


'''simple docstring'''

import argparse
import json
import os
from collections import OrderedDict

import numpy as np
import tensorflow as tf
import torch






def __UpperCamelCase   (			__lowerCamelCase		: Optional[Any]				)      ->     Tuple:







     '''simple docstring'''






     _a						=		os.path.join(args.tf_model_dir      ,  "parameters.json"				)
     _a						=		json.loads(open(__lowerCamelCase				).read()				)
     if not params:
          raise ValueError(
              F"It seems that the json file at {parameter_file} is empty. Make sure you have a correct json file."				)
     if not args.output.endswith(".pt"				):
          _a						=		args.output + ".pt"
     _a						=		OrderedDict()
     with tf.device("/CPU:0"				):
          _a						=		tf.train.load_checkpoint(args.tf_model_dir				)
          _a						=		reader.get_variable_to_shape_map()
          for key_name in shapes.keys():
               _a						=		reader.get_tensor(__lowerCamelCase				).astype(np.floataa				)
               if key_name.endswith("/adam_m"				) or key_name.endswith("/adam_v"				):
                    continue
               if key_name.startswith("pasts/"				):
                    if key_name.startswith("pasts/mlp"				):
                         _a						=		int(key_name[9]				)
                    elif key_name.startswith("pasts/out"				):
                         _a						=		8
                    _a						=		"model.sqout.%d.weight" % (player * 2)  # enter to nn.Sequencial with Tanh, so 2 at a time
                    _a						=		vnp.transpose([1, 0]				).copy()  # Mesh-Tensorflow is a diagonal matrix
                    _a						=		torch.tensor(__lowerCamelCase				)
               elif key_name.startswith("model/moe"				):
                    _a						=		int(key_name[9:].split("/"				)[0]				)
                    if key_name.endswith("/switch_gating/kernel"				):
                         _a						=		"model.blocks.%d.feed_forward.mlp.router.classifier.weight" % player
                         _a						=		vnp.transpose([1, 0]				).copy()  # Mesh-Tensorflow is a diagonal matrix
                         _a						=		torch.tensor(__lowerCamelCase				)
                    elif key_name.endswith("/softmlp/kernel"				):
                         _a						=		"model.blocks.%d.feed_forward.soft_bypass_mlp.weight" % player
                         _a						=		vnp.transpose([1, 0]				).copy()  # Mesh-Tensorflow is a diagonal matrix
                         _a						=		torch.tensor(__lowerCamelCase				)
                    elif key_name.endswith("/wo/kernel"				) or key_name.endswith("/wi/kernel"				):
                         _a						=		key_name[-9:-7]
                         for i in range(16				):
                              _a						=		"model.blocks.%d.feed_forward.mlp.experts.expert_%d.%s.weight" % (player, i, nlayer)
                              _a						=		(
                                  vnp[i].transpose([1, 0]				).copy()
                              )  # In Mesh-Tensorflow, it is one array, so it is divided
                              _a						=		torch.tensor(__lowerCamelCase				)
               elif key_name.startswith("model/mlp"				):
                    _a						=		int(key_name[9:].split("/"				)[0]				)
                    if key_name.endswith("/p1/kernel"				):
                         _a						=		"model.blocks.%d.feed_forward.mlp.wi.weight" % player
                         _a						=		vnp.transpose([1, 0]				).copy()  # Mesh-Tensorflow is a diagonal matrix
                         _a						=		torch.tensor(__lowerCamelCase				)
                    elif key_name.endswith("/p1/bias"				):
                         _a						=		"model.blocks.%d.feed_forward.mlp.wi.bias" % player
                         _a						=		vnp.copy()  # same because it is one dimensional
                         _a						=		torch.tensor(__lowerCamelCase				)
                    elif key_name.endswith("/p2/kernel"				):
                         _a						=		"model.blocks.%d.feed_forward.mlp.wo.weight" % player
                         _a						=		vnp.transpose([1, 0]				).copy()  # Mesh-Tensorflow is a diagonal matrix
                         _a						=		torch.tensor(__lowerCamelCase				)
                    elif key_name.endswith("/p2/bias"				):
                         _a						=		"model.blocks.%d.feed_forward.mlp.wo.bias" % player
                         _a						=		vnp.copy()  # same because it is one dimensional
                         _a						=		torch.tensor(__lowerCamelCase				)
               elif key_name.startswith("model/ln"				):
                    _a						=		int(key_name[8:].split("/"				)[0]				)
                    if key_name.endswith("/b"				):
                         _a						=		"model.blocks.%d.feed_forward.norm.bias" % player
                         _a						=		vnp.copy()  # same because it is one dimensional
                         _a						=		torch.tensor(__lowerCamelCase				)
                    elif key_name.endswith("/g"				):
