






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 	_UpperCAmelCase  :




      """simple docstring"""



      def __init__(   self    ,	_lowerCAmelCase    ,	_lowerCAmelCase=13    ,	_lowerCAmelCase=7    ,	_lowerCAmelCase=True    ,	_lowerCAmelCase=True    ,	_lowerCAmelCase=True    ,	_lowerCAmelCase=True    ,	_lowerCAmelCase=True    ,	_lowerCAmelCase=False    ,	_lowerCAmelCase=False    ,	_lowerCAmelCase=False    ,	_lowerCAmelCase=2    ,	_lowerCAmelCase=99    ,	_lowerCAmelCase=0    ,	_lowerCAmelCase=32    ,	_lowerCAmelCase=5    ,	_lowerCAmelCase=4    ,	_lowerCAmelCase=0.1    ,	_lowerCAmelCase=0.1    ,	_lowerCAmelCase=512    ,	_lowerCAmelCase=2    ,	_lowerCAmelCase=0.02    ,	_lowerCAmelCase=2    ,	_lowerCAmelCase=4    ,	_lowerCAmelCase="last"    ,	_lowerCAmelCase=True    ,	_lowerCAmelCase=None    ,	_lowerCAmelCase=0    ,	):



          '''simple docstring'''
          lowerCAmelCase__   :List[str]      							=				parent
          lowerCAmelCase__   :List[Any]      							=				batch_size
          lowerCAmelCase__   :List[Any]      							=				seq_length
          lowerCAmelCase__   :Dict      							=				is_training
          lowerCAmelCase__   :Any      							=				use_input_lengths
          lowerCAmelCase__   :Dict      							=				use_token_type_ids
          lowerCAmelCase__   :Tuple      							=				use_labels
          lowerCAmelCase__   :Tuple      							=				gelu_activation
          lowerCAmelCase__   :List[str]      							=				sinusoidal_embeddings
          lowerCAmelCase__   :Dict      							=				causal
          lowerCAmelCase__   :List[str]      							=				asm
          lowerCAmelCase__   :List[Any]      							=				n_langs
          lowerCAmelCase__   :Dict      							=				vocab_size
          lowerCAmelCase__   :Any      							=				n_special
          lowerCAmelCase__   :Dict      							=				hidden_size
          lowerCAmelCase__   :int      							=				num_hidden_layers
          lowerCAmelCase__   :str      							=				num_attention_heads
          lowerCAmelCase__   :str      							=				hidden_dropout_prob
          lowerCAmelCase__   :Optional[int]      							=				attention_probs_dropout_prob
          lowerCAmelCase__   :Optional[Any]      							=				max_position_embeddings
          lowerCAmelCase__   :List[str]      							=				type_sequence_label_size
          lowerCAmelCase__   :List[Any]      							=				initializer_range
          lowerCAmelCase__   :str      							=				num_labels
          lowerCAmelCase__   :Optional[int]      							=				num_choices
          lowerCAmelCase__   :Tuple      							=				summary_type
          lowerCAmelCase__   :Tuple      							=				use_proj
          lowerCAmelCase__   :str      							=				scope
          lowerCAmelCase__   :Optional[Any]      							=				bos_token_id
      def 			snake_case_      (   self		):



          '''simple docstring'''
          lowerCAmelCase__   :List[Any]      							=				ids_tensor([self.batch_size, self.seq_length]    ,	self.vocab_size		)
          lowerCAmelCase__   :List[str]      							=				random_attention_mask([self.batch_size, self.seq_length]		)

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

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

          lowerCAmelCase__   :str      							=				None
          lowerCAmelCase__   :str      							=				None
          lowerCAmelCase__   :Dict      							=				None
          if self.use_labels:
              lowerCAmelCase__   :Optional[Any]      							=				ids_tensor([self.batch_size]    ,	self.type_sequence_label_size		)
              lowerCAmelCase__   :Any      							=				ids_tensor([self.batch_size, self.seq_length]    ,	self.num_labels		)
              lowerCAmelCase__   :Optional[int]      							=				ids_tensor([self.batch_size]    ,	2		).float()
              lowerCAmelCase__   :Any      							=				ids_tensor([self.batch_size]    ,	self.num_choices		)

          lowerCAmelCase__   :Optional[int]      							=				self.get_config()

          return (
              config,
              input_ids,
              token_type_ids,
              input_lengths,
              sequence_labels,
              token_labels,
              is_impossible_labels,
              choice_labels,
              input_mask,
          )
      def 			snake_case_      (   self		):



