




import gc
import random
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DiffusionPipeline,
    EulerDiscreteScheduler,
    StableDiffusionXLImgaImgPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import floats_tensor, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    IMAGE_TO_IMAGE_IMAGE_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()





class  lowerCAmelCase__(    snake_case__    ,    snake_case__    ,    unittest.TestCase						):






    '''simple docstring'''





    A_  :     Optional[int]			= StableDiffusionXLImgaImgPipeline
    A_  :     Optional[int]			= TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'height', 'width'}
    A_  :     Dict			= PipelineTesterMixin.required_optional_params - {'latents'}
    A_  :     List[str]			= TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
    A_  :     Tuple			= IMAGE_TO_IMAGE_IMAGE_PARAMS
    A_  :     Optional[int]			= IMAGE_TO_IMAGE_IMAGE_PARAMS





    def 					_lowerCamelCase (	self				:   Optional[int]	):





       '''simple docstring'''
       torch.manual_seed(0	)
       UpperCAmelCase_							:      Optional[Any]						=   UNetaDConditionModel(
           block_out_channels=(32, 64)       ,   layers_per_block=2       ,   sample_size=32       ,   in_channels=4       ,   out_channels=4       ,   down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')       ,   up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')       ,   attention_head_dim=(2, 4)       ,   use_linear_projection=__snake_case       ,   addition_embed_type='''text_time'''       ,   addition_time_embed_dim=8       ,   transformer_layers_per_block=(1, 2)       ,   projection_class_embeddings_input_dim=80       ,   cross_attention_dim=64       ,   )
       UpperCAmelCase_							:      List[str]						=   EulerDiscreteScheduler(
           beta_start=0.00_085       ,   beta_end=0.012       ,   steps_offset=1       ,   beta_schedule='''scaled_linear'''       ,   timestep_spacing='''leading'''       ,   )
       torch.manual_seed(0	)
       UpperCAmelCase_							:      Any						=   AutoencoderKL(
           block_out_channels=[32, 64]       ,   in_channels=3       ,   out_channels=3       ,   down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']       ,   up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']       ,   latent_channels=4       ,   sample_size=128       ,   )
       torch.manual_seed(0	)
       UpperCAmelCase_							:      Dict						=   CLIPTextConfig(
           bos_token_id=0       ,   eos_token_id=2       ,   hidden_size=32       ,   intermediate_size=37       ,   layer_norm_eps=1E-05       ,   num_attention_heads=4       ,   num_hidden_layers=5       ,   pad_token_id=1       ,   vocab_size=1_000       ,   hidden_act='''gelu'''       ,   projection_dim=32       ,   )
       UpperCAmelCase_							:      Any						=   CLIPTextModel(__snake_case	)
       UpperCAmelCase_							:      Dict						=   CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''       ,   local_files_only=__snake_case	)

       UpperCAmelCase_							:      Any						=   CLIPTextModelWithProjection(__snake_case	)
       UpperCAmelCase_							:      List[str]						=   CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''       ,   local_files_only=__snake_case	)

       UpperCAmelCase_							:      Tuple						=   {
           '''unet''': unet,
           '''scheduler''': scheduler,
           '''vae''': vae,
           '''text_encoder''': text_encoder,
           '''tokenizer''': tokenizer,
           '''text_encoder_2''': text_encoder_a,
           '''tokenizer_2''': tokenizer_a,
           # "safety_checker": None,
           # "feature_extractor": None,
       }
       return components





    def 					_lowerCamelCase (	self				:   Union[str, Any]       ,   __snake_case				:   List[Any]       ,   __snake_case				:   Any=0	):





       '''simple docstring'''
       UpperCAmelCase_							:      Optional[int]						=   floats_tensor((1, 3, 32, 32)       ,   rng=random.Random(__snake_case	)	).to(__snake_case	)
       UpperCAmelCase_							:      str						=   image / 2 + 0.5
       if str(__snake_case	).startswith('''mps'''	):
          UpperCAmelCase_							:      Tuple						=   torch.manual_seed(__snake_case	)
       else:
          UpperCAmelCase_							:      Union[str, Any]						=   torch.Generator(device=__snake_case	).manual_seed(__snake_case	)
       UpperCAmelCase_							:      List[Any]						=   {
           '''prompt''': '''A painting of a squirrel eating a burger''',
           '''image''': image,
           '''generator''': generator,
           '''num_inference_steps''': 2,
           '''guidance_scale''': 5.0,
           '''output_type''': '''numpy''',
           '''strength''': 0.75,
       }
       return inputs





    def 					_lowerCamelCase (	self				:   Tuple	):





