






"""simple docstring"""







from __future__ import annotations

import unittest

from transformers import AutoTokenizer, PegasusConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
	import tensorflow as tf

	from transformers import TFAutoModelForSeqaSeqLM, TFPegasusForConditionalGeneration, TFPegasusModel







@require_tf
class        _SCREAMING_SNAKE_CASE						:





							"""simple docstring"""





							_SCREAMING_SNAKE_CASE    =PegasusConfig
							_SCREAMING_SNAKE_CASE    ={}
							_SCREAMING_SNAKE_CASE    ='gelu'




							def __init__(   self:     List[Any]				,		__A:     Optional[Any]				,		__A:     Optional[Any]=13				,		__A:     Any=7				,		__A:     Dict=True				,		__A:     Union[str, Any]=False				,		__A:     List[Any]=99				,		__A:     Dict=32				,		__A:     str=2				,		__A:     str=4				,		__A:     str=37				,		__A:     str=0.1				,		__A:     Union[str, Any]=0.1				,		__A:     List[str]=40				,		__A:     int=2				,		__A:     Optional[Any]=1				,		__A:     Any=0				,		):

												'''simple docstring'''

												a__          =  parent
												a__          =  batch_size
												a__          =  seq_length
												a__          =  is_training
												a__          =  use_labels
												a__          =  vocab_size
												a__          =  hidden_size
												a__          =  num_hidden_layers
												a__          =  num_attention_heads
												a__          =  intermediate_size

												a__          =  hidden_dropout_prob
												a__          =  attention_probs_dropout_prob
												a__          =  max_position_embeddings
												a__          =  eos_token_id
												a__          =  pad_token_id
												a__          =  bos_token_id




							def  lowercase		(   self:     Tuple    ):

												'''simple docstring'''

												a__          =  ids_tensor([self.batch_size, self.seq_length - 1]				,		self.vocab_size    )
												a__          =  tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size    )				,		1    )
												a__          =  tf.concat([input_ids, eos_tensor]				,		axis=1    )

												a__          =  ids_tensor([self.batch_size, self.seq_length]				,		self.vocab_size    )

												a__          =  self.config_cls(
												    vocab_size=self.vocab_size				,		d_model=self.hidden_size				,		encoder_layers=self.num_hidden_layers				,		decoder_layers=self.num_hidden_layers				,		encoder_attention_heads=self.num_attention_heads				,		decoder_attention_heads=self.num_attention_heads				,		encoder_ffn_dim=self.intermediate_size				,		decoder_ffn_dim=self.intermediate_size				,		dropout=self.hidden_dropout_prob				,		attention_dropout=self.attention_probs_dropout_prob				,		max_position_embeddings=self.max_position_embeddings				,		eos_token_ids=[2]				,		bos_token_id=self.bos_token_id				,		pad_token_id=self.pad_token_id				,		decoder_start_token_id=self.pad_token_id				,		**self.config_updates				,		)
												a__          =  prepare_pegasus_inputs_dict(__A				,		__A				,		__A    )
												return config, inputs_dict





							def  lowercase		(   self:     Tuple				,		__A:     List[str]				,		__A:     Optional[Any]    ):

												'''simple docstring'''

												a__          =  TFPegasusModel(config=__A    ).get_decoder()
												a__          =  inputs_dict['''input_ids''']

												a__          =  input_ids[:1, :]
												a__          =  inputs_dict['''attention_mask'''][:1, :]
												a__          =  inputs_dict['''head_mask''']
												a__          =  1

												# first forward pass
												a__          =  model(__A				,		attention_mask=__A				,		head_mask=__A				,		use_cache=__A    )

												a__       ,a__          =  outputs.to_tuple()

												# create hypothetical next token and extent to next_input_ids
												a__          =  ids_tensor((self.batch_size, 3)				,		config.vocab_size    )
												a__          =  tf.cast(ids_tensor((self.batch_size, 3)				,		2    )				,		tf.inta    )

												# append to next input_ids and
												a__          =  tf.concat([input_ids, next_tokens]				,		axis=-1    )
												a__          =  tf.concat([attention_mask, next_attn_mask]				,		axis=-1    )

												a__          =  model(__A				,		attention_mask=__A    )[0]
												a__          =  model(__A				,		attention_mask=__A				,		past_key_values=__A    )[0]

												self.parent.assertEqual(next_tokens.shape[1]				,		output_from_past.shape[1]    )

