





'''simple docstring'''






import re
import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin




class  UpperCAmelCase     (			snake_case_   ):
      _lowercase:			Union[str, Any]						     =   ['''image_processor''', '''tokenizer''']
      _lowercase:			int						     =   '''AutoImageProcessor'''
      _lowercase:			Optional[int]						     =   '''AutoTokenizer'''




      def __init__(			self			: int			,			__snake_case			: Tuple=None			,			__snake_case			: Optional[int]=None			,			**__snake_case			: Tuple  )				->      List[Any]:
            _lowerCAmelCase							     =							None
            if "feature_extractor" in kwargs:
                  warnings.warn(
                      """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
                      """ instead."""			,			__snake_case			,			)
                  _lowerCAmelCase							     =							kwargs.pop("""feature_extractor"""  )

            _lowerCAmelCase							     =							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__(__snake_case			,			__snake_case  )
            _lowerCAmelCase							     =							self.image_processor
            _lowerCAmelCase							     =							False




      def __call__(			self			: Dict			,			*__snake_case			: Optional[int]			,			**__snake_case			: Union[str, Any]  )				->      Tuple:
            # For backward compatibility
            if self._in_target_context_manager:
                  return self.current_processor(*__snake_case			,			**__snake_case  )

            _lowerCAmelCase							     =							kwargs.pop("""images"""			,			__snake_case  )
            _lowerCAmelCase							     =							kwargs.pop("""text"""			,			__snake_case  )
            if len(__snake_case  ) > 0:
                  _lowerCAmelCase							     =							args[0]
                  _lowerCAmelCase							     =							args[1:]

            if images is None and text is None:
                  raise ValueError("""You need to specify either an `images` or `text` input to process."""  )

            if images is not None:
                  _lowerCAmelCase							     =							self.image_processor(__snake_case			,			*__snake_case			,			**__snake_case  )
            if text is not None:
                  _lowerCAmelCase							     =							self.tokenizer(__snake_case			,			**__snake_case  )

            if text is None:
                  return inputs
            elif images is None:
                  return encodings
            else:
                  _lowerCAmelCase							     =							encodings["""input_ids"""]
                  return inputs




      def 				lowercase__    (			self			: List[Any]			,			*__snake_case			: Dict			,			**__snake_case			: List[str]  )				->      int:
            return self.tokenizer.batch_decode(*__snake_case			,			**__snake_case  )




      def 				lowercase__    (			self			: int			,			*__snake_case			: Tuple			,			**__snake_case			: Optional[Any]  )				->      Any:
            return self.tokenizer.decode(*__snake_case			,			**__snake_case  )




      @contextmanager
      def 				lowercase__    (			self			: int  )				->      Optional[Any]:
            warnings.warn(
                """`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your """
                """labels by using the argument `text` of the regular `__call__` method (either in the same call as """
                """your images inputs, or in a separate call."""  )
            _lowerCAmelCase							     =							True
            _lowerCAmelCase							     =							self.tokenizer
            yield
            _lowerCAmelCase							     =							self.image_processor
            _lowerCAmelCase							     =							False




      def 				lowercase__    (			self			: Optional[int]			,			__snake_case			: Union[str, Any]			,			__snake_case			: List[Any]=False			,			__snake_case			: Dict=None  )				->      Tuple:
            if added_vocab is None:
                  _lowerCAmelCase							     =							self.tokenizer.get_added_vocab()

