'''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 	__SCREAMING_SNAKE_CASE		:







  '''simple docstring'''






  def __init__(					self ,						snake_case_		):




       '''simple docstring'''

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


  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       UpperCAmelCase_				:  Optional[Any]  = ids_tensor([self.batch_size, self.seq_length] ,						self.vocab_size		)

       UpperCAmelCase_				:  Any  = None
       if self.use_input_mask:
            UpperCAmelCase_				:  Optional[Any]  = random_attention_mask([self.batch_size, self.seq_length]		)

       UpperCAmelCase_				:  Optional[int]  = None
       if self.use_token_type_ids:
            UpperCAmelCase_				:  Optional[Any]  = ids_tensor([self.batch_size, self.seq_length] ,						self.type_vocab_size		)

       UpperCAmelCase_				:  Dict  = None
       if self.use_mc_token_ids:
            UpperCAmelCase_				:  List[Any]  = ids_tensor([self.batch_size, self.num_choices] ,						self.seq_length		)

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

       UpperCAmelCase_				:  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=snake_case_ ,						)

       UpperCAmelCase_				:  List[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 _UpperCamelCase	(					self ,						snake_case_ ,						snake_case_ ,						snake_case_ ,						snake_case_ ,						snake_case_ ,						*snake_case_		):




       '''simple docstring'''

       UpperCAmelCase_				:  List[Any]  = TFGPTJModel(config=snake_case_		)
       UpperCAmelCase_				:  Any  = {
           'input_ids': input_ids,
           'attention_mask': input_mask,
           'token_type_ids': token_type_ids,
       }
       UpperCAmelCase_				:  Any  = model(snake_case_		)

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

       UpperCAmelCase_				:  Dict  = model(snake_case_		)

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


  def _UpperCamelCase	(					self ,						snake_case_ ,						snake_case_ ,						snake_case_ ,						snake_case_ ,						snake_case_ ,						*snake_case_		):




       '''simple docstring'''

       UpperCAmelCase_				:  Union[str, Any]  = TFGPTJModel(config=snake_case_		)

       # first forward pass
       UpperCAmelCase_				:  Union[str, Any]  = model(snake_case_ ,						token_type_ids=snake_case_ ,						use_cache=snake_case_		)
       UpperCAmelCase_				:  Union[str, Any]  = model(snake_case_ ,						token_type_ids=snake_case_		)
       UpperCAmelCase_				:  Union[str, Any]  = model(snake_case_ ,						token_type_ids=snake_case_ ,						use_cache=snake_case_		)

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

       UpperCAmelCase_  ,     UpperCAmelCase_				:  Dict  = outputs.to_tuple()

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

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

       UpperCAmelCase_				:  List[str]  = model(snake_case_ ,						token_type_ids=snake_case_		)['last_hidden_state']
       UpperCAmelCase_				:  Union[str, Any]  = model(snake_case_ ,						token_type_ids=snake_case_ ,						past_key_values=snake_case_		)[
           'last_hidden_state'
       ]

       # select random slice
       UpperCAmelCase_				:  int  = int(ids_tensor((1,) ,						shape_list(snake_case_		)[-1]		)		)
       UpperCAmelCase_				:  Dict  = output_from_no_past[:, -1, random_slice_idx]
       UpperCAmelCase_				:  List[Any]  = output_from_past[:, 0, random_slice_idx]

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


  def _UpperCamelCase	(					self ,						snake_case_ ,						snake_case_ ,						snake_case_ ,						snake_case_ ,						snake_case_ ,						*snake_case_		):




       '''simple docstring'''

       UpperCAmelCase_				:  Optional[int]  = TFGPTJModel(config=snake_case_		)

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

       # first forward pass
       UpperCAmelCase_  ,     UpperCAmelCase_				:  List[Any]  = model(snake_case_ ,						attention_mask=snake_case_		).to_tuple()

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

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

       # append to next input_ids and attn_mask
       UpperCAmelCase_				:  str  = tf.concat([input_ids, next_tokens] ,						axis=-1		)
       UpperCAmelCase_				:  List[str]  = tf.concat([attn_mask, tf.ones((shape_list(snake_case_		)[0], 1) ,						dtype=tf.intaa		)] ,						axis=1		)

       # get two different outputs
       UpperCAmelCase_				:  Tuple  = model(snake_case_ ,						attention_mask=snake_case_		)['last_hidden_state']
       UpperCAmelCase_				:  Optional[Any]  = model(snake_case_ ,						past_key_values=snake_case_ ,						attention_mask=snake_case_		)[
           'last_hidden_state'
       ]

