import unittest

from transformers import JukeboxTokenizer
from transformers.testing_utils import require_torch




class 					_UpperCAmelCase			(     unittest.TestCase  ):



 '''simple docstring'''




 __A			       =					JukeboxTokenizer
 __A			       =					{
     '''artist''': '''Zac Brown Band''',
     '''genres''': '''Country''',
     '''lyrics''': '''I met a traveller from an antique land,
        Who said "Two vast and trunkless legs of stone
        Stand in the desert. . . . Near them, on the sand,
        Half sunk a shattered visage lies, whose frown,
        And wrinkled lip, and sneer of cold command,
        Tell that its sculptor well those passions read
        Which yet survive, stamped on these lifeless things,
        The hand that mocked them, and the heart that fed;
        And on the pedestal, these words appear:
        My name is Ozymandias, King of Kings;
        Look on my Works, ye Mighty, and despair!
        Nothing beside remains. Round the decay
        Of that colossal Wreck, boundless and bare
        The lone and level sands stretch far away
        ''',
 }







 @require_torch
 def 		__UpperCAmelCase				(		self				:   str)						->	Optional[int]:


   """simple docstring"""







   import torch

   _UpperCamelCase           =    JukeboxTokenizer.from_pretrained("openai/jukebox-1b-lyrics")
   _UpperCamelCase           =    tokenizer(**self.metas)["""input_ids"""]
   # fmt: off
   _UpperCamelCase           =    [
       torch.tensor([[
           0, 0, 0, 7169, 507, 9, 76, 39, 31, 46, 76, 27,
           76, 46, 44, 27, 48, 31, 38, 38, 31, 44, 76, 32,
           44, 41, 39, 76, 27, 40, 76, 27, 40, 46, 35, 43,
           47, 31, 76, 38, 27, 40, 30, 64, 78, 76, 76, 76,
           76, 76, 76, 76, 76, 23, 34, 41, 76, 45, 27, 35,
           30, 76, 71, 20, 49, 41, 76, 48, 27, 45, 46, 76,
           27, 40, 30, 76, 46, 44, 47, 40, 37, 38, 31, 45,
           45, 76, 38, 31, 33, 45, 76, 41, 32, 76, 45, 46,
           41, 40, 31, 78, 76, 76, 76, 76, 76, 76, 76, 76,
           19, 46, 27, 40, 30, 76, 35, 40, 76, 46, 34, 31,
           76, 30, 31, 45, 31, 44, 46, 63, 76, 63, 76, 63,
           76, 63, 76, 14, 31, 27, 44, 76, 46, 34, 31, 39,
           64, 76, 41, 40, 76, 46, 34, 31, 76, 45, 27, 40,
           30, 64, 78, 76, 76, 76, 76, 76, 76, 76, 76, 8,
           27, 38, 32, 76, 45, 47, 40, 37, 76, 27, 76, 45,
           34, 27, 46, 46, 31, 44, 31, 30, 76, 48, 35, 45,
           27, 33, 31, 76, 38, 35, 31, 45, 64, 76, 49, 34,
           41, 45, 31, 76, 32, 44, 41, 49, 40, 64, 78, 76,
           76, 76, 76, 76, 76, 76, 76, 1, 40, 30, 76, 49,
           44, 35, 40, 37, 38, 31, 30, 76, 38, 35, 42, 64,
           76, 27, 40, 30, 76, 45, 40, 31, 31, 44, 76, 41,
           32, 76, 29, 41, 38, 30, 76, 29, 41, 39, 39, 27,