            _lowerCAmelCase							     =							{}

            while tokens:
                  _lowerCAmelCase							     =							re.search(R"""<s_(.*?)>"""			,			__snake_case			,			re.IGNORECASE  )
                  if start_token is None:
                        break
                  _lowerCAmelCase							     =							start_token.group(1  )
                  _lowerCAmelCase							     =							re.search(Rf"</s_{key}>"			,			__snake_case			,			re.IGNORECASE  )
                  _lowerCAmelCase							     =							start_token.group()
                  if end_token is None:
                        _lowerCAmelCase							     =							tokens.replace(__snake_case			,			""""""  )
                  else:
                        _lowerCAmelCase							     =							end_token.group()
                        _lowerCAmelCase							     =							re.escape(__snake_case  )
                        _lowerCAmelCase							     =							re.escape(__snake_case  )
                        _lowerCAmelCase							     =							re.search(f"{start_token_escaped}(.*?){end_token_escaped}"			,			__snake_case			,			re.IGNORECASE  )
                        if content is not None:
                              _lowerCAmelCase							     =							content.group(1  ).strip()
                              if r"<s_" in content and r"</s_" in content:  # non-leaf node
                                    _lowerCAmelCase							     =							self.tokenajson(__snake_case			,			is_inner_value=__snake_case			,			added_vocab=__snake_case  )
                                    if value:
                                          if len(__snake_case  ) == 1:
                                                _lowerCAmelCase							     =							value[0]
                                          _lowerCAmelCase							     =							value
                              else:  # leaf nodes
                                    _lowerCAmelCase							     =							[]
                                    for leaf in content.split(R"""<sep/>"""  ):
                                          _lowerCAmelCase							     =							leaf.strip()
                                          if leaf in added_vocab and leaf[0] == "<" and leaf[-2:] == "/>":
                                                _lowerCAmelCase							     =							leaf[1:-2]  # for categorical special tokens
                                          output[key].append(__snake_case  )
                                    if len(output[key]  ) == 1:
                                          _lowerCAmelCase							     =							output[key][0]

                        _lowerCAmelCase							     =							tokens[tokens.find(__snake_case  ) + len(__snake_case  ) :].strip()
                        if tokens[:6] == r"<sep/>":  # non-leaf nodes
                              return [output] + self.tokenajson(tokens[6:]			,			is_inner_value=__snake_case			,			added_vocab=__snake_case  )

            if len(__snake_case  ):
                  return [output] if is_inner_value else output
            else:
                  return [] if is_inner_value else {"text_sequence": tokens}




      @property
      def 				lowercase__    (			self			: Optional[int]  )				->      Union[str, Any]:
            warnings.warn(
                """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."""			,			__snake_case			,			)
            return self.image_processor_class




      @property
      def 				lowercase__    (			self			: List[Any]  )				->      Any:
            warnings.warn(
                """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."""			,			__snake_case			,			)
            return self.image_processor













"""simple docstring"""




import argparse
import json
import os
from collections import OrderedDict

import numpy as np
import tensorflow as tf
import torch






def       _SCREAMING_SNAKE_CASE		(	_lowercase						:    List[Any]					) ->str:






	'''simple docstring'''






