




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     lowerCamelCase__(					unittest.TestCase):
      def __init__(				self: Optional[int]    ,       UpperCamelCase_: Any    ,       UpperCamelCase_: int=2    ,       UpperCamelCase_: Optional[Any]=56    ,       UpperCamelCase_: Tuple=True    ,       UpperCamelCase_: Union[str, Any]=True    ,       UpperCamelCase_: Optional[Any]=True    ,       UpperCamelCase_: str=True    ,       UpperCamelCase_: str=99    ,       UpperCamelCase_: Tuple=32    ,       UpperCamelCase_: int=2    ,       UpperCamelCase_: Optional[int]=2    ,       UpperCamelCase_: Tuple=7    ,       UpperCamelCase_: Optional[int]="gelu_new"    ,       UpperCamelCase_: Any=0.1    ,       UpperCamelCase_: List[str]=0.1    ,       UpperCamelCase_: List[Any]=5_12    ,       UpperCamelCase_: Union[str, Any]=16    ,       UpperCamelCase_: int=2    ,       UpperCamelCase_: Dict=0.02    ,       UpperCamelCase_: Tuple=4    ,       UpperCamelCase_: Union[str, Any]="block_sparse"    ,       UpperCamelCase_: Optional[Any]=True    ,       UpperCamelCase_: Any=False    ,       UpperCamelCase_: Any=2    ,       UpperCamelCase_: int=3    ,       ):
             __lowerCamelCase   =						parent
             __lowerCamelCase   =						batch_size
             __lowerCamelCase   =						seq_length
             __lowerCamelCase   =						is_training
             __lowerCamelCase   =						use_attention_mask
             __lowerCamelCase   =						use_token_type_ids
             __lowerCamelCase   =						use_labels
             __lowerCamelCase   =						vocab_size
             __lowerCamelCase   =						hidden_size
             __lowerCamelCase   =						num_hidden_layers
             __lowerCamelCase   =						num_attention_heads
             __lowerCamelCase   =						intermediate_size
             __lowerCamelCase   =						hidden_act
             __lowerCamelCase   =						hidden_dropout_prob
             __lowerCamelCase   =						attention_probs_dropout_prob
             __lowerCamelCase   =						max_position_embeddings
             __lowerCamelCase   =						type_vocab_size
             __lowerCamelCase   =						type_sequence_label_size
             __lowerCamelCase   =						initializer_range
             __lowerCamelCase   =						num_choices

             __lowerCamelCase   =						rescale_embeddings
             __lowerCamelCase   =						attention_type
             __lowerCamelCase   =						use_bias
             __lowerCamelCase   =						block_size
             __lowerCamelCase   =						num_random_blocks
      def 			lowerCAmelCase__						(				self: int   ):
             __lowerCamelCase   =						ids_tensor([self.batch_size, self.seq_length]    ,       self.vocab_size   )

             __lowerCamelCase   =						None
             if self.use_attention_mask:
                    __lowerCamelCase   =						random_attention_mask([self.batch_size, self.seq_length]   )

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

             __lowerCamelCase   =						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=UpperCamelCase_    ,       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: Dict   ):
             __lowerCamelCase   =						self.prepare_config_and_inputs()
             __lowerCamelCase,   __lowerCamelCase,   __lowerCamelCase,   __lowerCamelCase   =						config_and_inputs
             __lowerCamelCase   =						{
                 """input_ids""": input_ids,
                 """token_type_ids""": token_type_ids,
                 """attention_mask""": attention_mask,
             }
             return config, inputs_dict





@require_flax
class     lowerCamelCase__(					__lowerCamelCase ,     unittest.TestCase):
      UpperCAmelCase__      :	Optional[int]    		=   (
          (
              FlaxBigBirdForCausalLM,
              FlaxBigBirdModel,
              FlaxBigBirdForPreTraining,
              FlaxBigBirdForMaskedLM,
              FlaxBigBirdForMultipleChoice,
              FlaxBigBirdForQuestionAnswering,
              FlaxBigBirdForSequenceClassification,
              FlaxBigBirdForTokenClassification,
          )
          if is_flax_available()
          else ()
      )

