


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 ,	lowerCAmelCase__					):





										"""simple docstring"""




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






							def 				UpperCamelCase__    (			self					):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:    Dict 		=				ids_tensor([self.batch_size, self.seq_length] ,	self.vocab_size					)

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

										SCREAMING_SNAKE_CASE_		:    List[str] 		=				None
										if self.use_token_type_ids:
													SCREAMING_SNAKE_CASE_		:    int 		=				ids_tensor([self.batch_size, self.seq_length] ,	self.type_vocab_size					)

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

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

										SCREAMING_SNAKE_CASE_		:    Tuple 		=				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=lowerCAmelCase__ ,	)

										SCREAMING_SNAKE_CASE_		:    List[str] 		=				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 				UpperCamelCase__    (			self ,	lowerCAmelCase__ ,	lowerCAmelCase__ ,	lowerCAmelCase__ ,	lowerCAmelCase__ ,	lowerCAmelCase__ ,	*lowerCAmelCase__					):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:    Tuple 		=				TFGPTJModel(config=lowerCAmelCase__					)
										SCREAMING_SNAKE_CASE_		:    List[Any] 		=				{
										    'input_ids': input_ids,
										    'attention_mask': input_mask,
										    'token_type_ids': token_type_ids,
										}
										SCREAMING_SNAKE_CASE_		:    Dict 		=				model(lowerCAmelCase__					)

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

										SCREAMING_SNAKE_CASE_		:    List[Any] 		=				model(lowerCAmelCase__					)

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






							def 				UpperCamelCase__    (			self ,	lowerCAmelCase__ ,	lowerCAmelCase__ ,	lowerCAmelCase__ ,	lowerCAmelCase__ ,	lowerCAmelCase__ ,	*lowerCAmelCase__					):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:    int 		=				TFGPTJModel(config=lowerCAmelCase__					)

										# first forward pass
										SCREAMING_SNAKE_CASE_		:    Any 		=				model(lowerCAmelCase__ ,	token_type_ids=lowerCAmelCase__ ,	use_cache=lowerCAmelCase__					)
										SCREAMING_SNAKE_CASE_		:    Optional[Any] 		=				model(lowerCAmelCase__ ,	token_type_ids=lowerCAmelCase__					)
										SCREAMING_SNAKE_CASE_		:    Any 		=				model(lowerCAmelCase__ ,	token_type_ids=lowerCAmelCase__ ,	use_cache=lowerCAmelCase__					)

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

										SCREAMING_SNAKE_CASE_		:    Optional[int] 		=				outputs.to_tuple()

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

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

										SCREAMING_SNAKE_CASE_		:    Optional[int] 		=				model(lowerCAmelCase__ ,	token_type_ids=lowerCAmelCase__					)['last_hidden_state']
										SCREAMING_SNAKE_CASE_		:    List[Any] 		=				model(lowerCAmelCase__ ,	token_type_ids=lowerCAmelCase__ ,	past_key_values=lowerCAmelCase__					)[
										    'last_hidden_state'
										]

										# select random slice
										SCREAMING_SNAKE_CASE_		:    List[str] 		=				int(ids_tensor((1,) ,	shape_list(lowerCAmelCase__					)[-1]					)					)
										SCREAMING_SNAKE_CASE_		:    Dict 		=				output_from_no_past[:, -1, random_slice_idx]
										SCREAMING_SNAKE_CASE_		:    Dict 		=				output_from_past[:, 0, random_slice_idx]

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






							def 				UpperCamelCase__    (			self ,	lowerCAmelCase__ ,	lowerCAmelCase__ ,	lowerCAmelCase__ ,	lowerCAmelCase__ ,	lowerCAmelCase__ ,	*lowerCAmelCase__					):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:    str 		=				TFGPTJModel(config=lowerCAmelCase__					)

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

										# first forward pass
										SCREAMING_SNAKE_CASE_		:    Union[str, Any] 		=				model(lowerCAmelCase__ ,	attention_mask=lowerCAmelCase__					).to_tuple()

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

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

										# append to next input_ids and attn_mask
										SCREAMING_SNAKE_CASE_		:    Optional[Any] 		=				tf.concat([input_ids, next_tokens] ,	axis=-1					)
										SCREAMING_SNAKE_CASE_		:    Tuple 		=				tf.concat([attn_mask, tf.ones((shape_list(lowerCAmelCase__					)[0], 1) ,	dtype=tf.intaa					)] ,	axis=1					)

