

'''simple docstring'''




import csv
import json
import logging
import os
import re
import subprocess
import tempfile
import unittest
import zipfile
from pathlib import Path
from typing import Optional
from unittest import mock

import torch

# We use TF to parse the logs
from accelerate import Accelerator
from accelerate.test_utils.testing import (
    MockingTestCase,
    TempDirTestCase,
    require_comet_ml,
    require_tensorboard,
    require_wandb,
    skip,
)
from accelerate.tracking import CometMLTracker, GeneralTracker
from accelerate.utils import ProjectConfiguration, is_comet_ml_available, is_tensorboard_available


if is_comet_ml_available():
       from comet_ml import OfflineExperiment

if is_tensorboard_available():
       import struct

       import tensorboard.compat.proto.event_pba as event_pba

_a				: Dict 							=						logging.getLogger(__name__)



@require_tensorboard
class        lowercase_	(		unittest.TestCase ):

  '''simple docstring'''





  def 						snake_case_			(     self     )							->				List[str]:


    """simple docstring"""
    UpperCAmelCase													=	'test_project_with_config'
    with tempfile.TemporaryDirectory() as dirpath:
      UpperCAmelCase													=	Accelerator(log_with='tensorboard'   , project_dir=a_     )
      UpperCAmelCase													=	{'num_iterations': 1_2, 'learning_rate': 1E-2, 'some_boolean': False, 'some_string': 'some_value'}
      accelerator.init_trackers(a_   , a_     )
      accelerator.end_training()
      for child in Path(F'''{dirpath}/{project_name}'''     ).glob('*/**'     ):
        UpperCAmelCase													=	list(filter(lambda a_     : x.is_file()   , child.iterdir()     )     )[0]
      self.assertNotEqual(str(a_     )   , ''     )
  def 						snake_case_			(     self     )							->				List[str]:


    """simple docstring"""
    UpperCAmelCase													=	'test_project_with_log'
    with tempfile.TemporaryDirectory() as dirpath:
      UpperCAmelCase													=	Accelerator(log_with='tensorboard'   , project_dir=a_     )
      accelerator.init_trackers(a_     )
      UpperCAmelCase													=	{'total_loss': 0.1, 'iteration': 1, 'my_text': 'some_value'}
      accelerator.log(a_   , step=0     )
      accelerator.end_training()
      # Logged values are stored in the outermost-tfevents file and can be read in as a TFRecord
      # Names are randomly generated each time
      UpperCAmelCase													=	list(filter(lambda a_     : x.is_file()   , Path(F'''{dirpath}/{project_name}'''     ).iterdir()     )     )[0]
      self.assertNotEqual(str(a_     )   , ''     )
  def 						snake_case_			(     self     )							->				int:


    """simple docstring"""
    UpperCAmelCase													=	'test_project_with_log'
    with tempfile.TemporaryDirectory() as dirpath:
      UpperCAmelCase													=	Accelerator(log_with='tensorboard'   , project_dir=a_     )
      accelerator.init_trackers(a_     )
      UpperCAmelCase													=	{'tensor': torch.tensor(1     )}
      accelerator.log(a_   , step=0     )
      accelerator.end_training()
      # Logged values are stored in the outermost-tfevents file and can be read in as a TFRecord
      # Names are randomly generated each time
      UpperCAmelCase													=	list(filter(lambda a_     : x.is_file()   , Path(F'''{dirpath}/{project_name}'''     ).iterdir()     )     )[0]
      # Reading implementation based on https://github.com/pytorch/pytorch/issues/45327#issuecomment-703757685
      with open(a_   , 'rb'     ) as f:
        UpperCAmelCase													=	f.read()
      UpperCAmelCase													=	False
      while data:
        UpperCAmelCase													=	struct.unpack('Q'   , data[:8]     )

        UpperCAmelCase													=	data[1_2 : 1_2 + int(header[0]     )]  # 8+4
        UpperCAmelCase													=	data[1_2 + int(header[0]     ) + 4 :]
        UpperCAmelCase													=	event_pba.Event()

        event.ParseFromString(a_     )
        if event.HasField('summary'     ):
          for value in event.summary.value:
            if value.simple_value == 1.0 and value.tag == "tensor":
              UpperCAmelCase													=	True
      self.assertTrue(a_   , 'Converted tensor was not found in the log file!'     )
  def 						snake_case_			(     self     )							->				Any:


    """simple docstring"""
    with self.assertRaisesRegex(a_   , 'Logging with `tensorboard` requires a `logging_dir`'     ):
      UpperCAmelCase													=	Accelerator(log_with='tensorboard'     )
    with tempfile.TemporaryDirectory() as dirpath:
      UpperCAmelCase													=	Accelerator(log_with='tensorboard'   , project_dir=a_     )





  def 						snake_case_			(     self     )							->				str:


