


'''simple docstring'''

from typing import Dict, List, Optional, Union

import numpy as np

from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType

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,
    is_valid_image,
    to_numpy_array,
    valid_images,
)
from ...utils import logging


if is_vision_available():
 import PIL

lowerCamelCase				:  List[str]    		=      logging.get_logger(__name__)

def _SCREAMING_SNAKE_CASE (A			)			->			List[List[ImageInput]]:






       """simple docstring"""







       if isinstance(A    ,					(list, tuple)			) and isinstance(videos[0]    ,					(list, tuple)			) and is_valid_image(videos[0][0]			):
              return videos

       elif isinstance(A    ,					(list, tuple)			) and is_valid_image(videos[0]			):
              return [videos]

       elif is_valid_image(A			):
              return [[videos]]

       raise ValueError(f"Could not make batched video from {videos}"			)

class     __lowerCAmelCase		(lowercase_	):





      '''simple docstring'''






      lowerCAmelCase__   :	Any       =				["""pixel_values"""]



      def __init__(self       :       int  ,		UpperCamelCase       :       bool = True  ,		UpperCamelCase       :       Dict[str, int] = None  ,		UpperCamelCase       :       PILImageResampling = PILImageResampling.BILINEAR  ,		UpperCamelCase       :       bool = True  ,		UpperCamelCase       :       Dict[str, int] = None  ,		UpperCamelCase       :       bool = True  ,		UpperCamelCase       :       Union[int, float] = 1 / 255  ,		UpperCamelCase       :       bool = True  ,		UpperCamelCase       :       bool = True  ,		UpperCamelCase       :       Optional[Union[float, List[float]]] = None  ,		UpperCamelCase       :       Optional[Union[float, List[float]]] = None  ,		**UpperCamelCase       :       Dict  ,		):

             '''simple docstring'''







             super().__init__(**UpperCamelCase					)
             lowercase__      =					size if size is not None else {'''shortest_edge''': 256}
             lowercase__      =					get_size_dict(UpperCamelCase  ,		default_to_square=UpperCamelCase					)
             lowercase__      =					crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
             lowercase__      =					get_size_dict(UpperCamelCase  ,		param_name='''crop_size'''					)

             lowercase__      =					do_resize
             lowercase__      =					size
             lowercase__      =					do_center_crop
             lowercase__      =					crop_size
             lowercase__      =					resample
             lowercase__      =					do_rescale
             lowercase__      =					rescale_factor
             lowercase__      =					offset
             lowercase__      =					do_normalize
             lowercase__      =					image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
             lowercase__      =					image_std if image_std is not None else IMAGENET_STANDARD_STD



      def  UpperCamelCase__						(self       :       Any  ,		UpperCamelCase       :       np.ndarray  ,		UpperCamelCase       :       Dict[str, int]  ,		UpperCamelCase       :       PILImageResampling = PILImageResampling.BILINEAR  ,		UpperCamelCase       :       Optional[Union[str, ChannelDimension]] = None  ,		**UpperCamelCase       :       Union[str, Any]  ,		):

             '''simple docstring'''







             lowercase__      =					get_size_dict(UpperCamelCase  ,		default_to_square=UpperCamelCase					)
             if "shortest_edge" in size:
                    lowercase__      =					get_resize_output_image_size(UpperCamelCase  ,		size['''shortest_edge''']  ,		default_to_square=UpperCamelCase					)
             elif "height" in size and "width" in size:
                    lowercase__      =					(size['''height'''], size['''width'''])
             else:
                    raise ValueError(f"Size must have 'height' and 'width' or 'shortest_edge' as keys. Got {size.keys()}"					)
             return resize(UpperCamelCase  ,		size=UpperCamelCase  ,		resample=UpperCamelCase  ,		data_format=UpperCamelCase  ,		**UpperCamelCase					)



      def  UpperCamelCase__						(self       :       Any  ,		UpperCamelCase       :       np.ndarray  ,		UpperCamelCase       :       Dict[str, int]  ,		UpperCamelCase       :       Optional[Union[str, ChannelDimension]] = None  ,		**UpperCamelCase       :       Optional[int]  ,		):

             '''simple docstring'''







             lowercase__      =					get_size_dict(UpperCamelCase					)
             if "height" not in size or "width" not in size:
                    raise ValueError(f"Size must have 'height' and 'width' as keys. Got {size.keys()}"					)
             return center_crop(UpperCamelCase  ,		size=(size['''height'''], size['''width'''])  ,		data_format=UpperCamelCase  ,		**UpperCamelCase					)



      def  UpperCamelCase__						(self       :       Optional[int]  ,		UpperCamelCase       :       np.ndarray  ,		UpperCamelCase       :       Union[int, float]  ,		UpperCamelCase       :       bool = True  ,		UpperCamelCase       :       Optional[Union[str, ChannelDimension]] = None  ,		**UpperCamelCase       :       Dict  ,		):

             '''simple docstring'''







             lowercase__      =					image.astype(np.floataa					)
             if offset:
                    lowercase__      =					image - (scale / 2)
             return rescale(UpperCamelCase  ,		scale=UpperCamelCase  ,		data_format=UpperCamelCase  ,		**UpperCamelCase					)



      def  UpperCamelCase__						(self       :       Dict  ,		UpperCamelCase       :       np.ndarray  ,		UpperCamelCase       :       Union[float, List[float]]  ,		UpperCamelCase       :       Union[float, List[float]]  ,		UpperCamelCase       :       Optional[Union[str, ChannelDimension]] = None  ,		**UpperCamelCase       :       List[Any]  ,		):