       '''simple docstring'''
       UpperCAmelCase_							:      Dict						=   '''cpu'''  # ensure determinism for the device-dependent torch.Generator
       UpperCAmelCase_							:      List[Any]						=   self.get_dummy_components()
       UpperCAmelCase_							:      Optional[Any]						=   StableDiffusionXLImgaImgPipeline(**__snake_case	)
       UpperCAmelCase_							:      Dict						=   sd_pipe.to(__snake_case	)
       sd_pipe.set_progress_bar_config(disable=__snake_case	)

       UpperCAmelCase_							:      int						=   self.get_dummy_inputs(__snake_case	)
       UpperCAmelCase_							:      str						=   sd_pipe(**__snake_case	).images
       UpperCAmelCase_							:      Optional[Any]						=   image[0, -3:, -3:, -1]

       assert image.shape == (1, 32, 32, 3)

       UpperCAmelCase_							:      Union[str, Any]						=   np.array([0.4_656, 0.4_840, 0.4_439, 0.6_698, 0.5_574, 0.4_524, 0.5_799, 0.5_943, 0.5_165]	)

       assert np.abs(image_slice.flatten() - expected_slice	).max() < 1E-2





    def 					_lowerCamelCase (	self				:   str	):





       '''simple docstring'''
       super().test_attention_slicing_forward_pass(expected_max_diff=3E-3	)





    def 					_lowerCamelCase (	self				:   Tuple	):





       '''simple docstring'''
       super().test_inference_batch_single_identical(expected_max_diff=3E-3	)





    def 					_lowerCamelCase (	self				:   Optional[Any]	):





       '''simple docstring'''
       pass





    def 					_lowerCamelCase (	self				:   str	):





       '''simple docstring'''
       UpperCAmelCase_							:      List[str]						=   self.get_dummy_components()
       UpperCAmelCase_							:      List[str]						=   StableDiffusionXLImgaImgPipeline(**__snake_case	)
       UpperCAmelCase_							:      List[str]						=   sd_pipe.to(__snake_case	)
       UpperCAmelCase_							:      List[str]						=   sd_pipe.to(__snake_case	)
       sd_pipe.set_progress_bar_config(disable=__snake_case	)

       # forward without prompt embeds
       UpperCAmelCase_							:      str						=   self.get_dummy_inputs(__snake_case	)
       UpperCAmelCase_							:      List[Any]						=   3 * ['''this is a negative prompt''']
       UpperCAmelCase_							:      str						=   negative_prompt
       UpperCAmelCase_							:      Union[str, Any]						=   3 * [inputs['''prompt''']]

       UpperCAmelCase_							:      int						=   sd_pipe(**__snake_case	)
       UpperCAmelCase_							:      List[Any]						=   output.images[0, -3:, -3:, -1]

       # forward with prompt embeds
       UpperCAmelCase_							:      Optional[int]						=   self.get_dummy_inputs(__snake_case	)
       UpperCAmelCase_							:      str						=   3 * ['''this is a negative prompt''']
       UpperCAmelCase_							:      str						=   3 * [inputs.pop('''prompt'''	)]

       (
           UpperCAmelCase_      
       )							:      Dict						=   sd_pipe.encode_prompt(__snake_case       ,   negative_prompt=__snake_case	)

       UpperCAmelCase_							:      Any						=   sd_pipe(
           **__snake_case       ,   prompt_embeds=__snake_case       ,   negative_prompt_embeds=__snake_case       ,   pooled_prompt_embeds=__snake_case       ,   negative_pooled_prompt_embeds=__snake_case       ,   )
       UpperCAmelCase_							:      Tuple						=   output.images[0, -3:, -3:, -1]

       # make sure that it's equal
       assert np.abs(image_slice_a.flatten() - image_slice_a.flatten()	).max() < 1E-4





@slow
@require_torch_gpu
class  lowerCAmelCase__(    unittest.TestCase						):