												# select random slice
												a__          =  int(ids_tensor((1,)				,		output_from_past.shape[-1]    )    )
												a__          =  output_from_no_past[:, -3:, random_slice_idx]
												a__          =  output_from_past[:, :, random_slice_idx]

												# test that outputs are equal for slice
												tf.debugging.assert_near(__A				,		__A				,		rtol=1e-3    )





def        SCREAMING_SNAKE_CASE       (      lowerCamelCase_ ,     lowerCamelCase_ ,     lowerCamelCase_ ,     lowerCamelCase_=None ,     lowerCamelCase_=None ,     lowerCamelCase_=None ,     lowerCamelCase_=None ,     lowerCamelCase_=None ,     ):
					if attention_mask is None:
										a__          =  tf.cast(tf.math.not_equal(lowerCamelCase_ ,     config.pad_token_id) ,     tf.inta)
					if decoder_attention_mask is None:
										a__          =  tf.concat(
										    [
										        tf.ones(decoder_input_ids[:, :1].shape ,     dtype=tf.inta),
										        tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:] ,     config.pad_token_id) ,     tf.inta),
										    ] ,     axis=-1 ,     )
					if head_mask is None:
										a__          =  tf.ones((config.encoder_layers, config.encoder_attention_heads))
					if decoder_head_mask is None:
										a__          =  tf.ones((config.decoder_layers, config.decoder_attention_heads))
					if cross_attn_head_mask is None:
										a__          =  tf.ones((config.decoder_layers, config.decoder_attention_heads))
					return {
					    "input_ids": input_ids,
					    "decoder_input_ids": decoder_input_ids,
					    "attention_mask": attention_mask,
					    "decoder_attention_mask": decoder_attention_mask,
					    "head_mask": head_mask,
					    "decoder_head_mask": decoder_head_mask,
					    "cross_attn_head_mask": cross_attn_head_mask,
					}







@require_tf
class        _SCREAMING_SNAKE_CASE						(  __snake_case       , __snake_case       , unittest.TestCase							):





							"""simple docstring"""





							_SCREAMING_SNAKE_CASE    =(TFPegasusForConditionalGeneration, TFPegasusModel) if is_tf_available() else ()
							_SCREAMING_SNAKE_CASE    =(TFPegasusForConditionalGeneration,) if is_tf_available() else ()
							_SCREAMING_SNAKE_CASE    =(
							    {
							        'conversational': TFPegasusForConditionalGeneration,
							        'feature-extraction': TFPegasusModel,
							        'summarization': TFPegasusForConditionalGeneration,
							        'text2text-generation': TFPegasusForConditionalGeneration,
							        'translation': TFPegasusForConditionalGeneration,
							    }
							    if is_tf_available()
							    else {}
							)
							_SCREAMING_SNAKE_CASE    =True
							_SCREAMING_SNAKE_CASE    =False
							_SCREAMING_SNAKE_CASE    =False




							def  lowercase		(   self:     Dict    ):

												'''simple docstring'''

												a__          =  TFPegasusModelTester(self    )
												a__          =  ConfigTester(self				,		config_class=__A    )




							def  lowercase		(   self:     str    ):

												'''simple docstring'''

												self.config_tester.run_common_tests()





							def  lowercase		(   self:     Dict    ):

												'''simple docstring'''

												a__          =  self.model_tester.prepare_config_and_inputs_for_common()
												self.model_tester.check_decoder_model_past_large_inputs(*__A    )







@require_sentencepiece
@require_tokenizers
@require_tf
class        _SCREAMING_SNAKE_CASE						(  unittest.TestCase							):





							"""simple docstring"""





