




"""simple docstring"""

from __future__ import annotations

import random
import unittest

from transformers import TransfoXLConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

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 (
           TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
           TFTransfoXLForSequenceClassification,
           TFTransfoXLLMHeadModel,
           TFTransfoXLModel,
       )





class     lowercase_	:



 '''simple docstring'''

 def __init__(			self	:	str   ,							_UpperCAmelCase	:	str   ,							):
       _A        =       parent
       _A        =       13
       _A        =       7
       _A        =       30
       _A        =       self.seq_length + self.mem_len
       _A        =       15
       _A        =       True
       _A        =       True
       _A        =       99
       _A        =       [10, 50, 80]
       _A        =       32
       _A        =       32
       _A        =       4
       _A        =       8
       _A        =       128
       _A        =       2
       _A        =       2
       _A        =       None
       _A        =       1
       _A        =       0
       _A        =       3
       _A        =       self.vocab_size - 1
       _A        =       0.01

 def       lowerCAmelCase_							(			self	:	List[Any]		):
       _A        =       ids_tensor([self.batch_size, self.seq_length]   ,							self.vocab_size		)
       _A        =       ids_tensor([self.batch_size, self.seq_length]   ,							self.vocab_size		)

       _A        =       None
       if self.use_labels:
             _A        =       ids_tensor([self.batch_size, self.seq_length]   ,							self.vocab_size		)

       _A        =       TransfoXLConfig(
           vocab_size=self.vocab_size   ,							mem_len=self.mem_len   ,							clamp_len=self.clamp_len   ,							cutoffs=self.cutoffs   ,							d_model=self.hidden_size   ,							d_embed=self.d_embed   ,							n_head=self.num_attention_heads   ,							d_head=self.d_head   ,							d_inner=self.d_inner   ,							div_val=self.div_val   ,							n_layer=self.num_hidden_layers   ,							eos_token_id=self.eos_token_id   ,							pad_token_id=self.vocab_size - 1   ,							init_range=self.init_range   ,							num_labels=self.num_labels   ,							)

       return (config, input_ids_a, input_ids_a, lm_labels)

 def       lowerCAmelCase_							(			self	:	Optional[Any]		):
       random.seed(self.seed		)
       tf.random.set_seed(self.seed		)

 def       lowerCAmelCase_							(			self	:	Dict   ,							_UpperCAmelCase	:	Any   ,							_UpperCAmelCase	:	Tuple   ,							_UpperCAmelCase	:	Dict   ,							_UpperCAmelCase	:	Union[str, Any]		):
       _A        =       TFTransfoXLModel(_UpperCAmelCase		)

       _A       ,   _A        =       model(_UpperCAmelCase		).to_tuple()

       _A        =       {'input_ids': input_ids_a, 'mems': mems_a}

       _A       ,   _A        =       model(_UpperCAmelCase		).to_tuple()

       self.parent.assertEqual(hidden_states_a.shape   ,							(self.batch_size, self.seq_length, self.hidden_size)		)
       self.parent.assertEqual(hidden_states_a.shape   ,							(self.batch_size, self.seq_length, self.hidden_size)		)
       self.parent.assertListEqual(
           [mem.shape for mem in mems_a]   ,							[(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers   ,							)
       self.parent.assertListEqual(
           [mem.shape for mem in mems_a]   ,							[(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers   ,							)

 def       lowerCAmelCase_							(			self	:	List[str]   ,							_UpperCAmelCase	:	Optional[int]   ,							_UpperCAmelCase	:	List[str]   ,							_UpperCAmelCase	:	Optional[int]   ,							_UpperCAmelCase	:	List[str]		):
       _A        =       TFTransfoXLLMHeadModel(_UpperCAmelCase		)

       _A       ,   _A        =       model(_UpperCAmelCase		).to_tuple()

       _A        =       {'input_ids': input_ids_a, 'labels': lm_labels}
       _A       ,   _A        =       model(_UpperCAmelCase		).to_tuple()

