
from __future__ import annotations

import unittest

from transformers import 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, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
     import tensorflow as tf

     from transformers import (
         TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
         TFXLMForMultipleChoice,
         TFXLMForQuestionAnsweringSimple,
         TFXLMForSequenceClassification,
         TFXLMForTokenClassification,
         TFXLMModel,
         TFXLMWithLMHeadModel,
         XLMConfig,
     )







class 	_snake_case						:




 def __init__(	self      ,						_lowerCamelCase      ,						):
       a		:Optional[Any]											= parent
       a		:Optional[int]											= 13
       a		:int											= 7
       a		:Optional[int]											= True
       a		:Any											= True
       a		:str											= True
       a		:Any											= True
       a		:Optional[int]											= True
       a		:List[Any]											= False
       a		:int											= False
       a		:List[str]											= False
       a		:List[Any]											= 2
       a		:str											= 99
       a		:Optional[int]											= 0
       a		:str											= 32
       a		:str											= 2
       a		:Optional[Any]											= 4
       a		:Any											= 0.1
       a		:List[Any]											= 0.1
       a		:List[Any]											= 512
       a		:List[str]											= 16
       a		:Optional[int]											= 2
       a		:Dict											= 0.02
       a		:Tuple											= 3
       a		:Dict											= 4
       a		:Dict											= 'last'
       a		:Dict											= True
       a		:Optional[int]											= None
       a		:Tuple											= 0




 def  SCREAMING_SNAKE_CASE__       (	self					):
       a		:Any											= ids_tensor([self.batch_size, self.seq_length]      ,						self.vocab_size					)
       a		:Tuple											= random_attention_mask([self.batch_size, self.seq_length]      ,						dtype=tf.floataa					)

       a		:Tuple											= None
       if self.use_input_lengths:
             a		:str											= (
                 ids_tensor([self.batch_size]      ,						vocab_size=2					) + self.seq_length - 2
             )  # small variation of seq_length

       a		:Optional[Any]											= None
       if self.use_token_type_ids:
             a		:Union[str, Any]											= ids_tensor([self.batch_size, self.seq_length]      ,						self.n_langs					)

       a		:Optional[int]											= None
       a		:Optional[int]											= None
       a		:Dict											= None
       if self.use_labels:
             a		:Tuple											= ids_tensor([self.batch_size]      ,						self.type_sequence_label_size					)
             a		:Any											= ids_tensor([self.batch_size, self.seq_length]      ,						self.num_labels					)
             a		:Optional[Any]											= ids_tensor([self.batch_size]      ,						2      ,						dtype=tf.floataa					)
             a		:Tuple											= ids_tensor([self.batch_size]      ,						self.num_choices					)

       a		:Union[str, Any]											= XLMConfig(
           vocab_size=self.vocab_size      ,						n_special=self.n_special      ,						emb_dim=self.hidden_size      ,						n_layers=self.num_hidden_layers      ,						n_heads=self.num_attention_heads      ,						dropout=self.hidden_dropout_prob      ,						attention_dropout=self.attention_probs_dropout_prob      ,						gelu_activation=self.gelu_activation      ,						sinusoidal_embeddings=self.sinusoidal_embeddings      ,						asm=self.asm      ,						causal=self.causal      ,						n_langs=self.n_langs      ,						max_position_embeddings=self.max_position_embeddings      ,						initializer_range=self.initializer_range      ,						summary_type=self.summary_type      ,						use_proj=self.use_proj      ,						bos_token_id=self.bos_token_id      ,						)

       return (
           config,
           input_ids,
           token_type_ids,
           input_lengths,
           sequence_labels,
           token_labels,
           is_impossible_labels,
           choice_labels,
           input_mask,
       )




 def  SCREAMING_SNAKE_CASE__       (	self      ,						_lowerCamelCase      ,						_lowerCamelCase      ,						_lowerCamelCase      ,						_lowerCamelCase      ,						_lowerCamelCase      ,						_lowerCamelCase      ,						_lowerCamelCase      ,						_lowerCamelCase      ,						_lowerCamelCase      ,						):
       a		:Union[str, Any]											= TFXLMModel(config=__a					)
       a		:Optional[int]											= {'input_ids': input_ids, 'lengths': input_lengths, 'langs': token_type_ids}
       a		:int											= model(__a					)

       a		:Tuple											= [input_ids, input_mask]
       a		:Union[str, Any]											= model(__a					)
       self.parent.assertEqual(result.last_hidden_state.shape      ,						(self.batch_size, self.seq_length, self.hidden_size)					)




