


'''simple docstring'''




import itertools
from dataclasses import dataclass
from typing import Any, Callable, Dict, List, Optional, Union

import pandas as pd
import pyarrow as pa

import datasets
import datasets.config
from datasets.features.features import require_storage_cast
from datasets.table import table_cast
from datasets.utils.py_utils import Literal


UpperCAmelCase_      :		Any          =		datasets.utils.logging.get_logger(__name__)

UpperCAmelCase_      :		int          =		["names", "prefix"]
UpperCAmelCase_      :		Union[str, Any]          =		["warn_bad_lines", "error_bad_lines", "mangle_dupe_cols"]
UpperCAmelCase_      :		Optional[int]          =		["encoding_errors", "on_bad_lines"]
UpperCAmelCase_      :		Union[str, Any]          =		["date_format"]







@dataclass
class 					a			(				datasets.BuilderConfig  ):
 '''simple docstring'''




 __lowerCAmelCase						: str							  =		","
 __lowerCAmelCase						: Optional[str]							  =		None
 __lowerCAmelCase						: Optional[Union[int, List[int], str]]							  =		"infer"
 __lowerCAmelCase						: Optional[List[str]]							  =		None
 __lowerCAmelCase						: Optional[List[str]]							  =		None
 __lowerCAmelCase						: Optional[Union[int, str, List[int], List[str]]]							  =		None
 __lowerCAmelCase						: Optional[Union[List[int], List[str]]]							  =		None
 __lowerCAmelCase						: Optional[str]							  =		None
 __lowerCAmelCase						: bool							  =		True
 __lowerCAmelCase						: Optional[Literal["c", "python", "pyarrow"]]							  =		None
 __lowerCAmelCase						: Dict[Union[int, str], Callable[[Any], Any]]							  =		None
 __lowerCAmelCase						: Optional[list]							  =		None
 __lowerCAmelCase						: Optional[list]							  =		None
 __lowerCAmelCase						: bool							  =		False
 __lowerCAmelCase						: Optional[Union[int, List[int]]]							  =		None
 __lowerCAmelCase						: Optional[int]							  =		None
 __lowerCAmelCase						: Optional[Union[str, List[str]]]							  =		None
 __lowerCAmelCase						: bool							  =		True
 __lowerCAmelCase						: bool							  =		True
 __lowerCAmelCase						: bool							  =		False
 __lowerCAmelCase						: bool							  =		True
 __lowerCAmelCase						: Optional[str]							  =		None
 __lowerCAmelCase						: str							  =		"."
 __lowerCAmelCase						: Optional[str]							  =		None
 __lowerCAmelCase						: str							  =		'"'
 __lowerCAmelCase						: int							  =		0
 __lowerCAmelCase						: Optional[str]							  =		None
 __lowerCAmelCase						: Optional[str]							  =		None
 __lowerCAmelCase						: Optional[str]							  =		None
 __lowerCAmelCase						: Optional[str]							  =		None
 __lowerCAmelCase						: bool							  =		True
 __lowerCAmelCase						: bool							  =		True
 __lowerCAmelCase						: int							  =		0
 __lowerCAmelCase						: bool							  =		True
 __lowerCAmelCase						: bool							  =		False
 __lowerCAmelCase						: Optional[str]							  =		None
 __lowerCAmelCase						: int							  =		1_0000
 __lowerCAmelCase						: Optional[datasets.Features]							  =		None
 __lowerCAmelCase						: Optional[str]							  =		"strict"
 __lowerCAmelCase						: Literal["error", "warn", "skip"]							  =		"error"
 __lowerCAmelCase						: Optional[str]							  =		None




 def   __UpperCamelCase   (    self      )      ->    List[str]:
    if self.delimiter is not None:
       _a					:  Dict						=  self.delimiter
    if self.column_names is not None:
       _a					:  Optional[Any]						=  self.column_names




