




import unittest

from transformers import XLMConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
   import torch

   from transformers import (
       XLMForMultipleChoice,
       XLMForQuestionAnswering,
       XLMForQuestionAnsweringSimple,
       XLMForSequenceClassification,
       XLMForTokenClassification,
       XLMModel,
       XLMWithLMHeadModel,
   )
   from transformers.models.xlm.modeling_xlm import XLM_PRETRAINED_MODEL_ARCHIVE_LIST





class        lowerCamelCase_							:


 '''simple docstring'''







 def __init__(							self  :		Optional[Any]				,				_lowerCAmelCase  :		str				,				_lowerCAmelCase  :		Optional[Any]=13				,				_lowerCAmelCase  :		List[str]=7				,				_lowerCAmelCase  :		Dict=True				,				_lowerCAmelCase  :		str=True				,				_lowerCAmelCase  :		Optional[Any]=True				,				_lowerCAmelCase  :		Tuple=True				,				_lowerCAmelCase  :		Union[str, Any]=True				,				_lowerCAmelCase  :		Optional[int]=False				,				_lowerCAmelCase  :		Tuple=False				,				_lowerCAmelCase  :		Tuple=False				,				_lowerCAmelCase  :		List[str]=2				,				_lowerCAmelCase  :		List[Any]=99				,				_lowerCAmelCase  :		List[Any]=0				,				_lowerCAmelCase  :		List[Any]=32				,				_lowerCAmelCase  :		Tuple=5				,				_lowerCAmelCase  :		int=4				,				_lowerCAmelCase  :		str=0.1				,				_lowerCAmelCase  :		Dict=0.1				,				_lowerCAmelCase  :		Optional[Any]=512				,				_lowerCAmelCase  :		Optional[Any]=2				,				_lowerCAmelCase  :		Tuple=0.02				,				_lowerCAmelCase  :		Optional[int]=2				,				_lowerCAmelCase  :		Union[str, Any]=4				,				_lowerCAmelCase  :		int="last"				,				_lowerCAmelCase  :		str=True				,				_lowerCAmelCase  :		Dict=None				,				_lowerCAmelCase  :		int=0				,				):
       SCREAMING_SNAKE_CASE_							=			parent
       SCREAMING_SNAKE_CASE_							=			batch_size
       SCREAMING_SNAKE_CASE_							=			seq_length
       SCREAMING_SNAKE_CASE_							=			is_training
       SCREAMING_SNAKE_CASE_							=			use_input_lengths
       SCREAMING_SNAKE_CASE_							=			use_token_type_ids
       SCREAMING_SNAKE_CASE_							=			use_labels
       SCREAMING_SNAKE_CASE_							=			gelu_activation
       SCREAMING_SNAKE_CASE_							=			sinusoidal_embeddings
       SCREAMING_SNAKE_CASE_							=			causal
       SCREAMING_SNAKE_CASE_							=			asm
       SCREAMING_SNAKE_CASE_							=			n_langs
       SCREAMING_SNAKE_CASE_							=			vocab_size
       SCREAMING_SNAKE_CASE_							=			n_special
       SCREAMING_SNAKE_CASE_							=			hidden_size
       SCREAMING_SNAKE_CASE_							=			num_hidden_layers
       SCREAMING_SNAKE_CASE_							=			num_attention_heads
       SCREAMING_SNAKE_CASE_							=			hidden_dropout_prob
       SCREAMING_SNAKE_CASE_							=			attention_probs_dropout_prob
       SCREAMING_SNAKE_CASE_							=			max_position_embeddings
       SCREAMING_SNAKE_CASE_							=			type_sequence_label_size
       SCREAMING_SNAKE_CASE_							=			initializer_range
       SCREAMING_SNAKE_CASE_							=			num_labels
       SCREAMING_SNAKE_CASE_							=			num_choices
       SCREAMING_SNAKE_CASE_							=			summary_type
       SCREAMING_SNAKE_CASE_							=			use_proj
       SCREAMING_SNAKE_CASE_							=			scope
       SCREAMING_SNAKE_CASE_							=			bos_token_id



 def       lowerCAmelCase_  (							self  :		Optional[Any]						):
       SCREAMING_SNAKE_CASE_							=			ids_tensor([self.batch_size, self.seq_length]				,				self.vocab_size						)
       SCREAMING_SNAKE_CASE_							=			random_attention_mask([self.batch_size, self.seq_length]						)

