code: 
  '''simple docstring'''
  
  
  
  from __future__ import annotations
  
  import unittest
  
  from transformers import AutoTokenizer, GPTJConfig, is_tf_available
  from transformers.testing_utils import require_tf, slow, tooslow
  
  from ...test_configuration_common import ConfigTester
  from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
  from ...test_pipeline_mixin import PipelineTesterMixin
  from ...utils.test_modeling_tf_core import TFCoreModelTesterMixin
  
  
  if is_tf_available():
  				import tensorflow as tf
  
  				from transformers.models.gptj.modeling_tf_gptj import (
  				    TFGPTJForCausalLM,
  				    TFGPTJForQuestionAnswering,
  				    TFGPTJForSequenceClassification,
  				    TFGPTJModel,
  				    shape_list,
  				)
  
  
  
  
  
  class 			A							:
  
  
  
  			def __init__(					self       ,     lowerCamelCase__       )			->		Optional[Any]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       parent
  									lowercase__							 =       13
  									lowercase__							 =       7
  									lowercase__							 =       True
  									lowercase__							 =       True
  									lowercase__							 =       True
  									lowercase__							 =       True
  									lowercase__							 =       True
  									lowercase__							 =       99
  									lowercase__							 =       32
  									lowercase__							 =       4
  									lowercase__							 =       2
  									lowercase__							 =       4
  									lowercase__							 =       37
  									lowercase__							 =       """gelu"""
  									lowercase__							 =       0.1
  									lowercase__							 =       0.1
  									lowercase__							 =       512
  									lowercase__							 =       16
  									lowercase__							 =       2
  									lowercase__							 =       0.02
  									lowercase__							 =       3
  									lowercase__							 =       4
  									lowercase__							 =       None
  									lowercase__							 =       self.vocab_size - 1
  									lowercase__							 =       self.vocab_size - 1
  									lowercase__							 =       self.vocab_size - 1
  
  
  
  			def A__ (					self       )			->		List[Any]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       ids_tensor([self.batch_size, self.seq_length]       ,     self.vocab_size       )
  
  									lowercase__							 =       None
  									if self.use_input_mask:
  															lowercase__							 =       random_attention_mask([self.batch_size, self.seq_length]       )
  
  									lowercase__							 =       None
  									if self.use_token_type_ids:
  															lowercase__							 =       ids_tensor([self.batch_size, self.seq_length]       ,     self.type_vocab_size       )
  
  									lowercase__							 =       None
  									if self.use_mc_token_ids:
  															lowercase__							 =       ids_tensor([self.batch_size, self.num_choices]       ,     self.seq_length       )
  
  									lowercase__							 =       None
  									lowercase__							 =       None
  									lowercase__							 =       None
  									if self.use_labels:
  															lowercase__							 =       ids_tensor([self.batch_size]       ,     self.type_sequence_label_size       )
  															lowercase__							 =       ids_tensor([self.batch_size, self.seq_length]       ,     self.num_labels       )
  															lowercase__							 =       ids_tensor([self.batch_size]       ,     self.num_choices       )
  
  									lowercase__							 =       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__       ,     )
  
  									lowercase__							 =       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 A__ (					self       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     *lowerCamelCase__       )			->		Dict:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       TFGPTJModel(config=lowerCamelCase__       )
  									lowercase__							 =       {
  									    """input_ids""": input_ids,
  									    """attention_mask""": input_mask,
  									    """token_type_ids""": token_type_ids,
  									}
  									lowercase__							 =       model(lowerCamelCase__       )
  
  									lowercase__							 =       [input_ids, None, input_mask]  # None is the input for 'past'
  									lowercase__							 =       model(lowerCamelCase__       )
  
  									lowercase__							 =       model(lowerCamelCase__       )
  
  									self.parent.assertEqual(result.last_hidden_state.shape       ,     (self.batch_size, self.seq_length, self.hidden_size)       )
  
  
  
  			def A__ (					self       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     *lowerCamelCase__       )			->		str:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       TFGPTJModel(config=lowerCamelCase__       )
  