 def  SCREAMING_SNAKE_CASE__       (	self      ,						_lowerCamelCase      ,						_lowerCamelCase      ,						_lowerCamelCase      ,						_lowerCamelCase      ,						_lowerCamelCase      ,						_lowerCamelCase      ,						_lowerCamelCase      ,						_lowerCamelCase      ,						_lowerCamelCase      ,						):
       a		:Any											= TFXLMWithLMHeadModel(__a					)

       a		:List[str]											= {'input_ids': input_ids, 'lengths': input_lengths, 'langs': token_type_ids}
       a		:Dict											= model(__a					)

       a		:Tuple											= outputs

       self.parent.assertEqual(result.logits.shape      ,						(self.batch_size, self.seq_length, self.vocab_size)					)




 def  SCREAMING_SNAKE_CASE__       (	self      ,						_lowerCamelCase      ,						_lowerCamelCase      ,						_lowerCamelCase      ,						_lowerCamelCase      ,						_lowerCamelCase      ,						_lowerCamelCase      ,						_lowerCamelCase      ,						_lowerCamelCase      ,						_lowerCamelCase      ,						):
       a		:Optional[Any]											= TFXLMForQuestionAnsweringSimple(__a					)

       a		:Any											= {'input_ids': input_ids, 'lengths': input_lengths}

       a		:List[str]											= model(__a					)

       self.parent.assertEqual(result.start_logits.shape      ,						(self.batch_size, self.seq_length)					)
       self.parent.assertEqual(result.end_logits.shape      ,						(self.batch_size, self.seq_length)					)




 def  SCREAMING_SNAKE_CASE__       (	self      ,						_lowerCamelCase      ,						_lowerCamelCase      ,						_lowerCamelCase      ,						_lowerCamelCase      ,						_lowerCamelCase      ,						_lowerCamelCase      ,						_lowerCamelCase      ,						_lowerCamelCase      ,						_lowerCamelCase      ,						):
       a		:Union[str, Any]											= TFXLMForSequenceClassification(__a					)

       a		:int											= {'input_ids': input_ids, 'lengths': input_lengths}

       a		:Dict											= model(__a					)

       self.parent.assertEqual(result.logits.shape      ,						(self.batch_size, self.type_sequence_label_size)					)




 def  SCREAMING_SNAKE_CASE__       (	self      ,						_lowerCamelCase      ,						_lowerCamelCase      ,						_lowerCamelCase      ,						_lowerCamelCase      ,						_lowerCamelCase      ,						_lowerCamelCase      ,						_lowerCamelCase      ,						_lowerCamelCase      ,						_lowerCamelCase      ,						):
       a		:Union[str, Any]											= self.num_labels
       a		:int											= TFXLMForTokenClassification(config=__a					)
       a		:Optional[Any]											= {'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}
       a		:Tuple											= model(__a					)
       self.parent.assertEqual(result.logits.shape      ,						(self.batch_size, self.seq_length, self.num_labels)					)




 def  SCREAMING_SNAKE_CASE__       (	self      ,						_lowerCamelCase      ,						_lowerCamelCase      ,						_lowerCamelCase      ,						_lowerCamelCase      ,						_lowerCamelCase      ,						_lowerCamelCase      ,						_lowerCamelCase      ,						_lowerCamelCase      ,						_lowerCamelCase      ,						):
       a		:Any											= self.num_choices
       a		:Dict											= TFXLMForMultipleChoice(config=__a					)
       a		:List[Any]											= tf.tile(tf.expand_dims(__a      ,						1					)      ,						(1, self.num_choices, 1)					)
       a		:Tuple											= tf.tile(tf.expand_dims(__a      ,						1					)      ,						(1, self.num_choices, 1)					)
       a		:int											= tf.tile(tf.expand_dims(__a      ,						1					)      ,						(1, self.num_choices, 1)					)
       a		:Dict											= {
           'input_ids': multiple_choice_inputs_ids,
           'attention_mask': multiple_choice_input_mask,
           'token_type_ids': multiple_choice_token_type_ids,
       }
       a		:List[Any]											= model(__a					)
       self.parent.assertEqual(result.logits.shape      ,						(self.batch_size, self.num_choices)					)




 def  SCREAMING_SNAKE_CASE__       (	self					):
       a		:Dict											= self.prepare_config_and_inputs()
       (
           a       
       )		:Dict											= config_and_inputs
       a		:Dict											= {
           'input_ids': input_ids,
           'token_type_ids': token_type_ids,
           'langs': token_type_ids,
           'lengths': input_lengths,
       }
       return config, inputs_dict







