
'''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     __snake_case			:




		def __init__(  self					:List[Any]			,    _SCREAMING_SNAKE_CASE					:Any)					->			Dict:






								'''simple docstring'''






								UpperCamelCase_     :	Union[str, Any]					     =     parent
								UpperCamelCase_     :	Union[str, Any]					     =     1_3
								UpperCamelCase_     :	Optional[int]					     =     7
								UpperCamelCase_     :	int					     =     True
								UpperCamelCase_     :	Any					     =     True
								UpperCamelCase_     :	Tuple					     =     True
								UpperCamelCase_     :	Optional[int]					     =     True
								UpperCamelCase_     :	Optional[Any]					     =     True
								UpperCamelCase_     :	str					     =     9_9
								UpperCamelCase_     :	Dict					     =     3_2
								UpperCamelCase_     :	str					     =     4
								UpperCamelCase_     :	str					     =     2
								UpperCamelCase_     :	Tuple					     =     4
								UpperCamelCase_     :	Optional[Any]					     =     3_7
								UpperCamelCase_     :	int					     =     """gelu"""
								UpperCamelCase_     :	Any					     =     0.1
								UpperCamelCase_     :	Optional[Any]					     =     0.1
								UpperCamelCase_     :	Optional[int]					     =     5_1_2
								UpperCamelCase_     :	Any					     =     1_6
								UpperCamelCase_     :	List[str]					     =     2
								UpperCamelCase_     :	str					     =     0.02
								UpperCamelCase_     :	Dict					     =     3
								UpperCamelCase_     :	int					     =     4
								UpperCamelCase_     :	Dict					     =     None
								UpperCamelCase_     :	Union[str, Any]					     =     self.vocab_size - 1
								UpperCamelCase_     :	Optional[int]					     =     self.vocab_size - 1
								UpperCamelCase_     :	Optional[Any]					     =     self.vocab_size - 1




		def 	_snake_case	(  self					:List[Any])					->			List[str]:






								'''simple docstring'''






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

								UpperCamelCase_     :	Optional[Any]					     =     None
								if self.use_input_mask:
														UpperCamelCase_     :	int					     =     random_attention_mask([self.batch_size, self.seq_length])

								UpperCamelCase_     :	str					     =     None
								if self.use_token_type_ids:
														UpperCamelCase_     :	Union[str, Any]					     =     ids_tensor([self.batch_size, self.seq_length]			,    self.type_vocab_size)

								UpperCamelCase_     :	List[Any]					     =     None
								if self.use_mc_token_ids:
														UpperCamelCase_     :	List[str]					     =     ids_tensor([self.batch_size, self.num_choices]			,    self.seq_length)

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

								UpperCamelCase_     :	List[str]					     =     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=_SCREAMING_SNAKE_CASE			,    )

								UpperCamelCase_     :	Optional[Any]					     =     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					:Dict			,    _SCREAMING_SNAKE_CASE					:Optional[Any]			,    _SCREAMING_SNAKE_CASE					:Tuple			,    _SCREAMING_SNAKE_CASE					:List[str]			,    _SCREAMING_SNAKE_CASE					:List[str]			,    _SCREAMING_SNAKE_CASE					:int			,    *_SCREAMING_SNAKE_CASE					:List[Any])					->			Any:






								'''simple docstring'''






								UpperCamelCase_     :	Tuple					     =     TFGPTJModel(config=_SCREAMING_SNAKE_CASE)
								UpperCamelCase_     :	Any					     =     {
								    """input_ids""": input_ids,
								    """attention_mask""": input_mask,
								    """token_type_ids""": token_type_ids,
								}
								UpperCamelCase_     :	Optional[int]					     =     model(_SCREAMING_SNAKE_CASE)

								UpperCamelCase_     :	Any					     =     [input_ids, None, input_mask]  # None is the input for 'past'
								UpperCamelCase_     :	Optional[int]					     =     model(_SCREAMING_SNAKE_CASE)