  									# first forward pass
  									lowercase__							 =       model(lowerCamelCase__       ,     token_type_ids=lowerCamelCase__       ,     use_cache=lowerCamelCase__       )
  									lowercase__							 =       model(lowerCamelCase__       ,     token_type_ids=lowerCamelCase__       )
  									lowercase__							 =       model(lowerCamelCase__       ,     token_type_ids=lowerCamelCase__       ,     use_cache=lowerCamelCase__       )
  
  									self.parent.assertTrue(len(lowerCamelCase__       ) == len(lowerCamelCase__       )       )
  									self.parent.assertTrue(len(lowerCamelCase__       ) == len(lowerCamelCase__       ) + 1       )
  
  									lowercase__ ,						lowercase__							 =       outputs.to_tuple()
  
  									# create hypothetical next token and extent to next_input_ids
  									lowercase__							 =       ids_tensor((self.batch_size, 1)       ,     config.vocab_size       )
  									lowercase__							 =       ids_tensor([self.batch_size, 1]       ,     self.type_vocab_size       )
  
  									# append to next input_ids and token_type_ids
  									lowercase__							 =       tf.concat([input_ids, next_tokens]       ,     axis=-1       )
  									lowercase__							 =       tf.concat([token_type_ids, next_token_types]       ,     axis=-1       )
  
  									lowercase__							 =       model(lowerCamelCase__       ,     token_type_ids=lowerCamelCase__       )["""last_hidden_state"""]
  									lowercase__							 =       model(lowerCamelCase__       ,     token_type_ids=lowerCamelCase__       ,     past_key_values=lowerCamelCase__       )[
  									    """last_hidden_state"""
  									]
  
  									# select random slice
  									lowercase__							 =       int(ids_tensor((1,)       ,     shape_list(lowerCamelCase__       )[-1]       )       )
  									lowercase__							 =       output_from_no_past[:, -1, random_slice_idx]
  									lowercase__							 =       output_from_past[:, 0, random_slice_idx]
  
  									# test that outputs are equal for slice
  									tf.debugging.assert_near(lowerCamelCase__       ,     lowerCamelCase__       ,     rtol=1e-6       )
  
  
  
  			def A__ (					self       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     *lowerCamelCase__       )			->		List[str]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       TFGPTJModel(config=lowerCamelCase__       )
  
  									# create attention mask
  									lowercase__							 =       self.seq_length // 2
  									lowercase__							 =       tf.ones((self.batch_size, half_seq_length)       ,     dtype=tf.intaa       )
  									lowercase__							 =       tf.zeros((self.batch_size, self.seq_length - half_seq_length)       ,     dtype=tf.intaa       )
  									lowercase__							 =       tf.concat([attn_mask_begin, attn_mask_end]       ,     axis=1       )
  
  									# first forward pass
  									lowercase__ ,						lowercase__							 =       model(lowerCamelCase__       ,     attention_mask=lowerCamelCase__       ).to_tuple()
  
  									# create hypothetical next token and extent to next_input_ids
  									lowercase__							 =       ids_tensor((self.batch_size, 1)       ,     config.vocab_size       )
  
  									# change a random masked slice from input_ids
  									lowercase__							 =       ids_tensor((1,)       ,     lowerCamelCase__       ).numpy() + 1
  									lowercase__							 =       ids_tensor((self.batch_size, self.seq_length)       ,     config.vocab_size       )
  									lowercase__							 =       tf.range(self.seq_length       ) == (self.seq_length - random_seq_idx_to_change)
  									lowercase__							 =       tf.transpose(
  									    tf.broadcast_to(tf.expand_dims(lowerCamelCase__       ,     -1       )       ,     (self.seq_length, self.batch_size)       )       )
  									lowercase__							 =       tf.where(lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       )
  
  									# append to next input_ids and attn_mask
  									lowercase__							 =       tf.concat([input_ids, next_tokens]       ,     axis=-1       )
  									lowercase__							 =       tf.concat([attn_mask, tf.ones((shape_list(lowerCamelCase__       )[0], 1)       ,     dtype=tf.intaa       )]       ,     axis=1       )
  
