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

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    is_torch_available,
    is_torch_tensor,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_torch_available():
	import torch

if is_vision_available():
	import PIL


lowercase__			:				Tuple		=	logging.get_logger(__name__)

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


							def constraint_to_multiple_of(snake_case__   ,  snake_case__   ,  snake_case__=0   ,  snake_case__=None							):
														lowerCAmelCase			=		round(val / multiple							) * multiple

														if max_val is not None and x > max_val:
																					lowerCAmelCase			=		math.floor(val / multiple							) * multiple

														if x < min_val:
																					lowerCAmelCase			=		math.ceil(val / multiple							) * multiple

														return x

							lowerCAmelCase			=		(output_size, output_size) if isinstance(UpperCamelCase__   ,  UpperCamelCase__							) else output_size

							lowerCAmelCase		,      lowerCAmelCase			=		get_image_size(UpperCamelCase__							)
							lowerCAmelCase		,      lowerCAmelCase			=		output_size

							# determine new height and width
							lowerCAmelCase			=		output_height / input_height
							lowerCAmelCase			=		output_width / input_width

							if keep_aspect_ratio:
														# scale as little as possible
														if abs(1 - scale_width							) < abs(1 - scale_height							):
																					# fit width
																					lowerCAmelCase			=		scale_width
														else:
																					# fit height
																					lowerCAmelCase			=		scale_height

							lowerCAmelCase			=		constraint_to_multiple_of(scale_height * input_height   ,  multiple=UpperCamelCase__							)
							lowerCAmelCase			=		constraint_to_multiple_of(scale_width * input_width   ,  multiple=UpperCamelCase__							)

							return (new_height, new_width)






class    lowercase_  (  UpperCamelCase_						):



	"""simple docstring"""





	UpperCAmelCase_  :					int   =				["""pixel_values"""]


	def __init__(							self		,			__SCREAMING_SNAKE_CASE = True		,			__SCREAMING_SNAKE_CASE = None		,			__SCREAMING_SNAKE_CASE = PILImageResampling.BILINEAR		,			__SCREAMING_SNAKE_CASE = False		,			__SCREAMING_SNAKE_CASE = 1		,			__SCREAMING_SNAKE_CASE = True		,			__SCREAMING_SNAKE_CASE = 1 / 255		,			__SCREAMING_SNAKE_CASE = True		,			__SCREAMING_SNAKE_CASE = None		,			__SCREAMING_SNAKE_CASE = None		,			**__SCREAMING_SNAKE_CASE		,			)						->None:
								super().__init__(**lowerCamelCase__				)
								lowerCAmelCase			=		size if size is not None else {'''height''': 384, '''width''': 384}
								lowerCAmelCase			=		get_size_dict(lowerCamelCase__				)
								lowerCAmelCase			=		do_resize
								lowerCAmelCase			=		size
								lowerCAmelCase			=		keep_aspect_ratio
								lowerCAmelCase			=		ensure_multiple_of
								lowerCAmelCase			=		resample
								lowerCAmelCase			=		do_rescale
								lowerCAmelCase			=		rescale_factor
								lowerCAmelCase			=		do_normalize
								lowerCAmelCase			=		image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
								lowerCAmelCase			=		image_std if image_std is not None else IMAGENET_STANDARD_STD


	def 			SCREAMING_SNAKE_CASE_				(							self		,			__SCREAMING_SNAKE_CASE		,			__SCREAMING_SNAKE_CASE		,			__SCREAMING_SNAKE_CASE = False		,			__SCREAMING_SNAKE_CASE = 1		,			__SCREAMING_SNAKE_CASE = PILImageResampling.BICUBIC		,			__SCREAMING_SNAKE_CASE = None		,			**__SCREAMING_SNAKE_CASE		,			)						->np.ndarray:
								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			=		get_resize_output_image_size(
								    lowerCamelCase__		,			output_size=(size['''height'''], size['''width'''])		,			keep_aspect_ratio=lowerCamelCase__		,			multiple=lowerCamelCase__		,			)
								return resize(lowerCamelCase__		,			size=lowerCamelCase__		,			resample=lowerCamelCase__		,			data_format=lowerCamelCase__		,			**lowerCamelCase__				)


	def 			SCREAMING_SNAKE_CASE_				(							self		,			__SCREAMING_SNAKE_CASE		,			__SCREAMING_SNAKE_CASE		,			__SCREAMING_SNAKE_CASE = None		,			**__SCREAMING_SNAKE_CASE		,			)						->Tuple:
								return rescale(lowerCamelCase__		,			scale=lowerCamelCase__		,			data_format=lowerCamelCase__		,			**lowerCamelCase__				)


