




import math
import unittest

from transformers import BioGptConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
    import torch

    from transformers import (
        BioGptForCausalLM,
        BioGptForSequenceClassification,
        BioGptForTokenClassification,
        BioGptModel,
        BioGptTokenizer,
    )
    from transformers.models.biogpt.modeling_biogpt import BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST







class __snake_case			:







   def __init__(      self		:						Dict					,						_snake_case		:						Optional[int]					,						_snake_case		:						Dict=13					,						_snake_case		:						Optional[Any]=7					,						_snake_case		:						List[str]=True					,						_snake_case		:						Optional[int]=True					,						_snake_case		:						Dict=False					,						_snake_case		:						Tuple=True					,						_snake_case		:						List[Any]=99					,						_snake_case		:						int=32					,						_snake_case		:						int=5					,						_snake_case		:						Optional[Any]=4					,						_snake_case		:						int=37					,						_snake_case		:						Tuple="gelu"					,						_snake_case		:						Optional[int]=0.1					,						_snake_case		:						str=0.1					,						_snake_case		:						str=512					,						_snake_case		:						Union[str, Any]=16					,						_snake_case		:						List[Any]=2					,						_snake_case		:						int=0.0_2					,						_snake_case		:						Dict=3					,						_snake_case		:						List[str]=4					,						_snake_case		:						Optional[Any]=None					,						):

        """simple docstring"""


        UpperCAmelCase_          =							parent
        UpperCAmelCase_          =							batch_size
        UpperCAmelCase_          =							seq_length
        UpperCAmelCase_          =							is_training
        UpperCAmelCase_          =							use_input_mask
        UpperCAmelCase_          =							use_token_type_ids
        UpperCAmelCase_          =							use_labels
        UpperCAmelCase_          =							vocab_size
        UpperCAmelCase_          =							hidden_size
        UpperCAmelCase_          =							num_hidden_layers
        UpperCAmelCase_          =							num_attention_heads
        UpperCAmelCase_          =							intermediate_size
        UpperCAmelCase_          =							hidden_act
        UpperCAmelCase_          =							hidden_dropout_prob
        UpperCAmelCase_          =							attention_probs_dropout_prob
        UpperCAmelCase_          =							max_position_embeddings
        UpperCAmelCase_          =							type_vocab_size
        UpperCAmelCase_          =							type_sequence_label_size
        UpperCAmelCase_          =							initializer_range
        UpperCAmelCase_          =							num_labels
        UpperCAmelCase_          =							num_choices
        UpperCAmelCase_          =							scope







   def lowerCamelCase						(      self		:						List[Any]):

        """simple docstring"""


        UpperCAmelCase_          =							ids_tensor([self.batch_size, self.seq_length]					,						self.vocab_size)

        UpperCAmelCase_          =							None
        if self.use_input_mask:
             UpperCAmelCase_          =							random_attention_mask([self.batch_size, self.seq_length])

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

        UpperCAmelCase_          =							None
        UpperCAmelCase_          =							None
        UpperCAmelCase_          =							None
        if self.use_labels:
             UpperCAmelCase_          =							ids_tensor([self.batch_size]					,						self.type_sequence_label_size)
             UpperCAmelCase_          =							ids_tensor([self.batch_size, self.seq_length]					,						self.num_labels)
             UpperCAmelCase_          =							ids_tensor([self.batch_size]					,						self.num_choices)

        UpperCAmelCase_          =							self.get_config()

        return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels







   def lowerCamelCase						(      self		:						List[str]):

        """simple docstring"""


        return BioGptConfig(
            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=_snake_case					,						initializer_range=self.initializer_range					,						)







   def lowerCamelCase						(      self		:						Any					,						_snake_case		:						Optional[int]					,						_snake_case		:						int					,						_snake_case		:						Optional[Any]					,						_snake_case		:						Union[str, Any]					,						_snake_case		:						Any					,						_snake_case		:						int					,						_snake_case		:						Optional[Any]):

        """simple docstring"""


        UpperCAmelCase_          =							BioGptModel(config=_snake_case)
        model.to(_snake_case)
        model.eval()
        UpperCAmelCase_          =							model(_snake_case					,						attention_mask=_snake_case)
        UpperCAmelCase_          =							model(_snake_case)
        self.parent.assertEqual(result.last_hidden_state.shape					,						(self.batch_size, self.seq_length, self.hidden_size))







   def lowerCamelCase						(      self		:						Tuple					,						_snake_case		:						Union[str, Any]					,						_snake_case		:						Dict					,						_snake_case		:						List[Any]					,						_snake_case		:						Tuple					,						_snake_case		:						Dict					,						_snake_case		:						Optional[Any]					,						_snake_case		:						Any					,						_snake_case		:						List[str]					,						_snake_case		:						Tuple					,						):