 @property
 def   __UpperCamelCase   (    self      )      ->    Any:
    _a					:  Dict						=  {
        'sep': self.sep,
        'header': self.header,
        'names': self.names,
        'index_col': self.index_col,
        'usecols': self.usecols,
        'prefix': self.prefix,
        'mangle_dupe_cols': self.mangle_dupe_cols,
        'engine': self.engine,
        'converters': self.converters,
        'true_values': self.true_values,
        'false_values': self.false_values,
        'skipinitialspace': self.skipinitialspace,
        'skiprows': self.skiprows,
        'nrows': self.nrows,
        'na_values': self.na_values,
        'keep_default_na': self.keep_default_na,
        'na_filter': self.na_filter,
        'verbose': self.verbose,
        'skip_blank_lines': self.skip_blank_lines,
        'thousands': self.thousands,
        'decimal': self.decimal,
        'lineterminator': self.lineterminator,
        'quotechar': self.quotechar,
        'quoting': self.quoting,
        'escapechar': self.escapechar,
        'comment': self.comment,
        'encoding': self.encoding,
        'dialect': self.dialect,
        'error_bad_lines': self.error_bad_lines,
        'warn_bad_lines': self.warn_bad_lines,
        'skipfooter': self.skipfooter,
        'doublequote': self.doublequote,
        'memory_map': self.memory_map,
        'float_precision': self.float_precision,
        'chunksize': self.chunksize,
        'encoding_errors': self.encoding_errors,
        'on_bad_lines': self.on_bad_lines,
        'date_format': self.date_format,
    }

    # some kwargs must not be passed if they don't have a default value
    # some others are deprecated and we can also not pass them if they are the default value
    for pd_read_csv_parameter in _PANDAS_READ_CSV_NO_DEFAULT_PARAMETERS + _PANDAS_READ_CSV_DEPRECATED_PARAMETERS:
       if pd_read_csv_kwargs[pd_read_csv_parameter] == getattr(CsvConfig()		,      lowerCamelCase_      ):
          del pd_read_csv_kwargs[pd_read_csv_parameter]

        # Remove 2.0 new arguments
    if not (datasets.config.PANDAS_VERSION.major >= 2):
       for pd_read_csv_parameter in _PANDAS_READ_CSV_NEW_2_0_0_PARAMETERS:
          del pd_read_csv_kwargs[pd_read_csv_parameter]

        # Remove 1.3 new arguments
    if not (datasets.config.PANDAS_VERSION.major >= 1 and datasets.config.PANDAS_VERSION.minor >= 3):
       for pd_read_csv_parameter in _PANDAS_READ_CSV_NEW_1_3_0_PARAMETERS:
          del pd_read_csv_kwargs[pd_read_csv_parameter]

    return pd_read_csv_kwargs







class 					a			(				datasets.ArrowBasedBuilder  ):
 '''simple docstring'''




 __lowerCAmelCase						: Optional[int]							  =		CsvConfig




 def   __UpperCamelCase   (    self      )      ->    Optional[Any]:
    return datasets.DatasetInfo(features=self.config.features      )




 def   __UpperCamelCase   (    self		,      lowerCamelCase_      )      ->    Tuple:
    if not self.config.data_files:
       raise ValueError(F'''At least one data file must be specified, but got data_files={self.config.data_files}'''      )
    _a					:  Optional[int]						=  dl_manager.download_and_extract(self.config.data_files      )
    if isinstance(lowerCamelCase_		,      (str, list, tuple)      ):
       _a					:  Any						=  data_files
       if isinstance(lowerCamelCase_		,      lowerCamelCase_      ):
          _a					:  Union[str, Any]						=  [files]
       _a					:  Tuple						=  [dl_manager.iter_files(lowerCamelCase_      ) for file in files]
       return [datasets.SplitGenerator(name=datasets.Split.TRAIN		,      gen_kwargs={'files': files}      )]
    _a					:  Union[str, Any]						=  []
    for split_name, files in data_files.items():
       if isinstance(lowerCamelCase_		,      lowerCamelCase_      ):
          _a					:  Any						=  [files]
       _a					:  Dict						=  [dl_manager.iter_files(lowerCamelCase_      ) for file in files]
       splits.append(datasets.SplitGenerator(name=lowerCamelCase_		,      gen_kwargs={'files': files}      )      )
    return splits