							_SCREAMING_SNAKE_CASE    =[
							    ' PG&E stated it scheduled the blackouts in response to forecasts for high winds amid dry conditions. The aim is to reduce the risk of wildfires. Nearly 800 thousand customers were scheduled to be affected by the shutoffs which were expected to last through at least midday tomorrow.',
							    ' The London trio are up for best UK act and best album, as well as getting two nominations in the best song category."We got told like this morning \'Oh I think you\'re nominated\'", said Dappy."And I was like \'Oh yeah, which one?\' And now we\'ve got nominated for four awards. I mean, wow!"Bandmate Fazer added: "We thought it\'s best of us to come down and mingle with everyone and say hello to the cameras. And now we find we\'ve got four nominations."The band have two shots at the best song prize, getting the nod for their Tynchy Stryder collaboration Number One, and single Strong Again.Their album Uncle B will also go up against records by the likes of Beyonce and Kanye West.N-Dubz picked up the best newcomer Mobo in 2007, but female member Tulisa said they wouldn\'t be too disappointed if they didn\'t win this time around."At the end of the day we\'re grateful to be where we are in our careers."If it don\'t happen then it don\'t happen - live to fight another day and keep on making albums and hits for the fans."Dappy also revealed they could be performing live several times on the night.The group will be doing Number One and also a possible rendition of the War Child single, I Got Soul.The charity song is a  re-working of The Killers\' All These Things That I\'ve Done and is set to feature artists like Chipmunk, Ironik and Pixie Lott.This year\'s Mobos will be held outside of London for the first time, in Glasgow on 30 September.N-Dubz said they were looking forward to performing for their Scottish fans and boasted about their recent shows north of the border."We just done Edinburgh the other day," said Dappy."We smashed up an N-Dubz show over there. We done Aberdeen about three or four months ago - we smashed up that show over there! Everywhere we go we smash it up!" ',
							]
							_SCREAMING_SNAKE_CASE    =[
							    'California\'s largest electricity provider has cut power to hundreds of thousands of customers in an effort to'
							    ' reduce the risk of wildfires.',
							    'N-Dubz have revealed they\'re "grateful" to have been nominated for four Mobo Awards.',
							]  # differs slightly from pytorch, likely due to numerical differences in linear layers
							_SCREAMING_SNAKE_CASE    ='google/pegasus-xsum'




							@cached_property
							def  lowercase		(   self:     Any    ):

												'''simple docstring'''

												return AutoTokenizer.from_pretrained(self.model_name    )




							@cached_property
							def  lowercase		(   self:     Optional[int]    ):

												'''simple docstring'''

												a__          =  TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name    )
												return model




							def  lowercase		(   self:     int				,		**__A:     Dict    ):

												'''simple docstring'''

												a__          =  self.translate_src_text(**__A    )
												assert self.expected_text == generated_words




							def  lowercase		(   self:     Optional[Any]				,		**__A:     Optional[int]    ):

												'''simple docstring'''

												a__          =  self.tokenizer(self.src_text				,		**__A				,		padding=__A				,		return_tensors='''tf'''    )
												a__          =  self.model.generate(
												    model_inputs.input_ids				,		attention_mask=model_inputs.attention_mask				,		num_beams=2				,		use_cache=__A				,		)
												a__          =  self.tokenizer.batch_decode(generated_ids.numpy()				,		skip_special_tokens=__A    )
												return generated_words





							@slow
							def  lowercase		(   self:     Any    ):

												'''simple docstring'''

												self._assert_generated_batch_equal_expected()










"""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'
	)
	__a       :	Union[str, Any]             = None
__a       :	Union[str, Any]             = {
    '7B': 1_1008,
    '13B': 1_3824,
    '30B': 1_7920,
    '65B': 2_2016,
    '70B': 2_8672,
}
__a       :	List[Any]             = {
    '7B': 1,
    '7Bf': 1,
    '13B': 2,
    '13Bf': 2,
    '30B': 4,
    '65B': 8,
    '70B': 8,
    '70Bf': 8,
}





def        SCREAMING_SNAKE_CASE       (      lowerCamelCase_ ,     lowerCamelCase_=1 ,     lowerCamelCase_=256):
					return multiple_of * ((int(ffn_dim_multiplier * int(8 * n / 3)) + multiple_of - 1) // multiple_of)





def        SCREAMING_SNAKE_CASE       (      lowerCamelCase_):
					with open(lowerCamelCase_ ,     '''r''') as f:
										return json.load(lowerCamelCase_)





def        SCREAMING_SNAKE_CASE       (      lowerCamelCase_ ,     lowerCamelCase_):
					with open(lowerCamelCase_ ,     '''w''') as f:
										json.dump(lowerCamelCase_ ,     lowerCamelCase_)





def        SCREAMING_SNAKE_CASE       (      lowerCamelCase_ ,     lowerCamelCase_ ,     lowerCamelCase_ ,     lowerCamelCase_=True):
					os.makedirs(lowerCamelCase_ ,     exist_ok=lowerCamelCase_)
					a__          =  os.path.join(lowerCamelCase_ ,     '''tmp''')
					os.makedirs(lowerCamelCase_ ,     exist_ok=lowerCamelCase_)

					a__          =  read_json(os.path.join(lowerCamelCase_ ,     '''params.json'''))
					a__          =  NUM_SHARDS[model_size]
					a__          =  params['''n_layers''']
					a__          =  params['''n_heads''']
					a__          =  n_heads // num_shards
					a__          =  params['''dim''']
					a__          =  dim // n_heads
					a__          =  10000.0
					a__          =  1.0 / (base ** (torch.arange(0 ,     lowerCamelCase_ ,     2).float() / dims_per_head))

					if "n_kv_heads" in params:
										a__          =  params['''n_kv_heads''']  # for GQA / MQA
										a__          =  n_heads_per_shard // num_key_value_heads
										a__          =  dim // num_key_value_heads
					else:  # compatibility with other checkpoints
										a__          =  n_heads
										a__          =  n_heads_per_shard
										a__          =  dim

