

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 						_snake_case(   SCREAMING_SNAKE_CASE__      :     Optional[int]   ,      SCREAMING_SNAKE_CASE__      :     List[str]   ,      SCREAMING_SNAKE_CASE__      :     int=None				)							->				Union[str, Any]:





    '''simple docstring'''






    assert torch_layer.weight.shape == weight.shape, f'{torch_layer} layer.weight does not match'
    A__   				=       nn.Parameter(SCREAMING_SNAKE_CASE__				)
    if bias is not None:
        assert torch_layer.bias.shape == bias.shape, f'{torch_layer} layer.bias does not match'
        A__   				=       nn.Parameter(SCREAMING_SNAKE_CASE__				)
def 						_snake_case(   SCREAMING_SNAKE_CASE__      :     Union[str, Any]   ,      SCREAMING_SNAKE_CASE__      :     Dict   ,      SCREAMING_SNAKE_CASE__      :     Optional[Any]				)							->				Any:





    '''simple docstring'''






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

    set_param(
        torch_layer.self_attention.query_key   ,      torch.tensor(SCREAMING_SNAKE_CASE__				).transpose(1   ,      2				).contiguous().view(-1   ,      SCREAMING_SNAKE_CASE__				)   ,      )
    set_param(
        torch_layer.self_attention.value   ,      torch.tensor(SCREAMING_SNAKE_CASE__				).transpose(1   ,      2				).contiguous().view(-1   ,      SCREAMING_SNAKE_CASE__				)   ,      )
    set_param(
        torch_layer.output.dense   ,      torch.tensor(SCREAMING_SNAKE_CASE__				).view(-1   ,      SCREAMING_SNAKE_CASE__				).contiguous().transpose(0   ,      1				)   ,      )
def 						_snake_case(   SCREAMING_SNAKE_CASE__      :     Union[str, Any]   ,      SCREAMING_SNAKE_CASE__      :     Optional[int]   ,      SCREAMING_SNAKE_CASE__      :     Dict				)							->				Optional[Any]:





    '''simple docstring'''






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

    set_param(
        torch_layer.self_attention.query   ,      torch.tensor(SCREAMING_SNAKE_CASE__				).transpose(1   ,      2				).contiguous().view(-1   ,      SCREAMING_SNAKE_CASE__				)   ,      )
    set_param(
        torch_layer.self_attention.key   ,      torch.tensor(SCREAMING_SNAKE_CASE__				).transpose(1   ,      2				).contiguous().view(-1   ,      SCREAMING_SNAKE_CASE__				)   ,      )
    set_param(
        torch_layer.self_attention.value   ,      torch.tensor(SCREAMING_SNAKE_CASE__				).transpose(1   ,      2				).contiguous().view(-1   ,      SCREAMING_SNAKE_CASE__				)   ,      )
    set_param(
        torch_layer.output.dense   ,      torch.tensor(SCREAMING_SNAKE_CASE__				).view(-1   ,      SCREAMING_SNAKE_CASE__				).contiguous().transpose(0   ,      1				)   ,      )
def 						_snake_case(   SCREAMING_SNAKE_CASE__      :     Union[str, Any]   ,      SCREAMING_SNAKE_CASE__      :     int   ,      SCREAMING_SNAKE_CASE__      :     int				)							->				Optional[Any]:





    '''simple docstring'''






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

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

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

    # Chunked Feed Forward
    if len(SCREAMING_SNAKE_CASE__				) == 4:
        A__   				=       intermediate_weights[2]

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

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

    # intermediate out
    A__   				=       np.asarray(intermediate_weights[4][0]				)
    A__   				=       np.asarray(intermediate_weights[4][1]				)
    set_param(
        torch_block.feed_forward.output.dense   ,      torch.tensor(SCREAMING_SNAKE_CASE__				).transpose(0   ,      1				).contiguous()   ,      torch.tensor(SCREAMING_SNAKE_CASE__				)   ,      )
def 						_snake_case(   SCREAMING_SNAKE_CASE__      :     Optional[Any]   ,      SCREAMING_SNAKE_CASE__      :     List[str]   ,      SCREAMING_SNAKE_CASE__      :     int				)							->				Optional[Any]:





    '''simple docstring'''






    A__   				=       torch_model.reformer

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

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

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

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

    # output embeddings
    A__   				=       np.asarray(weights[9][0]				)
    A__   				=       np.asarray(weights[9][1]				)
    set_param(
        torch_model.lm_head.decoder   ,      torch.tensor(SCREAMING_SNAKE_CASE__				).transpose(0   ,      1				).contiguous()   ,      torch.tensor(SCREAMING_SNAKE_CASE__				)   ,      )
def 						_snake_case(   SCREAMING_SNAKE_CASE__      :     int   ,      SCREAMING_SNAKE_CASE__      :     int   ,      SCREAMING_SNAKE_CASE__      :     Any				)							->				Any:





    '''simple docstring'''






    A__   				=       ReformerConfig.from_json_file(SCREAMING_SNAKE_CASE__				)
    print(f'Building PyTorch model from configuration: {config}'				)
    A__   				=       ReformerModelWithLMHead(SCREAMING_SNAKE_CASE__				)

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

    set_model_weights_in_torch(SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__   ,      config.hidden_size				)

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


if __name__ == "__main__":
     lowercase_			=  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."
     )
     lowercase_			=  parser.parse_args()
     convert_trax_checkpoint_to_pytorch(args.trax_model_pkl_path, args.config_file, args.pytorch_dump_path)




# XXX: we want transformers master here - in the absense of conftest manipulating sys.path:
# hack it in for now:
import sys
from pathlib import Path


lowercase_			=  Path(__file__).resolve().parents[3] / "src"
sys.path.insert(1, str(git_repo_path))

import dataclasses  # noqa
import io  # noqa
import itertools  # noqa
import json  # noqa
import os  # noqa
import unittest  # noqa
from copy import deepcopy  # noqa

from parameterized import parameterized  # noqa
from transformers import TrainingArguments, is_torch_available  # noqa
from transformers.deepspeed import is_deepspeed_available  # noqa
from transformers.file_utils import WEIGHTS_NAME  # noqa
from transformers.testing_utils import (  # noqa
    CaptureLogger,
    ExtendSysPath,
    TestCasePlus,
    execute_subprocess_async,
    get_gpu_count,
    mockenv_context,
    require_deepspeed,
    require_torch_gpu,
    require_torch_multi_gpu,
    slow,
)
from transformers.trainer_utils import set_seed  # noqa


set_seed(42)

lowercase_			=  {"base": "patrickvonplaten/wav2vec2_tiny_random", "robust": "patrickvonplaten/wav2vec2_tiny_random_robust"}

lowercase_			=  "zero2"
lowercase_			=  "zero3"
lowercase_			=  [ZEROa, ZEROa]
def 						_snake_case(   SCREAMING_SNAKE_CASE__      :     Optional[int]   ,      SCREAMING_SNAKE_CASE__      :     Tuple   ,      SCREAMING_SNAKE_CASE__      :     List[Any]				)							->				Dict:





    '''simple docstring'''






    A__   				=       parameterized.to_safe_name('_'.join(str(SCREAMING_SNAKE_CASE__				) for x in param.args				)				)
    return f'{func.__name__}_{param_based_name}'


# Cartesian-product of zero stages with models to test
lowercase_			=  list(itertools.product(stages, models.keys()))


@slow
@require_deepspeed
@require_torch_gpu
class 			A						(							_UpperCAmelCase			):







    """simple docstring"""





    @parameterized.expand(lowercase_,name_func=lowercase_		)
    def 						snake_case__					(				self			:	int,lowercase_			:	str,lowercase_			:	Any		)->   Optional[int]:





        '''simple docstring'''







        self.run_and_check(
            stage=lowercase_,model=lowercase_,distributed=lowercase_,fpaa=lowercase_,)