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

       SCREAMING_SNAKE_CASE_							=			None
       if self.use_token_type_ids:
             SCREAMING_SNAKE_CASE_							=			ids_tensor([self.batch_size, self.seq_length]				,				self.n_langs						)

       SCREAMING_SNAKE_CASE_							=			None
       SCREAMING_SNAKE_CASE_							=			None
       SCREAMING_SNAKE_CASE_							=			None
       if self.use_labels:
             SCREAMING_SNAKE_CASE_							=			ids_tensor([self.batch_size]				,				self.type_sequence_label_size						)
             SCREAMING_SNAKE_CASE_							=			ids_tensor([self.batch_size, self.seq_length]				,				self.num_labels						)
             SCREAMING_SNAKE_CASE_							=			ids_tensor([self.batch_size]				,				2						).float()
             SCREAMING_SNAKE_CASE_							=			ids_tensor([self.batch_size]				,				self.num_choices						)

       SCREAMING_SNAKE_CASE_							=			self.get_config()

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



 def       lowerCAmelCase_  (							self  :		List[str]						):
       return 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				,				num_labels=self.num_labels				,				bos_token_id=self.bos_token_id				,				)



 def       lowerCAmelCase_  (							self  :		Tuple				,				_lowerCAmelCase  :		Any				,				_lowerCAmelCase  :		List[str]				,				_lowerCAmelCase  :		str				,				_lowerCAmelCase  :		List[Any]				,				_lowerCAmelCase  :		List[Any]				,				_lowerCAmelCase  :		Optional[int]				,				_lowerCAmelCase  :		int				,				_lowerCAmelCase  :		Optional[Any]				,				_lowerCAmelCase  :		str				,				):
       SCREAMING_SNAKE_CASE_							=			XLMModel(config=snake_case__						)
       model.to(snake_case__						)
       model.eval()
       SCREAMING_SNAKE_CASE_							=			model(snake_case__				,				lengths=snake_case__				,				langs=snake_case__						)
       SCREAMING_SNAKE_CASE_							=			model(snake_case__				,				langs=snake_case__						)
       SCREAMING_SNAKE_CASE_							=			model(snake_case__						)
       self.parent.assertEqual(result.last_hidden_state.shape				,				(self.batch_size, self.seq_length, self.hidden_size)						)



 def       lowerCAmelCase_  (							self  :		Optional[int]				,				_lowerCAmelCase  :		Optional[Any]				,				_lowerCAmelCase  :		Optional[int]				,				_lowerCAmelCase  :		Tuple				,				_lowerCAmelCase  :		Optional[Any]				,				_lowerCAmelCase  :		Optional[int]				,				_lowerCAmelCase  :		List[str]				,				_lowerCAmelCase  :		Any				,				_lowerCAmelCase  :		Any				,				_lowerCAmelCase  :		List[Any]				,				):
       SCREAMING_SNAKE_CASE_							=			XLMWithLMHeadModel(snake_case__						)
       model.to(snake_case__						)
       model.eval()

       SCREAMING_SNAKE_CASE_							=			model(snake_case__				,				token_type_ids=snake_case__				,				labels=snake_case__						)
       self.parent.assertEqual(result.loss.shape				,				()						)
       self.parent.assertEqual(result.logits.shape				,				(self.batch_size, self.seq_length, self.vocab_size)						)



 def       lowerCAmelCase_  (							self  :		Optional[Any]				,				_lowerCAmelCase  :		Optional[Any]				,				_lowerCAmelCase  :		List[Any]				,				_lowerCAmelCase  :		Any				,				_lowerCAmelCase  :		List[str]				,				_lowerCAmelCase  :		Optional[Any]				,				_lowerCAmelCase  :		Tuple				,				_lowerCAmelCase  :		str				,				_lowerCAmelCase  :		int				,				_lowerCAmelCase  :		int				,				):
       SCREAMING_SNAKE_CASE_							=			XLMForQuestionAnsweringSimple(snake_case__						)
       model.to(snake_case__						)
       model.eval()

