


import json
import os
import sys
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile

from huggingface_hub import HfFolder, Repository, create_repo, delete_repo
from requests.exceptions import HTTPError

import transformers
from transformers import (
    CONFIG_MAPPING,
    FEATURE_EXTRACTOR_MAPPING,
    PROCESSOR_MAPPING,
    TOKENIZER_MAPPING,
    AutoConfig,
    AutoFeatureExtractor,
    AutoProcessor,
    AutoTokenizer,
    BertTokenizer,
    ProcessorMixin,
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
)
from transformers.testing_utils import TOKEN, USER, get_tests_dir, is_staging_test
from transformers.tokenization_utils import TOKENIZER_CONFIG_FILE
from transformers.utils import FEATURE_EXTRACTOR_NAME, is_tokenizers_available


sys.path.append(str(Path(__file__).parent.parent.parent.parent / """utils"""))

from test_module.custom_configuration import CustomConfig  # noqa E402
from test_module.custom_feature_extraction import CustomFeatureExtractor  # noqa E402
from test_module.custom_processing import CustomProcessor  # noqa E402
from test_module.custom_tokenization import CustomTokenizer  # noqa E402


UpperCAmelCase    =					get_tests_dir("""fixtures/dummy_feature_extractor_config.json""")
UpperCAmelCase    =					get_tests_dir("""fixtures/vocab.json""")
UpperCAmelCase    =					get_tests_dir("""fixtures""")







class lowerCAmelCase_		(				unittest.TestCase ):

 '''simple docstring'''

 __snake_case				=						["[UNK]", "[CLS]", "[SEP]", "[PAD]", "[MASK]", "bla", "blou"]



 def 		UpperCamelCase__  (			self      ):
       snake_case_ =   0



 def 		UpperCamelCase__  (			self      ):
       snake_case_ =   AutoProcessor.from_pretrained('''facebook/wav2vec2-base-960h'''      )
       self.assertIsInstance(_UpperCAmelCase	,   _UpperCAmelCase      )



 def 		UpperCamelCase__  (			self      ):
       with tempfile.TemporaryDirectory() as tmpdirname:
             snake_case_ =   WavaVecaConfig()
             snake_case_ =   AutoProcessor.from_pretrained('''facebook/wav2vec2-base-960h'''      )

             # save in new folder
             model_config.save_pretrained(_UpperCAmelCase      )
             processor.save_pretrained(_UpperCAmelCase      )

             snake_case_ =   AutoProcessor.from_pretrained(_UpperCAmelCase      )

       self.assertIsInstance(_UpperCAmelCase	,   _UpperCAmelCase      )



 def 		UpperCamelCase__  (			self      ):
       with tempfile.TemporaryDirectory() as tmpdirname:
             # copy relevant files
             copyfile(_UpperCAmelCase	,   os.path.join(_UpperCAmelCase	,   _UpperCAmelCase      )      )
             copyfile(_UpperCAmelCase	,   os.path.join(_UpperCAmelCase	,   '''vocab.json'''      )      )

             snake_case_ =   AutoProcessor.from_pretrained(_UpperCAmelCase      )

       self.assertIsInstance(_UpperCAmelCase	,   _UpperCAmelCase      )



 def 		UpperCamelCase__  (			self      ):
       with tempfile.TemporaryDirectory() as tmpdirname:
             snake_case_ =   WavaVecaFeatureExtractor()
             snake_case_ =   AutoTokenizer.from_pretrained('''facebook/wav2vec2-base-960h'''      )

             snake_case_ =   WavaVecaProcessor(_UpperCAmelCase	,   _UpperCAmelCase      )

             # save in new folder
             processor.save_pretrained(_UpperCAmelCase      )

             # drop `processor_class` in tokenizer
             with open(os.path.join(_UpperCAmelCase	,   _UpperCAmelCase      )	,   '''r'''      ) as f:
                   snake_case_ =   json.load(_UpperCAmelCase      )
                   config_dict.pop('''processor_class'''      )

             with open(os.path.join(_UpperCAmelCase	,   _UpperCAmelCase      )	,   '''w'''      ) as f:
                   f.write(json.dumps(_UpperCAmelCase      )      )

             snake_case_ =   AutoProcessor.from_pretrained(_UpperCAmelCase      )

       self.assertIsInstance(_UpperCAmelCase	,   _UpperCAmelCase      )



 def 		UpperCamelCase__  (			self      ):
       with tempfile.TemporaryDirectory() as tmpdirname:
             snake_case_ =   WavaVecaFeatureExtractor()
             snake_case_ =   AutoTokenizer.from_pretrained('''facebook/wav2vec2-base-960h'''      )

             snake_case_ =   WavaVecaProcessor(_UpperCAmelCase	,   _UpperCAmelCase      )

