"""simple docstring"""
import argparse
import collections

import numpy as np
import torch
from flax import traverse_util
from tax import checkpoints

from transformers import MTaConfig, UMTaEncoderModel, UMTaForConditionalGeneration
from transformers.utils import logging


logging.set_verbosity_info()





def 				lowerCAmelCase__      (     _UpperCamelCase    :			List[str]	,     _UpperCamelCase    :			Dict	,     _UpperCamelCase    :			Dict   )   ->			List[Any]:





						"""simple docstring"""






						return params[f"""{prefix}/{prefix}/relpos_bias/rel_embedding"""][:, i, :]





def 				lowerCAmelCase__      (     _UpperCamelCase    :			Any	,     _UpperCamelCase    :			List[Any]	,     _UpperCamelCase    :			int	,     _UpperCamelCase    :			Tuple="attention"   )   ->			Dict:





						"""simple docstring"""






						snake_case        =     snake_case        =     np.ascontiguousarray(params[f"""{prefix}/{prefix}/{layer_name}/key/kernel"""][:, i, :, :]   )
						snake_case        =     k_tmp.reshape(k_tmp.shape[0]	,     k_tmp.shape[1] * k_tmp.shape[2]   )
						snake_case        =     np.ascontiguousarray(params[f"""{prefix}/{prefix}/{layer_name}/out/kernel"""][:, i, :, :]   )
						snake_case        =     o_tmp.reshape(o_tmp.shape[0] * o_tmp.shape[1]	,     o_tmp.shape[2]   )
						snake_case        =     np.ascontiguousarray(params[f"""{prefix}/{prefix}/{layer_name}/query/kernel"""][:, i, :, :]   )
						snake_case        =     q_tmp.reshape(q_tmp.shape[0]	,     q_tmp.shape[1] * q_tmp.shape[2]   )
						snake_case        =     np.ascontiguousarray(params[f"""{prefix}/{prefix}/{layer_name}/value/kernel"""][:, i, :, :]   )
						snake_case        =     v_tmp.reshape(v_tmp.shape[0]	,     v_tmp.shape[1] * v_tmp.shape[2]   )
						return k, o, q, v





def 				lowerCAmelCase__      (     _UpperCamelCase    :			int	,     _UpperCamelCase    :			Union[str, Any]	,     _UpperCamelCase    :			List[Any]	,     _UpperCamelCase    :			Dict=False   )   ->			List[str]:





						"""simple docstring"""






						if split_mlp_wi:
												snake_case        =     params[f"""{prefix}/{prefix}/mlp/wi_0/kernel"""][:, i, :]
												snake_case        =     params[f"""{prefix}/{prefix}/mlp/wi_1/kernel"""][:, i, :]
												snake_case        =     (wi_a, wi_a)
						else:
												snake_case        =     params[f"""{prefix}/{prefix}/mlp/wi/kernel"""][:, i, :]

						snake_case        =     params[f"""{prefix}/{prefix}/mlp/wo/kernel"""][:, i, :]
						return wi, wo





def 				lowerCAmelCase__      (     _UpperCamelCase    :			int	,     _UpperCamelCase    :			Optional[Any]	,     _UpperCamelCase    :			Optional[Any]	,     _UpperCamelCase    :			str   )   ->			Dict:





						"""simple docstring"""






						return params[f"""{prefix}/{prefix}/{layer_name}/scale"""][:, i]





def 				lowerCAmelCase__      (     _UpperCamelCase    :			dict	,     *, _UpperCamelCase    :			int	,     _UpperCamelCase    :			bool	,     _UpperCamelCase    :			bool = False   )   ->			str:





						"""simple docstring"""






						snake_case        =     traverse_util.flatten_dict(variables['target']   )
						snake_case        =     {'/'.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
						snake_case        =     'encoder/encoder/mlp/wi_0/kernel' in old
						print('Split MLP:'	,     _UpperCamelCase   )

						snake_case        =     collections.OrderedDict()

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

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

												# Block i, layer 1 (MLP).
												snake_case        =     tax_layer_norm_lookup(_UpperCamelCase	,     _UpperCamelCase	,     'encoder'	,     'pre_mlp_layer_norm'   )
												snake_case   ,snake_case        =     tax_mlp_lookup(_UpperCamelCase	,     _UpperCamelCase	,     'encoder'	,     _UpperCamelCase   )
												snake_case        =     layer_norm
												if split_mlp_wi:
																		snake_case        =     wi[0].T
																		snake_case        =     wi[1].T
												else:
																		snake_case        =     wi.T
												snake_case        =     wo.T
												if scalable_attention:
																		# convert the rel_embedding of each layer
																		snake_case        =     tax_relpos_bias_lookup(
																		    _UpperCamelCase	,     _UpperCamelCase	,     'encoder'   ).T

						snake_case        =     old['encoder/encoder_norm/scale']

						if not scalable_attention:
												snake_case        =     tax_relpos_bias_lookup(
												    _UpperCamelCase	,     0	,     'encoder'   ).T
												snake_case        =     tax_relpos_bias_lookup(
												    _UpperCamelCase	,     0	,     'decoder'   ).T