@require_tf
class 	_snake_case						(   lowerCAmelCase_		,					lowerCAmelCase_		,					unittest.TestCase							):
 SCREAMING_SNAKE_CASE__   = (
     (
         TFXLMModel,
         TFXLMWithLMHeadModel,
         TFXLMForSequenceClassification,
         TFXLMForQuestionAnsweringSimple,
         TFXLMForTokenClassification,
         TFXLMForMultipleChoice,
     )
     if is_tf_available()
     else ()
 )
 SCREAMING_SNAKE_CASE__   = (
     (TFXLMWithLMHeadModel,) if is_tf_available() else ()
 )  # TODO (PVP): Check other models whether language generation is also applicable
 SCREAMING_SNAKE_CASE__   = (
     {
         'feature-extraction': TFXLMModel,
         'fill-mask': TFXLMWithLMHeadModel,
         'question-answering': TFXLMForQuestionAnsweringSimple,
         'text-classification': TFXLMForSequenceClassification,
         'text-generation': TFXLMWithLMHeadModel,
         'token-classification': TFXLMForTokenClassification,
         'zero-shot': TFXLMForSequenceClassification,
     }
     if is_tf_available()
     else {}
 )
 SCREAMING_SNAKE_CASE__   = False
 SCREAMING_SNAKE_CASE__   = False




 def  SCREAMING_SNAKE_CASE__       (	self      ,						_lowerCamelCase      ,						_lowerCamelCase      ,						_lowerCamelCase      ,						_lowerCamelCase      ,						_lowerCamelCase					):





       if (
           pipeline_test_casse_name == "QAPipelineTests"
           and tokenizer_name is not None
           and not tokenizer_name.endswith('''Fast'''					)
       ):
             # `QAPipelineTests` fails for a few models when the slower tokenizer are used.
             # (The slower tokenizers were never used for pipeline tests before the pipeline testing rework)
             # TODO: check (and possibly fix) the `QAPipelineTests` with slower tokenizer
             return True

       return False




 def  SCREAMING_SNAKE_CASE__       (	self					):
       a		:Optional[Any]											= TFXLMModelTester(self					)
       a		:Optional[int]											= ConfigTester(self      ,						config_class=__a      ,						emb_dim=37					)




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




 def  SCREAMING_SNAKE_CASE__       (	self					):
       a		:List[str]											= self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_xlm_model(*__a					)




 def  SCREAMING_SNAKE_CASE__       (	self					):
       a		:Union[str, Any]											= self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_xlm_lm_head(*__a					)




 def  SCREAMING_SNAKE_CASE__       (	self					):
       a		:int											= self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_xlm_qa(*__a					)




 def  SCREAMING_SNAKE_CASE__       (	self					):
       a		:Tuple											= self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_xlm_sequence_classif(*__a					)




 def  SCREAMING_SNAKE_CASE__       (	self					):
       a		:str											= self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_xlm_for_token_classification(*__a					)




 def  SCREAMING_SNAKE_CASE__       (	self					):
       a		:Dict											= self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_xlm_for_multiple_choice(*__a					)




 @slow
 def  SCREAMING_SNAKE_CASE__       (	self					):





       for model_name in TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             a		:Union[str, Any]											= TFXLMModel.from_pretrained(__a					)
             self.assertIsNotNone(__a					)







@require_tf
class 	_snake_case						(   unittest.TestCase							):




 @slow
 def  SCREAMING_SNAKE_CASE__       (	self					):
       a		:Tuple											= TFXLMWithLMHeadModel.from_pretrained('''xlm-mlm-en-2048'''					)
       a		:Any											= tf.convert_to_tensor([[14, 447]]      ,						dtype=tf.intaa					)  # the president
       a		:Optional[int]											= [
           14,
           447,
           14,
           447,
           14,
           447,
           14,
           447,
           14,
           447,
           14,
           447,
           14,
           447,
           14,
           447,
           14,
           447,
           14,
           447,
       ]  # the president the president the president the president the president the president the president the president the president the president
       # TODO(PVP): this and other input_ids I tried for generation give pretty bad results. Not sure why. Model might just not be made for auto-regressive inference
       a		:List[str]											= model.generate(__a      ,						do_sample=__a					)
       self.assertListEqual(output_ids[0].numpy().tolist()      ,						__a					)









import tempfile
import unittest

import numpy as np
import torch

from diffusers import ScoreSdeVeScheduler







class 	_snake_case						(   unittest.TestCase							):
 # TODO adapt with class SchedulerCommonTest (scheduler needs Numpy Integration)
 SCREAMING_SNAKE_CASE__   = (ScoreSdeVeScheduler,)
 SCREAMING_SNAKE_CASE__   = ()