										# get two different outputs
										SCREAMING_SNAKE_CASE_		:    int 		=				model(lowerCAmelCase__ ,	attention_mask=lowerCAmelCase__					)['last_hidden_state']
										SCREAMING_SNAKE_CASE_		:    Dict 		=				model(lowerCAmelCase__ ,	past_key_values=lowerCAmelCase__ ,	attention_mask=lowerCAmelCase__					)[
										    'last_hidden_state'
										]

										# select random slice
										SCREAMING_SNAKE_CASE_		:    Dict 		=				int(ids_tensor((1,) ,	shape_list(lowerCAmelCase__					)[-1]					)					)
										SCREAMING_SNAKE_CASE_		:    Union[str, Any] 		=				output_from_no_past[:, -1, random_slice_idx]
										SCREAMING_SNAKE_CASE_		:    Dict 		=				output_from_past[:, 0, random_slice_idx]

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






							def 				UpperCamelCase__    (			self ,	lowerCAmelCase__ ,	lowerCAmelCase__ ,	lowerCAmelCase__ ,	lowerCAmelCase__ ,	lowerCAmelCase__ ,	*lowerCAmelCase__					):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:    List[str] 		=				TFGPTJModel(config=lowerCAmelCase__					)

										SCREAMING_SNAKE_CASE_		:    Union[str, Any] 		=				input_ids[:1, :]
										SCREAMING_SNAKE_CASE_		:    Any 		=				input_mask[:1, :]
										SCREAMING_SNAKE_CASE_		:    Optional[int] 		=				token_type_ids[:1, :]
										SCREAMING_SNAKE_CASE_		:    Optional[int] 		=				1

										# first forward pass
										SCREAMING_SNAKE_CASE_		:    int 		=				model(lowerCAmelCase__ ,	attention_mask=lowerCAmelCase__ ,	token_type_ids=lowerCAmelCase__ ,	use_cache=lowerCAmelCase__					)

										SCREAMING_SNAKE_CASE_		:    Tuple 		=				outputs.to_tuple()

										# create hypothetical next token and extent to next_input_ids
										SCREAMING_SNAKE_CASE_		:    Optional[int] 		=				ids_tensor((self.batch_size, 3) ,	config.vocab_size					)
										SCREAMING_SNAKE_CASE_		:    List[str] 		=				ids_tensor((self.batch_size, 3) ,	2					)
										SCREAMING_SNAKE_CASE_		:    Dict 		=				ids_tensor((self.batch_size, 3) ,	self.type_vocab_size					)

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

										SCREAMING_SNAKE_CASE_		:    int 		=				model(
										    lowerCAmelCase__ ,	token_type_ids=lowerCAmelCase__ ,	attention_mask=lowerCAmelCase__					)['last_hidden_state']
										SCREAMING_SNAKE_CASE_		:    Dict 		=				model(
										    lowerCAmelCase__ ,	token_type_ids=lowerCAmelCase__ ,	attention_mask=lowerCAmelCase__ ,	past_key_values=lowerCAmelCase__ ,	)['last_hidden_state']
										self.parent.assertTrue(output_from_past.shape[1] == next_tokens.shape[1]					)

										# select random slice
										SCREAMING_SNAKE_CASE_		:    Union[str, Any] 		=				int(ids_tensor((1,) ,	shape_list(lowerCAmelCase__					)[-1]					)					)
										SCREAMING_SNAKE_CASE_		:    Dict 		=				output_from_no_past[:, -3:, random_slice_idx]
										SCREAMING_SNAKE_CASE_		:    int 		=				output_from_past[:, :, random_slice_idx]

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






							def 				UpperCamelCase__    (			self ,	lowerCAmelCase__ ,	lowerCAmelCase__ ,	lowerCAmelCase__ ,	lowerCAmelCase__ ,	lowerCAmelCase__ ,	*lowerCAmelCase__					):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:    Any 		=				TFGPTJForCausalLM(config=lowerCAmelCase__					)
										SCREAMING_SNAKE_CASE_		:    int 		=				{
										    'input_ids': input_ids,
										    'attention_mask': input_mask,
										    'token_type_ids': token_type_ids,
										}
										SCREAMING_SNAKE_CASE_		:    Tuple 		=				model(lowerCAmelCase__					)
										self.parent.assertEqual(result.logits.shape ,	(self.batch_size, self.seq_length, self.vocab_size)					)






							def 				UpperCamelCase__    (			self					):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:    Union[str, Any] 		=				self.prepare_config_and_inputs()

