"""simple docstring"""




import tempfile
import unittest

import numpy as np
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError

from transformers import BertConfig, is_flax_available
from transformers.testing_utils import TOKEN, USER, is_staging_test, require_flax


if is_flax_available():
			import os

			from flax.core.frozen_dict import unfreeze
			from flax.traverse_util import flatten_dict

			from transformers import FlaxBertModel

			__lowerCAmelCase    :							Tuple													=						"0.12"  # assumed parallelism: 8

@require_flax
@is_staging_test
class       a_      (     unittest.TestCase			):




					@classmethod
					def 						_SCREAMING_SNAKE_CASE						(	cls						:     Optional[int]     ):
							lowerCAmelCase__        =   TOKEN
							HfFolder.save_token(snake_case__     )




					@classmethod
					def 						_SCREAMING_SNAKE_CASE						(	cls						:     Optional[Any]     ):
							try:
									delete_repo(token=cls._token  ,						repo_id="""test-model-flax"""     )
							except HTTPError:
									pass

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




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Tuple     ):
							lowerCAmelCase__        =   BertConfig(
							    vocab_size=99  ,						hidden_size=32  ,						num_hidden_layers=5  ,						num_attention_heads=4  ,						intermediate_size=37     )
							lowerCAmelCase__        =   FlaxBertModel(snake_case__     )
							model.push_to_hub("""test-model-flax"""  ,						use_auth_token=self._token     )

							lowerCAmelCase__        =   FlaxBertModel.from_pretrained(F"""{USER}/test-model-flax"""     )

							lowerCAmelCase__        =   flatten_dict(unfreeze(model.params     )     )
							lowerCAmelCase__        =   flatten_dict(unfreeze(new_model.params     )     )

							for key in base_params.keys():
									lowerCAmelCase__        =   (base_params[key] - new_params[key]).sum().item()
									self.assertLessEqual(snake_case__  ,						1E-3  ,						msg=F"""{key} not identical"""     )

							# Reset repo
							delete_repo(token=self._token  ,						repo_id="""test-model-flax"""     )

							# Push to hub via save_pretrained
							with tempfile.TemporaryDirectory() as tmp_dir:
									model.save_pretrained(snake_case__  ,						repo_id="""test-model-flax"""  ,						push_to_hub=snake_case__  ,						use_auth_token=self._token     )

							lowerCAmelCase__        =   FlaxBertModel.from_pretrained(F"""{USER}/test-model-flax"""     )

							lowerCAmelCase__        =   flatten_dict(unfreeze(model.params     )     )
							lowerCAmelCase__        =   flatten_dict(unfreeze(new_model.params     )     )

							for key in base_params.keys():
									lowerCAmelCase__        =   (base_params[key] - new_params[key]).sum().item()
									self.assertLessEqual(snake_case__  ,						1E-3  ,						msg=F"""{key} not identical"""     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Optional[int]     ):
							lowerCAmelCase__        =   BertConfig(
							    vocab_size=99  ,						hidden_size=32  ,						num_hidden_layers=5  ,						num_attention_heads=4  ,						intermediate_size=37     )
							lowerCAmelCase__        =   FlaxBertModel(snake_case__     )
							model.push_to_hub("""valid_org/test-model-flax-org"""  ,						use_auth_token=self._token     )

							lowerCAmelCase__        =   FlaxBertModel.from_pretrained("""valid_org/test-model-flax-org"""     )

							lowerCAmelCase__        =   flatten_dict(unfreeze(model.params     )     )
							lowerCAmelCase__        =   flatten_dict(unfreeze(new_model.params     )     )

							for key in base_params.keys():
									lowerCAmelCase__        =   (base_params[key] - new_params[key]).sum().item()
									self.assertLessEqual(snake_case__  ,						1E-3  ,						msg=F"""{key} not identical"""     )