       _A       ,   _A        =       model([input_ids_a, mems_a]		).to_tuple()

       _A        =       {'input_ids': input_ids_a, 'mems': mems_a, 'labels': lm_labels}

       _A       ,   _A        =       model(_UpperCAmelCase		).to_tuple()

       self.parent.assertEqual(lm_logits_a.shape   ,							(self.batch_size, self.seq_length, self.vocab_size)		)
       self.parent.assertListEqual(
           [mem.shape for mem in mems_a]   ,							[(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers   ,							)

       self.parent.assertEqual(lm_logits_a.shape   ,							(self.batch_size, self.seq_length, self.vocab_size)		)
       self.parent.assertListEqual(
           [mem.shape for mem in mems_a]   ,							[(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers   ,							)

 def       lowerCAmelCase_							(			self	:	Optional[Any]   ,							_UpperCAmelCase	:	int   ,							_UpperCAmelCase	:	List[Any]   ,							_UpperCAmelCase	:	List[Any]   ,							_UpperCAmelCase	:	List[Any]		):
       _A        =       TFTransfoXLForSequenceClassification(_UpperCAmelCase		)
       _A        =       model(_UpperCAmelCase		)
       self.parent.assertEqual(result.logits.shape   ,							(self.batch_size, self.num_labels)		)



 def       lowerCAmelCase_							(			self	:	Union[str, Any]		):
       _A        =       self.prepare_config_and_inputs()
       ((_A)       ,   (_A)       ,   (_A)       ,   (_A))        =       config_and_inputs
       _A        =       {'input_ids': input_ids_a}
       return config, inputs_dict





@require_tf
class     lowercase_	(					__lowerCAmelCase      ,     __lowerCAmelCase      ,     unittest.TestCase							):



 '''simple docstring'''
 UpperCAmelCase   :					Optional[int]				  =     (
     (TFTransfoXLModel, TFTransfoXLLMHeadModel, TFTransfoXLForSequenceClassification) if is_tf_available() else ()
 )
 UpperCAmelCase   :					Optional[Any]				  =     () if is_tf_available() else ()
 UpperCAmelCase   :					str				  =     (
     {
         '''feature-extraction''': TFTransfoXLModel,
         '''text-classification''': TFTransfoXLForSequenceClassification,
         '''text-generation''': TFTransfoXLLMHeadModel,
         '''zero-shot''': TFTransfoXLForSequenceClassification,
     }
     if is_tf_available()
     else {}
 )
 # TODO: add this test when TFTransfoXLLMHead has a linear output layer implemented
 UpperCAmelCase   :					str				  =     False
 UpperCAmelCase   :					str				  =     False
 UpperCAmelCase   :					List[Any]				  =     False
 UpperCAmelCase   :					Union[str, Any]				  =     False

 def       lowerCAmelCase_							(			self	:	List[str]   ,							_UpperCAmelCase	:	Union[str, Any]   ,							_UpperCAmelCase	:	Optional[int]   ,							_UpperCAmelCase	:	List[str]   ,							_UpperCAmelCase	:	Any   ,							_UpperCAmelCase	:	List[str]		):
       if pipeline_test_casse_name == "TextGenerationPipelineTests":
             # Get `ValueError: AttributeError: 'NoneType' object has no attribute 'new_ones'` or `AssertionError`.
             # `TransfoXLConfig` was never used in pipeline tests: cannot create a simple
             # tokenizer.
             return True

       return False

 def       lowerCAmelCase_							(			self	:	List[Any]		):
       _A        =       TFTransfoXLModelTester(self		)
       _A        =       ConfigTester(self   ,							config_class=_UpperCAmelCase   ,							d_embed=37		)

 def       lowerCAmelCase_							(			self	:	List[str]		):
       self.config_tester.run_common_tests()

 def       lowerCAmelCase_							(			self	:	int		):
       self.model_tester.set_seed()
       _A        =       self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_transfo_xl_model(*_UpperCAmelCase		)