       # select random slice
       UpperCAmelCase_				:  Tuple  = int(ids_tensor((1,) ,						shape_list(snake_case_		)[-1]		)		)
       UpperCAmelCase_				:  Dict  = output_from_no_past[:, -1, random_slice_idx]
       UpperCAmelCase_				:  str  = output_from_past[:, 0, random_slice_idx]

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


  def _UpperCamelCase	(					self ,						snake_case_ ,						snake_case_ ,						snake_case_ ,						snake_case_ ,						snake_case_ ,						*snake_case_		):




       '''simple docstring'''

       UpperCAmelCase_				:  List[str]  = TFGPTJModel(config=snake_case_		)

       UpperCAmelCase_				:  Tuple  = input_ids[:1, :]
       UpperCAmelCase_				:  Union[str, Any]  = input_mask[:1, :]
       UpperCAmelCase_				:  List[str]  = token_type_ids[:1, :]
       UpperCAmelCase_				:  List[str]  = 1

       # first forward pass
       UpperCAmelCase_				:  Optional[Any]  = model(snake_case_ ,						attention_mask=snake_case_ ,						token_type_ids=snake_case_ ,						use_cache=snake_case_		)

       UpperCAmelCase_  ,     UpperCAmelCase_				:  Union[str, Any]  = outputs.to_tuple()

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

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

       UpperCAmelCase_				:  List[Any]  = model(
           snake_case_ ,						token_type_ids=snake_case_ ,						attention_mask=snake_case_		)['last_hidden_state']
       UpperCAmelCase_				:  Dict  = model(
           snake_case_ ,						token_type_ids=snake_case_ ,						attention_mask=snake_case_ ,						past_key_values=snake_case_ ,						)['last_hidden_state']
       self.parent.assertTrue(output_from_past.shape[1] == next_tokens.shape[1]		)

       # select random slice
       UpperCAmelCase_				:  int  = int(ids_tensor((1,) ,						shape_list(snake_case_		)[-1]		)		)
       UpperCAmelCase_				:  Optional[Any]  = output_from_no_past[:, -3:, random_slice_idx]
       UpperCAmelCase_				:  List[Any]  = output_from_past[:, :, random_slice_idx]

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


  def _UpperCamelCase	(					self ,						snake_case_ ,						snake_case_ ,						snake_case_ ,						snake_case_ ,						snake_case_ ,						*snake_case_		):




       '''simple docstring'''

       UpperCAmelCase_				:  Union[str, Any]  = TFGPTJForCausalLM(config=snake_case_		)
       UpperCAmelCase_				:  Dict  = {
           'input_ids': input_ids,
           'attention_mask': input_mask,
           'token_type_ids': token_type_ids,
       }
       UpperCAmelCase_				:  List[str]  = model(snake_case_		)
       self.parent.assertEqual(result.logits.shape ,						(self.batch_size, self.seq_length, self.vocab_size)		)






  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       UpperCAmelCase_				:  Dict  = self.prepare_config_and_inputs()

       (
           (
           UpperCAmelCase_							
       )  ,     (
           UpperCAmelCase_							
       )  ,     (
           UpperCAmelCase_							
       )  ,     (
           UpperCAmelCase_							
       )  ,     (
           UpperCAmelCase_							
       )  ,     (
           UpperCAmelCase_							
       )  ,     (
           UpperCAmelCase_							
       )  ,     (
           UpperCAmelCase_							
       )  ,     (
           UpperCAmelCase_							
       )  ,     							
       )				:  Optional[Any]  = config_and_inputs

       UpperCAmelCase_				:  Optional[int]  = {
           'input_ids': input_ids,
           'token_type_ids': token_type_ids,
           'attention_mask': input_mask,
       }
       return config, inputs_dict







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







  '''simple docstring'''






  lowerCamelCase_						:str		       =						(
      (TFGPTJForCausalLM, TFGPTJForSequenceClassification, TFGPTJForQuestionAnswering, TFGPTJModel)
      if is_tf_available()
      else ()
  )

  lowerCamelCase_						:Optional[Any]		       =						(TFGPTJForCausalLM,) if is_tf_available() else ()
  lowerCamelCase_						:Optional[Any]		       =						(
      {
          '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_						:Union[str, Any]		       =						False
  lowerCamelCase_						:Dict		       =						False
  lowerCamelCase_						:Any		       =						False


  def _UpperCamelCase	(					self ,						snake_case_ ,						snake_case_ ,						snake_case_ ,						snake_case_ ,						snake_case_		):




