



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

from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging


lowerCAmelCase__  	=      logging.get_logger(__name__)

lowerCAmelCase__  	=      {
    "google/long-t5-local-base": "https://huggingface.co/google/long-t5-local-base/blob/main/config.json",
    "google/long-t5-local-large": "https://huggingface.co/google/long-t5-local-large/blob/main/config.json",
    "google/long-t5-tglobal-base": "https://huggingface.co/google/long-t5-tglobal-base/blob/main/config.json",
    "google/long-t5-tglobal-large": "https://huggingface.co/google/long-t5-tglobal-large/blob/main/config.json",
}
class 	_a  (							lowerCamelCase_					):



	"""simple docstring"""



	__SCREAMING_SNAKE_CASE              =					'longt5'
	__SCREAMING_SNAKE_CASE              =					['past_key_values']
	__SCREAMING_SNAKE_CASE              =					{'hidden_size': 'd_model', 'num_attention_heads': 'num_heads', 'num_hidden_layers': 'num_layers'}


	def __init__(   self       ,  lowerCAmelCase_=32128       ,  lowerCAmelCase_=512       ,  lowerCAmelCase_=64       ,  lowerCAmelCase_=2048       ,  lowerCAmelCase_=6       ,  lowerCAmelCase_=None       ,  lowerCAmelCase_=8       ,  lowerCAmelCase_=127       ,  lowerCAmelCase_=16       ,  lowerCAmelCase_=32       ,  lowerCAmelCase_=128       ,  lowerCAmelCase_=0.1       ,  lowerCAmelCase_=1e-6       ,  lowerCAmelCase_=1.0       ,  lowerCAmelCase_="relu"       ,  lowerCAmelCase_=True       ,  lowerCAmelCase_="local"       ,  lowerCAmelCase_=True       ,  lowerCAmelCase_=0       ,  lowerCAmelCase_=1       ,  **lowerCAmelCase_       ,  ):
		_lowercase   =vocab_size
		_lowercase   =d_model
		_lowercase   =d_kv
		_lowercase   =d_ff
		_lowercase   =num_layers
		# default = symmetry
		_lowercase   =num_decoder_layers if num_decoder_layers is not None else self.num_layers
		_lowercase   =num_heads
		_lowercase   =local_radius
		_lowercase   =global_block_size
		_lowercase   =relative_attention_num_buckets
		_lowercase   =relative_attention_max_distance
		_lowercase   =dropout_rate
		_lowercase   =layer_norm_epsilon
		_lowercase   =initializer_factor
		_lowercase   =feed_forward_proj
		_lowercase   =encoder_attention_type
		_lowercase   =use_cache

		_lowercase   =self.feed_forward_proj.split("-"       )
		_lowercase   =act_info[-1]
		_lowercase   =act_info[0] == "gated"

		if len(lowerCAmelCase_       ) > 1 and act_info[0] != "gated" or len(lowerCAmelCase_       ) > 2:
			raise ValueError(
			    F'''`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer.'''
			    "Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. "
			    "'gated-gelu' or 'relu'"       )

		# for backwards compatibility
		if feed_forward_proj == "gated-gelu":
			_lowercase   ="gelu_new"

		super().__init__(
		    pad_token_id=lowerCAmelCase_       ,  eos_token_id=lowerCAmelCase_       ,  is_encoder_decoder=lowerCAmelCase_       ,  **lowerCAmelCase_       ,  )

class 	_a  (							lowerCamelCase_					):



	"""simple docstring"""



	@property
	def __lowerCAmelCase      (   self       ):
		_lowercase   ={
		    "input_ids": {0: "batch", 1: "encoder_sequence"},
		    "attention_mask": {0: "batch", 1: "encoder_sequence"},
		}
		if self.use_past:
			_lowercase   ="past_encoder_sequence + sequence"
			_lowercase   ={0: "batch"}
			_lowercase   ={0: "batch", 1: "past_decoder_sequence + sequence"}
		else:
			_lowercase   ={0: "batch", 1: "decoder_sequence"}
			_lowercase   ={0: "batch", 1: "decoder_sequence"}

		if self.use_past:
			self.fill_with_past_key_values_(lowerCAmelCase_       ,  direction="inputs"       )

		return common_inputs


	@property
	def __lowerCAmelCase      (   self       ):
		return 13