 def   __UpperCamelCase   (    self		,      lowerCamelCase_      )      ->    pa.Table:
    if self.config.features is not None:
       _a					:  List[Any]						=  self.config.features.arrow_schema
       if all(not require_storage_cast(lowerCamelCase_      ) for feature in self.config.features.values()      ):
          # cheaper cast
          _a					:  Union[str, Any]						=  pa.Table.from_arrays([pa_table[field.name] for field in schema]		,      schema=lowerCamelCase_      )
       else:
          # more expensive cast; allows str <-> int/float or str to Audio for example
          _a					:  List[Any]						=  table_cast(lowerCamelCase_		,      lowerCamelCase_      )
    return pa_table




 def   __UpperCamelCase   (    self		,      lowerCamelCase_      )      ->    Tuple:
    _a					:  Tuple						=  self.config.features.arrow_schema if self.config.features else None
    # dtype allows reading an int column as str
    _a					:  Dict						=  (
        {
            name: dtype.to_pandas_dtype() if not require_storage_cast(lowerCamelCase_      ) else object
            for name, dtype, feature in zip(schema.names		,      schema.types		,      self.config.features.values()      )
        }
        if schema is not None
        else None
    )
    for file_idx, file in enumerate(itertools.chain.from_iterable(lowerCamelCase_      )      ):
       _a					:  Optional[Any]						=  pd.read_csv(lowerCamelCase_		,      iterator=lowerCamelCase_		,      dtype=lowerCamelCase_		,      **self.config.pd_read_csv_kwargs      )
       try:
          for batch_idx, df in enumerate(lowerCamelCase_      ):
             _a					:  Union[str, Any]						=  pa.Table.from_pandas(lowerCamelCase_      )
             # Uncomment for debugging (will print the Arrow table size and elements)
             # logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
             # logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
             yield (file_idx, batch_idx), self._cast_table(lowerCamelCase_      )
       except ValueError as e:
          logger.error(F'''Failed to read file \'{file}\' with error {type(lowerCamelCase_      )}: {e}'''      )
          raise






'''simple docstring'''




import unittest

from parameterized import parameterized

from transformers import LlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, slow, 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 LlamaForCausalLM, LlamaForSequenceClassification, LlamaModel, LlamaTokenizer







class 					a			:
 '''simple docstring'''




 def __init__(    self		,      lowerCamelCase_		,      lowerCamelCase_=1_3		,      lowerCamelCase_=7		,      lowerCamelCase_=True		,      lowerCamelCase_=True		,      lowerCamelCase_=False		,      lowerCamelCase_=True		,      lowerCamelCase_=9_9		,      lowerCamelCase_=3_2		,      lowerCamelCase_=5		,      lowerCamelCase_=4		,      lowerCamelCase_=3_7		,      lowerCamelCase_="gelu"		,      lowerCamelCase_=0.1		,      lowerCamelCase_=0.1		,      lowerCamelCase_=5_1_2		,      lowerCamelCase_=1_6		,      lowerCamelCase_=2		,      lowerCamelCase_=0.02		,      lowerCamelCase_=3		,      lowerCamelCase_=4		,      lowerCamelCase_=None		,      )      ->    str:
    _a					:  int						=  parent
    _a					:  str						=  batch_size
    _a					:  Any						=  seq_length
    _a					:  Tuple						=  is_training
    _a					:  int						=  use_input_mask
    _a					:  Any						=  use_token_type_ids
    _a					:  List[Any]						=  use_labels
    _a					:  Optional[int]						=  vocab_size
    _a					:  str						=  hidden_size
    _a					:  Any						=  num_hidden_layers
    _a					:  List[Any]						=  num_attention_heads
    _a					:  Tuple						=  intermediate_size
    _a					:  Optional[int]						=  hidden_act
    _a					:  str						=  hidden_dropout_prob
    _a					:  Tuple						=  attention_probs_dropout_prob
    _a					:  List[Any]						=  max_position_embeddings
    _a					:  Union[str, Any]						=  type_vocab_size
    _a					:  Dict						=  type_sequence_label_size
    _a					:  List[Any]						=  initializer_range
    _a					:  Optional[Any]						=  num_labels
    _a					:  Dict						=  num_choices
    _a					:  Dict						=  scope




 def   __UpperCamelCase   (    self      )      ->    Dict:
    _a					:  Any						=  ids_tensor([self.batch_size, self.seq_length]		,      self.vocab_size      )