							# Reset repo
							delete_repo(token=self._token  ,						repo_id="""valid_org/test-model-flax-org"""     )

							# Push to hub via save_pretrained
							with tempfile.TemporaryDirectory() as tmp_dir:
									model.save_pretrained(
									    snake_case__  ,						repo_id="""valid_org/test-model-flax-org"""  ,						push_to_hub=snake_case__  ,						use_auth_token=self._token     )

							lowerCAmelCase__        =   FlaxBertModel.from_pretrained("""valid_org/test-model-flax-org"""     )

							lowerCAmelCase__        =   flatten_dict(unfreeze(model.params     )     )
							lowerCAmelCase__        =   flatten_dict(unfreeze(new_model.params     )     )

							for key in base_params.keys():
									lowerCAmelCase__        =   (base_params[key] - new_params[key]).sum().item()
									self.assertLessEqual(snake_case__  ,						1E-3  ,						msg=F"""{key} not identical"""     )







def 							_UpperCAmelCase		(       lowerCamelCase__			,  lowerCamelCase__     ):





		"""simple docstring"""




		lowerCAmelCase__        =   True
		lowerCAmelCase__        =   flatten_dict(modela.params     )
		lowerCAmelCase__        =   flatten_dict(modela.params     )
		for key in flat_params_a.keys():
				if np.sum(np.abs(flat_params_a[key] - flat_params_a[key]     )     ) > 1e-4:
						lowerCAmelCase__        =   False

		return models_are_equal






@require_flax
class       a_      (     unittest.TestCase			):




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Dict     ):
							lowerCAmelCase__        =   BertConfig.from_pretrained("""hf-internal-testing/tiny-bert-flax-only"""     )
							lowerCAmelCase__        =   FlaxBertModel(snake_case__     )

							lowerCAmelCase__        =   """bert"""
							with tempfile.TemporaryDirectory() as tmp_dir:
									model.save_pretrained(os.path.join(snake_case__  ,						snake_case__     )     )

									with self.assertRaises(snake_case__     ):
											lowerCAmelCase__        =   FlaxBertModel.from_pretrained(snake_case__     )

									lowerCAmelCase__        =   FlaxBertModel.from_pretrained(snake_case__  ,						subfolder=snake_case__     )

							self.assertTrue(check_models_equal(snake_case__  ,						snake_case__     )     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Optional[int]     ):
							lowerCAmelCase__        =   BertConfig.from_pretrained("""hf-internal-testing/tiny-bert-flax-only"""     )
							lowerCAmelCase__        =   FlaxBertModel(snake_case__     )

							lowerCAmelCase__        =   """bert"""
							with tempfile.TemporaryDirectory() as tmp_dir:
									model.save_pretrained(os.path.join(snake_case__  ,						snake_case__     )  ,						max_shard_size="""10KB"""     )

									with self.assertRaises(snake_case__     ):
											lowerCAmelCase__        =   FlaxBertModel.from_pretrained(snake_case__     )

									lowerCAmelCase__        =   FlaxBertModel.from_pretrained(snake_case__  ,						subfolder=snake_case__     )

							self.assertTrue(check_models_equal(snake_case__  ,						snake_case__     )     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Optional[int]     ):
							lowerCAmelCase__        =   """bert"""
							lowerCAmelCase__        =   """hf-internal-testing/tiny-random-bert-subfolder"""

							with self.assertRaises(snake_case__     ):
									lowerCAmelCase__        =   FlaxBertModel.from_pretrained(snake_case__     )

							lowerCAmelCase__        =   FlaxBertModel.from_pretrained(snake_case__  ,						subfolder=snake_case__     )

							self.assertIsNotNone(snake_case__     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Optional[Any]     ):
							lowerCAmelCase__        =   """bert"""
							lowerCAmelCase__        =   """hf-internal-testing/tiny-random-bert-sharded-subfolder"""
							with self.assertRaises(snake_case__     ):
									lowerCAmelCase__        =   FlaxBertModel.from_pretrained(snake_case__     )

							lowerCAmelCase__        =   FlaxBertModel.from_pretrained(snake_case__  ,						subfolder=snake_case__     )

							self.assertIsNotNone(snake_case__     )







"""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		(       lowerCamelCase__			,  lowerCamelCase__			,  lowerCamelCase__=None     ):





		"""simple docstring"""




		assert torch_layer.weight.shape == weight.shape, f"""{torch_layer} layer.weight does not match"""
		lowerCAmelCase__        =   nn.Parameter(lowerCamelCase__     )
		if bias is not None:
				assert torch_layer.bias.shape == bias.shape, f"""{torch_layer} layer.bias does not match"""
				lowerCAmelCase__        =   nn.Parameter(lowerCamelCase__     )







def 							_UpperCAmelCase		(       lowerCamelCase__			,  lowerCamelCase__			,  lowerCamelCase__     ):





