"""simple docstring"""




import argparse
import collections

import torch
from flax import traverse_util
from tax import checkpoints

from transformers import TaConfig, TaEncoderModel, TaForConditionalGeneration
from transformers.utils import logging


logging.set_verbosity_info()






def      UpperCAmelCase    (		UpperCamelCase__				,			UpperCamelCase__				,			UpperCamelCase__				,			UpperCamelCase__="attention" ):






							"""simple docstring"""





							A__		   =							params[F'''{prefix}/layers_{i}/{layer_name}/key/kernel''']
							A__		   =							params[F'''{prefix}/layers_{i}/{layer_name}/out/kernel''']
							A__		   =							params[F'''{prefix}/layers_{i}/{layer_name}/query/kernel''']
							A__		   =							params[F'''{prefix}/layers_{i}/{layer_name}/value/kernel''']
							return k, o, q, v






def      UpperCAmelCase    (		UpperCamelCase__				,			UpperCamelCase__				,			UpperCamelCase__				,			UpperCamelCase__=False ):






							"""simple docstring"""





							if split_mlp_wi:
														A__		   =							params[F'''{prefix}/layers_{i}/mlp/wi_0/kernel''']
														A__		   =							params[F'''{prefix}/layers_{i}/mlp/wi_1/kernel''']
														A__		   =							(wi_a, wi_a)
							else:
														A__		   =							params[F'''{prefix}/layers_{i}/mlp/wi/kernel''']

							A__		   =							params[F'''{prefix}/layers_{i}/mlp/wo/kernel''']
							return wi, wo






def      UpperCAmelCase    (		UpperCamelCase__				,			UpperCamelCase__				,			UpperCamelCase__				,			UpperCamelCase__ ):






							"""simple docstring"""





							return params[F'''{prefix}/layers_{i}/{layer_name}/scale''']






def      UpperCAmelCase    (		UpperCamelCase__				,			*, UpperCamelCase__				,			UpperCamelCase__ ):






							"""simple docstring"""





							A__		   =							traverse_util.flatten_dict(variables['target'] )
							A__		   =							{'/'.join(UpperCamelCase__ ): v for k, v in old.items()}

							# v1.1 models have a gated GeLU with wi_0 and wi_1 instead of wi
							A__		   =							'encoder/layers_0/mlp/wi_0/kernel' in old
							print('Split MLP:'				,			UpperCamelCase__ )

							A__		   =							collections.OrderedDict()

							# Shared embeddings.
							A__		   =							old['token_embedder/embedding']

							# Encoder.
							for i in range(UpperCamelCase__ ):
														# Block i, layer 0 (Self Attention).
														A__		   =							tax_layer_norm_lookup(UpperCamelCase__				,			UpperCamelCase__				,			'encoder'				,			'pre_attention_layer_norm' )
														A__   ,     A__   ,     A__   ,     A__		   =							tax_attention_lookup(UpperCamelCase__				,			UpperCamelCase__				,			'encoder'				,			'attention' )
														A__		   =							layer_norm
														A__		   =							k.T
														A__		   =							o.T
														A__		   =							q.T
														A__		   =							v.T

														# Block i, layer 1 (MLP).
														A__		   =							tax_layer_norm_lookup(UpperCamelCase__				,			UpperCamelCase__				,			'encoder'				,			'pre_mlp_layer_norm' )
														A__   ,     A__		   =							tax_mlp_lookup(UpperCamelCase__				,			UpperCamelCase__				,			'encoder'				,			UpperCamelCase__ )
														A__		   =							layer_norm
														if split_mlp_wi:
																					A__		   =							wi[0].T
																					A__		   =							wi[1].T
														else:
																					A__		   =							wi.T
														A__		   =							wo.T

							A__		   =							old[
							    'encoder/relpos_bias/rel_embedding'
							].T
							A__		   =							old['encoder/encoder_norm/scale']

							if not is_encoder_only:
														# Decoder.
														for i in range(UpperCamelCase__ ):
																					# Block i, layer 0 (Self Attention).
																					A__		   =							tax_layer_norm_lookup(UpperCamelCase__				,			UpperCamelCase__				,			'decoder'				,			'pre_self_attention_layer_norm' )
																					A__   ,     A__   ,     A__   ,     A__		   =							tax_attention_lookup(UpperCamelCase__				,			UpperCamelCase__				,			'decoder'				,			'self_attention' )
																					A__		   =							layer_norm
																					A__		   =							k.T
																					A__		   =							o.T
																					A__		   =							q.T
																					A__		   =							v.T

																					# Block i, layer 1 (Cross Attention).
																					A__		   =							tax_layer_norm_lookup(UpperCamelCase__				,			UpperCamelCase__				,			'decoder'				,			'pre_cross_attention_layer_norm' )
																					A__   ,     A__   ,     A__   ,     A__		   =							tax_attention_lookup(UpperCamelCase__				,			UpperCamelCase__				,			'decoder'				,			'encoder_decoder_attention' )
																					A__		   =							layer_norm
																					A__		   =							k.T
																					A__		   =							o.T
																					A__		   =							q.T
																					A__		   =							v.T