										(
										    SCREAMING_SNAKE_CASE_			
										)		:    List[str] 		=				config_and_inputs

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







@require_tf
class   __lowercase						(__SCREAMING_SNAKE_CASE  ,					__SCREAMING_SNAKE_CASE  ,					__SCREAMING_SNAKE_CASE  ,					unittest.TestCase     ):





							"""simple docstring"""







							_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 				UpperCamelCase__    (			self ,	lowerCAmelCase__ ,	lowerCAmelCase__ ,	lowerCAmelCase__ ,	lowerCAmelCase__ ,	lowerCAmelCase__					):





										"""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 				UpperCamelCase__    (			self					):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:    int 		=				TFGPTJModelTester(self					)
										SCREAMING_SNAKE_CASE_		:    Tuple 		=				ConfigTester(self ,	config_class=lowerCAmelCase__ ,	n_embd=3_7					)






							def 				UpperCamelCase__    (			self					):





										"""simple docstring"""




										self.config_tester.run_common_tests()






							def 				UpperCamelCase__    (			self					):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:    List[str] 		=				self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_gptj_model(*lowerCAmelCase__					)






							def 				UpperCamelCase__    (			self					):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:    List[str] 		=				self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_gptj_model_past(*lowerCAmelCase__					)






							def 				UpperCamelCase__    (			self					):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:    int 		=				self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_gptj_model_attention_mask_past(*lowerCAmelCase__					)






							def 				UpperCamelCase__    (			self					):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:    str 		=				self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_gptj_model_past_large_inputs(*lowerCAmelCase__					)






							def 				UpperCamelCase__    (			self					):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:    Union[str, Any] 		=				self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_gptj_lm_head_model(*lowerCAmelCase__					)






							@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 				UpperCamelCase__    (			self					):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:    Union[str, Any] 		=				TFGPTJModel.from_pretrained('EleutherAI/gpt-j-6B' ,	from_pt=lowerCAmelCase__					)
										self.assertIsNotNone(lowerCAmelCase__					)






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





										"""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 				UpperCamelCase__    (			self					):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:    Tuple 		=				TFGPTJForCausalLM.from_pretrained('EleutherAI/gpt-j-6B' ,	from_pt=lowerCAmelCase__					)
										SCREAMING_SNAKE_CASE_		:    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
										SCREAMING_SNAKE_CASE_		:    List[Any] 		=				[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
										SCREAMING_SNAKE_CASE_		:    List[str] 		=				model.generate(lowerCAmelCase__ ,	do_sample=lowerCAmelCase__					)
										self.assertListEqual(output_ids[0].numpy().tolist() ,	lowerCAmelCase__					)






							def 				UpperCamelCase__    (			self					):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:    List[Any] 		=				AutoTokenizer.from_pretrained('EleutherAI/gpt-j-6B' ,	revision='float16'					)
										SCREAMING_SNAKE_CASE_		:    Optional[Any] 		=				TFGPTJForCausalLM.from_pretrained('EleutherAI/gpt-j-6B' ,	revision='float16' ,	from_pt=lowerCAmelCase__					)

										SCREAMING_SNAKE_CASE_		:    str 		=				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'					):
													SCREAMING_SNAKE_CASE_		:    List[str] 		=				model.generate(**lowerCAmelCase__ ,	do_sample=lowerCAmelCase__ ,	seed=[4_2, 0]					)
										SCREAMING_SNAKE_CASE_		:    Optional[Any] 		=				tokenizer.decode(output_ids[0] ,	skip_special_tokens=lowerCAmelCase__					)

										SCREAMING_SNAKE_CASE_		:    Tuple 		=				'Today is a nice day and I’m going to go for a walk. I’'
										self.assertEqual(lowerCAmelCase__ ,	lowerCAmelCase__					)






							def 				UpperCamelCase__    (			self					):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:    Union[str, Any] 		=				TFGPTJForCausalLM.from_pretrained('EleutherAI/gpt-j-6B' ,	revision='float16' ,	from_pt=lowerCAmelCase__					)
										SCREAMING_SNAKE_CASE_		:    Optional[Any] 		=				AutoTokenizer.from_pretrained('EleutherAI/gpt-j-6B' ,	revision='float16'					)

