




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 	__lowerCAmelCase			:
							def __init__(		self		:Tuple	,   __magic_name__		:str       ):







								'''simple docstring'''







								a		       =							parent
								a		       =							13
								a		       =							7
								a		       =							True
								a		       =							True
								a		       =							True
								a		       =							True
								a		       =							True
								a		       =							99
								a		       =							32
								a		       =							4
								a		       =							2
								a		       =							4
								a		       =							37
								a		       =							"""gelu"""
								a		       =							0.1
								a		       =							0.1
								a		       =							512
								a		       =							16
								a		       =							2
								a		       =							0.02
								a		       =							3
								a		       =							4
								a		       =							None
								a		       =							self.vocab_size - 1
								a		       =							self.vocab_size - 1
								a		       =							self.vocab_size - 1
							def    lowerCamelCase__							(		self		:Union[str, Any]       ):







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

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

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

								a		       =							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=__magic_name__	,   )

								a		       =							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    lowerCamelCase__							(		self		:List[str]	,   __magic_name__		:Any	,   __magic_name__		:Tuple	,   __magic_name__		:str	,   __magic_name__		:Any	,   __magic_name__		:Optional[Any]	,   *__magic_name__		:Optional[Any]       ):







								'''simple docstring'''







								a		       =							TFGPTJModel(config=__magic_name__       )
								a		       =							{
								    """input_ids""": input_ids,
								    """attention_mask""": input_mask,
								    """token_type_ids""": token_type_ids,
								}
								a		       =							model(__magic_name__       )

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

								a		       =							model(__magic_name__       )

								self.parent.assertEqual(result.last_hidden_state.shape	,   (self.batch_size, self.seq_length, self.hidden_size)       )
							def    lowerCamelCase__							(		self		:List[str]	,   __magic_name__		:Optional[Any]	,   __magic_name__		:List[str]	,   __magic_name__		:str	,   __magic_name__		:Tuple	,   __magic_name__		:Union[str, Any]	,   *__magic_name__		:str       ):







								'''simple docstring'''







								a		       =							TFGPTJModel(config=__magic_name__       )

								# first forward pass
								a		       =							model(__magic_name__	,   token_type_ids=__magic_name__	,   use_cache=__magic_name__       )
								a		       =							model(__magic_name__	,   token_type_ids=__magic_name__       )
								a		       =							model(__magic_name__	,   token_type_ids=__magic_name__	,   use_cache=__magic_name__       )

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

								a       ,				a		       =							outputs.to_tuple()

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

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

								a		       =							model(__magic_name__	,   token_type_ids=__magic_name__       )["""last_hidden_state"""]
								a		       =							model(__magic_name__	,   token_type_ids=__magic_name__	,   past_key_values=__magic_name__       )[
								    """last_hidden_state"""
								]

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

								# test that outputs are equal for slice
								tf.debugging.assert_near(__magic_name__	,   __magic_name__	,   rtol=1E-6       )
							def    lowerCamelCase__							(		self		:List[Any]	,   __magic_name__		:List[str]	,   __magic_name__		:Union[str, Any]	,   __magic_name__		:Optional[int]	,   __magic_name__		:str	,   __magic_name__		:str	,   *__magic_name__		:str       ):







								'''simple docstring'''







								a		       =							TFGPTJModel(config=__magic_name__       )

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

								# first forward pass
								a       ,				a		       =							model(__magic_name__	,   attention_mask=__magic_name__       ).to_tuple()

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

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

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

								# get two different outputs
								a		       =							model(__magic_name__	,   attention_mask=__magic_name__       )["""last_hidden_state"""]
								a		       =							model(__magic_name__	,   past_key_values=__magic_name__	,   attention_mask=__magic_name__       )[
								    """last_hidden_state"""
								]

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

								# test that outputs are equal for slice
								tf.debugging.assert_near(__magic_name__	,   __magic_name__	,   rtol=1E-1_2       )
							def    lowerCamelCase__							(		self		:List[str]	,   __magic_name__		:List[Any]	,   __magic_name__		:List[str]	,   __magic_name__		:str	,   __magic_name__		:str	,   __magic_name__		:Optional[int]	,   *__magic_name__		:Any       ):







								'''simple docstring'''







								a		       =							TFGPTJModel(config=__magic_name__       )

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

								# first forward pass
								a		       =							model(__magic_name__	,   attention_mask=__magic_name__	,   token_type_ids=__magic_name__	,   use_cache=__magic_name__       )

								a       ,				a		       =							outputs.to_tuple()

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

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

								a		       =							model(
								    __magic_name__	,   token_type_ids=__magic_name__	,   attention_mask=__magic_name__       )["""last_hidden_state"""]
								a		       =							model(
								    __magic_name__	,   token_type_ids=__magic_name__	,   attention_mask=__magic_name__	,   past_key_values=__magic_name__	,   )["""last_hidden_state"""]
								self.parent.assertTrue(output_from_past.shape[1] == next_tokens.shape[1]       )

