



from pathlib import Path
from typing import List

from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import get_tests_dir, is_tool_test
from transformers.tools.agent_types import AGENT_TYPE_MAPPING, AgentAudio, AgentImage, AgentText


if is_torch_available():
						import torch

if is_vision_available():
						from PIL import Image


_snake_case		=  ["text", "image", "audio"]






def 				A				(					_lowerCamelCase  ):





							'''simple docstring'''




							_lowerCAmelCase     :							str    =    []

							for input_type in input_types:
														if input_type == "text":
																					inputs.append("Text input"  )
														elif input_type == "image":
																					inputs.append(
																					    Image.open(Path(get_tests_dir("fixtures/tests_samples/COCO"  )  ) / "000000039769.png"  ).resize((512, 512)  )  )
														elif input_type == "audio":
																					inputs.append(torch.ones(3_000  )  )
														elif isinstance(_lowerCamelCase				, _lowerCamelCase  ):
																					inputs.append(create_inputs(_lowerCamelCase  )  )
														else:
																					raise ValueError(F"Invalid type requested: {input_type}"  )

							return inputs






def 				A				(					_lowerCamelCase  ):





							'''simple docstring'''




							_lowerCAmelCase     :							Optional[int]    =    []

							for output in outputs:
														if isinstance(_lowerCamelCase				, (str, AgentText)  ):
																					output_types.append("text"  )
														elif isinstance(_lowerCamelCase				, (Image.Image, AgentImage)  ):
																					output_types.append("image"  )
														elif isinstance(_lowerCamelCase				, (torch.Tensor, AgentAudio)  ):
																					output_types.append("audio"  )
														else:
																					raise ValueError(F"Invalid output: {output}"  )

							return output_types



@is_tool_test
class   UpperCAmelCase_    :







							def    snake_case__      (   self):

														'''simple docstring'''

														self.assertTrue(hasattr(self.tool,			"inputs"))
														self.assertTrue(hasattr(self.tool,			"outputs"))

														_lowerCAmelCase     :							List[Any]    =    self.tool.inputs
														for _input in inputs:
																					if isinstance(_input,			__a):
																												for __input in _input:
																																			self.assertTrue(__input in authorized_types)
																					else:
																												self.assertTrue(_input in authorized_types)

														_lowerCAmelCase     :							str    =    self.tool.outputs
														for _output in outputs:
																					self.assertTrue(_output in authorized_types)







							def    snake_case__      (   self):

														'''simple docstring'''

														_lowerCAmelCase     :							List[Any]    =    create_inputs(self.tool.inputs)
														_lowerCAmelCase     :							Dict    =    self.tool(*__a)

														# There is a single output
														if len(self.tool.outputs) == 1:
																					_lowerCAmelCase     :							Dict    =    [outputs]

														self.assertListEqual(output_types(__a),			self.tool.outputs)







							def    snake_case__      (   self):

														'''simple docstring'''

														self.assertTrue(hasattr(self.tool,			"description"))
														self.assertTrue(hasattr(self.tool,			"default_checkpoint"))
														self.assertTrue(self.tool.description.startswith("This is a tool that"))







							def    snake_case__      (   self):

														'''simple docstring'''

														_lowerCAmelCase     :							Dict    =    create_inputs(self.tool.inputs)
														_lowerCAmelCase     :							Any    =    self.tool(*__a)

														if not isinstance(__a,			__a):
																					_lowerCAmelCase     :							str    =    [outputs]

														self.assertEqual(len(__a),			len(self.tool.outputs))

														for output, output_type in zip(__a,			self.tool.outputs):
																					_lowerCAmelCase     :							Any    =    AGENT_TYPE_MAPPING[output_type]
																					self.assertTrue(isinstance(__a,			__a))







							def    snake_case__      (   self):

														'''simple docstring'''

														_lowerCAmelCase     :							Tuple    =    create_inputs(self.tool.inputs)

