




from __future__ import annotations

import unittest

from transformers import AutoTokenizer, GPTJConfig, is_tf_available
from transformers.testing_utils import require_tf, slow, tooslow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
from ...utils.test_modeling_tf_core import TFCoreModelTesterMixin


if is_tf_available():
				import tensorflow as tf

				from transformers.models.gptj.modeling_tf_gptj import (
				    TFGPTJForCausalLM,
				    TFGPTJForQuestionAnswering,
				    TFGPTJForSequenceClassification,
				    TFGPTJModel,
				    shape_list,
				)
class 				__a :


					"""simple docstring"""


					def __init__(     self      :			int    ,_UpperCamelCase      :			Optional[int]						)		->       Optional[int]:


									'''simple docstring'''





									SCREAMING_SNAKE_CASE__       =parent
									SCREAMING_SNAKE_CASE__       =1_3
									SCREAMING_SNAKE_CASE__       =7
									SCREAMING_SNAKE_CASE__       =True
									SCREAMING_SNAKE_CASE__       =True
									SCREAMING_SNAKE_CASE__       =True
									SCREAMING_SNAKE_CASE__       =True
									SCREAMING_SNAKE_CASE__       =True
									SCREAMING_SNAKE_CASE__       =9_9
									SCREAMING_SNAKE_CASE__       =3_2
									SCREAMING_SNAKE_CASE__       =4
									SCREAMING_SNAKE_CASE__       =2
									SCREAMING_SNAKE_CASE__       =4
									SCREAMING_SNAKE_CASE__       =3_7
									SCREAMING_SNAKE_CASE__       ="""gelu"""
									SCREAMING_SNAKE_CASE__       =0.1
									SCREAMING_SNAKE_CASE__       =0.1
									SCREAMING_SNAKE_CASE__       =5_1_2
									SCREAMING_SNAKE_CASE__       =1_6
									SCREAMING_SNAKE_CASE__       =2
									SCREAMING_SNAKE_CASE__       =0.02
									SCREAMING_SNAKE_CASE__       =3
									SCREAMING_SNAKE_CASE__       =4
									SCREAMING_SNAKE_CASE__       =None
									SCREAMING_SNAKE_CASE__       =self.vocab_size - 1
									SCREAMING_SNAKE_CASE__       =self.vocab_size - 1
									SCREAMING_SNAKE_CASE__       =self.vocab_size - 1




					def   __A    (     self      :			List[str]						)		->       List[str]:


									'''simple docstring'''





									SCREAMING_SNAKE_CASE__       =ids_tensor([self.batch_size, self.seq_length]    ,self.vocab_size						)

									SCREAMING_SNAKE_CASE__       =None
									if self.use_input_mask:
													SCREAMING_SNAKE_CASE__       =random_attention_mask([self.batch_size, self.seq_length]						)

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

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

									SCREAMING_SNAKE_CASE__       =None
									SCREAMING_SNAKE_CASE__       =None
									SCREAMING_SNAKE_CASE__       =None
									if self.use_labels:
													SCREAMING_SNAKE_CASE__       =ids_tensor([self.batch_size]    ,self.type_sequence_label_size						)
													SCREAMING_SNAKE_CASE__       =ids_tensor([self.batch_size, self.seq_length]    ,self.num_labels						)
													SCREAMING_SNAKE_CASE__       =ids_tensor([self.batch_size]    ,self.num_choices						)

									SCREAMING_SNAKE_CASE__       =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_    ,)

									SCREAMING_SNAKE_CASE__       =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      :			Optional[int]    ,_UpperCamelCase      :			str    ,_UpperCamelCase      :			Any    ,_UpperCamelCase      :			Optional[Any]    ,_UpperCamelCase      :			Optional[Any]    ,_UpperCamelCase      :			int    ,*_UpperCamelCase      :			Union[str, Any]						)		->       str:


									'''simple docstring'''





									SCREAMING_SNAKE_CASE__       =TFGPTJModel(config=snake_case_						)
									SCREAMING_SNAKE_CASE__       ={
									    """input_ids""": input_ids,
									    """attention_mask""": input_mask,
									    """token_type_ids""": token_type_ids,
									}
									SCREAMING_SNAKE_CASE__       =model(snake_case_						)

