch.tensor(_lowerCAmelCase						).transpose(1 ,    2						).contiguous().view(-1 ,    _lowerCAmelCase						) ,    )
						set_param(
						    torch_layer.output.dense ,    torch.tensor(_lowerCAmelCase						).view(-1 ,    _lowerCAmelCase						).contiguous().transpose(0 ,    1						) ,    )

def  lowercase     (_lowerCAmelCase ,    _lowerCAmelCase ,    _lowerCAmelCase						):
						# layernorm 1
						__lowerCAmelCase     =  weights[0][0][0]
						__lowerCAmelCase     =  np.asarray(layer_norm_a[0]						)
						__lowerCAmelCase     =  np.asarray(layer_norm_a[1]						)
						set_param(
						    torch_block.attention.layer_norm ,    torch.tensor(_lowerCAmelCase						) ,    torch.tensor(_lowerCAmelCase						) ,    )

						# lsh weights + output
						__lowerCAmelCase     =  weights[0][1]
						if len(_lowerCAmelCase						) < 4:
												set_layer_weights_in_torch_lsh(_lowerCAmelCase ,    torch_block.attention ,    _lowerCAmelCase						)
						else:
												set_layer_weights_in_torch_local(_lowerCAmelCase ,    torch_block.attention ,    _lowerCAmelCase						)

						# intermediate weighs
						__lowerCAmelCase     =  weights[2][0][1][2]

						# Chunked Feed Forward
						if len(_lowerCAmelCase						) == 4:
												__lowerCAmelCase     =  intermediate_weights[2]

						# layernorm 2
						__lowerCAmelCase     =  np.asarray(intermediate_weights[0][0]						)
						__lowerCAmelCase     =  np.asarray(intermediate_weights[0][1]						)
						set_param(
						    torch_block.feed_forward.layer_norm ,    torch.tensor(_lowerCAmelCase						) ,    torch.tensor(_lowerCAmelCase						) ,    )

						# intermediate dense
						__lowerCAmelCase     =  np.asarray(intermediate_weights[1][0]						)
						__lowerCAmelCase     =  np.asarray(intermediate_weights[1][1]						)
						set_param(
						    torch_block.feed_forward.dense.dense ,    torch.tensor(_lowerCAmelCase						).transpose(0 ,    1						).contiguous() ,    torch.tensor(_lowerCAmelCase						) ,    )

						# intermediate out
						__lowerCAmelCase     =  np.asarray(intermediate_weights[4][0]						)
						__lowerCAmelCase     =  np.asarray(intermediate_weights[4][1]						)
						set_param(
						    torch_block.feed_forward.output.dense ,    torch.tensor(_lowerCAmelCase						).transpose(0 ,    1						).contiguous() ,    torch.tensor(_lowerCAmelCase						) ,    )

def  lowercase     (_lowerCAmelCase ,    _lowerCAmelCase ,    _lowerCAmelCase						):
						# reformer model
						__lowerCAmelCase     =  torch_model.reformer

						# word embeds
						__lowerCAmelCase     =  np.asarray(weights[1]						)
						set_param(
						    torch_model_reformer.embeddings.word_embeddings ,    torch.tensor(_lowerCAmelCase						) ,    )

						if isinstance(weights[3] ,    _lowerCAmelCase						):
												__lowerCAmelCase     =  torch_model_reformer.embeddings.position_embeddings
												for emb_idx in range(len(position_embeddings.weights						)						):
																		__lowerCAmelCase     =  np.asarray(weights[3][emb_idx][0]						)
																		assert (
																		    position_embeddings.weights[emb_idx].shape == emb_weights.shape
																		), f"""{position_embeddings[emb_idx]} emb does not match"""
																		__lowerCAmelCase     =  nn.Parameter(torch.tensor(_lowerCAmelCase						)						)

						__lowerCAmelCase     =  weights[5]
						assert len(torch_model_reformer.encoder.layers						) * 4 == len(
						    _lowerCAmelCase						), "HF and trax model do not have the same number of layers"
						for layer_idx, layer in enumerate(torch_model_reformer.encoder.layers						):
												__lowerCAmelCase     =  trax_layer_weights[4 * layer_idx : 4 * (layer_idx + 1)]
												set_block_weights_in_torch(_lowerCAmelCase ,    _lowerCAmelCase ,    _lowerCAmelCase						)

						# output layer norm
						__lowerCAmelCase     =  np.asarray(weights[7][0]						)
						__lowerCAmelCase     =  np.asarray(weights[7][1]						)
						set_param(
						    torch_model_reformer.encoder.layer_norm ,    torch.tensor(_lowerCAmelCase						) ,    torch.tensor(_lowerCAmelCase						) ,    )

						# output embeddings
						__lowerCAmelCase     =  np.asarray(weights[9][0]						)
						__lowerCAmelCase     =  np.asarray(weights[9][1]						)
						set_param(
						    torch_model.lm_head.decoder ,    torch.tensor(_lowerCAmelCase						).transpose(0 ,    1						).contiguous() ,    torch.tensor(_lowerCAmelCase						) ,    )

def  lowercase     (_lowerCAmelCase ,    _lowerCAmelCase ,    _lowerCAmelCase						):
						# Initialise PyTorch model
						__lowerCAmelCase     =  ReformerConfig.from_json_file(_lowerCAmelCase						)
						print(f"""Building PyTorch model from configuration: {config}"""						)
						__lowerCAmelCase     =  ReformerModelWithLMHead(_lowerCAmelCase						)

						with open(_lowerCAmelCase ,    """rb"""						) as f:
												__lowerCAmelCase     =  pickle.load(_lowerCAmelCase						)["""weights"""]

						set_model_weights_in_torch(_lowerCAmelCase ,    _lowerCAmelCase ,    config.hidden_size						)

						# Save pytorch-model
						print(f"""Save PyTorch model to {pytorch_dump_path}"""						)
						torch.save(model.state_dict() ,    _lowerCAmelCase						)


if __name__ == "__main__":
			SCREAMING_SNAKE_CASE_					     =	argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    '''--trax_model_pkl_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
			)
			parser.add_argument(
			    '''--config_file''',
			    default=None,
			    type=str,
			    required=True,
			    help=(
			        '''The config json file corresponding to the pre-trained Reformer model. \n'''
			        '''This specifies the model architecture.'''
			    ),
			)
			parser.add_argument(
			    '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
			)
			SCREAMING_SNAKE_CASE_					     =	parser.parse_args()
			convert_trax_checkpoint_to_pytorch(args.trax_model_pkl_path, args.config_file, args.pytorch_dump_path)
