




"""simple docstring"""





import unittest

from transformers import SpeechaTextaConfig
from transformers.testing_utils import is_torch_available, require_torch, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
							import torch

							from transformers.models.speech_to_text_a.modeling_speech_to_text_a import (
							    SpeechaTextaDecoder,
							    SpeechaTextaForCausalLM,
							)

@require_torch
class       __lowercase       :


				'''simple docstring'''


				def __init__(   self   :     int      ,SCREAMING_SNAKE_CASE_   :     Optional[Any]      ,SCREAMING_SNAKE_CASE_   :     Optional[Any]=9_9      ,SCREAMING_SNAKE_CASE_   :     List[str]=1_3      ,SCREAMING_SNAKE_CASE_   :     str=1_6      ,SCREAMING_SNAKE_CASE_   :     Tuple=7      ,SCREAMING_SNAKE_CASE_   :     Union[str, Any]=True      ,SCREAMING_SNAKE_CASE_   :     List[Any]=True      ,SCREAMING_SNAKE_CASE_   :     Tuple=True      ,SCREAMING_SNAKE_CASE_   :     Optional[Any]=False      ,SCREAMING_SNAKE_CASE_   :     Tuple=True      ,SCREAMING_SNAKE_CASE_   :     Dict=2      ,SCREAMING_SNAKE_CASE_   :     Tuple=3_2      ,SCREAMING_SNAKE_CASE_   :     List[str]=4      ,SCREAMING_SNAKE_CASE_   :     str=4      ,SCREAMING_SNAKE_CASE_   :     List[str]=3_0      ,SCREAMING_SNAKE_CASE_   :     Dict=0      ,SCREAMING_SNAKE_CASE_   :     List[Any]=1      ,SCREAMING_SNAKE_CASE_   :     Optional[Any]=2      ,SCREAMING_SNAKE_CASE_   :     Union[str, Any]=None      ,):
										snake_case__       :					Tuple										=					parent
										snake_case__       :					Optional[int]										=					batch_size
										snake_case__       :					str										=					decoder_seq_length
										# For common tests
										snake_case__       :					Any										=					self.decoder_seq_length
										snake_case__       :					Optional[int]										=					is_training
										snake_case__       :					Optional[int]										=					use_attention_mask
										snake_case__       :					Optional[Any]										=					use_labels

										snake_case__       :					Union[str, Any]										=					vocab_size
										snake_case__       :					Any										=					d_model
										snake_case__       :					Tuple										=					d_model
										snake_case__       :					Optional[int]										=					decoder_layers
										snake_case__       :					str										=					decoder_layers
										snake_case__       :					Any										=					decoder_ffn_dim
										snake_case__       :					List[Any]										=					decoder_attention_heads
										snake_case__       :					str										=					decoder_attention_heads
										snake_case__       :					Union[str, Any]										=					eos_token_id
										snake_case__       :					List[Any]										=					bos_token_id
										snake_case__       :					Any										=					pad_token_id
										snake_case__       :					List[str]										=					decoder_start_token_id
										snake_case__       :					Tuple										=					use_cache
										snake_case__       :					Optional[int]										=					max_position_embeddings

										snake_case__       :					str										=					None
										snake_case__       :					Optional[int]										=					decoder_seq_length
										snake_case__       :					Union[str, Any]										=					2
										snake_case__       :					str										=					1

				def       _lowerCamelCase					(   self   :     Union[str, Any]      ):
										snake_case__       :					List[str]										=					ids_tensor([self.batch_size, self.decoder_seq_length]      ,self.vocab_size      )

										snake_case__       :					Union[str, Any]										=					None
										if self.use_attention_mask:
																snake_case__       :					Optional[int]										=					ids_tensor([self.batch_size, self.decoder_seq_length]      ,vocab_size=2      )

										snake_case__       :					List[Any]										=					None
										if self.use_labels:
																snake_case__       :					List[Any]										=					ids_tensor([self.batch_size, self.decoder_seq_length]      ,self.vocab_size      )

										snake_case__       :					Dict										=					SpeechaTextaConfig(
										    vocab_size=self.vocab_size      ,d_model=self.d_model      ,decoder_layers=self.decoder_layers      ,decoder_ffn_dim=self.decoder_ffn_dim      ,decoder_attention_heads=self.decoder_attention_heads      ,eos_token_id=self.eos_token_id      ,bos_token_id=self.bos_token_id      ,use_cache=self.use_cache      ,pad_token_id=self.pad_token_id      ,decoder_start_token_id=self.decoder_start_token_id      ,max_position_embeddings=self.max_position_embeddings      ,)

										return (
										    config,
										    input_ids,
										    attention_mask,
										    lm_labels,
										)

				def       _lowerCamelCase					(   self   :     str      ,SCREAMING_SNAKE_CASE_   :     Dict      ,SCREAMING_SNAKE_CASE_   :     int      ,SCREAMING_SNAKE_CASE_   :     Dict      ,SCREAMING_SNAKE_CASE_   :     int      ,):
										snake_case__       :					Optional[int]										=					True
										snake_case__       :					Dict										=					SpeechaTextaDecoder(config=SCREAMING_SNAKE_CASE_      ).to(SCREAMING_SNAKE_CASE_      ).eval()
										snake_case__       :					List[str]										=					input_ids[:2]

										input_ids[input_ids == 0] += 1
										# first forward pass
										snake_case__       :					Dict										=					model(SCREAMING_SNAKE_CASE_      ,use_cache=SCREAMING_SNAKE_CASE_      )
										snake_case__       :					Any										=					model(SCREAMING_SNAKE_CASE_      )
										snake_case__       :					Tuple										=					model(SCREAMING_SNAKE_CASE_      ,use_cache=SCREAMING_SNAKE_CASE_      )