       SCREAMING_SNAKE_CASE_							=			model(snake_case__						)

       SCREAMING_SNAKE_CASE_							=			model(snake_case__				,				start_positions=snake_case__				,				end_positions=snake_case__						)
       SCREAMING_SNAKE_CASE_							=			outputs
       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       lowerCAmelCase_  (							self  :		int				,				_lowerCAmelCase  :		int				,				_lowerCAmelCase  :		Optional[Any]				,				_lowerCAmelCase  :		Dict				,				_lowerCAmelCase  :		Optional[Any]				,				_lowerCAmelCase  :		Union[str, Any]				,				_lowerCAmelCase  :		Any				,				_lowerCAmelCase  :		List[Any]				,				_lowerCAmelCase  :		Dict				,				_lowerCAmelCase  :		Tuple				,				):
       SCREAMING_SNAKE_CASE_							=			XLMForQuestionAnswering(snake_case__						)
       model.to(snake_case__						)
       model.eval()

       SCREAMING_SNAKE_CASE_							=			model(snake_case__						)

       SCREAMING_SNAKE_CASE_							=			model(
           snake_case__				,				start_positions=snake_case__				,				end_positions=snake_case__				,				cls_index=snake_case__				,				is_impossible=snake_case__				,				p_mask=snake_case__				,				)

       SCREAMING_SNAKE_CASE_							=			model(
           snake_case__				,				start_positions=snake_case__				,				end_positions=snake_case__				,				cls_index=snake_case__				,				is_impossible=snake_case__				,				)

       (SCREAMING_SNAKE_CASE_					)							=			result_with_labels.to_tuple()

       SCREAMING_SNAKE_CASE_							=			model(snake_case__				,				start_positions=snake_case__				,				end_positions=snake_case__						)

       (SCREAMING_SNAKE_CASE_					)							=			result_with_labels.to_tuple()

       self.parent.assertEqual(result_with_labels.loss.shape				,				()						)
       self.parent.assertEqual(result.start_top_log_probs.shape				,				(self.batch_size, model.config.start_n_top)						)
       self.parent.assertEqual(result.start_top_index.shape				,				(self.batch_size, model.config.start_n_top)						)
       self.parent.assertEqual(
           result.end_top_log_probs.shape				,				(self.batch_size, model.config.start_n_top * model.config.end_n_top)						)
       self.parent.assertEqual(
           result.end_top_index.shape				,				(self.batch_size, model.config.start_n_top * model.config.end_n_top)						)
       self.parent.assertEqual(result.cls_logits.shape				,				(self.batch_size,)						)



 def       lowerCAmelCase_  (							self  :		int				,				_lowerCAmelCase  :		Dict				,				_lowerCAmelCase  :		Any				,				_lowerCAmelCase  :		Any				,				_lowerCAmelCase  :		int				,				_lowerCAmelCase  :		Optional[int]				,				_lowerCAmelCase  :		Union[str, Any]				,				_lowerCAmelCase  :		int				,				_lowerCAmelCase  :		str				,				_lowerCAmelCase  :		Dict				,				):
       SCREAMING_SNAKE_CASE_							=			XLMForSequenceClassification(snake_case__						)
       model.to(snake_case__						)
       model.eval()

       SCREAMING_SNAKE_CASE_							=			model(snake_case__						)
       SCREAMING_SNAKE_CASE_							=			model(snake_case__				,				labels=snake_case__						)
       self.parent.assertEqual(result.loss.shape				,				()						)
       self.parent.assertEqual(result.logits.shape				,				(self.batch_size, self.type_sequence_label_size)						)



 def       lowerCAmelCase_  (							self  :		Any				,				_lowerCAmelCase  :		Any				,				_lowerCAmelCase  :		Any				,				_lowerCAmelCase  :		int				,				_lowerCAmelCase  :		str				,				_lowerCAmelCase  :		Optional[Any]				,				_lowerCAmelCase  :		str				,				_lowerCAmelCase  :		List[str]				,				_lowerCAmelCase  :		List[Any]				,				_lowerCAmelCase  :		Optional[int]				,				):
       SCREAMING_SNAKE_CASE_							=			self.num_labels
       SCREAMING_SNAKE_CASE_							=			XLMForTokenClassification(snake_case__						)
       model.to(snake_case__						)
       model.eval()