    """simple docstring"""
    UpperCAmelCase													=	ProjectConfiguration(total_limit=3_0     )
    with tempfile.TemporaryDirectory() as dirpath:
      UpperCAmelCase													=	Accelerator(log_with='tensorboard'   , project_dir=a_   , project_config=a_     )




@require_wandb
@mock.patch.dict(os.environ ,       {"WANDB_MODE": "offline"} )
class        lowercase_	(		a ,       a ):

  '''simple docstring'''





  def 						snake_case_			(     self     )							->				Optional[Any]:


    """simple docstring"""
    super().setUp()
    # wandb let's us override where logs are stored to via the WANDB_DIR env var
    self.add_mocks(mock.patch.dict(os.environ   , {'WANDB_DIR': self.tmpdir}     )     )
  @staticmethod
  def 						snake_case_			(     a_   , a_   , a_ = True     )							->				List[Any]:


    """simple docstring"""
    UpperCAmelCase													=	rF'''{section} ([\S\s]*?)\n\n'''
    if record:
      UpperCAmelCase													=	rF'''Record: {pattern}'''
    UpperCAmelCase													=	re.findall(a_   , a_     )[0]
    # A config
    if section == "config" or section == "history":
      UpperCAmelCase													=	re.findall(r'"([a-zA-Z0-9_.,]+)'   , a_     )
      return {key: val for key, val in zip(cleaned_record[0::2]   , cleaned_record[1::2]     )}
    # Everything else
    else:
      return dict(re.findall(r'(\w+): "([^\s]+)"'   , a_     )     )





  @skip
  def 						snake_case_			(     self     )							->				List[str]:


    """simple docstring"""
    UpperCAmelCase													=	'test_project_with_config'
    UpperCAmelCase													=	Accelerator(log_with='wandb'     )
    UpperCAmelCase													=	{'num_iterations': 1_2, 'learning_rate': 1E-2, 'some_boolean': False, 'some_string': 'some_value'}
    UpperCAmelCase													=	{'wandb': {'tags': ['my_tag']}}
    accelerator.init_trackers(a_   , a_   , a_     )
    UpperCAmelCase													=	{'total_loss': 0.1, 'iteration': 1, 'my_text': 'some_value'}
    accelerator.log(a_   , step=0     )
    accelerator.end_training()
    # The latest offline log is stored at wandb/latest-run/*.wandb
    for child in Path(F'''{self.tmpdir}/wandb/latest-run'''     ).glob('*'     ):
      if child.is_file() and child.suffix == ".wandb":
        UpperCAmelCase													=	subprocess.check_output(
            ['wandb', 'sync', '--view', '--verbose', str(a_     )]   , env=os.environ.copy()     ).decode('utf8'   , 'ignore'     )
        break

        # Check HPS through careful parsing and cleaning
    UpperCAmelCase													=	self.parse_log(a_   , 'config'     )
    self.assertEqual(logged_items['num_iterations']   , '12'     )
    self.assertEqual(logged_items['learning_rate']   , '0.01'     )
    self.assertEqual(logged_items['some_boolean']   , 'false'     )
    self.assertEqual(logged_items['some_string']   , 'some_value'     )
    self.assertEqual(logged_items['some_string']   , 'some_value'     )

    # Run tags
    UpperCAmelCase													=	self.parse_log(a_   , 'run'   , a_     )
    self.assertEqual(logged_items['tags']   , 'my_tag'     )

    # Actual logging
    UpperCAmelCase													=	self.parse_log(a_   , 'history'     )
    self.assertEqual(logged_items['total_loss']   , '0.1'     )
    self.assertEqual(logged_items['iteration']   , '1'     )
    self.assertEqual(logged_items['my_text']   , 'some_value'     )
    self.assertEqual(logged_items['_step']   , '0'     )




def       lowerCamelCase__		(						self      :   Union[str, Any]       ,   SCREAMING_SNAKE_CASE      :   str       ,   SCREAMING_SNAKE_CASE      :   str						):
  UpperCAmelCase													=	run_name
  UpperCAmelCase													=	OfflineExperiment(project_name=SCREAMING_SNAKE_CASE       ,   offline_directory=SCREAMING_SNAKE_CASE						)
  logger.info(f'''Initialized offline CometML project {self.run_name}'''						)
  logger.info('Make sure to log any initial configurations with `self.store_init_configuration` before training!'						)



@require_comet_ml
@mock.patch.object(a ,       "__init__" ,       a )
class        lowercase_	(		unittest.TestCase ):

  '''simple docstring'''





  @staticmethod
  def 						snake_case_			(     a_   , a_   , a_ = False     )							->				Optional[int]:


