





"""simple docstring"""




import argparse
import re

from flax.traverse_util import flatten_dict, unflatten_dict
from tax import checkpoints

from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration
from transformers.modeling_flax_pytorch_utils import load_flax_weights_in_pytorch_model
from transformers.utils import logging


logging.set_verbosity_info()


# should not include what is already done by the `from_pt` argument
UpperCamelCase__     =	{
    "/attention/": "/0/SelfAttention/",
    "/self_attention/": "/0/SelfAttention/",
    "/encoder_decoder_attention/": "/1/EncDecAttention/",
    "value": "v",
    "query": "q",
    "key": "k",
    "out": "o",
    "pre_self_attention_layer_norm": "0/layer_norm",
    "pre_cross_attention_layer_norm": "1/layer_norm",
    "pre_attention_layer_norm": "0/layer_norm",  # previously 1, but seems wrong
    "token_embedder": "shared",
    "encoder_norm": "final_layer_norm",
    "decoder_norm": "final_layer_norm",
    "relpos_bias/rel_embedding": "block/0/layer/0/SelfAttention/relative_attention_bias/weight",
    "router/router_weights/w/": "router/classifier/",
    "roer/roer_weights/w/": "router/classifier/",
    "logits_dense": "lm_head",
}





def       _UpperCamelCase       (a__				:Tuple    ):






							"""simple docstring"""





							UpperCamelCase__			       =     list(s_dict.keys()    )
							for key in keys:
														UpperCamelCase__			       =     R""".*/layers_(\d+)"""
														UpperCamelCase__			       =     key
														if re.match(lowercase__				,     lowercase__    ):
																					UpperCamelCase__			       =     re.sub(r"""layers_(\d+)"""				,     r"""block/\1/layer"""				,     lowercase__    )

														UpperCamelCase__			       =     R"""(encoder|decoder)\/"""

														if re.match(lowercase__				,     lowercase__    ):
																					UpperCamelCase__			       =     re.match(lowercase__				,     lowercase__    ).groups()
																					if groups[0] == "encoder":
																												UpperCamelCase__			       =     re.sub(r"""/mlp/"""				,     r"""/1/mlp/"""				,     lowercase__    )
																												UpperCamelCase__			       =     re.sub(r"""/pre_mlp_layer_norm/"""				,     r"""/1/layer_norm/"""				,     lowercase__    )

																					elif groups[0] == "decoder":
																												UpperCamelCase__			       =     re.sub(r"""/mlp/"""				,     r"""/2/mlp/"""				,     lowercase__    )
																												UpperCamelCase__			       =     re.sub(r"""/pre_mlp_layer_norm/"""				,     r"""/2/layer_norm/"""				,     lowercase__    )

        # 2. Convert other classic mappings
														for old_key, temp_key in MOE_LAYER_NAME_MAPPING.items():
																					if old_key in new_key:
																												UpperCamelCase__			       =     new_key.replace(lowercase__				,     lowercase__    )

														print(f"""{key} -> {new_key}"""    )
														UpperCamelCase__			       =     s_dict.pop(lowercase__    )

							if "encoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight" in s_dict:
														UpperCamelCase__			       =     s_dict[
														    """encoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight"""
														].T
							if "decoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight" in s_dict:
														UpperCamelCase__			       =     s_dict[
														    """decoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight"""
														].T

							# 3. Take extra care of the EXPERTS layer
							for key in list(s_dict.keys()    ):
														if "expert" in key:
																					UpperCamelCase__			       =     s_dict[key].shape[0]
																					UpperCamelCase__			       =     s_dict[key]
																					for idx in range(lowercase__    ):
																												UpperCamelCase__			       =     expert_weihts[idx]
																												print(f"""{key} -> {key.replace("expert/"				,     "nested fstring"    )}"""    )

																					s_dict.pop(lowercase__    )

							return s_dict


UpperCamelCase__     =	{
    "NUM_ENCODER_LAYERS": "num_layers",
    "NUM_DECODER_LAYERS": "num_decoder_layers",
    "NUM_HEADS": "num_heads",
    "HEAD_DIM": "d_kv",
    "EMBED_DIM": "d_model",
    "MLP_DIM": "d_ff",
    "NUM_SELECTED_EXPERTS": "num_selected_experts",
    "NUM_ENCODER_SPARSE_LAYERS": "num_sparse_encoder_layers",
    "NUM_DECODER_SPARSE_LAYERS": "num_sparse_decoder_layers",
    "dense.MlpBlock.activations": "feed_forward_proj",
}





def       _UpperCamelCase       (a__				:List[str]				,     a__				:Dict    ):