									SCREAMING_SNAKE_CASE__       =[input_ids, None, input_mask]  # None is the input for 'past'
									SCREAMING_SNAKE_CASE__       =model(snake_case_						)

									SCREAMING_SNAKE_CASE__       =model(snake_case_						)

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




					def   __A    (     self      :			Any    ,_UpperCamelCase      :			Union[str, Any]    ,_UpperCamelCase      :			Dict    ,_UpperCamelCase      :			int    ,_UpperCamelCase      :			Optional[Any]    ,_UpperCamelCase      :			List[str]    ,*_UpperCamelCase      :			int						)		->       List[str]:


									'''simple docstring'''





									SCREAMING_SNAKE_CASE__       =TFGPTJModel(config=snake_case_						)

									# first forward pass
									SCREAMING_SNAKE_CASE__       =model(snake_case_    ,token_type_ids=snake_case_    ,use_cache=snake_case_						)
									SCREAMING_SNAKE_CASE__       =model(snake_case_    ,token_type_ids=snake_case_						)
									SCREAMING_SNAKE_CASE__       =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						)

									SCREAMING_SNAKE_CASE__       =outputs.to_tuple()

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

									# append to next input_ids and token_type_ids
									SCREAMING_SNAKE_CASE__       =tf.concat([input_ids, next_tokens]    ,axis=-1						)
									SCREAMING_SNAKE_CASE__       =tf.concat([token_type_ids, next_token_types]    ,axis=-1						)

									SCREAMING_SNAKE_CASE__       =model(snake_case_    ,token_type_ids=snake_case_						)["""last_hidden_state"""]
									SCREAMING_SNAKE_CASE__       =model(snake_case_    ,token_type_ids=snake_case_    ,past_key_values=snake_case_						)[
									    """last_hidden_state"""
									]

									# select random slice
									SCREAMING_SNAKE_CASE__       =int(ids_tensor((1,)    ,shape_list(snake_case_						)[-1]						)						)
									SCREAMING_SNAKE_CASE__       =output_from_no_past[:, -1, random_slice_idx]
									SCREAMING_SNAKE_CASE__       =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   __A    (     self      :			Optional[Any]    ,_UpperCamelCase      :			Optional[int]    ,_UpperCamelCase      :			Any    ,_UpperCamelCase      :			Union[str, Any]    ,_UpperCamelCase      :			Dict    ,_UpperCamelCase      :			Tuple    ,*_UpperCamelCase      :			Optional[int]						)		->       Tuple:


									'''simple docstring'''





									SCREAMING_SNAKE_CASE__       =TFGPTJModel(config=snake_case_						)

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

									# first forward pass
									SCREAMING_SNAKE_CASE__       =model(snake_case_    ,attention_mask=snake_case_						).to_tuple()

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

									# change a random masked slice from input_ids
									SCREAMING_SNAKE_CASE__       =ids_tensor((1,)    ,snake_case_						).numpy() + 1
									SCREAMING_SNAKE_CASE__       =ids_tensor((self.batch_size, self.seq_length)    ,config.vocab_size						)
									SCREAMING_SNAKE_CASE__       =tf.range(self.seq_length						) == (self.seq_length - random_seq_idx_to_change)
									SCREAMING_SNAKE_CASE__       =tf.transpose(
									    tf.broadcast_to(tf.expand_dims(snake_case_    ,-1						)    ,(self.seq_length, self.batch_size)						)						)
									SCREAMING_SNAKE_CASE__       =tf.where(snake_case_    ,snake_case_    ,snake_case_						)

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

									# get two different outputs
									SCREAMING_SNAKE_CASE__       =model(snake_case_    ,attention_mask=snake_case_						)["""last_hidden_state"""]
									SCREAMING_SNAKE_CASE__       =model(snake_case_    ,past_key_values=snake_case_    ,attention_mask=snake_case_						)[
									    """last_hidden_state"""
									]