														_lowerCAmelCase     :							Tuple    =    []

														for _input, input_type in zip(__a,			self.tool.inputs):
																					if isinstance(__a,			__a):
																												_inputs.append([AGENT_TYPE_MAPPING[_input_type](_input) for _input_type in input_type])
																					else:
																												_inputs.append(AGENT_TYPE_MAPPING[input_type](_input))

        # Should not raise an error
														_lowerCAmelCase     :							Dict    =    self.tool(*__a)

														if not isinstance(__a,			__a):
																					_lowerCAmelCase     :							Any    =    [outputs]

														self.assertEqual(len(__a),			len(self.tool.outputs))





'''simple docstring'''






import argparse
import gc
import json
import os
import shutil
import warnings

import torch

from transformers import LlamaConfig, LlamaForCausalLM, LlamaTokenizer


try:
						from transformers import LlamaTokenizerFast
except ImportError as e:
						warnings.warn(e)
						warnings.warn(
						    "The converted tokenizer will be the `slow` tokenizer. To use the fast, update your `tokenizers` library and re-run the tokenizer conversion"
						)
						UpperCamelCase__:   str								=			None
UpperCamelCase__:   int								=			{
    "7B": 11008,
    "13B": 13824,
    "30B": 17920,
    "65B": 22016,
    "70B": 28672,
}
UpperCamelCase__:   List[Any]								=			{
    "7B": 1,
    "7Bf": 1,
    "13B": 2,
    "13Bf": 2,
    "30B": 4,
    "65B": 8,
    "70B": 8,
    "70Bf": 8,
}


def  snake_case_     (					_lowerCAmelCase  :						Tuple   ,      _lowerCAmelCase  :						Tuple=1   ,      _lowerCAmelCase  :						List[Any]=256  )      ->   Optional[Any]:
							return multiple_of * ((int(ffn_dim_multiplier * int(8 * n / 3  )  ) + multiple_of - 1) // multiple_of)


def  snake_case_     (					_lowerCAmelCase  :						List[str]  )      ->   str:
							with open(_lowerCAmelCase   ,      '''r'''  ) as f:
														return json.load(_lowerCAmelCase  )


def  snake_case_     (					_lowerCAmelCase  :						Union[str, Any]   ,      _lowerCAmelCase  :						Any  )      ->   Optional[Any]:
							with open(_lowerCAmelCase   ,      '''w'''  ) as f:
														json.dump(_lowerCAmelCase   ,      _lowerCAmelCase  )


def  snake_case_     (					_lowerCAmelCase  :						Dict   ,      _lowerCAmelCase  :						Tuple   ,      _lowerCAmelCase  :						Any   ,      _lowerCAmelCase  :						Any=True  )      ->   List[Any]:
							os.makedirs(_lowerCAmelCase   ,      exist_ok=_lowerCAmelCase  )
							UpperCAmelCase :       int 							=		os.path.join(_lowerCAmelCase   ,      '''tmp'''  )
							os.makedirs(_lowerCAmelCase   ,      exist_ok=_lowerCAmelCase  )

							UpperCAmelCase :       List[str] 							=		read_json(os.path.join(_lowerCAmelCase   ,      '''params.json'''  )  )
							UpperCAmelCase :       str 							=		NUM_SHARDS[model_size]
							UpperCAmelCase :       Any 							=		params['''n_layers''']
							UpperCAmelCase :       str 							=		params['''n_heads''']
							UpperCAmelCase :       Any 							=		n_heads // num_shards
							UpperCAmelCase :       List[str] 							=		params['''dim''']
							UpperCAmelCase :       Optional[Any] 							=		dim // n_heads
							UpperCAmelCase :       str 							=		1_0_0_0_0.0
							UpperCAmelCase :       Optional[int] 							=		1.0 / (base ** (torch.arange(0   ,      _lowerCAmelCase   ,      2  ).float() / dims_per_head))

							if "n_kv_heads" in params:
														UpperCAmelCase :       Tuple 							=		params['''n_kv_heads''']  # for GQA / MQA
														UpperCAmelCase :       Optional[int] 							=		n_heads_per_shard // num_key_value_heads
														UpperCAmelCase :       Optional[Any] 							=		dim // num_key_value_heads
							else:  # compatibility with other checkpoints
														UpperCAmelCase :       List[str] 							=		n_heads
														UpperCAmelCase :       Optional[int] 							=		n_heads_per_shard
														UpperCAmelCase :       List[str] 							=		dim