								UpperCamelCase_     :	Union[str, Any]					     =     model(_SCREAMING_SNAKE_CASE)

								self.parent.assertEqual(result.last_hidden_state.shape			,    (self.batch_size, self.seq_length, self.hidden_size))




		def 	_snake_case	(  self					:List[str]			,    _SCREAMING_SNAKE_CASE					:Optional[int]			,    _SCREAMING_SNAKE_CASE					:Union[str, Any]			,    _SCREAMING_SNAKE_CASE					:Optional[int]			,    _SCREAMING_SNAKE_CASE					:Union[str, Any]			,    _SCREAMING_SNAKE_CASE					:str			,    *_SCREAMING_SNAKE_CASE					:str)					->			str:






								'''simple docstring'''






								UpperCamelCase_     :	Union[str, Any]					     =     TFGPTJModel(config=_SCREAMING_SNAKE_CASE)

								# first forward pass
								UpperCamelCase_     :	Any					     =     model(_SCREAMING_SNAKE_CASE			,    token_type_ids=_SCREAMING_SNAKE_CASE			,    use_cache=_SCREAMING_SNAKE_CASE)
								UpperCamelCase_     :	Union[str, Any]					     =     model(_SCREAMING_SNAKE_CASE			,    token_type_ids=_SCREAMING_SNAKE_CASE)
								UpperCamelCase_     :	str					     =     model(_SCREAMING_SNAKE_CASE			,    token_type_ids=_SCREAMING_SNAKE_CASE			,    use_cache=_SCREAMING_SNAKE_CASE)

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

								UpperCamelCase_     ,UpperCamelCase_     :	str					     =     outputs.to_tuple()

								# create hypothetical next token and extent to next_input_ids
								UpperCamelCase_     :	int					     =     ids_tensor((self.batch_size, 1)			,    config.vocab_size)
								UpperCamelCase_     :	List[str]					     =     ids_tensor([self.batch_size, 1]			,    self.type_vocab_size)

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

								UpperCamelCase_     :	Tuple					     =     model(_SCREAMING_SNAKE_CASE			,    token_type_ids=_SCREAMING_SNAKE_CASE)["""last_hidden_state"""]
								UpperCamelCase_     :	Optional[Any]					     =     model(_SCREAMING_SNAKE_CASE			,    token_type_ids=_SCREAMING_SNAKE_CASE			,    past_key_values=_SCREAMING_SNAKE_CASE)[
								    """last_hidden_state"""
								]

								# select random slice
								UpperCamelCase_     :	Tuple					     =     int(ids_tensor((1,)			,    shape_list(_SCREAMING_SNAKE_CASE)[-1]))
								UpperCamelCase_     :	Dict					     =     output_from_no_past[:, -1, random_slice_idx]
								UpperCamelCase_     :	List[Any]					     =     output_from_past[:, 0, random_slice_idx]

								# test that outputs are equal for slice
								tf.debugging.assert_near(_SCREAMING_SNAKE_CASE			,    _SCREAMING_SNAKE_CASE			,    rtol=1E-6)




		def 	_snake_case	(  self					:Dict			,    _SCREAMING_SNAKE_CASE					:int			,    _SCREAMING_SNAKE_CASE					:int			,    _SCREAMING_SNAKE_CASE					:List[Any]			,    _SCREAMING_SNAKE_CASE					:List[Any]			,    _SCREAMING_SNAKE_CASE					:Any			,    *_SCREAMING_SNAKE_CASE					:Optional[Any])					->			int:






								'''simple docstring'''






								UpperCamelCase_     :	List[Any]					     =     TFGPTJModel(config=_SCREAMING_SNAKE_CASE)

								# create attention mask
								UpperCamelCase_     :	List[Any]					     =     self.seq_length // 2
								UpperCamelCase_     :	Optional[int]					     =     tf.ones((self.batch_size, half_seq_length)			,    dtype=tf.intaa)
								UpperCamelCase_     :	Union[str, Any]					     =     tf.zeros((self.batch_size, self.seq_length - half_seq_length)			,    dtype=tf.intaa)
								UpperCamelCase_     :	Optional[Any]					     =     tf.concat([attn_mask_begin, attn_mask_end]			,    axis=1)

