





'''simple docstring'''


import argparse
import json
import os

import tensorstore as ts
import torch
from flax import serialization
from flax.traverse_util import flatten_dict, unflatten_dict
from tensorflow.io import gfile

from transformers.modeling_utils import dtype_byte_size
from transformers.models.switch_transformers.convert_switch_transformers_original_flax_checkpoint_to_pytorch import (
    rename_keys,
)
from transformers.utils import WEIGHTS_INDEX_NAME, WEIGHTS_NAME
from transformers.utils.hub import convert_file_size_to_int


def  _lowerCamelCase   (				lowerCamelCase_ :							str     ,					lowerCamelCase_ :							Optional[Any]       ):



     """simple docstring"""





     if flax_key_tuple[-1] == "kernel" and flax_tensor.ndim == 3:
          # expert layer
          UpperCAmelCase_							:       Tuple       =				flax_key_tuple[:-1] + ('weight',)
          UpperCAmelCase_							:       List[Any]       =				torch.permute(lowerCamelCase_     ,					(0, 2, 1)       )
     elif flax_key_tuple[-1] == "kernel" and ".".join(lowerCamelCase_       ):
          # linear layer
          UpperCAmelCase_							:       Optional[int]       =				flax_key_tuple[:-1] + ('weight',)
          UpperCAmelCase_							:       Dict       =				flax_tensor.T
     elif flax_key_tuple[-1] in ["scale", "embedding"]:
          UpperCAmelCase_							:       int       =				flax_key_tuple[:-1] + ('weight',)

     return flax_key_tuple, flax_tensor


def  _lowerCamelCase   (				lowerCamelCase_ :							str     ,					lowerCamelCase_ :							Tuple     ,					lowerCamelCase_ :							List[str]       ):



     """simple docstring"""





     if "metadata" in layer:
          UpperCAmelCase_							:       List[str]       =				layer.split('metadata'       )
          UpperCAmelCase_							:       List[Any]       =				''.join(split_layer[0]       )[:-1]
          UpperCAmelCase_							:       Tuple       =				[tuple(('metadata' + split_layer[1]).split('/'       )       )]
     elif "kvstore" in layer:
          UpperCAmelCase_							:       Dict       =				layer.split('kvstore'       )
          UpperCAmelCase_							:       List[Any]       =				''.join(split_layer[0]       )[:-1]
          UpperCAmelCase_							:       Dict       =				[tuple(('kvstore' + split_layer[1]).split('/'       )       )]

     else:
          UpperCAmelCase_							:       List[str]       =				layer.split('/'       )
          UpperCAmelCase_							:       List[Any]       =				'/'.join(split_layer[:-1]       )
          UpperCAmelCase_							:       Any       =				(split_layer[-1],)

     if "kvstore/path" in layer:
          UpperCAmelCase_							:       Union[str, Any]       =				F'''{switch_checkpoint_path}/{checkpoint_info[layer]}'''
     elif "kvstore/driver" in layer:
          UpperCAmelCase_							:       List[str]       =				'file'
     else:
          UpperCAmelCase_							:       Optional[Any]       =				checkpoint_info[layer]

     return curr_real_layer_name, split_layer, content


def  _lowerCamelCase   (				lowerCamelCase_ :							str     ,					lowerCamelCase_ :							Union[str, Any]       ):



     """simple docstring"""





     UpperCAmelCase_							:       Dict       =				rename_keys(lowerCamelCase_       )
     UpperCAmelCase_							:       str       =				{}
     for k, v in current_block.items():
          UpperCAmelCase_							:       Optional[Any]       =				v
     UpperCAmelCase_							:       Any       =				new_current_block
     torch.save(lowerCamelCase_     ,					lowerCamelCase_       )


def  _lowerCamelCase   (				lowerCamelCase_ :							Dict     ,					lowerCamelCase_ :							str     ,					lowerCamelCase_ :							str     ,					lowerCamelCase_ :							List[str]     ,					lowerCamelCase_ :							str = WEIGHTS_NAME       ):



     """simple docstring"""