							"""simple docstring"""





							import regex as re

							with open(lowercase__				,     """r"""    ) as f:
														UpperCamelCase__			       =     f.read()

							UpperCamelCase__			       =     re.findall(r"""(.*) = ([0-9.]*)"""				,     lowercase__    )
							UpperCamelCase__			       =     {}
							for param, value in regex_match:
														if param in GIN_TO_CONFIG_MAPPING and value != "":
																					UpperCamelCase__			       =     float(lowercase__    ) if """.""" in value else int(lowercase__    )

							UpperCamelCase__			       =     re.findall(r"""(.*activations) = \(\'(.*)\',\)"""				,     lowercase__    )[0]
							UpperCamelCase__			       =     str(activation[1]    )

							UpperCamelCase__			       =     num_experts
							UpperCamelCase__			       =     SwitchTransformersConfig(**lowercase__    )
							return config





def       _UpperCamelCase       (a__				:Tuple				,     a__				:List[Any]				,     a__				:Dict=None				,     a__				:Any="./"				,     a__				:List[Any]=8    ):






							"""simple docstring"""





							print(f"""Loading flax weights from : {flax_checkpoint_path}"""    )
							UpperCamelCase__			       =     checkpoints.load_tax_checkpoint(lowercase__    )

							if gin_file is not None:
														UpperCamelCase__			       =     convert_gin_to_config(lowercase__				,     lowercase__    )
							else:
														UpperCamelCase__			       =     SwitchTransformersConfig.from_pretrained(lowercase__    )

							UpperCamelCase__			       =     SwitchTransformersForConditionalGeneration(lowercase__    )

							UpperCamelCase__			       =     flax_params["""target"""]
							UpperCamelCase__			       =     flatten_dict(lowercase__				,     sep="""/"""    )
							UpperCamelCase__			       =     rename_keys(lowercase__    )
							UpperCamelCase__			       =     unflatten_dict(lowercase__				,     sep="""/"""    )

							# Load the flax params in the PT model
							load_flax_weights_in_pytorch_model(lowercase__				,     lowercase__    )

							print(f"""Save PyTorch model to {pytorch_dump_path}"""    )
							pt_model.save_pretrained(lowercase__    )


if __name__ == "__main__":
					UpperCamelCase__     =	argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    "--switch_t5x_checkpoint_path",
					    default=None,
					    type=str,
					    required=True,
					    help=(
					        "The config json file corresponding to the pre-trained SwitchTransformers model. \nThis specifies the"
					        " model architecture. If not provided, a `gin_file` has to be provided."
					    ),
					)
					parser.add_argument(
					    "--gin_file",
					    default=None,
					    type=str,
					    required=False,
					    help="Path to the gin config file. If not provided, a `config_file` has to be passed   ",
					)
					parser.add_argument(
					    "--config_name", default=None, type=str, required=False, help="Config name of SwitchTransformers model."
					)
					parser.add_argument(
					    "--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output pytorch model."
					)
					parser.add_argument("--num_experts", default=8, type=int, required=False, help="Number of experts")
					UpperCamelCase__     =	parser.parse_args()
					convert_flax_checkpoint_to_pytorch(
					    args.switch_tax_checkpoint_path,
					    args.config_name,
					    args.gin_file,
					    args.pytorch_dump_folder_path,
					    args.num_experts,
					)











import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import is_speech_available, is_vision_available
from transformers.testing_utils import require_torch


if is_vision_available():
					from transformers import TvltImageProcessor

if is_speech_available():
					from transformers import TvltFeatureExtractor

from transformers import TvltProcessor

@require_torch
class     __SCREAMING_SNAKE_CASE   (       unittest.TestCase    ):






							def 			_lowerCamelCase						(  self					):
														UpperCamelCase__			       =     """ZinengTang/tvlt-base"""
														UpperCamelCase__			       =     tempfile.mkdtemp()






