



"""simple docstring"""






from typing import Dict, List, Optional, Tuple, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    flip_channel_order,
    get_resize_output_image_size,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_torch_available, is_torch_tensor, is_vision_available, logging


if is_vision_available():
 import PIL

if is_torch_available():
 import torch


__snake_case							:     Tuple   =					logging.get_logger(__name__)



class     A__     (						__SCREAMING_SNAKE_CASE							):




 '''simple docstring'''





 SCREAMING_SNAKE_CASE					   =			['''pixel_values''']

 def __init__(  self:	str ,    _SCREAMING_SNAKE_CASE:	List[str] = True ,    _SCREAMING_SNAKE_CASE:	Union[str, Any] = None ,    _SCREAMING_SNAKE_CASE:	Dict = PILImageResampling.BILINEAR ,    _SCREAMING_SNAKE_CASE:	int = True ,    _SCREAMING_SNAKE_CASE:	List[str] = 1 / 255 ,    _SCREAMING_SNAKE_CASE:	Union[str, Any] = True ,    _SCREAMING_SNAKE_CASE:	Union[str, Any] = None ,    _SCREAMING_SNAKE_CASE:	List[str] = True ,    **_SCREAMING_SNAKE_CASE:	Tuple ,    )       -> int:

    """simple docstring"""

    super().__init__(**A__)
    __lowerCAmelCase   :						Dict				= size if size is not None else {"""shortest_edge""": 224}
    __lowerCAmelCase   :						Dict				= get_size_dict(A__ ,    default_to_square=A__)
    __lowerCAmelCase   :						List[str]				= crop_size if crop_size is not None else {"""height""": 256, """width""": 256}
    __lowerCAmelCase   :						List[str]				= get_size_dict(A__ ,    param_name="crop_size")

    __lowerCAmelCase   :						str				= do_resize
    __lowerCAmelCase   :						str				= size
    __lowerCAmelCase   :						Optional[Any]				= resample
    __lowerCAmelCase   :						Optional[Any]				= do_rescale
    __lowerCAmelCase   :						str				= rescale_factor
    __lowerCAmelCase   :						Optional[Any]				= do_center_crop
    __lowerCAmelCase   :						Dict				= crop_size
    __lowerCAmelCase   :						Optional[Any]				= do_flip_channel_order

 def 							_SCREAMING_SNAKE_CASE     (  self:	Union[str, Any] ,    _SCREAMING_SNAKE_CASE:	List[Any] ,    _SCREAMING_SNAKE_CASE:	Dict ,    _SCREAMING_SNAKE_CASE:	str = PIL.Image.BILINEAR ,    _SCREAMING_SNAKE_CASE:	int = None ,    **_SCREAMING_SNAKE_CASE:	List[str] ,    )       -> Any:

    """simple docstring"""

    __lowerCAmelCase   :						Any				= get_size_dict(A__ ,    default_to_square=A__)
    if "shortest_edge" not in size:
       raise ValueError(F"""The `size` dictionary must contain the key `shortest_edge`. Got {size.keys()}""")
    __lowerCAmelCase   :						Union[str, Any]				= get_resize_output_image_size(A__ ,    size=size["shortest_edge"] ,    default_to_square=A__)
    return resize(A__ ,    size=A__ ,    resample=A__ ,    data_format=A__ ,    **A__)

 def 							_SCREAMING_SNAKE_CASE     (  self:	Tuple ,    _SCREAMING_SNAKE_CASE:	Any ,    _SCREAMING_SNAKE_CASE:	Any ,    _SCREAMING_SNAKE_CASE:	List[Any] = None ,    **_SCREAMING_SNAKE_CASE:	Dict ,    )       -> Tuple:

    """simple docstring"""