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

								# test that outputs are equal for slice
								tf.debugging.assert_near(__magic_name__	,   __magic_name__	,   rtol=1E-3       )
							def    lowerCamelCase__							(		self		:str	,   __magic_name__		:Optional[int]	,   __magic_name__		:Optional[Any]	,   __magic_name__		:Optional[Any]	,   __magic_name__		:str	,   __magic_name__		:int	,   *__magic_name__		:List[Any]       ):







								'''simple docstring'''







								a		       =							TFGPTJForCausalLM(config=__magic_name__       )
								a		       =							{
								    """input_ids""": input_ids,
								    """attention_mask""": input_mask,
								    """token_type_ids""": token_type_ids,
								}
								a		       =							model(__magic_name__       )
								self.parent.assertEqual(result.logits.shape	,   (self.batch_size, self.seq_length, self.vocab_size)       )





							def    lowerCamelCase__							(		self		:int       ):







								'''simple docstring'''







								a		       =							self.prepare_config_and_inputs()

								(
								    (
								    a
								)       ,				(
								    a
								)       ,				(
								    a
								)       ,				(
								    a
								)       ,				(
								    a
								)       ,				(
								    a
								)       ,				(
								    a
								)       ,				(
								    a
								)       ,				(
								    a
								)       ,				
								)		       =							config_and_inputs

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







@require_tf
class 	__lowerCAmelCase			(			__magic_name__   , __magic_name__   , __magic_name__   , unittest.TestCase				):
							UpperCamelCase__        =						(
							    (TFGPTJForCausalLM, TFGPTJForSequenceClassification, TFGPTJForQuestionAnswering, TFGPTJModel)
							    if is_tf_available()
							    else ()
							)

							UpperCamelCase__        =						(TFGPTJForCausalLM,) if is_tf_available() else ()
							UpperCamelCase__        =						(
							    {
							        '''feature-extraction''': TFGPTJModel,
							        '''question-answering''': TFGPTJForQuestionAnswering,
							        '''text-classification''': TFGPTJForSequenceClassification,
							        '''text-generation''': TFGPTJForCausalLM,
							        '''zero-shot''': TFGPTJForSequenceClassification,
							    }
							    if is_tf_available()
							    else {}
							)
							UpperCamelCase__        =						False
							UpperCamelCase__        =						False
							UpperCamelCase__        =						False
							UpperCamelCase__        =						False
							def    lowerCamelCase__							(		self		:Union[str, Any]	,   __magic_name__		:Any	,   __magic_name__		:Optional[int]	,   __magic_name__		:int	,   __magic_name__		:List[str]	,   __magic_name__		:Union[str, Any]       ):







								'''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    lowerCamelCase__							(		self		:Optional[int]       ):







								'''simple docstring'''







								a		       =							TFGPTJModelTester(self       )
								a		       =							ConfigTester(self	,   config_class=__magic_name__	,   n_embd=37       )
							def    lowerCamelCase__							(		self		:List[str]       ):







								'''simple docstring'''







								self.config_tester.run_common_tests()
							def    lowerCamelCase__							(		self		:str       ):







								'''simple docstring'''







								a		       =							self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_gptj_model(*__magic_name__       )
							def    lowerCamelCase__							(		self		:Optional[Any]       ):







								'''simple docstring'''







								a		       =							self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_gptj_model_past(*__magic_name__       )
							def    lowerCamelCase__							(		self		:Union[str, Any]       ):







								'''simple docstring'''







								a		       =							self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_gptj_model_attention_mask_past(*__magic_name__       )
							def    lowerCamelCase__							(		self		:Any       ):







								'''simple docstring'''







								a		       =							self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_gptj_model_past_large_inputs(*__magic_name__       )
							def    lowerCamelCase__							(		self		:List[str]       ):







								'''simple docstring'''







								a		       =							self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_gptj_lm_head_model(*__magic_name__       )
							@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    lowerCamelCase__							(		self		:str       ):







								'''simple docstring'''







								a		       =							TFGPTJModel.from_pretrained("""EleutherAI/gpt-j-6B"""	,   from_pt=__magic_name__       )
								self.assertIsNotNone(__magic_name__       )





							@unittest.skip(reason="""Currently, model embeddings are going to undergo a major refactor."""       )
							def    lowerCamelCase__							(		self		:str       ):







								'''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 	__lowerCAmelCase			(			unittest.TestCase				):
							def    lowerCamelCase__							(		self		:Any       ):