     UpperCAmelCase_							:       Tuple       =				convert_file_size_to_int(lowerCamelCase_       )
     UpperCAmelCase_							:       List[Any]       =				[]
     UpperCAmelCase_							:       Union[str, Any]       =				{}
     UpperCAmelCase_							:       List[Any]       =				0
     UpperCAmelCase_							:       Union[str, Any]       =				0

     os.makedirs(lowerCamelCase_     ,					exist_ok=lowerCamelCase_       )
     with gfile.GFile(switch_checkpoint_path + '/checkpoint'     ,					'rb'       ) as fp:
          UpperCAmelCase_							:       str       =				serialization.msgpack_restore(fp.read()       )['optimizer']['target']
          UpperCAmelCase_							:       Union[str, Any]       =				flatten_dict(lowerCamelCase_     ,					sep='/'       )

     UpperCAmelCase_							:       Union[str, Any]       =				{}
     for layer in checkpoint_info.keys():
          UpperCAmelCase_							:       int       =				get_key_and_tensorstore_dict(
              lowerCamelCase_     ,					lowerCamelCase_     ,					lowerCamelCase_       )
          if curr_real_layer_name in all_layers:
               UpperCAmelCase_							:       Optional[int]       =				content
          else:
               UpperCAmelCase_							:       Optional[int]       =				{split_layer[-1]: content}

     for key in all_layers.keys():
          # open tensorstore file
          UpperCAmelCase_							:       int       =				ts.open(unflatten_dict(all_layers[key]       )       ).result().read().result()
          UpperCAmelCase_							:       Any       =				torch.tensor(lowerCamelCase_       )
          UpperCAmelCase_							:       List[str]       =				raw_weights.numel() * dtype_byte_size(raw_weights.dtype       )

          # use the renaming pattern from the small conversion scripts
          UpperCAmelCase_							:       Tuple       =				rename_base_flax_keys(tuple(key.split('/'       )       )     ,					lowerCamelCase_       )
          UpperCAmelCase_							:       str       =				'/'.join(lowerCamelCase_       )

          # If this weight is going to tip up over the maximal size, we split.
          if current_block_size + weight_size > max_shard_size:
               UpperCAmelCase_							:       Dict       =				os.path.join(
                   lowerCamelCase_     ,					weights_name.replace('.bin'     ,					F'''-{len(lowerCamelCase_       )+1:05d}-of-???.bin'''       )       )
               rename_and_save_block(lowerCamelCase_     ,					lowerCamelCase_       )
               sharded_state_dicts.append(current_block.keys()       )
               del current_block
               UpperCAmelCase_							:       str       =				{}
               UpperCAmelCase_							:       List[Any]       =				0

          UpperCAmelCase_							:       Union[str, Any]       =				raw_weights.to(getattr(lowerCamelCase_     ,					lowerCamelCase_       )       )
          current_block_size += weight_size
          total_size += weight_size

     # Add the last block
     UpperCAmelCase_							:       Tuple       =				os.path.join(lowerCamelCase_     ,					weights_name.replace('.bin'     ,					F'''-{len(lowerCamelCase_       )+1:05d}-of-???.bin'''       )       )
     rename_and_save_block(lowerCamelCase_     ,					lowerCamelCase_       )
     sharded_state_dicts.append(current_block.keys()       )

     # If we only have one shard, we return it
     if len(lowerCamelCase_       ) == 1:
          return {weights_name: sharded_state_dicts[0]}, None

     # Otherwise, let's build the index
     UpperCAmelCase_							:       Any       =				{}
     UpperCAmelCase_							:       List[Any]       =				{}
     for idx, shard in enumerate(lowerCamelCase_       ):
          UpperCAmelCase_							:       Dict       =				weights_name.replace(
              '.bin'     ,					F'''-{idx+1:05d}-of-{len(lowerCamelCase_       ):05d}.bin'''       )  # len(sharded_state_dicts):05d}
          UpperCAmelCase_							:       Tuple       =				os.path.join(lowerCamelCase_     ,					weights_name.replace('.bin'     ,					F'''-{idx+1:05d}-of-???.bin'''       )       )
          os.rename(lowerCamelCase_     ,					os.path.join(lowerCamelCase_     ,					lowerCamelCase_       )       )
          UpperCAmelCase_							:       Dict       =				shard
          for key in shard:
               UpperCAmelCase_							:       Dict       =				shard_file