    '''simple docstring'''





    def 					_lowerCamelCase (	self				:   Dict	):





       '''simple docstring'''
       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()





    def 					_lowerCamelCase (	self				:   Any       ,   __snake_case				:   Tuple       ,   __snake_case				:   List[str]="cpu"       ,   __snake_case				:   List[Any]=torch.floataa       ,   __snake_case				:   Tuple=0	):





       '''simple docstring'''
       UpperCAmelCase_							:      Dict						=   torch.Generator(device=__snake_case	).manual_seed(__snake_case	)
       UpperCAmelCase_							:      Tuple						=   np.random.RandomState(__snake_case	).standard_normal((1, 4, 64, 64)	)
       UpperCAmelCase_							:      List[str]						=   torch.from_numpy(__snake_case	).to(device=__snake_case       ,   dtype=__snake_case	)
       UpperCAmelCase_							:      Dict						=   {
           '''prompt''': '''a photograph of an astronaut riding a horse''',
           '''latents''': latents,
           '''generator''': generator,
           '''num_inference_steps''': 3,
           '''guidance_scale''': 7.5,
           '''output_type''': '''numpy''',
       }
       return inputs





    def 					_lowerCamelCase (	self				:   Optional[Any]	):





       '''simple docstring'''
       UpperCAmelCase_							:      str						=   DiffusionPipeline.from_pretrained('''stabilityai/stable-diffusion-2-base'''	)
       pipe.to(__snake_case	)
       pipe.set_progress_bar_config(disable=__snake_case	)

       UpperCAmelCase_							:      Dict						=   self.get_inputs(__snake_case	)
       UpperCAmelCase_							:      Dict						=   pipe(**__snake_case	).images
       UpperCAmelCase_							:      Optional[Any]						=   image[0, -3:, -3:, -1].flatten()

       assert image.shape == (1, 512, 512, 3)
       UpperCAmelCase_							:      int						=   np.array([0.49_493, 0.47_896, 0.40_798, 0.54_214, 0.53_212, 0.48_202, 0.47_656, 0.46_329, 0.48_506]	)
       assert np.abs(image_slice - expected_slice	).max() < 7E-3



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				:   int       ,   __snake_case				:   List[Any]       ,   __snake_case				:   List[Any]=13       ,   __snake_case				:   str=7       ,   __snake_case				:   Union[str, Any]=True       ,   __snake_case				:   List[Any]=True       ,   __snake_case				:   str=True       ,   __snake_case				:   Optional[int]=True       ,   __snake_case				:   Optional[int]=True       ,   __snake_case				:   List[str]=False       ,   __snake_case				:   List[str]=False       ,   __snake_case				:   Tuple=False       ,   __snake_case				:   List[str]=2       ,   __snake_case				:   Optional[int]=99       ,   __snake_case				:   Tuple=0       ,   __snake_case				:   int=32       ,   __snake_case				:   Optional[int]=5       ,   __snake_case				:   str=4       ,   __snake_case				:   str=0.1       ,   __snake_case				:   Optional[int]=0.1       ,   __snake_case				:   List[str]=512       ,   __snake_case				:   Tuple=2       ,   __snake_case				:   List[Any]=0.02       ,   __snake_case				:   Any=2       ,   __snake_case				:   Optional[int]=4       ,   __snake_case				:   Optional[Any]="last"       ,   __snake_case				:   Dict=True       ,   __snake_case				:   Any=None       ,   __snake_case				:   str=0       ,   ):





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





    def 					_lowerCamelCase (	self				:   Optional[int]	):





       '''simple docstring'''
       UpperCAmelCase_							:      Tuple						=   ids_tensor([self.batch_size, self.seq_length]       ,   self.vocab_size	)
       UpperCAmelCase_							:      Union[str, Any]						=   random_attention_mask([self.batch_size, self.seq_length]	)

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

       UpperCAmelCase_							:      int						=   None
       if self.use_token_type_ids:
          UpperCAmelCase_							:      List[str]						=   ids_tensor([self.batch_size, self.seq_length]       ,   self.n_langs	)

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

       UpperCAmelCase_							:      List[Any]						=   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				:   Any	):