								# first forward pass
								UpperCamelCase_     ,UpperCamelCase_     :	Any					     =     model(_SCREAMING_SNAKE_CASE			,    attention_mask=_SCREAMING_SNAKE_CASE).to_tuple()

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

								# change a random masked slice from input_ids
								UpperCamelCase_     :	List[str]					     =     ids_tensor((1,)			,    _SCREAMING_SNAKE_CASE).numpy() + 1
								UpperCamelCase_     :	Tuple					     =     ids_tensor((self.batch_size, self.seq_length)			,    config.vocab_size)
								UpperCamelCase_     :	Union[str, Any]					     =     tf.range(self.seq_length) == (self.seq_length - random_seq_idx_to_change)
								UpperCamelCase_     :	str					     =     tf.transpose(
								    tf.broadcast_to(tf.expand_dims(_SCREAMING_SNAKE_CASE			,    -1)			,    (self.seq_length, self.batch_size)))
								UpperCamelCase_     :	Optional[Any]					     =     tf.where(_SCREAMING_SNAKE_CASE			,    _SCREAMING_SNAKE_CASE			,    _SCREAMING_SNAKE_CASE)

								# append to next input_ids and attn_mask
								UpperCamelCase_     :	int					     =     tf.concat([input_ids, next_tokens]			,    axis=-1)
								UpperCamelCase_     :	Any					     =     tf.concat([attn_mask, tf.ones((shape_list(_SCREAMING_SNAKE_CASE)[0], 1)			,    dtype=tf.intaa)]			,    axis=1)

								# get two different outputs
								UpperCamelCase_     :	int					     =     model(_SCREAMING_SNAKE_CASE			,    attention_mask=_SCREAMING_SNAKE_CASE)["""last_hidden_state"""]
								UpperCamelCase_     :	List[Any]					     =     model(_SCREAMING_SNAKE_CASE			,    past_key_values=_SCREAMING_SNAKE_CASE			,    attention_mask=_SCREAMING_SNAKE_CASE)[
								    """last_hidden_state"""
								]

								# select random slice
								UpperCamelCase_     :	str					     =     int(ids_tensor((1,)			,    shape_list(_SCREAMING_SNAKE_CASE)[-1]))
								UpperCamelCase_     :	Optional[int]					     =     output_from_no_past[:, -1, random_slice_idx]
								UpperCamelCase_     :	Optional[int]					     =     output_from_past[:, 0, random_slice_idx]

								# test that outputs are equal for slice
								tf.debugging.assert_near(_SCREAMING_SNAKE_CASE			,    _SCREAMING_SNAKE_CASE			,    rtol=1E-12)




		def 	_snake_case	(  self					:Union[str, Any]			,    _SCREAMING_SNAKE_CASE					:Union[str, Any]			,    _SCREAMING_SNAKE_CASE					:int			,    _SCREAMING_SNAKE_CASE					:Union[str, Any]			,    _SCREAMING_SNAKE_CASE					:Optional[Any]			,    _SCREAMING_SNAKE_CASE					:List[str]			,    *_SCREAMING_SNAKE_CASE					:Any)					->			Any:






								'''simple docstring'''






								UpperCamelCase_     :	Optional[Any]					     =     TFGPTJModel(config=_SCREAMING_SNAKE_CASE)

								UpperCamelCase_     :	Optional[Any]					     =     input_ids[:1, :]
								UpperCamelCase_     :	int					     =     input_mask[:1, :]
								UpperCamelCase_     :	List[str]					     =     token_type_ids[:1, :]
								UpperCamelCase_     :	Tuple					     =     1

								# first forward pass
								UpperCamelCase_     :	Optional[Any]					     =     model(_SCREAMING_SNAKE_CASE			,    attention_mask=_SCREAMING_SNAKE_CASE			,    token_type_ids=_SCREAMING_SNAKE_CASE			,    use_cache=_SCREAMING_SNAKE_CASE)

								UpperCamelCase_     ,UpperCamelCase_     :	int					     =     outputs.to_tuple()