           40, 30, 64, 78, 76, 76, 76, 76, 76, 76, 76, 76,
           20, 31, 38, 38, 76, 46, 34, 27, 46, 76, 35, 46,
           45, 76, 45, 29, 47, 38, 42, 46, 41, 44, 76, 49,
           31, 38, 38, 76, 46, 34, 41, 45, 31, 76, 42, 27,
           45, 45, 35, 41, 40, 45, 76, 44, 31, 27, 30, 78,
           76, 76, 76, 76, 76, 76, 76, 76, 23, 34, 35, 29,
           34, 76, 51, 31, 46, 76, 45, 47, 44, 48, 35, 48,
           31, 64, 76, 45, 46, 27, 39, 42, 31, 30, 76, 41,
           40, 76, 46, 34, 31, 45, 31, 76, 38, 35, 32, 31,
           38, 31, 45, 45, 76, 46, 34, 35, 40, 33, 45, 64,
           78, 76, 76, 76, 76, 76, 76, 76, 76, 20, 34, 31,
           76, 34, 27, 40, 30, 76, 46, 34, 27, 46, 76, 39,
           41, 29, 37, 31, 30, 76, 46, 34, 31, 39, 64, 76,
           27, 40, 30, 76, 46, 34, 31, 76, 34, 31, 27, 44,
           46, 76, 46, 34, 27, 46, 76, 32, 31, 30, 66, 78,
           76, 76, 76, 76, 76, 76, 76, 76, 1, 40, 30, 76,
           41, 40, 76, 46, 34, 31, 76, 42, 31, 30, 31, 45,
           46, 27, 38, 64, 76, 46, 34, 31, 45, 31, 76, 49,
           41, 44, 30, 45, 76, 27, 42, 42, 31, 27, 44, 65,
           78, 76, 76, 76, 76, 76, 76, 76, 76, 13, 51, 76,
           40, 27, 39, 31, 76, 35, 45, 76, 15, 52, 51, 39,
           27, 40, 30, 35, 27, 45, 64, 76, 11, 35, 40, 33,
           76, 41, 32, 76, 11, 35, 40, 33, 45, 66, 78, 76,
           76, 76, 76, 76, 76, 76, 76, 12, 41, 41, 37, 76,
           41, 40, 76, 39, 51, 76, 23, 41, 44, 37, 45, 64,
           76, 51, 31, 76, 13, 35, 33, 34, 46, 51, 64, 76,
           27, 40, 30, 76, 30, 31, 45, 42, 27, 35, 44, 67,
           78, 76, 76, 76, 76, 76, 76, 76, 76, 14, 41, 46,
           34, 35, 40, 33, 76, 28, 31, 45, 35, 30, 31, 76,
           44, 31, 39, 27, 35, 40, 45, 63, 76, 18, 41, 47,
           40, 30, 76, 46, 34, 31, 76, 30, 31, 29, 27, 51,
           78, 76, 76, 76, 76, 76, 76, 76, 76, 15, 32, 76,
           46, 34, 27, 46, 76, 29, 41, 38, 41, 45, 45, 27,
           38, 76, 23, 44, 31, 29, 37, 64, 76, 28, 41, 47,
           40, 30, 38, 31, 45, 45, 76, 27, 40, 30, 76, 28,
           27, 44, 31, 78, 76, 76, 76, 76, 76, 76, 76, 76,
           20, 34, 31, 76, 38, 41, 40, 31, 76, 27, 40, 30,
           76, 38, 31, 48, 31, 38, 76, 45, 27, 40, 30, 45,
           76, 45, 46, 44, 31, 46, 29, 34, 76, 32, 27, 44,
           76, 27, 49, 27, 51, 78, 76, 76, 76, 76, 76, 76,
           76, 76]]),
       torch.tensor([[0, 0, 0, 1069, 11]]),
       torch.tensor([[0, 0, 0, 1069, 11]]),
   ]
   # fmt: on
   self.assertTrue(torch.allclose(tokens[0]							, EXPECTED_OUTPUT[0]))
   self.assertTrue(torch.allclose(tokens[1]							, EXPECTED_OUTPUT[1]))
   self.assertTrue(torch.allclose(tokens[2]							, EXPECTED_OUTPUT[2]))







 @require_torch
 def 		__UpperCAmelCase				(		self				:   Optional[int])						->	Optional[int]:


   """simple docstring"""







   import torch

   _UpperCamelCase           =    JukeboxTokenizer.from_pretrained("openai/jukebox-5b-lyrics")
   _UpperCamelCase           =    tokenizer(**self.metas)["""input_ids"""]
   # fmt: off
   _UpperCamelCase           =    [
       torch.tensor([[
           0, 0, 0, 1069, 11, -1, -1, -1, -1, 9, 77, 39,
           31, 46, 77, 27, 77, 46, 44, 27, 48, 31, 38, 38,
           31, 44, 77, 32, 44, 41, 39, 77, 27, 40, 77, 27,
           40, 46, 35, 43, 47, 31, 77, 38, 27, 40, 30, 64,
           79, 77, 77, 77, 77, 77, 77, 77, 77, 23, 34, 41,
           77, 45, 27, 35, 30, 77, 72, 20, 49, 41, 77, 48,
           27, 45, 46, 77, 27, 40, 30, 77, 46, 44, 47, 40,
           37, 38, 31, 45, 45, 77, 38, 31, 33, 45, 77, 41,
           32, 77, 45, 46, 41, 40, 31, 79, 77, 77, 77, 77,
           77, 77, 77, 77, 19, 46, 27, 40, 30, 77, 35, 40,
           77, 46, 34, 31, 77, 30, 31, 45, 31, 44, 46, 63,
           77, 63, 77, 63, 77, 63, 77, 14, 31, 27, 44, 77,
           46, 34, 31, 39, 64, 77, 41, 40, 77, 46, 34, 31,
           77, 45, 27, 40, 30, 64, 79, 77, 77, 77, 77, 77,
           77, 77, 77, 8, 27, 38, 32, 77, 45, 47, 40, 37,
           77, 27, 77, 45, 34, 27, 46, 46, 31, 44, 31, 30,
           77, 48, 35, 45, 27, 33, 31, 77, 38, 35, 31, 45,
           64, 77, 49, 34, 41, 45, 31, 77, 32, 44, 41, 49,
           40, 64, 79, 77, 77, 77, 77, 77, 77, 77, 77, 1,
           40, 30, 77, 49, 44, 35, 40, 37, 38, 31, 30, 77,
           38, 35, 42, 64, 77, 27, 40, 30, 77, 45, 40, 31,
           31, 44, 77, 41, 32, 77, 29, 41, 38, 30, 77, 29,
           41, 39, 39, 27, 40, 30, 64, 79, 77, 77, 77, 77,
           77, 77, 77, 77, 20, 31, 38, 38, 77, 46, 34, 27,
           46, 77, 35, 46, 45, 77, 45, 29, 47, 38, 42, 46,
           41, 44, 77, 49, 31, 38, 38, 77, 46, 34, 41, 45,
           31, 77, 42, 27, 45, 45, 35, 41, 40, 45, 77, 44,
           31, 27, 30, 79, 77, 77, 77, 77, 77, 77, 77, 77,
           23, 34, 35, 29, 34, 77, 51, 31, 46, 77, 45, 47,
           44, 48, 35, 48, 31, 64, 77, 45, 46, 27, 39, 42,
           31, 30, 77, 41, 40, 77, 46, 34, 31, 45, 31, 77,
           38, 35, 32, 31, 38, 31, 45, 45, 77, 46, 34, 35,
           40, 33, 45, 64, 79, 77, 77, 77, 77, 77, 77, 77,
           77, 20, 34, 31, 77, 34, 27, 40, 30, 77, 46, 34,
           27, 46, 77, 39, 41, 29, 37, 31, 30, 77, 46, 34,
           31, 39, 64, 77, 27, 40, 30, 77, 46, 34, 31, 77,
           34, 31, 27, 44, 46, 77, 46, 34, 27, 46, 77, 32,
           31, 30, 66, 79, 77, 77, 77, 77, 77, 77, 77, 77,
           1, 40, 30, 77, 41, 40, 77, 46, 34, 31, 77, 42,
           31, 30, 31, 45, 46, 27, 38, 64, 77, 46, 34, 31,
           45, 31, 77, 49, 41, 44, 30, 45, 77, 27, 42, 42,
           31, 27, 44, 65, 79, 77, 77, 77, 77, 77, 77, 77,
           77, 13, 51, 77, 40, 27, 39, 31, 77, 35, 45, 77,
           15, 52, 51, 39, 27, 40, 30, 35, 27, 45, 64, 77,
           11, 35, 40, 33, 77, 41, 32, 77, 11, 35, 40, 33,
           45, 66, 79, 77, 77, 77, 77, 77, 77, 77, 77, 12,
           41, 41, 37, 77, 41, 40, 77, 39, 51, 77, 23, 41,
           44, 37, 45, 64, 77, 51, 31, 77, 13, 35, 33, 34,
           46, 51, 64, 77, 27, 40, 30, 77, 30, 31, 45, 42,
           27, 35, 44, 67, 79, 77, 77, 77, 77, 77, 77, 77,
           77, 14, 41, 46, 34, 35, 40, 33, 77, 28, 31, 45,
           35, 30, 31, 77, 44, 31, 39, 27, 35, 40, 45, 63,
           77, 18, 41, 47, 40, 30, 77, 46, 34, 31, 77, 30,
           31, 29, 27, 51, 79, 77, 77, 77, 77, 77, 77, 77,
           77, 15, 32, 77, 46, 34, 27, 46, 77, 29, 41, 38,
           41, 45, 45, 27, 38, 77, 23, 44, 31, 29, 37, 64,
           77, 28, 41, 47, 40, 30, 38, 31, 45, 45, 77, 27,
           40, 30, 77, 28, 27, 44, 31, 79, 77, 77, 77, 77,
           77, 77, 77, 77, 20, 34, 31, 77, 38, 41, 40, 31,
           77, 27, 40, 30, 77, 38, 31, 48, 31, 38, 77, 45,
           27, 40, 30, 45, 77, 45, 46, 44, 31, 46, 29, 34,
           77, 32, 27, 44, 77, 27, 49, 27, 51, 79, 77, 77,
           77, 77, 77, 77, 77, 77]]),
       torch.tensor([[0, 0, 0, 1069, 11, -1, -1, -1, -1]]),
       torch.tensor([[0, 0, 0, 1069, 11, -1, -1, -1, -1]]),
   ]
   # fmt: on
   self.assertTrue(torch.allclose(tokens[0]							, EXPECTED_OUTPUT[0]))
   self.assertTrue(torch.allclose(tokens[1]							, EXPECTED_OUTPUT[1]))
   self.assertTrue(torch.allclose(tokens[2]							, EXPECTED_OUTPUT[2]))