    __lowerCAmelCase   :						Optional[Any]				= get_size_dict(A__)
    if "height" not in size or "width" not in size:
       raise ValueError(F"""The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}""")
    return center_crop(A__ ,    size=(size["height"], size["width"]) ,    data_format=A__ ,    **A__)

 def 							_SCREAMING_SNAKE_CASE     (  self:	Optional[Any] ,    _SCREAMING_SNAKE_CASE:	Optional[int] ,    _SCREAMING_SNAKE_CASE:	List[str] ,    _SCREAMING_SNAKE_CASE:	List[str] = None ,    **_SCREAMING_SNAKE_CASE:	int ,    )       -> Dict:

    """simple docstring"""

    return rescale(A__ ,    scale=A__ ,    data_format=A__ ,    **A__)

 def 							_SCREAMING_SNAKE_CASE     (  self:	List[Any] ,    _SCREAMING_SNAKE_CASE:	List[Any] ,    _SCREAMING_SNAKE_CASE:	Any = None)       -> Any:

    """simple docstring"""

    return flip_channel_order(A__ ,    data_format=A__)

 def 							_SCREAMING_SNAKE_CASE     (  self:	Tuple ,    _SCREAMING_SNAKE_CASE:	List[Any] ,    _SCREAMING_SNAKE_CASE:	Union[str, Any] = None ,    _SCREAMING_SNAKE_CASE:	Union[str, Any] = None ,    _SCREAMING_SNAKE_CASE:	Union[str, Any] = None ,    _SCREAMING_SNAKE_CASE:	Tuple = None ,    _SCREAMING_SNAKE_CASE:	Any = None ,    _SCREAMING_SNAKE_CASE:	List[Any] = None ,    _SCREAMING_SNAKE_CASE:	List[str] = None ,    _SCREAMING_SNAKE_CASE:	Optional[int] = None ,    _SCREAMING_SNAKE_CASE:	str = None ,    _SCREAMING_SNAKE_CASE:	Optional[int] = ChannelDimension.FIRST ,    **_SCREAMING_SNAKE_CASE:	Union[str, Any] ,    )       -> List[str]:

    """simple docstring"""

    __lowerCAmelCase   :						List[str]				= do_resize if do_resize is not None else self.do_resize
    __lowerCAmelCase   :						Union[str, Any]				= resample if resample is not None else self.resample
    __lowerCAmelCase   :						Optional[Any]				= do_rescale if do_rescale is not None else self.do_rescale
    __lowerCAmelCase   :						int				= rescale_factor if rescale_factor is not None else self.rescale_factor
    __lowerCAmelCase   :						int				= do_center_crop if do_center_crop is not None else self.do_center_crop
    __lowerCAmelCase   :						Union[str, Any]				= (
        do_flip_channel_order if do_flip_channel_order is not None else self.do_flip_channel_order
    )

    __lowerCAmelCase   :						Union[str, Any]				= size if size is not None else self.size
    __lowerCAmelCase   :						Any				= get_size_dict(A__ ,    default_to_square=A__)
    __lowerCAmelCase   :						Optional[Any]				= crop_size if crop_size is not None else self.crop_size
    __lowerCAmelCase   :						List[Any]				= get_size_dict(A__ ,    param_name="crop_size")

    __lowerCAmelCase   :						Dict				= make_list_of_images(A__)

    if not valid_images(A__):
       raise ValueError(
           "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
           "torch.Tensor, tf.Tensor or jax.ndarray.")

    if do_resize and size is None:
       raise ValueError("Size must be specified if do_resize is True.")

    if do_rescale and rescale_factor is None:
       raise ValueError("Rescale factor must be specified if do_rescale is True.")

    if do_center_crop and crop_size is None:
       raise ValueError("Crop size must be specified if do_center_crop is True.")