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

										snake_case__       :					List[Any]										=					outputs['''past_key_values''']

										# create hypothetical next token and extent to next_input_ids
										snake_case__       :					List[str]										=					ids_tensor((2, 1)      ,config.vocab_size - 1      ) + 1

										# append to next input_ids and
										snake_case__       :					int										=					torch.cat([input_ids, next_tokens]      ,dim=-1      )

										print(SCREAMING_SNAKE_CASE_      )

										snake_case__       :					List[Any]										=					model(SCREAMING_SNAKE_CASE_      )['''last_hidden_state''']
										snake_case__       :					Optional[Any]										=					model(SCREAMING_SNAKE_CASE_      ,past_key_values=SCREAMING_SNAKE_CASE_      )['''last_hidden_state''']

										# select random slice
										snake_case__       :					int										=					ids_tensor((1,)      ,output_from_past.shape[-1]      ).item()
										snake_case__       :					str										=					output_from_no_past[:, next_input_ids.shape[-1] - 1, random_slice_idx].detach()
										snake_case__       :					Optional[int]										=					output_from_past[:, 0, random_slice_idx].detach()

										# test that outputs are equal for slice
										assert torch.allclose(SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_      ,atol=1E-3      )





				def       _lowerCamelCase					(   self   :     Tuple      ):
										snake_case__       :					str										=					self.prepare_config_and_inputs()
										(
										    (
										    snake_case__
										)			,	(
										    snake_case__
										)			,	(
										    snake_case__
										)			,	(
										    snake_case__
										)			,	
										)       :					Optional[int]										=					config_and_inputs

										snake_case__       :					Optional[Any]										=					{
										    '''input_ids''': input_ids,
										    '''attention_mask''': attention_mask,
										}
										return config, inputs_dict







@require_torch
class       __lowercase       (			snake_case_						,   snake_case_						,   snake_case_						,   unittest.TestCase	):


				'''simple docstring'''


				_lowerCAmelCase				=(SpeechaTextaDecoder, SpeechaTextaForCausalLM) if is_torch_available() else ()
				_lowerCAmelCase				=(SpeechaTextaForCausalLM,) if is_torch_available() else ()
				_lowerCAmelCase				={"text-generation": SpeechaTextaForCausalLM} if is_torch_available() else {}
				_lowerCAmelCase				=True
				_lowerCAmelCase				=False

				def       _lowerCamelCase					(   self   :     str      ,):
										snake_case__       :					str										=					SpeechaTextaStandaloneDecoderModelTester(self      ,is_training=SCREAMING_SNAKE_CASE_      )
										snake_case__       :					Optional[int]										=					ConfigTester(self      ,config_class=SCREAMING_SNAKE_CASE_      )

				def       _lowerCamelCase					(   self   :     Optional[Any]      ):
										pass

				def       _lowerCamelCase					(   self   :     Optional[Any]      ):
										pass

				def       _lowerCamelCase					(   self   :     List[str]      ):
										pass

				def       _lowerCamelCase					(   self   :     Dict      ):
										self.config_tester.run_common_tests()

				def       _lowerCamelCase					(   self   :     Union[str, Any]      ):
										snake_case__       :					Optional[int]										=					self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_decoder_model_past(*SCREAMING_SNAKE_CASE_      )





				def       _lowerCamelCase					(   self   :     Union[str, Any]      ):
										return








"""simple docstring"""





def 		lowerCAmelCase							( lowerCamelCase      ,			lowerCamelCase		):
						'''simple docstring'''


						snake_case__       :					Any										=					len(lowerCamelCase		)
						# Create a stack and push index of fist element to it
						snake_case__       :					str										=					[]
						st.append(0		)

						# Span value of first element is always 1
						snake_case__       :					Union[str, Any]										=					1

						# Calculate span values for rest of the elements
						for i in range(1      ,			lowerCamelCase		):
												# Pop elements from stack while stack is not
												# empty and top of stack is smaller than price[i]
												while len(lowerCamelCase		) > 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
												snake_case__       :					Dict										=					i + 1 if len(lowerCamelCase		) <= 0 else (i - st[0])

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

def 		lowerCAmelCase							( lowerCamelCase      ,			lowerCamelCase		):
						'''simple docstring'''


						for i in range(0      ,			lowerCamelCase		):
												print(arr[i]      ,			end=''' '''		)


# Driver program to test above function
_lowercase	      =    [10, 4, 5, 90, 1_20, 80]
_lowercase	      =    [0 for i in range(len(price) + 1)]

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

print_array(S, len(price))