from __future__ import annotations

import unittest

from transformers import BlenderbotConfig, BlenderbotTokenizer, is_tf_available
from transformers.testing_utils import require_tf, require_tokenizers, slow
from transformers.utils import cached_property

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
 import tensorflow as tf

 from transformers import TFAutoModelForSeqaSeqLM, TFBlenderbotForConditionalGeneration, TFBlenderbotModel


@require_tf
class 		_SCREAMING_SNAKE_CASE     :
     __SCREAMING_SNAKE_CASE     					=    BlenderbotConfig
     __SCREAMING_SNAKE_CASE     					=    {}
     __SCREAMING_SNAKE_CASE     					=    '''gelu'''


     def __init__(  self							,					A_							,					A_=13							,					A_=7							,					A_=True							,					A_=False							,					A_=99							,					A_=32							,					A_=2							,					A_=4							,					A_=37							,					A_=0.1							,					A_=0.1							,					A_=20							,					A_=2							,					A_=1							,					A_=0							,					):
       _UpperCAmelCase		:							List[str]							=     parent
       _UpperCAmelCase		:							Union[str, Any]							=     batch_size
       _UpperCAmelCase		:							Optional[Any]							=     seq_length
       _UpperCAmelCase		:							List[Any]							=     is_training
       _UpperCAmelCase		:							Union[str, Any]							=     use_labels
       _UpperCAmelCase		:							Tuple							=     vocab_size
       _UpperCAmelCase		:							Optional[int]							=     hidden_size
       _UpperCAmelCase		:							int							=     num_hidden_layers
       _UpperCAmelCase		:							List[Any]							=     num_attention_heads
       _UpperCAmelCase		:							List[Any]							=     intermediate_size

       _UpperCAmelCase		:							Union[str, Any]							=     hidden_dropout_prob
       _UpperCAmelCase		:							str							=     attention_probs_dropout_prob
       _UpperCAmelCase		:							Optional[int]							=     max_position_embeddings
       _UpperCAmelCase		:							Optional[Any]							=     eos_token_id
       _UpperCAmelCase		:							Optional[Any]							=     pad_token_id
       _UpperCAmelCase		:							Tuple							=     bos_token_id


     def        __snake_case(  self    ):
       _UpperCAmelCase		:							Optional[Any]							=     ids_tensor([self.batch_size, self.seq_length - 1]							,					self.vocab_size    )
       _UpperCAmelCase		:							Tuple							=     tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size    )							,					1    )
       _UpperCAmelCase		:							List[Any]							=     tf.concat([input_ids, eos_tensor]							,					axis=1    )