       '''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 					_lowerCamelCase (	self				:   Optional[Any]       ,   __snake_case				:   int       ,   __snake_case				:   int       ,   __snake_case				:   List[Any]       ,   __snake_case				:   List[Any]       ,   __snake_case				:   List[str]       ,   __snake_case				:   List[str]       ,   __snake_case				:   Optional[int]       ,   __snake_case				:   Union[str, Any]       ,   __snake_case				:   Dict       ,   ):





       '''simple docstring'''
       UpperCAmelCase_							:      Any						=   XLMModel(config=__snake_case	)
       model.to(__snake_case	)
       model.eval()
       UpperCAmelCase_							:      Optional[int]						=   model(__snake_case       ,   lengths=__snake_case       ,   langs=__snake_case	)
       UpperCAmelCase_							:      Any						=   model(__snake_case       ,   langs=__snake_case	)
       UpperCAmelCase_							:      Any						=   model(__snake_case	)
       self.parent.assertEqual(result.last_hidden_state.shape       ,   (self.batch_size, self.seq_length, self.hidden_size)	)





    def 					_lowerCamelCase (	self				:   str       ,   __snake_case				:   Dict       ,   __snake_case				:   Any       ,   __snake_case				:   Optional[int]       ,   __snake_case				:   Union[str, Any]       ,   __snake_case				:   Dict       ,   __snake_case				:   str       ,   __snake_case				:   Tuple       ,   __snake_case				:   Union[str, Any]       ,   __snake_case				:   int       ,   ):





       '''simple docstring'''
       UpperCAmelCase_							:      Any						=   XLMWithLMHeadModel(__snake_case	)
       model.to(__snake_case	)
       model.eval()

       UpperCAmelCase_							:      Optional[Any]						=   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[int]       ,   __snake_case				:   Tuple       ,   __snake_case				:   Any       ,   __snake_case				:   Tuple       ,   __snake_case				:   List[str]       ,   __snake_case				:   Optional[int]       ,   __snake_case				:   List[str]       ,   __snake_case				:   Optional[Any]       ,   __snake_case				:   str       ,   __snake_case				:   List[str]       ,   ):





       '''simple docstring'''
       UpperCAmelCase_							:      Optional[Any]						=   XLMForQuestionAnsweringSimple(__snake_case	)
       model.to(__snake_case	)
       model.eval()

       UpperCAmelCase_							:      Optional[int]						=   model(__snake_case	)

       UpperCAmelCase_							:      Any						=   model(__snake_case       ,   start_positions=__snake_case       ,   end_positions=__snake_case	)
       UpperCAmelCase_							:      Optional[Any]						=   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				:   Any       ,   __snake_case				:   int       ,   __snake_case				:   Dict       ,   __snake_case				:   Union[str, Any]       ,   __snake_case				:   Union[str, Any]       ,   __snake_case				:   List[str]       ,   __snake_case				:   Tuple       ,   __snake_case				:   str       ,   __snake_case				:   Tuple       ,   __snake_case				:   int       ,   ):





       '''simple docstring'''
       UpperCAmelCase_							:      List[str]						=   XLMForQuestionAnswering(__snake_case	)
       model.to(__snake_case	)
       model.eval()

       UpperCAmelCase_							:      Union[str, Any]						=   model(__snake_case	)

       UpperCAmelCase_							:      List[str]						=   model(
           __snake_case       ,   start_positions=__snake_case       ,   end_positions=__snake_case       ,   cls_index=__snake_case       ,   is_impossible=__snake_case       ,   p_mask=__snake_case       ,   )

       UpperCAmelCase_							:      Optional[Any]						=   model(
           __snake_case       ,   start_positions=__snake_case       ,   end_positions=__snake_case       ,   cls_index=__snake_case       ,   is_impossible=__snake_case       ,   )

       ((UpperCAmelCase_)		,      )							:      Union[str, Any]						=   result_with_labels.to_tuple()

       UpperCAmelCase_							:      Optional[int]						=   model(__snake_case       ,   start_positions=__snake_case       ,   end_positions=__snake_case	)

