




'''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 		snake_case_							(_a			:							List[str]					,     _a			:							List[Any]					,     _a			:							List[Any]  ):
    return params[F"{prefix}/{prefix}/relpos_bias/rel_embedding"][:, i, :]





def 		snake_case_							(_a			:							Dict					,     _a			:							Optional[int]					,     _a			:							Union[str, Any]					,     _a			:							Union[str, Any]="attention"  ):
    UpperCAmelCase     =	UpperCAmelCase     =	np.ascontiguousarray(params[F"{prefix}/{prefix}/{layer_name}/key/kernel"][:, i, :, :]  )
    UpperCAmelCase     =	k_tmp.reshape(k_tmp.shape[0]					,     k_tmp.shape[1] * k_tmp.shape[2]  )
    UpperCAmelCase     =	np.ascontiguousarray(params[F"{prefix}/{prefix}/{layer_name}/out/kernel"][:, i, :, :]  )
    UpperCAmelCase     =	o_tmp.reshape(o_tmp.shape[0] * o_tmp.shape[1]					,     o_tmp.shape[2]  )
    UpperCAmelCase     =	np.ascontiguousarray(params[F"{prefix}/{prefix}/{layer_name}/query/kernel"][:, i, :, :]  )
    UpperCAmelCase     =	q_tmp.reshape(q_tmp.shape[0]					,     q_tmp.shape[1] * q_tmp.shape[2]  )
    UpperCAmelCase     =	np.ascontiguousarray(params[F"{prefix}/{prefix}/{layer_name}/value/kernel"][:, i, :, :]  )
    UpperCAmelCase     =	v_tmp.reshape(v_tmp.shape[0]					,     v_tmp.shape[1] * v_tmp.shape[2]  )
    return k, o, q, v





def 		snake_case_							(_a			:							Dict					,     _a			:							str					,     _a			:							Dict					,     _a			:							Dict=False  ):
    if split_mlp_wi:
        UpperCAmelCase     =	params[F"{prefix}/{prefix}/mlp/wi_0/kernel"][:, i, :]
        UpperCAmelCase     =	params[F"{prefix}/{prefix}/mlp/wi_1/kernel"][:, i, :]
        UpperCAmelCase     =	(wi_a, wi_a)
    else:
        UpperCAmelCase     =	params[F"{prefix}/{prefix}/mlp/wi/kernel"][:, i, :]

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





def 		snake_case_							(_a			:							str					,     _a			:							str					,     _a			:							Dict					,     _a			:							Dict  ):
    return params[F"{prefix}/{prefix}/{layer_name}/scale"][:, i]





def 		snake_case_							(_a			:							dict					,     *, _a			:							int					,     _a			:							bool					,     _a			:							bool = False  ):
    UpperCAmelCase     =	traverse_util.flatten_dict(variables['''target''']  )
    UpperCAmelCase     =	{'''/'''.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
    UpperCAmelCase     =	'''encoder/encoder/mlp/wi_0/kernel''' in old
    print('''Split MLP:'''					,     _a  )

    UpperCAmelCase     =	collections.OrderedDict()

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

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

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

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

    if not scalable_attention:
        UpperCAmelCase     =	tax_relpos_bias_lookup(
            _a					,     0					,     '''encoder'''  ).T
        UpperCAmelCase     =	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).
            UpperCAmelCase     =	tax_layer_norm_lookup(_a					,     _a					,     '''decoder'''					,     '''pre_self_attention_layer_norm'''  )
            UpperCAmelCase ,  UpperCAmelCase ,  UpperCAmelCase ,  UpperCAmelCase     =	tax_attention_lookup(_a					,     _a					,     '''decoder'''					,     '''self_attention'''  )
            UpperCAmelCase     =	layer_norm
            UpperCAmelCase     =	k.T
            UpperCAmelCase     =	o.T
            UpperCAmelCase     =	q.T
            UpperCAmelCase     =	v.T

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

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

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

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

    return new





def 		snake_case_							(_a			:							List[Any]					,     _a			:							bool  ):
    UpperCAmelCase     =	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:
        UpperCAmelCase     =	state_dict['''shared.weight''']