       SCREAMING_SNAKE_CASE_							=			model(snake_case__				,				attention_mask=snake_case__				,				labels=snake_case__						)
       self.parent.assertEqual(result.logits.shape				,				(self.batch_size, self.seq_length, self.num_labels)						)



 def       lowerCAmelCase_  (							self  :		Optional[int]				,				_lowerCAmelCase  :		Any				,				_lowerCAmelCase  :		str				,				_lowerCAmelCase  :		Tuple				,				_lowerCAmelCase  :		Tuple				,				_lowerCAmelCase  :		Tuple				,				_lowerCAmelCase  :		Tuple				,				_lowerCAmelCase  :		Union[str, Any]				,				_lowerCAmelCase  :		List[Any]				,				_lowerCAmelCase  :		Union[str, Any]				,				):
       SCREAMING_SNAKE_CASE_							=			self.num_choices
       SCREAMING_SNAKE_CASE_							=			XLMForMultipleChoice(config=snake_case__						)
       model.to(snake_case__						)
       model.eval()
       SCREAMING_SNAKE_CASE_							=			input_ids.unsqueeze(1						).expand(-1				,				self.num_choices				,				-1						).contiguous()
       SCREAMING_SNAKE_CASE_							=			token_type_ids.unsqueeze(1						).expand(-1				,				self.num_choices				,				-1						).contiguous()
       SCREAMING_SNAKE_CASE_							=			input_mask.unsqueeze(1						).expand(-1				,				self.num_choices				,				-1						).contiguous()
       SCREAMING_SNAKE_CASE_							=			model(
           snake_case__				,				attention_mask=snake_case__				,				token_type_ids=snake_case__				,				labels=snake_case__				,				)
       self.parent.assertEqual(result.logits.shape				,				(self.batch_size, self.num_choices)						)




 def       lowerCAmelCase_  (							self  :		Optional[Any]						):
       SCREAMING_SNAKE_CASE_							=			self.prepare_config_and_inputs()
       (
           SCREAMING_SNAKE_CASE_					
       )							=			config_and_inputs
       SCREAMING_SNAKE_CASE_							=			{"input_ids": input_ids, "token_type_ids": token_type_ids, "lengths": input_lengths}
       return config, inputs_dict





@require_torch
class        lowerCamelCase_							(      A_       ,   A_       ,   A_       ,   unittest.TestCase					):


 '''simple docstring'''







 lowercase_				    =    (
     (
         XLMModel,
         XLMWithLMHeadModel,
         XLMForQuestionAnswering,
         XLMForSequenceClassification,
         XLMForQuestionAnsweringSimple,
         XLMForTokenClassification,
         XLMForMultipleChoice,
     )
     if is_torch_available()
     else ()
 )
 lowercase_				    =    (
     (XLMWithLMHeadModel,) if is_torch_available() else ()
 )  # TODO (PVP): Check other models whether language generation is also applicable
 lowercase_				    =    (
     {
         "feature-extraction": XLMModel,
         "fill-mask": XLMWithLMHeadModel,
         "question-answering": XLMForQuestionAnsweringSimple,
         "text-classification": XLMForSequenceClassification,
         "text-generation": XLMWithLMHeadModel,
         "token-classification": XLMForTokenClassification,
         "zero-shot": XLMForSequenceClassification,
     }
     if is_torch_available()
     else {}
 )



 def       lowerCAmelCase_  (							self  :		List[str]				,				_lowerCAmelCase  :		Tuple				,				_lowerCAmelCase  :		int				,				_lowerCAmelCase  :		List[Any]				,				_lowerCAmelCase  :		Dict				,				_lowerCAmelCase  :		List[str]						):