  									# get two different outputs
  									lowercase__							 =       model(lowerCamelCase__       ,     attention_mask=lowerCamelCase__       )["""last_hidden_state"""]
  									lowercase__							 =       model(lowerCamelCase__       ,     past_key_values=lowerCamelCase__       ,     attention_mask=lowerCamelCase__       )[
  									    """last_hidden_state"""
  									]
  
  									# select random slice
  									lowercase__							 =       int(ids_tensor((1,)       ,     shape_list(lowerCamelCase__       )[-1]       )       )
  									lowercase__							 =       output_from_no_past[:, -1, random_slice_idx]
  									lowercase__							 =       output_from_past[:, 0, random_slice_idx]
  
  									# test that outputs are equal for slice
  									tf.debugging.assert_near(lowerCamelCase__       ,     lowerCamelCase__       ,     rtol=1e-12       )
  
  
  
  			def A__ (					self       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     *lowerCamelCase__       )			->		Tuple:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       TFGPTJModel(config=lowerCamelCase__       )
  
  									lowercase__							 =       input_ids[:1, :]
  									lowercase__							 =       input_mask[:1, :]
  									lowercase__							 =       token_type_ids[:1, :]
  									lowercase__							 =       1
  
  									# first forward pass
  									lowercase__							 =       model(lowerCamelCase__       ,     attention_mask=lowerCamelCase__       ,     token_type_ids=lowerCamelCase__       ,     use_cache=lowerCamelCase__       )
  
  									lowercase__ ,						lowercase__							 =       outputs.to_tuple()
  
  									# create hypothetical next token and extent to next_input_ids
  									lowercase__							 =       ids_tensor((self.batch_size, 3)       ,     config.vocab_size       )
  									lowercase__							 =       ids_tensor((self.batch_size, 3)       ,     2       )
  									lowercase__							 =       ids_tensor((self.batch_size, 3)       ,     self.type_vocab_size       )
  
  									# append to next input_ids and token_type_ids
  									lowercase__							 =       tf.concat([input_ids, next_tokens]       ,     axis=-1       )
  									lowercase__							 =       tf.concat([input_mask, next_attn_mask]       ,     axis=-1       )
  									lowercase__							 =       tf.concat([token_type_ids, next_token_types]       ,     axis=-1       )
  
  									lowercase__							 =       model(
  									    lowerCamelCase__       ,     token_type_ids=lowerCamelCase__       ,     attention_mask=lowerCamelCase__       )["""last_hidden_state"""]
  									lowercase__							 =       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
  									lowercase__							 =       int(ids_tensor((1,)       ,     shape_list(lowerCamelCase__       )[-1]       )       )
  									lowercase__							 =       output_from_no_past[:, -3:, random_slice_idx]
  									lowercase__							 =       output_from_past[:, :, random_slice_idx]
  
  									# test that outputs are equal for slice
  									tf.debugging.assert_near(lowerCamelCase__       ,     lowerCamelCase__       ,     rtol=1e-3       )
  
  
  
  			def A__ (					self       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     *lowerCamelCase__       )			->		List[str]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       TFGPTJForCausalLM(config=lowerCamelCase__       )
  									lowercase__							 =       {
  									    """input_ids""": input_ids,
  									    """attention_mask""": input_mask,
  									    """token_type_ids""": token_type_ids,
  									}
  									lowercase__							 =       model(lowerCamelCase__       )
  									self.parent.assertEqual(result.logits.shape       ,     (self.batch_size, self.seq_length, self.vocab_size)       )
  
  
  
  			def A__ (					self       )			->		List[Any]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       self.prepare_config_and_inputs()
  
  									(
  									    (
  									    lowercase__
  									) ,						(
  									    lowercase__
  									) ,						(
  									    lowercase__
  									) ,						(
  									    lowercase__
  									) ,						(
  									    lowercase__
  									) ,						(
  									    lowercase__
  									) ,						(
  									    lowercase__
  									) ,						(
  									    lowercase__
  									) ,						(
  									    lowercase__
  									) ,						
  									)							 =       config_and_inputs
  
  									lowercase__							 =       {
  									    """input_ids""": input_ids,
  									    """token_type_ids""": token_type_ids,
  									    """attention_mask""": input_mask,
  									}
  									return config, inputs_dict
  