                         _a						=		"model.blocks.%d.feed_forward.norm.weight" % player
                         _a						=		vnp.copy()  # same because it is one dimensional
                         _a						=		torch.tensor(__lowerCamelCase				)
               elif key_name.startswith("model/att"				):
                    _a						=		int(key_name[9:].split("/"				)[0]				)
                    if key_name.endswith("/qkv/kernel"				):
                         _a						=		vnp.copy()  # Compute same dimension as Mesh-tensorflow using einsum
                         _a						=		state[:, 0, :, :]
                         _a						=		state[:, 1, :, :]
                         _a						=		state[:, 2, :, :]
                         _a						=		(
                             state_q.reshape([state_q.shape[0], state_q.shape[1] * state_q.shape[2]]				)
                             .transpose([1, 0]				)
                             .copy()
                         )  # Mesh-Tensorflow is a diagonal matrix
                         _a						=		(
                             state_k.reshape([state_k.shape[0], state_k.shape[1] * state_k.shape[2]]				)
                             .transpose([1, 0]				)
                             .copy()
                         )  # Mesh-Tensorflow is a diagonal matrix
                         _a						=		(
                             state_v.reshape([state_v.shape[0], state_v.shape[1] * state_v.shape[2]]				)
                             .transpose([1, 0]				)
                             .copy()
                         )  # Mesh-Tensorflow is a diagonal matrix
                         _a						=		"model.blocks.%d.self_attn.self_attn.q_proj.weight" % player
                         _a						=		torch.tensor(__lowerCamelCase				)
                         _a						=		"model.blocks.%d.self_attn.self_attn.k_proj.weight" % player
                         _a						=		torch.tensor(__lowerCamelCase				)
                         _a						=		"model.blocks.%d.self_attn.self_attn.v_proj.weight" % player
                         _a						=		torch.tensor(__lowerCamelCase				)
                    elif key_name.endswith("/o/kernel"				):
                         _a						=		"model.blocks.%d.self_attn.self_attn.out_proj.weight" % player
                         _a						=		(
                             vnp.reshape([vnp.shape[0] * vnp.shape[1], vnp.shape[2]]				).transpose([1, 0]				).copy()
                         )  # Mesh-Tensorflow is a diagonal matrix
                         _a						=		torch.tensor(__lowerCamelCase				)
               elif key_name.startswith("model/an"				):
                    _a						=		int(key_name[8:].split("/"				)[0]				)
                    if key_name.endswith("/b"				):
                         _a						=		"model.blocks.%d.self_attn.norm.bias" % player
                         _a						=		vnp.copy()  # same because it is one dimensional
                         _a						=		torch.tensor(__lowerCamelCase				)
                    elif key_name.endswith("/g"				):
                         _a						=		"model.blocks.%d.self_attn.norm.weight" % player
                         _a						=		vnp.copy()  # same because it is one dimensional
                         _a						=		torch.tensor(__lowerCamelCase				)
               elif (
                   key_name.startswith("model/wte"				)
                   or key_name.startswith("model/wpe"				)
                   or key_name.startswith("model/ete"				)
               ):
                    _a						=		{"wte": "embed_tokens", "wpe": "position_embeddings", "ete": "extra_position_embeddings"}[
                        key_name[-3:]
                    ]
                    _a						=		"model.%s.weight" % nlayer
                    _a						=		vnp.copy()  # same in embedded
                    _a						=		torch.tensor(__lowerCamelCase				)
                    if key_name.startswith("model/wte"				):
                         _a						=		"lm_head.weight"
                         _a						=		vnp.copy()  # same in embedded
                         _a						=		torch.tensor(__lowerCamelCase				)
               elif key_name.startswith("model/wob"				):
                    _a						=		"final_logits_bias"
                    _a						=		vnp.copy()  # same in embedded
                    _a						=		state.reshape((1, -1)				)
                    _a						=		torch.tensor(__lowerCamelCase				)
               elif key_name == "model/dense/kernel":
                    _a						=		"model.last_project.weight"
                    _a						=		vnp.transpose([1, 0]				).copy()  # Mesh-Tensorflow is a diagonal matrix
                    _a						=		torch.tensor(__lowerCamelCase				)
               elif key_name == "model/dense_1/bias":
                    _a						=		"model.last_project.bias"
                    _a						=		vnp.copy()  # same because it is one dimensional
                    _a						=		torch.tensor(__lowerCamelCase				)
     torch.save(__lowerCamelCase      ,  args.output				)