							# permute for sliced rotary
							def permute(_lowerCAmelCase  :						Tuple   ,      _lowerCAmelCase  :						Optional[Any]=n_heads   ,      _lowerCAmelCase  :						int=dim   ,      _lowerCAmelCase  :						Dict=dim  ):
														return w.view(_lowerCAmelCase   ,      dima // n_heads // 2   ,      2   ,      _lowerCAmelCase  ).transpose(1   ,      2  ).reshape(_lowerCAmelCase   ,      _lowerCAmelCase  )

							print(f"""Fetching all parameters from the checkpoint at {input_base_path}."""  )
							# Load weights
							if model_size == "7B":
														# Not sharded
														# (The sharded implementation would also work, but this is simpler.)
														UpperCAmelCase :       int 							=		torch.load(os.path.join(_lowerCAmelCase   ,      '''consolidated.00.pth'''  )   ,      map_location='''cpu'''  )
							else:
														# Sharded
														UpperCAmelCase :       Optional[Any] 							=		[
														    torch.load(os.path.join(_lowerCAmelCase   ,      f"""consolidated.{i:02d}.pth"""  )   ,      map_location='''cpu'''  )
														    for i in range(_lowerCAmelCase  )
														]
							UpperCAmelCase :       Any 							=		0
							UpperCAmelCase :       str 							=		{'''weight_map''': {}}
							for layer_i in range(_lowerCAmelCase  ):
														UpperCAmelCase :       Optional[Any] 							=		f"""pytorch_model-{layer_i + 1}-of-{n_layers + 1}.bin"""
														if model_size == "7B":
																					# Unsharded
																					UpperCAmelCase :       Optional[int] 							=		{
																					    f"""model.layers.{layer_i}.self_attn.q_proj.weight""": permute(
																					        loaded[f"""layers.{layer_i}.attention.wq.weight"""]  ),
																					    f"""model.layers.{layer_i}.self_attn.k_proj.weight""": permute(
																					        loaded[f"""layers.{layer_i}.attention.wk.weight"""]  ),
																					    f"""model.layers.{layer_i}.self_attn.v_proj.weight""": loaded[f"""layers.{layer_i}.attention.wv.weight"""],
																					    f"""model.layers.{layer_i}.self_attn.o_proj.weight""": loaded[f"""layers.{layer_i}.attention.wo.weight"""],
																					    f"""model.layers.{layer_i}.mlp.gate_proj.weight""": loaded[f"""layers.{layer_i}.feed_forward.w1.weight"""],
																					    f"""model.layers.{layer_i}.mlp.down_proj.weight""": loaded[f"""layers.{layer_i}.feed_forward.w2.weight"""],
																					    f"""model.layers.{layer_i}.mlp.up_proj.weight""": loaded[f"""layers.{layer_i}.feed_forward.w3.weight"""],
																					    f"""model.layers.{layer_i}.input_layernorm.weight""": loaded[f"""layers.{layer_i}.attention_norm.weight"""],
																					    f"""model.layers.{layer_i}.post_attention_layernorm.weight""": loaded[f"""layers.{layer_i}.ffn_norm.weight"""],
																					}
														else:
																					# Sharded
																					# Note that attention.w{q,k,v,o}, feed_fordward.w[1,2,3], attention_norm.weight and ffn_norm.weight share
																					# the same storage object, saving attention_norm and ffn_norm will save other weights too, which is
																					# redundant as other weights will be stitched from multiple shards. To avoid that, they are cloned.

