




import json
import os
from typing import Optional, Tuple

import regex as re

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


_lowerCamelCase     :		List[str]								=       logging.get_logger(__name__)

_lowerCamelCase     :		int								=       {
    """vocab_file""": """vocab.json""",
    """merges_file""": """merges.txt""",
}

_lowerCamelCase     :		Optional[Any]								=       {
    """vocab_file""": {"""ctrl""": """https://raw.githubusercontent.com/salesforce/ctrl/master/ctrl-vocab.json"""},
    """merges_file""": {"""ctrl""": """https://raw.githubusercontent.com/salesforce/ctrl/master/ctrl-merges.txt"""},
}

_lowerCamelCase     :		Any								=       {
    """ctrl""": 256,
}

_lowerCamelCase     :		str								=       {
    """Pregnancy""": 168629,
    """Christianity""": 7675,
    """Explain""": 106423,
    """Fitness""": 63440,
    """Saving""": 63163,
    """Ask""": 27171,
    """Ass""": 95985,
    """Joke""": 163509,
    """Questions""": 45622,
    """Thoughts""": 49605,
    """Retail""": 52342,
    """Feminism""": 164338,
    """Writing""": 11992,
    """Atheism""": 192263,
    """Netflix""": 48616,
    """Computing""": 39639,
    """Opinion""": 43213,
    """Alone""": 44967,
    """Funny""": 58917,
    """Gaming""": 40358,
    """Human""": 4088,
    """India""": 1331,
    """Joker""": 77138,
    """Diet""": 36206,
    """Legal""": 11859,
    """Norman""": 4939,
    """Tip""": 72689,
    """Weight""": 52343,
    """Movies""": 46273,
    """Running""": 23425,
    """Science""": 2090,
    """Horror""": 37793,
    """Confession""": 60572,
    """Finance""": 12250,
    """Politics""": 16360,
    """Scary""": 191985,
    """Support""": 12654,
    """Technologies""": 32516,
    """Teenage""": 66160,
    """Event""": 32769,
    """Learned""": 67460,
    """Notion""": 182770,
    """Wikipedia""": 37583,
    """Books""": 6665,
    """Extract""": 76050,
    """Confessions""": 102701,
    """Conspiracy""": 75932,
    """Links""": 63674,
    """Narcissus""": 150425,
    """Relationship""": 54766,
    """Relationships""": 134796,
    """Reviews""": 41671,
    """News""": 4256,
    """Translation""": 26820,
    """multilingual""": 128406,
}







def SCREAMING_SNAKE_CASE    (	lowercase_						)       ->	int:



       """simple docstring"""

       A__							=   set()
       A__							=   word[0]
       for char in word[1:]:
              pairs.add((prev_char, char)						)
              A__							=   char

       A__							=   set(lowercase_						)
       return pairs


class     UpperCamelCase_   (			UpperCAmelCase__					):

    '''simple docstring'''






    UpperCAmelCase__							  =				VOCAB_FILES_NAMES
    UpperCAmelCase__							  =				PRETRAINED_VOCAB_FILES_MAP
    UpperCAmelCase__							  =				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    UpperCAmelCase__							  =				CONTROL_CODES


    def __init__(      self				:     int ,			UpperCAmelCase__				:     Optional[Any] ,			UpperCAmelCase__				:     Union[str, Any] ,			UpperCAmelCase__				:     List[Any]="<unk>" ,			**UpperCAmelCase__				:     Dict)						->Any:





           '''simple docstring'''

           super().__init__(unk_token=UpperCAmelCase__ ,			**UpperCAmelCase__)

           with open(UpperCAmelCase__ ,			encoding='''utf-8''') as vocab_handle:
                  A__							=   json.load(UpperCAmelCase__)
           A__							=   {v: k for k, v in self.encoder.items()}
           with open(UpperCAmelCase__ ,			encoding='''utf-8''') as merges_handle:
                  A__							=   merges_handle.read().split('''\n''')[1:-1]
           A__							=   [tuple(merge.split()) for merge in merges]
           A__							=   dict(zip(UpperCAmelCase__ ,			range(len(UpperCAmelCase__))))
           A__							=   {}


