'''simple docstring'''
import unittest

from transformers import BigBirdConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
  import jax

  from transformers.models.big_bird.modeling_flax_big_bird import (
      FlaxBigBirdForCausalLM,
      FlaxBigBirdForMaskedLM,
      FlaxBigBirdForMultipleChoice,
      FlaxBigBirdForPreTraining,
      FlaxBigBirdForQuestionAnswering,
      FlaxBigBirdForSequenceClassification,
      FlaxBigBirdForTokenClassification,
      FlaxBigBirdModel,
  )






class 			__lowercase		(						unittest.TestCase						):







     """simple docstring"""






     def __init__(		self     ,			_lowerCamelCase     ,			_lowerCamelCase=2     ,			_lowerCamelCase=5_6     ,			_lowerCamelCase=True     ,			_lowerCamelCase=True     ,			_lowerCamelCase=True     ,			_lowerCamelCase=True     ,			_lowerCamelCase=9_9     ,			_lowerCamelCase=3_2     ,			_lowerCamelCase=2     ,			_lowerCamelCase=2     ,			_lowerCamelCase=7     ,			_lowerCamelCase="gelu_new"     ,			_lowerCamelCase=0.1     ,			_lowerCamelCase=0.1     ,			_lowerCamelCase=5_1_2     ,			_lowerCamelCase=1_6     ,			_lowerCamelCase=2     ,			_lowerCamelCase=0.0_2     ,			_lowerCamelCase=4     ,			_lowerCamelCase="block_sparse"     ,			_lowerCamelCase=True     ,			_lowerCamelCase=False     ,			_lowerCamelCase=2     ,			_lowerCamelCase=3     ,			):
         __UpperCamelCase	:	List[str]		    =  parent
         __UpperCamelCase	:	Dict		    =  batch_size
         __UpperCamelCase	:	Union[str, Any]		    =  seq_length
         __UpperCamelCase	:	int		    =  is_training
         __UpperCamelCase	:	List[Any]		    =  use_attention_mask
         __UpperCamelCase	:	Any		    =  use_token_type_ids
         __UpperCamelCase	:	Union[str, Any]		    =  use_labels
         __UpperCamelCase	:	List[Any]		    =  vocab_size
         __UpperCamelCase	:	List[Any]		    =  hidden_size
         __UpperCamelCase	:	Any		    =  num_hidden_layers
         __UpperCamelCase	:	str		    =  num_attention_heads
         __UpperCamelCase	:	Union[str, Any]		    =  intermediate_size
         __UpperCamelCase	:	Union[str, Any]		    =  hidden_act
         __UpperCamelCase	:	Union[str, Any]		    =  hidden_dropout_prob
         __UpperCamelCase	:	Optional[Any]		    =  attention_probs_dropout_prob
         __UpperCamelCase	:	List[Any]		    =  max_position_embeddings
         __UpperCamelCase	:	Union[str, Any]		    =  type_vocab_size
         __UpperCamelCase	:	Tuple		    =  type_sequence_label_size
         __UpperCamelCase	:	str		    =  initializer_range
         __UpperCamelCase	:	Tuple		    =  num_choices

         __UpperCamelCase	:	List[Any]		    =  rescale_embeddings
         __UpperCamelCase	:	List[Any]		    =  attention_type
         __UpperCamelCase	:	List[Any]		    =  use_bias
         __UpperCamelCase	:	Union[str, Any]		    =  block_size
         __UpperCamelCase	:	Optional[int]		    =  num_random_blocks







     def lowerCAmelCase     (		self    ):
         __UpperCamelCase	:	str		    =  ids_tensor([self.batch_size, self.seq_length]     ,			self.vocab_size    )

         __UpperCamelCase	:	Optional[int]		    =  None
         if self.use_attention_mask:
             __UpperCamelCase	:	str		    =  random_attention_mask([self.batch_size, self.seq_length]    )

         __UpperCamelCase	:	Optional[Any]		    =  None
         if self.use_token_type_ids:
             __UpperCamelCase	:	Optional[Any]		    =  ids_tensor([self.batch_size, self.seq_length]     ,			self.type_vocab_size    )

         __UpperCamelCase	:	str		    =  BigBirdConfig(
             vocab_size=self.vocab_size     ,			hidden_size=self.hidden_size     ,			num_hidden_layers=self.num_hidden_layers     ,			num_attention_heads=self.num_attention_heads     ,			intermediate_size=self.intermediate_size     ,			hidden_act=self.hidden_act     ,			hidden_dropout_prob=self.hidden_dropout_prob     ,			attention_probs_dropout_prob=self.attention_probs_dropout_prob     ,			max_position_embeddings=self.max_position_embeddings     ,			type_vocab_size=self.type_vocab_size     ,			is_decoder=_lowerCamelCase     ,			initializer_range=self.initializer_range     ,			attention_type=self.attention_type     ,			block_size=self.block_size     ,			num_random_blocks=self.num_random_blocks     ,			use_bias=self.use_bias     ,			rescale_embeddings=self.rescale_embeddings     ,			)

         return config, input_ids, token_type_ids, attention_mask







     def lowerCAmelCase     (		self    ):
         __UpperCamelCase	:	Optional[Any]		    =  self.prepare_config_and_inputs()
         __UpperCamelCase    ,					__UpperCamelCase    ,					__UpperCamelCase    ,					__UpperCamelCase	:	Any		    =  config_and_inputs
         __UpperCamelCase	:	Optional[Any]		    =  {
             'input_ids': input_ids,
             'token_type_ids': token_type_ids,
             'attention_mask': attention_mask,
         }
         return config, inputs_dict






