




"""simple docstring"""







from typing import Dict, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import flip_channel_order, resize, to_channel_dimension_format, to_pil_image
from ...image_utils import (
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_pytesseract_available, is_vision_available, logging, requires_backends


if is_vision_available():
		import PIL

# soft dependency
if is_pytesseract_available():
		import pytesseract

a      =							logging.get_logger(__name__)





def 							lowercase					(snake_case__   : Tuple      ,       snake_case__   : Optional[int]      ,       snake_case__   : int )     ->  Optional[int]:


				'''simple docstring'''






				return [
				    int(1_000 * (box[0] / width) ),
				    int(1_000 * (box[1] / height) ),
				    int(1_000 * (box[2] / width) ),
				    int(1_000 * (box[3] / height) ),
				]





def 							lowercase					(snake_case__   : np.ndarray      ,       snake_case__   : Optional[str]      ,       snake_case__   : Optional[str] = None )     ->  Optional[Any]:


				'''simple docstring'''






				lowerCAmelCase  =				tesseract_config if tesseract_config is not None else """"""

				# apply OCR
				lowerCAmelCase  =				to_pil_image(snake_case__ )
				lowerCAmelCase ,						lowerCAmelCase  =				pil_image.size
				lowerCAmelCase  =				pytesseract.image_to_data(snake_case__      ,       lang=snake_case__      ,       output_type="""dict"""      ,       config=snake_case__ )
				lowerCAmelCase ,						lowerCAmelCase ,						lowerCAmelCase ,						lowerCAmelCase ,						lowerCAmelCase  =				data["""text"""], data["""left"""], data["""top"""], data["""width"""], data["""height"""]

				# filter empty words and corresponding coordinates
				lowerCAmelCase  =				[idx for idx, word in enumerate(snake_case__ ) if not word.strip()]
				lowerCAmelCase  =				[word for idx, word in enumerate(snake_case__ ) if idx not in irrelevant_indices]
				lowerCAmelCase  =				[coord for idx, coord in enumerate(snake_case__ ) if idx not in irrelevant_indices]
				lowerCAmelCase  =				[coord for idx, coord in enumerate(snake_case__ ) if idx not in irrelevant_indices]
				lowerCAmelCase  =				[coord for idx, coord in enumerate(snake_case__ ) if idx not in irrelevant_indices]
				lowerCAmelCase  =				[coord for idx, coord in enumerate(snake_case__ ) if idx not in irrelevant_indices]

				# turn coordinates into (left, top, left+width, top+height) format
				lowerCAmelCase  =				[]
				for x, y, w, h in zip(snake_case__      ,       snake_case__      ,       snake_case__      ,       snake_case__ ):
								lowerCAmelCase  =				[x, y, x + w, y + h]
								actual_boxes.append(snake_case__ )

				# finally, normalize the bounding boxes
				lowerCAmelCase  =				[]
				for box in actual_boxes:
								normalized_boxes.append(normalize_box(snake_case__      ,       snake_case__      ,       snake_case__ ) )

				assert len(snake_case__ ) == len(snake_case__ ), "Not as many words as there are bounding boxes"

				return words, normalized_boxes






class       SCREAMING_SNAKE_CASE__					(					_a					):
						_a    						=						['pixel_values']


						def __init__(							self				:			Any						,						lowerCAmelCase				:			bool = True						,						lowerCAmelCase				:			Dict[str, int] = None						,						lowerCAmelCase				:			PILImageResampling = PILImageResampling.BILINEAR						,						lowerCAmelCase				:			bool = True						,						lowerCAmelCase				:			Optional[str] = None						,						lowerCAmelCase				:			Optional[str] = ""						,						**lowerCAmelCase				:			Optional[int]						,						):
										super().__init__(**lowerCAmelCase					)
										lowerCAmelCase  =				size if size is not None else {"""height""": 224, """width""": 224}
										lowerCAmelCase  =				get_size_dict(lowerCAmelCase					)

										lowerCAmelCase  =				do_resize
										lowerCAmelCase  =				size
										lowerCAmelCase  =				resample
										lowerCAmelCase  =				apply_ocr
										lowerCAmelCase  =				ocr_lang
										lowerCAmelCase  =				tesseract_config


						def   __lowercase (							self				:			Union[str, Any]						,						lowerCAmelCase				:			np.ndarray						,						lowerCAmelCase				:			Dict[str, int]						,						lowerCAmelCase				:			PILImageResampling = PILImageResampling.BILINEAR						,						lowerCAmelCase				:			Optional[Union[str, ChannelDimension]] = None						,						**lowerCAmelCase				:			int						,						):
										lowerCAmelCase  =				get_size_dict(lowerCAmelCase					)
										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()}'''					)
										lowerCAmelCase  =				(size["""height"""], size["""width"""])
										return resize(lowerCAmelCase						,						size=lowerCAmelCase						,						resample=lowerCAmelCase						,						data_format=lowerCAmelCase						,						**lowerCAmelCase					)