    @require_torch_multi_gpu
    @parameterized.expand(lowercase_,name_func=lowercase_		)
    def 						snake_case__					(				self			:	Union[str, Any],lowercase_			:	Optional[Any],lowercase_			:	List[Any]		)->   int:





        '''simple docstring'''







        self.run_and_check(
            stage=lowercase_,model=lowercase_,distributed=lowercase_,fpaa=lowercase_,)


    @parameterized.expand(lowercase_,name_func=lowercase_		)
    def 						snake_case__					(				self			:	List[str],lowercase_			:	List[str],lowercase_			:	List[Any]		)->   Any:





        '''simple docstring'''







        self.run_and_check(
            stage=lowercase_,model=lowercase_,distributed=lowercase_,fpaa=lowercase_,)


    @require_torch_multi_gpu
    @parameterized.expand(lowercase_,name_func=lowercase_		)
    def 						snake_case__					(				self			:	Dict,lowercase_			:	Optional[Any],lowercase_			:	List[Any]		)->   Optional[int]:





        '''simple docstring'''







        self.run_and_check(
            stage=lowercase_,model=lowercase_,distributed=lowercase_,fpaa=lowercase_,)


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





        '''simple docstring'''







        pass


    def 						snake_case__					(				self			:	int,lowercase_			:	str,lowercase_			:	str,lowercase_			:	int = 1_0,lowercase_			:	bool = True,lowercase_			:	bool = True,lowercase_			:	bool = True,)->   Union[str, Any]:





        '''simple docstring'''







        A__   				=       models[model]

        A__   				=       self.run_trainer(
            stage=lowercase_,model_name=lowercase_,eval_steps=lowercase_,num_train_epochs=1,distributed=lowercase_,fpaa=lowercase_,)

        self.do_checks(lowercase_		)

        return output_dir


    def 						snake_case__					(				self			:	Union[str, Any],lowercase_			:	str,lowercase_			:	str,lowercase_			:	int = 1_0,lowercase_			:	int = 1,lowercase_			:	bool = True,lowercase_			:	bool = True,)->   Any:





        '''simple docstring'''







        A__   				=       self.get_auto_remove_tmp_dir('./xxx',after=lowercase_		)
        A__   				=       F'\n            --model_name_or_path {model_name}\n            --dataset_name hf-internal-testing/librispeech_asr_dummy\n            --dataset_config_name clean\n            --train_split_name validation\n            --validation_split_name validation\n            --output_dir {output_dir}\n            --num_train_epochs {str(lowercase_		)}\n            --per_device_train_batch_size 2\n            --per_device_eval_batch_size 2\n            --evaluation_strategy steps\n            --learning_rate 5e-4\n            --warmup_steps 8\n            --orthography timit\n            --preprocessing_num_workers 1\n            --group_by_length\n            --freeze_feature_extractor\n            --report_to none\n            --save_steps 0\n            --eval_steps {eval_steps}\n            --report_to none\n        '.split()

        if fpaa:
            args.extend(['--fp16']		)

        # currently ds_config_wav2vec2_zero.json requires "zero_optimization.find_unused_parameters": true,
        # hence the separate config files
        A__   				=       F'--deepspeed {self.test_file_dir_str}/ds_config_wav2vec2_{stage}.json'.split()
        A__   				=       [F'{self.examples_dir_str}/research_projects/wav2vec2/run_asr.py']
        A__   				=       self.get_launcher(lowercase_		)

        A__   				=       launcher + script + args + ds_args
        # keep for quick debug
        # print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] +cmd)); die
        execute_subprocess_async(lowercase_,env=self.get_env()		)

        return output_dir


    def 						snake_case__					(				self			:	Any,lowercase_			:	int=False		)->   Tuple:





        '''simple docstring'''







        A__   				=       min(2,get_gpu_count()		) if distributed else 1
        return F'deepspeed --num_nodes 1 --num_gpus {num_gpus}'.split()