@require_flax
class 			__lowercase		(						_lowerCamelCase			,							unittest.TestCase						):







     """simple docstring"""






     SCREAMING_SNAKE_CASE__		   =						(
         (
             FlaxBigBirdForCausalLM,
             FlaxBigBirdModel,
             FlaxBigBirdForPreTraining,
             FlaxBigBirdForMaskedLM,
             FlaxBigBirdForMultipleChoice,
             FlaxBigBirdForQuestionAnswering,
             FlaxBigBirdForSequenceClassification,
             FlaxBigBirdForTokenClassification,
         )
         if is_flax_available()
         else ()
     )

     SCREAMING_SNAKE_CASE__		   =						False
     SCREAMING_SNAKE_CASE__		   =						False







     def lowerCAmelCase     (		self    ):
         __UpperCamelCase	:	int		    =  FlaxBigBirdModelTester(self    )







     @slow
     # copied from `test_modeling_flax_common` because it takes much longer than other models
     def lowerCAmelCase     (		self    ):
         super().test_from_pretrained_save_pretrained()







     @slow
     # copied from `test_modeling_flax_common` because it takes much longer than other models
     def lowerCAmelCase     (		self    ):
         super().test_from_pretrained_with_no_automatic_init()







     @slow
     # copied from `test_modeling_flax_common` because it takes much longer than other models
     def lowerCAmelCase     (		self    ):
         super().test_no_automatic_init()







     @slow
     # copied from `test_modeling_flax_common` because it takes much longer than other models
     def lowerCAmelCase     (		self    ):
         super().test_hidden_states_output()







     @slow
     def lowerCAmelCase     (		self    ):
         for model_class_name in self.all_model_classes:
             __UpperCamelCase	:	List[str]		    =  model_class_name.from_pretrained('google/bigbird-roberta-base'    )
             self.assertIsNotNone(_lowerCamelCase    )







     def lowerCAmelCase     (		self    ):
         if self.test_attn_probs:
             super().test_attention_outputs()







     @slow
     # copied from `test_modeling_flax_common` because it takes much longer than other models
     def lowerCAmelCase     (		self    ):
         __UpperCamelCase    ,					__UpperCamelCase	:	str		    =  self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
             with self.subTest(model_class.__name__    ):
                 __UpperCamelCase	:	Any		    =  self._prepare_for_class(_lowerCamelCase     ,			_lowerCamelCase    )
                 __UpperCamelCase	:	str		    =  model_class(_lowerCamelCase    )

                 @jax.jit
                 def model_jitted(_lowerCamelCase     ,			_lowerCamelCase=None     ,			**_lowerCamelCase    ):
                     return model(input_ids=_lowerCamelCase     ,			attention_mask=_lowerCamelCase     ,			**_lowerCamelCase    )

                 with self.subTest('JIT Enabled'    ):
                     __UpperCamelCase	:	Optional[int]		    =  model_jitted(**_lowerCamelCase    ).to_tuple()

                 with self.subTest('JIT Disabled'    ):
                     with jax.disable_jit():
                         __UpperCamelCase	:	Optional[Any]		    =  model_jitted(**_lowerCamelCase    ).to_tuple()

                 self.assertEqual(len(_lowerCamelCase    )     ,			len(_lowerCamelCase    )    )
                 for jitted_output, output in zip(_lowerCamelCase     ,			_lowerCamelCase    ):
                     self.assertEqual(jitted_output.shape     ,			output.shape    )







     def lowerCAmelCase     (		self     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase=1E-5     ,			_lowerCamelCase="outputs"     ,			_lowerCamelCase=None    ):
         # `bigbird_block_sparse_attention` in `FlaxBigBird` returns `attention_probs = None`, while in PyTorch version,
         # an effort was done to return `attention_probs` (yet to be verified).
         if name.startswith('outputs.attentions'    ):
             return
         else:
             super().check_pt_flax_outputs(_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase    )




'''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  _UpperCamelCase (     _a	:	int				,     _a	:	Tuple				,     _a	:	Dict=None			):




    """simple docstring"""



    assert torch_layer.weight.shape == weight.shape, f"""{torch_layer} layer.weight does not match"""
    __UpperCamelCase	:	List[Any]		    =  nn.Parameter(_a			)
    if bias is not None:
        assert torch_layer.bias.shape == bias.shape, f"""{torch_layer} layer.bias does not match"""
        __UpperCamelCase	:	Dict		    =  nn.Parameter(_a			)







def  _UpperCamelCase (     _a	:	Tuple				,     _a	:	str				,     _a	:	List[str]			):




