




import math
from typing import Optional

import torch
import torch.utils.checkpoint as checkpoint
from torch import nn

from ....modeling_utils import PreTrainedModel
from ....utils import add_start_docstrings, logging
from ...bert.modeling_bert import BertModel
from .configuration_retribert import RetriBertConfig


__UpperCamelCase      : Optional[Any]					      =   logging.get_logger(__name__)

__UpperCamelCase      : Any					      =   [
    "yjernite/retribert-base-uncased",
    # See all RetriBert models at https://huggingface.co/models?filter=retribert
]

class 	__lowerCAmelCase			(			__magic_name__				):
							UpperCamelCase__        =						RetriBertConfig
							UpperCamelCase__        =						None
							UpperCamelCase__        =						'''retribert'''
							def    lowerCamelCase__							(		self		:str	,   __magic_name__		:Any       ):







								'''simple docstring'''







								if isinstance(__magic_name__	,   nn.Linear       ):
									module.weight.data.normal_(mean=0.0	,   std=self.config.initializer_range       )
									if module.bias is not None:
										module.bias.data.zero_()
								elif isinstance(__magic_name__	,   nn.Embedding       ):
									module.weight.data.normal_(mean=0.0	,   std=self.config.initializer_range       )
									if module.padding_idx is not None:
										module.weight.data[module.padding_idx].zero_()
								elif isinstance(__magic_name__	,   nn.LayerNorm       ):
									module.bias.data.zero_()
									module.weight.data.fill_(1.0       )







__UpperCamelCase      : List[Any]					      =   R"\n\n    This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the\n    library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads\n    etc.)\n\n    This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.\n    Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage\n    and behavior.\n\n    Parameters:\n        config ([`RetriBertConfig`]): Model configuration class with all the parameters of the model.\n            Initializing with a config file does not load the weights associated with the model, only the\n            configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.\n"

@add_start_docstrings(
    '''Bert Based model to embed queries or document for document retrieval.'''   , __magic_name__   , )
class 	__lowerCAmelCase			(			__magic_name__				):
							def __init__(		self		:Optional[Any]	,   __magic_name__		:RetriBertConfig       ):







								'''simple docstring'''







								super().__init__(__magic_name__       )
								a		       =							config.projection_dim

								a		       =							BertModel(__magic_name__       )
								a		       =							None if config.share_encoders else BertModel(__magic_name__       )
								a		       =							nn.Dropout(config.hidden_dropout_prob       )
								a		       =							nn.Linear(config.hidden_size	,   config.projection_dim	,   bias=__magic_name__       )
								a		       =							nn.Linear(config.hidden_size	,   config.projection_dim	,   bias=__magic_name__       )

								a		       =							nn.CrossEntropyLoss(reduction="""mean"""       )

								# Initialize weights and apply final processing
								self.post_init()
							def    lowerCamelCase__							(		self		:List[Any]	,   __magic_name__		:List[Any]	,   __magic_name__		:List[str]	,   __magic_name__		:Optional[Any]	,   __magic_name__		:str=-1	,   ):







								'''simple docstring'''







								if checkpoint_batch_size < 0 or input_ids.shape[0] < checkpoint_batch_size:
									return sent_encoder(__magic_name__	,   attention_mask=__magic_name__       )[1]
								else:
									# prepare implicit variables
									a		       =							input_ids.device
									a		       =							input_ids.size()
									a		       =							torch.zeros(__magic_name__	,   dtype=torch.long	,   device=__magic_name__       )
									a		       =							[None] * sent_encoder.config.num_hidden_layers
									a		       =							sent_encoder.get_extended_attention_mask(
									    __magic_name__	,   __magic_name__       )

									# define function for checkpointing
									def partial_encode(*__magic_name__		:Any       ):
										a		       =							sent_encoder.encoder(
										    inputs[0]	,   attention_mask=inputs[1]	,   head_mask=__magic_name__	,   )
										a		       =							encoder_outputs[0]
										a		       =							sent_encoder.pooler(__magic_name__       )
										return pooled_output

									# run embedding layer on everything at once
									a		       =							sent_encoder.embeddings(
									    input_ids=__magic_name__	,   position_ids=__magic_name__	,   token_type_ids=__magic_name__	,   inputs_embeds=__magic_name__       )
									# run encoding and pooling on one mini-batch at a time
									a		       =							[]
									for b in range(math.ceil(input_ids.shape[0] / checkpoint_batch_size       )       ):
										a		       =							embedding_output[b * checkpoint_batch_size : (b + 1) * checkpoint_batch_size]
										a		       =							extended_attention_mask[b * checkpoint_batch_size : (b + 1) * checkpoint_batch_size]
										a		       =							checkpoint.checkpoint(__magic_name__	,   __magic_name__	,   __magic_name__       )
										pooled_output_list.append(__magic_name__       )
									return torch.cat(__magic_name__	,   dim=0       )
							def    lowerCamelCase__							(		self		:Dict	,   __magic_name__		:Dict	,   __magic_name__		:Union[str, Any]=None	,   __magic_name__		:List[str]=-1	,   ):







								'''simple docstring'''







								a		       =							self.embed_sentences_checkpointed(
								    __magic_name__	,   __magic_name__	,   self.bert_query	,   __magic_name__	,   )
								return self.project_query(__magic_name__       )
							def    lowerCamelCase__							(		self		:Tuple	,   __magic_name__		:str	,   __magic_name__		:List[Any]=None	,   __magic_name__		:List[str]=-1	,   ):







								'''simple docstring'''







								a		       =							self.embed_sentences_checkpointed(
								    __magic_name__	,   __magic_name__	,   self.bert_query if self.bert_doc is None else self.bert_doc	,   __magic_name__	,   )
								return self.project_doc(__magic_name__       )





							def    lowerCamelCase__							(		self		:Any	,   __magic_name__		:torch.LongTensor	,   __magic_name__		:Optional[torch.FloatTensor]	,   __magic_name__		:torch.LongTensor	,   __magic_name__		:Optional[torch.FloatTensor]	,   __magic_name__		:int = -1	,   ):







								'''simple docstring'''







								a		       =							input_ids_query.device
								a		       =							self.embed_questions(__magic_name__	,   __magic_name__	,   __magic_name__       )
								a		       =							self.embed_answers(__magic_name__	,   __magic_name__	,   __magic_name__       )
								a		       =							torch.mm(__magic_name__	,   a_reps.t()       )
								a		       =							self.ce_loss(__magic_name__	,   torch.arange(compare_scores.shape[1]       ).to(__magic_name__       )       )
								a		       =							self.ce_loss(compare_scores.t()	,   torch.arange(compare_scores.shape[0]       ).to(__magic_name__       )       )
								a		       =							(loss_qa + loss_aq) / 2
								return loss







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__       )

