



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







import os
import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device


if is_torch_available():
      import torch

      from transformers import GPTaLMHeadModel

@require_torch
@require_sentencepiece
@require_tokenizers
class 	__snake_case					(      unittest.TestCase ):






    '''simple docstring'''





    @slow
    @unittest.skip('''Model is not available.'''				)
    def 						A		( self    :		str				):

      """simple docstring"""





      __lowerCAmelCase								=					'''nvidia/megatron-gpt2-345m/'''
      if "MYDIR" in os.environ:
        __lowerCAmelCase								=					os.path.join(os.environ['''MYDIR''']      ,    snake_case				)
      __lowerCAmelCase								=					GPTaLMHeadModel.from_pretrained(snake_case				)
      model.to(snake_case				)
      model.half()

      __lowerCAmelCase								=					torch.tensor(
          [[101, 7_110, 1_005, 1_056, 2_023, 11_333, 17_413, 1_029, 102]]      ,    device=snake_case      ,    dtype=torch.long      ,    )

      with torch.no_grad():
        __lowerCAmelCase								=					model(snake_case				).logits

      __lowerCAmelCase								=					torch.Size((1, 9, 50_257)				)
      self.assertEqual(output.shape      ,    snake_case				)

      __lowerCAmelCase								=					torch.tensor(
          [
              4.9414,
              -0.2920,
              -1.2148,
              -4.0273,
              -0.5161,
              -5.2109,
              -1.2412,
              -1.8301,
              -1.7734,
              -4.7148,
              -0.2317,
              -1.0811,
              -2.1777,
              0.4141,
              -3.7969,
              -4.0586,
              -2.5332,
              -3.3809,
              4.3867,
          ]      ,    device=snake_case      ,    dtype=torch.half      ,    )

      for i in range(19				):
        __lowerCAmelCase						,    __lowerCAmelCase								=					8 * i // 17, 2_792 * i  # along the diagonal
        __lowerCAmelCase						,    __lowerCAmelCase								=					output[0, r, c], expected_diag[i]
        __lowerCAmelCase								=					f'''row={r} col={c} computed={computed} expected={expected}'''
        self.assertAlmostEqual(snake_case      ,    snake_case      ,    delta=1e-4      ,    msg=snake_case				)