    _a					:  str						=  None
    if self.use_input_mask:
       _a					:  str						=  random_attention_mask([self.batch_size, self.seq_length]      )

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

    _a					:  Union[str, Any]						=  None
    _a					:  Any						=  None
    _a					:  Optional[Any]						=  None
    if self.use_labels:
       _a					:  Dict						=  ids_tensor([self.batch_size]		,      self.type_sequence_label_size      )
       _a					:  str						=  ids_tensor([self.batch_size, self.seq_length]		,      self.num_labels      )
       _a					:  List[Any]						=  ids_tensor([self.batch_size]		,      self.num_choices      )

    _a					:  List[str]						=  self.get_config()

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




 def   __UpperCamelCase   (    self      )      ->    List[Any]:
    return LlamaConfig(
        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=lowerCamelCase_		,      initializer_range=self.initializer_range		,      )




 def   __UpperCamelCase   (    self		,      lowerCamelCase_		,      lowerCamelCase_		,      lowerCamelCase_		,      lowerCamelCase_		,      lowerCamelCase_		,      lowerCamelCase_		,      lowerCamelCase_      )      ->    int:
    _a					:  List[Any]						=  LlamaModel(config=lowerCamelCase_      )
    model.to(lowerCamelCase_      )
    model.eval()
    _a					:  Any						=  model(lowerCamelCase_		,      attention_mask=lowerCamelCase_      )
    _a					:  List[Any]						=  model(lowerCamelCase_      )
    self.parent.assertEqual(result.last_hidden_state.shape		,      (self.batch_size, self.seq_length, self.hidden_size)      )




 def   __UpperCamelCase   (    self		,      lowerCamelCase_		,      lowerCamelCase_		,      lowerCamelCase_		,      lowerCamelCase_		,      lowerCamelCase_		,      lowerCamelCase_		,      lowerCamelCase_		,      lowerCamelCase_		,      lowerCamelCase_		,      )      ->    Any:
    _a					:  Dict						=  True
    _a					:  Tuple						=  LlamaModel(lowerCamelCase_      )
    model.to(lowerCamelCase_      )
    model.eval()
    _a					:  List[Any]						=  model(
        lowerCamelCase_		,      attention_mask=lowerCamelCase_		,      encoder_hidden_states=lowerCamelCase_		,      encoder_attention_mask=lowerCamelCase_		,      )
    _a					:  Optional[Any]						=  model(
        lowerCamelCase_		,      attention_mask=lowerCamelCase_		,      encoder_hidden_states=lowerCamelCase_		,      )
    _a					:  str						=  model(lowerCamelCase_		,      attention_mask=lowerCamelCase_      )
    self.parent.assertEqual(result.last_hidden_state.shape		,      (self.batch_size, self.seq_length, self.hidden_size)      )




 def   __UpperCamelCase   (    self		,      lowerCamelCase_		,      lowerCamelCase_		,      lowerCamelCase_		,      lowerCamelCase_		,      lowerCamelCase_		,      lowerCamelCase_		,      lowerCamelCase_		,      lowerCamelCase_		,      lowerCamelCase_		,      )      ->    Dict:
    _a					:  Dict						=  LlamaForCausalLM(config=lowerCamelCase_      )
    model.to(lowerCamelCase_      )
    model.eval()
    _a					:  int						=  model(lowerCamelCase_		,      attention_mask=lowerCamelCase_		,      labels=lowerCamelCase_      )
    self.parent.assertEqual(result.logits.shape		,      (self.batch_size, self.seq_length, self.vocab_size)      )




 def   __UpperCamelCase   (    self		,      lowerCamelCase_		,      lowerCamelCase_		,      lowerCamelCase_		,      lowerCamelCase_		,      lowerCamelCase_		,      lowerCamelCase_		,      lowerCamelCase_		,      lowerCamelCase_		,      lowerCamelCase_		,      )      ->    str:
    _a					:  Optional[Any]						=  True
    _a					:  Tuple						=  True
    _a					:  Optional[int]						=  LlamaForCausalLM(config=lowerCamelCase_      )
    model.to(lowerCamelCase_      )
    model.eval()