        """simple docstring"""


        UpperCAmelCase_          =							BioGptForCausalLM(config=_snake_case)
        model.to(_snake_case)
        model.eval()
        UpperCAmelCase_          =							model(_snake_case					,						attention_mask=_snake_case					,						token_type_ids=_snake_case					,						labels=_snake_case)
        self.parent.assertEqual(result.logits.shape					,						(self.batch_size, self.seq_length, self.vocab_size))







   def lowerCamelCase						(      self		:						Dict					,						_snake_case		:						str					,						_snake_case		:						Optional[Any]					,						_snake_case		:						Optional[Any]					,						_snake_case		:						Optional[Any]					,						_snake_case		:						str					,						*_snake_case		:						Any):

        """simple docstring"""


        UpperCAmelCase_          =							BioGptModel(config=_snake_case)
        model.to(_snake_case)
        model.eval()

        # create attention mask
        UpperCAmelCase_          =							torch.ones(input_ids.shape					,						dtype=torch.long					,						device=_snake_case)
        UpperCAmelCase_          =							self.seq_length // 2
        UpperCAmelCase_          =							0

        # first forward pass
        UpperCAmelCase_     ,       UpperCAmelCase_          =							model(_snake_case					,						attention_mask=_snake_case).to_tuple()

        # create hypothetical next token and extent to next_input_ids
        UpperCAmelCase_          =							ids_tensor((self.batch_size, 1)					,						config.vocab_size)

        # change a random masked slice from input_ids
        UpperCAmelCase_          =							ids_tensor((1,)					,						_snake_case).item() + 1
        UpperCAmelCase_          =							ids_tensor((self.batch_size, 1)					,						config.vocab_size).squeeze(-1)
        UpperCAmelCase_          =							random_other_next_tokens

        # append to next input_ids and attn_mask
        UpperCAmelCase_          =							torch.cat([input_ids, next_tokens]					,						dim=-1)
        UpperCAmelCase_          =							torch.cat(
            [attn_mask, torch.ones((attn_mask.shape[0], 1)					,						dtype=torch.long					,						device=_snake_case)]					,						dim=1					,						)

        # get two different outputs
        UpperCAmelCase_          =							model(_snake_case					,						attention_mask=_snake_case)['''last_hidden_state''']
        UpperCAmelCase_          =							model(_snake_case					,						past_key_values=_snake_case					,						attention_mask=_snake_case)['''last_hidden_state''']

        # select random slice
        UpperCAmelCase_          =							ids_tensor((1,)					,						output_from_past.shape[-1]).item()
        UpperCAmelCase_          =							output_from_no_past[:, -1, random_slice_idx].detach()
        UpperCAmelCase_          =							output_from_past[:, 0, random_slice_idx].detach()

        # test that outputs are equal for slice
        self.parent.assertTrue(torch.allclose(_snake_case					,						_snake_case					,						atol=1e-3))







   def lowerCamelCase						(      self		:						Optional[int]					,						_snake_case		:						List[str]					,						_snake_case		:						List[str]					,						_snake_case		:						List[str]					,						_snake_case		:						List[str]					,						_snake_case		:						int					,						*_snake_case		:						Union[str, Any]):

        """simple docstring"""


        UpperCAmelCase_          =							BioGptModel(config=_snake_case).to(_snake_case).eval()

        UpperCAmelCase_          =							torch.ones(input_ids.shape					,						dtype=torch.long					,						device=_snake_case)

        # first forward pass
        UpperCAmelCase_          =							model(_snake_case					,						attention_mask=_snake_case					,						use_cache=_snake_case)

        UpperCAmelCase_     ,       UpperCAmelCase_          =							outputs.to_tuple()

        # create hypothetical multiple next token and extent to next_input_ids
        UpperCAmelCase_          =							ids_tensor((self.batch_size, 3)					,						config.vocab_size)
        UpperCAmelCase_          =							ids_tensor((self.batch_size, 3)					,						2)

        # append to next input_ids and
        UpperCAmelCase_          =							torch.cat([input_ids, next_tokens]					,						dim=-1)
        UpperCAmelCase_          =							torch.cat([attention_mask, next_attn_mask]					,						dim=-1)