								'''simple docstring'''







								a		       =							TFGPTJForCausalLM.from_pretrained("""EleutherAI/gpt-j-6B"""	,   from_pt=__magic_name__       )
								a		       =							tf.convert_to_tensor([[464, 3290]]	,   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
								a		       =							[464, 3290, 318, 257, 582, 338, 1266, 1545, 13, 632, 318, 257, 9112, 1_5185, 11, 290, 340, 318, 257, 1545]
								# fmt: on
								a		       =							model.generate(__magic_name__	,   do_sample=__magic_name__       )
								self.assertListEqual(output_ids[0].numpy().tolist()	,   __magic_name__       )
							def    lowerCamelCase__							(		self		:List[str]       ):







								'''simple docstring'''







								a		       =							AutoTokenizer.from_pretrained("""EleutherAI/gpt-j-6B"""	,   revision="""float16"""       )
								a		       =							TFGPTJForCausalLM.from_pretrained("""EleutherAI/gpt-j-6B"""	,   revision="""float16"""	,   from_pt=__magic_name__       )

								a		       =							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"""       ):
									a		       =							model.generate(**__magic_name__	,   do_sample=__magic_name__	,   seed=[42, 0]       )
								a		       =							tokenizer.decode(output_ids[0]	,   skip_special_tokens=__magic_name__       )

								a		       =							"""Today is a nice day and I’m going to go for a walk. I’"""
								self.assertEqual(__magic_name__	,   __magic_name__       )
							def    lowerCamelCase__							(		self		:Optional[Any]       ):







								'''simple docstring'''







								a		       =							TFGPTJForCausalLM.from_pretrained("""EleutherAI/gpt-j-6B"""	,   revision="""float16"""	,   from_pt=__magic_name__       )
								a		       =							AutoTokenizer.from_pretrained("""EleutherAI/gpt-j-6B"""	,   revision="""float16"""       )

								a		       =							"""left"""

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

								# use different length sentences to test batching
								a		       =							[
								    """Hello, my dog is a little""",
								    """Today, I""",
								]
								a		       =							[
								    """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    lowerCamelCase__							(		self		:List[Any]       ):







								'''simple docstring'''







								a       ,				a       ,				a       ,				a		       =							self._get_beam_search_test_objects()

								a		       =							tokenizer(__magic_name__	,   return_tensors="""tf"""	,   padding=__magic_name__       )
								a		       =							model.generate(**__magic_name__	,   do_sample=__magic_name__	,   num_beams=2       )
								a		       =							tokenizer.batch_decode(__magic_name__	,   skip_special_tokens=__magic_name__       )
								self.assertListEqual(__magic_name__	,   __magic_name__       )
							def    lowerCamelCase__							(		self		:Optional[int]       ):







								'''simple docstring'''







								a       ,				a       ,				a       ,				a		       =							self._get_beam_search_test_objects()

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





							def    lowerCamelCase__							(		self		:Optional[int]       ):







								'''simple docstring'''







								a       ,				a       ,				a       ,				a		       =							self._get_beam_search_test_objects()

								a		       =							tokenizer(__magic_name__	,   return_tensors="""tf"""	,   padding=__magic_name__       )
								a		       =							tf.function(model.generate	,   jit_compile=__magic_name__       )
								a		       =							xla_generate(**__magic_name__	,   do_sample=__magic_name__	,   num_beams=2       )
								a		       =							tokenizer.batch_decode(__magic_name__	,   skip_special_tokens=__magic_name__       )
								self.assertListEqual(__magic_name__	,   __magic_name__       )







import math





def 	__A	(			__lowerCamelCase					)   -> bool:

	if 1 < number < 4:
		# 2 and 3 are primes
		return True
	elif number < 2 or number % 2 == 0 or number % 3 == 0:
		# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
		return False

	# All primes number are in format of 6k +/- 1
	for i in range(5							,    int(math.sqrt(__lowerCamelCase					) + 1					)							,    6					):
		if number % i == 0 or number % (i + 2) == 0:
			return False
	return True





def 	__A	(			__lowerCamelCase = 6008_5147_5143					)   -> int:

	try:
		a		       =							int(__lowerCamelCase					)
	except (TypeError, ValueError):
		raise TypeError("""Parameter n must be int or castable to int."""					)
	if n <= 0:
		raise ValueError("""Parameter n must be greater than or equal to one."""					)
	a		       =							0
	if is_prime(__lowerCamelCase					):
		return n
	while n % 2 == 0:
		n //= 2
	if is_prime(__lowerCamelCase					):
		return n
	for i in range(3							,    int(math.sqrt(__lowerCamelCase					)					) + 1							,    2					):
		if n % i == 0:
			if is_prime(n // i					):
				a		       =							n // i
				break
			elif is_prime(__lowerCamelCase					):
				a		       =							i
	return max_number


if __name__ == "__main__":
		print(F'{solution() = }')