    """simple docstring"""



    __UpperCamelCase	:	List[Any]		    =  np.asarray(weights[0]			)
    __UpperCamelCase	:	str		    =  np.asarray(weights[1]			)
    __UpperCamelCase	:	Optional[int]		    =  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  _UpperCamelCase (     _a	:	Union[str, Any]				,     _a	:	Any				,     _a	:	List[Any]			):




    """simple docstring"""



    __UpperCamelCase	:	Optional[int]		    =  np.asarray(weights[0]			)
    __UpperCamelCase	:	Tuple		    =  np.asarray(weights[1]			)
    __UpperCamelCase	:	Tuple		    =  np.asarray(weights[2]			)
    __UpperCamelCase	:	Optional[int]		    =  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  _UpperCamelCase (     _a	:	Dict				,     _a	:	str				,     _a	:	List[Any]			):




    """simple docstring"""



    __UpperCamelCase	:	Optional[Any]		    =  weights[0][0][0]
    __UpperCamelCase	:	Tuple		    =  np.asarray(layer_norm_a[0]			)
    __UpperCamelCase	:	List[str]		    =  np.asarray(layer_norm_a[1]			)
    set_param(
        torch_block.attention.layer_norm				,     torch.tensor(_a			)				,     torch.tensor(_a			)				,     )

    # lsh weights + output
    __UpperCamelCase	:	int		    =  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	:	int		    =  weights[2][0][1][2]

    # Chunked Feed Forward
    if len(_a			) == 4:
        __UpperCamelCase	:	Optional[Any]		    =  intermediate_weights[2]

    # layernorm 2
    __UpperCamelCase	:	int		    =  np.asarray(intermediate_weights[0][0]			)
    __UpperCamelCase	:	Tuple		    =  np.asarray(intermediate_weights[0][1]			)
    set_param(
        torch_block.feed_forward.layer_norm				,     torch.tensor(_a			)				,     torch.tensor(_a			)				,     )

    # intermediate dense
    __UpperCamelCase	:	Optional[Any]		    =  np.asarray(intermediate_weights[1][0]			)
    __UpperCamelCase	:	int		    =  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	:	Dict		    =  np.asarray(intermediate_weights[4][0]			)
    __UpperCamelCase	:	List[str]		    =  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  _UpperCamelCase (     _a	:	int				,     _a	:	str				,     _a	:	Union[str, Any]			):




    """simple docstring"""



    __UpperCamelCase	:	Union[str, Any]		    =  torch_model.reformer

    # word embeds
    __UpperCamelCase	:	Tuple		    =  np.asarray(weights[1]			)
    set_param(
        torch_model_reformer.embeddings.word_embeddings				,     torch.tensor(_a			)				,     )

    if isinstance(weights[3]				,     _a			):
        __UpperCamelCase	:	Dict		    =  torch_model_reformer.embeddings.position_embeddings
        for emb_idx in range(len(position_embeddings.weights			)			):
            __UpperCamelCase	:	str		    =  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	:	Tuple		    =  nn.Parameter(torch.tensor(_a			)			)

    __UpperCamelCase	:	int		    =  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	:	Any		    =  trax_layer_weights[4 * layer_idx : 4 * (layer_idx + 1)]
        set_block_weights_in_torch(_a				,     _a				,     _a			)

    # output layer norm
    __UpperCamelCase	:	Optional[Any]		    =  np.asarray(weights[7][0]			)
    __UpperCamelCase	:	Any		    =  np.asarray(weights[7][1]			)
    set_param(
        torch_model_reformer.encoder.layer_norm				,     torch.tensor(_a			)				,     torch.tensor(_a			)				,     )

    # output embeddings
    __UpperCamelCase	:	List[Any]		    =  np.asarray(weights[9][0]			)
    __UpperCamelCase	:	Union[str, Any]		    =  np.asarray(weights[9][1]			)
    set_param(
        torch_model.lm_head.decoder				,     torch.tensor(_a			).transpose(0				,     1			).contiguous()				,     torch.tensor(_a			)				,     )







def  _UpperCamelCase (     _a	:	Optional[int]				,     _a	:	List[Any]				,     _a	:	Optional[Any]			):




    """simple docstring"""



    __UpperCamelCase	:	str		    =  ReformerConfig.from_json_file(_a			)
    print(f"""Building PyTorch model from configuration: {config}"""			)
    __UpperCamelCase	:	List[str]		    =  ReformerModelWithLMHead(_a			)

    with open(_a				,     'rb'			) as f:
        __UpperCamelCase	:	Tuple		    =  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__":
  a=  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.'''
  )
  a=  parser.parse_args()
  convert_trax_checkpoint_to_pytorch(args.trax_model_pkl_path, args.config_file, args.pytorch_dump_path)



