


import argparse
import json
import os
from collections import OrderedDict

import numpy as np
import tensorflow as tf
import torch





def lowerCamelCase      (      lowerCAmelCase    :     str		):





	"""simple docstring"""







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


if __name__ == "__main__":
							lowerCAmelCase				:Tuple        =      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				:Tuple        =      parser.parse_args()
							convert_tf_gptsan_to_pt(args)



'''simple docstring'''

# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

from ..models.auto import AutoModelForVisionaSeq
from ..utils import requires_backends
from .base import PipelineTool


if TYPE_CHECKING:
							from PIL import Image
class _lowerCamelCase	(						lowercase__							):



		'''simple docstring'''







		A_     :      Any				   =    """Salesforce/blip-image-captioning-base"""
		A_     :      Any				   =    (
		    """This is a tool that generates a description of an image. It takes an input named `image` which should be the """
		    """image to caption, and returns a text that contains the description in English."""
		)
		A_     :      List[str]				   =    """image_captioner"""
		A_     :      Optional[Any]				   =    AutoModelForVisionaSeq

		A_     :      Any				   =    ["""image"""]
		A_     :      Union[str, Any]				   =    ["""text"""]







		def __init__( self		:						Dict  ,					*_A		:						Dict  ,					**_A		:						Any      )  ->			Dict:
			requires_backends(self  ,					['vision']      )
			super().__init__(*_A  ,					**_A      )







		def        __lowerCAmelCase		( self		:						Tuple  ,					_A		:						"Image"      )  ->			Union[str, Any]:
			return self.pre_processor(images=_A  ,					return_tensors='pt'      )







		def        __lowerCAmelCase		( self		:						Optional[Any]  ,					_A		:						str      )  ->			List[Any]:
			return self.model.generate(**_A      )







		def        __lowerCAmelCase		( self		:						int  ,					_A		:						Optional[int]      )  ->			Union[str, Any]:
			return self.pre_processor.batch_decode(_A  ,					skip_special_tokens=_A      )[0].strip()