          '''simple docstring'''
          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 			snake_case_      (   self    ,	_lowerCAmelCase    ,	_lowerCAmelCase    ,	_lowerCAmelCase    ,	_lowerCAmelCase    ,	_lowerCAmelCase    ,	_lowerCAmelCase    ,	_lowerCAmelCase    ,	_lowerCAmelCase    ,	_lowerCAmelCase    ,	):



          '''simple docstring'''
          lowerCAmelCase__   :Dict      							=				XLMModel(config=_lowerCAmelCase		)
          model.to(_lowerCAmelCase		)
          model.eval()
          lowerCAmelCase__   :Any      							=				model(_lowerCAmelCase    ,	lengths=_lowerCAmelCase    ,	langs=_lowerCAmelCase		)
          lowerCAmelCase__   :Tuple      							=				model(_lowerCAmelCase    ,	langs=_lowerCAmelCase		)
          lowerCAmelCase__   :Union[str, Any]      							=				model(_lowerCAmelCase		)
          self.parent.assertEqual(result.last_hidden_state.shape    ,	(self.batch_size, self.seq_length, self.hidden_size)		)
      def 			snake_case_      (   self    ,	_lowerCAmelCase    ,	_lowerCAmelCase    ,	_lowerCAmelCase    ,	_lowerCAmelCase    ,	_lowerCAmelCase    ,	_lowerCAmelCase    ,	_lowerCAmelCase    ,	_lowerCAmelCase    ,	_lowerCAmelCase    ,	):



          '''simple docstring'''
          lowerCAmelCase__   :Tuple      							=				XLMWithLMHeadModel(_lowerCAmelCase		)
          model.to(_lowerCAmelCase		)
          model.eval()

          lowerCAmelCase__   :int      							=				model(_lowerCAmelCase    ,	token_type_ids=_lowerCAmelCase    ,	labels=_lowerCAmelCase		)
          self.parent.assertEqual(result.loss.shape    ,	()		)
          self.parent.assertEqual(result.logits.shape    ,	(self.batch_size, self.seq_length, self.vocab_size)		)
      def 			snake_case_      (   self    ,	_lowerCAmelCase    ,	_lowerCAmelCase    ,	_lowerCAmelCase    ,	_lowerCAmelCase    ,	_lowerCAmelCase    ,	_lowerCAmelCase    ,	_lowerCAmelCase    ,	_lowerCAmelCase    ,	_lowerCAmelCase    ,	):



          '''simple docstring'''
          lowerCAmelCase__   :Tuple      							=				XLMForQuestionAnsweringSimple(_lowerCAmelCase		)
          model.to(_lowerCAmelCase		)
          model.eval()

          lowerCAmelCase__   :Tuple      							=				model(_lowerCAmelCase		)

          lowerCAmelCase__   :Tuple      							=				model(_lowerCAmelCase    ,	start_positions=_lowerCAmelCase    ,	end_positions=_lowerCAmelCase		)
          lowerCAmelCase__   :Dict      							=				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 			snake_case_      (   self    ,	_lowerCAmelCase    ,	_lowerCAmelCase    ,	_lowerCAmelCase    ,	_lowerCAmelCase    ,	_lowerCAmelCase    ,	_lowerCAmelCase    ,	_lowerCAmelCase    ,	_lowerCAmelCase    ,	_lowerCAmelCase    ,	):



          '''simple docstring'''
          lowerCAmelCase__   :Optional[int]      							=				XLMForQuestionAnswering(_lowerCAmelCase		)
          model.to(_lowerCAmelCase		)
          model.eval()

          lowerCAmelCase__   :List[str]      							=				model(_lowerCAmelCase		)

          lowerCAmelCase__   :Any      							=				model(
              _lowerCAmelCase    ,	start_positions=_lowerCAmelCase    ,	end_positions=_lowerCAmelCase    ,	cls_index=_lowerCAmelCase    ,	is_impossible=_lowerCAmelCase    ,	p_mask=_lowerCAmelCase    ,	)

          lowerCAmelCase__   :Optional[int]      							=				model(
              _lowerCAmelCase    ,	start_positions=_lowerCAmelCase    ,	end_positions=_lowerCAmelCase    ,	cls_index=_lowerCAmelCase    ,	is_impossible=_lowerCAmelCase    ,	)

