






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 				__a				:





		'''simple docstring'''



		def __init__(    self			,     UpperCamelCase__							):
						SCREAMING_SNAKE_CASE_ :						List[Any]    				= parent
						SCREAMING_SNAKE_CASE_ :						Any    				= 13
						SCREAMING_SNAKE_CASE_ :						Optional[Any]    				= 7
						SCREAMING_SNAKE_CASE_ :						Dict    				= True
						SCREAMING_SNAKE_CASE_ :						str    				= True
						SCREAMING_SNAKE_CASE_ :						Optional[int]    				= True
						SCREAMING_SNAKE_CASE_ :						Tuple    				= True
						SCREAMING_SNAKE_CASE_ :						List[str]    				= True
						SCREAMING_SNAKE_CASE_ :						List[str]    				= 99
						SCREAMING_SNAKE_CASE_ :						Optional[Any]    				= 32
						SCREAMING_SNAKE_CASE_ :						Optional[int]    				= 4
						SCREAMING_SNAKE_CASE_ :						List[Any]    				= 2
						SCREAMING_SNAKE_CASE_ :						Union[str, Any]    				= 4
						SCREAMING_SNAKE_CASE_ :						int    				= 37
						SCREAMING_SNAKE_CASE_ :						Dict    				= 'gelu'
						SCREAMING_SNAKE_CASE_ :						Optional[int]    				= 0.1
						SCREAMING_SNAKE_CASE_ :						Optional[Any]    				= 0.1
						SCREAMING_SNAKE_CASE_ :						Union[str, Any]    				= 512
						SCREAMING_SNAKE_CASE_ :						Optional[int]    				= 16
						SCREAMING_SNAKE_CASE_ :						List[Any]    				= 2
						SCREAMING_SNAKE_CASE_ :						List[str]    				= 0.02
						SCREAMING_SNAKE_CASE_ :						Tuple    				= 3
						SCREAMING_SNAKE_CASE_ :						Union[str, Any]    				= 4
						SCREAMING_SNAKE_CASE_ :						str    				= None
						SCREAMING_SNAKE_CASE_ :						Tuple    				= self.vocab_size - 1
						SCREAMING_SNAKE_CASE_ :						List[str]    				= self.vocab_size - 1
						SCREAMING_SNAKE_CASE_ :						Dict    				= self.vocab_size - 1


		def 	__snake_case      (    self							):
						SCREAMING_SNAKE_CASE_ :						List[Any]    				= ids_tensor([self.batch_size, self.seq_length]			,     self.vocab_size							)

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

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

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

						SCREAMING_SNAKE_CASE_ :						Optional[Any]    				= None
						SCREAMING_SNAKE_CASE_ :						List[Any]    				= None
						SCREAMING_SNAKE_CASE_ :						Any    				= None
						if self.use_labels:
										SCREAMING_SNAKE_CASE_ :						Dict    				= ids_tensor([self.batch_size]			,     self.type_sequence_label_size							)
										SCREAMING_SNAKE_CASE_ :						str    				= 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_ :						Union[str, Any]    				= 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=UpperCamelCase__			,     )

						SCREAMING_SNAKE_CASE_ :						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			,     UpperCamelCase__			,     UpperCamelCase__			,     UpperCamelCase__			,     UpperCamelCase__			,     UpperCamelCase__			,     *UpperCamelCase__							):
						SCREAMING_SNAKE_CASE_ :						Optional[Any]    				= TFGPTJModel(config=UpperCamelCase__							)
						SCREAMING_SNAKE_CASE_ :						Tuple    				= {
						    'input_ids': input_ids,
						    'attention_mask': input_mask,
						    'token_type_ids': token_type_ids,
						}
						SCREAMING_SNAKE_CASE_ :						int    				= model(UpperCamelCase__							)

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

						SCREAMING_SNAKE_CASE_ :						Tuple    				= model(UpperCamelCase__							)