             # save in new folder
             processor.save_pretrained(_UpperCAmelCase      )

             # drop `processor_class` in feature extractor
             with open(os.path.join(_UpperCAmelCase	,   _UpperCAmelCase      )	,   '''r'''      ) as f:
                   snake_case_ =   json.load(_UpperCAmelCase      )
                   config_dict.pop('''processor_class'''      )

             with open(os.path.join(_UpperCAmelCase	,   _UpperCAmelCase      )	,   '''w'''      ) as f:
                   f.write(json.dumps(_UpperCAmelCase      )      )

             snake_case_ =   AutoProcessor.from_pretrained(_UpperCAmelCase      )

       self.assertIsInstance(_UpperCAmelCase	,   _UpperCAmelCase      )



 def 		UpperCamelCase__  (			self      ):
       with tempfile.TemporaryDirectory() as tmpdirname:
             snake_case_ =   WavaVecaConfig(processor_class='''Wav2Vec2Processor'''      )
             model_config.save_pretrained(_UpperCAmelCase      )
             # copy relevant files
             copyfile(_UpperCAmelCase	,   os.path.join(_UpperCAmelCase	,   '''vocab.json'''      )      )
             # create emtpy sample processor
             with open(os.path.join(_UpperCAmelCase	,   _UpperCAmelCase      )	,   '''w'''      ) as f:
                   f.write('''{}'''      )

             snake_case_ =   AutoProcessor.from_pretrained(_UpperCAmelCase      )

       self.assertIsInstance(_UpperCAmelCase	,   _UpperCAmelCase      )



 def 		UpperCamelCase__  (			self      ):
       # If remote code is not set, we will time out when asking whether to load the model.
       with self.assertRaises(_UpperCAmelCase      ):
             snake_case_ =   AutoProcessor.from_pretrained('''hf-internal-testing/test_dynamic_processor'''      )
       # If remote code is disabled, we can't load this config.
       with self.assertRaises(_UpperCAmelCase      ):
             snake_case_ =   AutoProcessor.from_pretrained(
                 '''hf-internal-testing/test_dynamic_processor'''	,   trust_remote_code=_UpperCAmelCase      )

       snake_case_ =   AutoProcessor.from_pretrained('''hf-internal-testing/test_dynamic_processor'''	,   trust_remote_code=_UpperCAmelCase      )
       self.assertTrue(processor.special_attribute_present      )
       self.assertEqual(processor.__class__.__name__	,   '''NewProcessor'''      )

       snake_case_ =   processor.feature_extractor
       self.assertTrue(feature_extractor.special_attribute_present      )
       self.assertEqual(feature_extractor.__class__.__name__	,   '''NewFeatureExtractor'''      )

       snake_case_ =   processor.tokenizer
       self.assertTrue(tokenizer.special_attribute_present      )
       if is_tokenizers_available():
             self.assertEqual(tokenizer.__class__.__name__	,   '''NewTokenizerFast'''      )

             # Test we can also load the slow version
             snake_case_ =   AutoProcessor.from_pretrained(
                 '''hf-internal-testing/test_dynamic_processor'''	,   trust_remote_code=_UpperCAmelCase	,   use_fast=_UpperCAmelCase      )
             snake_case_ =   new_processor.tokenizer
             self.assertTrue(new_tokenizer.special_attribute_present      )
             self.assertEqual(new_tokenizer.__class__.__name__	,   '''NewTokenizer'''      )
       else:
             self.assertEqual(tokenizer.__class__.__name__	,   '''NewTokenizer'''      )



 def 		UpperCamelCase__  (			self      ):
       try:
             AutoConfig.register('''custom'''	,   _UpperCAmelCase      )
             AutoFeatureExtractor.register(_UpperCAmelCase	,   _UpperCAmelCase      )
             AutoTokenizer.register(_UpperCAmelCase	,   slow_tokenizer_class=_UpperCAmelCase      )
             AutoProcessor.register(_UpperCAmelCase	,   _UpperCAmelCase      )
             # Trying to register something existing in the Transformers library will raise an error
             with self.assertRaises(_UpperCAmelCase      ):
                   AutoProcessor.register(_UpperCAmelCase	,   _UpperCAmelCase      )