          ((lowerCAmelCase__)							,)   :Any      							=				result_with_labels.to_tuple()

          lowerCAmelCase__   :Union[str, Any]      							=				model(_lowerCAmelCase    ,	start_positions=_lowerCAmelCase    ,	end_positions=_lowerCAmelCase		)

          ((lowerCAmelCase__)							,)   :Union[str, Any]      							=				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 			snake_case_      (   self    ,	_lowerCAmelCase    ,	_lowerCAmelCase    ,	_lowerCAmelCase    ,	_lowerCAmelCase    ,	_lowerCAmelCase    ,	_lowerCAmelCase    ,	_lowerCAmelCase    ,	_lowerCAmelCase    ,	_lowerCAmelCase    ,	):



          '''simple docstring'''
          lowerCAmelCase__   :List[Any]      							=				XLMForSequenceClassification(_lowerCAmelCase		)
          model.to(_lowerCAmelCase		)
          model.eval()

          lowerCAmelCase__   :Union[str, Any]      							=				model(_lowerCAmelCase		)
          lowerCAmelCase__   :int      							=				model(_lowerCAmelCase    ,	labels=_lowerCAmelCase		)
          self.parent.assertEqual(result.loss.shape    ,	()		)
          self.parent.assertEqual(result.logits.shape    ,	(self.batch_size, self.type_sequence_label_size)		)
      def 			snake_case_      (   self    ,	_lowerCAmelCase    ,	_lowerCAmelCase    ,	_lowerCAmelCase    ,	_lowerCAmelCase    ,	_lowerCAmelCase    ,	_lowerCAmelCase    ,	_lowerCAmelCase    ,	_lowerCAmelCase    ,	_lowerCAmelCase    ,	):



          '''simple docstring'''
          lowerCAmelCase__   :str      							=				self.num_labels
          lowerCAmelCase__   :Union[str, Any]      							=				XLMForTokenClassification(_lowerCAmelCase		)
          model.to(_lowerCAmelCase		)
          model.eval()

          lowerCAmelCase__   :List[str]      							=				model(_lowerCAmelCase    ,	attention_mask=_lowerCAmelCase    ,	labels=_lowerCAmelCase		)
          self.parent.assertEqual(result.logits.shape    ,	(self.batch_size, self.seq_length, self.num_labels)		)
      def 			snake_case_      (   self    ,	_lowerCAmelCase    ,	_lowerCAmelCase    ,	_lowerCAmelCase    ,	_lowerCAmelCase    ,	_lowerCAmelCase    ,	_lowerCAmelCase    ,	_lowerCAmelCase    ,	_lowerCAmelCase    ,	_lowerCAmelCase    ,	):



          '''simple docstring'''
          lowerCAmelCase__   :Optional[Any]      							=				self.num_choices
          lowerCAmelCase__   :List[str]      							=				XLMForMultipleChoice(config=_lowerCAmelCase		)
          model.to(_lowerCAmelCase		)
          model.eval()
          lowerCAmelCase__   :Optional[int]      							=				input_ids.unsqueeze(1		).expand(-1    ,	self.num_choices    ,	-1		).contiguous()
          lowerCAmelCase__   :Optional[Any]      							=				token_type_ids.unsqueeze(1		).expand(-1    ,	self.num_choices    ,	-1		).contiguous()
          lowerCAmelCase__   :Optional[Any]      							=				input_mask.unsqueeze(1		).expand(-1    ,	self.num_choices    ,	-1		).contiguous()
          lowerCAmelCase__   :str      							=				model(
              _lowerCAmelCase    ,	attention_mask=_lowerCAmelCase    ,	token_type_ids=_lowerCAmelCase    ,	labels=_lowerCAmelCase    ,	)
          self.parent.assertEqual(result.logits.shape    ,	(self.batch_size, self.num_choices)		)



      def 			snake_case_      (   self		):