    # first forward pass
    _a					:  str						=  model(
        lowerCamelCase_		,      attention_mask=lowerCamelCase_		,      encoder_hidden_states=lowerCamelCase_		,      encoder_attention_mask=lowerCamelCase_		,      use_cache=lowerCamelCase_		,      )
    _a					:  Union[str, Any]						=  outputs.past_key_values

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

    # append to next input_ids and
    _a					:  Optional[Any]						=  torch.cat([input_ids, next_tokens]		,      dim=-1      )
    _a					:  List[Any]						=  torch.cat([input_mask, next_mask]		,      dim=-1      )

    _a					:  Any						=  model(
        lowerCamelCase_		,      attention_mask=lowerCamelCase_		,      encoder_hidden_states=lowerCamelCase_		,      encoder_attention_mask=lowerCamelCase_		,      output_hidden_states=lowerCamelCase_		,      )['hidden_states'][0]
    _a					:  List[Any]						=  model(
        lowerCamelCase_		,      attention_mask=lowerCamelCase_		,      encoder_hidden_states=lowerCamelCase_		,      encoder_attention_mask=lowerCamelCase_		,      past_key_values=lowerCamelCase_		,      output_hidden_states=lowerCamelCase_		,      )['hidden_states'][0]

    # select random slice
    _a					:  List[Any]						=  ids_tensor((1,)		,      output_from_past.shape[-1]      ).item()
    _a					:  int						=  output_from_no_past[:, -3:, random_slice_idx].detach()
    _a					:  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(lowerCamelCase_		,      lowerCamelCase_		,      atol=1e-3      )      )




 def   __UpperCamelCase   (    self      )      ->    Tuple:
    _a					:  Optional[int]						=  self.prepare_config_and_inputs()
    (
        (
        _a
    )		,						(
        _a
    )		,						(
        _a
    )		,						(
        _a
    )		,						(
        _a
    )		,						(
        _a
    )		,						(
        _a
    )		,						
    )					:  Dict						=  config_and_inputs
    _a					:  Tuple						=  {'input_ids': input_ids, 'attention_mask': input_mask}
    return config, inputs_dict







@require_torch
class 					a			(				snake_case__			,							snake_case__			,							snake_case__			,							unittest.TestCase  ):
 '''simple docstring'''




 __lowerCAmelCase						: Optional[int]							  =		(LlamaModel, LlamaForCausalLM, LlamaForSequenceClassification) if is_torch_available() else ()
 __lowerCAmelCase						: int							  =		(LlamaForCausalLM,) if is_torch_available() else ()
 __lowerCAmelCase						: int							  =		(
     {
         """feature-extraction""": LlamaModel,
         """text-classification""": LlamaForSequenceClassification,
         """text-generation""": LlamaForCausalLM,
         """zero-shot""": LlamaForSequenceClassification,
     }
     if is_torch_available()
     else {}
 )
 __lowerCAmelCase						: str							  =		False
 __lowerCAmelCase						: List[Any]							  =		False




 def   __UpperCamelCase   (    self      )      ->    str:
    _a					:  Optional[int]						=  LlamaModelTester(self      )
    _a					:  Tuple						=  ConfigTester(self		,      config_class=lowerCamelCase_		,      hidden_size=3_7      )




 def   __UpperCamelCase   (    self      )      ->    Any:
    self.config_tester.run_common_tests()




 def   __UpperCamelCase   (    self      )      ->    Union[str, Any]:
    _a					:  Dict						=  self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_model(*lowerCamelCase_      )




 def   __UpperCamelCase   (    self      )      ->    Optional[Any]:
    _a					:  Tuple						=  self.model_tester.prepare_config_and_inputs()
    for type in ["absolute", "relative_key", "relative_key_query"]:
       _a					:  List[str]						=  type
       self.model_tester.create_and_check_model(*lowerCamelCase_      )