    @property
    def 	SCREAMING_SNAKE_CASE							(      self				:     Optional[Any])						->int:





           '''simple docstring'''

           return len(self.encoder)


    def 	SCREAMING_SNAKE_CASE							(      self				:     Optional[int])						->List[str]:





           '''simple docstring'''

           return dict(self.encoder ,			**self.added_tokens_encoder)


    def 	SCREAMING_SNAKE_CASE							(      self				:     str ,			UpperCAmelCase__				:     List[str])						->str:





           '''simple docstring'''

           if token in self.cache:
                  return self.cache[token]
           A__							=   tuple(UpperCAmelCase__)
           A__							=   tuple(list(word[:-1]) + [word[-1] + '''</w>'''])
           A__							=   get_pairs(UpperCAmelCase__)

           if not pairs:
                  return token

           while True:
                  A__							=   min(UpperCAmelCase__ ,			key=lambda UpperCAmelCase__: self.bpe_ranks.get(UpperCAmelCase__ ,			float('''inf''')))
                  if bigram not in self.bpe_ranks:
                         break
                  A__			,							A__							=   bigram
                  A__							=   []
                  A__							=   0
                  while i < len(UpperCAmelCase__):
                         try:
                                A__							=   word.index(UpperCAmelCase__ ,			UpperCAmelCase__)
                         except ValueError:
                                new_word.extend(word[i:])
                                break
                         else:
                                new_word.extend(word[i:j])
                                A__							=   j

                         if word[i] == first and i < len(UpperCAmelCase__) - 1 and word[i + 1] == second:
                                new_word.append(first + second)
                                i += 2
                         else:
                                new_word.append(word[i])
                                i += 1
                  A__							=   tuple(UpperCAmelCase__)
                  A__							=   new_word
                  if len(UpperCAmelCase__) == 1:
                         break
                  else:
                         A__							=   get_pairs(UpperCAmelCase__)
           A__							=   '''@@ '''.join(UpperCAmelCase__)
           A__							=   word[:-4]
           A__							=   word
           return word


    def 	SCREAMING_SNAKE_CASE							(      self				:     Tuple ,			UpperCAmelCase__				:     Tuple)						->List[Any]:





           '''simple docstring'''

           A__							=   []

           A__							=   re.findall(R'''\S+\n?''' ,			UpperCAmelCase__)

           for token in words:
                  split_tokens.extend(list(self.bpe(UpperCAmelCase__).split(''' ''')))
           return split_tokens


    def 	SCREAMING_SNAKE_CASE							(      self				:     List[str] ,			UpperCAmelCase__				:     Optional[Any])						->Optional[int]:





           '''simple docstring'''

           return self.encoder.get(UpperCAmelCase__ ,			self.encoder.get(self.unk_token))


    def 	SCREAMING_SNAKE_CASE							(      self				:     Union[str, Any] ,			UpperCAmelCase__				:     List[Any])						->List[Any]:





           '''simple docstring'''

           return self.decoder.get(UpperCAmelCase__ ,			self.unk_token)


    def 	SCREAMING_SNAKE_CASE							(      self				:     Optional[int] ,			UpperCAmelCase__				:     Tuple)						->Tuple:





           '''simple docstring'''

           A__							=   ''' '''.join(UpperCAmelCase__).replace('''@@ ''' ,			'''''').strip()
           return out_string





    def 	SCREAMING_SNAKE_CASE							(      self				:     str ,			UpperCAmelCase__				:     str ,			UpperCAmelCase__				:     Optional[str] = None)						->Tuple[str]:





           '''simple docstring'''

           if not os.path.isdir(UpperCAmelCase__):
                  logger.error(f"""Vocabulary path ({save_directory}) should be a directory""")
                  return
           A__							=   os.path.join(
               UpperCAmelCase__ ,			(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''])
           A__							=   os.path.join(
               UpperCAmelCase__ ,			(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''merges_file'''])

           with open(UpperCAmelCase__ ,			'''w''' ,			encoding='''utf-8''') as f:
                  f.write(json.dumps(self.encoder ,			indent=2 ,			sort_keys=UpperCAmelCase__ ,			ensure_ascii=UpperCAmelCase__) + '''\n''')