          '''simple docstring'''
          lowerCAmelCase__   :Dict      							=				self.prepare_config_and_inputs()
          (
              (
              lowerCAmelCase__
          )							,(
              lowerCAmelCase__
          )							,(
              lowerCAmelCase__
          )							,(
              lowerCAmelCase__
          )							,(
              lowerCAmelCase__
          )							,(
              lowerCAmelCase__
          )							,(
              lowerCAmelCase__
          )							,(
              lowerCAmelCase__
          )							,(
              lowerCAmelCase__
          )							,
          )   :str      							=				config_and_inputs
          lowerCAmelCase__   :Tuple      							=				{"input_ids": input_ids, "token_type_ids": token_type_ids, "lengths": input_lengths}
          return config, inputs_dict







@require_torch
class 	_UpperCAmelCase  (  _A				,							_A				,							_A				,							unittest.TestCase		):




      """simple docstring"""



      A          =    (
          (
              XLMModel,
              XLMWithLMHeadModel,
              XLMForQuestionAnswering,
              XLMForSequenceClassification,
              XLMForQuestionAnsweringSimple,
              XLMForTokenClassification,
              XLMForMultipleChoice,
          )
          if is_torch_available()
          else ()
      )
      A          =    (
          (XLMWithLMHeadModel,) if is_torch_available() else ()
      )  # TODO (PVP): Check other models whether language generation is also applicable
      A          =    (
          {
              '''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 			snake_case_      (   self    ,	_lowerCAmelCase    ,	_lowerCAmelCase    ,	_lowerCAmelCase    ,	_lowerCAmelCase    ,	_lowerCAmelCase		):



          '''simple docstring'''
          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 			snake_case_      (   self    ,	_lowerCAmelCase    ,	_lowerCAmelCase    ,	_lowerCAmelCase=False		):



          '''simple docstring'''
          lowerCAmelCase__   :int      							=				super()._prepare_for_class(_lowerCAmelCase    ,	_lowerCAmelCase    ,	return_labels=_lowerCAmelCase		)

          if return_labels:
              if model_class.__name__ == "XLMForQuestionAnswering":
                  lowerCAmelCase__   :Optional[Any]      							=				torch.zeros(
                      self.model_tester.batch_size    ,	dtype=torch.long    ,	device=_lowerCAmelCase		)
                  lowerCAmelCase__   :List[str]      							=				torch.zeros(
                      self.model_tester.batch_size    ,	dtype=torch.long    ,	device=_lowerCAmelCase		)

          return inputs_dict
      def 			snake_case_      (   self		):



          '''simple docstring'''
          lowerCAmelCase__   :List[str]      							=				XLMModelTester(self		)
          lowerCAmelCase__   :Optional[Any]      							=				ConfigTester(self    ,	config_class=_lowerCAmelCase    ,	emb_dim=37		)
      def 			snake_case_      (   self		):



          '''simple docstring'''
          self.config_tester.run_common_tests()
      def 			snake_case_      (   self		):



          '''simple docstring'''
          lowerCAmelCase__   :Dict      							=				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_xlm_model(*_lowerCAmelCase		)
      def 			snake_case_      (   self		):



          '''simple docstring'''
          lowerCAmelCase__   :int      							=				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_xlm_lm_head(*_lowerCAmelCase		)
      def 			snake_case_      (   self		):



          '''simple docstring'''
          lowerCAmelCase__   :int      							=				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_xlm_simple_qa(*_lowerCAmelCase		)
      def 			snake_case_      (   self		):



          '''simple docstring'''
          lowerCAmelCase__   :int      							=				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_xlm_qa(*_lowerCAmelCase		)
      def 			snake_case_      (   self		):



          '''simple docstring'''
          lowerCAmelCase__   :List[str]      							=				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_xlm_sequence_classif(*_lowerCAmelCase		)
      def 			snake_case_      (   self		):



          '''simple docstring'''
          lowerCAmelCase__   :str      							=				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_xlm_token_classif(*_lowerCAmelCase		)
      def 			snake_case_      (   self		):



          '''simple docstring'''
          lowerCAmelCase__   :Tuple      							=				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_xlm_for_multiple_choice(*_lowerCAmelCase		)
      def 			snake_case_      (   self    ,	_lowerCAmelCase    ,	_lowerCAmelCase    ,	_lowerCAmelCase    ,	_lowerCAmelCase    ,	_lowerCAmelCase    ,	_lowerCAmelCase=False    ,	_lowerCAmelCase=1		):