       _UpperCAmelCase		:							int							=     ids_tensor([self.batch_size, self.seq_length]							,					self.vocab_size    )

       _UpperCAmelCase		:							List[Any]							=     self.config_cls(
           vocab_size=self.vocab_size							,					d_model=self.hidden_size							,					encoder_layers=self.num_hidden_layers							,					decoder_layers=self.num_hidden_layers							,					encoder_attention_heads=self.num_attention_heads							,					decoder_attention_heads=self.num_attention_heads							,					encoder_ffn_dim=self.intermediate_size							,					decoder_ffn_dim=self.intermediate_size							,					dropout=self.hidden_dropout_prob							,					attention_dropout=self.attention_probs_dropout_prob							,					max_position_embeddings=self.max_position_embeddings							,					eos_token_ids=[2]							,					bos_token_id=self.bos_token_id							,					pad_token_id=self.pad_token_id							,					decoder_start_token_id=self.pad_token_id							,					**self.config_updates							,					)
       _UpperCAmelCase		:							Dict							=     prepare_blenderbot_inputs_dict(A_							,					A_							,					A_    )
       return config, inputs_dict




     def        __snake_case(  self							,					A_							,					A_    ):
       _UpperCAmelCase		:							Union[str, Any]							=     TFBlenderbotModel(config=A_    ).get_decoder()
       _UpperCAmelCase		:							List[Any]							=     inputs_dict["""input_ids"""]

       _UpperCAmelCase		:							Any							=     input_ids[:1, :]
       _UpperCAmelCase		:							List[str]							=     inputs_dict["""attention_mask"""][:1, :]
       _UpperCAmelCase		:							Tuple							=     inputs_dict["""head_mask"""]
       _UpperCAmelCase		:							Union[str, Any]							=     1

       # first forward pass
       _UpperCAmelCase		:							List[str]							=     model(A_							,					attention_mask=A_							,					head_mask=A_							,					use_cache=A_    )

       _UpperCAmelCase,_UpperCAmelCase		:							List[str]							=     outputs.to_tuple()

       # create hypothetical next token and extent to next_input_ids
       _UpperCAmelCase		:							List[Any]							=     ids_tensor((self.batch_size, 3)							,					config.vocab_size    )
       _UpperCAmelCase		:							List[str]							=     tf.cast(ids_tensor((self.batch_size, 3)							,					2    )							,					tf.inta    )

       # append to next input_ids and
       _UpperCAmelCase		:							Dict							=     tf.concat([input_ids, next_tokens]							,					axis=-1    )
       _UpperCAmelCase		:							Optional[Any]							=     tf.concat([attention_mask, next_attn_mask]							,					axis=-1    )

       _UpperCAmelCase		:							Tuple							=     model(A_							,					attention_mask=A_    )[0]
       _UpperCAmelCase		:							int							=     model(A_							,					attention_mask=A_							,					past_key_values=A_    )[0]

       self.parent.assertEqual(next_tokens.shape[1]							,					output_from_past.shape[1]    )

       # select random slice
       _UpperCAmelCase		:							Tuple							=     int(ids_tensor((1,)							,					output_from_past.shape[-1]    )    )
       _UpperCAmelCase		:							str							=     output_from_no_past[:, -3:, random_slice_idx]
       _UpperCAmelCase		:							str							=     output_from_past[:, :, random_slice_idx]