										SCREAMING_SNAKE_CASE_		:    Optional[Any] 		=				'left'

										# Define PAD Token = EOS Token = 50256
										SCREAMING_SNAKE_CASE_		:    Optional[Any] 		=				tokenizer.eos_token
										SCREAMING_SNAKE_CASE_		:    Optional[Any] 		=				model.config.eos_token_id

										# use different length sentences to test batching
										SCREAMING_SNAKE_CASE_		:    Optional[Any] 		=				[
										    'Hello, my dog is a little',
										    'Today, I',
										]
										SCREAMING_SNAKE_CASE_		:    Dict 		=				[
										    '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 				UpperCamelCase__    (			self					):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:    Optional[Any] 		=				self._get_beam_search_test_objects()

										SCREAMING_SNAKE_CASE_		:    Tuple 		=				tokenizer(lowerCAmelCase__ ,	return_tensors='tf' ,	padding=lowerCAmelCase__					)
										SCREAMING_SNAKE_CASE_		:    Tuple 		=				model.generate(**lowerCAmelCase__ ,	do_sample=lowerCAmelCase__ ,	num_beams=2					)
										SCREAMING_SNAKE_CASE_		:    List[str] 		=				tokenizer.batch_decode(lowerCAmelCase__ ,	skip_special_tokens=lowerCAmelCase__					)
										self.assertListEqual(lowerCAmelCase__ ,	lowerCAmelCase__					)






							def 				UpperCamelCase__    (			self					):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:    List[Any] 		=				self._get_beam_search_test_objects()

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






							def 				UpperCamelCase__    (			self					):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:    Any 		=				self._get_beam_search_test_objects()

										SCREAMING_SNAKE_CASE_		:    List[Any] 		=				tokenizer(lowerCAmelCase__ ,	return_tensors='tf' ,	padding=lowerCAmelCase__					)
										SCREAMING_SNAKE_CASE_		:    Union[str, Any] 		=				tf.function(model.generate ,	jit_compile=lowerCAmelCase__					)
										SCREAMING_SNAKE_CASE_		:    Tuple 		=				xla_generate(**lowerCAmelCase__ ,	do_sample=lowerCAmelCase__ ,	num_beams=2					)
										SCREAMING_SNAKE_CASE_		:    Tuple 		=				tokenizer.batch_decode(lowerCAmelCase__ ,	skip_special_tokens=lowerCAmelCase__					)
										self.assertListEqual(lowerCAmelCase__ ,	lowerCAmelCase__					)




import numpy as np







class   __lowercase						:





							"""simple docstring"""







							def __init__(			self ,	lowerCAmelCase__=5 ,	lowerCAmelCase__=5					):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:    List[str] 		=				depth
										SCREAMING_SNAKE_CASE_		:    Union[str, Any] 		=				0
										SCREAMING_SNAKE_CASE_		:    int 		=				None
										SCREAMING_SNAKE_CASE_		:    List[Any] 		=				None
										SCREAMING_SNAKE_CASE_		:    Optional[int] 		=				min_leaf_size
										SCREAMING_SNAKE_CASE_		:    Optional[Any] 		=				None






							def 				UpperCamelCase__    (			self ,	lowerCAmelCase__ ,	lowerCAmelCase__					):





										"""simple docstring"""




										if labels.ndim != 1:
													print('Error: Input labels must be one dimensional'					)

										return np.mean((labels - prediction) ** 2					)






							def 				UpperCamelCase__    (			self ,	lowerCAmelCase__ ,	lowerCAmelCase__					):





										"""simple docstring"""




										if x.ndim != 1:
													print('Error: Input data set must be one dimensional'					)
													return
										if len(lowerCAmelCase__					) != len(lowerCAmelCase__					):
													print('Error: X and y have different lengths'					)
													return
										if y.ndim != 1:
													print('Error: Data set labels must be one dimensional'					)
													return

										if len(lowerCAmelCase__					) < 2 * self.min_leaf_size:
													SCREAMING_SNAKE_CASE_		:    Optional[int] 		=				np.mean(lowerCAmelCase__					)
													return

										if self.depth == 1:
													SCREAMING_SNAKE_CASE_		:    Tuple 		=				np.mean(lowerCAmelCase__					)
													return