          '''simple docstring'''
          self.assertIsInstance(_lowerCAmelCase    ,	_lowerCAmelCase		)
          self.assertListEqual(
              [isinstance(_lowerCAmelCase    ,	_lowerCAmelCase		) for iter_attentions in attentions]    ,	[True] * len(_lowerCAmelCase		)		)
          self.assertEqual(len(_lowerCAmelCase		)    ,	(max_length - min_length) * num_beam_groups		)

          for idx, iter_attentions in enumerate(_lowerCAmelCase		):
              # adds PAD dummy token
              lowerCAmelCase__   :List[str]      							=				min_length + idx + 1
              lowerCAmelCase__   :Any      							=				min_length + idx + 1

              lowerCAmelCase__   :Any      							=				(
                  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(_lowerCAmelCase		)		)
      def 			snake_case_      (   self    ,	_lowerCAmelCase    ,	_lowerCAmelCase    ,	_lowerCAmelCase    ,	_lowerCAmelCase    ,	_lowerCAmelCase    ,	_lowerCAmelCase=False    ,	_lowerCAmelCase=1		):



          '''simple docstring'''
          self.assertIsInstance(_lowerCAmelCase    ,	_lowerCAmelCase		)
          self.assertListEqual(
              [isinstance(_lowerCAmelCase    ,	_lowerCAmelCase		) for iter_hidden_states in hidden_states]    ,	[True] * len(_lowerCAmelCase		)    ,	)
          self.assertEqual(len(_lowerCAmelCase		)    ,	(max_length - min_length) * num_beam_groups		)

          for idx, iter_hidden_states in enumerate(_lowerCAmelCase		):
              # adds PAD dummy token
              lowerCAmelCase__   :Any      							=				min_length + idx + 1
              lowerCAmelCase__   :str      							=				(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(_lowerCAmelCase		)    ,	)
          pass



      @slow
      def 			snake_case_      (   self		):



          '''simple docstring'''
          for model_name in XLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              lowerCAmelCase__   :Dict      							=				XLMModel.from_pretrained(_lowerCAmelCase		)
              self.assertIsNotNone(_lowerCAmelCase		)







@require_torch
class 	_UpperCAmelCase  (  unittest.TestCase		):




      """simple docstring"""



      @slow
      def 			snake_case_      (   self		):



          '''simple docstring'''
          lowerCAmelCase__   :Dict      							=				XLMWithLMHeadModel.from_pretrained("xlm-mlm-en-2048"		)
          model.to(_lowerCAmelCase		)
          lowerCAmelCase__   :Union[str, Any]      							=				torch.tensor([[14, 447]]    ,	dtype=torch.long    ,	device=_lowerCAmelCase		)  # the president
          lowerCAmelCase__   :Dict      							=				[
              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
          lowerCAmelCase__   :Tuple      							=				model.generate(_lowerCAmelCase    ,	do_sample=_lowerCAmelCase		)
          self.assertListEqual(output_ids[0].cpu().numpy().tolist()    ,	_lowerCAmelCase		)











from math import acos, sin
from typing import List, Tuple, Union

import numpy as np
import torch
from PIL import Image

from ...models import AutoencoderKL, UNetaDConditionModel
from ...schedulers import DDIMScheduler, DDPMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import AudioPipelineOutput, BaseOutput, DiffusionPipeline, ImagePipelineOutput
from .mel import Mel







class 	_UpperCAmelCase  (  _A		):




      """simple docstring"""



      A          =    ['''vqvae''']
      def __init__(   self    ,	_lowerCAmelCase    ,	_lowerCAmelCase    ,	_lowerCAmelCase    ,	_lowerCAmelCase    ,	):



          '''simple docstring'''
          super().__init__()
          self.register_modules(unet=_lowerCAmelCase    ,	scheduler=_lowerCAmelCase    ,	mel=_lowerCAmelCase    ,	vqvae=_lowerCAmelCase		)
      def 			snake_case_      (   self		):



          '''simple docstring'''
          return 50 if isinstance(self.scheduler    ,	_lowerCAmelCase		) else 1_000
      @torch.no_grad()
      def __call__(   self    ,	_lowerCAmelCase = 1    ,	_lowerCAmelCase = None    ,	_lowerCAmelCase = None    ,	_lowerCAmelCase = 0    ,	_lowerCAmelCase = 0    ,	_lowerCAmelCase = None    ,	_lowerCAmelCase = None    ,	_lowerCAmelCase = 0    ,	_lowerCAmelCase = 0    ,	_lowerCAmelCase = None    ,	_lowerCAmelCase = 0    ,	_lowerCAmelCase = None    ,	_lowerCAmelCase = None    ,	_lowerCAmelCase=True    ,	):