							def 			_lowerCamelCase						(  self					,	**__lowerCAmelCase					):
														return TvltImageProcessor.from_pretrained(self.checkpoint					,	**__lowerCAmelCase					)






							def 			_lowerCamelCase						(  self					,	**__lowerCAmelCase					):
														return TvltFeatureExtractor.from_pretrained(self.checkpoint					,	**__lowerCAmelCase					)






							def 			_lowerCamelCase						(  self					):
														shutil.rmtree(self.tmpdirname					)






							def 			_lowerCamelCase						(  self					):
														UpperCamelCase__			       =     self.get_image_processor()
														UpperCamelCase__			       =     self.get_feature_extractor()

														UpperCamelCase__			       =     TvltProcessor(image_processor=__lowerCAmelCase					,	feature_extractor=__lowerCAmelCase					)
														processor.save_pretrained(self.tmpdirname					)
														UpperCamelCase__			       =     TvltProcessor.from_pretrained(self.tmpdirname					)

														self.assertIsInstance(processor.feature_extractor					,	__lowerCAmelCase					)
														self.assertIsInstance(processor.image_processor					,	__lowerCAmelCase					)






							def 			_lowerCamelCase						(  self					):
														UpperCamelCase__			       =     self.get_image_processor()
														UpperCamelCase__			       =     self.get_feature_extractor()

														UpperCamelCase__			       =     TvltProcessor(image_processor=__lowerCAmelCase					,	feature_extractor=__lowerCAmelCase					)

														UpperCamelCase__			       =     np.ones([12000]					)

														UpperCamelCase__			       =     feature_extractor(__lowerCAmelCase					,	return_tensors="""np"""					)
														UpperCamelCase__			       =     processor(audio=__lowerCAmelCase					,	return_tensors="""np"""					)

														for key in audio_dict.keys():
																					self.assertAlmostEqual(audio_dict[key].sum()					,	input_processor[key].sum()					,	delta=1E-2					)






							def 			_lowerCamelCase						(  self					):
														UpperCamelCase__			       =     self.get_image_processor()
														UpperCamelCase__			       =     self.get_feature_extractor()

														UpperCamelCase__			       =     TvltProcessor(image_processor=__lowerCAmelCase					,	feature_extractor=__lowerCAmelCase					)

														UpperCamelCase__			       =     np.ones([3, 224, 224]					)

														UpperCamelCase__			       =     image_processor(__lowerCAmelCase					,	return_tensors="""np"""					)
														UpperCamelCase__			       =     processor(images=__lowerCAmelCase					,	return_tensors="""np"""					)

														for key in image_dict.keys():
																					self.assertAlmostEqual(image_dict[key].sum()					,	input_processor[key].sum()					,	delta=1E-2					)






							def 			_lowerCamelCase						(  self					):
														UpperCamelCase__			       =     self.get_image_processor()
														UpperCamelCase__			       =     self.get_feature_extractor()

														UpperCamelCase__			       =     TvltProcessor(image_processor=__lowerCAmelCase					,	feature_extractor=__lowerCAmelCase					)

														UpperCamelCase__			       =     np.ones([12000]					)
														UpperCamelCase__			       =     np.ones([3, 224, 224]					)

														UpperCamelCase__			       =     processor(audio=__lowerCAmelCase					,	images=__lowerCAmelCase					)

														self.assertListEqual(list(inputs.keys()					)					,	["""audio_values""", """audio_mask""", """pixel_values""", """pixel_mask"""]					)

														# test if it raises when no input is passed
														with pytest.raises(__lowerCAmelCase					):
																					processor()







							def 			_lowerCamelCase						(  self					):
														UpperCamelCase__			       =     self.get_image_processor()
														UpperCamelCase__			       =     self.get_feature_extractor()

														UpperCamelCase__			       =     TvltProcessor(image_processor=__lowerCAmelCase					,	feature_extractor=__lowerCAmelCase					)

														self.assertListEqual(
														    processor.model_input_names					,	image_processor.model_input_names + feature_extractor.model_input_names					,	msg="""`processor` and `image_processor`+`feature_extractor` model input names do not match"""					,	)