      UpperCAmelCase__      :	Optional[int]    		=   False
      UpperCAmelCase__      :	Optional[int]    		=   False
      def 			lowerCAmelCase__						(				self: List[str]   ):
             __lowerCamelCase   =						FlaxBigBirdModelTester(self   )
      @slow
      # copied from `test_modeling_flax_common` because it takes much longer than other models
      def 			lowerCAmelCase__						(				self: Optional[Any]   ):
             super().test_from_pretrained_save_pretrained()
      @slow
      # copied from `test_modeling_flax_common` because it takes much longer than other models
      def 			lowerCAmelCase__						(				self: List[Any]   ):
             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: List[Any]   ):
             super().test_no_automatic_init()
      @slow
      # copied from `test_modeling_flax_common` because it takes much longer than other models
      def 			lowerCAmelCase__						(				self: List[str]   ):
             super().test_hidden_states_output()
      @slow
      def 			lowerCAmelCase__						(				self: Optional[Any]   ):
             for model_class_name in self.all_model_classes:
                    __lowerCamelCase   =						model_class_name.from_pretrained("""google/bigbird-roberta-base"""   )
                    self.assertIsNotNone(UpperCamelCase_   )
      def 			lowerCAmelCase__						(				self: Dict   ):
             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: List[Any]   ):
             __lowerCamelCase,   __lowerCamelCase   =						self.model_tester.prepare_config_and_inputs_for_common()

             for model_class in self.all_model_classes:
                    with self.subTest(model_class.__name__   ):
                           __lowerCamelCase   =						self._prepare_for_class(UpperCamelCase_    ,       UpperCamelCase_   )
                           __lowerCamelCase   =						model_class(UpperCamelCase_   )

                           @jax.jit
                           def model_jitted(UpperCamelCase_: Tuple    ,       UpperCamelCase_: List[Any]=None    ,       **UpperCamelCase_: Union[str, Any]   ):
                                  return model(input_ids=UpperCamelCase_    ,       attention_mask=UpperCamelCase_    ,       **UpperCamelCase_   )

                           with self.subTest("""JIT Enabled"""   ):
                                  __lowerCamelCase   =						model_jitted(**UpperCamelCase_   ).to_tuple()

                           with self.subTest("""JIT Disabled"""   ):
                                  with jax.disable_jit():
                                         __lowerCamelCase   =						model_jitted(**UpperCamelCase_   ).to_tuple()

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




      def 			lowerCAmelCase__						(				self: List[Any]    ,       UpperCamelCase_: int    ,       UpperCamelCase_: List[str]    ,       UpperCamelCase_: Any    ,       UpperCamelCase_: Dict=1E-5    ,       UpperCamelCase_: List[str]="outputs"    ,       UpperCamelCase_: List[str]=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(UpperCamelCase_    ,       UpperCamelCase_    ,       UpperCamelCase_    ,       UpperCamelCase_    ,       UpperCamelCase_    ,       UpperCamelCase_   )








"""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      __lowerCamelCase   (							a_     :		Union[str, Any] ,   a_     :		Tuple ,   a_     :		str=None							)			->      Union[str, Any]:
      # set parameter of one layer
      assert torch_layer.weight.shape == weight.shape, f'''{torch_layer} layer.weight does not match'''
      __SCREAMING_SNAKE_CASE   :Dict						=  nn.Parameter(a_							)
      if bias is not None:
            assert torch_layer.bias.shape == bias.shape, f'''{torch_layer} layer.bias does not match'''
            __SCREAMING_SNAKE_CASE   :Optional[int]						=  nn.Parameter(a_							)



def      __lowerCamelCase   (							a_     :		Dict ,   a_     :		str ,   a_     :		Optional[int]							)			->      Any:
      # set torch weights for 1-to-1 comparison
      __SCREAMING_SNAKE_CASE   :List[Any]						=  np.asarray(weights[0]							)
      __SCREAMING_SNAKE_CASE   :Optional[int]						=  np.asarray(weights[1]							)
      __SCREAMING_SNAKE_CASE   :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      __lowerCamelCase   (							a_     :		List[Any] ,   a_     :		Dict ,   a_     :		List[str]							)			->      Union[str, Any]:
      # set torch weights for 1-to-1 comparison
      __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  np.asarray(weights[0]							)
      __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  np.asarray(weights[1]							)
      __SCREAMING_SNAKE_CASE   :Any						=  np.asarray(weights[2]							)
      __SCREAMING_SNAKE_CASE   :Dict						=  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      __lowerCamelCase   (							a_     :		Any ,   a_     :		List[str] ,   a_     :		Optional[int]							)			->      Union[str, Any]:
      # layernorm 1
      __SCREAMING_SNAKE_CASE   :Any						=  weights[0][0][0]
      __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  np.asarray(layer_norm_a[0]							)
      __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  np.asarray(layer_norm_a[1]							)
      set_param(
          torch_block.attention.layer_norm ,   torch.tensor(a_							) ,   torch.tensor(a_							) ,   )