if __name__ == "__main__":
   lowercase__				     =		argparse.ArgumentParser(
       description="model converter.", formatter_class=argparse.ArgumentDefaultsHelpFormatter
   )
   parser.add_argument("--tf_model_dir", metavar="PATH", type=str, required=True, help="import model")
   parser.add_argument("--output", metavar="PATH", type=str, required=True, help="output model")
   lowercase__				     =		parser.parse_args()
   convert_tf_gptsan_to_pt(args)





'''simple docstring'''

import warnings
from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType





class 						__SCREAMING_SNAKE_CASE					(      lowerCamelCase__ ):
     UpperCAmelCase 					=				['''image_processor''', '''tokenizer''']
     UpperCAmelCase 					=				'''LayoutLMv2ImageProcessor'''
     UpperCAmelCase 					=				('''LayoutXLMTokenizer''', '''LayoutXLMTokenizerFast''')






     def __init__(			self     ,							__UpperCamelCase=None     ,							__UpperCamelCase=None     ,							**__UpperCamelCase		)       ->    str:
          if "feature_extractor" in kwargs:
               warnings.warn(
                   "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
                   " instead."     ,							__UpperCamelCase     ,							)
               _a						=		kwargs.pop("feature_extractor"		)

          _a						=		image_processor if image_processor is not None else feature_extractor
          if image_processor is None:
               raise ValueError("You need to specify an `image_processor`."		)
          if tokenizer is None:
               raise ValueError("You need to specify a `tokenizer`."		)

          super().__init__(__UpperCamelCase     ,							__UpperCamelCase		)






     def __call__(			self     ,							__UpperCamelCase     ,							__UpperCamelCase = None     ,							__UpperCamelCase = None     ,							__UpperCamelCase = None     ,							__UpperCamelCase = None     ,							__UpperCamelCase = True     ,							__UpperCamelCase = False     ,							__UpperCamelCase = None     ,							__UpperCamelCase = None     ,							__UpperCamelCase = 0     ,							__UpperCamelCase = None     ,							__UpperCamelCase = None     ,							__UpperCamelCase = None     ,							__UpperCamelCase = False     ,							__UpperCamelCase = False     ,							__UpperCamelCase = False     ,							__UpperCamelCase = False     ,							__UpperCamelCase = True     ,							__UpperCamelCase = None     ,							**__UpperCamelCase     ,							)       ->    BatchEncoding:
          # verify input
          if self.image_processor.apply_ocr and (boxes is not None):
               raise ValueError(
                   "You cannot provide bounding boxes "
                   "if you initialized the image processor with apply_ocr set to True."		)

          if self.image_processor.apply_ocr and (word_labels is not None):
               raise ValueError(
                   "You cannot provide word labels if you initialized the image processor with apply_ocr set to True."		)

          if return_overflowing_tokens is True and return_offsets_mapping is False:
               raise ValueError("You cannot return overflowing tokens without returning the offsets mapping."		)

          # first, apply the image processor
          _a						=		self.image_processor(images=__UpperCamelCase     ,							return_tensors=__UpperCamelCase		)

          # second, apply the tokenizer
          if text is not None and self.image_processor.apply_ocr and text_pair is None:
               if isinstance(__UpperCamelCase     ,							__UpperCamelCase		):
                    _a						=		[text]  # add batch dimension (as the image processor always adds a batch dimension)
               _a						=		features["words"]

          _a						=		self.tokenizer(
              text=text if text is not None else features["words"]     ,							text_pair=text_pair if text_pair is not None else None     ,							boxes=boxes if boxes is not None else features["boxes"]     ,							word_labels=__UpperCamelCase     ,							add_special_tokens=__UpperCamelCase     ,							padding=__UpperCamelCase     ,							truncation=__UpperCamelCase     ,							max_length=__UpperCamelCase     ,							stride=__UpperCamelCase     ,							pad_to_multiple_of=__UpperCamelCase     ,							return_token_type_ids=__UpperCamelCase     ,							return_attention_mask=__UpperCamelCase     ,							return_overflowing_tokens=__UpperCamelCase     ,							return_special_tokens_mask=__UpperCamelCase     ,							return_offsets_mapping=__UpperCamelCase     ,							return_length=__UpperCamelCase     ,							verbose=__UpperCamelCase     ,							return_tensors=__UpperCamelCase     ,							**__UpperCamelCase     ,							)

          # add pixel values
          _a						=		features.pop("pixel_values"		)
          if return_overflowing_tokens is True:
               _a						=		self.get_overflowing_images(__UpperCamelCase     ,							encoded_inputs["overflow_to_sample_mapping"]		)
          _a						=		images

          return encoded_inputs






     def  a_					(			self     ,							__UpperCamelCase     ,							__UpperCamelCase		)       ->    Dict:
          # in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
          _a						=		[]
          for sample_idx in overflow_to_sample_mapping:
               images_with_overflow.append(images[sample_idx]		)

          if len(__UpperCamelCase		) != len(__UpperCamelCase		):
               raise ValueError(
                   "Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got"
                   f" {len(__UpperCamelCase		)} and {len(__UpperCamelCase		)}"		)

          return images_with_overflow






     def  a_					(			self     ,							*__UpperCamelCase     ,							**__UpperCamelCase		)       ->    Tuple:
          return self.tokenizer.batch_decode(*__UpperCamelCase     ,							**__UpperCamelCase		)






     def  a_					(			self     ,							*__UpperCamelCase     ,							**__UpperCamelCase		)       ->    List[Any]:
          return self.tokenizer.decode(*__UpperCamelCase     ,							**__UpperCamelCase		)






     @property
     def  a_					(			self		)       ->    int:
          return ["input_ids", "bbox", "attention_mask", "image"]






     @property
     def  a_					(			self		)       ->    Dict:
          warnings.warn(
              "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."     ,							__UpperCamelCase     ,							)
          return self.image_processor_class






     @property
     def  a_					(			self		)       ->    int:
          warnings.warn(
              "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."     ,							__UpperCamelCase     ,							)
          return self.image_processor