																					# Block i, layer 2 (MLP).
																					A__		   =							tax_layer_norm_lookup(UpperCamelCase__				,			UpperCamelCase__				,			'decoder'				,			'pre_mlp_layer_norm' )
																					A__   ,     A__		   =							tax_mlp_lookup(UpperCamelCase__				,			UpperCamelCase__				,			'decoder'				,			UpperCamelCase__ )
																					A__		   =							layer_norm
																					if split_mlp_wi:
																												A__		   =							wi[0].T
																												A__		   =							wi[1].T
																					else:
																												A__		   =							wi.T
																					A__		   =							wo.T

														A__		   =							old['decoder/decoder_norm/scale']
														A__		   =							old[
														    'decoder/relpos_bias/rel_embedding'
														].T

														# LM Head (only in v1.1 checkpoints, in v1.0 embeddings are used instead)
														if "decoder/logits_dense/kernel" in old:
																					A__		   =							old['decoder/logits_dense/kernel'].T

							return new






def      UpperCAmelCase    (		UpperCamelCase__				,			UpperCamelCase__ ):






							"""simple docstring"""





							A__		   =							collections.OrderedDict([(k, torch.from_numpy(v.copy() )) for (k, v) in converted_params.items()] )

							# Add what is missing.
							if "encoder.embed_tokens.weight" not in state_dict:
														A__		   =							state_dict['shared.weight']

							if not is_encoder_only:
														if "decoder.embed_tokens.weight" not in state_dict:
																					A__		   =							state_dict['shared.weight']

														if "lm_head.weight" not in state_dict:  # For old 1.0 models.
																					print('Using shared word embeddings as lm_head.' )
																					A__		   =							state_dict['shared.weight']

							return state_dict






def      UpperCAmelCase    (		UpperCamelCase__				,			UpperCamelCase__				,			UpperCamelCase__				,			UpperCamelCase__ ):






							"""simple docstring"""





							A__		   =							checkpoints.load_tax_checkpoint(UpperCamelCase__ )
							A__		   =							convert_tax_to_pytorch(UpperCamelCase__				,			num_layers=config.num_layers				,			is_encoder_only=UpperCamelCase__ )
							A__		   =							make_state_dict(UpperCamelCase__				,			UpperCamelCase__ )
							model.load_state_dict(UpperCamelCase__				,			strict=UpperCamelCase__ )






def      UpperCAmelCase    (		UpperCamelCase__				,			UpperCamelCase__				,			UpperCamelCase__				,			UpperCamelCase__ = False ):






							"""simple docstring"""





							A__		   =							TaConfig.from_json_file(UpperCamelCase__ )
							print(F'''Building PyTorch model from configuration: {config}''' )
							# Non-v1.1 checkpoints could also use T5Model, but this works for all.
							# The v1.0 checkpoints will simply have an LM head that is the word embeddings.
							if is_encoder_only:
														A__		   =							TaEncoderModel(UpperCamelCase__ )
							else:
														A__		   =							TaForConditionalGeneration(UpperCamelCase__ )

							# Load weights from tf checkpoint
							load_tax_weights_in_ta(UpperCamelCase__				,			UpperCamelCase__				,			UpperCamelCase__				,			UpperCamelCase__ )

							# Save pytorch-model
							print(F'''Save PyTorch model to {pytorch_dump_path}''' )
							model.save_pretrained(UpperCamelCase__ )

							# Verify that we can load the checkpoint.
							model.from_pretrained(UpperCamelCase__ )
							print('Done' )


if __name__ == "__main__":
				__lowerCamelCase							 =     argparse.ArgumentParser(description="Converts a native T5X checkpoint into a PyTorch checkpoint.")
				# Required parameters
				parser.add_argument(
				    "--t5x_checkpoint_path", default=None, type=str, required=True, help="Path to the T5X checkpoint."
				)
				parser.add_argument(
				    "--config_file",
				    default=None,
				    type=str,
				    required=True,
				    help="The config json file corresponding to the pre-trained T5 model.\nThis specifies the model architecture.",
				)
				parser.add_argument(
				    "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
				)
				parser.add_argument(
				    "--is_encoder_only", action="store_true", help="Check if the model is encoder-decoder model", default=False
				)
				__lowerCamelCase							 =     parser.parse_args()
				convert_tax_checkpoint_to_pytorch(
				    args.tax_checkpoint_path, args.config_file, args.pytorch_dump_path, args.is_encoder_only
				)






"""simple docstring"""




# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


__lowerCamelCase							 =     {
    "configuration_vivit": ["VIVIT_PRETRAINED_CONFIG_ARCHIVE_MAP", "VivitConfig"],
}
try:
				if not is_vision_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__lowerCamelCase							 =     ["VivitImageProcessor"]


try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__lowerCamelCase							 =     [
				    "VIVIT_PRETRAINED_MODEL_ARCHIVE_LIST",
				    "VivitModel",
				    "VivitPreTrainedModel",
				    "VivitForVideoClassification",
				]


if TYPE_CHECKING:
				from .configuration_vivit import VIVIT_PRETRAINED_CONFIG_ARCHIVE_MAP, VivitConfig

				try:
								if not is_vision_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .image_processing_vivit import VivitImageProcessor

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_vivit import (
								    VIVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
								    VivitForVideoClassification,
								    VivitModel,
								    VivitPreTrainedModel,
								)


else:
				import sys

				__lowerCamelCase							 =     _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





