
"""simple docstring"""






import doctest
import glob
import importlib
import inspect
import os
import re
from contextlib import contextmanager
from functools import wraps
from unittest.mock import patch

import numpy as np
import pytest
from absl.testing import parameterized

import datasets
from datasets import load_metric

from .utils import for_all_test_methods, local, slow


# mark all tests as integration
__UpperCAmelCase       =  pytest.mark.integration


__UpperCAmelCase       =  {"comet"}
__UpperCAmelCase       =  importlib.util.find_spec('fairseq') is not None

__UpperCAmelCase       =  {"code_eval"}
__UpperCAmelCase       =  os.name == "nt"

__UpperCAmelCase       =  {"bertscore", "frugalscore", "perplexity"}
__UpperCAmelCase       =  importlib.util.find_spec('transformers') is not None






def 						_snake_case       (	lowercase__		:  Dict		)  ->				List[Any]:


			'''simple docstring'''





			@wraps(__A		)
			def wrapper(self		:  Optional[int]      ,     lowercase__		:  Tuple		):
						if not _has_fairseq and metric_name in REQUIRE_FAIRSEQ:
									self.skipTest("""\"test requires Fairseq\""""		)
						else:
									test_case(self      ,     __A		)

			return wrapper






def 						_snake_case       (	lowercase__		:  Tuple		)  ->				List[str]:


			'''simple docstring'''





			@wraps(__A		)
			def wrapper(self		:  str      ,     lowercase__		:  int		):
						if not _has_transformers and metric_name in REQUIRE_TRANSFORMERS:
									self.skipTest("""\"test requires transformers\""""		)
						else:
									test_case(self      ,     __A		)

			return wrapper






def 						_snake_case       (	lowercase__		:  int		)  ->				List[Any]:


			'''simple docstring'''





			@wraps(__A		)
			def wrapper(self		:  List[Any]      ,     lowercase__		:  str		):
						if _on_windows and metric_name in UNSUPPORTED_ON_WINDOWS:
									self.skipTest("""\"test not supported on Windows\""""		)
						else:
									test_case(self      ,     __A		)

			return wrapper






def 						_snake_case       (	)  ->				int:


			'''simple docstring'''





			lowerCAmelCase_    :Union[str, Any]			     =  [metric_dir.split(os.sep		)[-2] for metric_dir in glob.glob("""./metrics/*/"""		)]
			return [{"testcase_name": x, "metric_name": x} for x in metrics if x != "gleu"]  # gleu is unfinished




@parameterized.named_parameters(get_local_metric_names()							)
@for_all_test_methods(
    A__	, A__	, A__							)
@local
class 			_SCREAMING_SNAKE_CASE					(			parameterized.TestCase							):
			UpperCAmelCase_  :List[Any]				 =						{}
			UpperCAmelCase_  :List[str]				 =						None






			@pytest.mark.filterwarnings("""ignore:metric_module_factory is deprecated:FutureWarning"""       )
			@pytest.mark.filterwarnings("""ignore:load_metric is deprecated:FutureWarning"""       )
			def  __lowerCAmelCase (					self       ,   __A       )			->     Union[str, Any]:
						lowerCAmelCase_    :Any			     =  """[...]"""
						lowerCAmelCase_    :int			     =  importlib.import_module(
						    datasets.load.metric_module_factory(os.path.join("""metrics"""       ,   _snake_case       )       ).module_path       )
						lowerCAmelCase_    :List[Any]			     =  datasets.load.import_main_class(metric_module.__name__       ,   dataset=_snake_case       )
						# check parameters
						lowerCAmelCase_    :Optional[int]			     =  inspect.signature(metric._compute       ).parameters
						self.assertTrue(all(p.kind != p.VAR_KEYWORD for p in parameters.values()       )       )  # no **kwargs
						# run doctest
						with self.patch_intensive_calls(_snake_case       ,   metric_module.__name__       ):
									with self.use_local_metrics():
												try:
															lowerCAmelCase_    :List[str]			     =  doctest.testmod(_snake_case       ,   verbose=_snake_case       ,   raise_on_error=_snake_case       )
												except doctest.UnexpectedException as e:
															raise e.exc_info[1]  # raise the exception that doctest caught
						self.assertEqual(results.failed       ,   0       )
						self.assertGreater(results.attempted       ,   1       )