						def   __lowercase (							self				:			Optional[int]						,						lowerCAmelCase				:			ImageInput						,						lowerCAmelCase				:			bool = None						,						lowerCAmelCase				:			Dict[str, int] = None						,						lowerCAmelCase				:			PILImageResampling = None						,						lowerCAmelCase				:			bool = None						,						lowerCAmelCase				:			Optional[str] = None						,						lowerCAmelCase				:			Optional[str] = None						,						lowerCAmelCase				:			Optional[Union[str, TensorType]] = None						,						lowerCAmelCase				:			ChannelDimension = ChannelDimension.FIRST						,						**lowerCAmelCase				:			Tuple						,						):
										lowerCAmelCase  =				do_resize if do_resize is not None else self.do_resize
										lowerCAmelCase  =				size if size is not None else self.size
										lowerCAmelCase  =				get_size_dict(lowerCAmelCase					)
										lowerCAmelCase  =				resample if resample is not None else self.resample
										lowerCAmelCase  =				apply_ocr if apply_ocr is not None else self.apply_ocr
										lowerCAmelCase  =				ocr_lang if ocr_lang is not None else self.ocr_lang
										lowerCAmelCase  =				tesseract_config if tesseract_config is not None else self.tesseract_config

										lowerCAmelCase  =				make_list_of_images(lowerCAmelCase					)

										if not valid_images(lowerCAmelCase					):
														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."""					)

										# All transformations expect numpy arrays.
										lowerCAmelCase  =				[to_numpy_array(lowerCAmelCase					) for image in images]

										if apply_ocr:
														requires_backends(self						,						"""pytesseract"""					)
														lowerCAmelCase  =				[]
														lowerCAmelCase  =				[]
														for image in images:
																		lowerCAmelCase ,						lowerCAmelCase  =				apply_tesseract(lowerCAmelCase						,						lowerCAmelCase						,						lowerCAmelCase					)
																		words_batch.append(lowerCAmelCase					)
																		boxes_batch.append(lowerCAmelCase					)

										if do_resize:
														lowerCAmelCase  =				[self.resize(image=lowerCAmelCase						,						size=lowerCAmelCase						,						resample=lowerCAmelCase					) for image in images]

										# flip color channels from RGB to BGR (as Detectron2 requires this)
										lowerCAmelCase  =				[flip_channel_order(lowerCAmelCase					) for image in images]
										lowerCAmelCase  =				[to_channel_dimension_format(lowerCAmelCase						,						lowerCAmelCase					) for image in images]

										lowerCAmelCase  =				BatchFeature(data={"""pixel_values""": images}						,						tensor_type=lowerCAmelCase					)

										if apply_ocr:
														lowerCAmelCase  =				words_batch
														lowerCAmelCase  =				boxes_batch
										return data











"""simple docstring"""







import argparse
from argparse import Namespace

import torch
from torch import nn

from transformers import XGLMConfig, XGLMForCausalLM





def 							lowercase					(snake_case__   : Union[str, Any] )     ->  List[str]:


				'''simple docstring'''






				lowerCAmelCase  =				[
				    """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] )     ->  Optional[int]:


				'''simple docstring'''






				lowerCAmelCase ,						lowerCAmelCase  =				emb.weight.shape
				lowerCAmelCase  =				nn.Linear(snake_case__      ,       snake_case__      ,       bias=snake_case__ )
				lowerCAmelCase  =				emb.weight.data
				return lin_layer





def 							lowercase					(snake_case__   : str )     ->  Optional[Any]:


				'''simple docstring'''






				lowerCAmelCase  =				torch.load(snake_case__      ,       map_location="""cpu""" )
				lowerCAmelCase  =				Namespace(**checkpoint["""cfg"""]["""model"""] )
				lowerCAmelCase  =				checkpoint["""model"""]
				remove_ignore_keys_(snake_case__ )
				lowerCAmelCase  =				state_dict["""decoder.embed_tokens.weight"""].shape[0]

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

				lowerCAmelCase  =				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  =				XGLMForCausalLM(snake_case__ )
				lowerCAmelCase  =				model.load_state_dict(snake_case__      ,       strict=snake_case__ )
				print(snake_case__ )
				lowerCAmelCase  =				make_linear_from_emb(model.model.embed_tokens )

				return model


if __name__ == "__main__":
		a      =							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.')
		a      =							parser.parse_args()
		a      =							convert_fairseq_xglm_checkpoint_from_disk(args.fairseq_path)
		model.save_pretrained(args.pytorch_dump_folder_path)