					# permute for sliced rotary
					def permute(lowerCamelCase_ ,     lowerCamelCase_=n_heads ,     lowerCamelCase_=dim ,     lowerCamelCase_=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.)
										a__          =  torch.load(os.path.join(lowerCamelCase_ ,     '''consolidated.00.pth''') ,     map_location='''cpu''')
					else:
										# Sharded
										a__          =  [
										    torch.load(os.path.join(lowerCamelCase_ ,     f'consolidated.{i:02d}.pth') ,     map_location='''cpu''')
										    for i in range(lowerCamelCase_)
										]
					a__          =  0
					a__          =  {'''weight_map''': {}}
					for layer_i in range(lowerCamelCase_):
										a__          =  f'pytorch_model-{layer_i + 1}-of-{n_layers + 1}.bin'
										if model_size == "7B":
															# Unsharded
															a__          =  {
															    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.

															a__          =  {
															    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(),
															}
															a__          =  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_))
															a__          =  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_ ,     )
															a__          =  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_)

															a__          =  torch.cat(
															    [loaded[i][f'layers.{layer_i}.attention.wo.weight'] for i in range(lowerCamelCase_)] ,     dim=1)
															a__          =  torch.cat(
															    [loaded[i][f'layers.{layer_i}.feed_forward.w1.weight'] for i in range(lowerCamelCase_)] ,     dim=0)
															a__          =  torch.cat(
															    [loaded[i][f'layers.{layer_i}.feed_forward.w2.weight'] for i in range(lowerCamelCase_)] ,     dim=1)
															a__          =  torch.cat(
															    [loaded[i][f'layers.{layer_i}.feed_forward.w3.weight'] for i in range(lowerCamelCase_)] ,     dim=0)

										a__          =  inv_freq
										for k, v in state_dict.items():
															a__          =  filename
															param_count += v.numel()
										torch.save(lowerCamelCase_ ,     os.path.join(lowerCamelCase_ ,     lowerCamelCase_))

					a__          =  f'pytorch_model-{n_layers + 1}-of-{n_layers + 1}.bin'
					if model_size == "7B":
										# Unsharded
										a__          =  {
										    '''model.embed_tokens.weight''': loaded['''tok_embeddings.weight'''],
										    '''model.norm.weight''': loaded['''norm.weight'''],
										    '''lm_head.weight''': loaded['''output.weight'''],
										}
					else:
										a__          =  {
										    '''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():
										a__          =  filename
										param_count += v.numel()
					torch.save(lowerCamelCase_ ,     os.path.join(lowerCamelCase_ ,     lowerCamelCase_))

					# Write configs
					a__          =  {'''total_size''': param_count * 2}
					write_json(lowerCamelCase_ ,     os.path.join(lowerCamelCase_ ,     '''pytorch_model.bin.index.json'''))
					a__          =  params['''ffn_dim_multiplier'''] if '''ffn_dim_multiplier''' in params else 1
					a__          =  params['''multiple_of'''] if '''multiple_of''' in params else 256
					a__          =  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.''')
					a__          =  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        SCREAMING_SNAKE_CASE       (      lowerCamelCase_ ,     lowerCamelCase_):
					# Initialize the tokenizer based on the `spm` model
					a__          =  LlamaTokenizer if LlamaTokenizerFast is None else LlamaTokenizerFast
					print(f'Saving a {tokenizer_class.__name__} to {tokenizer_path}.')
					a__          =  tokenizer_class(lowerCamelCase_)
					tokenizer.save_pretrained(lowerCamelCase_)





def        SCREAMING_SNAKE_CASE       (      ):
					a__          =  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`.''')
					a__          =  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 ,     )
					a__          =  os.path.join(args.input_dir ,     '''tokenizer.model''')
					write_tokenizer(args.output_dir ,     lowerCamelCase_)


if __name__ == "__main__":
	main()