           A__							=   0
           with open(UpperCAmelCase__ ,			'''w''' ,			encoding='''utf-8''') as writer:
                  writer.write('''#version: 0.2\n''')
                  for bpe_tokens, token_index in sorted(self.bpe_ranks.items() ,			key=lambda UpperCAmelCase__: kv[1]):
                         if index != token_index:
                                logger.warning(
                                    f"""Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."""
                                    ''' Please check that the tokenizer is not corrupted!''')
                                A__							=   token_index
                         writer.write(''' '''.join(UpperCAmelCase__) + '''\n''')
                         index += 1

           return vocab_file, merge_file

    # def decode(self, token_ids, skip_special_tokens=False, clean_up_tokenization_spaces=True):
    #     filtered_tokens = ' '.join(self.convert_ids_to_tokens(token_ids, skip_special_tokens=skip_special_tokens))
    #     tokens_generated_so_far = re.sub('(@@ )', '', string=filtered_tokens)
    #     tokens_generated_so_far = re.sub('(@@ ?$)', '', string=tokens_generated_so_far)
    #     return ''.join(tokens_generated_so_far)








import tempfile
import unittest

import numpy as np

import transformers
from transformers import GPTaTokenizer, GPTJConfig, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, tooslow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
  import jax
  import jax.numpy as jnp

  from transformers.modeling_flax_pytorch_utils import (
      convert_pytorch_state_dict_to_flax,
      load_flax_weights_in_pytorch_model,
  )
  from transformers.models.gptj.modeling_flax_gptj import FlaxGPTJForCausalLM, FlaxGPTJModel

if is_torch_available():
  import torch


class     UpperCamelCase_   :

    '''simple docstring'''






    def __init__(      self				:     str ,			UpperCAmelCase__				:     List[Any] ,			UpperCAmelCase__				:     Any=14 ,			UpperCAmelCase__				:     Any=7 ,			UpperCAmelCase__				:     Dict=True ,			UpperCAmelCase__				:     int=True ,			UpperCAmelCase__				:     Tuple=False ,			UpperCAmelCase__				:     Optional[int]=True ,			UpperCAmelCase__				:     Tuple=99 ,			UpperCAmelCase__				:     Optional[Any]=32 ,			UpperCAmelCase__				:     Tuple=4 ,			UpperCAmelCase__				:     Any=4 ,			UpperCAmelCase__				:     int=4 ,			UpperCAmelCase__				:     Optional[Any]=37 ,			UpperCAmelCase__				:     Union[str, Any]="gelu" ,			UpperCAmelCase__				:     Optional[int]=0.1 ,			UpperCAmelCase__				:     int=0.1 ,			UpperCAmelCase__				:     Optional[Any]=512 ,			UpperCAmelCase__				:     Union[str, Any]=0.02 ,			)						->Any:





           '''simple docstring'''

           A__							=   parent
           A__							=   batch_size
           A__							=   seq_length
           A__							=   is_training
           A__							=   use_input_mask
           A__							=   use_token_type_ids
           A__							=   use_labels
           A__							=   vocab_size
           A__							=   hidden_size
           A__							=   rotary_dim
           A__							=   num_hidden_layers
           A__							=   num_attention_heads
           A__							=   intermediate_size
           A__							=   hidden_act
           A__							=   hidden_dropout_prob
           A__							=   attention_probs_dropout_prob
           A__							=   max_position_embeddings
           A__							=   initializer_range
           A__							=   None
           A__							=   vocab_size - 1
           A__							=   vocab_size - 1
           A__							=   vocab_size - 1


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





           '''simple docstring'''

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

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

           A__							=   GPTJConfig(
               vocab_size=self.vocab_size ,			n_embd=self.hidden_size ,			n_layer=self.num_hidden_layers ,			n_head=self.num_attention_heads ,			n_positions=self.max_position_embeddings ,			use_cache=UpperCAmelCase__ ,			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 (config, input_ids, input_mask)


    def 	SCREAMING_SNAKE_CASE							(      self				:     Any)						->Optional[int]:





           '''simple docstring'''

           A__							=   self.prepare_config_and_inputs()
           A__			,							A__			,							A__							=   config_and_inputs
           A__							=   {'''input_ids''': input_ids, '''attention_mask''': attention_mask}
           return config, inputs_dict


    def 	SCREAMING_SNAKE_CASE							(      self				:     Any ,			UpperCAmelCase__				:     int ,			UpperCAmelCase__				:     Tuple ,			UpperCAmelCase__				:     Union[str, Any] ,			UpperCAmelCase__				:     List[Any])						->Optional[int]:





           '''simple docstring'''

           A__							=   20
           A__							=   model_class_name(UpperCAmelCase__)

           A__							=   model.init_cache(input_ids.shape[0] ,			UpperCAmelCase__)
           A__							=   jnp.ones((input_ids.shape[0], max_decoder_length) ,			dtype='''i4''')

           A__							=   jnp.broadcast_to(
               jnp.arange(input_ids.shape[-1] - 1)[None, :] ,			(input_ids.shape[0], input_ids.shape[-1] - 1))
           A__							=   model(
               input_ids[:, :-1] ,			attention_mask=UpperCAmelCase__ ,			past_key_values=UpperCAmelCase__ ,			position_ids=UpperCAmelCase__ ,			)

           A__							=   jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]] ,			dtype='''i4''')
           A__							=   model(
               input_ids[:, -1:] ,			attention_mask=UpperCAmelCase__ ,			past_key_values=outputs_cache.past_key_values ,			position_ids=UpperCAmelCase__ ,			)

           A__							=   model(UpperCAmelCase__)

           A__							=   np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])))
           self.parent.assertTrue(diff < 1e-3 ,			msg=f"""Max diff is {diff}""")





    def 	SCREAMING_SNAKE_CASE							(      self				:     Tuple ,			UpperCAmelCase__				:     Optional[Any] ,			UpperCAmelCase__				:     Dict ,			UpperCAmelCase__				:     List[Any] ,			UpperCAmelCase__				:     int)						->Any:





           '''simple docstring'''

           A__							=   20
           A__							=   model_class_name(UpperCAmelCase__)

           A__							=   jnp.concatenate(
               [attention_mask, jnp.zeros((attention_mask.shape[0], max_decoder_length - attention_mask.shape[1]))] ,			axis=-1 ,			)

           A__							=   model.init_cache(input_ids.shape[0] ,			UpperCAmelCase__)
           A__							=   jnp.broadcast_to(
               jnp.arange(input_ids.shape[-1] - 1)[None, :] ,			(input_ids.shape[0], input_ids.shape[-1] - 1))

           A__							=   model(
               input_ids[:, :-1] ,			attention_mask=UpperCAmelCase__ ,			past_key_values=UpperCAmelCase__ ,			position_ids=UpperCAmelCase__ ,			)
           A__							=   jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]] ,			dtype='''i4''')
           A__							=   model(
               input_ids[:, -1:] ,			past_key_values=outputs_cache.past_key_values ,			attention_mask=UpperCAmelCase__ ,			position_ids=UpperCAmelCase__ ,			)

           A__							=   model(UpperCAmelCase__ ,			attention_mask=UpperCAmelCase__)

           A__							=   np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])))
           self.parent.assertTrue(diff < 1e-3 ,			msg=f"""Max diff is {diff}""")


@require_flax
class     UpperCamelCase_   (			UpperCAmelCase__		, UpperCAmelCase__		, unittest.TestCase					):

    '''simple docstring'''






    UpperCAmelCase__							  =				(FlaxGPTJModel, FlaxGPTJForCausalLM) if is_flax_available() else ()
    UpperCAmelCase__							  =				(FlaxGPTJForCausalLM,) if is_flax_available() else ()


    def 	SCREAMING_SNAKE_CASE							(      self				:     Optional[Any])						->List[Any]:





           '''simple docstring'''

           A__							=   FlaxGPTJModelTester(self)