 def   __UpperCamelCase   (    self      )      ->    List[Any]:
    _a		,						_a					:  int						=  self.model_tester.prepare_config_and_inputs_for_common()
    _a					:  Optional[Any]						=  3
    _a					:  Union[str, Any]						=  input_dict['input_ids']
    _a					:  List[Any]						=  input_ids.ne(1      ).to(lowerCamelCase_      )
    _a					:  int						=  ids_tensor([self.model_tester.batch_size]		,      self.model_tester.type_sequence_label_size      )
    _a					:  Any						=  LlamaForSequenceClassification(lowerCamelCase_      )
    model.to(lowerCamelCase_      )
    model.eval()
    _a					:  List[Any]						=  model(lowerCamelCase_		,      attention_mask=lowerCamelCase_		,      labels=lowerCamelCase_      )
    self.assertEqual(result.logits.shape		,      (self.model_tester.batch_size, self.model_tester.num_labels)      )




 def   __UpperCamelCase   (    self      )      ->    List[str]:
    _a		,						_a					:  Tuple						=  self.model_tester.prepare_config_and_inputs_for_common()
    _a					:  List[str]						=  3
    _a					:  List[str]						=  'single_label_classification'
    _a					:  Union[str, Any]						=  input_dict['input_ids']
    _a					:  str						=  input_ids.ne(1      ).to(lowerCamelCase_      )
    _a					:  Any						=  ids_tensor([self.model_tester.batch_size]		,      self.model_tester.type_sequence_label_size      )
    _a					:  str						=  LlamaForSequenceClassification(lowerCamelCase_      )
    model.to(lowerCamelCase_      )
    model.eval()
    _a					:  List[Any]						=  model(lowerCamelCase_		,      attention_mask=lowerCamelCase_		,      labels=lowerCamelCase_      )
    self.assertEqual(result.logits.shape		,      (self.model_tester.batch_size, self.model_tester.num_labels)      )




 def   __UpperCamelCase   (    self      )      ->    Tuple:
    _a		,						_a					:  Union[str, Any]						=  self.model_tester.prepare_config_and_inputs_for_common()
    _a					:  Union[str, Any]						=  3
    _a					:  str						=  'multi_label_classification'
    _a					:  Union[str, Any]						=  input_dict['input_ids']
    _a					:  Union[str, Any]						=  input_ids.ne(1      ).to(lowerCamelCase_      )
    _a					:  str						=  ids_tensor(
        [self.model_tester.batch_size, config.num_labels]		,      self.model_tester.type_sequence_label_size      ).to(torch.float      )
    _a					:  str						=  LlamaForSequenceClassification(lowerCamelCase_      )
    model.to(lowerCamelCase_      )
    model.eval()
    _a					:  int						=  model(lowerCamelCase_		,      attention_mask=lowerCamelCase_		,      labels=lowerCamelCase_      )
    self.assertEqual(result.logits.shape		,      (self.model_tester.batch_size, self.model_tester.num_labels)      )




 @unittest.skip('LLaMA buffers include complex numbers, which breaks this test'      )
 def   __UpperCamelCase   (    self      )      ->    Optional[int]:
    pass




 @parameterized.expand([('linear',), ('dynamic',)]      )
 def   __UpperCamelCase   (    self		,      lowerCamelCase_      )      ->    List[str]:
    _a		,						_a					:  Dict						=  self.model_tester.prepare_config_and_inputs_for_common()
    _a					:  Union[str, Any]						=  ids_tensor([1, 1_0]		,      config.vocab_size      )
    _a					:  Optional[Any]						=  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
    _a					:  int						=  LlamaModel(lowerCamelCase_      )
    original_model.to(lowerCamelCase_      )
    original_model.eval()
    _a					:  str						=  original_model(lowerCamelCase_      ).last_hidden_state
    _a					:  Union[str, Any]						=  original_model(lowerCamelCase_      ).last_hidden_state

    set_seed(4_2      )  # Fixed seed at init time so the two models get the same random weights
    _a					:  Optional[Any]						=  {'type': scaling_type, 'factor': 10.0}
    _a					:  Any						=  LlamaModel(lowerCamelCase_      )
    scaled_model.to(lowerCamelCase_      )
    scaled_model.eval()
    _a					:  Optional[int]						=  scaled_model(lowerCamelCase_      ).last_hidden_state
    _a					:  int						=  scaled_model(lowerCamelCase_      ).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(lowerCamelCase_		,      lowerCamelCase_		,      atol=1e-5      )      )
    else:
       self.assertFalse(torch.allclose(lowerCamelCase_		,      lowerCamelCase_		,      atol=1e-5      )      )