 @property
 def  SCREAMING_SNAKE_CASE__       (	self					):
       a		:Tuple											= 4
       a		:List[str]											= 3
       a		:Any											= 8
       a		:Any											= 8

       a		:List[str]											= torch.rand((batch_size, num_channels, height, width)					)

       return sample




 @property
 def  SCREAMING_SNAKE_CASE__       (	self					):
       a		:int											= 4
       a		:Union[str, Any]											= 3
       a		:Optional[Any]											= 8
       a		:Tuple											= 8

       a		:Dict											= batch_size * num_channels * height * width
       a		:Optional[int]											= torch.arange(_lowerCamelCase					)
       a		:List[Any]											= sample.reshape(_lowerCamelCase      ,						_lowerCamelCase      ,						_lowerCamelCase      ,						_lowerCamelCase					)
       a		:Union[str, Any]											= sample / num_elems
       a		:Dict											= sample.permute(3      ,						0      ,						1      ,						2					)

       return sample




 def  SCREAMING_SNAKE_CASE__       (	self					):
       def model(_lowerCamelCase      ,						_lowerCamelCase      ,						*_lowerCamelCase					):
             return sample * t / (t + 1)

       return model




 def  SCREAMING_SNAKE_CASE__       (	self      ,						**_lowerCamelCase					):
       a		:str											= {
           '''num_train_timesteps''': 2000,
           '''snr''': 0.15,
           '''sigma_min''': 0.01,
           '''sigma_max''': 1348,
           '''sampling_eps''': 1e-5,
       }

       config.update(**_lowerCamelCase					)
       return config




 def  SCREAMING_SNAKE_CASE__       (	self      ,						_lowerCamelCase=0      ,						**_lowerCamelCase					):
       a		:Optional[int]											= dict(self.forward_default_kwargs					)

       for scheduler_class in self.scheduler_classes:
             a		:Union[str, Any]											= self.dummy_sample
             a		:Tuple											= 0.1 * sample

             a		:Union[str, Any]											= self.get_scheduler_config(**_lowerCamelCase					)
             a		:Union[str, Any]											= scheduler_class(**_lowerCamelCase					)

             with tempfile.TemporaryDirectory() as tmpdirname:
                   scheduler.save_config(_lowerCamelCase					)
                   a		:Optional[int]											= scheduler_class.from_pretrained(_lowerCamelCase					)

             a		:List[str]											= scheduler.step_pred(
                 _lowerCamelCase      ,						_lowerCamelCase      ,						_lowerCamelCase      ,						generator=torch.manual_seed(0					)      ,						**_lowerCamelCase					).prev_sample
             a		:Dict											= new_scheduler.step_pred(
                 _lowerCamelCase      ,						_lowerCamelCase      ,						_lowerCamelCase      ,						generator=torch.manual_seed(0					)      ,						**_lowerCamelCase					).prev_sample

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

             a		:int											= scheduler.step_correct(_lowerCamelCase      ,						_lowerCamelCase      ,						generator=torch.manual_seed(0					)      ,						**_lowerCamelCase					).prev_sample
             a		:Optional[Any]											= new_scheduler.step_correct(
                 _lowerCamelCase      ,						_lowerCamelCase      ,						generator=torch.manual_seed(0					)      ,						**_lowerCamelCase					).prev_sample

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




 def  SCREAMING_SNAKE_CASE__       (	self      ,						_lowerCamelCase=0      ,						**_lowerCamelCase					):
       a		:Dict											= dict(self.forward_default_kwargs					)
       kwargs.update(_lowerCamelCase					)

       for scheduler_class in self.scheduler_classes:
             a		:int											= self.dummy_sample
             a		:str											= 0.1 * sample

             a		:Optional[Any]											= self.get_scheduler_config()
             a		:Union[str, Any]											= scheduler_class(**_lowerCamelCase					)

             with tempfile.TemporaryDirectory() as tmpdirname:
                   scheduler.save_config(_lowerCamelCase					)
                   a		:Union[str, Any]											= scheduler_class.from_pretrained(_lowerCamelCase					)

             a		:Union[str, Any]											= scheduler.step_pred(
                 _lowerCamelCase      ,						_lowerCamelCase      ,						_lowerCamelCase      ,						generator=torch.manual_seed(0					)      ,						**_lowerCamelCase					).prev_sample
             a		:Optional[int]											= new_scheduler.step_pred(
                 _lowerCamelCase      ,						_lowerCamelCase      ,						_lowerCamelCase      ,						generator=torch.manual_seed(0					)      ,						**_lowerCamelCase					).prev_sample