    # Add the metadata
     UpperCAmelCase_							:       Optional[Any]       =				{'total_size': total_size}
     UpperCAmelCase_							:       List[str]       =				{'metadata': metadata, 'weight_map': weight_map}

     with open(os.path.join(lowerCamelCase_     ,					lowerCamelCase_       )     ,					'w'     ,					encoding='utf-8'       ) as f:
          UpperCAmelCase_							:       int       =				json.dumps(lowerCamelCase_     ,					indent=2     ,					sort_keys=lowerCamelCase_       ) + '\n'
          f.write(lowerCamelCase_       )

     return metadata, index


if __name__ == "__main__":
 snake_case__					:      Union[str, Any]				      =  argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     '''--switch_t5x_checkpoint_path''',
     default='''/mnt/disks/disk_switch/original_checkpoints/switch-xxl-128/checkpoint_634600''',
     type=str,
     required=False,
     help='''Path to a directory containing a folder per layer. Follows the original Google format.''',
 )
 parser.add_argument('''--max_shard_size''', default='''10GB''', required=False, help='''Max shard size''')
 parser.add_argument('''--dtype''', default='''bfloat16''', type=str, required=False, help='''dtype of the saved model''')
 parser.add_argument(
     '''--pytorch_dump_folder_path''',
     default='''/mnt/disks/disk_switch/original_checkpoints/switch-xxl-128-converted''',
     type=str,
     required=False,
     help='''Path to the output pytorch model.''',
 )
 snake_case__					:      Any				      =  parser.parse_args()
 shard_on_the_fly(
     args.switch_tax_checkpoint_path,
     args.pytorch_dump_folder_path,
     args.max_shard_size,
     args.dtype,
 )





def  _lowerCamelCase   (				):



     """simple docstring"""





     from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration, TaTokenizer

     UpperCAmelCase_							:       Optional[Any]       =				SwitchTransformersConfig.from_pretrained('google/switch-base-8'       )
     config.save_pretrained('/home/arthur_huggingface_co/transformers/switch_converted'       )
     UpperCAmelCase_							:       str       =				SwitchTransformersForConditionalGeneration.from_pretrained(
         '/home/arthur_huggingface_co/transformers/switch_converted'     ,					device_map='auto'       )

     UpperCAmelCase_							:       Dict       =				TaTokenizer.from_pretrained('t5-small'       )
     UpperCAmelCase_							:       Optional[Any]       =				'A <extra_id_0> walks into a bar a orders a <extra_id_1> with <extra_id_2> pinch of <extra_id_3>.'

     UpperCAmelCase_							:       int       =				tokenizer(lowerCamelCase_     ,					return_tensors='pt'       ).input_ids
     UpperCAmelCase_							:       Union[str, Any]       =				model.generate(lowerCamelCase_     ,					decoder_start_token_id=0       )
     print(tokenizer.decode(out[0]       )       )







'''simple docstring'''


import unittest

from parameterized import parameterized

from transformers import OpenLlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
 import torch

 from transformers import OpenLlamaForCausalLM, OpenLlamaForSequenceClassification, OpenLlamaModel






class 					__SCREAMING_SNAKE_CASE						:







  '''simple docstring'''






  def __init__(			self				,		snake_case_				,		snake_case_=1_3				,		snake_case_=7				,		snake_case_=True				,		snake_case_=True				,		snake_case_=False				,		snake_case_=True				,		snake_case_=9_9				,		snake_case_=3_2				,		snake_case_=5				,		snake_case_=4				,		snake_case_=3_7				,		snake_case_="gelu"				,		snake_case_=0.1				,		snake_case_=0.1				,		snake_case_=5_1_2				,		snake_case_=1_6				,		snake_case_=2				,		snake_case_=0.02				,		snake_case_=3				,		snake_case_=4				,		snake_case_=None				,		):




       '''simple docstring'''