		"""simple docstring"""




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

		set_param(
		    torch_layer.self_attention.query_key			,  torch.tensor(lowerCamelCase__     ).transpose(1			,  2     ).contiguous().view(-1			,  lowerCamelCase__     )			,  )
		set_param(
		    torch_layer.self_attention.value			,  torch.tensor(lowerCamelCase__     ).transpose(1			,  2     ).contiguous().view(-1			,  lowerCamelCase__     )			,  )
		set_param(
		    torch_layer.output.dense			,  torch.tensor(lowerCamelCase__     ).view(-1			,  lowerCamelCase__     ).contiguous().transpose(0			,  1     )			,  )







def 							_UpperCAmelCase		(       lowerCamelCase__			,  lowerCamelCase__			,  lowerCamelCase__     ):





		"""simple docstring"""




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

		set_param(
		    torch_layer.self_attention.query			,  torch.tensor(lowerCamelCase__     ).transpose(1			,  2     ).contiguous().view(-1			,  lowerCamelCase__     )			,  )
		set_param(
		    torch_layer.self_attention.key			,  torch.tensor(lowerCamelCase__     ).transpose(1			,  2     ).contiguous().view(-1			,  lowerCamelCase__     )			,  )
		set_param(
		    torch_layer.self_attention.value			,  torch.tensor(lowerCamelCase__     ).transpose(1			,  2     ).contiguous().view(-1			,  lowerCamelCase__     )			,  )
		set_param(
		    torch_layer.output.dense			,  torch.tensor(lowerCamelCase__     ).view(-1			,  lowerCamelCase__     ).contiguous().transpose(0			,  1     )			,  )







def 							_UpperCAmelCase		(       lowerCamelCase__			,  lowerCamelCase__			,  lowerCamelCase__     ):





		"""simple docstring"""




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

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

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

		# Chunked Feed Forward
		if len(lowerCamelCase__     ) == 4:
				lowerCAmelCase__        =   intermediate_weights[2]

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

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

		# intermediate out
		lowerCAmelCase__        =   np.asarray(intermediate_weights[4][0]     )
		lowerCAmelCase__        =   np.asarray(intermediate_weights[4][1]     )
		set_param(
		    torch_block.feed_forward.output.dense			,  torch.tensor(lowerCamelCase__     ).transpose(0			,  1     ).contiguous()			,  torch.tensor(lowerCamelCase__     )			,  )







def 							_UpperCAmelCase		(       lowerCamelCase__			,  lowerCamelCase__			,  lowerCamelCase__     ):





		"""simple docstring"""




		lowerCAmelCase__        =   torch_model.reformer

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

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

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

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

		# output embeddings
		lowerCAmelCase__        =   np.asarray(weights[9][0]     )
		lowerCAmelCase__        =   np.asarray(weights[9][1]     )
		set_param(
		    torch_model.lm_head.decoder			,  torch.tensor(lowerCamelCase__     ).transpose(0			,  1     ).contiguous()			,  torch.tensor(lowerCamelCase__     )			,  )







def 							_UpperCAmelCase		(       lowerCamelCase__			,  lowerCamelCase__			,  lowerCamelCase__     ):





		"""simple docstring"""




		lowerCAmelCase__        =   ReformerConfig.from_json_file(lowerCamelCase__     )
		print(f"""Building PyTorch model from configuration: {config}"""     )
		lowerCAmelCase__        =   ReformerModelWithLMHead(lowerCamelCase__     )

		with open(lowerCamelCase__			,  """rb"""     ) as f:
				lowerCAmelCase__        =   pickle.load(lowerCamelCase__     )["""weights"""]

		set_model_weights_in_torch(lowerCamelCase__			,  lowerCamelCase__			,  config.hidden_size     )

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


if __name__ == "__main__":
			__lowerCAmelCase    :							List[str]													=						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    :							Union[str, Any]													=						parser.parse_args()
			convert_trax_checkpoint_to_pytorch(args.trax_model_pkl_path, args.config_file, args.pytorch_dump_path)