	def 			SCREAMING_SNAKE_CASE_				(							self		,			__SCREAMING_SNAKE_CASE		,			__SCREAMING_SNAKE_CASE		,			__SCREAMING_SNAKE_CASE		,			__SCREAMING_SNAKE_CASE = None		,			**__SCREAMING_SNAKE_CASE		,			)						->np.ndarray:
								return normalize(lowerCamelCase__		,			mean=lowerCamelCase__		,			std=lowerCamelCase__		,			data_format=lowerCamelCase__		,			**lowerCamelCase__				)


	def 			SCREAMING_SNAKE_CASE_				(							self		,			__SCREAMING_SNAKE_CASE		,			__SCREAMING_SNAKE_CASE = None		,			__SCREAMING_SNAKE_CASE = None		,			__SCREAMING_SNAKE_CASE = None		,			__SCREAMING_SNAKE_CASE = None		,			__SCREAMING_SNAKE_CASE = None		,			__SCREAMING_SNAKE_CASE = None		,			__SCREAMING_SNAKE_CASE = None		,			__SCREAMING_SNAKE_CASE = None		,			__SCREAMING_SNAKE_CASE = None		,			__SCREAMING_SNAKE_CASE = None		,			__SCREAMING_SNAKE_CASE = None		,			__SCREAMING_SNAKE_CASE = ChannelDimension.FIRST		,			**__SCREAMING_SNAKE_CASE		,			)						->PIL.Image.Image:
								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			=		keep_aspect_ratio if keep_aspect_ratio is not None else self.keep_aspect_ratio
								lowerCAmelCase			=		ensure_multiple_of if ensure_multiple_of is not None else self.ensure_multiple_of
								lowerCAmelCase			=		resample if resample is not None else self.resample
								lowerCAmelCase			=		do_rescale if do_rescale is not None else self.do_rescale
								lowerCAmelCase			=		rescale_factor if rescale_factor is not None else self.rescale_factor
								lowerCAmelCase			=		do_normalize if do_normalize is not None else self.do_normalize
								lowerCAmelCase			=		image_mean if image_mean is not None else self.image_mean
								lowerCAmelCase			=		image_std if image_std is not None else self.image_std

								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 or resample is None:
															raise ValueError('''Size and resample 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_normalize and (image_mean is None or image_std is None):
															raise ValueError('''Image mean and std must be specified if do_normalize is True.'''				)

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

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

								if do_rescale:
															lowerCAmelCase			=		[self.rescale(image=lowerCamelCase__		,			scale=lowerCamelCase__				) for image in images]

								if do_normalize:
															lowerCAmelCase			=		[self.normalize(image=lowerCamelCase__		,			mean=lowerCamelCase__		,			std=lowerCamelCase__				) for image in images]

								lowerCAmelCase			=		[to_channel_dimension_format(lowerCamelCase__		,			lowerCamelCase__				) for image in images]

								lowerCAmelCase			=		{'''pixel_values''': images}
								return BatchFeature(data=lowerCamelCase__		,			tensor_type=lowerCamelCase__				)




	def 			SCREAMING_SNAKE_CASE_				(							self		,			__SCREAMING_SNAKE_CASE		,			__SCREAMING_SNAKE_CASE = None				)						->List[str]:
								lowerCAmelCase			=		outputs.logits

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

															if is_torch_tensor(lowerCamelCase__				):
																						lowerCAmelCase			=		target_sizes.numpy()

															lowerCAmelCase			=		[]

															for idx in range(len(lowerCamelCase__				)				):
																						lowerCAmelCase			=		torch.nn.functional.interpolate(
																						    logits[idx].unsqueeze(dim=0				)		,			size=target_sizes[idx]		,			mode='''bilinear'''		,			align_corners=lowerCamelCase__				)
																						lowerCAmelCase			=		resized_logits[0].argmax(dim=0				)
																						semantic_segmentation.append(lowerCamelCase__				)
								else:
															lowerCAmelCase			=		logits.argmax(dim=1				)
															lowerCAmelCase			=		[semantic_segmentation[i] for i in range(semantic_segmentation.shape[0]				)]

								return semantic_segmentation









import argparse
import os
import pickle
import sys

import torch

from transformers import TransfoXLConfig, TransfoXLLMHeadModel, load_tf_weights_in_transfo_xl
from transformers.models.transfo_xl import tokenization_transfo_xl as data_utils
from transformers.models.transfo_xl.tokenization_transfo_xl import CORPUS_NAME, VOCAB_FILES_NAMES
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()