       # test that outputs are equal for slice
       tf.debugging.assert_near(A_							,					A_							,					rtol=1e-3    )




def    a__    (		snake_case__					:							Optional[Any]						,   snake_case__					:							str						,   snake_case__					:							Dict						,   snake_case__					:							str=None						,   snake_case__					:							Optional[int]=None						,   snake_case__					:							str=None						,   snake_case__					:							List[str]=None						,   snake_case__					:							List[str]=None						,   ):
  if attention_mask is None:
    _UpperCAmelCase		:							List[Any]							=     tf.cast(tf.math.not_equal(snake_case__						,   config.pad_token_id     )						,   tf.inta     )
  if decoder_attention_mask is None:
    _UpperCAmelCase		:							str							=     tf.concat(
        [
            tf.ones(decoder_input_ids[:, :1].shape						,   dtype=tf.inta     ),
            tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:]						,   config.pad_token_id     )						,   tf.inta     ),
        ]						,   axis=-1						,   )
  if head_mask is None:
    _UpperCAmelCase		:							List[str]							=     tf.ones((config.encoder_layers, config.encoder_attention_heads)     )
  if decoder_head_mask is None:
    _UpperCAmelCase		:							Tuple							=     tf.ones((config.decoder_layers, config.decoder_attention_heads)     )
  if cross_attn_head_mask is None:
    _UpperCAmelCase		:							Dict							=     tf.ones((config.decoder_layers, config.decoder_attention_heads)     )
  return {
      "input_ids": input_ids,
      "decoder_input_ids": decoder_input_ids,
      "attention_mask": attention_mask,
      "decoder_attention_mask": decoder_attention_mask,
      "head_mask": head_mask,
      "decoder_head_mask": decoder_head_mask,
      "cross_attn_head_mask": cross_attn_head_mask,
  }


@require_tf
class 		_SCREAMING_SNAKE_CASE     (						A							,	A							,	unittest.TestCase     ):
     __SCREAMING_SNAKE_CASE     					=    (TFBlenderbotForConditionalGeneration, TFBlenderbotModel) if is_tf_available() else ()
     __SCREAMING_SNAKE_CASE     					=    (TFBlenderbotForConditionalGeneration,) if is_tf_available() else ()
     __SCREAMING_SNAKE_CASE     					=    (
         {
             '''conversational''': TFBlenderbotForConditionalGeneration,
             '''feature-extraction''': TFBlenderbotModel,
             '''summarization''': TFBlenderbotForConditionalGeneration,
             '''text2text-generation''': TFBlenderbotForConditionalGeneration,
             '''translation''': TFBlenderbotForConditionalGeneration,
         }
         if is_tf_available()
         else {}
     )
     __SCREAMING_SNAKE_CASE     					=    True
     __SCREAMING_SNAKE_CASE     					=    False
     __SCREAMING_SNAKE_CASE     					=    False


     def        __snake_case(  self    ):
       _UpperCAmelCase		:							Union[str, Any]							=     TFBlenderbotModelTester(self    )
       _UpperCAmelCase		:							Any							=     ConfigTester(self							,					config_class=A_    )


     def        __snake_case(  self    ):
       self.config_tester.run_common_tests()




     def        __snake_case(  self    ):
       _UpperCAmelCase		:							Tuple							=     self.model_tester.prepare_config_and_inputs_for_common()
       self.model_tester.check_decoder_model_past_large_inputs(*A_    )


@require_tokenizers
@require_tf
class 		_SCREAMING_SNAKE_CASE     (						unittest.TestCase     ):
     __SCREAMING_SNAKE_CASE     					=    ['''My friends are cool but they eat too many carbs.''']
     __SCREAMING_SNAKE_CASE     					=    '''facebook/blenderbot-400M-distill'''


     @cached_property
     def        __snake_case(  self    ):
       return BlenderbotTokenizer.from_pretrained(self.model_name    )


     @cached_property
     def        __snake_case(  self    ):
       _UpperCAmelCase		:							List[Any]							=     TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name    )
       return model




     @slow
     def        __snake_case(  self    ):
       _UpperCAmelCase		:							Any							=     self.tokenizer(self.src_text							,					return_tensors="""tf"""    )
       _UpperCAmelCase		:							List[Any]							=     self.model.generate(
           model_inputs.input_ids							,					)
       _UpperCAmelCase		:							List[str]							=     self.tokenizer.batch_decode(generated_ids.numpy()							,					skip_special_tokens=A_    )[0]
       assert (
           generated_words
           == " That's unfortunate. Are they trying to lose weight or are they just trying to be healthier?"
       )