        UpperCAmelCase_          =							model(_snake_case					,						attention_mask=_snake_case)['''last_hidden_state''']
        UpperCAmelCase_          =							model(_snake_case					,						attention_mask=_snake_case					,						past_key_values=_snake_case)[
            '''last_hidden_state'''
        ]

        # select random slice
        UpperCAmelCase_          =							ids_tensor((1,)					,						output_from_past.shape[-1]).item()
        UpperCAmelCase_          =							output_from_no_past[:, -3:, random_slice_idx].detach()
        UpperCAmelCase_          =							output_from_past[:, :, random_slice_idx].detach()

        self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1])

        # test that outputs are equal for slice
        self.parent.assertTrue(torch.allclose(_snake_case					,						_snake_case					,						atol=1e-3))







   def lowerCamelCase						(      self		:						Optional[int]					,						_snake_case		:						Optional[int]					,						_snake_case		:						str					,						_snake_case		:						Optional[Any]					,						_snake_case		:						str					,						_snake_case		:						Any					,						*_snake_case		:						Dict					,						_snake_case		:						Optional[Any]=False):

        """simple docstring"""


        UpperCAmelCase_          =							BioGptForCausalLM(_snake_case)
        model.to(_snake_case)
        if gradient_checkpointing:
             model.gradient_checkpointing_enable()

        UpperCAmelCase_          =							model(_snake_case					,						labels=_snake_case)
        self.parent.assertEqual(result.loss.shape					,						())
        self.parent.assertEqual(result.logits.shape					,						(self.batch_size, self.seq_length, self.vocab_size))
        result.loss.backward()







   def lowerCamelCase						(      self		:						Tuple					,						_snake_case		:						List[Any]					,						*_snake_case		:						int):

        """simple docstring"""


        UpperCAmelCase_          =							BioGptModel(_snake_case)
        UpperCAmelCase_          =							model.config.initializer_range / math.sqrt(2 * model.config.num_hidden_layers)
        for key in model.state_dict().keys():
             if "c_proj" in key and "weight" in key:
                  self.parent.assertLessEqual(abs(torch.std(model.state_dict()[key]) - model_std)					,						0.0_0_1)
                  self.parent.assertLessEqual(abs(torch.mean(model.state_dict()[key]) - 0.0)					,						0.0_1)







   def lowerCamelCase						(      self		:						Tuple					,						_snake_case		:						Dict					,						_snake_case		:						Optional[int]					,						_snake_case		:						List[str]					,						_snake_case		:						Any					,						_snake_case		:						int					,						*_snake_case		:						List[str]):

        """simple docstring"""


        UpperCAmelCase_          =							self.num_labels
        UpperCAmelCase_          =							BioGptForTokenClassification(_snake_case)
        model.to(_snake_case)
        model.eval()
        UpperCAmelCase_          =							model(_snake_case					,						attention_mask=_snake_case					,						token_type_ids=_snake_case)
        self.parent.assertEqual(result.logits.shape					,						(self.batch_size, self.seq_length, self.num_labels))







   def lowerCamelCase						(      self		:						Optional[int]):

        """simple docstring"""


        UpperCAmelCase_          =							self.prepare_config_and_inputs()
        (
            (
            UpperCAmelCase_
        )     ,       (
            UpperCAmelCase_
        )     ,       (
            UpperCAmelCase_
        )     ,       (
            UpperCAmelCase_
        )     ,       (
            UpperCAmelCase_
        )     ,       (
            UpperCAmelCase_
        )     ,       (
            UpperCAmelCase_
        )     ,       
        )          =							config_and_inputs
        UpperCAmelCase_          =							{'''input_ids''': input_ids, '''attention_mask''': input_mask}
        return config, inputs_dict







@require_torch
class __snake_case			(			a ,  a ,  a ,  unittest.TestCase		):
   UpperCAmelCase__  :    Optional[Any]          =  (
       (BioGptModel, BioGptForCausalLM, BioGptForSequenceClassification, BioGptForTokenClassification)
       if is_torch_available()
       else ()
   )
   UpperCAmelCase__  :    str          =  (BioGptForCausalLM,) if is_torch_available() else ()
   UpperCAmelCase__  :    Any          =  (
       {
           '''feature-extraction''': BioGptModel,
           '''text-classification''': BioGptForSequenceClassification,
           '''text-generation''': BioGptForCausalLM,
           '''token-classification''': BioGptForTokenClassification,
           '''zero-shot''': BioGptForSequenceClassification,
       }
       if is_torch_available()
       else {}
   )
   UpperCAmelCase__  :    Optional[Any]          =  False







   def lowerCamelCase						(      self		:						Tuple):