# We do this to be able to load python 2 datasets pickles
# See e.g. https://stackoverflow.com/questions/2121874/python-pickling-after-changing-a-modules-directory/2121918#2121918
__A        =    data_utils.TransfoXLTokenizer
__A        =    data_utils.TransfoXLCorpus
__A        =    data_utils
__A        =    data_utils







def 			lowerCamelCase_		(       UpperCamelCase__				:      List[str] ,     UpperCamelCase__				:      Optional[int] ,     UpperCamelCase__				:      Union[str, Any] ,     UpperCamelCase__				:      Optional[Any]      )     ->       Union[str, Any]:





				"""simple docstring"""


				if transfo_xl_dataset_file:
								# Convert a pre-processed corpus (see original TensorFlow repo)
								with open(UpperCamelCase__ ,     'rb'      ) as fp:
												__lowerCamelCase		       =    pickle.load(UpperCamelCase__ ,     encoding='latin1'      )
								# Save vocabulary and dataset cache as Dictionaries (should be better than pickles for the long-term)
								__lowerCamelCase		       =    pytorch_dump_folder_path + '/' + VOCAB_FILES_NAMES['pretrained_vocab_file']
								print(F"""Save vocabulary to {pytorch_vocab_dump_path}"""      )
								__lowerCamelCase		       =    corpus.vocab.__dict__
								torch.save(UpperCamelCase__ ,     UpperCamelCase__      )

								__lowerCamelCase		       =    corpus.__dict__
								corpus_dict_no_vocab.pop('vocab' ,     UpperCamelCase__      )
								__lowerCamelCase		       =    pytorch_dump_folder_path + '/' + CORPUS_NAME
								print(F"""Save dataset to {pytorch_dataset_dump_path}"""      )
								torch.save(UpperCamelCase__ ,     UpperCamelCase__      )

				if tf_checkpoint_path:
								# Convert a pre-trained TensorFlow model
								__lowerCamelCase		       =    os.path.abspath(UpperCamelCase__      )
								__lowerCamelCase		       =    os.path.abspath(UpperCamelCase__      )

								print(F"""Converting Transformer XL checkpoint from {tf_path} with config at {config_path}."""      )
								# Initialise PyTorch model
								if transfo_xl_config_file == "":
												__lowerCamelCase		       =    TransfoXLConfig()
								else:
												__lowerCamelCase		       =    TransfoXLConfig.from_json_file(UpperCamelCase__      )
								print(F"""Building PyTorch model from configuration: {config}"""      )
								__lowerCamelCase		       =    TransfoXLLMHeadModel(UpperCamelCase__      )

								__lowerCamelCase		       =    load_tf_weights_in_transfo_xl(UpperCamelCase__ ,     UpperCamelCase__ ,     UpperCamelCase__      )
								# Save pytorch-model
								__lowerCamelCase		       =    os.path.join(UpperCamelCase__ ,     UpperCamelCase__      )
								__lowerCamelCase		       =    os.path.join(UpperCamelCase__ ,     UpperCamelCase__      )
								print(F"""Save PyTorch model to {os.path.abspath(UpperCamelCase__      )}"""      )
								torch.save(model.state_dict() ,     UpperCamelCase__      )
								print(F"""Save configuration file to {os.path.abspath(UpperCamelCase__      )}"""      )
								with open(UpperCamelCase__ ,     'w' ,     encoding='utf-8'      ) as f:
												f.write(config.to_json_string()      )


if __name__ == "__main__":
					__A        =    argparse.ArgumentParser()
					parser.add_argument(
					    "--pytorch_dump_folder_path",
					    default=None,
					    type=str,
					    required=True,
					    help="Path to the folder to store the PyTorch model or dataset/vocab.",
					)
					parser.add_argument(
					    "--tf_checkpoint_path",
					    default="",
					    type=str,
					    help="An optional path to a TensorFlow checkpoint path to be converted.",
					)
					parser.add_argument(
					    "--transfo_xl_config_file",
					    default="",
					    type=str,
					    help=(
					        "An optional config json file corresponding to the pre-trained BERT model. \n"
					        "This specifies the model architecture."
					    ),
					)
					parser.add_argument(
					    "--transfo_xl_dataset_file",
					    default="",
					    type=str,
					    help="An optional dataset file to be converted in a vocabulary.",
					)
					__A        =    parser.parse_args()
					convert_transfo_xl_checkpoint_to_pytorch(
					    args.tf_checkpoint_path,
					    args.transfo_xl_config_file,
					    args.pytorch_dump_folder_path,
					    args.transfo_xl_dataset_file,
					)