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


		def 	__snake_case      (    self			,     UpperCamelCase__			,     UpperCamelCase__			,     UpperCamelCase__			,     UpperCamelCase__			,     UpperCamelCase__			,     *UpperCamelCase__							):
						SCREAMING_SNAKE_CASE_ :						Optional[Any]    				= TFGPTJModel(config=UpperCamelCase__							)

						# first forward pass
						SCREAMING_SNAKE_CASE_ :						List[str]    				= model(UpperCamelCase__			,     token_type_ids=UpperCamelCase__			,     use_cache=UpperCamelCase__							)
						SCREAMING_SNAKE_CASE_ :						str    				= model(UpperCamelCase__			,     token_type_ids=UpperCamelCase__							)
						SCREAMING_SNAKE_CASE_ :						List[str]    				= model(UpperCamelCase__			,     token_type_ids=UpperCamelCase__			,     use_cache=UpperCamelCase__							)

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

						SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_ :						List[str]    				= outputs.to_tuple()

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

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

						SCREAMING_SNAKE_CASE_ :						Tuple    				= model(UpperCamelCase__			,     token_type_ids=UpperCamelCase__							)['last_hidden_state']
						SCREAMING_SNAKE_CASE_ :						Tuple    				= model(UpperCamelCase__			,     token_type_ids=UpperCamelCase__			,     past_key_values=UpperCamelCase__							)[
						    'last_hidden_state'
						]

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

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


		def 	__snake_case      (    self			,     UpperCamelCase__			,     UpperCamelCase__			,     UpperCamelCase__			,     UpperCamelCase__			,     UpperCamelCase__			,     *UpperCamelCase__							):
						SCREAMING_SNAKE_CASE_ :						str    				= TFGPTJModel(config=UpperCamelCase__							)

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

						# first forward pass
						SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_ :						str    				= model(UpperCamelCase__			,     attention_mask=UpperCamelCase__							).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_ :						str    				= ids_tensor((1,)			,     UpperCamelCase__							).numpy() + 1
						SCREAMING_SNAKE_CASE_ :						int    				= 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_ :						Optional[Any]    				= tf.transpose(
						    tf.broadcast_to(tf.expand_dims(UpperCamelCase__			,     -1							)			,     (self.seq_length, self.batch_size)							)							)
						SCREAMING_SNAKE_CASE_ :						Dict    				= tf.where(UpperCamelCase__			,     UpperCamelCase__			,     UpperCamelCase__							)

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

						# get two different outputs
						SCREAMING_SNAKE_CASE_ :						str    				= model(UpperCamelCase__			,     attention_mask=UpperCamelCase__							)['last_hidden_state']
						SCREAMING_SNAKE_CASE_ :						List[Any]    				= model(UpperCamelCase__			,     past_key_values=UpperCamelCase__			,     attention_mask=UpperCamelCase__							)[
						    'last_hidden_state'
						]

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

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


		def 	__snake_case      (    self			,     UpperCamelCase__			,     UpperCamelCase__			,     UpperCamelCase__			,     UpperCamelCase__			,     UpperCamelCase__			,     *UpperCamelCase__							):
						SCREAMING_SNAKE_CASE_ :						Optional[Any]    				= TFGPTJModel(config=UpperCamelCase__							)

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

						# first forward pass
						SCREAMING_SNAKE_CASE_ :						Any    				= model(UpperCamelCase__			,     attention_mask=UpperCamelCase__			,     token_type_ids=UpperCamelCase__			,     use_cache=UpperCamelCase__							)

						SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_ :						Dict    				= outputs.to_tuple()

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

						# append to next input_ids and token_type_ids
						SCREAMING_SNAKE_CASE_ :						Union[str, Any]    				= tf.concat([input_ids, next_tokens]			,     axis=-1							)
						SCREAMING_SNAKE_CASE_ :						int    				= 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_ :						Union[str, Any]    				= model(
						    UpperCamelCase__			,     token_type_ids=UpperCamelCase__			,     attention_mask=UpperCamelCase__							)['last_hidden_state']
						SCREAMING_SNAKE_CASE_ :						Optional[Any]    				= model(
						    UpperCamelCase__			,     token_type_ids=UpperCamelCase__			,     attention_mask=UpperCamelCase__			,     past_key_values=UpperCamelCase__			,     )['last_hidden_state']
						self.parent.assertTrue(output_from_past.shape[1] == next_tokens.shape[1]							)