    """simple docstring"""
    for log in log_list:
      UpperCAmelCase													=	json.loads(a_     )['payload']
      if is_param and "param" in j.keys():
        if j["param"]["paramName"] == key:
          return j["param"]["paramValue"]
      if "log_other" in j.keys():
        if j["log_other"]["key"] == key:
          return j["log_other"]["val"]
      if "metric" in j.keys():
        if j["metric"]["metricName"] == key:
          return j["metric"]["metricValue"]
  def 						snake_case_			(     self     )							->				List[str]:


    """simple docstring"""
    with tempfile.TemporaryDirectory() as d:
      UpperCAmelCase													=	CometMLTracker('test_project_with_config'   , a_     )
      UpperCAmelCase													=	Accelerator(log_with=a_     )
      UpperCAmelCase													=	{'num_iterations': 1_2, 'learning_rate': 1E-2, 'some_boolean': False, 'some_string': 'some_value'}
      accelerator.init_trackers(a_   , a_     )
      accelerator.end_training()
      UpperCAmelCase													=	os.listdir(a_     )[0]  # Comet is nice, it's just a zip file here
      # We parse the raw logs
      UpperCAmelCase													=	os.path.join(a_   , a_     )
      UpperCAmelCase													=	zipfile.ZipFile(a_   , 'r'     )
      UpperCAmelCase													=	archive.open('messages.json'     ).read().decode('utf-8'     )
    UpperCAmelCase													=	log.split('\n'     )[:-1]
    self.assertEqual(self.get_value_from_key(a_   , 'num_iterations'   , a_     )   , 1_2     )
    self.assertEqual(self.get_value_from_key(a_   , 'learning_rate'   , a_     )   , 0.01     )
    self.assertEqual(self.get_value_from_key(a_   , 'some_boolean'   , a_     )   , a_     )
    self.assertEqual(self.get_value_from_key(a_   , 'some_string'   , a_     )   , 'some_value'     )





  def 						snake_case_			(     self     )							->				Union[str, Any]:


    """simple docstring"""
    with tempfile.TemporaryDirectory() as d:
      UpperCAmelCase													=	CometMLTracker('test_project_with_config'   , a_     )
      UpperCAmelCase													=	Accelerator(log_with=a_     )
      accelerator.init_trackers(a_     )
      UpperCAmelCase													=	{'total_loss': 0.1, 'iteration': 1, 'my_text': 'some_value'}
      accelerator.log(a_   , step=0     )
      accelerator.end_training()
      UpperCAmelCase													=	os.listdir(a_     )[0]  # Comet is nice, it's just a zip file here
      # We parse the raw logs
      UpperCAmelCase													=	os.path.join(a_   , a_     )
      UpperCAmelCase													=	zipfile.ZipFile(a_   , 'r'     )
      UpperCAmelCase													=	archive.open('messages.json'     ).read().decode('utf-8'     )
    UpperCAmelCase													=	log.split('\n'     )[:-1]
    self.assertEqual(self.get_value_from_key(a_   , 'curr_step'   , a_     )   , 0     )
    self.assertEqual(self.get_value_from_key(a_   , 'total_loss'     )   , 0.1     )
    self.assertEqual(self.get_value_from_key(a_   , 'iteration'     )   , 1     )
    self.assertEqual(self.get_value_from_key(a_   , 'my_text'     )   , 'some_value'     )




class        lowercase_	(		a ):

  '''simple docstring'''





  __lowerCAmelCase					: int      =   [
      "total_loss",
      "iteration",
      "my_text",
      "learning_rate",
      "num_iterations",
      "some_boolean",
      "some_string",
  ]

  __lowerCAmelCase					: Optional[Any]      =   "my_custom_tracker"
  __lowerCAmelCase					: Optional[Any]      =   False
  def __init__(     self   , a_     )							->				Optional[int]:


    """simple docstring"""
    UpperCAmelCase													=	open(F'''{dir}/log.csv'''   , 'w+'     )
    UpperCAmelCase													=	csv.DictWriter(self.f   , fieldnames=self._col_names     )
    self.writer.writeheader()
  @property
  def 						snake_case_			(     self     )							->				Optional[int]:


    """simple docstring"""
    return self.writer
  def 						snake_case_			(     self   , a_     )							->				Any:


    """simple docstring"""
    logger.info('Call init'     )
    self.writer.writerow(a_     )
  def 						snake_case_			(     self   , a_   , a_     )							->				Union[str, Any]:


    """simple docstring"""
    logger.info('Call log'     )
    self.writer.writerow(a_     )





  def 						snake_case_			(     self     )							->				Optional[int]:


    """simple docstring"""
    self.f.close()




class        lowercase_	(		unittest.TestCase ):

  '''simple docstring'''





  def 						snake_case_			(     self     )							->				List[str]:


    """simple docstring"""
    with tempfile.TemporaryDirectory() as d:
      UpperCAmelCase													=	MyCustomTracker(a_     )
      UpperCAmelCase													=	Accelerator(log_with=a_     )
      UpperCAmelCase													=	{'num_iterations': 1_2, 'learning_rate': 1E-2, 'some_boolean': False, 'some_string': 'some_value'}
      accelerator.init_trackers('Some name'   , a_     )
      accelerator.end_training()
      with open(F'''{d}/log.csv'''   , 'r'     ) as f:
        UpperCAmelCase													=	csv.DictReader(a_     )
        UpperCAmelCase													=	next(a_     )
        UpperCAmelCase													=	{
            'total_loss': '',
            'iteration': '',
            'my_text': '',
            'learning_rate': '0.01',
            'num_iterations': '12',
            'some_boolean': 'False',
            'some_string': 'some_value',
        }
        self.assertDictEqual(a_   , a_     )





  def 						snake_case_			(     self     )							->				Optional[Any]:


    """simple docstring"""
    with tempfile.TemporaryDirectory() as d:
      UpperCAmelCase													=	MyCustomTracker(a_     )
      UpperCAmelCase													=	Accelerator(log_with=a_     )
      accelerator.init_trackers('Some name'     )
      UpperCAmelCase													=	{'total_loss': 0.1, 'iteration': 1, 'my_text': 'some_value'}
      accelerator.log(a_   , step=0     )
      accelerator.end_training()
      with open(F'''{d}/log.csv'''   , 'r'     ) as f:
        UpperCAmelCase													=	csv.DictReader(a_     )
        UpperCAmelCase													=	next(a_     )
        UpperCAmelCase													=	{
            'total_loss': '0.1',
            'iteration': '1',
            'my_text': 'some_value',
            'learning_rate': '',
            'num_iterations': '',
            'some_boolean': '',
            'some_string': '',
        }
        self.assertDictEqual(a_   , a_     )





'''simple docstring'''




from __future__ import annotations

import unittest

from transformers import AutoTokenizer, GPTJConfig, is_tf_available
from transformers.testing_utils import require_tf, slow, tooslow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
from ...utils.test_modeling_tf_core import TFCoreModelTesterMixin


if is_tf_available():
  import tensorflow as tf

  from transformers.models.gptj.modeling_tf_gptj import (
      TFGPTJForCausalLM,
      TFGPTJForQuestionAnswering,
      TFGPTJForSequenceClassification,
      TFGPTJModel,
      shape_list,
  )



class        lowercase_	:

  '''simple docstring'''





  def __init__(     self   , a_     )							->				Any:


    """simple docstring"""
    UpperCAmelCase													=	parent
    UpperCAmelCase													=	1_3
    UpperCAmelCase													=	7
    UpperCAmelCase													=	True
    UpperCAmelCase													=	True
    UpperCAmelCase													=	True
    UpperCAmelCase													=	True
    UpperCAmelCase													=	True
    UpperCAmelCase													=	9_9
    UpperCAmelCase													=	3_2
    UpperCAmelCase													=	4
    UpperCAmelCase													=	2
    UpperCAmelCase													=	4
    UpperCAmelCase													=	3_7
    UpperCAmelCase													=	'gelu'
    UpperCAmelCase													=	0.1
    UpperCAmelCase													=	0.1
    UpperCAmelCase													=	5_1_2
    UpperCAmelCase													=	1_6
    UpperCAmelCase													=	2
    UpperCAmelCase													=	0.02
    UpperCAmelCase													=	3
    UpperCAmelCase													=	4
    UpperCAmelCase													=	None
    UpperCAmelCase													=	self.vocab_size - 1
    UpperCAmelCase													=	self.vocab_size - 1
    UpperCAmelCase													=	self.vocab_size - 1
  def 						snake_case_			(     self     )							->				List[str]:


    """simple docstring"""
    UpperCAmelCase													=	ids_tensor([self.batch_size, self.seq_length]   , self.vocab_size     )

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

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

    UpperCAmelCase													=	None
    if self.use_mc_token_ids:
      UpperCAmelCase													=	ids_tensor([self.batch_size, self.num_choices]   , self.seq_length     )

    UpperCAmelCase													=	None
    UpperCAmelCase													=	None
    UpperCAmelCase													=	None
    if self.use_labels:
      UpperCAmelCase													=	ids_tensor([self.batch_size]   , self.type_sequence_label_size     )
      UpperCAmelCase													=	ids_tensor([self.batch_size, self.seq_length]   , self.num_labels     )
      UpperCAmelCase													=	ids_tensor([self.batch_size]   , self.num_choices     )