             '''simple docstring'''







             return normalize(UpperCamelCase  ,		mean=UpperCamelCase  ,		std=UpperCamelCase  ,		data_format=UpperCamelCase  ,		**UpperCamelCase					)



      def  UpperCamelCase__						(self       :       Optional[int]  ,		UpperCamelCase       :       ImageInput  ,		UpperCamelCase       :       bool = None  ,		UpperCamelCase       :       Dict[str, int] = None  ,		UpperCamelCase       :       PILImageResampling = None  ,		UpperCamelCase       :       bool = None  ,		UpperCamelCase       :       Dict[str, int] = None  ,		UpperCamelCase       :       bool = None  ,		UpperCamelCase       :       float = None  ,		UpperCamelCase       :       bool = None  ,		UpperCamelCase       :       bool = None  ,		UpperCamelCase       :       Optional[Union[float, List[float]]] = None  ,		UpperCamelCase       :       Optional[Union[float, List[float]]] = None  ,		UpperCamelCase       :       Optional[ChannelDimension] = ChannelDimension.FIRST  ,		):

             '''simple docstring'''







             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_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.'''					)

             if offset and not do_rescale:
                    raise ValueError('''For offset, do_rescale must also be set to True.'''					)

             # All transformations expect numpy arrays.
             lowercase__      =					to_numpy_array(UpperCamelCase					)

             if do_resize:
                    lowercase__      =					self.resize(image=UpperCamelCase  ,		size=UpperCamelCase  ,		resample=UpperCamelCase					)

             if do_center_crop:
                    lowercase__      =					self.center_crop(UpperCamelCase  ,		size=UpperCamelCase					)

             if do_rescale:
                    lowercase__      =					self.rescale(image=UpperCamelCase  ,		scale=UpperCamelCase  ,		offset=UpperCamelCase					)

             if do_normalize:
                    lowercase__      =					self.normalize(image=UpperCamelCase  ,		mean=UpperCamelCase  ,		std=UpperCamelCase					)

             lowercase__      =					to_channel_dimension_format(UpperCamelCase  ,		UpperCamelCase					)
             return image






      def  UpperCamelCase__						(self       :       Union[str, Any]  ,		UpperCamelCase       :       ImageInput  ,		UpperCamelCase       :       bool = None  ,		UpperCamelCase       :       Dict[str, int] = None  ,		UpperCamelCase       :       PILImageResampling = None  ,		UpperCamelCase       :       bool = None  ,		UpperCamelCase       :       Dict[str, int] = None  ,		UpperCamelCase       :       bool = None  ,		UpperCamelCase       :       float = None  ,		UpperCamelCase       :       bool = None  ,		UpperCamelCase       :       bool = None  ,		UpperCamelCase       :       Optional[Union[float, List[float]]] = None  ,		UpperCamelCase       :       Optional[Union[float, List[float]]] = None  ,		UpperCamelCase       :       Optional[Union[str, TensorType]] = None  ,		UpperCamelCase       :       ChannelDimension = ChannelDimension.FIRST  ,		**UpperCamelCase       :       Optional[int]  ,		):

             '''simple docstring'''







             lowercase__      =					do_resize if do_resize is not None else self.do_resize
             lowercase__      =					resample if resample is not None else self.resample
             lowercase__      =					do_center_crop if do_center_crop is not None else self.do_center_crop
             lowercase__      =					do_rescale if do_rescale is not None else self.do_rescale
             lowercase__      =					rescale_factor if rescale_factor is not None else self.rescale_factor
             lowercase__      =					offset if offset is not None else self.offset
             lowercase__      =					do_normalize if do_normalize is not None else self.do_normalize
             lowercase__      =					image_mean if image_mean is not None else self.image_mean
             lowercase__      =					image_std if image_std is not None else self.image_std

             lowercase__      =					size if size is not None else self.size
             lowercase__      =					get_size_dict(UpperCamelCase  ,		default_to_square=UpperCamelCase					)
             lowercase__      =					crop_size if crop_size is not None else self.crop_size
             lowercase__      =					get_size_dict(UpperCamelCase  ,		param_name='''crop_size'''					)

             if not valid_images(UpperCamelCase					):
                    raise ValueError(
                        '''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
                        '''torch.Tensor, tf.Tensor or jax.ndarray.'''					)

             lowercase__      =					make_batched(UpperCamelCase					)

             lowercase__      =					[
                 [
                     self._preprocess_image(
                         image=UpperCamelCase  ,		do_resize=UpperCamelCase  ,		size=UpperCamelCase  ,		resample=UpperCamelCase  ,		do_center_crop=UpperCamelCase  ,		crop_size=UpperCamelCase  ,		do_rescale=UpperCamelCase  ,		rescale_factor=UpperCamelCase  ,		offset=UpperCamelCase  ,		do_normalize=UpperCamelCase  ,		image_mean=UpperCamelCase  ,		image_std=UpperCamelCase  ,		data_format=UpperCamelCase  ,		)
                     for img in video
                 ]
                 for video in videos
             ]

             lowercase__      =					{'''pixel_values''': videos}
             return BatchFeature(data=UpperCamelCase  ,		tensor_type=UpperCamelCase					)








'''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 (A			)			->			Dict:






       """simple docstring"""







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


if __name__ == "__main__":
 lowerCamelCase				:  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')
 lowerCamelCase				:  Dict    		=      parser.parse_args()
 convert_tf_gptsan_to_pt(args)