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





           '''simple docstring'''

           for model_class_name in self.all_model_classes:
                  A__			,							A__			,							A__							=   self.model_tester.prepare_config_and_inputs()
                  self.model_tester.check_use_cache_forward(UpperCAmelCase__ ,			UpperCAmelCase__ ,			UpperCAmelCase__ ,			UpperCAmelCase__)


    def 	SCREAMING_SNAKE_CASE							(      self				:     Optional[int])						->Any:





           '''simple docstring'''

           for model_class_name in self.all_model_classes:
                  A__			,							A__			,							A__							=   self.model_tester.prepare_config_and_inputs()
                  self.model_tester.check_use_cache_forward_with_attn_mask(
                      UpperCAmelCase__ ,			UpperCAmelCase__ ,			UpperCAmelCase__ ,			UpperCAmelCase__)


    @tooslow
    def 	SCREAMING_SNAKE_CASE							(      self				:     Optional[Any])						->Dict:





           '''simple docstring'''

           A__							=   GPTaTokenizer.from_pretrained('''gpt2''' ,			pad_token='''<|endoftext|>''' ,			padding_side='''left''')
           A__							=   tokenizer(['''Hello this is a long string''', '''Hey'''] ,			return_tensors='''np''' ,			padding=UpperCAmelCase__ ,			truncation=UpperCAmelCase__)

           A__							=   FlaxGPTJForCausalLM.from_pretrained('''EleutherAI/gpt-j-6B''')
           A__							=   False
           A__							=   model.config.eos_token_id

           A__							=   jax.jit(model.generate)

           A__							=   jit_generate(
               inputs['''input_ids'''] ,			attention_mask=inputs['''attention_mask'''] ,			pad_token_id=tokenizer.pad_token_id).sequences

           A__							=   tokenizer.batch_decode(UpperCAmelCase__ ,			skip_special_tokens=UpperCAmelCase__)

           A__							=   [
               '''Hello this is a long string of text.\n\nI\'m trying to get the text of the''',
               '''Hey, I\'m a little late to the party. I\'m going to''',
           ]

           self.assertListEqual(UpperCAmelCase__ ,			UpperCAmelCase__)


    @is_pt_flax_cross_test
    def 	SCREAMING_SNAKE_CASE							(      self				:     Optional[int])						->int:





           '''simple docstring'''

           A__			,							A__							=   self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
                  with self.subTest(model_class.__name__):
                         # prepare inputs
                         A__							=   self._prepare_for_class(UpperCAmelCase__ ,			UpperCAmelCase__)
                         A__							=   {k: torch.tensor(v.tolist()) for k, v in prepared_inputs_dict.items()}

                         # load corresponding PyTorch class
                         A__							=   model_class.__name__[4:]  # Skip the "Flax" at the beginning
                         A__							=   getattr(UpperCAmelCase__ ,			UpperCAmelCase__)

                         A__			,							A__							=   pt_inputs['''input_ids'''].shape
                         A__							=   np.random.randint(0 ,			seq_length - 1 ,			size=(batch_size,))
                         for batch_idx, start_index in enumerate(UpperCAmelCase__):
                                A__							=   0
                                A__							=   1
                                A__							=   0
                                A__							=   1
                         A__							=   pt_model_class(UpperCAmelCase__).eval()
                         A__							=   model_class(UpperCAmelCase__ ,			dtype=jnp.floataa)

                         A__							=   convert_pytorch_state_dict_to_flax(pt_model.state_dict() ,			UpperCAmelCase__)
                         A__							=   fx_state

                         with torch.no_grad():
                                A__							=   pt_model(**UpperCAmelCase__).to_tuple()

                         A__							=   fx_model(**UpperCAmelCase__).to_tuple()
                         self.assertEqual(len(UpperCAmelCase__) ,			len(UpperCAmelCase__) ,			'''Output lengths differ between Flax and PyTorch''')
                         for fx_output, pt_output in zip(UpperCAmelCase__ ,			UpperCAmelCase__):
                                self.assert_almost_equals(fx_output[:, -1] ,			pt_output[:, -1].numpy() ,			4e-2)