       '''simple docstring'''

       if (
           pipeline_test_casse_name == "QAPipelineTests"
           and tokenizer_name is not None
           and not tokenizer_name.endswith('Fast'		)
       ):
            # `QAPipelineTests` fails for a few models when the slower tokenizer are used.
            # (The slower tokenizers were never used for pipeline tests before the pipeline testing rework)
            # TODO: check (and possibly fix) the `QAPipelineTests` with slower tokenizer
            return True

       return False


  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       UpperCAmelCase_				:  str  = TFGPTJModelTester(self		)
       UpperCAmelCase_				:  Optional[int]  = ConfigTester(self ,						config_class=snake_case_ ,						n_embd=3_7		)


  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       self.config_tester.run_common_tests()


  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       UpperCAmelCase_				:  Optional[Any]  = self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_gptj_model(*snake_case_		)


  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       UpperCAmelCase_				:  Tuple  = self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_gptj_model_past(*snake_case_		)


  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       UpperCAmelCase_				:  Dict  = self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_gptj_model_attention_mask_past(*snake_case_		)


  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       UpperCAmelCase_				:  int  = self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_gptj_model_past_large_inputs(*snake_case_		)


  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       UpperCAmelCase_				:  Dict  = self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_gptj_lm_head_model(*snake_case_		)


  @slow
  @unittest.skipIf(
      not is_tf_available() or len(tf.config.list_physical_devices('GPU'		)		) > 0 ,						'skip testing on GPU for now to avoid GPU OOM.' ,						)
  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       UpperCAmelCase_				:  List[Any]  = TFGPTJModel.from_pretrained('EleutherAI/gpt-j-6B' ,						from_pt=snake_case_		)
       self.assertIsNotNone(snake_case_		)






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




       '''simple docstring'''

       super().test_resize_token_embeddings()







@require_tf
@tooslow
# Marked as @tooslow due to GPU OOM -- but still useful to run locally. Requires ~39GB of RAM.
class 	__SCREAMING_SNAKE_CASE		(			unittest.TestCase			):







  '''simple docstring'''






  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       UpperCAmelCase_				:  List[str]  = TFGPTJForCausalLM.from_pretrained('EleutherAI/gpt-j-6B' ,						from_pt=snake_case_		)
       UpperCAmelCase_				:  int  = tf.convert_to_tensor([[4_6_4, 3_2_9_0]] ,						dtype=tf.intaa		)  # The dog
       # fmt: off
       # The dog is a man's best friend. It is a loyal companion, and it is a friend
       UpperCAmelCase_				:  Union[str, Any]  = [4_6_4, 3_2_9_0, 3_1_8, 2_5_7, 5_8_2, 3_3_8, 1_2_6_6, 1_5_4_5, 1_3, 6_3_2, 3_1_8, 2_5_7, 9_1_1_2, 1_5_1_8_5, 1_1, 2_9_0, 3_4_0, 3_1_8, 2_5_7, 1_5_4_5]
       # fmt: on
       UpperCAmelCase_				:  Dict  = model.generate(snake_case_ ,						do_sample=snake_case_		)
       self.assertListEqual(output_ids[0].numpy().tolist() ,						snake_case_		)


  def _UpperCamelCase	(					self		):




       '''simple docstring'''

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

       UpperCAmelCase_				:  List[str]  = tokenizer('Today is a nice day and' ,						return_tensors='tf'		)
       # forces the generation to happen on CPU, to avoid GPU-related quirks
       with tf.device(':/CPU:0'		):
            UpperCAmelCase_				:  Optional[Any]  = model.generate(**snake_case_ ,						do_sample=snake_case_ ,						seed=[4_2, 0]		)
       UpperCAmelCase_				:  Dict  = tokenizer.decode(output_ids[0] ,						skip_special_tokens=snake_case_		)

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


  def _UpperCamelCase	(					self		):




       '''simple docstring'''

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

       UpperCAmelCase_				:  Optional[int]  = 'left'

       # Define PAD Token = EOS Token = 50256
       UpperCAmelCase_				:  Dict  = tokenizer.eos_token
       UpperCAmelCase_				:  Any  = model.config.eos_token_id

       # use different length sentences to test batching
       UpperCAmelCase_				:  int  = [
           'Hello, my dog is a little',
           'Today, I',
       ]
       UpperCAmelCase_				:  Tuple  = [
           'Hello, my dog is a little over a year old and has been diagnosed with hip dysplasia',
           'Today, I’m going to be talking about a topic that’',
       ]
       return model, tokenizer, sentences, expected_output_sentences


  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       UpperCAmelCase_  ,     UpperCAmelCase_  ,     UpperCAmelCase_  ,     UpperCAmelCase_				:  Optional[Any]  = self._get_beam_search_test_objects()