             # Now that the config is registered, it can be used as any other config with the auto-API
             snake_case_ =   CustomFeatureExtractor.from_pretrained(_UpperCAmelCase      )

             with tempfile.TemporaryDirectory() as tmp_dir:
                   snake_case_ =   os.path.join(_UpperCAmelCase	,   '''vocab.txt'''      )
                   with open(_UpperCAmelCase	,   '''w'''	,   encoding='''utf-8'''      ) as vocab_writer:
                         vocab_writer.write(''''''.join([x + '''\n''' for x in self.vocab_tokens]      )      )
                   snake_case_ =   CustomTokenizer(_UpperCAmelCase      )

             snake_case_ =   CustomProcessor(_UpperCAmelCase	,   _UpperCAmelCase      )

             with tempfile.TemporaryDirectory() as tmp_dir:
                   processor.save_pretrained(_UpperCAmelCase      )
                   snake_case_ =   AutoProcessor.from_pretrained(_UpperCAmelCase      )
                   self.assertIsInstance(_UpperCAmelCase	,   _UpperCAmelCase      )

       finally:
             if "custom" in CONFIG_MAPPING._extra_content:
                   del CONFIG_MAPPING._extra_content["custom"]
             if CustomConfig in FEATURE_EXTRACTOR_MAPPING._extra_content:
                   del FEATURE_EXTRACTOR_MAPPING._extra_content[CustomConfig]
             if CustomConfig in TOKENIZER_MAPPING._extra_content:
                   del TOKENIZER_MAPPING._extra_content[CustomConfig]
             if CustomConfig in PROCESSOR_MAPPING._extra_content:
                   del PROCESSOR_MAPPING._extra_content[CustomConfig]



 def 		UpperCamelCase__  (			self      ):







       class lowerCAmelCase_		(				lowerCamelCase__ ):

        '''simple docstring'''

        __snake_case				=						False







       class lowerCAmelCase_		(				lowerCamelCase__ ):

        '''simple docstring'''

        __snake_case				=						False







       class lowerCAmelCase_		(				lowerCamelCase__ ):

        '''simple docstring'''

        __snake_case				=						"AutoFeatureExtractor"
        __snake_case				=						"AutoTokenizer"
        __snake_case				=						False

       try:
             AutoConfig.register('''custom'''	,   _UpperCAmelCase      )
             AutoFeatureExtractor.register(_UpperCAmelCase	,   _UpperCAmelCase      )
             AutoTokenizer.register(_UpperCAmelCase	,   slow_tokenizer_class=_UpperCAmelCase      )
             AutoProcessor.register(_UpperCAmelCase	,   _UpperCAmelCase      )
             # If remote code is not set, the default is to use local classes.
             snake_case_ =   AutoProcessor.from_pretrained('''hf-internal-testing/test_dynamic_processor'''      )
             self.assertEqual(processor.__class__.__name__	,   '''NewProcessor'''      )
             self.assertFalse(processor.special_attribute_present      )
             self.assertFalse(processor.feature_extractor.special_attribute_present      )
             self.assertFalse(processor.tokenizer.special_attribute_present      )

             # If remote code is disabled, we load the local ones.
             snake_case_ =   AutoProcessor.from_pretrained(
                 '''hf-internal-testing/test_dynamic_processor'''	,   trust_remote_code=_UpperCAmelCase      )
             self.assertEqual(processor.__class__.__name__	,   '''NewProcessor'''      )
             self.assertFalse(processor.special_attribute_present      )
             self.assertFalse(processor.feature_extractor.special_attribute_present      )
             self.assertFalse(processor.tokenizer.special_attribute_present      )