			@slow
			def  __lowerCAmelCase (					self       ,   __A       )			->     Tuple:
						lowerCAmelCase_    :Dict			     =  """[...]"""
						lowerCAmelCase_    :List[str]			     =  importlib.import_module(
						    datasets.load.metric_module_factory(os.path.join("""metrics"""       ,   _snake_case       )       ).module_path       )
						# run doctest
						with self.use_local_metrics():
									lowerCAmelCase_    :List[str]			     =  doctest.testmod(_snake_case       ,   verbose=_snake_case       ,   raise_on_error=_snake_case       )
						self.assertEqual(results.failed       ,   0       )
						self.assertGreater(results.attempted       ,   1       )






			@contextmanager
			def  __lowerCAmelCase (					self       ,   __A       ,   __A       )			->     Any:


						if metric_name in self.INTENSIVE_CALLS_PATCHER:
									with self.INTENSIVE_CALLS_PATCHER[metric_name](_snake_case       ):
												yield
						else:
									yield






			@contextmanager
			def  __lowerCAmelCase (					self       )			->     Tuple:


						def load_local_metric(__A       ,   *__A       ,   **__A       ):
									return load_metric(os.path.join("""metrics"""       ,   _snake_case       )       ,   *_snake_case       ,   **_snake_case       )

						with patch("""datasets.load_metric"""       ) as mock_load_metric:
									lowerCAmelCase_    :List[str]			     =  load_local_metric
									yield






			@classmethod
			def  __lowerCAmelCase (					cls       ,   __A       )			->     Dict:


						def wrapper(__A       ):
									lowerCAmelCase_    :List[str]			     =  contextmanager(_snake_case       )
									lowerCAmelCase_    :Union[str, Any]			     =  patcher
									return patcher

						return wrapper






@LocalMetricTest.register_intensive_calls_patcher("""bleurt"""		)
def 						_snake_case       (	lowercase__		:  Any		)  ->				List[str]:


			'''simple docstring'''





			import tensorflow.compat.va as tf
			from bleurt.score import Predictor

			tf.flags.DEFINE_string("""sv"""      ,     """"""      ,     """"""		)  # handle pytest cli flags




			class 			_SCREAMING_SNAKE_CASE					(			A__							):






						def  __lowerCAmelCase (					self       ,   __A       )			->     Union[str, Any]:
									assert len(input_dict["""input_ids"""]       ) == 2
									return np.array([1.0_3, 1.0_4]       )

    # mock predict_fn which is supposed to do a forward pass with a bleurt model
			with patch("""bleurt.score._create_predictor"""		) as mock_create_predictor:
						lowerCAmelCase_    :str			     =  MockedPredictor()
						yield






@LocalMetricTest.register_intensive_calls_patcher("""bertscore"""		)
def 						_snake_case       (	lowercase__		:  int		)  ->				Tuple:


			'''simple docstring'''





			import torch

			def bert_cos_score_idf(lowercase__		:  str      ,     lowercase__		:  Optional[Any]      ,     *lowercase__		:  List[Any]      ,     **lowercase__		:  List[str]		):
						return torch.tensor([[1.0, 1.0, 1.0]] * len(__A		)		)

			# mock get_model which is supposed to do download a bert model
			# mock bert_cos_score_idf which is supposed to do a forward pass with a bert model
			with patch("""bert_score.scorer.get_model"""		), patch(
			    """bert_score.scorer.bert_cos_score_idf"""		) as mock_bert_cos_score_idf:
						lowerCAmelCase_    :Dict			     =  bert_cos_score_idf
						yield






@LocalMetricTest.register_intensive_calls_patcher("""comet"""		)
def 						_snake_case       (	lowercase__		:  List[str]		)  ->				Optional[Any]:


			'''simple docstring'''





			def load_from_checkpoint(lowercase__		:  str		):




						class 			_SCREAMING_SNAKE_CASE					:






									def  __lowerCAmelCase (					self       ,   __A       ,   *__A       ,   **__A       )			->     int:
												assert len(_snake_case       ) == 2
												lowerCAmelCase_    :Optional[int]			     =  [0.1_9, 0.9_2]
												return scores, sum(_snake_case       ) / len(_snake_case       )




						return Model()

