
"""simple docstring"""


import argparse
import json
import os
from collections import OrderedDict

import numpy as np
import tensorflow as tf
import torch






def       _snake_case				(		snake_case__   :     Dict     ):
	A						   =   os.path.join(args.tf_model_dir	,      'parameters.json'     )
	A						   =   json.loads(open(snake_case__     ).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(snake_case__     ).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(snake_case__     )
			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(snake_case__     )
				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(snake_case__     )
				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(snake_case__     )
			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(snake_case__     )
				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(snake_case__     )
				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(snake_case__     )
				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(snake_case__     )
			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(snake_case__     )
				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(snake_case__     )
			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(snake_case__     )
					A						   =   'model.blocks.%d.self_attn.self_attn.k_proj.weight' % player
					A						   =   torch.tensor(snake_case__     )
					A						   =   'model.blocks.%d.self_attn.self_attn.v_proj.weight' % player
					A						   =   torch.tensor(snake_case__     )
				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(snake_case__     )
			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(snake_case__     )
				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(snake_case__     )
			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(snake_case__     )
				if key_name.startswith('model/wte'     ):
					A						   =   'lm_head.weight'
					A						   =   vnp.copy()  # same in embedded
					A						   =   torch.tensor(snake_case__     )
			elif key_name.startswith('model/wob'     ):
				A						   =   'final_logits_bias'
				A						   =   vnp.copy()  # same in embedded
				A						   =   state.reshape((1, -1)     )
				A						   =   torch.tensor(snake_case__     )
			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(snake_case__     )
			elif key_name == "model/dense_1/bias":
				A						   =   'model.last_project.bias'
				A						   =   vnp.copy()  # same because it is one dimensional
				A						   =   torch.tensor(snake_case__     )
	torch.save(snake_case__	,      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"""


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,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_torch_available, is_torch_tensor, logging


if is_torch_available():
		import torch


_lowercase							   =				logging.get_logger(__name__)







class 				lowerCAmelCase_				(      _lowercase    ):



						'''simple docstring'''


						_lowerCamelCase:    Dict      =    ['''pixel_values''']


						def __init__( self     :      Optional[Any]  ,A_     :      bool = True  ,A_     :      Optional[Dict[str, int]] = None  ,A_     :      PILImageResampling = PILImageResampling.BILINEAR  ,A_     :      bool = True  ,A_     :      Dict[str, int] = None  ,A_     :      bool = True  ,A_     :      Union[int, float] = 1 / 255  ,A_     :      bool = True  ,A_     :      Optional[Union[float, List[float]]] = None  ,A_     :      Optional[Union[float, List[float]]] = None  ,**A_     :      Optional[Any]  ,)     ->   None:
							super().__init__(**A_      )
							A						   =   size if size is not None else {'shortest_edge': 256}
							A						   =   get_size_dict(A_  ,default_to_square=A_      )
							A						   =   crop_size if crop_size is not None else {'height': 224, 'width': 224}
							A						   =   get_size_dict(A_  ,param_name='crop_size'      )
							A						   =   do_resize
							A						   =   size
							A						   =   resample
							A						   =   do_center_crop
							A						   =   crop_size
							A						   =   do_rescale
							A						   =   rescale_factor
							A						   =   do_normalize
							A						   =   image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
							A						   =   image_std if image_std is not None else IMAGENET_STANDARD_STD


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Union[str, Any]  ,A_     :      np.ndarray  ,A_     :      Dict[str, int]  ,A_     :      PILImageResampling = PILImageResampling.BICUBIC  ,A_     :      Optional[Union[str, ChannelDimension]] = None  ,**A_     :      int  ,)     ->   np.ndarray:
							A						   =   get_size_dict(A_  ,default_to_square=A_      )
							if "shortest_edge" not in size:
								raise ValueError(F'The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}'      )
							A						   =   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     :      List[Any]  ,A_     :      np.ndarray  ,A_     :      Dict[str, int]  ,A_     :      Optional[Union[str, ChannelDimension]] = None  ,**A_     :      int  ,)     ->   np.ndarray:
							A						   =   get_size_dict(A_      )
							if "height" not in size or "width" not in size:
								raise ValueError(F'The `size` parameter 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     :      List[Any]  ,A_     :      np.ndarray  ,A_     :      float  ,A_     :      Optional[Union[str, ChannelDimension]] = None  ,**A_     :      List[str]      )     ->   np.ndarray:
							return rescale(A_  ,scale=A_  ,data_format=A_  ,**A_      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Optional[Any]  ,A_     :      np.ndarray  ,A_     :      Union[float, List[float]]  ,A_     :      Union[float, List[float]]  ,A_     :      Optional[Union[str, ChannelDimension]] = None  ,**A_     :      Any  ,)     ->   np.ndarray:
							return normalize(A_  ,mean=A_  ,std=A_  ,data_format=A_  ,**A_      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Any  ,A_     :      ImageInput  ,A_     :      Optional[bool] = None  ,A_     :      Dict[str, int] = None  ,A_     :      PILImageResampling = None  ,A_     :      bool = None  ,A_     :      Dict[str, int] = None  ,A_     :      Optional[bool] = None  ,A_     :      Optional[float] = None  ,A_     :      Optional[bool] = None  ,A_     :      Optional[Union[float, List[float]]] = None  ,A_     :      Optional[Union[float, List[float]]] = None  ,A_     :      Optional[Union[str, TensorType]] = None  ,A_     :      Union[str, ChannelDimension] = ChannelDimension.FIRST  ,**A_     :      Tuple  ,)     ->   List[Any]:
							A						   =   do_resize if do_resize is not None else self.do_resize
							A						   =   size if size is not None else self.size
							A						   =   get_size_dict(A_  ,default_to_square=A_      )
							A						   =   resample if resample is not None else self.resample
							A						   =   do_center_crop if do_center_crop is not None else self.do_center_crop
							A						   =   crop_size if crop_size is not None else self.crop_size
							A						   =   get_size_dict(A_  ,param_name='crop_size'      )
							A						   =   do_rescale if do_rescale is not None else self.do_rescale
							A						   =   rescale_factor if rescale_factor is not None else self.rescale_factor
							A						   =   do_normalize if do_normalize is not None else self.do_normalize
							A						   =   image_mean if image_mean is not None else self.image_mean
							A						   =   image_std if image_std is not None else self.image_std

							A						   =   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_center_crop and crop_size is None:
								raise ValueError('Crop size must be specified if do_center_crop 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.
							A						   =   [to_numpy_array(A_      ) for image in images]

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

							if do_center_crop:
								A						   =   [self.center_crop(image=A_  ,size=A_      ) for image in images]

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

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

							A						   =   [to_channel_dimension_format(A_  ,A_      ) for image in images]

							A						   =   {'pixel_values': images}
							return BatchFeature(data=A_  ,tensor_type=A_      )




						def 						_SCREAMING_SNAKE_CASE      ( self     :      Optional[int]  ,A_     :      Union[str, Any]  ,A_     :      List[Tuple] = None      )     ->   str:
							A						   =   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_      ):
									A						   =   target_sizes.numpy()

								A						   =   []

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

							return semantic_segmentation