    UpperCAmelCase													=	GPTJConfig(
        vocab_size=self.vocab_size   , n_embd=self.hidden_size   , n_layer=self.num_hidden_layers   , n_head=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   , n_positions=self.max_position_embeddings   , type_vocab_size=self.type_vocab_size   , initializer_range=self.initializer_range   , bos_token_id=self.bos_token_id   , eos_token_id=self.eos_token_id   , pad_token_id=self.pad_token_id   , rotary_dim=self.rotary_dim   , return_dict=a_   , )

    UpperCAmelCase													=	ids_tensor([self.num_hidden_layers, self.num_attention_heads]   , 2     )

    return (
        config,
        input_ids,
        input_mask,
        head_mask,
        token_type_ids,
        mc_token_ids,
        sequence_labels,
        token_labels,
        choice_labels,
    )
  def 						snake_case_			(     self   , a_   , a_   , a_   , a_   , a_   , *a_     )							->				Dict:


    """simple docstring"""
    UpperCAmelCase													=	TFGPTJModel(config=a_     )
    UpperCAmelCase													=	{
        'input_ids': input_ids,
        'attention_mask': input_mask,
        'token_type_ids': token_type_ids,
    }
    UpperCAmelCase													=	model(a_     )

    UpperCAmelCase													=	[input_ids, None, input_mask]  # None is the input for 'past'
    UpperCAmelCase													=	model(a_     )

    UpperCAmelCase													=	model(a_     )

    self.parent.assertEqual(result.last_hidden_state.shape   , (self.batch_size, self.seq_length, self.hidden_size)     )
  def 						snake_case_			(     self   , a_   , a_   , a_   , a_   , a_   , *a_     )							->				Tuple:


    """simple docstring"""
    UpperCAmelCase													=	TFGPTJModel(config=a_     )

    # first forward pass
    UpperCAmelCase													=	model(a_   , token_type_ids=a_   , use_cache=a_     )
    UpperCAmelCase													=	model(a_   , token_type_ids=a_     )
    UpperCAmelCase													=	model(a_   , token_type_ids=a_   , use_cache=a_     )

    self.parent.assertTrue(len(a_     ) == len(a_     )     )
    self.parent.assertTrue(len(a_     ) == len(a_     ) + 1     )

    UpperCAmelCase							,      UpperCAmelCase													=	outputs.to_tuple()

    # create hypothetical next token and extent to next_input_ids
    UpperCAmelCase													=	ids_tensor((self.batch_size, 1)   , config.vocab_size     )
    UpperCAmelCase													=	ids_tensor([self.batch_size, 1]   , self.type_vocab_size     )

    # append to next input_ids and token_type_ids
    UpperCAmelCase													=	tf.concat([input_ids, next_tokens]   , axis=-1     )
    UpperCAmelCase													=	tf.concat([token_type_ids, next_token_types]   , axis=-1     )

    UpperCAmelCase													=	model(a_   , token_type_ids=a_     )['last_hidden_state']
    UpperCAmelCase													=	model(a_   , token_type_ids=a_   , past_key_values=a_     )[
        'last_hidden_state'
    ]

    # select random slice
    UpperCAmelCase													=	int(ids_tensor((1,)   , shape_list(a_     )[-1]     )     )
    UpperCAmelCase													=	output_from_no_past[:, -1, random_slice_idx]
    UpperCAmelCase													=	output_from_past[:, 0, random_slice_idx]

    # test that outputs are equal for slice
    tf.debugging.assert_near(a_   , a_   , rtol=1E-6     )
  def 						snake_case_			(     self   , a_   , a_   , a_   , a_   , a_   , *a_     )							->				Tuple:


    """simple docstring"""
    UpperCAmelCase													=	TFGPTJModel(config=a_     )

    # create attention mask
    UpperCAmelCase													=	self.seq_length // 2
    UpperCAmelCase													=	tf.ones((self.batch_size, half_seq_length)   , dtype=tf.intaa     )
    UpperCAmelCase													=	tf.zeros((self.batch_size, self.seq_length - half_seq_length)   , dtype=tf.intaa     )
    UpperCAmelCase													=	tf.concat([attn_mask_begin, attn_mask_end]   , axis=1     )

    # first forward pass
    UpperCAmelCase							,      UpperCAmelCase													=	model(a_   , attention_mask=a_     ).to_tuple()

    # create hypothetical next token and extent to next_input_ids
    UpperCAmelCase													=	ids_tensor((self.batch_size, 1)   , config.vocab_size     )

    # change a random masked slice from input_ids
    UpperCAmelCase													=	ids_tensor((1,)   , a_     ).numpy() + 1
    UpperCAmelCase													=	ids_tensor((self.batch_size, self.seq_length)   , config.vocab_size     )
    UpperCAmelCase													=	tf.range(self.seq_length     ) == (self.seq_length - random_seq_idx_to_change)
    UpperCAmelCase													=	tf.transpose(
        tf.broadcast_to(tf.expand_dims(a_   , -1     )   , (self.seq_length, self.batch_size)     )     )
    UpperCAmelCase													=	tf.where(a_   , a_   , a_     )