       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       lowerCAmelCase_  (							self  :		Optional[int]				,				_lowerCAmelCase  :		Optional[Any]				,				_lowerCAmelCase  :		Dict				,				_lowerCAmelCase  :		str=False						):
       SCREAMING_SNAKE_CASE_							=			super()._prepare_for_class(snake_case__				,				snake_case__				,				return_labels=snake_case__						)

       if return_labels:
             if model_class.__name__ == "XLMForQuestionAnswering":
                   SCREAMING_SNAKE_CASE_							=			torch.zeros(
                       self.model_tester.batch_size				,				dtype=torch.long				,				device=snake_case__						)
                   SCREAMING_SNAKE_CASE_							=			torch.zeros(
                       self.model_tester.batch_size				,				dtype=torch.long				,				device=snake_case__						)

       return inputs_dict



 def       lowerCAmelCase_  (							self  :		Dict						):
       SCREAMING_SNAKE_CASE_							=			XLMModelTester(self						)
       SCREAMING_SNAKE_CASE_							=			ConfigTester(self				,				config_class=snake_case__				,				emb_dim=37						)



 def       lowerCAmelCase_  (							self  :		Dict						):
       self.config_tester.run_common_tests()



 def       lowerCAmelCase_  (							self  :		int						):
       SCREAMING_SNAKE_CASE_							=			self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_xlm_model(*snake_case__						)



 def       lowerCAmelCase_  (							self  :		Tuple						):
       SCREAMING_SNAKE_CASE_							=			self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_xlm_lm_head(*snake_case__						)



 def       lowerCAmelCase_  (							self  :		str						):
       SCREAMING_SNAKE_CASE_							=			self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_xlm_simple_qa(*snake_case__						)



 def       lowerCAmelCase_  (							self  :		List[str]						):
       SCREAMING_SNAKE_CASE_							=			self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_xlm_qa(*snake_case__						)



 def       lowerCAmelCase_  (							self  :		List[Any]						):
       SCREAMING_SNAKE_CASE_							=			self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_xlm_sequence_classif(*snake_case__						)



 def       lowerCAmelCase_  (							self  :		List[Any]						):
       SCREAMING_SNAKE_CASE_							=			self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_xlm_token_classif(*snake_case__						)



 def       lowerCAmelCase_  (							self  :		List[str]						):
       SCREAMING_SNAKE_CASE_							=			self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_xlm_for_multiple_choice(*snake_case__						)



 def       lowerCAmelCase_  (							self  :		Optional[int]				,				_lowerCAmelCase  :		Union[str, Any]				,				_lowerCAmelCase  :		str				,				_lowerCAmelCase  :		List[Any]				,				_lowerCAmelCase  :		int				,				_lowerCAmelCase  :		Optional[Any]				,				_lowerCAmelCase  :		Optional[Any]=False				,				_lowerCAmelCase  :		int=1						):
       self.assertIsInstance(snake_case__				,				snake_case__						)
       self.assertListEqual(
           [isinstance(snake_case__				,				snake_case__						) for iter_attentions in attentions]				,				[True] * len(snake_case__						)						)
       self.assertEqual(len(snake_case__						)				,				(max_length - min_length) * num_beam_groups						)

       for idx, iter_attentions in enumerate(snake_case__						):
             # adds PAD dummy token
             SCREAMING_SNAKE_CASE_							=			min_length + idx + 1
             SCREAMING_SNAKE_CASE_							=			min_length + idx + 1

             SCREAMING_SNAKE_CASE_							=			(
                 batch_size * num_beam_groups,
                 config.num_attention_heads,
                 tgt_len,
                 src_len,
             )
             # check attn size
             self.assertListEqual(
                 [layer_attention.shape for layer_attention in iter_attentions]				,				[expected_shape] * len(snake_case__						)						)



 def       lowerCAmelCase_  (							self  :		Optional[Any]				,				_lowerCAmelCase  :		Union[str, Any]				,				_lowerCAmelCase  :		List[str]				,				_lowerCAmelCase  :		Dict				,				_lowerCAmelCase  :		Tuple				,				_lowerCAmelCase  :		Dict				,				_lowerCAmelCase  :		Optional[int]=False				,				_lowerCAmelCase  :		str=1						):
       self.assertIsInstance(snake_case__				,				snake_case__						)
       self.assertListEqual(
           [isinstance(snake_case__				,				snake_case__						) for iter_hidden_states in hidden_states]				,				[True] * len(snake_case__						)				,				)
       self.assertEqual(len(snake_case__						)				,				(max_length - min_length) * num_beam_groups						)