       UpperCAmelCase_							:       Union[str, Any]       =				parent
       UpperCAmelCase_							:       List[str]       =				batch_size
       UpperCAmelCase_							:       Dict       =				seq_length
       UpperCAmelCase_							:       Any       =				is_training
       UpperCAmelCase_							:       List[Any]       =				use_input_mask
       UpperCAmelCase_							:       Tuple       =				use_token_type_ids
       UpperCAmelCase_							:       Tuple       =				use_labels
       UpperCAmelCase_							:       Dict       =				vocab_size
       UpperCAmelCase_							:       Union[str, Any]       =				hidden_size
       UpperCAmelCase_							:       Optional[Any]       =				num_hidden_layers
       UpperCAmelCase_							:       Union[str, Any]       =				num_attention_heads
       UpperCAmelCase_							:       Optional[int]       =				intermediate_size
       UpperCAmelCase_							:       Dict       =				hidden_act
       UpperCAmelCase_							:       Any       =				hidden_dropout_prob
       UpperCAmelCase_							:       Optional[Any]       =				attention_probs_dropout_prob
       UpperCAmelCase_							:       Optional[int]       =				max_position_embeddings
       UpperCAmelCase_							:       List[Any]       =				type_vocab_size
       UpperCAmelCase_							:       Optional[Any]       =				type_sequence_label_size
       UpperCAmelCase_							:       List[Any]       =				initializer_range
       UpperCAmelCase_							:       List[Any]       =				num_labels
       UpperCAmelCase_							:       Any       =				num_choices
       UpperCAmelCase_							:       List[str]       =				scope


  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       UpperCAmelCase_							:       List[str]       =				ids_tensor([self.batch_size, self.seq_length]				,		self.vocab_size						)

       UpperCAmelCase_							:       List[str]       =				None
       if self.use_input_mask:
            UpperCAmelCase_							:       Optional[int]       =				random_attention_mask([self.batch_size, self.seq_length]						)

       UpperCAmelCase_							:       int       =				None
       if self.use_token_type_ids:
            UpperCAmelCase_							:       Dict       =				ids_tensor([self.batch_size, self.seq_length]				,		self.type_vocab_size						)

       UpperCAmelCase_							:       Tuple       =				None
       UpperCAmelCase_							:       Any       =				None
       UpperCAmelCase_							:       str       =				None
       if self.use_labels:
            UpperCAmelCase_							:       str       =				ids_tensor([self.batch_size]				,		self.type_sequence_label_size						)
            UpperCAmelCase_							:       Optional[int]       =				ids_tensor([self.batch_size, self.seq_length]				,		self.num_labels						)
            UpperCAmelCase_							:       Any       =				ids_tensor([self.batch_size]				,		self.num_choices						)

       UpperCAmelCase_							:       int       =				self.get_config()

       return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels


  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       return OpenLlamaConfig(
           vocab_size=self.vocab_size				,		hidden_size=self.hidden_size				,		num_hidden_layers=self.num_hidden_layers				,		num_attention_heads=self.num_attention_heads				,		intermediate_size=self.intermediate_size				,		hidden_act=self.hidden_act				,		hidden_dropout_prob=self.hidden_dropout_prob				,		attention_probs_dropout_prob=self.attention_probs_dropout_prob				,		max_position_embeddings=self.max_position_embeddings				,		type_vocab_size=self.type_vocab_size				,		is_decoder=snake_case_				,		initializer_range=self.initializer_range				,		use_stable_embedding=snake_case_				,		)


  def _UpperCamelCase					(			self				,		snake_case_				,		snake_case_				,		snake_case_				,		snake_case_				,		snake_case_				,		snake_case_				,		snake_case_						):




       '''simple docstring'''

       UpperCAmelCase_							:       str       =				OpenLlamaModel(config=snake_case_						)
       model.to(snake_case_						)
       model.eval()
       UpperCAmelCase_							:       str       =				model(snake_case_				,		attention_mask=snake_case_						)
       UpperCAmelCase_							:       Dict       =				model(snake_case_						)
       self.parent.assertEqual(result.last_hidden_state.shape				,		(self.batch_size, self.seq_length, self.hidden_size)						)


  def _UpperCamelCase					(			self				,		snake_case_				,		snake_case_				,		snake_case_				,		snake_case_				,		snake_case_				,		snake_case_				,		snake_case_				,		snake_case_				,		snake_case_				,		):