									# select random slice
									SCREAMING_SNAKE_CASE__       =int(ids_tensor((1,)    ,shape_list(snake_case_						)[-1]						)						)
									SCREAMING_SNAKE_CASE__       =output_from_no_past[:, -1, random_slice_idx]
									SCREAMING_SNAKE_CASE__       =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   __A    (     self      :			Optional[Any]    ,_UpperCamelCase      :			Optional[Any]    ,_UpperCamelCase      :			Dict    ,_UpperCamelCase      :			Any    ,_UpperCamelCase      :			List[str]    ,_UpperCamelCase      :			Tuple    ,*_UpperCamelCase      :			List[Any]						)		->       Tuple:


									'''simple docstring'''





									SCREAMING_SNAKE_CASE__       =TFGPTJModel(config=snake_case_						)

									SCREAMING_SNAKE_CASE__       =input_ids[:1, :]
									SCREAMING_SNAKE_CASE__       =input_mask[:1, :]
									SCREAMING_SNAKE_CASE__       =token_type_ids[:1, :]
									SCREAMING_SNAKE_CASE__       =1

									# first forward pass
									SCREAMING_SNAKE_CASE__       =model(snake_case_    ,attention_mask=snake_case_    ,token_type_ids=snake_case_    ,use_cache=snake_case_						)

									SCREAMING_SNAKE_CASE__       =outputs.to_tuple()

									# create hypothetical next token and extent to next_input_ids
									SCREAMING_SNAKE_CASE__       =ids_tensor((self.batch_size, 3)    ,config.vocab_size						)
									SCREAMING_SNAKE_CASE__       =ids_tensor((self.batch_size, 3)    ,2						)
									SCREAMING_SNAKE_CASE__       =ids_tensor((self.batch_size, 3)    ,self.type_vocab_size						)

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

									SCREAMING_SNAKE_CASE__       =model(
									    snake_case_    ,token_type_ids=snake_case_    ,attention_mask=snake_case_						)["""last_hidden_state"""]
									SCREAMING_SNAKE_CASE__       =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
									SCREAMING_SNAKE_CASE__       =int(ids_tensor((1,)    ,shape_list(snake_case_						)[-1]						)						)
									SCREAMING_SNAKE_CASE__       =output_from_no_past[:, -3:, random_slice_idx]
									SCREAMING_SNAKE_CASE__       =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   __A    (     self      :			Optional[int]    ,_UpperCamelCase      :			int    ,_UpperCamelCase      :			Union[str, Any]    ,_UpperCamelCase      :			Any    ,_UpperCamelCase      :			Union[str, Any]    ,_UpperCamelCase      :			Dict    ,*_UpperCamelCase      :			Dict						)		->       int:


									'''simple docstring'''





									SCREAMING_SNAKE_CASE__       =TFGPTJForCausalLM(config=snake_case_						)
									SCREAMING_SNAKE_CASE__       ={
									    """input_ids""": input_ids,
									    """attention_mask""": input_mask,
									    """token_type_ids""": token_type_ids,
									}
									SCREAMING_SNAKE_CASE__       =model(snake_case_						)
									self.parent.assertEqual(result.logits.shape    ,(self.batch_size, self.seq_length, self.vocab_size)						)




					def   __A    (     self      :			Any						)		->       Optional[int]:


									'''simple docstring'''





									SCREAMING_SNAKE_CASE__       =self.prepare_config_and_inputs()

									(
									    SCREAMING_SNAKE_CASE__   
									)       =config_and_inputs

									SCREAMING_SNAKE_CASE__       ={
									    """input_ids""": input_ids,
									    """token_type_ids""": token_type_ids,
									    """attention_mask""": input_mask,
									}
									return config, inputs_dict






@require_tf
class 				__a (			_a							,					_a							,					_a							,					unittest.TestCase					):


					"""simple docstring"""


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

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




					def   __A    (     self      :			int    ,_UpperCamelCase      :			int    ,_UpperCamelCase      :			Optional[int]    ,_UpperCamelCase      :			List[str]    ,_UpperCamelCase      :			Union[str, Any]    ,_UpperCamelCase      :			Optional[int]						)		->       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      :			List[Any]						)		->       str:


									'''simple docstring'''





									SCREAMING_SNAKE_CASE__       =TFGPTJModelTester(self						)
									SCREAMING_SNAKE_CASE__       =ConfigTester(self    ,config_class=snake_case_    ,n_embd=3_7						)




					def   __A    (     self      :			List[Any]						)		->       Tuple:


									'''simple docstring'''





									self.config_tester.run_common_tests()




					def   __A    (     self      :			Optional[Any]						)		->       List[str]:


									'''simple docstring'''





									SCREAMING_SNAKE_CASE__       =self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_gptj_model(*snake_case_						)




					def   __A    (     self      :			Dict						)		->       Union[str, Any]:


									'''simple docstring'''





									SCREAMING_SNAKE_CASE__       =self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_gptj_model_past(*snake_case_						)




					def   __A    (     self      :			Any						)		->       List[Any]:


									'''simple docstring'''





									SCREAMING_SNAKE_CASE__       =self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_gptj_model_attention_mask_past(*snake_case_						)




					def   __A    (     self      :			Union[str, Any]						)		->       Optional[Any]:


									'''simple docstring'''





									SCREAMING_SNAKE_CASE__       =self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_gptj_model_past_large_inputs(*snake_case_						)




					def   __A    (     self      :			Dict						)		->       Optional[int]:


									'''simple docstring'''





									SCREAMING_SNAKE_CASE__       =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   __A    (     self      :			List[str]						)		->       int:


									'''simple docstring'''





									SCREAMING_SNAKE_CASE__       =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   __A    (     self      :			Tuple						)		->       Tuple:


									'''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					):


					"""simple docstring"""


					def   __A    (     self      :			List[Any]						)		->       Any:


									'''simple docstring'''





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




					def   __A    (     self      :			Optional[int]						)		->       Union[str, Any]:


									'''simple docstring'''





									SCREAMING_SNAKE_CASE__       =AutoTokenizer.from_pretrained("""EleutherAI/gpt-j-6B"""    ,revision="""float16"""						)
									SCREAMING_SNAKE_CASE__       =TFGPTJForCausalLM.from_pretrained("""EleutherAI/gpt-j-6B"""    ,revision="""float16"""    ,from_pt=snake_case_						)

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

									SCREAMING_SNAKE_CASE__       ="""Today is a nice day and I’m going to go for a walk. I’"""
									self.assertEqual(snake_case_    ,snake_case_						)




					def   __A    (     self      :			Any						)		->       Any:


									'''simple docstring'''





									SCREAMING_SNAKE_CASE__       =TFGPTJForCausalLM.from_pretrained("""EleutherAI/gpt-j-6B"""    ,revision="""float16"""    ,from_pt=snake_case_						)
									SCREAMING_SNAKE_CASE__       =AutoTokenizer.from_pretrained("""EleutherAI/gpt-j-6B"""    ,revision="""float16"""						)

									SCREAMING_SNAKE_CASE__       ="""left"""

									# Define PAD Token = EOS Token = 50256
									SCREAMING_SNAKE_CASE__       =tokenizer.eos_token
									SCREAMING_SNAKE_CASE__       =model.config.eos_token_id

									# use different length sentences to test batching
									SCREAMING_SNAKE_CASE__       =[
									    """Hello, my dog is a little""",
									    """Today, I""",
									]
									SCREAMING_SNAKE_CASE__       =[
									    """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      :			str						)		->       Tuple:


									'''simple docstring'''





									SCREAMING_SNAKE_CASE__       =self._get_beam_search_test_objects()

									SCREAMING_SNAKE_CASE__       =tokenizer(snake_case_    ,return_tensors="""tf"""    ,padding=snake_case_						)
									SCREAMING_SNAKE_CASE__       =model.generate(**snake_case_    ,do_sample=snake_case_    ,num_beams=2						)
									SCREAMING_SNAKE_CASE__       =tokenizer.batch_decode(snake_case_    ,skip_special_tokens=snake_case_						)
									self.assertListEqual(snake_case_    ,snake_case_						)




					def   __A    (     self      :			Tuple						)		->       Optional[int]:


									'''simple docstring'''





									SCREAMING_SNAKE_CASE__       =self._get_beam_search_test_objects()

									SCREAMING_SNAKE_CASE__       =tokenizer(snake_case_    ,return_tensors="""tf"""    ,padding=snake_case_						)
									SCREAMING_SNAKE_CASE__       =tokenizer(sentences[0]    ,return_tensors="""tf"""						)
									SCREAMING_SNAKE_CASE__       =model.generate(**snake_case_    ,do_sample=snake_case_    ,num_beams=2						)
									SCREAMING_SNAKE_CASE__       =(
									    shape_list(inputs_non_padded["""input_ids"""]						)[-1]
									    - tf.reduce_sum(tf.cast(inputs["""attention_mask"""][-1]    ,tf.intaa						)						).numpy()
									)
									SCREAMING_SNAKE_CASE__       =tokenizer(sentences[1]    ,return_tensors="""tf"""						)
									SCREAMING_SNAKE_CASE__       =model.generate(
									    **snake_case_    ,do_sample=snake_case_    ,num_beams=2    ,max_length=model.config.max_length - num_paddings						)
									SCREAMING_SNAKE_CASE__       =tokenizer.decode(output_non_padded[0]    ,skip_special_tokens=snake_case_						)
									SCREAMING_SNAKE_CASE__       =tokenizer.decode(output_padded[0]    ,skip_special_tokens=snake_case_						)
									self.assertListEqual(snake_case_    ,[non_padded_sentence, padded_sentence]						)




					def   __A    (     self      :			Union[str, Any]						)		->       Any:


									'''simple docstring'''





									SCREAMING_SNAKE_CASE__       =self._get_beam_search_test_objects()

									SCREAMING_SNAKE_CASE__       =tokenizer(snake_case_    ,return_tensors="""tf"""    ,padding=snake_case_						)
									SCREAMING_SNAKE_CASE__       =tf.function(model.generate    ,jit_compile=snake_case_						)
									SCREAMING_SNAKE_CASE__       =xla_generate(**snake_case_    ,do_sample=snake_case_    ,num_beams=2						)
									SCREAMING_SNAKE_CASE__       =tokenizer.batch_decode(snake_case_    ,skip_special_tokens=snake_case_						)
									self.assertListEqual(snake_case_    ,snake_case_						)












def      UpperCAmelCase_   (     __UpperCamelCase,    __UpperCamelCase     ):
    SCREAMING_SNAKE_CASE__       =len(__UpperCamelCase     )
    # Create a stack and push index of fist element to it
    SCREAMING_SNAKE_CASE__       =[]
    st.append(0     )

    # Span value of first element is always 1
    SCREAMING_SNAKE_CASE__       =1

    # Calculate span values for rest of the elements
    for i in range(1,    __UpperCamelCase     ):
        # Pop elements from stack while stack is not
        # empty and top of stack is smaller than price[i]
        while len(__UpperCamelCase     ) > 0 and price[st[0]] <= price[i]:
            st.pop()

        # If stack becomes empty, then price[i] is greater
        # than all elements on left of it, i.e. price[0],
        # price[1], ..price[i-1]. Else the price[i]  is
        # greater than elements after top of stack
        SCREAMING_SNAKE_CASE__       =i + 1 if len(__UpperCamelCase     ) <= 0 else (i - st[0])

        # Push this element to stack
        st.append(__UpperCamelCase     )



def      UpperCAmelCase_   (     __UpperCamelCase,    __UpperCamelCase     ):
    for i in range(0,    __UpperCamelCase     ):
        print(arr[i],    end=""" """     )


# Driver program to test above function
lowerCamelCase_  						=       [10, 4, 5, 90, 120, 80]
lowerCamelCase_  						=       [0 for i in range(len(price) + 1)]

# Fill the span values in array S[]
calculation_span(price, S)




print_array(S, len(price))