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







@require_torch
class 					a			(				unittest.TestCase  ):
 '''simple docstring'''




 @unittest.skip('Logits are not exactly the same, once we fix the instabalities somehow, will update!'      )
 @slow
 def   __UpperCamelCase   (    self      )      ->    int:
    _a					:  Optional[Any]						=  [1, 3_0_6, 4_6_5_8, 2_7_8, 6_5_9_3, 3_1_0, 2_8_3_4, 3_3_8]
    _a					:  Optional[int]						=  LlamaForCausalLM.from_pretrained('meta-llama/Llama-2-7b-hf'		,      device_map='auto'      )
    _a					:  Tuple						=  model(torch.tensor([input_ids]      )      )
    # Expected mean on dim = -1
    _a					:  List[Any]						=  torch.tensor([[-6.6550, -4.1227, -4.9859, -3.2406, 0.8262, -3.0033, 1.2964, -3.3699]]      )
    torch.testing.assert_close(out.mean(-1      )		,      lowerCamelCase_		,      atol=1e-2		,      rtol=1e-2      )
    # slicing logits[0, 0, 0:30]
    # fmt: off
    _a					:  str						=  torch.tensor([-12.8281, -7.4453, -0.4639, -8.0625, -7.2500, -8.0000, -6.4883, -7.7695, -7.8438, -7.0312, -6.2188, -7.1328, -1.8496, 1.9961, -8.6250, -6.7227, -12.8281, -6.9492, -7.0742, -7.7852, -7.5820, -7.9062, -6.9375, -7.9805, -8.3438, -8.1562, -8.0469, -7.6250, -7.7422, -7.3398,]      )
    # fmt: on
    torch.testing.assert_close(out[0, 0, :3_0]		,      lowerCamelCase_		,      atol=1e-5		,      rtol=1e-5      )




 @unittest.skip('Logits are not exactly the same, once we fix the instabalities somehow, will update!'      )
 @slow
 def   __UpperCamelCase   (    self      )      ->    Optional[int]:
    _a					:  Any						=  [1, 3_0_6, 4_6_5_8, 2_7_8, 6_5_9_3, 3_1_0, 2_8_3_4, 3_3_8]
    _a					:  Any						=  LlamaForCausalLM.from_pretrained('meta-llama/Llama-2-13b-hf'		,      device_map='auto'      )
    _a					:  List[Any]						=  model(torch.tensor(lowerCamelCase_      )      )
    # Expected mean on dim = -1
    _a					:  Optional[int]						=  torch.tensor([[-2.0622, -1.2794, -1.1638, -0.9788, -1.4603, -1.0238, -1.7893, -1.4411]]      )
    torch.testing.assert_close(out.mean(-1      )		,      lowerCamelCase_		,      atol=1e-2		,      rtol=1e-2      )
    # slicing logits[0, 0, 0:30]
    # fmt: off
    _a					:  List[Any]						=  torch.tensor([-8.1406, -8.0547, 2.7461, -1.2344, -0.1448, -1.8262, -1.0020, -1.8154, -1.6895, -1.8516, -2.3574, -0.9277, 3.7598, 6.5742, -1.2998, -0.1177, -8.1406, -2.9688, -2.9199, -3.1699, -3.5254, -2.3555, -2.7988, -3.4141, -2.8262, -4.5195, -3.3379, -3.3164, -2.7832, -3.0273]      )
    # fmt: on
    torch.testing.assert_close(out[0, 0, :3_0]		,      lowerCamelCase_		,      atol=1e-5		,      rtol=1e-5      )