 def       lowerCAmelCase_							(			self	:	List[str]		):
       self.model_tester.set_seed()
       _A        =       self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_transfo_xl_lm_head(*_UpperCAmelCase		)

 def       lowerCAmelCase_							(			self	:	List[Any]		):
       _A        =       self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_transfo_xl_for_sequence_classification(*_UpperCAmelCase		)

 def       lowerCAmelCase_							(			self	:	Tuple		):
       _A       ,   _A        =       self.model_tester.prepare_config_and_inputs_for_common()
       _A        =       [TFTransfoXLForSequenceClassification]

       for model_class in self.all_model_classes:
             _A        =       model_class(_UpperCAmelCase		)
             assert isinstance(model.get_input_embeddings()   ,							tf.keras.layers.Layer		)
             if model_class in list_other_models_with_output_ebd:
                   _A        =       model.get_output_embeddings()
                   assert isinstance(_UpperCAmelCase   ,							tf.keras.layers.Layer		)
                   _A        =       model.get_bias()
                   assert name is None
             else:
                   _A        =       model.get_output_embeddings()
                   assert x is None
                   _A        =       model.get_bias()
                   assert name is None

 def       lowerCAmelCase_							(			self	:	Union[str, Any]		):
       # TODO JP: Make TransfoXL XLA compliant
       pass

 @slow
 def       lowerCAmelCase_							(			self	:	Optional[Any]		):
       for model_name in TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             _A        =       TFTransfoXLModel.from_pretrained(_UpperCAmelCase		)
             self.assertIsNotNone(_UpperCAmelCase		)



 @unittest.skip(reason='This model doesn\'t play well with fit() due to not returning a single loss.'		)
 def       lowerCAmelCase_							(			self	:	Any		):
       pass





@require_tf
class     lowercase_	(					unittest.TestCase							):



 '''simple docstring'''

 @unittest.skip('Skip test until #12651 is resolved.'		)
 @slow
 def       lowerCAmelCase_							(			self	:	List[str]		):
       _A        =       TFTransfoXLLMHeadModel.from_pretrained('transfo-xl-wt103'		)
       # fmt: off
       _A        =       tf.convert_to_tensor([[33,1_297,2,1,1_009,4,1_109,11_739,4_762,358,5,25,245,22,1_706,17,20_098,5,3_215,21,37,1_110,3,13,1_041,4,24,603,490,2,71_477,20_098,104_447,2,20_961,1,2_604,4,1,329,3,6_224,831,16_002,2,8,603,78_967,29_546,23,803,20,25,416,5,8,232,4,277,6,1_855,4_601,3,29_546,54,8,3_609,5,57_211,49,4,1,277,18,8,1_755,15_691,3,341,25,416,693,42_573,71,17,401,94,31,17_919,2,29_546,7_873,18,1,435,23,11_011,755,5,5_167,3,7_983,98,84,2,29_546,3_267,8,3_609,4,1,4_865,1_075,2,6_087,71,6,346,8,5_854,3,29_546,824,1_400,1_868,2,19,160,2,311,8,5_496,2,20_920,17,25,15_097,3,24,24,0]]   ,							dtype=tf.intaa		)  # noqa: E231
       # fmt: on
       #  In 1991 , the remains of Russian Tsar Nicholas II and his family
       #  ( except for Alexei and Maria ) are discovered .
       #  The voice of Nicholas's young son , Tsarevich Alexei Nikolaevich , narrates the
       #  remainder of the story . 1883 Western Siberia ,
       #  a young Grigori Rasputin is asked by his father and a group of men to perform magic .
       #  Rasputin has a vision and denounces one of the men as a horse thief . Although his
       #  father initially slaps him for making such an accusation , Rasputin watches as the
       #  man is chased outside and beaten . Twenty years later , Rasputin sees a vision of
       #  the Virgin Mary , prompting him to become a priest . Rasputin quickly becomes famous ,
       #  with people , even a bishop , begging for his blessing . <eod> </s> <eos>