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

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


		def 	__snake_case      (    self			,     UpperCamelCase__			,     UpperCamelCase__			,     UpperCamelCase__			,     UpperCamelCase__			,     UpperCamelCase__			,     *UpperCamelCase__							):
						SCREAMING_SNAKE_CASE_ :						int    				= TFGPTJForCausalLM(config=UpperCamelCase__							)
						SCREAMING_SNAKE_CASE_ :						int    				= {
						    'input_ids': input_ids,
						    'attention_mask': input_mask,
						    'token_type_ids': token_type_ids,
						}
						SCREAMING_SNAKE_CASE_ :						Optional[int]    				= model(UpperCamelCase__							)
						self.parent.assertEqual(result.logits.shape			,     (self.batch_size, self.seq_length, self.vocab_size)							)






		def 	__snake_case      (    self							):
						SCREAMING_SNAKE_CASE_ :						Optional[int]    				= self.prepare_config_and_inputs()

						(
						    (
						    SCREAMING_SNAKE_CASE_
						)				,							(
						    SCREAMING_SNAKE_CASE_
						)				,							(
						    SCREAMING_SNAKE_CASE_
						)				,							(
						    SCREAMING_SNAKE_CASE_
						)				,							(
						    SCREAMING_SNAKE_CASE_
						)				,							(
						    SCREAMING_SNAKE_CASE_
						)				,							(
						    SCREAMING_SNAKE_CASE_
						)				,							(
						    SCREAMING_SNAKE_CASE_
						)				,							(
						    SCREAMING_SNAKE_CASE_
						)				,							
						) :						Optional[int]    				= 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 				__a				(	__A			,						__A			,						__A			,						unittest.TestCase    ):





		'''simple docstring'''



		UpperCAmelCase__   :					List[Any]					     =    (
		    (TFGPTJForCausalLM, TFGPTJForSequenceClassification, TFGPTJForQuestionAnswering, TFGPTJModel)
		    if is_tf_available()
		    else ()
		)

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


		def 	__snake_case      (    self			,     UpperCamelCase__			,     UpperCamelCase__			,     UpperCamelCase__			,     UpperCamelCase__			,     UpperCamelCase__							):
						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							):
						SCREAMING_SNAKE_CASE_ :						Optional[int]    				= TFGPTJModelTester(self							)
						SCREAMING_SNAKE_CASE_ :						Union[str, Any]    				= ConfigTester(self			,     config_class=UpperCamelCase__			,     n_embd=37							)


		def 	__snake_case      (    self							):
						self.config_tester.run_common_tests()


		def 	__snake_case      (    self							):
						SCREAMING_SNAKE_CASE_ :						str    				= self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_gptj_model(*UpperCamelCase__							)


		def 	__snake_case      (    self							):
						SCREAMING_SNAKE_CASE_ :						List[Any]    				= self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_gptj_model_past(*UpperCamelCase__							)


		def 	__snake_case      (    self							):
						SCREAMING_SNAKE_CASE_ :						Any    				= self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_gptj_model_attention_mask_past(*UpperCamelCase__							)


		def 	__snake_case      (    self							):
						SCREAMING_SNAKE_CASE_ :						List[str]    				= self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_gptj_model_past_large_inputs(*UpperCamelCase__							)


		def 	__snake_case      (    self							):
						SCREAMING_SNAKE_CASE_ :						Optional[Any]    				= self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_gptj_lm_head_model(*UpperCamelCase__							)


		@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							):
						SCREAMING_SNAKE_CASE_ :						Any    				= TFGPTJModel.from_pretrained('EleutherAI/gpt-j-6B'			,     from_pt=UpperCamelCase__							)
						self.assertIsNotNone(UpperCamelCase__							)