    # All transformations expect numpy arrays.
    __lowerCAmelCase   :						str				= [to_numpy_array(A__) for image in images]

    if do_resize:
       __lowerCAmelCase   :						Optional[Any]				= [self.resize(image=A__ ,    size=A__ ,    resample=A__) for image in images]

    if do_center_crop:
       __lowerCAmelCase   :						Dict				= [self.center_crop(image=A__ ,    size=A__) for image in images]

    if do_rescale:
       __lowerCAmelCase   :						int				= [self.rescale(image=A__ ,    scale=A__) for image in images]

    # the pretrained checkpoints assume images are BGR, not RGB
    if do_flip_channel_order:
       __lowerCAmelCase   :						List[Any]				= [self.flip_channel_order(image=A__) for image in images]

    __lowerCAmelCase   :						Any				= [to_channel_dimension_format(A__ ,    A__) for image in images]

    __lowerCAmelCase   :						Any				= {"""pixel_values""": images}
    return BatchFeature(data=A__ ,    tensor_type=A__)







 def 							_SCREAMING_SNAKE_CASE     (  self:	List[Any] ,    _SCREAMING_SNAKE_CASE:	Optional[int] ,    _SCREAMING_SNAKE_CASE:	List[str] = None)       -> List[Any]:

    """simple docstring"""

    __lowerCAmelCase   :						Union[str, Any]				= outputs.logits

    # Resize logits and compute semantic segmentation maps
    if target_sizes is not None:
       if len(A__) != len(A__):
          raise ValueError(
              "Make sure that you pass in as many target sizes as the batch dimension of the logits")

       if is_torch_tensor(A__):
          __lowerCAmelCase   :						Tuple				= target_sizes.numpy()

       __lowerCAmelCase   :						List[Any]				= []

       for idx in range(len(A__)):
          __lowerCAmelCase   :						Optional[Any]				= torch.nn.functional.interpolate(
              logits[idx].unsqueeze(dim=0) ,    size=target_sizes[idx] ,    mode="bilinear" ,    align_corners=A__)
          __lowerCAmelCase   :						List[Any]				= resized_logits[0].argmax(dim=0)
          semantic_segmentation.append(A__)
    else:
       __lowerCAmelCase   :						int				= logits.argmax(dim=1)
       __lowerCAmelCase   :						Any				= [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0])]

    return semantic_segmentation




"""simple docstring"""






import argparse
from argparse import Namespace

import torch
from torch import nn

from transformers import XGLMConfig, XGLMForCausalLM


def     _lowercase       (						__snake_case			)		->     Tuple:
			__lowerCAmelCase   :						Dict				= [
			    "decoder.version",
			    "decoder.output_projection.weight",
			    "_float_tensor",
			    "decoder.embed_positions._float_tensor",
			]
			for k in ignore_keys:
						state_dict.pop(__snake_case      ,__snake_case			)


def     _lowercase       (						__snake_case			)		->     List[str]:
			__lowerCAmelCase		,			__lowerCAmelCase   :						List[Any]				= emb.weight.shape
			__lowerCAmelCase   :						str				= nn.Linear(__snake_case      ,__snake_case      ,bias=__snake_case			)
			__lowerCAmelCase   :						Optional[int]				= emb.weight.data
			return lin_layer


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

			__lowerCAmelCase   :						Optional[int]				= {key.replace("decoder"      ,"model"			): val for key, val in state_dict.items()}

			__lowerCAmelCase   :						List[Any]				= XGLMConfig(
			    vocab_size=__snake_case      ,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      ,)

			__lowerCAmelCase   :						Union[str, Any]				= XGLMForCausalLM(__snake_case			)
			__lowerCAmelCase   :						Tuple				= model.load_state_dict(__snake_case      ,strict=__snake_case			)
			print(__snake_case			)
			__lowerCAmelCase   :						List[str]				= make_linear_from_emb(model.model.embed_tokens			)

			return model


if __name__ == "__main__":
	__snake_case							:     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							:     Optional[Any]   =					parser.parse_args()
	__snake_case							:     Optional[Any]   =					convert_fairseq_xglm_checkpoint_from_disk(args.fairseq_path)
	model.save_pretrained(args.pytorch_dump_folder_path)