       '''simple docstring'''

       UpperCAmelCase_							:       List[Any]       =				True
       UpperCAmelCase_							:       Dict       =				OpenLlamaModel(snake_case_						)
       model.to(snake_case_						)
       model.eval()
       UpperCAmelCase_							:       Optional[Any]       =				model(
           snake_case_				,		attention_mask=snake_case_				,		encoder_hidden_states=snake_case_				,		encoder_attention_mask=snake_case_				,		)
       UpperCAmelCase_							:       Any       =				model(
           snake_case_				,		attention_mask=snake_case_				,		encoder_hidden_states=snake_case_				,		)
       UpperCAmelCase_							:       Optional[Any]       =				model(snake_case_				,		attention_mask=snake_case_						)
       self.parent.assertEqual(result.last_hidden_state.shape				,		(self.batch_size, self.seq_length, self.hidden_size)						)


  def _UpperCamelCase					(			self				,		snake_case_				,		snake_case_				,		snake_case_				,		snake_case_				,		snake_case_				,		snake_case_				,		snake_case_				,		snake_case_				,		snake_case_				,		):




       '''simple docstring'''

       UpperCAmelCase_							:       Optional[Any]       =				OpenLlamaForCausalLM(config=snake_case_						)
       model.to(snake_case_						)
       model.eval()
       UpperCAmelCase_							:       Union[str, Any]       =				model(snake_case_				,		attention_mask=snake_case_				,		labels=snake_case_						)
       self.parent.assertEqual(result.logits.shape				,		(self.batch_size, self.seq_length, self.vocab_size)						)


  def _UpperCamelCase					(			self				,		snake_case_				,		snake_case_				,		snake_case_				,		snake_case_				,		snake_case_				,		snake_case_				,		snake_case_				,		snake_case_				,		snake_case_				,		):




       '''simple docstring'''

       UpperCAmelCase_							:       Optional[Any]       =				True
       UpperCAmelCase_							:       List[Any]       =				True
       UpperCAmelCase_							:       Tuple       =				OpenLlamaForCausalLM(config=snake_case_						)
       model.to(snake_case_						)
       model.eval()

       # first forward pass
       UpperCAmelCase_							:       Tuple       =				model(
           snake_case_				,		attention_mask=snake_case_				,		encoder_hidden_states=snake_case_				,		encoder_attention_mask=snake_case_				,		use_cache=snake_case_				,		)
       UpperCAmelCase_							:       int       =				outputs.past_key_values

       # create hypothetical multiple next token and extent to next_input_ids
       UpperCAmelCase_							:       Optional[int]       =				ids_tensor((self.batch_size, 3)				,		config.vocab_size						)
       UpperCAmelCase_							:       Optional[int]       =				ids_tensor((self.batch_size, 3)				,		vocab_size=2						)

       # append to next input_ids and
       UpperCAmelCase_							:       Union[str, Any]       =				torch.cat([input_ids, next_tokens]				,		dim=-1						)
       UpperCAmelCase_							:       Tuple       =				torch.cat([input_mask, next_mask]				,		dim=-1						)

       UpperCAmelCase_							:       List[Any]       =				model(
           snake_case_				,		attention_mask=snake_case_				,		encoder_hidden_states=snake_case_				,		encoder_attention_mask=snake_case_				,		output_hidden_states=snake_case_				,		)['hidden_states'][0]
       UpperCAmelCase_							:       List[str]       =				model(
           snake_case_				,		attention_mask=snake_case_				,		encoder_hidden_states=snake_case_				,		encoder_attention_mask=snake_case_				,		past_key_values=snake_case_				,		output_hidden_states=snake_case_				,		)['hidden_states'][0]