    # append to next input_ids and attn_mask
    UpperCAmelCase													=	tf.concat([input_ids, next_tokens]   , axis=-1     )
    UpperCAmelCase													=	tf.concat([attn_mask, tf.ones((shape_list(a_     )[0], 1)   , dtype=tf.intaa     )]   , axis=1     )

    # get two different outputs
    UpperCAmelCase													=	model(a_   , attention_mask=a_     )['last_hidden_state']
    UpperCAmelCase													=	model(a_   , past_key_values=a_   , attention_mask=a_     )[
        'last_hidden_state'
    ]

    # select random slice
    UpperCAmelCase													=	int(ids_tensor((1,)   , shape_list(a_     )[-1]     )     )
    UpperCAmelCase													=	output_from_no_past[:, -1, random_slice_idx]
    UpperCAmelCase													=	output_from_past[:, 0, random_slice_idx]

    # test that outputs are equal for slice
    tf.debugging.assert_near(a_   , a_   , rtol=1E-12     )
  def 						snake_case_			(     self   , a_   , a_   , a_   , a_   , a_   , *a_     )							->				Any:


    """simple docstring"""
    UpperCAmelCase													=	TFGPTJModel(config=a_     )

    UpperCAmelCase													=	input_ids[:1, :]
    UpperCAmelCase													=	input_mask[:1, :]
    UpperCAmelCase													=	token_type_ids[:1, :]
    UpperCAmelCase													=	1

    # first forward pass
    UpperCAmelCase													=	model(a_   , attention_mask=a_   , token_type_ids=a_   , use_cache=a_     )

    UpperCAmelCase							,      UpperCAmelCase													=	outputs.to_tuple()

    # create hypothetical next token and extent to next_input_ids
    UpperCAmelCase													=	ids_tensor((self.batch_size, 3)   , config.vocab_size     )
    UpperCAmelCase													=	ids_tensor((self.batch_size, 3)   , 2     )
    UpperCAmelCase													=	ids_tensor((self.batch_size, 3)   , self.type_vocab_size     )

    # append to next input_ids and token_type_ids
    UpperCAmelCase													=	tf.concat([input_ids, next_tokens]   , axis=-1     )
    UpperCAmelCase													=	tf.concat([input_mask, next_attn_mask]   , axis=-1     )
    UpperCAmelCase													=	tf.concat([token_type_ids, next_token_types]   , axis=-1     )

    UpperCAmelCase													=	model(
        a_   , token_type_ids=a_   , attention_mask=a_     )['last_hidden_state']
    UpperCAmelCase													=	model(
        a_   , token_type_ids=a_   , attention_mask=a_   , past_key_values=a_   , )['last_hidden_state']
    self.parent.assertTrue(output_from_past.shape[1] == next_tokens.shape[1]     )

    # select random slice
    UpperCAmelCase													=	int(ids_tensor((1,)   , shape_list(a_     )[-1]     )     )
    UpperCAmelCase													=	output_from_no_past[:, -3:, random_slice_idx]
    UpperCAmelCase													=	output_from_past[:, :, random_slice_idx]

    # test that outputs are equal for slice
    tf.debugging.assert_near(a_   , a_   , rtol=1E-3     )
  def 						snake_case_			(     self   , a_   , a_   , a_   , a_   , a_   , *a_     )							->				Union[str, Any]:


    """simple docstring"""
    UpperCAmelCase													=	TFGPTJForCausalLM(config=a_     )
    UpperCAmelCase													=	{
        'input_ids': input_ids,
        'attention_mask': input_mask,
        'token_type_ids': token_type_ids,
    }
    UpperCAmelCase													=	model(a_     )
    self.parent.assertEqual(result.logits.shape   , (self.batch_size, self.seq_length, self.vocab_size)     )





  def 						snake_case_			(     self     )							->				Dict:


    """simple docstring"""
    UpperCAmelCase													=	self.prepare_config_and_inputs()

    (
        (
        UpperCAmelCase
    )							,      (
        UpperCAmelCase
    )							,      (
        UpperCAmelCase
    )							,      (
        UpperCAmelCase
    )							,      (
        UpperCAmelCase
    )							,      (
        UpperCAmelCase
    )							,      (
        UpperCAmelCase
    )							,      (
        UpperCAmelCase
    )							,      (
        UpperCAmelCase
    )							,      
    )													=	config_and_inputs

    UpperCAmelCase													=	{
        'input_ids': input_ids,
        'token_type_ids': token_type_ids,
        'attention_mask': input_mask,
    }
    return config, inputs_dict




@require_tf
class        lowercase_	(		a ,       a ,       a ,       unittest.TestCase ):

  '''simple docstring'''