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

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

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

																		if scalable_attention:
																								# convert the rel_embedding of each layer
																								snake_case        =     tax_relpos_bias_lookup(_UpperCamelCase	,     _UpperCamelCase	,     'decoder'   ).T

												snake_case        =     old['decoder/decoder_norm/scale']

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

						return new





def 				lowerCAmelCase__      (     _UpperCamelCase    :			Union[str, Any]	,     _UpperCamelCase    :			bool   )   ->			str:





						"""simple docstring"""






						snake_case        =     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:
												snake_case        =     state_dict['shared.weight']

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

						return state_dict





def 				lowerCAmelCase__      (     _UpperCamelCase    :			Optional[Any]	,     _UpperCamelCase    :			Optional[int]	,     _UpperCamelCase    :			List[Any]	,     _UpperCamelCase    :			Tuple	,     _UpperCamelCase    :			Any   )   ->			Optional[Any]:





						"""simple docstring"""






						snake_case        =     checkpoints.load_tax_checkpoint(_UpperCamelCase   )
						snake_case        =     convert_tax_to_pytorch(
						    _UpperCamelCase	,     num_layers=config.num_layers	,     is_encoder_only=_UpperCamelCase	,     scalable_attention=_UpperCamelCase   )
						snake_case        =     make_state_dict(_UpperCamelCase	,     _UpperCamelCase   )
						model.load_state_dict(_UpperCamelCase	,     strict=_UpperCamelCase   )





def 				lowerCAmelCase__      (     _UpperCamelCase    :			str	,     _UpperCamelCase    :			Optional[Any]	,     _UpperCamelCase    :			int	,     _UpperCamelCase    :			bool = False	,     _UpperCamelCase    :			bool = False	,     )   ->			Optional[int]:





						"""simple docstring"""






						snake_case        =     MTaConfig.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:
												snake_case        =     UMTaEncoderModel(_UpperCamelCase   )
						else:
												snake_case        =     UMTaForConditionalGeneration(_UpperCamelCase   )

						# Load weights from tf checkpoint
						load_tax_weights_in_ta(_UpperCamelCase	,     _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__":
					SCREAMING_SNAKE_CASE__   =					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
					)
					parser.add_argument(
					    "--scalable_attention",
					    action="store_true",
					    help="Whether the model uses scaled attention (umt5 model)",
					    default=False,
					)
					SCREAMING_SNAKE_CASE__   =					parser.parse_args()
					convert_tax_checkpoint_to_pytorch(
					    args.tax_checkpoint_path,
					    args.config_file,
					    args.pytorch_dump_path,
					    args.is_encoder_only,
					    args.scalable_attention,
					)



"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging


SCREAMING_SNAKE_CASE__   =					logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__   =					{
    "microsoft/swinv2-tiny-patch4-window8-256": (
        "https://huggingface.co/microsoft/swinv2-tiny-patch4-window8-256/resolve/main/config.json"
    ),
}



class 					lowerCAmelCase_      (     lowerCAmelCase   ):



				"""simple docstring"""
				_lowerCAmelCase       :      Tuple     					=  """swinv2"""

				_lowerCAmelCase       :      Any     					=  {
				    """num_attention_heads""": """num_heads""",
				    """num_hidden_layers""": """num_layers""",
				}

				def __init__(			self						,      lowerCAmelCase=2_24						,      lowerCAmelCase=4						,      lowerCAmelCase=3						,      lowerCAmelCase=96						,      lowerCAmelCase=[2, 2, 6, 2]						,      lowerCAmelCase=[3, 6, 12, 24]						,      lowerCAmelCase=7						,      lowerCAmelCase=4.0						,      lowerCAmelCase=True						,      lowerCAmelCase=0.0						,      lowerCAmelCase=0.0						,      lowerCAmelCase=0.1						,      lowerCAmelCase="gelu"						,      lowerCAmelCase=False						,      lowerCAmelCase=0.02						,      lowerCAmelCase=1E-5						,      lowerCAmelCase=32						,      **lowerCAmelCase						,      ):






										"""simple docstring"""

										super().__init__(**lowerCAmelCase      )

										snake_case        =     image_size
										snake_case        =     patch_size
										snake_case        =     num_channels
										snake_case        =     embed_dim
										snake_case        =     depths
										snake_case        =     len(lowerCAmelCase      )
										snake_case        =     num_heads
										snake_case        =     window_size
										snake_case        =     mlp_ratio
										snake_case        =     qkv_bias
										snake_case        =     hidden_dropout_prob
										snake_case        =     attention_probs_dropout_prob
										snake_case        =     drop_path_rate
										snake_case        =     hidden_act
										snake_case        =     use_absolute_embeddings
										snake_case        =     layer_norm_eps
										snake_case        =     initializer_range
										snake_case        =     encoder_stride
										# we set the hidden_size attribute in order to make Swinv2 work with VisionEncoderDecoderModel
										# this indicates the channel dimension after the last stage of the model
										snake_case        =     int(embed_dim * 2 ** (len(lowerCAmelCase      ) - 1)      )
										snake_case        =     (0, 0, 0, 0)