       # select random slice
       UpperCAmelCase_							:       Union[str, Any]       =				ids_tensor((1,)				,		output_from_past.shape[-1]						).item()
       UpperCAmelCase_							:       List[Any]       =				output_from_no_past[:, -3:, random_slice_idx].detach()
       UpperCAmelCase_							:       Optional[Any]       =				output_from_past[:, :, random_slice_idx].detach()

       self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]						)

       # test that outputs are equal for slice
       self.parent.assertTrue(torch.allclose(snake_case_				,		snake_case_				,		atol=1E-3						)						)






  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       UpperCAmelCase_							:       int       =				self.prepare_config_and_inputs()
       (
           (
           UpperCAmelCase_
       )       ,      (
           UpperCAmelCase_
       )       ,      (
           UpperCAmelCase_
       )       ,      (
           UpperCAmelCase_
       )       ,      (
           UpperCAmelCase_
       )       ,      (
           UpperCAmelCase_
       )       ,      (
           UpperCAmelCase_
       )       ,      
       )							:       Tuple       =				config_and_inputs
       UpperCAmelCase_							:       List[Any]       =				{'input_ids': input_ids, 'attention_mask': input_mask}
       return config, inputs_dict







@require_torch
class 					__SCREAMING_SNAKE_CASE						(	lowerCamelCase_						,     lowerCamelCase_						,     lowerCamelCase_						,     unittest.TestCase	):







  '''simple docstring'''






  lowerCamelCase_		:Tuple						   =		(
      (OpenLlamaModel, OpenLlamaForCausalLM, OpenLlamaForSequenceClassification) if is_torch_available() else ()
  )
  lowerCamelCase_		:Tuple						   =		(OpenLlamaForCausalLM,) if is_torch_available() else ()
  lowerCamelCase_		:Union[str, Any]						   =		(
      {
          '''feature-extraction''': OpenLlamaModel,
          '''text-classification''': OpenLlamaForSequenceClassification,
          '''text-generation''': OpenLlamaForCausalLM,
          '''zero-shot''': OpenLlamaForSequenceClassification,
      }
      if is_torch_available()
      else {}
  )
  lowerCamelCase_		:str						   =		False
  lowerCamelCase_		:Optional[int]						   =		False


  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       UpperCAmelCase_							:       List[str]       =				OpenLlamaModelTester(self						)
       UpperCAmelCase_							:       List[Any]       =				ConfigTester(self				,		config_class=snake_case_				,		hidden_size=3_7						)


  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       self.config_tester.run_common_tests()


  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       UpperCAmelCase_							:       Any       =				self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*snake_case_						)


  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       UpperCAmelCase_							:       Optional[int]       =				self.model_tester.prepare_config_and_inputs()
       for type in ["absolute", "relative_key", "relative_key_query"]:
            UpperCAmelCase_							:       int       =				type
            self.model_tester.create_and_check_model(*snake_case_						)


  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       UpperCAmelCase_       ,      UpperCAmelCase_							:       List[Any]       =				self.model_tester.prepare_config_and_inputs_for_common()
       UpperCAmelCase_							:       Optional[Any]       =				3
       UpperCAmelCase_							:       Union[str, Any]       =				input_dict['input_ids']
       UpperCAmelCase_							:       int       =				input_ids.ne(1						).to(snake_case_						)
       UpperCAmelCase_							:       str       =				ids_tensor([self.model_tester.batch_size]				,		self.model_tester.type_sequence_label_size						)
       UpperCAmelCase_							:       Any       =				OpenLlamaForSequenceClassification(snake_case_						)
       model.to(snake_case_						)
       model.eval()
       UpperCAmelCase_							:       Optional[Any]       =				model(snake_case_				,		attention_mask=snake_case_				,		labels=snake_case_						)
       self.assertEqual(result.logits.shape				,		(self.model_tester.batch_size, self.model_tester.num_labels)						)


