



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       _snake_case				:






   '''simple docstring'''






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

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

       UpperCAmelCase_     :       Tuple  		=					None
       UpperCAmelCase_     :       int  		=					decoder_seq_length
       UpperCAmelCase_     :       List[Any]  		=					2
       UpperCAmelCase_     :       str  		=					1


   def       A__    (    self:     Optional[Any]							)	->      Optional[int]:
       UpperCAmelCase_     :       Optional[int]  		=					ids_tensor([self.batch_size, self.decoder_seq_length]  ,self.vocab_size							)

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

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

       UpperCAmelCase_     :       Optional[int]  		=					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:     Optional[Any]  ,lowerCamelCase_:     Dict  ,lowerCamelCase_:     str  ,lowerCamelCase_:     str  ,lowerCamelCase_:     List[Any]  ,)	->      List[str]:
       UpperCAmelCase_     :       Dict  		=					True
       UpperCAmelCase_     :       Dict  		=					SpeechaTextaDecoder(config=lowerCamelCase_							).to(lowerCamelCase_							).eval()
       UpperCAmelCase_     :       Any  		=					input_ids[:2]

       input_ids[input_ids == 0] += 1
       # first forward pass
       UpperCAmelCase_     :       Optional[Any]  		=					model(lowerCamelCase_  ,use_cache=lowerCamelCase_							)
       UpperCAmelCase_     :       List[str]  		=					model(lowerCamelCase_							)
       UpperCAmelCase_     :       int  		=					model(lowerCamelCase_  ,use_cache=lowerCamelCase_							)

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

       UpperCAmelCase_     :       Dict  		=					outputs["""past_key_values"""]

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

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

       print(lowerCamelCase_							)

       UpperCAmelCase_     :       Dict  		=					model(lowerCamelCase_							)["""last_hidden_state"""]
       UpperCAmelCase_     :       int  		=					model(lowerCamelCase_  ,past_key_values=lowerCamelCase_							)["""last_hidden_state"""]

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

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






   def       A__    (    self:     Optional[Any]							)	->      List[str]:
       UpperCAmelCase_     :       Dict  		=					self.prepare_config_and_inputs()
       (
           (
           UpperCAmelCase_
       )		, (
           UpperCAmelCase_
       )		, (
           UpperCAmelCase_
       )		, (
           UpperCAmelCase_
       )		, 
       )     :       Dict  		=					config_and_inputs

       UpperCAmelCase_     :       Any  		=					{
           """input_ids""": input_ids,
           """attention_mask""": attention_mask,
       }
       return config, inputs_dict







@require_torch
class       _snake_case				(     __snake_case ,							__snake_case ,							__snake_case ,							unittest.TestCase ):






   '''simple docstring'''






   A__      : Optional[Any]       =						(SpeechaTextaDecoder, SpeechaTextaForCausalLM) if is_torch_available() else ()
   A__      : List[Any]       =						(SpeechaTextaForCausalLM,) if is_torch_available() else ()
   A__      : Any       =						{"text-generation": SpeechaTextaForCausalLM} if is_torch_available() else {}
   A__      : Optional[int]       =						True
   A__      : Union[str, Any]       =						False


   def       A__    (    self:     Tuple  ,)	->      Any:
       UpperCAmelCase_     :       Any  		=					SpeechaTextaStandaloneDecoderModelTester(self  ,is_training=lowerCamelCase_							)
       UpperCAmelCase_     :       List[Any]  		=					ConfigTester(self  ,config_class=lowerCamelCase_							)


   def       A__    (    self:     Dict							)	->      int:
       pass


   def       A__    (    self:     str							)	->      int:
       pass


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


   def       A__    (    self:     List[str]							)	->      int:
       self.config_tester.run_common_tests()


   def       A__    (    self:     int							)	->      Dict:
       UpperCAmelCase_     :       List[str]  		=					self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_decoder_model_past(*lowerCamelCase_							)