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

								# append to next input_ids and token_type_ids
								UpperCamelCase_     :	Union[str, Any]					     =     tf.concat([input_ids, next_tokens]			,    axis=-1)
								UpperCamelCase_     :	List[Any]					     =     tf.concat([input_mask, next_attn_mask]			,    axis=-1)
								UpperCamelCase_     :	List[str]					     =     tf.concat([token_type_ids, next_token_types]			,    axis=-1)

								UpperCamelCase_     :	int					     =     model(
								    _SCREAMING_SNAKE_CASE			,    token_type_ids=_SCREAMING_SNAKE_CASE			,    attention_mask=_SCREAMING_SNAKE_CASE)["""last_hidden_state"""]
								UpperCamelCase_     :	Optional[Any]					     =     model(
								    _SCREAMING_SNAKE_CASE			,    token_type_ids=_SCREAMING_SNAKE_CASE			,    attention_mask=_SCREAMING_SNAKE_CASE			,    past_key_values=_SCREAMING_SNAKE_CASE			,    )["""last_hidden_state"""]
								self.parent.assertTrue(output_from_past.shape[1] == next_tokens.shape[1])

								# select random slice
								UpperCamelCase_     :	List[str]					     =     int(ids_tensor((1,)			,    shape_list(_SCREAMING_SNAKE_CASE)[-1]))
								UpperCamelCase_     :	List[Any]					     =     output_from_no_past[:, -3:, random_slice_idx]
								UpperCamelCase_     :	List[str]					     =     output_from_past[:, :, random_slice_idx]

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




		def 	_snake_case	(  self					:Optional[int]			,    _SCREAMING_SNAKE_CASE					:Dict			,    _SCREAMING_SNAKE_CASE					:Dict			,    _SCREAMING_SNAKE_CASE					:List[str]			,    _SCREAMING_SNAKE_CASE					:Optional[int]			,    _SCREAMING_SNAKE_CASE					:Dict			,    *_SCREAMING_SNAKE_CASE					:Union[str, Any])					->			Dict:






								'''simple docstring'''






								UpperCamelCase_     :	Dict					     =     TFGPTJForCausalLM(config=_SCREAMING_SNAKE_CASE)
								UpperCamelCase_     :	Any					     =     {
								    """input_ids""": input_ids,
								    """attention_mask""": input_mask,
								    """token_type_ids""": token_type_ids,
								}
								UpperCamelCase_     :	Union[str, Any]					     =     model(_SCREAMING_SNAKE_CASE)
								self.parent.assertEqual(result.logits.shape			,    (self.batch_size, self.seq_length, self.vocab_size))





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






								'''simple docstring'''






								UpperCamelCase_     :	Tuple					     =     self.prepare_config_and_inputs()

								(
								    (
								    UpperCamelCase_
								)     ,(
								    UpperCamelCase_
								)     ,(
								    UpperCamelCase_
								)     ,(
								    UpperCamelCase_
								)     ,(
								    UpperCamelCase_
								)     ,(
								    UpperCamelCase_
								)     ,(
								    UpperCamelCase_
								)     ,(
								    UpperCamelCase_
								)     ,(
								    UpperCamelCase_
								)     ,
								)     :	Any					     =     config_and_inputs

								UpperCamelCase_     :	Dict					     =     {
								    """input_ids""": input_ids,
								    """token_type_ids""": token_type_ids,
								    """attention_mask""": input_mask,
								}
								return config, inputs_dict







@require_tf
class     __snake_case			(    _a					,			_a					,			_a					,			unittest.TestCase	):
		a_          =					(
		    (TFGPTJForCausalLM, TFGPTJForSequenceClassification, TFGPTJForQuestionAnswering, TFGPTJModel)
		    if is_tf_available()
		    else ()
		)

		a_          =					(TFGPTJForCausalLM,) if is_tf_available() else ()
		a_          =					(
		    {
		        "feature-extraction": TFGPTJModel,
		        "question-answering": TFGPTJForQuestionAnswering,
		        "text-classification": TFGPTJForSequenceClassification,
		        "text-generation": TFGPTJForCausalLM,
		        "zero-shot": TFGPTJForSequenceClassification,
		    }
		    if is_tf_available()
		    else {}
		)
		a_          =					False
		a_          =					False
		a_          =					False
		a_          =					False




		def 	_snake_case	(  self					:Optional[int]			,    _SCREAMING_SNAKE_CASE					:Optional[Any]			,    _SCREAMING_SNAKE_CASE					:List[Any]			,    _SCREAMING_SNAKE_CASE					:str			,    _SCREAMING_SNAKE_CASE					:Any			,    _SCREAMING_SNAKE_CASE					:str)					->			Optional[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					:str)					->			int:






								'''simple docstring'''






								UpperCamelCase_     :	List[Any]					     =     TFGPTJModelTester(self)
								UpperCamelCase_     :	List[str]					     =     ConfigTester(self			,    config_class=_SCREAMING_SNAKE_CASE			,    n_embd=3_7)




		def 	_snake_case	(  self					:Optional[int])					->			Tuple:






								'''simple docstring'''






								self.config_tester.run_common_tests()




		def 	_snake_case	(  self					:int)					->			Any:






								'''simple docstring'''






								UpperCamelCase_     :	int					     =     self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_gptj_model(*_SCREAMING_SNAKE_CASE)




		def 	_snake_case	(  self					:Optional[Any])					->			Dict:






								'''simple docstring'''






								UpperCamelCase_     :	Dict					     =     self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_gptj_model_past(*_SCREAMING_SNAKE_CASE)




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






								'''simple docstring'''






								UpperCamelCase_     :	Dict					     =     self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_gptj_model_attention_mask_past(*_SCREAMING_SNAKE_CASE)




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






								'''simple docstring'''






								UpperCamelCase_     :	int					     =     self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_gptj_model_past_large_inputs(*_SCREAMING_SNAKE_CASE)




		def 	_snake_case	(  self					:int)					->			str:






								'''simple docstring'''






								UpperCamelCase_     :	List[Any]					     =     self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_gptj_lm_head_model(*_SCREAMING_SNAKE_CASE)




		@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					:Union[str, Any])					->			int:






								'''simple docstring'''






								UpperCamelCase_     :	List[str]					     =     TFGPTJModel.from_pretrained("""EleutherAI/gpt-j-6B"""			,    from_pt=_SCREAMING_SNAKE_CASE)
								self.assertIsNotNone(_SCREAMING_SNAKE_CASE)





		@unittest.skip(reason="""Currently, model embeddings are going to undergo a major refactor.""")
		def 	_snake_case	(  self					:Optional[int])					->			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     __snake_case			(    unittest.TestCase	):




		def 	_snake_case	(  self					:str)					->			Dict:






								'''simple docstring'''






								UpperCamelCase_     :	Tuple					     =     TFGPTJForCausalLM.from_pretrained("""EleutherAI/gpt-j-6B"""			,    from_pt=_SCREAMING_SNAKE_CASE)
								UpperCamelCase_     :	int					     =     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_     :	int					     =     [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_     :	Union[str, Any]					     =     model.generate(_SCREAMING_SNAKE_CASE			,    do_sample=_SCREAMING_SNAKE_CASE)
								self.assertListEqual(output_ids[0].numpy().tolist()			,    _SCREAMING_SNAKE_CASE)




		def 	_snake_case	(  self					:Tuple)					->			str:






								'''simple docstring'''






								UpperCamelCase_     :	Union[str, Any]					     =     AutoTokenizer.from_pretrained("""EleutherAI/gpt-j-6B"""			,    revision="""float16""")
								UpperCamelCase_     :	str					     =     TFGPTJForCausalLM.from_pretrained("""EleutherAI/gpt-j-6B"""			,    revision="""float16"""			,    from_pt=_SCREAMING_SNAKE_CASE)

								UpperCamelCase_     :	Optional[int]					     =     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_     :	List[str]					     =     model.generate(**_SCREAMING_SNAKE_CASE			,    do_sample=_SCREAMING_SNAKE_CASE			,    seed=[4_2, 0])
								UpperCamelCase_     :	Union[str, Any]					     =     tokenizer.decode(output_ids[0]			,    skip_special_tokens=_SCREAMING_SNAKE_CASE)