	a      :		Union[str, Any]	=				os.path.join(args.tf_model_dir    ,       "parameters.json"					)
	a      :		str	=				json.loads(open(_lowercase					).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      :		str	=				args.output + ".pt"
	a      :		Dict	=				OrderedDict()
	with tf.device("/CPU:0"					):
		a      :		Optional[int]	=				tf.train.load_checkpoint(args.tf_model_dir					)
		a      :		Optional[Any]	=				reader.get_variable_to_shape_map()
		for key_name in shapes.keys():
			a      :		Dict	=				reader.get_tensor(_lowercase					).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      :		Union[str, Any]	=				int(key_name[9]					)
				elif key_name.startswith("pasts/out"					):
					a      :		Optional[int]	=				8
				a      :		Dict	=				"model.sqout.%d.weight" % (player * 2)  # enter to nn.Sequencial with Tanh, so 2 at a time
				a      :		int	=				vnp.transpose([1, 0]					).copy()  # Mesh-Tensorflow is a diagonal matrix
				a      :		Dict	=				torch.tensor(_lowercase					)
			elif key_name.startswith("model/moe"					):
				a      :		List[str]	=				int(key_name[9:].split("/"					)[0]					)
				if key_name.endswith("/switch_gating/kernel"					):
					a      :		str	=				"model.blocks.%d.feed_forward.mlp.router.classifier.weight" % player
					a      :		Tuple	=				vnp.transpose([1, 0]					).copy()  # Mesh-Tensorflow is a diagonal matrix
					a      :		List[str]	=				torch.tensor(_lowercase					)
				elif key_name.endswith("/softmlp/kernel"					):
					a      :		Optional[int]	=				"model.blocks.%d.feed_forward.soft_bypass_mlp.weight" % player
					a      :		Tuple	=				vnp.transpose([1, 0]					).copy()  # Mesh-Tensorflow is a diagonal matrix
					a      :		List[Any]	=				torch.tensor(_lowercase					)
				elif key_name.endswith("/wo/kernel"					) or key_name.endswith("/wi/kernel"					):
					a      :		Any	=				key_name[-9:-7]
					for i in range(16					):
						a      :		List[Any]	=				"model.blocks.%d.feed_forward.mlp.experts.expert_%d.%s.weight" % (player, i, nlayer)
						a      :		str	=				(
						    vnp[i].transpose([1, 0]					).copy()
						)  # In Mesh-Tensorflow, it is one array, so it is divided
						a      :		Dict	=				torch.tensor(_lowercase					)
			elif key_name.startswith("model/mlp"					):
				a      :		Union[str, Any]	=				int(key_name[9:].split("/"					)[0]					)
				if key_name.endswith("/p1/kernel"					):
					a      :		str	=				"model.blocks.%d.feed_forward.mlp.wi.weight" % player
					a      :		Dict	=				vnp.transpose([1, 0]					).copy()  # Mesh-Tensorflow is a diagonal matrix
					a      :		Optional[int]	=				torch.tensor(_lowercase					)
				elif key_name.endswith("/p1/bias"					):
					a      :		str	=				"model.blocks.%d.feed_forward.mlp.wi.bias" % player
					a      :		List[Any]	=				vnp.copy()  # same because it is one dimensional
					a      :		Tuple	=				torch.tensor(_lowercase					)
				elif key_name.endswith("/p2/kernel"					):
					a      :		Union[str, Any]	=				"model.blocks.%d.feed_forward.mlp.wo.weight" % player
					a      :		Tuple	=				vnp.transpose([1, 0]					).copy()  # Mesh-Tensorflow is a diagonal matrix
					a      :		List[Any]	=				torch.tensor(_lowercase					)
				elif key_name.endswith("/p2/bias"					):
					a      :		Dict	=				"model.blocks.%d.feed_forward.mlp.wo.bias" % player
					a      :		List[str]	=				vnp.copy()  # same because it is one dimensional
					a      :		str	=				torch.tensor(_lowercase					)
			elif key_name.startswith("model/ln"					):
				a      :		List[str]	=				int(key_name[8:].split("/"					)[0]					)
				if key_name.endswith("/b"					):
					a      :		Optional[Any]	=				"model.blocks.%d.feed_forward.norm.bias" % player
					a      :		Tuple	=				vnp.copy()  # same because it is one dimensional
					a      :		int	=				torch.tensor(_lowercase					)
				elif key_name.endswith("/g"					):
					a      :		Optional[Any]	=				"model.blocks.%d.feed_forward.norm.weight" % player