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

             a		:List[str]											= scheduler.step_correct(_lowerCamelCase      ,						_lowerCamelCase      ,						generator=torch.manual_seed(0					)      ,						**_lowerCamelCase					).prev_sample
             a		:Tuple											= new_scheduler.step_correct(
                 _lowerCamelCase      ,						_lowerCamelCase      ,						generator=torch.manual_seed(0					)      ,						**_lowerCamelCase					).prev_sample

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




 def  SCREAMING_SNAKE_CASE__       (	self					):
       for timesteps in [10, 100, 1000]:
             self.check_over_configs(num_train_timesteps=_lowerCamelCase					)




 def  SCREAMING_SNAKE_CASE__       (	self					):
       for sigma_min, sigma_max in zip([0.0001, 0.001, 0.01]      ,						[1, 100, 1000]					):
             self.check_over_configs(sigma_min=_lowerCamelCase      ,						sigma_max=_lowerCamelCase					)




 def  SCREAMING_SNAKE_CASE__       (	self					):
       for t in [0.1, 0.5, 0.75]:
             self.check_over_forward(time_step=_lowerCamelCase					)




 def  SCREAMING_SNAKE_CASE__       (	self					):
       a		:Dict											= dict(self.forward_default_kwargs					)

       a		:Optional[Any]											= self.scheduler_classes[0]
       a		:Union[str, Any]											= self.get_scheduler_config()
       a		:List[Any]											= scheduler_class(**_lowerCamelCase					)

       a		:Union[str, Any]											= 3

       a		:List[Any]											= self.dummy_model()
       a		:Dict											= self.dummy_sample_deter

       scheduler.set_sigmas(_lowerCamelCase					)
       scheduler.set_timesteps(_lowerCamelCase					)
       a		:str											= torch.manual_seed(0					)

       for i, t in enumerate(scheduler.timesteps					):
             a		:Optional[int]											= scheduler.sigmas[i]

             for _ in range(scheduler.config.correct_steps					):
                   with torch.no_grad():
                         a		:Dict											= model(_lowerCamelCase      ,						_lowerCamelCase					)
                   a		:int											= scheduler.step_correct(_lowerCamelCase      ,						_lowerCamelCase      ,						generator=_lowerCamelCase      ,						**_lowerCamelCase					).prev_sample

             with torch.no_grad():
                   a		:Any											= model(_lowerCamelCase      ,						_lowerCamelCase					)

             a		:Optional[Any]											= scheduler.step_pred(_lowerCamelCase      ,						_lowerCamelCase      ,						_lowerCamelCase      ,						generator=_lowerCamelCase      ,						**_lowerCamelCase					)
             a							,       a		:Any											= output.prev_sample, output.prev_sample_mean

       a		:Dict											= torch.sum(torch.abs(_lowerCamelCase					)					)
       a		:int											= torch.mean(torch.abs(_lowerCamelCase					)					)

       assert np.isclose(result_sum.item()      ,						143_7275_8528.0					)
       assert np.isclose(result_mean.item()      ,						1871_4530.0					)




 def  SCREAMING_SNAKE_CASE__       (	self					):
       a		:str											= dict(self.forward_default_kwargs					)

       a		:Union[str, Any]											= kwargs.pop('''num_inference_steps'''      ,						_lowerCamelCase					)

       for scheduler_class in self.scheduler_classes:
             a		:Optional[int]											= self.get_scheduler_config()
             a		:str											= scheduler_class(**_lowerCamelCase					)

             a		:List[str]											= self.dummy_sample
             a		:List[Any]											= 0.1 * sample

             if num_inference_steps is not None and hasattr(_lowerCamelCase      ,						'''set_timesteps'''					):
                   scheduler.set_timesteps(_lowerCamelCase					)
             elif num_inference_steps is not None and not hasattr(_lowerCamelCase      ,						'''set_timesteps'''					):
                   a		:Dict											= num_inference_steps

             a		:Tuple											= scheduler.step_pred(_lowerCamelCase      ,						0      ,						_lowerCamelCase      ,						generator=torch.manual_seed(0					)      ,						**_lowerCamelCase					).prev_sample
             a		:Tuple											= scheduler.step_pred(_lowerCamelCase      ,						1      ,						_lowerCamelCase      ,						generator=torch.manual_seed(0					)      ,						**_lowerCamelCase					).prev_sample

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