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

    return state_dict





def 		snake_case_							(_a			:							Optional[Any]					,     _a			:							Union[str, Any]					,     _a			:							Tuple					,     _a			:							Optional[int]					,     _a			:							Optional[int]  ):
    UpperCAmelCase     =	checkpoints.load_tax_checkpoint(_a  )
    UpperCAmelCase     =	convert_tax_to_pytorch(
        _a					,     num_layers=config.num_layers					,     is_encoder_only=_a					,     scalable_attention=_a  )
    UpperCAmelCase     =	make_state_dict(_a					,     _a  )
    model.load_state_dict(_a					,     strict=_a  )





def 		snake_case_							(_a			:							str					,     _a			:							Optional[Any]					,     _a			:							Any					,     _a			:							bool = False					,     _a			:							bool = False					,     ):
    UpperCAmelCase     =	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:
        UpperCAmelCase     =	UMTaEncoderModel(_a  )
    else:
        UpperCAmelCase     =	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__":
   A									=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,
   )
   A									=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 Union

from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
   from PIL import Image

   from ..image_utils import load_image

if is_torch_available():
   from ..models.auto.modeling_auto import MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING

A									=logging.get_logger(__name__)


@add_end_docstrings(__a				)
class     _a						(			__a				):
       def __init__(			self		:			Optional[int]     ,       *lowercase		:			Any     ,       **lowercase		:			Optional[int]       ):



           '''simple docstring'''

           super().__init__(*lowercase     ,       **lowercase       )
           self.check_model_type(lowercase       )
       def 					A   (			self		:			List[str]     ,       lowercase		:			str=None     ,       lowercase		:			List[str]=None     ,       lowercase		:			List[str]=None     ,       **lowercase		:			List[str]       ):



           '''simple docstring'''

           UpperCAmelCase ,  UpperCAmelCase     =	{}, {}
           if padding is not None:
               UpperCAmelCase     =	padding
           if truncation is not None:
               UpperCAmelCase     =	truncation
           if top_k is not None:
               UpperCAmelCase     =	top_k
           return preprocess_params, {}, postprocess_params
       def __call__(			self		:			Optional[Any]     ,       lowercase		:			Union["Image.Image", str]     ,       lowercase		:			str = None     ,       **lowercase		:			Optional[int]       ):



           '''simple docstring'''

           if isinstance(lowercase     ,       (Image.Image, str)       ) and isinstance(lowercase     ,       lowercase       ):
               UpperCAmelCase     =	{'''image''': image, '''question''': question}
           else:
               UpperCAmelCase     =	image
           UpperCAmelCase     =	super().__call__(lowercase     ,       **lowercase       )
           return results
       def 					A   (			self		:			List[Any]     ,       lowercase		:			List[str]     ,       lowercase		:			Any=False     ,       lowercase		:			Any=False       ):



           '''simple docstring'''

           UpperCAmelCase     =	load_image(inputs['''image''']       )
           UpperCAmelCase     =	self.tokenizer(
               inputs['''question''']     ,       return_tensors=self.framework     ,       padding=lowercase     ,       truncation=lowercase       )
           UpperCAmelCase     =	self.image_processor(images=lowercase     ,       return_tensors=self.framework       )
           model_inputs.update(lowercase       )
           return model_inputs
       def 					A   (			self		:			Dict     ,       lowercase		:			Optional[Any]       ):



           '''simple docstring'''

           UpperCAmelCase     =	self.model(**lowercase       )
           return model_outputs







       def 					A   (			self		:			Union[str, Any]     ,       lowercase		:			int     ,       lowercase		:			Optional[Any]=5       ):



           '''simple docstring'''

           if top_k > self.model.config.num_labels:
               UpperCAmelCase     =	self.model.config.num_labels

           if self.framework == "pt":
               UpperCAmelCase     =	model_outputs.logits.sigmoid()[0]
               UpperCAmelCase ,  UpperCAmelCase     =	probs.topk(lowercase       )
           else:
               raise ValueError(f"Unsupported framework: {self.framework}"       )

           UpperCAmelCase     =	scores.tolist()
           UpperCAmelCase     =	ids.tolist()
           return [{"score": score, "answer": self.model.config.idalabel[_id]} for score, _id in zip(lowercase     ,       lowercase       )]