       # fmt: off
       _A        =       [33,1_297,2,1,1_009,4,1_109,11_739,4_762,358,5,25,245,22,1_706,17,20_098,5,3_215,21,37,1_110,3,13,1_041,4,24,603,490,2,71_477,20_098,104_447,2,20_961,1,2_604,4,1,329,3,6_224,831,16_002,2,8,603,78_967,29_546,23,803,20,25,416,5,8,232,4,277,6,1_855,4_601,3,29_546,54,8,3_609,5,57_211,49,4,1,277,18,8,1_755,15_691,3,341,25,416,693,42_573,71,17,401,94,31,17_919,2,29_546,7_873,18,1,435,23,11_011,755,5,5_167,3,7_983,98,84,2,29_546,3_267,8,3_609,4,1,4_865,1_075,2,6_087,71,6,346,8,5_854,3,29_546,824,1_400,1_868,2,19,160,2,311,8,5_496,2,20_920,17,25,15_097,3,24,24,0,33,1,1_857,2,1,1_009,4,1_109,11_739,4_762,358,5,25,245,28,1_110,3,13,1_041,4,24,603,490,2,71_477,20_098,104_447,2,20_961,1,2_604,4,1,329,3,0]  # noqa: E231
       # fmt: on
       #  In 1991, the remains of Russian Tsar Nicholas II and his family (
       #  except for Alexei and Maria ) are discovered. The voice of young son,
       #  Tsarevich Alexei Nikolaevich, narrates the remainder of the story.
       #  1883 Western Siberia, a young Grigori Rasputin is asked by his father
       #  and a group of men to perform magic. Rasputin has a vision and
       #  denounces one of the men as a horse thief. Although his father initially
       #  slaps him for making such an accusation, Rasputin watches as the man
       #  is chased outside and beaten. Twenty years later, Rasputin sees a vision
       #  of the Virgin Mary, prompting him to become a priest.
       #  Rasputin quickly becomes famous, with people, even a bishop, begging for
       #  his blessing. <unk> <unk> <eos> In the 1990s, the remains of Russian Tsar
       # Nicholas II and his family were discovered. The voice of <unk> young son,
       # Tsarevich Alexei Nikolaevich, narrates the remainder of the story.<eos>

       _A        =       model.generate(_UpperCAmelCase   ,							max_length=200   ,							do_sample=_UpperCAmelCase		)
       self.assertListEqual(output_ids[0].numpy().tolist()   ,							_UpperCAmelCase		)







"""simple docstring"""

import tempfile

import torch

from diffusers import IPNDMScheduler

from .test_schedulers import SchedulerCommonTest





class     lowercase_	(					__lowerCAmelCase							):



 '''simple docstring'''
 UpperCAmelCase   :					Union[str, Any]				  =     (IPNDMScheduler,)
 UpperCAmelCase   :					Optional[Any]				  =     (('''num_inference_steps''', 50),)

 def       lowerCAmelCase_							(			self	:	Union[str, Any]   ,							**_UpperCAmelCase	:	List[Any]		):
       _A        =       {'num_train_timesteps': 1_000}
       config.update(**_UpperCAmelCase		)
       return config

 def       lowerCAmelCase_							(			self	:	Tuple   ,							_UpperCAmelCase	:	Optional[int]=0   ,							**_UpperCAmelCase	:	Union[str, Any]		):
       _A        =       dict(self.forward_default_kwargs		)
       _A        =       kwargs.pop('num_inference_steps'   ,							_UpperCAmelCase		)
       _A        =       self.dummy_sample
       _A        =       0.1 * sample
       _A        =       [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

       for scheduler_class in self.scheduler_classes:
             _A        =       self.get_scheduler_config(**_UpperCAmelCase		)
             _A        =       scheduler_class(**_UpperCAmelCase		)
             scheduler.set_timesteps(_UpperCAmelCase		)
             # copy over dummy past residuals
             _A        =       dummy_past_residuals[:]

             if time_step is None:
                   _A        =       scheduler.timesteps[len(scheduler.timesteps		) // 2]

             with tempfile.TemporaryDirectory() as tmpdirname:
                   scheduler.save_config(_UpperCAmelCase		)
                   _A        =       scheduler_class.from_pretrained(_UpperCAmelCase		)
                   new_scheduler.set_timesteps(_UpperCAmelCase		)
                   # copy over dummy past residuals
                   _A        =       dummy_past_residuals[:]

