





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__   (							snake_case_     :  Union[str, Any]	,				snake_case_     :  Dict	,				snake_case_     :  Dict=None	)				->    Optional[Any]:
			# set parameter of one layer
			assert torch_layer.weight.shape == weight.shape, f"""{torch_layer} layer.weight does not match"""
			__snake_case								=       nn.Parameter(lowerCamelCase__	)
			if bias is not None:
						assert torch_layer.bias.shape == bias.shape, f"""{torch_layer} layer.bias does not match"""
						__snake_case								=       nn.Parameter(lowerCamelCase__	)






def lowerCamelCase__   (							snake_case_     :  List[str]	,				snake_case_     :  Union[str, Any]	,				snake_case_     :  int	)				->    Dict:
			# set torch weights for 1-to-1 comparison
			__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(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 lowerCamelCase__   (							snake_case_     :  Optional[Any]	,				snake_case_     :  List[str]	,				snake_case_     :  str	)				->    Dict:
			# set torch weights for 1-to-1 comparison
			__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(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 lowerCamelCase__   (							snake_case_     :  Tuple	,				snake_case_     :  Optional[int]	,				snake_case_     :  str	)				->    int:
			# layernorm 1
			__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(lowerCamelCase__	)	,				torch.tensor(lowerCamelCase__	)	,				)

			# lsh weights + output
			__snake_case								=       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
			__snake_case								=       weights[2][0][1][2]

			# Chunked Feed Forward
			if len(lowerCamelCase__	) == 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(lowerCamelCase__	)	,				torch.tensor(lowerCamelCase__	)	,				)

			# 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(lowerCamelCase__	).transpose(0	,				1	).contiguous()	,				torch.tensor(lowerCamelCase__	)	,				)

			# 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(lowerCamelCase__	).transpose(0	,				1	).contiguous()	,				torch.tensor(lowerCamelCase__	)	,				)






def lowerCamelCase__   (							snake_case_     :  int	,				snake_case_     :  Union[str, Any]	,				snake_case_     :  str	)				->    str:
			# reformer model
			__snake_case								=       torch_model.reformer

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

			if isinstance(weights[3]	,				lowerCamelCase__	):
						__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(lowerCamelCase__	)	)

			__snake_case								=       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	):
						__snake_case								=       trax_layer_weights[4 * layer_idx : 4 * (layer_idx + 1)]
						set_block_weights_in_torch(lowerCamelCase__	,				lowerCamelCase__	,				lowerCamelCase__	)

			# 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(lowerCamelCase__	)	,				torch.tensor(lowerCamelCase__	)	,				)

			# 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(lowerCamelCase__	).transpose(0	,				1	).contiguous()	,				torch.tensor(lowerCamelCase__	)	,				)






def lowerCamelCase__   (							snake_case_     :  str	,				snake_case_     :  Optional[int]	,				snake_case_     :  Optional[int]	)				->    Tuple:
			# Initialise PyTorch model
			__snake_case								=       ReformerConfig.from_json_file(lowerCamelCase__	)
			print(f"""Building PyTorch model from configuration: {config}"""	)
			__snake_case								=       ReformerModelWithLMHead(lowerCamelCase__	)

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








from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


snake_case_           =      {
    'configuration_git': ['GIT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GitConfig', 'GitVisionConfig'],
    'processing_git': ['GitProcessor'],
}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    snake_case_           =      [
        'GIT_PRETRAINED_MODEL_ARCHIVE_LIST',
        'GitForCausalLM',
        'GitModel',
        'GitPreTrainedModel',
        'GitVisionModel',
    ]

if TYPE_CHECKING:
    from .configuration_git import GIT_PRETRAINED_CONFIG_ARCHIVE_MAP, GitConfig, GitVisionConfig
    from .processing_git import GitProcessor

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_git import (
            GIT_PRETRAINED_MODEL_ARCHIVE_LIST,
            GitForCausalLM,
            GitModel,
            GitPreTrainedModel,
            GitVisionModel,
        )

else:
    import sys

    snake_case_           =      _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)