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





		'''simple docstring'''



		def 	__snake_case      (    self							):
						SCREAMING_SNAKE_CASE_ :						Dict    				= TFGPTJForCausalLM.from_pretrained('EleutherAI/gpt-j-6B'			,     from_pt=UpperCamelCase__							)
						SCREAMING_SNAKE_CASE_ :						Tuple    				= 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
						SCREAMING_SNAKE_CASE_ :						Dict    				= [464, 3290, 318, 257, 582, 338, 1266, 1545, 13, 632, 318, 257, 9112, 15185, 11, 290, 340, 318, 257, 1545]
						# fmt: on
						SCREAMING_SNAKE_CASE_ :						Union[str, Any]    				= model.generate(UpperCamelCase__			,     do_sample=UpperCamelCase__							)
						self.assertListEqual(output_ids[0].numpy().tolist()			,     UpperCamelCase__							)


		def 	__snake_case      (    self							):
						SCREAMING_SNAKE_CASE_ :						Any    				= AutoTokenizer.from_pretrained('EleutherAI/gpt-j-6B'			,     revision='float16'							)
						SCREAMING_SNAKE_CASE_ :						List[str]    				= TFGPTJForCausalLM.from_pretrained('EleutherAI/gpt-j-6B'			,     revision='float16'			,     from_pt=UpperCamelCase__							)

						SCREAMING_SNAKE_CASE_ :						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'							):
										SCREAMING_SNAKE_CASE_ :						str    				= model.generate(**UpperCamelCase__			,     do_sample=UpperCamelCase__			,     seed=[42, 0]							)
						SCREAMING_SNAKE_CASE_ :						Dict    				= tokenizer.decode(output_ids[0]			,     skip_special_tokens=UpperCamelCase__							)

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


		def 	__snake_case      (    self							):
						SCREAMING_SNAKE_CASE_ :						Optional[Any]    				= TFGPTJForCausalLM.from_pretrained('EleutherAI/gpt-j-6B'			,     revision='float16'			,     from_pt=UpperCamelCase__							)
						SCREAMING_SNAKE_CASE_ :						Union[str, 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_ :						List[str]    				= tokenizer.eos_token
						SCREAMING_SNAKE_CASE_ :						Union[str, Any]    				= model.config.eos_token_id

						# use different length sentences to test batching
						SCREAMING_SNAKE_CASE_ :						Dict    				= [
						    'Hello, my dog is a little',
						    'Today, I',
						]
						SCREAMING_SNAKE_CASE_ :						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							):
						# Confirms that we get the expected results with left-padded beam search
						SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_ :						Tuple    				= self._get_beam_search_test_objects()

						SCREAMING_SNAKE_CASE_ :						int    				= tokenizer(UpperCamelCase__			,     return_tensors='tf'			,     padding=UpperCamelCase__							)
						SCREAMING_SNAKE_CASE_ :						Any    				= model.generate(**UpperCamelCase__			,     do_sample=UpperCamelCase__			,     num_beams=2							)
						SCREAMING_SNAKE_CASE_ :						str    				= tokenizer.batch_decode(UpperCamelCase__			,     skip_special_tokens=UpperCamelCase__							)
						self.assertListEqual(UpperCamelCase__			,     UpperCamelCase__							)


		def 	__snake_case      (    self							):
						# Confirms that left-padding is working properly
						SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_ :						List[Any]    				= self._get_beam_search_test_objects()

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






		def 	__snake_case      (    self							):
						# Confirms that XLA is working properly
						SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_ :						Optional[int]    				= self._get_beam_search_test_objects()

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







def        _lowerCamelCase(      lowerCAmelCase__					:		float     ,     lowerCAmelCase__					:		float     ,     lowerCAmelCase__					:		float   ):





				'''simple docstring'''







				if mass < 0:
								raise ValueError('The mass of the body cannot be negative'   )
				if radius <= 0:
								raise ValueError('The radius is always a positive non zero integer'   )
				return (mass * (velocity) ** 2) / radius


if __name__ == "__main__":
				import doctest

				doctest.testmod(verbose=True)