             # If remote is enabled, we load from the Hub.
             snake_case_ =   AutoProcessor.from_pretrained(
                 '''hf-internal-testing/test_dynamic_processor'''	,   trust_remote_code=_UpperCAmelCase      )
             self.assertEqual(processor.__class__.__name__	,   '''NewProcessor'''      )
             self.assertTrue(processor.special_attribute_present      )
             self.assertTrue(processor.feature_extractor.special_attribute_present      )
             self.assertTrue(processor.tokenizer.special_attribute_present      )

       finally:
             if "custom" in CONFIG_MAPPING._extra_content:
                   del CONFIG_MAPPING._extra_content["custom"]
             if CustomConfig in FEATURE_EXTRACTOR_MAPPING._extra_content:
                   del FEATURE_EXTRACTOR_MAPPING._extra_content[CustomConfig]
             if CustomConfig in TOKENIZER_MAPPING._extra_content:
                   del TOKENIZER_MAPPING._extra_content[CustomConfig]
             if CustomConfig in PROCESSOR_MAPPING._extra_content:
                   del PROCESSOR_MAPPING._extra_content[CustomConfig]



 def 		UpperCamelCase__  (			self      ):
       snake_case_ =   AutoProcessor.from_pretrained('''hf-internal-testing/tiny-random-bert'''      )
       self.assertEqual(processor.__class__.__name__	,   '''BertTokenizerFast'''      )





 def 		UpperCamelCase__  (			self      ):
       snake_case_ =   AutoProcessor.from_pretrained('''hf-internal-testing/tiny-random-convnext'''      )
       self.assertEqual(processor.__class__.__name__	,   '''ConvNextImageProcessor'''      )







@is_staging_test
class lowerCAmelCase_		(				unittest.TestCase ):

 '''simple docstring'''

 __snake_case				=						["[UNK]", "[CLS]", "[SEP]", "[PAD]", "[MASK]", "bla", "blou"]



 @classmethod
 def 		UpperCamelCase__  (			cls      ):
       snake_case_ =   TOKEN
       HfFolder.save_token(_UpperCAmelCase      )



 @classmethod
 def 		UpperCamelCase__  (			cls      ):
       try:
             delete_repo(token=cls._token	,   repo_id='''test-processor'''      )
       except HTTPError:
             pass

       try:
             delete_repo(token=cls._token	,   repo_id='''valid_org/test-processor-org'''      )
       except HTTPError:
             pass

       try:
             delete_repo(token=cls._token	,   repo_id='''test-dynamic-processor'''      )
       except HTTPError:
             pass



 def 		UpperCamelCase__  (			self      ):
       snake_case_ =   WavaVecaProcessor.from_pretrained(_UpperCAmelCase      )
       with tempfile.TemporaryDirectory() as tmp_dir:
             processor.save_pretrained(
                 os.path.join(_UpperCAmelCase	,   '''test-processor'''      )	,   push_to_hub=_UpperCAmelCase	,   use_auth_token=self._token      )

             snake_case_ =   WavaVecaProcessor.from_pretrained(F'''{USER}/test-processor'''      )
             for k, v in processor.feature_extractor.__dict__.items():
                   self.assertEqual(_UpperCAmelCase	,   getattr(new_processor.feature_extractor	,   _UpperCAmelCase      )      )
             self.assertDictEqual(new_processor.tokenizer.get_vocab()	,   processor.tokenizer.get_vocab()      )



 def 		UpperCamelCase__  (			self      ):
       snake_case_ =   WavaVecaProcessor.from_pretrained(_UpperCAmelCase      )

       with tempfile.TemporaryDirectory() as tmp_dir:
             processor.save_pretrained(
                 os.path.join(_UpperCAmelCase	,   '''test-processor-org'''      )	,   push_to_hub=_UpperCAmelCase	,   use_auth_token=self._token	,   organization='''valid_org'''	,   )

             snake_case_ =   WavaVecaProcessor.from_pretrained('''valid_org/test-processor-org'''      )
             for k, v in processor.feature_extractor.__dict__.items():
                   self.assertEqual(_UpperCAmelCase	,   getattr(new_processor.feature_extractor	,   _UpperCAmelCase      )      )
             self.assertDictEqual(new_processor.tokenizer.get_vocab()	,   processor.tokenizer.get_vocab()      )





 def 		UpperCamelCase__  (			self      ):
       CustomFeatureExtractor.register_for_auto_class()
       CustomTokenizer.register_for_auto_class()
       CustomProcessor.register_for_auto_class()

       snake_case_ =   CustomFeatureExtractor.from_pretrained(_UpperCAmelCase      )

       with tempfile.TemporaryDirectory() as tmp_dir:
             snake_case_ =   os.path.join(_UpperCAmelCase	,   '''vocab.txt'''      )
             with open(_UpperCAmelCase	,   '''w'''	,   encoding='''utf-8'''      ) as vocab_writer:
                   vocab_writer.write(''''''.join([x + '''\n''' for x in self.vocab_tokens]      )      )
             snake_case_ =   CustomTokenizer(_UpperCAmelCase      )

       snake_case_ =   CustomProcessor(_UpperCAmelCase	,   _UpperCAmelCase      )

       with tempfile.TemporaryDirectory() as tmp_dir:
             create_repo(F'''{USER}/test-dynamic-processor'''	,   token=self._token      )
             snake_case_ =   Repository(_UpperCAmelCase	,   clone_from=F'''{USER}/test-dynamic-processor'''	,   token=self._token      )
             processor.save_pretrained(_UpperCAmelCase      )