        """simple docstring"""


        UpperCAmelCase_          =							BioGptModelTester(self)
        UpperCAmelCase_          =							ConfigTester(self					,						config_class=_snake_case					,						hidden_size=37)







   def lowerCamelCase						(      self		:						Tuple):

        """simple docstring"""


        self.config_tester.run_common_tests()







   def lowerCamelCase						(      self		:						Optional[Any]):

        """simple docstring"""


        UpperCAmelCase_          =							self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*_snake_case)







   def lowerCamelCase						(      self		:						Optional[Any]):

        """simple docstring"""


        UpperCAmelCase_          =							self.model_tester.prepare_config_and_inputs()
        for type in ["absolute", "relative_key", "relative_key_query"]:
             UpperCAmelCase_          =							type
             self.model_tester.create_and_check_model(*_snake_case)







   def lowerCamelCase						(      self		:						Dict):

        """simple docstring"""


        UpperCAmelCase_          =							self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_biogpt_model_attention_mask_past(*_snake_case)







   def lowerCamelCase						(      self		:						int):

        """simple docstring"""


        UpperCAmelCase_          =							self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_forward_and_backwards(*_snake_case					,						gradient_checkpointing=_snake_case)







   def lowerCamelCase						(      self		:						Tuple):

        """simple docstring"""


        UpperCAmelCase_          =							self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_biogpt_model_past_large_inputs(*_snake_case)







   def lowerCamelCase						(      self		:						Union[str, Any]):

        """simple docstring"""


        UpperCAmelCase_          =							self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_biogpt_weight_initialization(*_snake_case)







   def lowerCamelCase						(      self		:						Dict):

        """simple docstring"""


        UpperCAmelCase_          =							self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_biogpt_for_token_classification(*_snake_case)







   @slow
   def lowerCamelCase						(      self		:						str):

        """simple docstring"""


        UpperCAmelCase_          =							BioGptForCausalLM.from_pretrained('''microsoft/biogpt''')
        model.to(_snake_case)
        UpperCAmelCase_          =							BioGptTokenizer.from_pretrained('''microsoft/biogpt''')

        UpperCAmelCase_          =							'''left'''

        # Define PAD Token = EOS Token = 50256
        UpperCAmelCase_          =							tokenizer.eos_token
        UpperCAmelCase_          =							model.config.eos_token_id

        # use different length sentences to test batching
        UpperCAmelCase_          =							[
            '''Hello, my dog is a little''',
            '''Today, I''',
        ]

        UpperCAmelCase_          =							tokenizer(_snake_case					,						return_tensors='''pt'''					,						padding=_snake_case)
        UpperCAmelCase_          =							inputs['''input_ids'''].to(_snake_case)

        UpperCAmelCase_          =							model.generate(
            input_ids=_snake_case					,						attention_mask=inputs['''attention_mask'''].to(_snake_case)					,						)

        UpperCAmelCase_          =							tokenizer(sentences[0]					,						return_tensors='''pt''').input_ids.to(_snake_case)
        UpperCAmelCase_          =							model.generate(input_ids=_snake_case)

        UpperCAmelCase_          =							inputs_non_padded.shape[-1] - inputs['''attention_mask'''][-1].long().sum().cpu().item()
        UpperCAmelCase_          =							tokenizer(sentences[1]					,						return_tensors='''pt''').input_ids.to(_snake_case)
        UpperCAmelCase_          =							model.generate(input_ids=_snake_case					,						max_length=model.config.max_length - num_paddings)

        UpperCAmelCase_          =							tokenizer.batch_decode(_snake_case					,						skip_special_tokens=_snake_case)
        UpperCAmelCase_          =							tokenizer.decode(output_non_padded[0]					,						skip_special_tokens=_snake_case)
        UpperCAmelCase_          =							tokenizer.decode(output_padded[0]					,						skip_special_tokens=_snake_case)

        UpperCAmelCase_          =							[
            '''Hello, my dog is a little bit bigger than a little bit.''',
            '''Today, I have a good idea of how to use the information''',
        ]
        self.assertListEqual(_snake_case					,						_snake_case)
        self.assertListEqual(_snake_case					,						[non_padded_sentence, padded_sentence])







   @slow
   def lowerCamelCase						(      self		:						Optional[int]):

