
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 							UpperCAmelCase   :




	'''simple docstring'''





	def __init__( self      :				Dict							,A      :				Tuple							,A      :				Union[str, Any]=99							,A      :				Dict=13							,A      :				int=16							,A      :				int=7							,A      :				Optional[int]=True							,A      :				str=True							,A      :				Optional[Any]=True							,A      :				Optional[int]=False							,A      :				Dict=True							,A      :				Any=2							,A      :				List[Any]=32							,A      :				Optional[Any]=4							,A      :				Tuple=4							,A      :				List[Any]=30							,A      :				Optional[Any]=0							,A      :				str=1							,A      :				List[Any]=2							,A      :				List[Any]=None							,):
					__A         =				parent
					__A         =				batch_size
					__A         =				decoder_seq_length
					# For common tests
					__A         =				self.decoder_seq_length
					__A         =				is_training
					__A         =				use_attention_mask
					__A         =				use_labels

					__A         =				vocab_size
					__A         =				d_model
					__A         =				d_model
					__A         =				decoder_layers
					__A         =				decoder_layers
					__A         =				decoder_ffn_dim
					__A         =				decoder_attention_heads
					__A         =				decoder_attention_heads
					__A         =				eos_token_id
					__A         =				bos_token_id
					__A         =				pad_token_id
					__A         =				decoder_start_token_id
					__A         =				use_cache
					__A         =				max_position_embeddings

					__A         =				None
					__A         =				decoder_seq_length
					__A         =				2
					__A         =				1






	def        UpperCamelCase_						( self      :				Tuple				):
					__A         =				ids_tensor([self.batch_size, self.decoder_seq_length]							,self.vocab_size				)

					__A         =				None
					if self.use_attention_mask:
									__A         =				ids_tensor([self.batch_size, self.decoder_seq_length]							,vocab_size=2				)

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

					__A         =				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        UpperCamelCase_						( self      :				Tuple							,A      :				List[str]							,A      :				Tuple							,A      :				Optional[int]							,A      :				List[str]							,):
					__A         =				True
					__A         =				SpeechaTextaDecoder(config=A				).to(A				).eval()
					__A         =				input_ids[:2]

					input_ids[input_ids == 0] += 1
					# first forward pass
					__A         =				model(A							,use_cache=A				)
					__A         =				model(A				)
					__A         =				model(A							,use_cache=A				)

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

					__A         =				outputs["past_key_values"]

					# create hypothetical next token and extent to next_input_ids
					__A         =				ids_tensor((2, 1)							,config.vocab_size - 1				) + 1

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

					print(A				)

					__A         =				model(A				)["last_hidden_state"]
					__A         =				model(A							,past_key_values=A				)["last_hidden_state"]

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

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







	def        UpperCamelCase_						( self      :				Any				):
					__A         =				self.prepare_config_and_inputs()
					(
					    (
					    __A
					)    ,							(
					    __A
					)    ,							(
					    __A
					)    ,							(
					    __A
					)    ,							
					)         =				config_and_inputs

					__A         =				{
					    "input_ids": input_ids,
					    "attention_mask": attention_mask,
					}
					return config, inputs_dict





@require_torch
class 							UpperCAmelCase   (						__SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    unittest.TestCase						):




	'''simple docstring'''





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






	def        UpperCamelCase_						( self      :				Any							,):
					__A         =				SpeechaTextaStandaloneDecoderModelTester(self							,is_training=A				)
					__A         =				ConfigTester(self							,config_class=A				)






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






	def        UpperCamelCase_						( self      :				int				):
					pass






	def        UpperCamelCase_						( self      :				Tuple				):
					pass






	def        UpperCamelCase_						( self      :				str				):
					self.config_tester.run_common_tests()






	def        UpperCamelCase_						( self      :				Dict				):
					__A         =				self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_decoder_model_past(*A				)







	def        UpperCamelCase_						( self      :				str				):
					return


from typing import Any





class 							UpperCAmelCase   :




	'''simple docstring'''





	def __init__( self      :				Union[str, Any]				):
					__A         =				[]
					__A         =				0






	def __str__( self      :				Tuple				):
					__A         =				"<" + str(self.stack				)[1:-1] + ">"
					return printed






	def        UpperCamelCase_						( self      :				Optional[int]							,A      :				Any				):
					self.stack.append(A				)
					__A         =				self.length + 1






	def        UpperCamelCase_						( self      :				Any				):
					self.rotate(1				)
					__A         =				self.stack[self.length - 1]
					__A         =				self.stack[:-1]
					self.rotate(self.length - 1				)
					__A         =				self.length - 1
					return dequeued






	def        UpperCamelCase_						( self      :				Tuple							,A      :				int				):
					for _ in range(A				):
									__A         =				self.stack[0]
									__A         =				self.stack[1:]
									self.put(A				)
									__A         =				self.length - 1






	def        UpperCamelCase_						( self      :				Optional[Any]				):
					__A         =				self.get()
					self.put(A				)
					self.rotate(self.length - 1				)
					return front






	def        UpperCamelCase_						( self      :				Optional[Any]				):
					return self.length