          '''simple docstring'''
          lowerCAmelCase__   :str      							=				steps or self.get_default_steps()
          self.scheduler.set_timesteps(_lowerCAmelCase		)
          lowerCAmelCase__   :Dict      							=				step_generator or generator
          # For backwards compatibility
          if type(self.unet.config.sample_size		) == int:
              lowerCAmelCase__   :Dict      							=				(self.unet.config.sample_size, self.unet.config.sample_size)
          if noise is None:
              lowerCAmelCase__   :Optional[int]      							=				randn_tensor(
                  (
                      batch_size,
                      self.unet.config.in_channels,
                      self.unet.config.sample_size[0],
                      self.unet.config.sample_size[1],
                  )    ,	generator=_lowerCAmelCase    ,	device=self.device    ,	)
          lowerCAmelCase__   :Union[str, Any]      							=				noise
          lowerCAmelCase__   :Any      							=				None

          if audio_file is not None or raw_audio is not None:
              self.mel.load_audio(_lowerCAmelCase    ,	_lowerCAmelCase		)
              lowerCAmelCase__   :Dict      							=				self.mel.audio_slice_to_image(_lowerCAmelCase		)
              lowerCAmelCase__   :List[str]      							=				np.frombuffer(input_image.tobytes()    ,	dtype="uint8"		).reshape(
                  (input_image.height, input_image.width)		)
              lowerCAmelCase__   :Tuple      							=				(input_image / 255) * 2 - 1
              lowerCAmelCase__   :Any      							=				torch.tensor(input_image[np.newaxis, :, :]    ,	dtype=torch.float		).to(self.device		)

              if self.vqvae is not None:
                  lowerCAmelCase__   :str      							=				self.vqvae.encode(torch.unsqueeze(_lowerCAmelCase    ,	0		)		).latent_dist.sample(
                      generator=_lowerCAmelCase		)[0]
                  lowerCAmelCase__   :Dict      							=				self.vqvae.config.scaling_factor * input_images

              if start_step > 0:
                  lowerCAmelCase__   :Dict      							=				self.scheduler.add_noise(_lowerCAmelCase    ,	_lowerCAmelCase    ,	self.scheduler.timesteps[start_step - 1]		)

              lowerCAmelCase__   :Optional[Any]      							=				(
                  self.unet.config.sample_size[1] * self.mel.get_sample_rate() / self.mel.x_res / self.mel.hop_length
              )
              lowerCAmelCase__   :Dict      							=				int(mask_start_secs * pixels_per_second		)
              lowerCAmelCase__   :Tuple      							=				int(mask_end_secs * pixels_per_second		)
              lowerCAmelCase__   :str      							=				self.scheduler.add_noise(_lowerCAmelCase    ,	_lowerCAmelCase    ,	torch.tensor(self.scheduler.timesteps[start_step:]		)		)

          for step, t in enumerate(self.progress_bar(self.scheduler.timesteps[start_step:]		)		):
              if isinstance(self.unet    ,	_lowerCAmelCase		):
                  lowerCAmelCase__   :Optional[Any]      							=				self.unet(_lowerCAmelCase    ,	_lowerCAmelCase    ,	_lowerCAmelCase		)["sample"]
              else:
                  lowerCAmelCase__   :Dict      							=				self.unet(_lowerCAmelCase    ,	_lowerCAmelCase		)["sample"]

              if isinstance(self.scheduler    ,	_lowerCAmelCase		):
                  lowerCAmelCase__   :Any      							=				self.scheduler.step(
                      model_output=_lowerCAmelCase    ,	timestep=_lowerCAmelCase    ,	sample=_lowerCAmelCase    ,	eta=_lowerCAmelCase    ,	generator=_lowerCAmelCase    ,	)["prev_sample"]
              else:
                  lowerCAmelCase__   :List[str]      							=				self.scheduler.step(
                      model_output=_lowerCAmelCase    ,	timestep=_lowerCAmelCase    ,	sample=_lowerCAmelCase    ,	generator=_lowerCAmelCase    ,	)["prev_sample"]