								UpperCamelCase_     :	List[Any]					     =     """Today is a nice day and I’m going to go for a walk. I’"""
								self.assertEqual(_SCREAMING_SNAKE_CASE			,    _SCREAMING_SNAKE_CASE)




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






								'''simple docstring'''






								UpperCamelCase_     :	str					     =     TFGPTJForCausalLM.from_pretrained("""EleutherAI/gpt-j-6B"""			,    revision="""float16"""			,    from_pt=_SCREAMING_SNAKE_CASE)
								UpperCamelCase_     :	Any					     =     AutoTokenizer.from_pretrained("""EleutherAI/gpt-j-6B"""			,    revision="""float16""")

								UpperCamelCase_     :	Union[str, Any]					     =     """left"""

								# Define PAD Token = EOS Token = 50256
								UpperCamelCase_     :	List[str]					     =     tokenizer.eos_token
								UpperCamelCase_     :	Any					     =     model.config.eos_token_id

								# use different length sentences to test batching
								UpperCamelCase_     :	List[Any]					     =     [
								    """Hello, my dog is a little""",
								    """Today, I""",
								]
								UpperCamelCase_     :	Optional[int]					     =     [
								    """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					:Dict)					->			Any:






								'''simple docstring'''






								UpperCamelCase_     ,UpperCamelCase_     ,UpperCamelCase_     ,UpperCamelCase_     :	Optional[int]					     =     self._get_beam_search_test_objects()

								UpperCamelCase_     :	Any					     =     tokenizer(_SCREAMING_SNAKE_CASE			,    return_tensors="""tf"""			,    padding=_SCREAMING_SNAKE_CASE)
								UpperCamelCase_     :	List[Any]					     =     model.generate(**_SCREAMING_SNAKE_CASE			,    do_sample=_SCREAMING_SNAKE_CASE			,    num_beams=2)
								UpperCamelCase_     :	Optional[int]					     =     tokenizer.batch_decode(_SCREAMING_SNAKE_CASE			,    skip_special_tokens=_SCREAMING_SNAKE_CASE)
								self.assertListEqual(_SCREAMING_SNAKE_CASE			,    _SCREAMING_SNAKE_CASE)




		def 	_snake_case	(  self					:List[str])					->			Any:






								'''simple docstring'''






								UpperCamelCase_     ,UpperCamelCase_     ,UpperCamelCase_     ,UpperCamelCase_     :	int					     =     self._get_beam_search_test_objects()

								UpperCamelCase_     :	Tuple					     =     tokenizer(_SCREAMING_SNAKE_CASE			,    return_tensors="""tf"""			,    padding=_SCREAMING_SNAKE_CASE)
								UpperCamelCase_     :	Union[str, Any]					     =     tokenizer(sentences[0]			,    return_tensors="""tf""")
								UpperCamelCase_     :	str					     =     model.generate(**_SCREAMING_SNAKE_CASE			,    do_sample=_SCREAMING_SNAKE_CASE			,    num_beams=2)
								UpperCamelCase_     :	Optional[Any]					     =     (
								    shape_list(inputs_non_padded["""input_ids"""])[-1]
								    - tf.reduce_sum(tf.cast(inputs["""attention_mask"""][-1]			,    tf.intaa)).numpy()
								)
								UpperCamelCase_     :	Tuple					     =     tokenizer(sentences[1]			,    return_tensors="""tf""")
								UpperCamelCase_     :	Tuple					     =     model.generate(
								    **_SCREAMING_SNAKE_CASE			,    do_sample=_SCREAMING_SNAKE_CASE			,    num_beams=2			,    max_length=model.config.max_length - num_paddings)
								UpperCamelCase_     :	List[Any]					     =     tokenizer.decode(output_non_padded[0]			,    skip_special_tokens=_SCREAMING_SNAKE_CASE)
								UpperCamelCase_     :	Tuple					     =     tokenizer.decode(output_padded[0]			,    skip_special_tokens=_SCREAMING_SNAKE_CASE)
								self.assertListEqual(_SCREAMING_SNAKE_CASE			,    [non_padded_sentence, padded_sentence])