  
  
  
  
  @require_tf
  class 			A							(       __UpperCAmelCase		,						__UpperCAmelCase		,						__UpperCAmelCase		,						unittest.TestCase				):
  			lowerCamelCase							:							Optional[Any]       		=  (
  			    (TFGPTJForCausalLM, TFGPTJForSequenceClassification, TFGPTJForQuestionAnswering, TFGPTJModel)
  			    if is_tf_available()
  			    else ()
  			)
  
  			lowerCamelCase							:							Optional[int]       		=  (TFGPTJForCausalLM,) if is_tf_available() else ()
  			lowerCamelCase							:							str       		=  (
  			    {
  			        """feature-extraction""": TFGPTJModel,
  			        """question-answering""": TFGPTJForQuestionAnswering,
  			        """text-classification""": TFGPTJForSequenceClassification,
  			        """text-generation""": TFGPTJForCausalLM,
  			        """zero-shot""": TFGPTJForSequenceClassification,
  			    }
  			    if is_tf_available()
  			    else {}
  			)
  			lowerCamelCase							:							Optional[Any]       		=  False
  			lowerCamelCase							:							int       		=  False
  			lowerCamelCase							:							str       		=  False
  			lowerCamelCase							:							Tuple       		=  False
  
  
  
  			def A__ (					self       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       )			->		List[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 A__ (					self       )			->		Optional[int]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       TFGPTJModelTester(self       )
  									lowercase__							 =       ConfigTester(self       ,     config_class=lowerCamelCase__       ,     n_embd=37       )
  
  
  
  			def A__ (					self       )			->		Optional[Any]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									self.config_tester.run_common_tests()
  
  
  
  			def A__ (					self       )			->		Dict:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       self.model_tester.prepare_config_and_inputs()
  									self.model_tester.create_and_check_gptj_model(*lowerCamelCase__       )
  
  
  
  			def A__ (					self       )			->		int:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       self.model_tester.prepare_config_and_inputs()
  									self.model_tester.create_and_check_gptj_model_past(*lowerCamelCase__       )
  
  
  
  			def A__ (					self       )			->		Optional[int]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       self.model_tester.prepare_config_and_inputs()
  									self.model_tester.create_and_check_gptj_model_attention_mask_past(*lowerCamelCase__       )
  
  
  
  			def A__ (					self       )			->		int:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       self.model_tester.prepare_config_and_inputs()
  									self.model_tester.create_and_check_gptj_model_past_large_inputs(*lowerCamelCase__       )
  
  
  
  			def A__ (					self       )			->		Any:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       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 A__ (					self       )			->		List[str]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       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 A__ (					self       )			->		Union[str, Any]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									super().test_resize_token_embeddings()
  
  
  
  
  
  @require_tf
  @tooslow
  # Marked as @tooslow due to GPU OOM -- but still useful to run locally. Requires ~39GB of RAM.
  class 			A							(       unittest.TestCase				):
  
  
  
  			def A__ (					self       )			->		Optional[Any]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       TFGPTJForCausalLM.from_pretrained("""EleutherAI/gpt-j-6B"""       ,     from_pt=lowerCamelCase__       )
  									lowercase__							 =       tf.convert_to_tensor([[464, 3_290]]       ,     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
  									lowercase__							 =       [464, 3_290, 318, 257, 582, 338, 1_266, 1_545, 13, 632, 318, 257, 9_112, 15_185, 11, 290, 340, 318, 257, 1_545]
  									# fmt: on
  									lowercase__							 =       model.generate(lowerCamelCase__       ,     do_sample=lowerCamelCase__       )
  									self.assertListEqual(output_ids[0].numpy().tolist()       ,     lowerCamelCase__       )
  
