
'''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 					snake_case_		(				__SCREAMING_SNAKE_CASE					: Optional[Any]      ,      __SCREAMING_SNAKE_CASE					: Optional[int]      ,      __SCREAMING_SNAKE_CASE					: Dict=None							):



						"""simple docstring"""





						assert torch_layer.weight.shape == weight.shape, F'''{torch_layer} layer.weight does not match'''
						lowercase_	:     int													=       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'''
												lowercase_	:     Any													=       nn.Parameter(__SCREAMING_SNAKE_CASE							)
def 					snake_case_		(				__SCREAMING_SNAKE_CASE					: List[Any]      ,      __SCREAMING_SNAKE_CASE					: List[Any]      ,      __SCREAMING_SNAKE_CASE					: List[Any]							):



						"""simple docstring"""





						lowercase_	:     Optional[int]													=       np.asarray(weights[0]							)
						lowercase_	:     Optional[Any]													=       np.asarray(weights[1]							)
						lowercase_	:     Optional[int]													=       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 					snake_case_		(				__SCREAMING_SNAKE_CASE					: Tuple      ,      __SCREAMING_SNAKE_CASE					: Dict      ,      __SCREAMING_SNAKE_CASE					: Union[str, Any]							):



						"""simple docstring"""





						lowercase_	:     Union[str, Any]													=       np.asarray(weights[0]							)
						lowercase_	:     Any													=       np.asarray(weights[1]							)
						lowercase_	:     Optional[int]													=       np.asarray(weights[2]							)
						lowercase_	:     int													=       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 					snake_case_		(				__SCREAMING_SNAKE_CASE					: Optional[int]      ,      __SCREAMING_SNAKE_CASE					: Dict      ,      __SCREAMING_SNAKE_CASE					: Any							):



						"""simple docstring"""





						lowercase_	:     Union[str, Any]													=       weights[0][0][0]
						lowercase_	:     Optional[Any]													=       np.asarray(layer_norm_a[0]							)
						lowercase_	:     List[Any]													=       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
						lowercase_	:     Dict													=       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
						lowercase_	:     Dict													=       weights[2][0][1][2]

						# Chunked Feed Forward
						if len(__SCREAMING_SNAKE_CASE							) == 4:
												lowercase_	:     Any													=       intermediate_weights[2]

						# layernorm 2
						lowercase_	:     List[Any]													=       np.asarray(intermediate_weights[0][0]							)
						lowercase_	:     Any													=       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
						lowercase_	:     List[str]													=       np.asarray(intermediate_weights[1][0]							)
						lowercase_	:     List[str]													=       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
						lowercase_	:     int													=       np.asarray(intermediate_weights[4][0]							)
						lowercase_	:     Union[str, Any]													=       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 					snake_case_		(				__SCREAMING_SNAKE_CASE					: Union[str, Any]      ,      __SCREAMING_SNAKE_CASE					: str      ,      __SCREAMING_SNAKE_CASE					: List[Any]							):



						"""simple docstring"""





						lowercase_	:     Tuple													=       torch_model.reformer

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

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

						lowercase_	:     Tuple													=       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							):
												lowercase_	:     Dict													=       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
						lowercase_	:     List[str]													=       np.asarray(weights[7][0]							)
						lowercase_	:     Optional[Any]													=       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
						lowercase_	:     Optional[int]													=       np.asarray(weights[9][0]							)
						lowercase_	:     Any													=       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 					snake_case_		(				__SCREAMING_SNAKE_CASE					: Dict      ,      __SCREAMING_SNAKE_CASE					: List[Any]      ,      __SCREAMING_SNAKE_CASE					: List[str]							):



						"""simple docstring"""





						lowercase_	:     int													=       ReformerConfig.from_json_file(__SCREAMING_SNAKE_CASE							)
						print(F'''Building PyTorch model from configuration: {config}'''							)
						lowercase_	:     Optional[Any]													=       ReformerModelWithLMHead(__SCREAMING_SNAKE_CASE							)

						with open(__SCREAMING_SNAKE_CASE      ,      '''rb'''							) as f:
												lowercase_	:     List[str]													=       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__":
							_lowercase :  Union[str, 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."
							)
							_lowercase :  str    							=       parser.parse_args()
							convert_trax_checkpoint_to_pytorch(args.trax_model_pkl_path, args.config_file, args.pytorch_dump_path)





from ...utils import (
    OptionalDependencyNotAvailable,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)


try:
						if not (is_transformers_available() and is_torch_available() and is_transformers_version('>=', '4.25.0')):
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						from ...utils.dummy_torch_and_transformers_objects import UnCLIPImageVariationPipeline, UnCLIPPipeline
else:
						from .pipeline_unclip import UnCLIPPipeline
						from .pipeline_unclip_image_variation import UnCLIPImageVariationPipeline
						from .text_proj import UnCLIPTextProjModel