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






								'''simple docstring'''






								UpperCamelCase_     ,UpperCamelCase_     ,UpperCamelCase_     ,UpperCamelCase_     :	List[Any]					     =     self._get_beam_search_test_objects()

								UpperCamelCase_     :	Optional[int]					     =     tokenizer(_SCREAMING_SNAKE_CASE			,    return_tensors="""tf"""			,    padding=_SCREAMING_SNAKE_CASE)
								UpperCamelCase_     :	List[Any]					     =     tf.function(model.generate			,    jit_compile=_SCREAMING_SNAKE_CASE)
								UpperCamelCase_     :	str					     =     xla_generate(**_SCREAMING_SNAKE_CASE			,    do_sample=_SCREAMING_SNAKE_CASE			,    num_beams=2)
								UpperCamelCase_     :	List[Any]					     =     tokenizer.batch_decode(_SCREAMING_SNAKE_CASE			,    skip_special_tokens=_SCREAMING_SNAKE_CASE)
								self.assertListEqual(_SCREAMING_SNAKE_CASE			,    _SCREAMING_SNAKE_CASE)






'''simple docstring'''


__SCREAMING_SNAKE_CASE						=      [chr(i) for i in range(32, 126)]
__SCREAMING_SNAKE_CASE						=      list(range(len(alphabets)))
__SCREAMING_SNAKE_CASE						=      list(range(len(alphabets)))
__SCREAMING_SNAKE_CASE						=      list(range(len(alphabets)))
__SCREAMING_SNAKE_CASE						=      list(reversed(range(len(alphabets))))
__SCREAMING_SNAKE_CASE						=      []
__SCREAMING_SNAKE_CASE						=      __SCREAMING_SNAKE_CASE						=      __SCREAMING_SNAKE_CASE						=      0
def 					__magic_name__(       ):
						global gear_one_pos
						global gear_two_pos
						global gear_three_pos
						UpperCamelCase_     :	List[Any]					     =     gear_one[0]
						gear_one.append(UpperCAmelCase__	)
						del gear_one[0]
						gear_one_pos += 1
						if gear_one_pos % int(len(UpperCAmelCase__	)	) == 0:
												UpperCamelCase_     :	List[str]					     =     gear_two[0]
												gear_two.append(UpperCAmelCase__	)
												del gear_two[0]
												gear_two_pos += 1
												if gear_two_pos % int(len(UpperCAmelCase__	)	) == 0:
																		UpperCamelCase_     :	List[Any]					     =     gear_three[0]
																		gear_three.append(UpperCAmelCase__	)
																		del gear_three[0]
																		gear_three_pos += 1
def 					__magic_name__(       UpperCAmelCase__ :       int	):
						UpperCamelCase_     :	Any					     =     alphabets.index(UpperCAmelCase__	)
						UpperCamelCase_     :	List[str]					     =     gear_one[target]
						UpperCamelCase_     :	str					     =     gear_two[target]
						UpperCamelCase_     :	int					     =     gear_three[target]
						UpperCamelCase_     :	Union[str, Any]					     =     reflector[target]
						UpperCamelCase_     :	Dict					     =     gear_three.index(UpperCAmelCase__	)
						UpperCamelCase_     :	Optional[int]					     =     gear_two.index(UpperCAmelCase__	)
						UpperCamelCase_     :	Optional[int]					     =     gear_one.index(UpperCAmelCase__	)
						code.append(alphabets[target]	)
						rotator()


if __name__ == "__main__":
	__SCREAMING_SNAKE_CASE						=      list(input("Type your message:\n"))
	while True:
		try:
			__SCREAMING_SNAKE_CASE						=      int(input("Please set token:(must be only digits)\n"))
			break
		except Exception as error:
			print(error)
	for _ in range(token):
		rotator()
	for j in decode:
		engine(j)
	print("\n" + "".join(code))
	print(
	    f"\nYour Token is {token} please write it down.\nIf you want to decode "
	    "this message again you should input same digits as token!"
	)