										SCREAMING_SNAKE_CASE_		:    int 		=				0
										SCREAMING_SNAKE_CASE_		:    List[str] 		=				self.mean_squared_error(lowerCAmelCase__ ,	np.mean(lowerCAmelCase__					)					) * 2
										for i in range(len(lowerCAmelCase__					)					):
													if len(x[:i]					) < self.min_leaf_size:
																continue
													elif len(x[i:]					) < self.min_leaf_size:
																continue
													else:
																SCREAMING_SNAKE_CASE_		:    Dict 		=				self.mean_squared_error(x[:i] ,	np.mean(y[:i]					)					)
																SCREAMING_SNAKE_CASE_		:    int 		=				self.mean_squared_error(x[i:] ,	np.mean(y[i:]					)					)
																SCREAMING_SNAKE_CASE_		:    Any 		=				error_left + error_right
																if error < min_error:
																			SCREAMING_SNAKE_CASE_		:    Union[str, Any] 		=				i
																			SCREAMING_SNAKE_CASE_		:    int 		=				error

										if best_split != 0:
													SCREAMING_SNAKE_CASE_		:    int 		=				x[:best_split]
													SCREAMING_SNAKE_CASE_		:    Optional[Any] 		=				y[:best_split]
													SCREAMING_SNAKE_CASE_		:    Union[str, Any] 		=				x[best_split:]
													SCREAMING_SNAKE_CASE_		:    str 		=				y[best_split:]

													SCREAMING_SNAKE_CASE_		:    str 		=				x[best_split]
													SCREAMING_SNAKE_CASE_		:    Optional[Any] 		=				DecisionTree(
													    depth=self.depth - 1 ,	min_leaf_size=self.min_leaf_size					)
													SCREAMING_SNAKE_CASE_		:    int 		=				DecisionTree(
													    depth=self.depth - 1 ,	min_leaf_size=self.min_leaf_size					)
													self.left.train(lowerCAmelCase__ ,	lowerCAmelCase__					)
													self.right.train(lowerCAmelCase__ ,	lowerCAmelCase__					)
										else:
													SCREAMING_SNAKE_CASE_		:    List[Any] 		=				np.mean(lowerCAmelCase__					)

										return






							def 				UpperCamelCase__    (			self ,	lowerCAmelCase__					):





										"""simple docstring"""




										if self.prediction is not None:
													return self.prediction
										elif self.left or self.right is not None:
													if x >= self.decision_boundary:
																return self.right.predict(lowerCAmelCase__					)
													else:
																return self.left.predict(lowerCAmelCase__					)
										else:
													print('Error: Decision tree not yet trained'					)
													return None







class   __lowercase						:





							"""simple docstring"""







							@staticmethod
							def 				UpperCamelCase__    (			lowerCAmelCase__ ,	lowerCAmelCase__					):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:    Optional[Any] 		=				float(0					)
										for label in labels:
													squared_error_sum += (label - prediction) ** 2

										return float(squared_error_sum / labels.size					)







def 	a__ (							):
			SCREAMING_SNAKE_CASE_		:    List[str] 		=				np.arange(-1.0,			1.0,			0.0_05							)
			SCREAMING_SNAKE_CASE_		:    Dict 		=				np.sin(A__							)

			SCREAMING_SNAKE_CASE_		:    str 		=				DecisionTree(depth=1_0,			min_leaf_size=1_0							)
			tree.train(A__,			A__							)

			SCREAMING_SNAKE_CASE_		:    Dict 		=				(np.random.rand(1_0							) * 2) - 1
			SCREAMING_SNAKE_CASE_		:    Any 		=				np.array([tree.predict(A__							) for x in test_cases]							)
			SCREAMING_SNAKE_CASE_		:    Optional[int] 		=				np.mean((predictions - test_cases) ** 2							)

			print('Test values: ' + str(A__							)							)
			print('Predictions: ' + str(A__							)							)
			print('Average error: ' + str(A__							)							)


if __name__ == "__main__":
				main()
				import doctest

				doctest.testmod(name='mean_squarred_error', verbose=True)