       UpperCAmelCase_				:  int  = tokenizer(snake_case_ ,						return_tensors='tf' ,						padding=snake_case_		)
       UpperCAmelCase_				:  Dict  = model.generate(**snake_case_ ,						do_sample=snake_case_ ,						num_beams=2		)
       UpperCAmelCase_				:  int  = tokenizer.batch_decode(snake_case_ ,						skip_special_tokens=snake_case_		)
       self.assertListEqual(snake_case_ ,						snake_case_		)


  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       UpperCAmelCase_  ,     UpperCAmelCase_  ,     UpperCAmelCase_  ,     UpperCAmelCase_				:  str  = self._get_beam_search_test_objects()

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






  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       UpperCAmelCase_  ,     UpperCAmelCase_  ,     UpperCAmelCase_  ,     UpperCAmelCase_				:  List[str]  = self._get_beam_search_test_objects()

       UpperCAmelCase_				:  str  = tokenizer(snake_case_ ,						return_tensors='tf' ,						padding=snake_case_		)
       UpperCAmelCase_				:  Any  = tf.function(model.generate ,						jit_compile=snake_case_		)
       UpperCAmelCase_				:  Tuple  = xla_generate(**snake_case_ ,						do_sample=snake_case_ ,						num_beams=2		)
       UpperCAmelCase_				:  str  = tokenizer.batch_decode(snake_case_ ,						skip_special_tokens=snake_case_		)
       self.assertListEqual(snake_case_ ,						snake_case_		)









from dataclasses import dataclass
from typing import Optional, Tuple, Union

import flax
import jax.numpy as jnp
from scipy import integrate

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import (
    CommonSchedulerState,
    FlaxKarrasDiffusionSchedulers,
    FlaxSchedulerMixin,
    FlaxSchedulerOutput,
    broadcast_to_shape_from_left,
)







@flax.struct.dataclass
class 					__lowerCamelCase							:
      lowerCamelCase_      : CommonSchedulerState

      # setable values
      lowerCamelCase_      : jnp.ndarray
      lowerCamelCase_      : jnp.ndarray
      lowerCamelCase_      : jnp.ndarray
      lowerCamelCase_      : Optional[int]  			=   None

      # running values
      lowerCamelCase_      : Optional[jnp.ndarray]  			=   None







      @classmethod
      def       lowerCAmelCase_       (	cls				,    lowerCamelCase				,    lowerCamelCase				,    lowerCamelCase				,    lowerCamelCase  )		->			Union[str, Any]:
        return cls(common=lowerCamelCase				,    init_noise_sigma=lowerCamelCase				,    timesteps=lowerCamelCase				,    sigmas=lowerCamelCase  )







@dataclass
class 					__lowerCamelCase							(      __snake_case      ):
      lowerCamelCase_      : LMSDiscreteSchedulerState







class 					__lowerCamelCase							(      __snake_case    ,				__snake_case      ):
      lowerCamelCase_      : str  			=   [e.name for e in FlaxKarrasDiffusionSchedulers]

      lowerCamelCase_      : jnp.dtype







      @property
      def       lowerCAmelCase_       (	self  )		->			List[str]:
        return True







      @register_to_config
      def __init__(	self				,    lowerCamelCase = 1000				,    lowerCamelCase = 0.0001				,    lowerCamelCase = 0.02				,    lowerCamelCase = "linear"				,    lowerCamelCase = None				,    lowerCamelCase = "epsilon"				,    lowerCamelCase = jnp.floataa				,    )		->			Optional[Any]:
        snake_case_					= dtype







      def       lowerCAmelCase_       (	self				,    lowerCamelCase = None  )		->			LMSDiscreteSchedulerState:
        if common is None:
          snake_case_					= CommonSchedulerState.create(self  )

        snake_case_					= jnp.arange(0				,    self.config.num_train_timesteps  ).round()[::-1]
        snake_case_					= ((1 - common.alphas_cumprod) / common.alphas_cumprod) ** 0.5