					a      :		List[str]	=				vnp.copy()  # same because it is one dimensional
					a      :		Tuple	=				torch.tensor(_lowercase					)
			elif key_name.startswith("model/att"					):
				a      :		Optional[Any]	=				int(key_name[9:].split("/"					)[0]					)
				if key_name.endswith("/qkv/kernel"					):
					a      :		Union[str, Any]	=				vnp.copy()  # Compute same dimension as Mesh-tensorflow using einsum
					a      :		List[str]	=				state[:, 0, :, :]
					a      :		Dict	=				state[:, 1, :, :]
					a      :		Union[str, Any]	=				state[:, 2, :, :]
					a      :		str	=				(
					    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      :		List[str]	=				(
					    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      :		Dict	=				(
					    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      :		List[Any]	=				"model.blocks.%d.self_attn.self_attn.q_proj.weight" % player
					a      :		Union[str, Any]	=				torch.tensor(_lowercase					)
					a      :		Any	=				"model.blocks.%d.self_attn.self_attn.k_proj.weight" % player
					a      :		List[str]	=				torch.tensor(_lowercase					)
					a      :		Optional[Any]	=				"model.blocks.%d.self_attn.self_attn.v_proj.weight" % player
					a      :		Optional[Any]	=				torch.tensor(_lowercase					)
				elif key_name.endswith("/o/kernel"					):
					a      :		Any	=				"model.blocks.%d.self_attn.self_attn.out_proj.weight" % player
					a      :		Optional[int]	=				(
					    vnp.reshape([vnp.shape[0] * vnp.shape[1], vnp.shape[2]]					).transpose([1, 0]					).copy()
					)  # Mesh-Tensorflow is a diagonal matrix
					a      :		Tuple	=				torch.tensor(_lowercase					)
			elif key_name.startswith("model/an"					):
				a      :		List[str]	=				int(key_name[8:].split("/"					)[0]					)
				if key_name.endswith("/b"					):
					a      :		Optional[int]	=				"model.blocks.%d.self_attn.norm.bias" % player
					a      :		Union[str, Any]	=				vnp.copy()  # same because it is one dimensional
					a      :		List[Any]	=				torch.tensor(_lowercase					)
				elif key_name.endswith("/g"					):
					a      :		Any	=				"model.blocks.%d.self_attn.norm.weight" % player
					a      :		str	=				vnp.copy()  # same because it is one dimensional
					a      :		Any	=				torch.tensor(_lowercase					)
			elif (
			    key_name.startswith("model/wte"					)
			    or key_name.startswith("model/wpe"					)
			    or key_name.startswith("model/ete"					)
			):
				a      :		Optional[int]	=				{"wte": "embed_tokens", "wpe": "position_embeddings", "ete": "extra_position_embeddings"}[
				    key_name[-3:]
				]
				a      :		Tuple	=				"model.%s.weight" % nlayer
				a      :		Any	=				vnp.copy()  # same in embedded
				a      :		Tuple	=				torch.tensor(_lowercase					)
				if key_name.startswith("model/wte"					):
					a      :		Optional[int]	=				"lm_head.weight"
					a      :		Optional[int]	=				vnp.copy()  # same in embedded
					a      :		Optional[int]	=				torch.tensor(_lowercase					)
			elif key_name.startswith("model/wob"					):
				a      :		Optional[int]	=				"final_logits_bias"
				a      :		Optional[Any]	=				vnp.copy()  # same in embedded
				a      :		Optional[int]	=				state.reshape((1, -1)					)
				a      :		List[Any]	=				torch.tensor(_lowercase					)
			elif key_name == "model/dense/kernel":
				a      :		Optional[int]	=				"model.last_project.weight"
				a      :		Union[str, Any]	=				vnp.transpose([1, 0]					).copy()  # Mesh-Tensorflow is a diagonal matrix
				a      :		List[Any]	=				torch.tensor(_lowercase					)
			elif key_name == "model/dense_1/bias":
				a      :		Dict	=				"model.last_project.bias"
				a      :		Optional[Any]	=				vnp.copy()  # same because it is one dimensional
				a      :		Any	=				torch.tensor(_lowercase					)
	torch.save(_lowercase    ,       args.output					)


if __name__ == "__main__":
		a						: Optional[int]     	=			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''')
		a						: Tuple     	=			parser.parse_args()
		convert_tf_gptsan_to_pt(args)