             _A        =       scheduler.step(_UpperCAmelCase   ,							_UpperCAmelCase   ,							_UpperCAmelCase   ,							**_UpperCAmelCase		).prev_sample
             _A        =       new_scheduler.step(_UpperCAmelCase   ,							_UpperCAmelCase   ,							_UpperCAmelCase   ,							**_UpperCAmelCase		).prev_sample

             assert torch.sum(torch.abs(output - new_output		)		) < 1E-5, "Scheduler outputs are not identical"

             _A        =       scheduler.step(_UpperCAmelCase   ,							_UpperCAmelCase   ,							_UpperCAmelCase   ,							**_UpperCAmelCase		).prev_sample
             _A        =       new_scheduler.step(_UpperCAmelCase   ,							_UpperCAmelCase   ,							_UpperCAmelCase   ,							**_UpperCAmelCase		).prev_sample

             assert torch.sum(torch.abs(output - new_output		)		) < 1E-5, "Scheduler outputs are not identical"

 def       lowerCAmelCase_							(			self	:	str		):
       pass

 def       lowerCAmelCase_							(			self	:	Optional[Any]   ,							_UpperCAmelCase	:	Any=0   ,							**_UpperCAmelCase	:	Any		):
       _A        =       dict(self.forward_default_kwargs		)
       _A        =       kwargs.pop('num_inference_steps'   ,							_UpperCAmelCase		)
       _A        =       self.dummy_sample
       _A        =       0.1 * sample
       _A        =       [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

       for scheduler_class in self.scheduler_classes:
             _A        =       self.get_scheduler_config()
             _A        =       scheduler_class(**_UpperCAmelCase		)
             scheduler.set_timesteps(_UpperCAmelCase		)

             # copy over dummy past residuals (must be after setting timesteps)
             _A        =       dummy_past_residuals[:]

             if time_step is None:
                   _A        =       scheduler.timesteps[len(scheduler.timesteps		) // 2]

             with tempfile.TemporaryDirectory() as tmpdirname:
                   scheduler.save_config(_UpperCAmelCase		)
                   _A        =       scheduler_class.from_pretrained(_UpperCAmelCase		)
                   # copy over dummy past residuals
                   new_scheduler.set_timesteps(_UpperCAmelCase		)

                   # copy over dummy past residual (must be after setting timesteps)
                   _A        =       dummy_past_residuals[:]

             _A        =       scheduler.step(_UpperCAmelCase   ,							_UpperCAmelCase   ,							_UpperCAmelCase   ,							**_UpperCAmelCase		).prev_sample
             _A        =       new_scheduler.step(_UpperCAmelCase   ,							_UpperCAmelCase   ,							_UpperCAmelCase   ,							**_UpperCAmelCase		).prev_sample

             assert torch.sum(torch.abs(output - new_output		)		) < 1E-5, "Scheduler outputs are not identical"

             _A        =       scheduler.step(_UpperCAmelCase   ,							_UpperCAmelCase   ,							_UpperCAmelCase   ,							**_UpperCAmelCase		).prev_sample
             _A        =       new_scheduler.step(_UpperCAmelCase   ,							_UpperCAmelCase   ,							_UpperCAmelCase   ,							**_UpperCAmelCase		).prev_sample

             assert torch.sum(torch.abs(output - new_output		)		) < 1E-5, "Scheduler outputs are not identical"

 def       lowerCAmelCase_							(			self	:	List[str]   ,							**_UpperCAmelCase	:	Optional[int]		):
       _A        =       self.scheduler_classes[0]
       _A        =       self.get_scheduler_config(**_UpperCAmelCase		)
       _A        =       scheduler_class(**_UpperCAmelCase		)

