


"""simple docstring"""




import argparse
from argparse import Namespace

import torch
from torch import nn

from transformers import XGLMConfig, XGLMForCausalLM







def     lowerCAmelCase_					( __lowerCamelCase     ):
							__snake_case		:      Dict										=       [
							    "decoder.version",
							    "decoder.output_projection.weight",
							    "_float_tensor",
							    "decoder.embed_positions._float_tensor",
							]
							for k in ignore_keys:
														state_dict.pop(__lowerCamelCase							,     __lowerCamelCase     )







def     lowerCAmelCase_					( __lowerCamelCase     ):
							__snake_case		:      Any										=       emb.weight.shape
							__snake_case		:      Optional[Any]										=       nn.Linear(__lowerCamelCase							,     __lowerCamelCase							,     bias=__lowerCamelCase     )
							__snake_case		:      Optional[Any]										=       emb.weight.data
							return lin_layer







def     lowerCAmelCase_					( __lowerCamelCase     ):
							__snake_case		:      List[Any]										=       torch.load(__lowerCamelCase							,     map_location="cpu"     )
							__snake_case		:      Dict										=       Namespace(**checkpoint["cfg"]["model"]     )
							__snake_case		:      Dict										=       checkpoint["model"]
							remove_ignore_keys_(__lowerCamelCase     )
							__snake_case		:      Union[str, Any]										=       state_dict["decoder.embed_tokens.weight"].shape[0]

							__snake_case		:      str										=       {key.replace("decoder"							,     "model"     ): val for key, val in state_dict.items()}

							__snake_case		:      Union[str, Any]										=       XGLMConfig(
							    vocab_size=__lowerCamelCase							,     max_position_embeddings=args.max_target_positions							,     num_layers=args.decoder_layers							,     attention_heads=args.decoder_attention_heads							,     ffn_dim=args.decoder_ffn_embed_dim							,     d_model=args.decoder_embed_dim							,     layerdrop=args.decoder_layerdrop							,     dropout=args.dropout							,     attention_dropout=args.attention_dropout							,     activation_dropout=args.activation_dropout							,     activation_function="gelu"							,     scale_embedding=not args.no_scale_embedding							,     tie_word_embeddings=args.share_decoder_input_output_embed							,     )

							__snake_case		:      Optional[int]										=       XGLMForCausalLM(__lowerCamelCase     )
							__snake_case		:      Dict										=       model.load_state_dict(__lowerCamelCase							,     strict=__lowerCamelCase     )
							print(__lowerCamelCase     )
							__snake_case		:      Optional[Any]										=       make_linear_from_emb(model.model.embed_tokens     )

							return model


if __name__ == "__main__":
			_snake_case					:		List[str]      =    argparse.ArgumentParser()
			# Required parameters
			parser.add_argument("fairseq_path", type=str, help="path to a model.pt on local filesystem.")
			parser.add_argument("pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
			_snake_case					:		int      =    parser.parse_args()
			_snake_case					:		Optional[int]      =    convert_fairseq_xglm_checkpoint_from_disk(args.fairseq_path)
			model.save_pretrained(args.pytorch_dump_folder_path)





from typing import List, Union

from ..utils import (
    add_end_docstrings,
    is_tf_available,
    is_torch_available,
    is_vision_available,
    logging,
    requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
			from PIL import Image

			from ..image_utils import load_image

if is_tf_available():
			import tensorflow as tf

			from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
			from ..tf_utils import stable_softmax

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

_snake_case					:		Optional[int]      =    logging.get_logger(__name__)

@add_end_docstrings(_lowerCAmelCase				)
class     a		(_lowerCAmelCase				):




							"""simple docstring"""


							def __init__(							self	:  Tuple   ,     *lowerCamelCase	:  Any   ,     **lowerCamelCase	:  Tuple    )     ->   int:
														super().__init__(*lowerCamelCase   ,     **lowerCamelCase    )
														requires_backends(self   ,     "vision"    )
														self.check_model_type(
														    TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
														    if self.framework == "tf"
														    else MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING    )

							def    __snake_case (							self	:  List[str]   ,     lowerCamelCase	:  Optional[Any]=None    )     ->   Optional[int]:
														__snake_case		:      Optional[Any]										=       {}
														if top_k is not None:
																					__snake_case		:      List[Any]										=       top_k
														return {}, {}, postprocess_params

							def __call__(							self	:  List[Any]   ,     lowerCamelCase	:  Union[str, List[str], "Image.Image", List["Image.Image"]]   ,     **lowerCamelCase	:  Dict    )     ->   Optional[int]:
														return super().__call__(lowerCamelCase   ,     **lowerCamelCase    )

							def    __snake_case (							self	:  Optional[Any]   ,     lowerCamelCase	:  List[Any]    )     ->   int:
														__snake_case		:      Any										=       load_image(lowerCamelCase    )
														__snake_case		:      str										=       self.image_processor(images=lowerCamelCase   ,     return_tensors=self.framework    )
														return model_inputs

							def    __snake_case (							self	:  int   ,     lowerCamelCase	:  List[str]    )     ->   Tuple:
														__snake_case		:      List[Any]										=       self.model(**lowerCamelCase    )
														return model_outputs






							def    __snake_case (							self	:  List[Any]   ,     lowerCamelCase	:  Any   ,     lowerCamelCase	:  Optional[Any]=5    )     ->   List[str]:
														if top_k > self.model.config.num_labels:
																					__snake_case		:      int										=       self.model.config.num_labels

														if self.framework == "pt":
																					__snake_case		:      Optional[Any]										=       model_outputs.logits.softmax(-1    )[0]
																					__snake_case			,      __snake_case		:      List[str]										=       probs.topk(lowerCamelCase    )
														elif self.framework == "tf":
																					__snake_case		:      Tuple										=       stable_softmax(model_outputs.logits   ,     axis=-1    )[0]
																					__snake_case		:      Optional[Any]										=       tf.math.top_k(lowerCamelCase   ,     k=lowerCamelCase    )
																					__snake_case			,      __snake_case		:      List[Any]										=       topk.values.numpy(), topk.indices.numpy()
														else:
																					raise ValueError(F'Unsupported framework: {self.framework}'    )

														__snake_case		:      Any										=       scores.tolist()
														__snake_case		:      Optional[int]										=       ids.tolist()
														return [{"score": score, "label": self.model.config.idalabel[_id]} for score, _id in zip(lowerCamelCase   ,     lowerCamelCase    )]