              if mask is not None:
                  if mask_start > 0:
                      lowerCAmelCase__   :List[Any]      							=				mask[:, step, :, :mask_start]
                  if mask_end > 0:
                      lowerCAmelCase__   :Optional[Any]      							=				mask[:, step, :, -mask_end:]

          if self.vqvae is not None:
              # 0.18215 was scaling factor used in training to ensure unit variance
              lowerCAmelCase__   :Any      							=				1 / self.vqvae.config.scaling_factor * images
              lowerCAmelCase__   :List[Any]      							=				self.vqvae.decode(_lowerCAmelCase		)["sample"]

          lowerCAmelCase__   :Dict      							=				(images / 2 + 0.5).clamp(0    ,	1		)
          lowerCAmelCase__   :Union[str, Any]      							=				images.cpu().permute(0    ,	2    ,	3    ,	1		).numpy()
          lowerCAmelCase__   :Optional[int]      							=				(images * 255).round().astype("uint8"		)
          lowerCAmelCase__   :Optional[int]      							=				list(
              (Image.fromarray(_[:, :, 0]		) for _ in images)
              if images.shape[3] == 1
              else (Image.fromarray(_lowerCAmelCase    ,	mode="RGB"		).convert("L"		) for _ in images)		)

          lowerCAmelCase__   :Optional[Any]      							=				[self.mel.image_to_audio(_lowerCAmelCase		) for _ in images]
          if not return_dict:
              return images, (self.mel.get_sample_rate(), audios)

          return BaseOutput(**AudioPipelineOutput(np.array(_lowerCAmelCase		)[:, np.newaxis, :]		)    ,	**ImagePipelineOutput(_lowerCAmelCase		)		)
      @torch.no_grad()
      def 			snake_case_      (   self    ,	_lowerCAmelCase    ,	_lowerCAmelCase = 50		):



          '''simple docstring'''
          assert isinstance(self.scheduler    ,	_lowerCAmelCase		)
          self.scheduler.set_timesteps(_lowerCAmelCase		)
          lowerCAmelCase__   :Any      							=				np.array(
              [np.frombuffer(image.tobytes()    ,	dtype="uint8"		).reshape((1, image.height, image.width)		) for image in images]		)
          lowerCAmelCase__   :Dict      							=				(sample / 255) * 2 - 1
          lowerCAmelCase__   :Optional[Any]      							=				torch.Tensor(_lowerCAmelCase		).to(self.device		)

          for t in self.progress_bar(torch.flip(self.scheduler.timesteps    ,	(0,)		)		):
              lowerCAmelCase__   :List[Any]      							=				t - self.scheduler.config.num_train_timesteps // self.scheduler.num_inference_steps
              lowerCAmelCase__   :Any      							=				self.scheduler.alphas_cumprod[t]
              lowerCAmelCase__   :List[Any]      							=				(
                  self.scheduler.alphas_cumprod[prev_timestep]
                  if prev_timestep >= 0
                  else self.scheduler.final_alpha_cumprod
              )
              lowerCAmelCase__   :List[str]      							=				1 - alpha_prod_t
              lowerCAmelCase__   :List[Any]      							=				self.unet(_lowerCAmelCase    ,	_lowerCAmelCase		)["sample"]
              lowerCAmelCase__   :int      							=				(1 - alpha_prod_t_prev) ** 0.5 * model_output
              lowerCAmelCase__   :List[Any]      							=				(sample - pred_sample_direction) * alpha_prod_t_prev ** (-0.5)
              lowerCAmelCase__   :Union[str, Any]      							=				sample * alpha_prod_t ** 0.5 + beta_prod_t ** 0.5 * model_output

          return sample



      @staticmethod
      def 			snake_case_      (   _lowerCAmelCase    ,	_lowerCAmelCase    ,	_lowerCAmelCase		):



          '''simple docstring'''
          lowerCAmelCase__   :List[str]      							=				acos(torch.dot(torch.flatten(_lowerCAmelCase		)    ,	torch.flatten(_lowerCAmelCase		)		) / torch.norm(_lowerCAmelCase		) / torch.norm(_lowerCAmelCase		)		)
          return sin((1 - alpha) * theta		) * xa / sin(_lowerCAmelCase		) + sin(alpha * theta		) * xa / sin(_lowerCAmelCase		)