			# mock load_from_checkpoint which is supposed to do download a bert model
			# mock load_from_checkpoint which is supposed to do download a bert model
			with patch("""comet.download_model"""		) as mock_download_model:
						lowerCAmelCase_    :List[Any]			     =  None
						with patch("""comet.load_from_checkpoint"""		) as mock_load_from_checkpoint:
									lowerCAmelCase_    :Optional[int]			     =  load_from_checkpoint
									yield






def 						_snake_case       (	)  ->				int:


			'''simple docstring'''





			lowerCAmelCase_    :str			     =  load_metric(os.path.join("""metrics"""      ,     """seqeval"""		)		)
			lowerCAmelCase_    :Any			     =  """ERROR"""
			lowerCAmelCase_    :List[Any]			     =  f"""Scheme should be one of [IOB1, IOB2, IOE1, IOE2, IOBES, BILOU], got {wrong_scheme}"""
			with pytest.raises(__A      ,     match=re.escape(__A		)		):
						metric.compute(predictions=[]      ,     references=[]      ,     scheme=__A		)












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    A						(__A   :    Optional[int]   ,				__A   :    int   ,				__A   :    str=None       )   ->      List[Any]:







     """simple docstring"""


     assert torch_layer.weight.shape == weight.shape, F"""{torch_layer} layer.weight does not match"""
     UpperCAmelCase_          =							nn.Parameter(__A       )
     if bias is not None:
          assert torch_layer.bias.shape == bias.shape, F"""{torch_layer} layer.bias does not match"""
          UpperCAmelCase_          =							nn.Parameter(__A       )


def    A						(__A   :    Tuple   ,				__A   :    Dict   ,				__A   :    str       )   ->      Tuple:







     """simple docstring"""


     UpperCAmelCase_          =							np.asarray(weights[0]       )
     UpperCAmelCase_          =							np.asarray(weights[1]       )
     UpperCAmelCase_          =							np.asarray(weights[2]       )

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


def    A						(__A   :    Optional[Any]   ,				__A   :    Any   ,				__A   :    List[Any]       )   ->      int:







     """simple docstring"""


     UpperCAmelCase_          =							np.asarray(weights[0]       )
     UpperCAmelCase_          =							np.asarray(weights[1]       )
     UpperCAmelCase_          =							np.asarray(weights[2]       )
     UpperCAmelCase_          =							np.asarray(weights[3]       )

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


def    A						(__A   :    int   ,				__A   :    Union[str, Any]   ,				__A   :    List[str]       )   ->      List[Any]:







     """simple docstring"""


     UpperCAmelCase_          =							weights[0][0][0]
     UpperCAmelCase_          =							np.asarray(layer_norm_a[0]       )
     UpperCAmelCase_          =							np.asarray(layer_norm_a[1]       )
     set_param(
         torch_block.attention.layer_norm   ,				torch.tensor(__A       )   ,				torch.tensor(__A       )   ,				)

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

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

     # Chunked Feed Forward
     if len(__A       ) == 4:
          UpperCAmelCase_          =							intermediate_weights[2]

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

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

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


def    A						(__A   :    Optional[int]   ,				__A   :    Tuple   ,				__A   :    Any       )   ->      Tuple:







     """simple docstring"""


     UpperCAmelCase_          =							torch_model.reformer

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

     if isinstance(weights[3]   ,				__A       ):
          UpperCAmelCase_          =							torch_model_reformer.embeddings.position_embeddings
          for emb_idx in range(len(position_embeddings.weights       )       ):
               UpperCAmelCase_          =							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"""
               UpperCAmelCase_          =							nn.Parameter(torch.tensor(__A       )       )

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

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

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


def    A						(__A   :    Tuple   ,				__A   :    int   ,				__A   :    str       )   ->      Union[str, Any]:







     """simple docstring"""


     UpperCAmelCase_          =							ReformerConfig.from_json_file(__A       )
     print(F"""Building PyTorch model from configuration: {config}"""       )
     UpperCAmelCase_          =							ReformerModelWithLMHead(__A       )

     with open(__A   ,				'''rb'''       ) as f:
          UpperCAmelCase_          =							pickle.load(__A       )['''weights''']

     set_model_weights_in_torch(__A   ,				__A   ,				config.hidden_size       )

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


if __name__ == "__main__":
    snake_case_ : Optional[Any]    		=       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."
    )
    snake_case_ : List[Any]    		=       parser.parse_args()
    convert_trax_checkpoint_to_pytorch(args.trax_model_pkl_path, args.config_file, args.pytorch_dump_path)