        # standard deviation of the initial noise distribution
        snake_case_					= sigmas.max()

        return LMSDiscreteSchedulerState.create(
            common=lowerCamelCase				,    init_noise_sigma=lowerCamelCase				,    timesteps=lowerCamelCase				,    sigmas=lowerCamelCase				,    )







      def       lowerCAmelCase_       (	self				,    lowerCamelCase				,    lowerCamelCase				,    lowerCamelCase  )		->			jnp.ndarray:
        ((snake_case_)		,							)					= jnp.where(state.timesteps == timestep				,    size=1  )
        snake_case_					= step_index[0]

        snake_case_					= state.sigmas[step_index]
        snake_case_					= sample / ((sigma**2 + 1) ** 0.5)
        return sample







      def       lowerCAmelCase_       (	self				,    lowerCamelCase				,    lowerCamelCase				,    lowerCamelCase				,    lowerCamelCase  )		->			Any:

        def lms_derivative(lowerCamelCase  ):
          snake_case_					= 1.0
          for k in range(lowerCamelCase  ):
            if current_order == k:
              continue
            prod *= (tau - state.sigmas[t - k]) / (state.sigmas[t - current_order] - state.sigmas[t - k])
          return prod

        snake_case_					= integrate.quad(lowerCamelCase				,    state.sigmas[t]				,    state.sigmas[t + 1]				,    epsrel=1e-4  )[0]

        return integrated_coeff







      def       lowerCAmelCase_       (	self				,    lowerCamelCase				,    lowerCamelCase				,    lowerCamelCase = ()  )		->			LMSDiscreteSchedulerState:
        snake_case_					= jnp.linspace(self.config.num_train_timesteps - 1				,    0				,    lowerCamelCase				,    dtype=self.dtype  )

        snake_case_					= jnp.floor(lowerCamelCase  ).astype(jnp.intaa  )
        snake_case_					= jnp.ceil(lowerCamelCase  ).astype(jnp.intaa  )

        snake_case_					= jnp.mod(lowerCamelCase				,    1.0  )

        snake_case_					= ((1 - state.common.alphas_cumprod) / state.common.alphas_cumprod) ** 0.5
        snake_case_					= (1 - frac) * sigmas[low_idx] + frac * sigmas[high_idx]
        snake_case_					= jnp.concatenate([sigmas, jnp.array([0.0]				,    dtype=self.dtype  )]  )

        snake_case_					= timesteps.astype(jnp.intaa  )

        # initial running values
        snake_case_					= jnp.zeros((0,) + shape				,    dtype=self.dtype  )

        return state.replace(
            timesteps=lowerCamelCase				,    sigmas=lowerCamelCase				,    num_inference_steps=lowerCamelCase				,    derivatives=lowerCamelCase				,    )







      def       lowerCAmelCase_       (	self				,    lowerCamelCase				,    lowerCamelCase				,    lowerCamelCase				,    lowerCamelCase				,    lowerCamelCase = 4				,    lowerCamelCase = True				,    )		->			Union[FlaxLMSSchedulerOutput, Tuple]:
        if state.num_inference_steps is None:
          raise ValueError(
              """Number of inference steps is 'None', you need to run 'set_timesteps' after creating the scheduler"""  )

        snake_case_					= state.sigmas[timestep]

        # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
        if self.config.prediction_type == "epsilon":
          snake_case_					= sample - sigma * model_output
        elif self.config.prediction_type == "v_prediction":
          # * c_out + input * c_skip
          snake_case_					= model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
        else:
          raise ValueError(
              f'''prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`'''  )

        # 2. Convert to an ODE derivative
        snake_case_					= (sample - pred_original_sample) / sigma
        snake_case_					= state.replace(derivatives=jnp.append(state.derivatives				,    lowerCamelCase  )  )
        if len(state.derivatives  ) > order:
          snake_case_					= state.replace(derivatives=jnp.delete(state.derivatives				,    0  )  )

        # 3. Compute linear multistep coefficients
        snake_case_					= min(timestep + 1				,    lowerCamelCase  )
        snake_case_					= [self.get_lms_coefficient(lowerCamelCase				,    lowerCamelCase				,    lowerCamelCase				,    lowerCamelCase  ) for curr_order in range(lowerCamelCase  )]

        # 4. Compute previous sample based on the derivatives path
        snake_case_					= sample + sum(
            coeff * derivative for coeff, derivative in zip(lowerCamelCase				,    reversed(state.derivatives  )  )  )

        if not return_dict:
          return (prev_sample, state)

        return FlaxLMSSchedulerOutput(prev_sample=lowerCamelCase				,    state=lowerCamelCase  )







      def       lowerCAmelCase_       (	self				,    lowerCamelCase				,    lowerCamelCase				,    lowerCamelCase				,    lowerCamelCase				,    )		->			jnp.ndarray:
        snake_case_					= state.sigmas[timesteps].flatten()
        snake_case_					= broadcast_to_shape_from_left(lowerCamelCase				,    noise.shape  )

        snake_case_					= original_samples + noise * sigma

        return noisy_samples







      def __len__(	self  )		->			Optional[Any]:
        return self.config.num_train_timesteps