  __lowerCAmelCase					: Any      =   (
      (TFGPTJForCausalLM, TFGPTJForSequenceClassification, TFGPTJForQuestionAnswering, TFGPTJModel)
      if is_tf_available()
      else ()
  )

  __lowerCAmelCase					: Optional[int]      =   (TFGPTJForCausalLM,) if is_tf_available() else ()
  __lowerCAmelCase					: Union[str, Any]      =   (
      {
          "feature-extraction": TFGPTJModel,
          "question-answering": TFGPTJForQuestionAnswering,
          "text-classification": TFGPTJForSequenceClassification,
          "text-generation": TFGPTJForCausalLM,
          "zero-shot": TFGPTJForSequenceClassification,
      }
      if is_tf_available()
      else {}
  )
  __lowerCAmelCase					: int      =   False
  __lowerCAmelCase					: List[Any]      =   False
  __lowerCAmelCase					: Any      =   False
  __lowerCAmelCase					: Tuple      =   False
  def 						snake_case_			(     self   , a_   , a_   , a_   , a_   , a_     )							->				int:


    """simple docstring"""
    if (
        pipeline_test_casse_name == "QAPipelineTests"
        and tokenizer_name is not None
        and not tokenizer_name.endswith('Fast'     )
    ):
      # `QAPipelineTests` fails for a few models when the slower tokenizer are used.
      # (The slower tokenizers were never used for pipeline tests before the pipeline testing rework)
      # TODO: check (and possibly fix) the `QAPipelineTests` with slower tokenizer
      return True

    return False
  def 						snake_case_			(     self     )							->				List[Any]:


    """simple docstring"""
    UpperCAmelCase													=	TFGPTJModelTester(self     )
    UpperCAmelCase													=	ConfigTester(self   , config_class=a_   , n_embd=3_7     )
  def 						snake_case_			(     self     )							->				Any:


    """simple docstring"""
    self.config_tester.run_common_tests()
  def 						snake_case_			(     self     )							->				Union[str, Any]:


    """simple docstring"""
    UpperCAmelCase													=	self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_gptj_model(*a_     )
  def 						snake_case_			(     self     )							->				List[Any]:


    """simple docstring"""
    UpperCAmelCase													=	self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_gptj_model_past(*a_     )
  def 						snake_case_			(     self     )							->				Optional[int]:


    """simple docstring"""
    UpperCAmelCase													=	self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_gptj_model_attention_mask_past(*a_     )
  def 						snake_case_			(     self     )							->				List[Any]:


    """simple docstring"""
    UpperCAmelCase													=	self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_gptj_model_past_large_inputs(*a_     )
  def 						snake_case_			(     self     )							->				List[Any]:


    """simple docstring"""
    UpperCAmelCase													=	self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_gptj_lm_head_model(*a_     )
  @slow
  @unittest.skipIf(
      not is_tf_available() or len(tf.config.list_physical_devices('GPU'     )     ) > 0   , 'skip testing on GPU for now to avoid GPU OOM.'   , )
  def 						snake_case_			(     self     )							->				int:


    """simple docstring"""
    UpperCAmelCase													=	TFGPTJModel.from_pretrained('EleutherAI/gpt-j-6B'   , from_pt=a_     )
    self.assertIsNotNone(a_     )





  @unittest.skip(reason='Currently, model embeddings are going to undergo a major refactor.'     )
  def 						snake_case_			(     self     )							->				List[Any]:


    """simple docstring"""
    super().test_resize_token_embeddings()




@require_tf
@tooslow
# Marked as @tooslow due to GPU OOM -- but still useful to run locally. Requires ~39GB of RAM.
class        lowercase_	(		unittest.TestCase ):

  '''simple docstring'''





  def 						snake_case_			(     self     )							->				Any:


    """simple docstring"""
    UpperCAmelCase													=	TFGPTJForCausalLM.from_pretrained('EleutherAI/gpt-j-6B'   , from_pt=a_     )
    UpperCAmelCase													=	tf.convert_to_tensor([[4_6_4, 3_2_9_0]]   , dtype=tf.intaa     )  # The dog
    # fmt: off
    # The dog is a man's best friend. It is a loyal companion, and it is a friend
    UpperCAmelCase													=	[4_6_4, 3_2_9_0, 3_1_8, 2_5_7, 5_8_2, 3_3_8, 1_2_6_6, 1_5_4_5, 1_3, 6_3_2, 3_1_8, 2_5_7, 9_1_1_2, 1_5_1_8_5, 1_1, 2_9_0, 3_4_0, 3_1_8, 2_5_7, 1_5_4_5]
    # fmt: on
    UpperCAmelCase													=	model.generate(a_   , do_sample=a_     )
    self.assertListEqual(output_ids[0].numpy().tolist()   , a_     )
  def 						snake_case_			(     self     )							->				List[str]:


    """simple docstring"""
    UpperCAmelCase													=	AutoTokenizer.from_pretrained('EleutherAI/gpt-j-6B'   , revision='float16'     )
    UpperCAmelCase													=	TFGPTJForCausalLM.from_pretrained('EleutherAI/gpt-j-6B'   , revision='float16'   , from_pt=a_     )

    UpperCAmelCase													=	tokenizer('Today is a nice day and'   , return_tensors='tf'     )
    # forces the generation to happen on CPU, to avoid GPU-related quirks
    with tf.device(':/CPU:0'     ):
      UpperCAmelCase													=	model.generate(**a_   , do_sample=a_   , seed=[4_2, 0]     )
    UpperCAmelCase													=	tokenizer.decode(output_ids[0]   , skip_special_tokens=a_     )

    UpperCAmelCase													=	'Today is a nice day and I’m going to go for a walk. I’'
    self.assertEqual(a_   , a_     )
  def 						snake_case_			(     self     )							->				Dict:


    """simple docstring"""
    UpperCAmelCase													=	TFGPTJForCausalLM.from_pretrained('EleutherAI/gpt-j-6B'   , revision='float16'   , from_pt=a_     )
    UpperCAmelCase													=	AutoTokenizer.from_pretrained('EleutherAI/gpt-j-6B'   , revision='float16'     )

    UpperCAmelCase													=	'left'

    # Define PAD Token = EOS Token = 50256
    UpperCAmelCase													=	tokenizer.eos_token
    UpperCAmelCase													=	model.config.eos_token_id

    # use different length sentences to test batching
    UpperCAmelCase													=	[
        'Hello, my dog is a little',
        'Today, I',
    ]
    UpperCAmelCase													=	[
        'Hello, my dog is a little over a year old and has been diagnosed with hip dysplasia',
        'Today, I’m going to be talking about a topic that’',
    ]
    return model, tokenizer, sentences, expected_output_sentences
  def 						snake_case_			(     self     )							->				int:


    """simple docstring"""
    # Confirms that we get the expected results with left-padded beam search
    UpperCAmelCase							,      UpperCAmelCase							,      UpperCAmelCase							,      UpperCAmelCase													=	self._get_beam_search_test_objects()

    UpperCAmelCase													=	tokenizer(a_   , return_tensors='tf'   , padding=a_     )
    UpperCAmelCase													=	model.generate(**a_   , do_sample=a_   , num_beams=2     )
    UpperCAmelCase													=	tokenizer.batch_decode(a_   , skip_special_tokens=a_     )
    self.assertListEqual(a_   , a_     )
  def 						snake_case_			(     self     )							->				List[str]:


    """simple docstring"""
    # Confirms that left-padding is working properly
    UpperCAmelCase							,      UpperCAmelCase							,      UpperCAmelCase							,      UpperCAmelCase													=	self._get_beam_search_test_objects()

    UpperCAmelCase													=	tokenizer(a_   , return_tensors='tf'   , padding=a_     )
    UpperCAmelCase													=	tokenizer(sentences[0]   , return_tensors='tf'     )
    UpperCAmelCase													=	model.generate(**a_   , do_sample=a_   , num_beams=2     )
    UpperCAmelCase													=	(
        shape_list(inputs_non_padded['input_ids']     )[-1]
        - tf.reduce_sum(tf.cast(inputs['attention_mask'][-1]   , tf.intaa     )     ).numpy()
    )
    UpperCAmelCase													=	tokenizer(sentences[1]   , return_tensors='tf'     )
    UpperCAmelCase													=	model.generate(
        **a_   , do_sample=a_   , num_beams=2   , max_length=model.config.max_length - num_paddings     )
    UpperCAmelCase													=	tokenizer.decode(output_non_padded[0]   , skip_special_tokens=a_     )
    UpperCAmelCase													=	tokenizer.decode(output_padded[0]   , skip_special_tokens=a_     )
    self.assertListEqual(a_   , [non_padded_sentence, padded_sentence]     )





  def 						snake_case_			(     self     )							->				List[Any]:


    """simple docstring"""
    # Confirms that XLA is working properly
    UpperCAmelCase							,      UpperCAmelCase							,      UpperCAmelCase							,      UpperCAmelCase													=	self._get_beam_search_test_objects()

    UpperCAmelCase													=	tokenizer(a_   , return_tensors='tf'   , padding=a_     )
    UpperCAmelCase													=	tf.function(model.generate   , jit_compile=a_     )
    UpperCAmelCase													=	xla_generate(**a_   , do_sample=a_   , num_beams=2     )
    UpperCAmelCase													=	tokenizer.batch_decode(a_   , skip_special_tokens=a_     )
    self.assertListEqual(a_   , a_     )