 @unittest.skip('Logits are not exactly the same, once we fix the instabalities somehow, will update!'      )
 @slow
 def   __UpperCamelCase   (    self      )      ->    Any:
    _a					:  Optional[int]						=  [1, 3_0_6, 4_6_5_8, 2_7_8, 6_5_9_3, 3_1_0, 2_8_3_4, 3_3_8]
    _a					:  Union[str, Any]						=  LlamaForCausalLM.from_pretrained('meta-llama/Llama-2-13b-chat-hf'		,      device_map='auto'      )
    _a					:  str						=  model(torch.tensor(lowerCamelCase_      )      )
    # Expected mean on dim = -1
    _a					:  int						=  torch.tensor([[-0.8562, -1.8520, -0.7551, -0.4162, -1.5161, -1.2038, -2.4823, -2.3254]]      )
    torch.testing.assert_close(out.mean(-1      )		,      lowerCamelCase_		,      atol=1e-2		,      rtol=1e-2      )
    # slicing logits[0, 0, 0:30]
    # fmt: off
    _a					:  List[Any]						=  torch.tensor([-2.2227, 4.8828, 0.9023, -0.4578, -0.7871, -0.1033, -0.6221, -0.5786, -0.7803, -1.0674, -1.2920, -0.1570, 0.8008, 2.0723, -0.9497, 0.2771, -2.2227, -0.7612, -1.4346, -1.2061, -1.6426, -0.3000, -0.7139, -1.1934, -1.8691, -1.6973, -1.5947, -1.2705, -0.3523, -0.5513]      )
    # fmt: on
    torch.testing.assert_close(out.mean(-1      )		,      lowerCamelCase_		,      atol=1e-2		,      rtol=1e-2      )




 @unittest.skip(
     'Logits are not exactly the same, once we fix the instabalities somehow, will update! Also it is gonna be a `too_slow` test'      )
 @slow
 def   __UpperCamelCase   (    self      )      ->    Dict:
    _a					:  Dict						=  [1, 3_0_6, 4_6_5_8, 2_7_8, 6_5_9_3, 3_1_0, 2_8_3_4, 3_3_8]
    _a					:  str						=  LlamaForCausalLM.from_pretrained('meta-llama/Llama-2-70b-hf'		,      device_map='auto'      )
    _a					:  Dict						=  model(torch.tensor(lowerCamelCase_      )      )

    _a					:  str						=  torch.tensor(
        [[-4.2327, -3.3360, -4.6665, -4.7631, -1.8180, -3.4170, -1.4211, -3.1810]]		,      dtype=torch.floataa      )
    torch.testing.assert_close(out.mean(-1      )		,      lowerCamelCase_		,      atol=1e-2		,      rtol=1e-2      )
    # fmt: off
    _a					:  Any						=  torch.tensor([-9.4922, -3.9551, 1.7998, -5.6758, -5.1055, -5.8984, -4.8320, -6.8086, -6.5391, -5.6172, -5.5820, -5.5352, 1.7881, 3.6289, -6.5117, -3.4785, -9.5000, -6.0352, -6.8125, -6.0195, -6.6836, -5.4727, -6.2812, -6.0391, -7.3398, -7.4297, -7.4844, -6.5820, -5.8789, -5.5312]      )
    # fmt: on
    torch.testing.assert_close(out[0, 0, :3_0]		,      lowerCamelCase_		,      atol=1e-5		,      rtol=1e-5      )




 @unittest.skip('Model is curently gated'      )
 @slow
 def   __UpperCamelCase   (    self      )      ->    Optional[Any]:
    _a					:  List[Any]						=  'Simply put, the theory of relativity states that 1) the laws of physics are the same everywhere in the universe and 2) the passage of time and the length of objects can vary depending on the observer\'s frame of reference.\n\nThe first part of the theory, that the laws of physics are the same everywhere, is known as the "princi'
    _a					:  List[Any]						=  'Simply put, the theory of relativity states that '
    _a					:  List[Any]						=  LlamaTokenizer.from_pretrained('meta-llama/Llama-2-13b-chat-hf'      )
    _a					:  Any						=  tokenizer.encode(lowerCamelCase_		,      return_tensors='pt'      )
    _a					:  Union[str, Any]						=  LlamaForCausalLM.from_pretrained(
        'meta-llama/Llama-2-13b-chat-hf'		,      device_map='sequential'		,      use_safetensors=lowerCamelCase_      )

    # greedy generation outputs
    _a					:  List[Any]						=  model.generate(lowerCamelCase_		,      max_new_tokens=6_4		,      top_p=lowerCamelCase_		,      temperature=1		,      do_sample=lowerCamelCase_      )
    _a					:  Dict						=  tokenizer.decode(generated_ids[0]		,      skip_special_tokens=lowerCamelCase_      )
    self.assertEqual(lowerCamelCase_		,      lowerCamelCase_      )