                         with tempfile.TemporaryDirectory() as tmpdirname:
                                pt_model.save_pretrained(UpperCAmelCase__)
                                A__							=   model_class.from_pretrained(UpperCAmelCase__ ,			from_pt=UpperCAmelCase__)

                         A__							=   fx_model_loaded(**UpperCAmelCase__).to_tuple()
                         self.assertEqual(
                             len(UpperCAmelCase__) ,			len(UpperCAmelCase__) ,			'''Output lengths differ between Flax and PyTorch''')
                         for fx_output_loaded, pt_output in zip(UpperCAmelCase__ ,			UpperCAmelCase__):
                                self.assert_almost_equals(fx_output_loaded[:, -1] ,			pt_output[:, -1].numpy() ,			4e-2)


    @is_pt_flax_cross_test
    def 	SCREAMING_SNAKE_CASE							(      self				:     Any)						->Optional[int]:





           '''simple docstring'''

           A__			,							A__							=   self.model_tester.prepare_config_and_inputs_for_common()
           for model_class in self.all_model_classes:
                  with self.subTest(model_class.__name__):
                         # prepare inputs
                         A__							=   self._prepare_for_class(UpperCAmelCase__ ,			UpperCAmelCase__)
                         A__							=   {k: torch.tensor(v.tolist()) for k, v in prepared_inputs_dict.items()}

                         # load corresponding PyTorch class
                         A__							=   model_class.__name__[4:]  # Skip the "Flax" at the beginning
                         A__							=   getattr(UpperCAmelCase__ ,			UpperCAmelCase__)

                         A__							=   pt_model_class(UpperCAmelCase__).eval()
                         A__							=   model_class(UpperCAmelCase__ ,			dtype=jnp.floataa)

                         A__							=   load_flax_weights_in_pytorch_model(UpperCAmelCase__ ,			fx_model.params)
                         A__			,							A__							=   pt_inputs['''input_ids'''].shape
                         A__							=   np.random.randint(0 ,			seq_length - 1 ,			size=(batch_size,))
                         for batch_idx, start_index in enumerate(UpperCAmelCase__):
                                A__							=   0
                                A__							=   1
                                A__							=   0
                                A__							=   1

                         # make sure weights are tied in PyTorch
                         pt_model.tie_weights()

                         with torch.no_grad():
                                A__							=   pt_model(**UpperCAmelCase__).to_tuple()

                         A__							=   fx_model(**UpperCAmelCase__).to_tuple()
                         self.assertEqual(len(UpperCAmelCase__) ,			len(UpperCAmelCase__) ,			'''Output lengths differ between Flax and PyTorch''')
                         for fx_output, pt_output in zip(UpperCAmelCase__ ,			UpperCAmelCase__):
                                self.assert_almost_equals(fx_output[:, -1] ,			pt_output[:, -1].numpy() ,			4e-2)

                         with tempfile.TemporaryDirectory() as tmpdirname:
                                fx_model.save_pretrained(UpperCAmelCase__)
                                A__							=   pt_model_class.from_pretrained(UpperCAmelCase__ ,			from_flax=UpperCAmelCase__)

                         with torch.no_grad():
                                A__							=   pt_model_loaded(**UpperCAmelCase__).to_tuple()

                         self.assertEqual(
                             len(UpperCAmelCase__) ,			len(UpperCAmelCase__) ,			'''Output lengths differ between Flax and PyTorch''')
                         for fx_output, pt_output in zip(UpperCAmelCase__ ,			UpperCAmelCase__):
                                self.assert_almost_equals(fx_output[:, -1] ,			pt_output[:, -1].numpy() ,			4e-2)





    @tooslow
    def 	SCREAMING_SNAKE_CASE							(      self				:     Tuple)						->Optional[Any]:





           '''simple docstring'''

           for model_class_name in self.all_model_classes:
                  A__							=   model_class_name.from_pretrained('''EleutherAI/gpt-j-6B''')
                  A__							=   model(np.ones((1, 1)))
                  self.assertIsNotNone(UpperCAmelCase__)