																					UpperCAmelCase :       List[str] 							=		{
																					    f"""model.layers.{layer_i}.input_layernorm.weight""": loaded[0][
																					        f"""layers.{layer_i}.attention_norm.weight"""
																					    ].clone(),
																					    f"""model.layers.{layer_i}.post_attention_layernorm.weight""": loaded[0][
																					        f"""layers.{layer_i}.ffn_norm.weight"""
																					    ].clone(),
																					}
																					UpperCAmelCase :       Union[str, Any] 							=		permute(
																					    torch.cat(
																					        [
																					            loaded[i][f"""layers.{layer_i}.attention.wq.weight"""].view(_lowerCAmelCase   ,      _lowerCAmelCase   ,      _lowerCAmelCase  )
																					            for i in range(_lowerCAmelCase  )
																					        ]   ,      dim=0   ,      ).reshape(_lowerCAmelCase   ,      _lowerCAmelCase  )  )
																					UpperCAmelCase :       Optional[Any] 							=		permute(
																					    torch.cat(
																					        [
																					            loaded[i][f"""layers.{layer_i}.attention.wk.weight"""].view(
																					                _lowerCAmelCase   ,      _lowerCAmelCase   ,      _lowerCAmelCase  )
																					            for i in range(_lowerCAmelCase  )
																					        ]   ,      dim=0   ,      ).reshape(_lowerCAmelCase   ,      _lowerCAmelCase  )   ,      _lowerCAmelCase   ,      _lowerCAmelCase   ,      _lowerCAmelCase   ,      )
																					UpperCAmelCase :       str 							=		torch.cat(
																					    [
																					        loaded[i][f"""layers.{layer_i}.attention.wv.weight"""].view(
																					            _lowerCAmelCase   ,      _lowerCAmelCase   ,      _lowerCAmelCase  )
																					        for i in range(_lowerCAmelCase  )
																					    ]   ,      dim=0   ,      ).reshape(_lowerCAmelCase   ,      _lowerCAmelCase  )

																					UpperCAmelCase :       Optional[int] 							=		torch.cat(
																					    [loaded[i][f"""layers.{layer_i}.attention.wo.weight"""] for i in range(_lowerCAmelCase  )]   ,      dim=1  )
																					UpperCAmelCase :       Any 							=		torch.cat(
																					    [loaded[i][f"""layers.{layer_i}.feed_forward.w1.weight"""] for i in range(_lowerCAmelCase  )]   ,      dim=0  )
																					UpperCAmelCase :       str 							=		torch.cat(
																					    [loaded[i][f"""layers.{layer_i}.feed_forward.w2.weight"""] for i in range(_lowerCAmelCase  )]   ,      dim=1  )
																					UpperCAmelCase :       Tuple 							=		torch.cat(
																					    [loaded[i][f"""layers.{layer_i}.feed_forward.w3.weight"""] for i in range(_lowerCAmelCase  )]   ,      dim=0  )

														UpperCAmelCase :       Any 							=		inv_freq
														for k, v in state_dict.items():
																					UpperCAmelCase :       List[Any] 							=		filename
																					param_count += v.numel()
														torch.save(_lowerCAmelCase   ,      os.path.join(_lowerCAmelCase   ,      _lowerCAmelCase  )  )

							UpperCAmelCase :       Optional[int] 							=		f"""pytorch_model-{n_layers + 1}-of-{n_layers + 1}.bin"""
							if model_size == "7B":
														# Unsharded
														UpperCAmelCase :       str 							=		{
														    '''model.embed_tokens.weight''': loaded['''tok_embeddings.weight'''],
														    '''model.norm.weight''': loaded['''norm.weight'''],
														    '''lm_head.weight''': loaded['''output.weight'''],
														}
							else:
														UpperCAmelCase :       Any 							=		{
														    '''model.norm.weight''': loaded[0]['''norm.weight'''],
														    '''model.embed_tokens.weight''': torch.cat(
														        [loaded[i]['''tok_embeddings.weight'''] for i in range(_lowerCAmelCase  )]   ,      dim=1  ),
														    '''lm_head.weight''': torch.cat([loaded[i]['''output.weight'''] for i in range(_lowerCAmelCase  )]   ,      dim=0  ),
														}

							for k, v in state_dict.items():
														UpperCAmelCase :       Optional[int] 							=		filename
														param_count += v.numel()
							torch.save(_lowerCAmelCase   ,      os.path.join(_lowerCAmelCase   ,      _lowerCAmelCase  )  )

