

'''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 							_UpperCamelCase				(   lowerCAmelCase__:					Union[str, Any]		,lowerCAmelCase__:					List[str]		,lowerCAmelCase__:					Dict   )      -> Optional[int]:
							return params[F"""{prefix}/{prefix}/relpos_bias/rel_embedding"""][:, i, :]




def 							_UpperCamelCase				(   lowerCAmelCase__:					Optional[int]		,lowerCAmelCase__:					str		,lowerCAmelCase__:					Tuple		,lowerCAmelCase__:					List[Any]="attention"   )      -> Optional[Any]:
							SCREAMING_SNAKE_CASE_				=				np.ascontiguousarray(params[F"""{prefix}/{prefix}/{layer_name}/key/kernel"""][:, i, :, :]   )
							SCREAMING_SNAKE_CASE_				=				k_tmp.reshape(k_tmp.shape[0]		,k_tmp.shape[1] * k_tmp.shape[2]   )
							SCREAMING_SNAKE_CASE_				=				np.ascontiguousarray(params[F"""{prefix}/{prefix}/{layer_name}/out/kernel"""][:, i, :, :]   )
							SCREAMING_SNAKE_CASE_				=				o_tmp.reshape(o_tmp.shape[0] * o_tmp.shape[1]		,o_tmp.shape[2]   )
							SCREAMING_SNAKE_CASE_				=				np.ascontiguousarray(params[F"""{prefix}/{prefix}/{layer_name}/query/kernel"""][:, i, :, :]   )
							SCREAMING_SNAKE_CASE_				=				q_tmp.reshape(q_tmp.shape[0]		,q_tmp.shape[1] * q_tmp.shape[2]   )
							SCREAMING_SNAKE_CASE_				=				np.ascontiguousarray(params[F"""{prefix}/{prefix}/{layer_name}/value/kernel"""][:, i, :, :]   )
							SCREAMING_SNAKE_CASE_				=				v_tmp.reshape(v_tmp.shape[0]		,v_tmp.shape[1] * v_tmp.shape[2]   )
							return k, o, q, v




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







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

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




def 							_UpperCamelCase				(   lowerCAmelCase__:					Optional[int]		,lowerCAmelCase__:					Dict		,lowerCAmelCase__:					Union[str, Any]		,lowerCAmelCase__:					Dict   )      -> Optional[Any]:
							return params[F"""{prefix}/{prefix}/{layer_name}/scale"""][:, i]




def 							_UpperCamelCase				(   lowerCAmelCase__:					Optional[Any]		,*, lowerCAmelCase__:					Optional[Any]		,lowerCAmelCase__:					List[str]		,lowerCAmelCase__:					str = False   )      -> Tuple:
							SCREAMING_SNAKE_CASE_				=				traverse_util.flatten_dict(variables['target']   )
							SCREAMING_SNAKE_CASE_				=				{'/'.join(_A   ): v for k, v in old.items()}

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

							SCREAMING_SNAKE_CASE_				=				collections.OrderedDict()

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

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

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

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

							if not scalable_attention:
														SCREAMING_SNAKE_CASE_				=				tax_relpos_bias_lookup(
														    _A		,0		,'encoder'   ).T
														SCREAMING_SNAKE_CASE_				=				tax_relpos_bias_lookup(
														    _A		,0		,'decoder'   ).T

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

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

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

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

														SCREAMING_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:
																					SCREAMING_SNAKE_CASE_				=				old['decoder/logits_dense/kernel'].T

							return new




def 							_UpperCamelCase				(   lowerCAmelCase__:					Union[str, Any]		,lowerCAmelCase__:					List[str]   )      -> List[Any]:
							SCREAMING_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:
														SCREAMING_SNAKE_CASE_				=				state_dict['shared.weight']

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

							return state_dict




def 							_UpperCamelCase				(   lowerCAmelCase__:					Dict		,lowerCAmelCase__:					Tuple		,lowerCAmelCase__:					Union[str, Any]		,lowerCAmelCase__:					Any		,lowerCAmelCase__:					List[Any]   )      -> str:
							SCREAMING_SNAKE_CASE_				=				checkpoints.load_tax_checkpoint(_A   )
							SCREAMING_SNAKE_CASE_				=				convert_tax_to_pytorch(
							    _A		,num_layers=config.num_layers		,is_encoder_only=_A		,scalable_attention=_A   )
							SCREAMING_SNAKE_CASE_				=				make_state_dict(_A		,_A   )
							model.load_state_dict(_A		,strict=_A   )




def 							_UpperCamelCase				(   lowerCAmelCase__:					str		,lowerCAmelCase__:					Optional[int]		,lowerCAmelCase__:					str		,lowerCAmelCase__:					List[Any] = False		,lowerCAmelCase__:					List[Any] = False		,)      -> Optional[int]:
							SCREAMING_SNAKE_CASE_				=				MTaConfig.from_json_file(_A   )
							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:
														SCREAMING_SNAKE_CASE_				=				UMTaEncoderModel(_A   )
							else:
														SCREAMING_SNAKE_CASE_				=				UMTaForConditionalGeneration(_A   )

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

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

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


if __name__ == "__main__":
							SCREAMING_SNAKE_CASE							:				List[str]     	=			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							:				Any     	=			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 typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


lowerCAmelCase     :  int											=    {
    """configuration_owlvit""": [
        """OWLVIT_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """OwlViTConfig""",
        """OwlViTOnnxConfig""",
        """OwlViTTextConfig""",
        """OwlViTVisionConfig""",
    ],
    """processing_owlvit""": ["""OwlViTProcessor"""],
}


try:
       if not is_vision_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCAmelCase     :  Dict											=    ["""OwlViTFeatureExtractor"""]
       lowerCAmelCase     :  int											=    ["""OwlViTImageProcessor"""]

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCAmelCase     :  Optional[Any]											=    [
           """OWLVIT_PRETRAINED_MODEL_ARCHIVE_LIST""",
           """OwlViTModel""",
           """OwlViTPreTrainedModel""",
           """OwlViTTextModel""",
           """OwlViTVisionModel""",
           """OwlViTForObjectDetection""",
       ]

if TYPE_CHECKING:
       from .configuration_owlvit import (
           OWLVIT_PRETRAINED_CONFIG_ARCHIVE_MAP,
           OwlViTConfig,
           OwlViTOnnxConfig,
           OwlViTTextConfig,
           OwlViTVisionConfig,
       )
       from .processing_owlvit import OwlViTProcessor

       try:
              if not is_vision_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .feature_extraction_owlvit import OwlViTFeatureExtractor
              from .image_processing_owlvit import OwlViTImageProcessor

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_owlvit import (
                  OWLVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
                  OwlViTForObjectDetection,
                  OwlViTModel,
                  OwlViTPreTrainedModel,
                  OwlViTTextModel,
                  OwlViTVisionModel,
              )

else:
       import sys

       lowerCAmelCase     :  Union[str, Any]											=    _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