      # lsh weights + output
      __SCREAMING_SNAKE_CASE   :List[Any]						=  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
      __SCREAMING_SNAKE_CASE   :List[Any]						=  weights[2][0][1][2]

      # Chunked Feed Forward
      if len(a_							) == 4:
            __SCREAMING_SNAKE_CASE   :List[str]						=  intermediate_weights[2]

      # layernorm 2
      __SCREAMING_SNAKE_CASE   :Tuple						=  np.asarray(intermediate_weights[0][0]							)
      __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  np.asarray(intermediate_weights[0][1]							)
      set_param(
          torch_block.feed_forward.layer_norm ,   torch.tensor(a_							) ,   torch.tensor(a_							) ,   )

      # intermediate dense
      __SCREAMING_SNAKE_CASE   :int						=  np.asarray(intermediate_weights[1][0]							)
      __SCREAMING_SNAKE_CASE   :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
      __SCREAMING_SNAKE_CASE   :str						=  np.asarray(intermediate_weights[4][0]							)
      __SCREAMING_SNAKE_CASE   :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      __lowerCamelCase   (							a_     :		List[str] ,   a_     :		str ,   a_     :		List[Any]							)			->      Optional[Any]:
      # reformer model
      __SCREAMING_SNAKE_CASE   :Dict						=  torch_model.reformer

      # word embeds
      __SCREAMING_SNAKE_CASE   :List[Any]						=  np.asarray(weights[1]							)
      set_param(
          torch_model_reformer.embeddings.word_embeddings ,   torch.tensor(a_							) ,   )

      if isinstance(weights[3] ,   a_							):
            __SCREAMING_SNAKE_CASE   :List[Any]						=  torch_model_reformer.embeddings.position_embeddings
            for emb_idx in range(len(position_embeddings.weights							)							):
                  __SCREAMING_SNAKE_CASE   :List[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'''
                  __SCREAMING_SNAKE_CASE   :str						=  nn.Parameter(torch.tensor(a_							)							)

      __SCREAMING_SNAKE_CASE   :Optional[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							):
            __SCREAMING_SNAKE_CASE   :Dict						=  trax_layer_weights[4 * layer_idx : 4 * (layer_idx + 1)]
            set_block_weights_in_torch(a_ ,   a_ ,   a_							)

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

      # output embeddings
      __SCREAMING_SNAKE_CASE   :Optional[int]						=  np.asarray(weights[9][0]							)
      __SCREAMING_SNAKE_CASE   :str						=  np.asarray(weights[9][1]							)
      set_param(
          torch_model.lm_head.decoder ,   torch.tensor(a_							).transpose(0 ,   1							).contiguous() ,   torch.tensor(a_							) ,   )



def      __lowerCamelCase   (							a_     :		Any ,   a_     :		Dict ,   a_     :		Dict							)			->      Tuple:
      # Initialise PyTorch model
      __SCREAMING_SNAKE_CASE   :List[str]						=  ReformerConfig.from_json_file(a_							)
      print(f'''Building PyTorch model from configuration: {config}'''							)
      __SCREAMING_SNAKE_CASE   :List[Any]						=  ReformerModelWithLMHead(a_							)

      with open(a_ ,   '''rb'''							) as f:
            __SCREAMING_SNAKE_CASE   :Any						=  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__":
  lowerCamelCase_								=		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."
  )
  lowerCamelCase_								=		parser.parse_args()
  convert_trax_checkpoint_to_pytorch(args.trax_model_pkl_path, args.config_file, args.pytorch_dump_path)