       ((UpperCAmelCase_)		,      )							:      str						=   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				:   str       ,   __snake_case				:   Dict       ,   __snake_case				:   str       ,   __snake_case				:   Optional[Any]       ,   __snake_case				:   Dict       ,   __snake_case				:   Optional[int]       ,   __snake_case				:   Dict       ,   __snake_case				:   List[str]       ,   __snake_case				:   Optional[Any]       ,   __snake_case				:   Any       ,   ):





       '''simple docstring'''
       UpperCAmelCase_							:      List[str]						=   XLMForSequenceClassification(__snake_case	)
       model.to(__snake_case	)
       model.eval()

       UpperCAmelCase_							:      Union[str, Any]						=   model(__snake_case	)
       UpperCAmelCase_							:      Optional[int]						=   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       ,   __snake_case				:   Union[str, Any]       ,   __snake_case				:   Any       ,   __snake_case				:   List[str]       ,   __snake_case				:   Optional[Any]       ,   __snake_case				:   Tuple       ,   __snake_case				:   str       ,   __snake_case				:   Optional[int]       ,   __snake_case				:   Tuple       ,   __snake_case				:   int       ,   ):





       '''simple docstring'''
       UpperCAmelCase_							:      List[Any]						=   self.num_labels
       UpperCAmelCase_							:      Optional[int]						=   XLMForTokenClassification(__snake_case	)
       model.to(__snake_case	)
       model.eval()

       UpperCAmelCase_							:      List[str]						=   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				:   List[Any]       ,   __snake_case				:   str       ,   __snake_case				:   List[str]       ,   __snake_case				:   Any       ,   __snake_case				:   Tuple       ,   __snake_case				:   Any       ,   __snake_case				:   Union[str, Any]       ,   __snake_case				:   Dict       ,   __snake_case				:   Dict       ,   __snake_case				:   Optional[int]       ,   ):





       '''simple docstring'''
       UpperCAmelCase_							:      int						=   self.num_choices
       UpperCAmelCase_							:      int						=   XLMForMultipleChoice(config=__snake_case	)
       model.to(__snake_case	)
       model.eval()
       UpperCAmelCase_							:      int						=   input_ids.unsqueeze(1	).expand(-1       ,   self.num_choices       ,   -1	).contiguous()
       UpperCAmelCase_							:      int						=   token_type_ids.unsqueeze(1	).expand(-1       ,   self.num_choices       ,   -1	).contiguous()
       UpperCAmelCase_							:      List[Any]						=   input_mask.unsqueeze(1	).expand(-1       ,   self.num_choices       ,   -1	).contiguous()
       UpperCAmelCase_							:      Any						=   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				:   List[str]	):





       '''simple docstring'''
       UpperCAmelCase_							:      Union[str, Any]						=   self.prepare_config_and_inputs()
       (
           (
           UpperCAmelCase_
       )		,      (
           UpperCAmelCase_
       )		,      (
           UpperCAmelCase_
       )		,      (
           UpperCAmelCase_
       )		,      (
           UpperCAmelCase_
       )		,      (
           UpperCAmelCase_
       )		,      (
           UpperCAmelCase_
       )		,      (
           UpperCAmelCase_
       )		,      (
           UpperCAmelCase_
       )		,      
       )							:      Union[str, Any]						=   config_and_inputs
       UpperCAmelCase_							:      str						=   {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''lengths''': input_lengths}
       return config, inputs_dict





@require_torch
class  lowerCAmelCase__(    snake_case__    ,    snake_case__    ,    snake_case__    ,    unittest.TestCase						):






    '''simple docstring'''





    A_  :     Dict			= (
        (
            XLMModel,
            XLMWithLMHeadModel,
            XLMForQuestionAnswering,
            XLMForSequenceClassification,
            XLMForQuestionAnsweringSimple,
            XLMForTokenClassification,
            XLMForMultipleChoice,
        )
        if is_torch_available()
        else ()
    )
    A_  :     List[str]			= (
        (XLMWithLMHeadModel,) if is_torch_available() else ()
    )  # TODO (PVP): Check other models whether language generation is also applicable
    A_  :     Optional[int]			= (
        {
            '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				:   str       ,   __snake_case				:   Optional[int]       ,   __snake_case				:   Optional[Any]       ,   __snake_case				:   Optional[int]       ,   __snake_case				:   Union[str, Any]       ,   __snake_case				:   Optional[Any]	):