       for idx, iter_hidden_states in enumerate(snake_case__						):
             # adds PAD dummy token
             SCREAMING_SNAKE_CASE_							=			min_length + idx + 1
             SCREAMING_SNAKE_CASE_							=			(batch_size * num_beam_groups, seq_len, config.hidden_size)
             # check hidden size
             self.assertListEqual(
                 [layer_hidden_states.shape for layer_hidden_states in iter_hidden_states]				,				[expected_shape] * len(snake_case__						)				,				)
       pass




 @slow
 def       lowerCAmelCase_  (							self  :		Optional[int]						):


       for model_name in XLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             SCREAMING_SNAKE_CASE_							=			XLMModel.from_pretrained(snake_case__						)
             self.assertIsNotNone(snake_case__						)





@require_torch
class        lowerCamelCase_							(      unittest.TestCase					):


 '''simple docstring'''







 @slow
 def       lowerCAmelCase_  (							self  :		Optional[Any]						):
       SCREAMING_SNAKE_CASE_							=			XLMWithLMHeadModel.from_pretrained('xlm-mlm-en-2048'						)
       model.to(snake_case__						)
       SCREAMING_SNAKE_CASE_							=			torch.tensor([[14, 447]]				,				dtype=torch.long				,				device=snake_case__						)  # the president
       SCREAMING_SNAKE_CASE_							=			[
           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
       SCREAMING_SNAKE_CASE_							=			model.generate(snake_case__				,				do_sample=snake_case__						)
       self.assertListEqual(output_ids[0].cpu().numpy().tolist()				,				snake_case__						)

import torch

from diffusers import DDIMParallelScheduler

from .test_schedulers import SchedulerCommonTest






class   UpperCAmelCase		(						A_ ):
   A__					:		Dict						     =     (DDIMParallelScheduler,)
   A__					:		Tuple						     =     (("eta", 0.0), ("num_inference_steps", 50))



   def 				_SCREAMING_SNAKE_CASE   (self		:					Tuple    ,						**snake_case__		:					Optional[int] )		->	Optional[Any]:


          '''simple docstring'''


          snake_case       :   Any							=      {
              "num_train_timesteps": 10_00,
              "beta_start": 0.0001,
              "beta_end": 0.02,
              "beta_schedule": "linear",
              "clip_sample": True,
          }

          config.update(**snake_case__ )
          return config



   def 				_SCREAMING_SNAKE_CASE   (self		:					Dict    ,						**snake_case__		:					Optional[int] )		->	Any:


          '''simple docstring'''


          snake_case       :   List[Any]							=      self.scheduler_classes[0]
          snake_case       :   Any							=      self.get_scheduler_config(**snake_case__ )
          snake_case       :   Any							=      scheduler_class(**snake_case__ )

          snake_case				,					snake_case       :   Union[str, Any]							=      10, 0.0

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

          scheduler.set_timesteps(snake_case__ )

          for t in scheduler.timesteps:
                 snake_case       :   Optional[int]							=      model(snake_case__    ,						snake_case__ )
                 snake_case       :   List[str]							=      scheduler.step(snake_case__    ,						snake_case__    ,						snake_case__    ,						snake_case__ ).prev_sample

          return sample



   def 				_SCREAMING_SNAKE_CASE   (self		:					Union[str, Any] )		->	str:


          '''simple docstring'''


          for timesteps in [1_00, 5_00, 10_00]:
                 self.check_over_configs(num_train_timesteps=snake_case__ )



   def 				_SCREAMING_SNAKE_CASE   (self		:					str )		->	int:


          '''simple docstring'''


          for steps_offset in [0, 1]:
                 self.check_over_configs(steps_offset=snake_case__ )

          snake_case       :   Optional[int]							=      self.scheduler_classes[0]
          snake_case       :   Optional[int]							=      self.get_scheduler_config(steps_offset=1 )
          snake_case       :   Union[str, Any]							=      scheduler_class(**snake_case__ )
          scheduler.set_timesteps(5 )
          assert torch.equal(scheduler.timesteps    ,						torch.LongTensor([8_01, 6_01, 4_01, 2_01, 1] ) )



   def 				_SCREAMING_SNAKE_CASE   (self		:					int )		->	Tuple:


          '''simple docstring'''


          for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1]    ,						[0.002, 0.02, 0.2, 2] ):
                 self.check_over_configs(beta_start=snake_case__    ,						beta_end=snake_case__ )



   def 				_SCREAMING_SNAKE_CASE   (self		:					Union[str, Any] )		->	Union[str, Any]:


          '''simple docstring'''


          for schedule in ["linear", "squaredcos_cap_v2"]:
                 self.check_over_configs(beta_schedule=snake_case__ )



   def 				_SCREAMING_SNAKE_CASE   (self		:					str )		->	Dict:


          '''simple docstring'''


          for prediction_type in ["epsilon", "v_prediction"]:
                 self.check_over_configs(prediction_type=snake_case__ )



   def 				_SCREAMING_SNAKE_CASE   (self		:					Optional[Any] )		->	List[str]:


          '''simple docstring'''


          for clip_sample in [True, False]:
                 self.check_over_configs(clip_sample=snake_case__ )



   def 				_SCREAMING_SNAKE_CASE   (self		:					List[Any] )		->	List[Any]:


          '''simple docstring'''


          for timestep_spacing in ["trailing", "leading"]:
                 self.check_over_configs(timestep_spacing=snake_case__ )



   def 				_SCREAMING_SNAKE_CASE   (self		:					Optional[int] )		->	List[Any]:


          '''simple docstring'''


          for rescale_betas_zero_snr in [True, False]:
                 self.check_over_configs(rescale_betas_zero_snr=snake_case__ )



   def 				_SCREAMING_SNAKE_CASE   (self		:					Optional[Any] )		->	Optional[Any]:


          '''simple docstring'''


          self.check_over_configs(thresholding=snake_case__ )
          for threshold in [0.5, 1.0, 2.0]:
                 for prediction_type in ["epsilon", "v_prediction"]:
                        self.check_over_configs(
                            thresholding=snake_case__    ,						prediction_type=snake_case__    ,						sample_max_value=snake_case__    ,						)



   def 				_SCREAMING_SNAKE_CASE   (self		:					Any )		->	Any:


          '''simple docstring'''


          for t in [1, 10, 49]:
                 self.check_over_forward(time_step=snake_case__ )



   def 				_SCREAMING_SNAKE_CASE   (self		:					Optional[int] )		->	Any:


          '''simple docstring'''


          for t, num_inference_steps in zip([1, 10, 50]    ,						[10, 50, 5_00] ):
                 self.check_over_forward(time_step=snake_case__    ,						num_inference_steps=snake_case__ )



   def 				_SCREAMING_SNAKE_CASE   (self		:					List[str] )		->	Optional[Any]:


          '''simple docstring'''


          for t, eta in zip([1, 10, 49]    ,						[0.0, 0.5, 1.0] ):
                 self.check_over_forward(time_step=snake_case__    ,						eta=snake_case__ )



   def 				_SCREAMING_SNAKE_CASE   (self		:					Optional[Any] )		->	Optional[int]:


          '''simple docstring'''


          snake_case       :   Dict							=      self.scheduler_classes[0]
          snake_case       :   Tuple							=      self.get_scheduler_config()
          snake_case       :   Dict							=      scheduler_class(**snake_case__ )

          assert torch.sum(torch.abs(scheduler._get_variance(0    ,						0 ) - 0.0 ) ) < 1e-5
          assert torch.sum(torch.abs(scheduler._get_variance(4_20    ,						4_00 ) - 0.14771 ) ) < 1e-5
          assert torch.sum(torch.abs(scheduler._get_variance(9_80    ,						9_60 ) - 0.32460 ) ) < 1e-5
          assert torch.sum(torch.abs(scheduler._get_variance(0    ,						0 ) - 0.0 ) ) < 1e-5
          assert torch.sum(torch.abs(scheduler._get_variance(4_87    ,						4_86 ) - 0.00979 ) ) < 1e-5
          assert torch.sum(torch.abs(scheduler._get_variance(9_99    ,						9_98 ) - 0.02 ) ) < 1e-5



   def 				_SCREAMING_SNAKE_CASE   (self		:					Optional[int] )		->	Dict:


          '''simple docstring'''


          snake_case       :   Union[str, Any]							=      self.scheduler_classes[0]
          snake_case       :   List[Any]							=      self.get_scheduler_config()
          snake_case       :   int							=      scheduler_class(**snake_case__ )

          snake_case				,					snake_case       :   Any							=      10, 0.0
          scheduler.set_timesteps(snake_case__ )

          snake_case       :   Optional[Any]							=      self.dummy_model()
          snake_case       :   str							=      self.dummy_sample_deter
          snake_case       :   Dict							=      self.dummy_sample_deter + 0.1
          snake_case       :   Dict							=      self.dummy_sample_deter - 0.1

          snake_case       :   Optional[Any]							=      samplea.shape[0]
          snake_case       :   str							=      torch.stack([samplea, samplea, samplea]    ,						dim=0 )
          snake_case       :   Tuple							=      torch.arange(snake_case__ )[0:3, None].repeat(1    ,						snake_case__ )

          snake_case       :   Tuple							=      model(samples.flatten(0    ,						1 )    ,						timesteps.flatten(0    ,						1 ) )
          snake_case       :   List[str]							=      scheduler.batch_step_no_noise(snake_case__    ,						timesteps.flatten(0    ,						1 )    ,						samples.flatten(0    ,						1 )    ,						snake_case__ )

          snake_case       :   Dict							=      torch.sum(torch.abs(snake_case__ ) )
          snake_case       :   List[Any]							=      torch.mean(torch.abs(snake_case__ ) )

          assert abs(result_sum.item() - 1147.7904 ) < 1e-2
          assert abs(result_mean.item() - 0.4982 ) < 1e-3



   def 				_SCREAMING_SNAKE_CASE   (self		:					Optional[Any] )		->	Optional[Any]:


          '''simple docstring'''


          snake_case       :   List[Any]							=      self.full_loop()

          snake_case       :   Optional[Any]							=      torch.sum(torch.abs(snake_case__ ) )
          snake_case       :   List[Any]							=      torch.mean(torch.abs(snake_case__ ) )

          assert abs(result_sum.item() - 172.0067 ) < 1e-2
          assert abs(result_mean.item() - 0.223967 ) < 1e-3



   def 				_SCREAMING_SNAKE_CASE   (self		:					str )		->	Union[str, Any]:


          '''simple docstring'''


          snake_case       :   Dict							=      self.full_loop(prediction_type="v_prediction" )

          snake_case       :   int							=      torch.sum(torch.abs(snake_case__ ) )
          snake_case       :   Optional[int]							=      torch.mean(torch.abs(snake_case__ ) )

          assert abs(result_sum.item() - 52.5302 ) < 1e-2
          assert abs(result_mean.item() - 0.0684 ) < 1e-3



   def 				_SCREAMING_SNAKE_CASE   (self		:					Any )		->	Optional[Any]:


          '''simple docstring'''


          snake_case       :   Dict							=      self.full_loop(set_alpha_to_one=snake_case__    ,						beta_start=0.01 )
          snake_case       :   str							=      torch.sum(torch.abs(snake_case__ ) )
          snake_case       :   Optional[Any]							=      torch.mean(torch.abs(snake_case__ ) )

          assert abs(result_sum.item() - 149.8295 ) < 1e-2
          assert abs(result_mean.item() - 0.1951 ) < 1e-3



   def 				_SCREAMING_SNAKE_CASE   (self		:					int )		->	Optional[Any]:


          '''simple docstring'''


          snake_case       :   int							=      self.full_loop(set_alpha_to_one=snake_case__    ,						beta_start=0.01 )
          snake_case       :   Tuple							=      torch.sum(torch.abs(snake_case__ ) )
          snake_case       :   List[Any]							=      torch.mean(torch.abs(snake_case__ ) )

          assert abs(result_sum.item() - 149.0784 ) < 1e-2
          assert abs(result_mean.item() - 0.1941 ) < 1e-3