  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       UpperCAmelCase_       ,      UpperCAmelCase_							:       Any       =				self.model_tester.prepare_config_and_inputs_for_common()
       UpperCAmelCase_							:       List[str]       =				3
       UpperCAmelCase_							:       str       =				'single_label_classification'
       UpperCAmelCase_							:       List[str]       =				input_dict['input_ids']
       UpperCAmelCase_							:       Optional[Any]       =				input_ids.ne(1						).to(snake_case_						)
       UpperCAmelCase_							:       List[str]       =				ids_tensor([self.model_tester.batch_size]				,		self.model_tester.type_sequence_label_size						)
       UpperCAmelCase_							:       Optional[int]       =				OpenLlamaForSequenceClassification(snake_case_						)
       model.to(snake_case_						)
       model.eval()
       UpperCAmelCase_							:       List[Any]       =				model(snake_case_				,		attention_mask=snake_case_				,		labels=snake_case_						)
       self.assertEqual(result.logits.shape				,		(self.model_tester.batch_size, self.model_tester.num_labels)						)


  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       UpperCAmelCase_       ,      UpperCAmelCase_							:       Optional[int]       =				self.model_tester.prepare_config_and_inputs_for_common()
       UpperCAmelCase_							:       List[str]       =				3
       UpperCAmelCase_							:       int       =				'multi_label_classification'
       UpperCAmelCase_							:       Dict       =				input_dict['input_ids']
       UpperCAmelCase_							:       int       =				input_ids.ne(1						).to(snake_case_						)
       UpperCAmelCase_							:       str       =				ids_tensor(
           [self.model_tester.batch_size, config.num_labels]				,		self.model_tester.type_sequence_label_size						).to(torch.float						)
       UpperCAmelCase_							:       Union[str, Any]       =				OpenLlamaForSequenceClassification(snake_case_						)
       model.to(snake_case_						)
       model.eval()
       UpperCAmelCase_							:       Optional[Any]       =				model(snake_case_				,		attention_mask=snake_case_				,		labels=snake_case_						)
       self.assertEqual(result.logits.shape				,		(self.model_tester.batch_size, self.model_tester.num_labels)						)


  @unittest.skip('Open-Llama buffers include complex numbers, which breaks this test'						)
  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       pass






  @parameterized.expand([('linear',), ('dynamic',)]						)
  def _UpperCamelCase					(			self				,		snake_case_						):




       '''simple docstring'''

       UpperCAmelCase_       ,      UpperCAmelCase_							:       Optional[Any]       =				self.model_tester.prepare_config_and_inputs_for_common()
       UpperCAmelCase_							:       int       =				ids_tensor([1, 1_0]				,		config.vocab_size						)
       UpperCAmelCase_							:       Tuple       =				ids_tensor([1, int(config.max_position_embeddings * 1.5						)]				,		config.vocab_size						)

       set_seed(4_2						)  # Fixed seed at init time so the two models get the same random weights
       UpperCAmelCase_							:       Tuple       =				OpenLlamaModel(snake_case_						)
       original_model.to(snake_case_						)
       original_model.eval()
       UpperCAmelCase_							:       List[str]       =				original_model(snake_case_						).last_hidden_state
       UpperCAmelCase_							:       Any       =				original_model(snake_case_						).last_hidden_state

       set_seed(4_2						)  # Fixed seed at init time so the two models get the same random weights
       UpperCAmelCase_							:       Tuple       =				{'type': scaling_type, 'factor': 10.0}
       UpperCAmelCase_							:       List[str]       =				OpenLlamaModel(snake_case_						)
       scaled_model.to(snake_case_						)
       scaled_model.eval()
       UpperCAmelCase_							:       Tuple       =				scaled_model(snake_case_						).last_hidden_state
       UpperCAmelCase_							:       Optional[int]       =				scaled_model(snake_case_						).last_hidden_state

       # Dynamic scaling does not change the RoPE embeddings until it receives an input longer than the original
       # maximum sequence length, so the outputs for the short input should match.
       if scaling_type == "dynamic":
            self.assertTrue(torch.allclose(snake_case_				,		snake_case_				,		atol=1E-5						)						)
       else:
            self.assertFalse(torch.allclose(snake_case_				,		snake_case_				,		atol=1E-5						)						)

       # The output should be different for long inputs
       self.assertFalse(torch.allclose(snake_case_				,		snake_case_				,		atol=1E-5						)						)