							# Write configs
							UpperCAmelCase :       Union[str, Any] 							=		{'''total_size''': param_count * 2}
							write_json(_lowerCAmelCase   ,      os.path.join(_lowerCAmelCase   ,      '''pytorch_model.bin.index.json'''  )  )
							UpperCAmelCase :       int 							=		params['''ffn_dim_multiplier'''] if '''ffn_dim_multiplier''' in params else 1
							UpperCAmelCase :       Tuple 							=		params['''multiple_of'''] if '''multiple_of''' in params else 256
							UpperCAmelCase :       Any 							=		LlamaConfig(
							    hidden_size=_lowerCAmelCase   ,      intermediate_size=compute_intermediate_size(_lowerCAmelCase   ,      _lowerCAmelCase   ,      _lowerCAmelCase  )   ,      num_attention_heads=params['''n_heads''']   ,      num_hidden_layers=params['''n_layers''']   ,      rms_norm_eps=params['''norm_eps''']   ,      num_key_value_heads=_lowerCAmelCase   ,      )
							config.save_pretrained(_lowerCAmelCase  )

							# Make space so we can load the model properly now.
							del state_dict
							del loaded
							gc.collect()

							print('''Loading the checkpoint in a Llama model.'''  )
							UpperCAmelCase :       Optional[int] 							=		LlamaForCausalLM.from_pretrained(_lowerCAmelCase   ,      torch_dtype=torch.floataa   ,      low_cpu_mem_usage=_lowerCAmelCase  )
							# Avoid saving this as part of the config.
							del model.config._name_or_path

							print('''Saving in the Transformers format.'''  )
							model.save_pretrained(_lowerCAmelCase   ,      safe_serialization=_lowerCAmelCase  )
							shutil.rmtree(_lowerCAmelCase  )


def  snake_case_     (					_lowerCAmelCase  :						int   ,      _lowerCAmelCase  :						Union[str, Any]  )      ->   List[str]:
							# Initialize the tokenizer based on the `spm` model
							UpperCAmelCase :       Dict 							=		LlamaTokenizer if LlamaTokenizerFast is None else LlamaTokenizerFast
							print(f"""Saving a {tokenizer_class.__name__} to {tokenizer_path}."""  )
							UpperCAmelCase :       List[Any] 							=		tokenizer_class(_lowerCAmelCase  )
							tokenizer.save_pretrained(_lowerCAmelCase  )


def  snake_case_     (					)      ->   List[Any]:
							UpperCAmelCase :       int 							=		argparse.ArgumentParser()
							parser.add_argument(
							    '''--input_dir'''   ,      help='''Location of LLaMA weights, which contains tokenizer.model and model folders'''   ,      )
							parser.add_argument(
							    '''--model_size'''   ,      choices=['''7B''', '''7Bf''', '''13B''', '''13Bf''', '''30B''', '''65B''', '''70B''', '''70Bf''', '''tokenizer_only''']   ,      )
							parser.add_argument(
							    '''--output_dir'''   ,      help='''Location to write HF model and tokenizer'''   ,      )
							parser.add_argument('''--safe_serialization'''   ,      type=_lowerCAmelCase   ,      help='''Whether or not to save using `safetensors`.'''  )
							UpperCAmelCase :       List[Any] 							=		parser.parse_args()
							if args.model_size != "tokenizer_only":
														write_model(
														    model_path=args.output_dir   ,      input_base_path=os.path.join(args.input_dir   ,      args.model_size  )   ,      model_size=args.model_size   ,      safe_serialization=args.safe_serialization   ,      )
							UpperCAmelCase :       Optional[int] 							=		os.path.join(args.input_dir   ,      '''tokenizer.model'''  )
							write_tokenizer(args.output_dir   ,      _lowerCAmelCase  )


if __name__ == "__main__":
						main()