  
  
  			def A__ (					self       )			->		Union[str, Any]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       AutoTokenizer.from_pretrained("""EleutherAI/gpt-j-6B"""       ,     revision="""float16"""       )
  									lowercase__							 =       TFGPTJForCausalLM.from_pretrained("""EleutherAI/gpt-j-6B"""       ,     revision="""float16"""       ,     from_pt=lowerCamelCase__       )
  
  									lowercase__							 =       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"""       ):
  															lowercase__							 =       model.generate(**lowerCamelCase__       ,     do_sample=lowerCamelCase__       ,     seed=[42, 0]       )
  									lowercase__							 =       tokenizer.decode(output_ids[0]       ,     skip_special_tokens=lowerCamelCase__       )
  
  									lowercase__							 =       """Today is a nice day and I’m going to go for a walk. I’"""
  									self.assertEqual(lowerCamelCase__       ,     lowerCamelCase__       )
  
  
  
  			def A__ (					self       )			->		List[str]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       TFGPTJForCausalLM.from_pretrained("""EleutherAI/gpt-j-6B"""       ,     revision="""float16"""       ,     from_pt=lowerCamelCase__       )
  									lowercase__							 =       AutoTokenizer.from_pretrained("""EleutherAI/gpt-j-6B"""       ,     revision="""float16"""       )
  
  									lowercase__							 =       """left"""
  
  									# Define PAD Token = EOS Token = 50256
  									lowercase__							 =       tokenizer.eos_token
  									lowercase__							 =       model.config.eos_token_id
  
  									# use different length sentences to test batching
  									lowercase__							 =       [
  									    """Hello, my dog is a little""",
  									    """Today, I""",
  									]
  									lowercase__							 =       [
  									    """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 A__ (					self       )			->		Optional[int]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__ ,						lowercase__ ,						lowercase__ ,						lowercase__							 =       self._get_beam_search_test_objects()
  
  									lowercase__							 =       tokenizer(lowerCamelCase__       ,     return_tensors="""tf"""       ,     padding=lowerCamelCase__       )
  									lowercase__							 =       model.generate(**lowerCamelCase__       ,     do_sample=lowerCamelCase__       ,     num_beams=2       )
  									lowercase__							 =       tokenizer.batch_decode(lowerCamelCase__       ,     skip_special_tokens=lowerCamelCase__       )
  									self.assertListEqual(lowerCamelCase__       ,     lowerCamelCase__       )
  
  
  
  			def A__ (					self       )			->		Optional[Any]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__ ,						lowercase__ ,						lowercase__ ,						lowercase__							 =       self._get_beam_search_test_objects()
  
  									lowercase__							 =       tokenizer(lowerCamelCase__       ,     return_tensors="""tf"""       ,     padding=lowerCamelCase__       )
  									lowercase__							 =       tokenizer(sentences[0]       ,     return_tensors="""tf"""       )
  									lowercase__							 =       model.generate(**lowerCamelCase__       ,     do_sample=lowerCamelCase__       ,     num_beams=2       )
  									lowercase__							 =       (
  									    shape_list(inputs_non_padded["""input_ids"""]       )[-1]
  									    - tf.reduce_sum(tf.cast(inputs["""attention_mask"""][-1]       ,     tf.intaa       )       ).numpy()
  									)
  									lowercase__							 =       tokenizer(sentences[1]       ,     return_tensors="""tf"""       )
  									lowercase__							 =       model.generate(
  									    **lowerCamelCase__       ,     do_sample=lowerCamelCase__       ,     num_beams=2       ,     max_length=model.config.max_length - num_paddings       )
  									lowercase__							 =       tokenizer.decode(output_non_padded[0]       ,     skip_special_tokens=lowerCamelCase__       )
  									lowercase__							 =       tokenizer.decode(output_padded[0]       ,     skip_special_tokens=lowerCamelCase__       )
  									self.assertListEqual(lowerCamelCase__       ,     [non_padded_sentence, padded_sentence]       )
  