       '''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 					_lowerCamelCase (	self				:   Union[str, Any]       ,   __snake_case				:   Any       ,   __snake_case				:   List[Any]       ,   __snake_case				:   str=False	):





       '''simple docstring'''
       UpperCAmelCase_							:      int						=   super()._prepare_for_class(__snake_case       ,   __snake_case       ,   return_labels=__snake_case	)

       if return_labels:
          if model_class.__name__ == "XLMForQuestionAnswering":
             UpperCAmelCase_							:      List[str]						=   torch.zeros(
                 self.model_tester.batch_size       ,   dtype=torch.long       ,   device=__snake_case	)
             UpperCAmelCase_							:      Dict						=   torch.zeros(
                 self.model_tester.batch_size       ,   dtype=torch.long       ,   device=__snake_case	)

       return inputs_dict





    def 					_lowerCamelCase (	self				:   Dict	):





       '''simple docstring'''
       UpperCAmelCase_							:      Union[str, Any]						=   XLMModelTester(self	)
       UpperCAmelCase_							:      List[Any]						=   ConfigTester(self       ,   config_class=__snake_case       ,   emb_dim=37	)





    def 					_lowerCamelCase (	self				:   Union[str, Any]	):





       '''simple docstring'''
       self.config_tester.run_common_tests()





    def 					_lowerCamelCase (	self				:   Any	):





       '''simple docstring'''
       UpperCAmelCase_							:      Any						=   self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_xlm_model(*__snake_case	)





    def 					_lowerCamelCase (	self				:   Union[str, Any]	):





       '''simple docstring'''
       UpperCAmelCase_							:      Tuple						=   self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_xlm_lm_head(*__snake_case	)





    def 					_lowerCamelCase (	self				:   Tuple	):





       '''simple docstring'''
       UpperCAmelCase_							:      str						=   self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_xlm_simple_qa(*__snake_case	)





    def 					_lowerCamelCase (	self				:   int	):





       '''simple docstring'''
       UpperCAmelCase_							:      List[Any]						=   self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_xlm_qa(*__snake_case	)





    def 					_lowerCamelCase (	self				:   List[str]	):





       '''simple docstring'''
       UpperCAmelCase_							:      List[Any]						=   self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_xlm_sequence_classif(*__snake_case	)





    def 					_lowerCamelCase (	self				:   int	):





       '''simple docstring'''
       UpperCAmelCase_							:      Union[str, Any]						=   self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_xlm_token_classif(*__snake_case	)





    def 					_lowerCamelCase (	self				:   str	):





       '''simple docstring'''
       UpperCAmelCase_							:      Optional[int]						=   self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_xlm_for_multiple_choice(*__snake_case	)





    def 					_lowerCamelCase (	self				:   str       ,   __snake_case				:   str       ,   __snake_case				:   Union[str, Any]       ,   __snake_case				:   List[Any]       ,   __snake_case				:   str       ,   __snake_case				:   Optional[int]       ,   __snake_case				:   Union[str, Any]=False       ,   __snake_case				:   Optional[Any]=1	):





       '''simple docstring'''
       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
          UpperCAmelCase_							:      Dict						=   min_length + idx + 1
          UpperCAmelCase_							:      List[Any]						=   min_length + idx + 1

          UpperCAmelCase_							:      Optional[int]						=   (
              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				:   List[Any]       ,   __snake_case				:   List[str]       ,   __snake_case				:   int       ,   __snake_case				:   Optional[int]       ,   __snake_case				:   Dict       ,   __snake_case				:   Any       ,   __snake_case				:   Optional[Any]=False       ,   __snake_case				:   str=1	):





       '''simple docstring'''
       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
          UpperCAmelCase_							:      str						=   min_length + idx + 1
          UpperCAmelCase_							:      int						=   (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				:   int	):





       '''simple docstring'''
       for model_name in XLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
          UpperCAmelCase_							:      Any						=   XLMModel.from_pretrained(__snake_case	)
          self.assertIsNotNone(__snake_case	)





@require_torch
class  lowerCAmelCase__(    unittest.TestCase						):






    '''simple docstring'''





    @slow
    def 					_lowerCamelCase (	self				:   str	):





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