        """simple docstring"""


        for model_name in BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             UpperCAmelCase_          =							BioGptModel.from_pretrained(_snake_case)
             self.assertIsNotNone(_snake_case)







   def lowerCamelCase						(      self		:						Any):

        """simple docstring"""


        UpperCAmelCase_     ,       UpperCAmelCase_          =							self.model_tester.prepare_config_and_inputs_for_common()
        UpperCAmelCase_          =							3
        UpperCAmelCase_          =							input_dict['''input_ids''']
        UpperCAmelCase_          =							input_ids.ne(1).to(_snake_case)
        UpperCAmelCase_          =							ids_tensor([self.model_tester.batch_size]					,						self.model_tester.type_sequence_label_size)
        UpperCAmelCase_          =							BioGptForSequenceClassification(_snake_case)
        model.to(_snake_case)
        model.eval()
        UpperCAmelCase_          =							model(_snake_case					,						attention_mask=_snake_case					,						labels=_snake_case)
        self.assertEqual(result.logits.shape					,						(self.model_tester.batch_size, self.model_tester.num_labels))







   def lowerCamelCase						(      self		:						Any):

        """simple docstring"""


        UpperCAmelCase_     ,       UpperCAmelCase_          =							self.model_tester.prepare_config_and_inputs_for_common()
        UpperCAmelCase_          =							3
        UpperCAmelCase_          =							'''multi_label_classification'''
        UpperCAmelCase_          =							input_dict['''input_ids''']
        UpperCAmelCase_          =							input_ids.ne(1).to(_snake_case)
        UpperCAmelCase_          =							ids_tensor(
            [self.model_tester.batch_size, config.num_labels]					,						self.model_tester.type_sequence_label_size).to(torch.float)
        UpperCAmelCase_          =							BioGptForSequenceClassification(_snake_case)
        model.to(_snake_case)
        model.eval()
        UpperCAmelCase_          =							model(_snake_case					,						attention_mask=_snake_case					,						labels=_snake_case)
        self.assertEqual(result.logits.shape					,						(self.model_tester.batch_size, self.model_tester.num_labels))







@require_torch
class __snake_case			(			unittest.TestCase		):







   @slow
   def lowerCamelCase						(      self		:						Any):

        """simple docstring"""


        UpperCAmelCase_          =							BioGptForCausalLM.from_pretrained('''microsoft/biogpt''')
        UpperCAmelCase_          =							torch.tensor([[2, 4805, 9, 656, 21]])
        UpperCAmelCase_          =							model(_snake_case)[0]

        UpperCAmelCase_          =							42384

        UpperCAmelCase_          =							torch.Size((1, 5, vocab_size))
        self.assertEqual(output.shape					,						_snake_case)

        UpperCAmelCase_          =							torch.tensor(
            [[[-9.5_2_3_6, -9.8_9_1_8, 1_0.4_5_5_7], [-1_1.0_4_6_9, -9.6_4_2_3, 8.1_0_2_2], [-8.8_6_6_4, -7.8_8_2_6, 5.5_3_2_5]]])

        self.assertTrue(torch.allclose(output[:, :3, :3]					,						_snake_case					,						atol=1e-4))







   @slow
   def lowerCamelCase						(      self		:						Dict):

        """simple docstring"""


        UpperCAmelCase_          =							BioGptTokenizer.from_pretrained('''microsoft/biogpt''')
        UpperCAmelCase_          =							BioGptForCausalLM.from_pretrained('''microsoft/biogpt''')
        model.to(_snake_case)

        torch.manual_seed(0)
        UpperCAmelCase_          =							tokenizer('''COVID-19 is'''					,						return_tensors='''pt''').to(_snake_case)
        UpperCAmelCase_          =							model.generate(
            **_snake_case					,						min_length=100					,						max_length=1024					,						num_beams=5					,						early_stopping=_snake_case					,						)
        UpperCAmelCase_          =							tokenizer.decode(output_ids[0]					,						skip_special_tokens=_snake_case)

        UpperCAmelCase_          =							(
            '''COVID-19 is a global pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the'''
            ''' causative agent of coronavirus disease 2019 (COVID-19), which has spread to more than 200 countries and'''
            ''' territories, including the United States (US), Canada, Australia, New Zealand, the United Kingdom (UK),'''
            ''' and the United States of America (USA), as of March 11, 2020, with more than 800,000 confirmed cases and'''
            ''' more than 800,000 deaths.'''
        )
        self.assertEqual(_snake_case					,						_snake_case)












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)






