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						:

				def __init__(       self    :      Union[str, Any]    ,      _lowercase    :      str    ,      _lowercase    :      Tuple=9_9    ,      _lowercase    :      List[Any]=1_3    ,      _lowercase    :      Dict=1_6    ,      _lowercase    :      Dict=7    ,      _lowercase    :      Tuple=True    ,      _lowercase    :      Optional[Any]=True    ,      _lowercase    :      List[Any]=True    ,      _lowercase    :      str=False    ,      _lowercase    :      Tuple=True    ,      _lowercase    :      Optional[int]=2    ,      _lowercase    :      Optional[Any]=3_2    ,      _lowercase    :      Dict=4    ,      _lowercase    :      List[str]=4    ,      _lowercase    :      str=3_0    ,      _lowercase    :      Optional[Any]=0    ,      _lowercase    :      Dict=1    ,      _lowercase    :      Optional[Any]=2    ,      _lowercase    :      List[str]=None    ,      ):


										'''simple docstring'''
										UpperCAmelCase__   		=						parent
										UpperCAmelCase__   		=						batch_size
										UpperCAmelCase__   		=						decoder_seq_length
										# For common tests
										UpperCAmelCase__   		=						self.decoder_seq_length
										UpperCAmelCase__   		=						is_training
										UpperCAmelCase__   		=						use_attention_mask
										UpperCAmelCase__   		=						use_labels

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

										UpperCAmelCase__   		=						None
										UpperCAmelCase__   		=						decoder_seq_length
										UpperCAmelCase__   		=						2
										UpperCAmelCase__   		=						1

				def     A	(       self    :      Union[str, Any]       ):


										'''simple docstring'''
										UpperCAmelCase__   		=						ids_tensor([self.batch_size, self.decoder_seq_length]    ,      self.vocab_size       )

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

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

										UpperCAmelCase__   		=						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     A	(       self    :      int    ,      _lowercase    :      Any    ,      _lowercase    :      Any    ,      _lowercase    :      Optional[int]    ,      _lowercase    :      Optional[int]    ,      ):


										'''simple docstring'''
										UpperCAmelCase__   		=						True
										UpperCAmelCase__   		=						SpeechaTextaDecoder(config=_lowercase       ).to(_lowercase       ).eval()
										UpperCAmelCase__   		=						input_ids[:2]

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

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

										UpperCAmelCase__   		=						outputs["past_key_values"]

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

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

										print(_lowercase       )

										UpperCAmelCase__   		=						model(_lowercase       )["last_hidden_state"]
										UpperCAmelCase__   		=						model(_lowercase    ,      past_key_values=_lowercase       )["last_hidden_state"]

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

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

				def     A	(       self    :      Optional[Any]       ):


										'''simple docstring'''
										UpperCAmelCase__   		=						self.prepare_config_and_inputs()
										(
										    UpperCAmelCase__						
										)   		=						config_and_inputs

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


@require_torch
class      _lowercase						(				__lowerCamelCase , __lowerCamelCase , __lowerCamelCase , unittest.TestCase					):
				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     A	(       self    :      str    ,      ):


										'''simple docstring'''
										UpperCAmelCase__   		=						SpeechaTextaStandaloneDecoderModelTester(self    ,      is_training=_lowercase       )
										UpperCAmelCase__   		=						ConfigTester(self    ,      config_class=_lowercase       )

				def     A	(       self    :      List[str]       ):


										'''simple docstring'''
										pass

				def     A	(       self    :      Optional[Any]       ):


										'''simple docstring'''
										pass

				def     A	(       self    :      Union[str, Any]       ):


										'''simple docstring'''
										pass

				def     A	(       self    :      Optional[Any]       ):


										'''simple docstring'''
										self.config_tester.run_common_tests()

				def     A	(       self    :      Dict       ):


										'''simple docstring'''
										UpperCAmelCase__   		=						self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_decoder_model_past(*_lowercase       )

				def     A	(       self    :      Optional[int]       ):


										'''simple docstring'''
										return


"""simple docstring"""







import safs


class      _lowercase						(				safs.SaFileSystem					):
				pass