             # This has added the proper auto_map field to the feature extractor config
             self.assertDictEqual(
                 processor.feature_extractor.auto_map	,   {
                     '''AutoFeatureExtractor''': '''custom_feature_extraction.CustomFeatureExtractor''',
                     '''AutoProcessor''': '''custom_processing.CustomProcessor''',
                 }	,   )

             # This has added the proper auto_map field to the tokenizer config
             with open(os.path.join(_UpperCAmelCase	,   '''tokenizer_config.json'''      )      ) as f:
                   snake_case_ =   json.load(_UpperCAmelCase      )
             self.assertDictEqual(
                 tokenizer_config['''auto_map''']	,   {
                     '''AutoTokenizer''': ['''custom_tokenization.CustomTokenizer''', None],
                     '''AutoProcessor''': '''custom_processing.CustomProcessor''',
                 }	,   )

             # The code has been copied from fixtures
             self.assertTrue(os.path.isfile(os.path.join(_UpperCAmelCase	,   '''custom_feature_extraction.py'''      )      )      )
             self.assertTrue(os.path.isfile(os.path.join(_UpperCAmelCase	,   '''custom_tokenization.py'''      )      )      )
             self.assertTrue(os.path.isfile(os.path.join(_UpperCAmelCase	,   '''custom_processing.py'''      )      )      )

             repo.push_to_hub()

       snake_case_ =   AutoProcessor.from_pretrained(F'''{USER}/test-dynamic-processor'''	,   trust_remote_code=_UpperCAmelCase      )
       # Can't make an isinstance check because the new_processor is from the CustomProcessor class of a dynamic module
       self.assertEqual(new_processor.__class__.__name__	,   '''CustomProcessor'''      )



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					(SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE=None      )->						Any:




      """simple docstring"""

      assert torch_layer.weight.shape == weight.shape, f'''{torch_layer} layer.weight does not match'''
      snake_case_ =   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'''
            snake_case_ =   nn.Parameter(SCREAMING_SNAKE_CASE      )






def  __lowerCAmelCase					(SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      )->						int:




      """simple docstring"""

      snake_case_ =   np.asarray(weights[0]      )
      snake_case_ =   np.asarray(weights[1]      )
      snake_case_ =   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  __lowerCAmelCase					(SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      )->						Tuple:




      """simple docstring"""

      snake_case_ =   np.asarray(weights[0]      )
      snake_case_ =   np.asarray(weights[1]      )
      snake_case_ =   np.asarray(weights[2]      )
      snake_case_ =   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  __lowerCAmelCase					(SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      )->						int:




      """simple docstring"""

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

      # Chunked Feed Forward
      if len(SCREAMING_SNAKE_CASE      ) == 4:
            snake_case_ =   intermediate_weights[2]

      # layernorm 2
      snake_case_ =   np.asarray(intermediate_weights[0][0]      )
      snake_case_ =   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
      snake_case_ =   np.asarray(intermediate_weights[1][0]      )
      snake_case_ =   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
      snake_case_ =   np.asarray(intermediate_weights[4][0]      )
      snake_case_ =   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  __lowerCAmelCase					(SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      )->						str:




      """simple docstring"""

      snake_case_ =   torch_model.reformer

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

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

      snake_case_ =   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      ):
            snake_case_ =   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
      snake_case_ =   np.asarray(weights[7][0]      )
      snake_case_ =   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
      snake_case_ =   np.asarray(weights[9][0]      )
      snake_case_ =   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  __lowerCAmelCase					(SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      )->						int:




      """simple docstring"""

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

      with open(SCREAMING_SNAKE_CASE      ,      '''rb'''      ) as f:
            snake_case_ =   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__":
   UpperCAmelCase    =					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."""
   )
   UpperCAmelCase    =					parser.parse_args()
   convert_trax_checkpoint_to_pytorch(args.trax_model_pkl_path, args.config_file, args.pytorch_dump_path)