  
  
  			def A__ (					self       )			->		List[str]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__ ,						lowercase__ ,						lowercase__ ,						lowercase__							 =       self._get_beam_search_test_objects()
  
  									lowercase__							 =       tokenizer(lowerCamelCase__       ,     return_tensors="""tf"""       ,     padding=lowerCamelCase__       )
  									lowercase__							 =       tf.function(model.generate       ,     jit_compile=lowerCamelCase__       )
  									lowercase__							 =       xla_generate(**lowerCamelCase__       ,     do_sample=lowerCamelCase__       ,     num_beams=2       )
  									lowercase__							 =       tokenizer.batch_decode(lowerCamelCase__       ,     skip_special_tokens=lowerCamelCase__       )
  									self.assertListEqual(lowerCamelCase__       ,     lowerCamelCase__       )
  
  
  
  
  
  
  
style_context: 
  '''simple docstring'''
  
  
  
  # DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch
  
  from dataclasses import dataclass
  from typing import Optional, Tuple, Union
  
  import flax
  import jax.numpy as jnp
  from jax import random
  
  from ..configuration_utils import ConfigMixin, register_to_config
  from .scheduling_utils_flax import FlaxSchedulerMixin, FlaxSchedulerOutput, broadcast_to_shape_from_left
  
  
  
  
  
  @flax.struct.dataclass
  class 			A							:
  			# setable values
  			lowerCamelCase							:							Optional[jnp.ndarray]       		=  None
  			lowerCamelCase							:							Optional[jnp.ndarray]       		=  None
  			lowerCamelCase							:							Optional[jnp.ndarray]       		=  None
  
  
  
  			@classmethod
  			def A__ (					cls       )			->		Optional[int]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									return cls()
  
  
  
  
  
  @dataclass
  class 			A							(       __UpperCAmelCase				):
  			lowerCamelCase							:							ScoreSdeVeSchedulerState
  			lowerCamelCase							:							jnp.ndarray
  			lowerCamelCase							:							Optional[jnp.ndarray]       		=  None
  
  
  
  
  
  class 			A							(       __UpperCAmelCase		,						__UpperCAmelCase				):
  
  
  
  			@property
  			def A__ (					self       )			->		Tuple:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									return True
  
  
  
  			@register_to_config
  			def __init__(					self       ,     lowerCamelCase__ = 2_000       ,     lowerCamelCase__ = 0.15       ,     lowerCamelCase__ = 0.01       ,     lowerCamelCase__ = 13_48.0       ,     lowerCamelCase__ = 1e-5       ,     lowerCamelCase__ = 1       ,     )			->		str:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									pass
  
  
  
  			def A__ (					self       )			->		List[Any]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       ScoreSdeVeSchedulerState.create()
  									return self.set_sigmas(
  									    lowerCamelCase__       ,     self.config.num_train_timesteps       ,     self.config.sigma_min       ,     self.config.sigma_max       ,     self.config.sampling_eps       ,     )
  
  
  
  			def A__ (					self       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__ = ()       ,     lowerCamelCase__ = None       )			->		ScoreSdeVeSchedulerState:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       sampling_eps if sampling_eps is not None else self.config.sampling_eps
  
  									lowercase__							 =       jnp.linspace(1       ,     lowerCamelCase__       ,     lowerCamelCase__       )
  									return state.replace(timesteps=lowerCamelCase__       )
  
  
  
  			def A__ (					self       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__ = None       ,     lowerCamelCase__ = None       ,     lowerCamelCase__ = None       ,     )			->		ScoreSdeVeSchedulerState:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       sigma_min if sigma_min is not None else self.config.sigma_min
  									lowercase__							 =       sigma_max if sigma_max is not None else self.config.sigma_max
  									lowercase__							 =       sampling_eps if sampling_eps is not None else self.config.sampling_eps
  									if state.timesteps is None:
  															lowercase__							 =       self.set_timesteps(lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       )
  
  									lowercase__							 =       jnp.exp(jnp.linspace(jnp.log(lowerCamelCase__       )       ,     jnp.log(lowerCamelCase__       )       ,     lowerCamelCase__       )       )
  									lowercase__							 =       jnp.array([sigma_min * (sigma_max / sigma_min) ** t for t in state.timesteps]       )
  
  									return state.replace(discrete_sigmas=lowerCamelCase__       ,     sigmas=lowerCamelCase__       )
  