       _A        =       10
       _A        =       self.dummy_model()
       _A        =       self.dummy_sample_deter
       scheduler.set_timesteps(_UpperCAmelCase		)

       for i, t in enumerate(scheduler.timesteps		):
             _A        =       model(_UpperCAmelCase   ,							_UpperCAmelCase		)
             _A        =       scheduler.step(_UpperCAmelCase   ,							_UpperCAmelCase   ,							_UpperCAmelCase		).prev_sample

       for i, t in enumerate(scheduler.timesteps		):
             _A        =       model(_UpperCAmelCase   ,							_UpperCAmelCase		)
             _A        =       scheduler.step(_UpperCAmelCase   ,							_UpperCAmelCase   ,							_UpperCAmelCase		).prev_sample

       return sample

 def       lowerCAmelCase_							(			self	:	Union[str, Any]		):
       _A        =       dict(self.forward_default_kwargs		)

       _A        =       kwargs.pop('num_inference_steps'   ,							_UpperCAmelCase		)

       for scheduler_class in self.scheduler_classes:
             _A        =       self.get_scheduler_config()
             _A        =       scheduler_class(**_UpperCAmelCase		)

             _A        =       self.dummy_sample
             _A        =       0.1 * sample

             if num_inference_steps is not None and hasattr(_UpperCAmelCase   ,							'set_timesteps'		):
                   scheduler.set_timesteps(_UpperCAmelCase		)
             elif num_inference_steps is not None and not hasattr(_UpperCAmelCase   ,							'set_timesteps'		):
                   _A        =       num_inference_steps

             # copy over dummy past residuals (must be done after set_timesteps)
             _A        =       [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
             _A        =       dummy_past_residuals[:]

             _A        =       scheduler.timesteps[5]
             _A        =       scheduler.timesteps[6]

             _A        =       scheduler.step(_UpperCAmelCase   ,							_UpperCAmelCase   ,							_UpperCAmelCase   ,							**_UpperCAmelCase		).prev_sample
             _A        =       scheduler.step(_UpperCAmelCase   ,							_UpperCAmelCase   ,							_UpperCAmelCase   ,							**_UpperCAmelCase		).prev_sample

             self.assertEqual(output_a.shape   ,							sample.shape		)
             self.assertEqual(output_a.shape   ,							output_a.shape		)

             _A        =       scheduler.step(_UpperCAmelCase   ,							_UpperCAmelCase   ,							_UpperCAmelCase   ,							**_UpperCAmelCase		).prev_sample
             _A        =       scheduler.step(_UpperCAmelCase   ,							_UpperCAmelCase   ,							_UpperCAmelCase   ,							**_UpperCAmelCase		).prev_sample

             self.assertEqual(output_a.shape   ,							sample.shape		)
             self.assertEqual(output_a.shape   ,							output_a.shape		)

 def       lowerCAmelCase_							(			self	:	Tuple		):
       for timesteps in [100, 1_000]:
             self.check_over_configs(num_train_timesteps=_UpperCAmelCase   ,							time_step=_UpperCAmelCase		)

 def       lowerCAmelCase_							(			self	:	List[Any]		):
       for t, num_inference_steps in zip([1, 5, 10]   ,							[10, 50, 100]		):
             self.check_over_forward(num_inference_steps=_UpperCAmelCase   ,							time_step=_UpperCAmelCase		)



 def       lowerCAmelCase_							(			self	:	Optional[int]		):
       _A        =       self.full_loop()
       _A        =       torch.mean(torch.abs(_UpperCAmelCase		)		)

       assert abs(result_mean.item() - 2_540_529		) < 10