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








'''simple docstring'''







import sys
from collections.abc import Mapping

import numpy as np
import pyarrow as pa

from .. import config
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter






class        a__     (      TensorFormatter[Mapping, np.ndarray, Mapping]    ):
     '''simple docstring'''







     def __init__(self     :						Tuple					,					lowercase__     :						Dict=None					,					**lowercase__     :						List[Any]       )	->							str:
           super().__init__(features=lowercase__       )
           __A		 =   np_array_kwargs






     def      _UpperCamelCase (self     :						int					,					lowercase__     :						Tuple       )	->							List[Any]:
           if isinstance(lowercase__					,					lowercase__       ):
                 if column and all(
                     isinstance(lowercase__					,					np.ndarray       ) and x.shape == column[0].shape and x.dtype == column[0].dtype for x in column       ):
                       return np.stack(lowercase__       )
                 else:
                       # don't use np.array(column, dtype=object)
                       # since it fails in certain cases
                       # see https://stackoverflow.com/q/51005699
                       __A		 =   np.empty(len(lowercase__       )					,					dtype=lowercase__       )
                       __A		 =   column
                       return out
           return column






     def      _UpperCamelCase (self     :						Optional[Any]					,					lowercase__     :						Optional[int]       )	->							Tuple:
           if isinstance(lowercase__					,					(str, bytes, type(lowercase__       ))       ):
                 return value
           elif isinstance(lowercase__					,					(np.character, np.ndarray)       ) and np.issubdtype(value.dtype					,					np.character       ):
                 return value
           elif isinstance(lowercase__					,					np.number       ):
                 return value

           __A		 =   {}

           if isinstance(lowercase__					,					np.ndarray       ) and np.issubdtype(value.dtype					,					np.integer       ):
                 __A		 =   {"dtype": np.intaa}
           elif isinstance(lowercase__					,					np.ndarray       ) and np.issubdtype(value.dtype					,					np.floating       ):
                 __A		 =   {"dtype": np.floataa}
           elif config.PIL_AVAILABLE and "PIL" in sys.modules:
                 import PIL.Image

                 if isinstance(lowercase__					,					PIL.Image.Image       ):
                       return np.asarray(lowercase__					,					**self.np_array_kwargs       )

           return np.asarray(lowercase__					,					**{**default_dtype, **self.np_array_kwargs}       )






     def      _UpperCamelCase (self     :						List[Any]					,					lowercase__     :						str       )	->							Optional[Any]:
           # support for torch, tf, jax etc.
           if config.TORCH_AVAILABLE and "torch" in sys.modules:
                 import torch

                 if isinstance(lowercase__					,					torch.Tensor       ):
                       return self._tensorize(data_struct.detach().cpu().numpy()[()]       )
           if hasattr(lowercase__					,					"__array__"       ) and not isinstance(lowercase__					,					(np.ndarray, np.character, np.number)       ):
                 __A		 =   data_struct.__array__()
           # support for nested types like struct of list of struct
           if isinstance(lowercase__					,					np.ndarray       ):
                 if data_struct.dtype == object:
                       return self._consolidate([self.recursive_tensorize(lowercase__       ) for substruct in data_struct]       )
           if isinstance(lowercase__					,					(list, tuple)       ):
                 return self._consolidate([self.recursive_tensorize(lowercase__       ) for substruct in data_struct]       )
           return self._tensorize(lowercase__       )






     def      _UpperCamelCase (self     :						Optional[Any]					,					lowercase__     :						dict       )	->							Dict:
           return map_nested(self._recursive_tensorize					,					lowercase__					,					map_list=lowercase__       )






     def      _UpperCamelCase (self     :						Optional[int]					,					lowercase__     :						pa.Table       )	->							Mapping:
           __A		 =   self.numpy_arrow_extractor().extract_row(lowercase__       )
           __A		 =   self.python_features_decoder.decode_row(lowercase__       )
           return self.recursive_tensorize(lowercase__       )






     def      _UpperCamelCase (self     :						List[Any]					,					lowercase__     :						pa.Table       )	->							np.ndarray:
           __A		 =   self.numpy_arrow_extractor().extract_column(lowercase__       )
           __A		 =   self.python_features_decoder.decode_column(lowercase__					,					pa_table.column_names[0]       )
           __A		 =   self.recursive_tensorize(lowercase__       )
           __A		 =   self._consolidate(lowercase__       )
           return column






     def      _UpperCamelCase (self     :						Optional[Any]					,					lowercase__     :						pa.Table       )	->							Mapping:
           __A		 =   self.numpy_arrow_extractor().extract_batch(lowercase__       )
           __A		 =   self.python_features_decoder.decode_batch(lowercase__       )
           __A		 =   self.recursive_tensorize(lowercase__       )
           for column_name in batch:
                 __A		 =   self._consolidate(batch[column_name]       )
           return batch