  
  
  			def A__ (					self       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       )			->		List[str]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									return jnp.where(timesteps == 0       ,     jnp.zeros_like(lowerCamelCase__       )       ,     state.discrete_sigmas[timesteps - 1]       )
  
  
  
  			def A__ (					self       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__ = True       ,     )			->		Union[FlaxSdeVeOutput, Tuple]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									if state.timesteps is None:
  															raise ValueError(
  															    """`state.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"""       )
  
  									lowercase__							 =       timestep * jnp.ones(
  									    sample.shape[0]       ,     )
  									lowercase__							 =       (timestep * (len(state.timesteps       ) - 1)).long()
  
  									lowercase__							 =       state.discrete_sigmas[timesteps]
  									lowercase__							 =       self.get_adjacent_sigma(lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       )
  									lowercase__							 =       jnp.zeros_like(lowerCamelCase__       )
  									lowercase__							 =       (sigma**2 - adjacent_sigma**2) ** 0.5
  
  									# equation 6 in the paper: the model_output modeled by the network is grad_x log pt(x)
  									# also equation 47 shows the analog from SDE models to ancestral sampling methods
  									lowercase__							 =       diffusion.flatten()
  									lowercase__							 =       broadcast_to_shape_from_left(lowerCamelCase__       ,     sample.shape       )
  									lowercase__							 =       drift - diffusion**2 * model_output
  
  									#  equation 6: sample noise for the diffusion term of
  									lowercase__							 =       random.split(lowerCamelCase__       ,     num=1       )
  									lowercase__							 =       random.normal(key=lowerCamelCase__       ,     shape=sample.shape       )
  									lowercase__							 =       sample - drift  # subtract because `dt` is a small negative timestep
  									# TODO is the variable diffusion the correct scaling term for the noise?
  									lowercase__							 =       prev_sample_mean + diffusion * noise  # add impact of diffusion field g
  
  									if not return_dict:
  															return (prev_sample, prev_sample_mean, state)
  
  									return FlaxSdeVeOutput(prev_sample=lowerCamelCase__       ,     prev_sample_mean=lowerCamelCase__       ,     state=lowerCamelCase__       )
  
  
  
  			def A__ (					self       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__ = True       ,     )			->		Union[FlaxSdeVeOutput, Tuple]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									if state.timesteps is None:
  															raise ValueError(
  															    """`state.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"""       )
  
  									# For small batch sizes, the paper "suggest replacing norm(z) with sqrt(d), where d is the dim. of z"
  									# sample noise for correction
  									lowercase__							 =       random.split(lowerCamelCase__       ,     num=1       )
  									lowercase__							 =       random.normal(key=lowerCamelCase__       ,     shape=sample.shape       )
  
  									# compute step size from the model_output, the noise, and the snr
  									lowercase__							 =       jnp.linalg.norm(lowerCamelCase__       )
  									lowercase__							 =       jnp.linalg.norm(lowerCamelCase__       )
  									lowercase__							 =       (self.config.snr * noise_norm / grad_norm) ** 2 * 2
  									lowercase__							 =       step_size * jnp.ones(sample.shape[0]       )
  
  									# compute corrected sample: model_output term and noise term
  									lowercase__							 =       step_size.flatten()
  									lowercase__							 =       broadcast_to_shape_from_left(lowerCamelCase__       ,     sample.shape       )
  									lowercase__							 =       sample + step_size * model_output
  									lowercase__							 =       prev_sample_mean + ((step_size * 2) ** 0.5) * noise
  
  									if not return_dict:
  															return (prev_sample, state)
  
  									return FlaxSdeVeOutput(prev_sample=lowerCamelCase__       ,     state=lowerCamelCase__       )
  
  
  
  			def __len__(					self       )			->		Union[str, Any]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									return self.config.num_train_timesteps
  
  
  
  
  
  
  