


import tempfile
import unittest

from transformers import TaConfig, is_torch_available
from transformers.testing_utils import (
    require_sentencepiece,
    require_tokenizers,
    require_torch,
    slow,
    torch_device,
)

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


if is_torch_available():
   import torch

   from transformers import AutoTokenizer, UMTaForConditionalGeneration, UMTaForQuestionAnswering, UMTaModel







class lowerCAmelCase_		:

 '''simple docstring'''

 def __init__(			self	,   _UpperCAmelCase	,   _UpperCAmelCase=99	,   _UpperCAmelCase=13	,   _UpperCAmelCase=7	,   _UpperCAmelCase=9	,   _UpperCAmelCase=True	,   _UpperCAmelCase=True	,   _UpperCAmelCase=False	,   _UpperCAmelCase=32	,   _UpperCAmelCase=5	,   _UpperCAmelCase=4	,   _UpperCAmelCase=37	,   _UpperCAmelCase=8	,   _UpperCAmelCase=0.1	,   _UpperCAmelCase=0.002	,   _UpperCAmelCase=1	,   _UpperCAmelCase=0	,   _UpperCAmelCase=0	,   _UpperCAmelCase=None	,   _UpperCAmelCase=None	,   ):
       snake_case_ =   parent
       snake_case_ =   batch_size
       snake_case_ =   encoder_seq_length
       snake_case_ =   decoder_seq_length
       # For common tests
       snake_case_ =   self.decoder_seq_length
       snake_case_ =   is_training
       snake_case_ =   use_attention_mask
       snake_case_ =   use_labels
       snake_case_ =   vocab_size
       snake_case_ =   hidden_size
       snake_case_ =   num_hidden_layers
       snake_case_ =   num_attention_heads
       snake_case_ =   d_ff
       snake_case_ =   relative_attention_num_buckets
       snake_case_ =   dropout_rate
       snake_case_ =   initializer_factor
       snake_case_ =   eos_token_id
       snake_case_ =   pad_token_id
       snake_case_ =   decoder_start_token_id
       snake_case_ =   None
       snake_case_ =   decoder_layers



 def 		UpperCamelCase__  (			self      ):
       return TaConfig.from_pretrained('''google/umt5-base'''      )



 def 		UpperCamelCase__  (			self	,   _UpperCAmelCase	,   _UpperCAmelCase	,   _UpperCAmelCase	,   _UpperCAmelCase=None	,   _UpperCAmelCase=None	,   _UpperCAmelCase=None	,   _UpperCAmelCase=None	,   _UpperCAmelCase=None	,   ):
       if attention_mask is None:
             snake_case_ =   input_ids.ne(config.pad_token_id      )
       if decoder_attention_mask is None:
             snake_case_ =   decoder_input_ids.ne(config.pad_token_id      )
       if head_mask is None:
             snake_case_ =   torch.ones(config.num_hidden_layers	,   config.num_attention_heads	,   device=_UpperCAmelCase      )
       if decoder_head_mask is None:
             snake_case_ =   torch.ones(config.num_decoder_layers	,   config.num_attention_heads	,   device=_UpperCAmelCase      )
       if cross_attn_head_mask is None:
             snake_case_ =   torch.ones(
                 config.num_decoder_layers	,   config.num_attention_heads	,   device=_UpperCAmelCase      )
       return {
           "input_ids": input_ids,
           "decoder_input_ids": decoder_input_ids,
           "attention_mask": attention_mask,
           "decoder_attention_mask": decoder_attention_mask,
           "head_mask": head_mask,
           "decoder_head_mask": decoder_head_mask,
           "cross_attn_head_mask": cross_attn_head_mask,
       }



 def 		UpperCamelCase__  (			self      ):
       snake_case_ =   ids_tensor([self.batch_size, self.encoder_seq_length]	,   self.vocab_size      )
       snake_case_ =   ids_tensor([self.batch_size, self.decoder_seq_length]	,   self.vocab_size      )

       # we need to clamp the input ids here to avoid having pad token in between
       # this is because for NllbMoe the position_ids are prepared such that
       # all pad tokens have pos id = 2 and rest are between 2..seq_length
       # and the seq_length here is seq_length - num_pad_tokens
       # but when using past, there is no way of knowing if the past input ids had
       # pad tokens in them, which results in incorrect seq_lenth and which in turn results in
       # position_ids being off by num_pad_tokens in past input
       snake_case_ =   input_ids.clamp(self.pad_token_id + 1      )
       snake_case_ =   decoder_input_ids.clamp(self.pad_token_id + 1      )

       snake_case_ =   self.get_config()
       snake_case_ =   config.num_attention_heads
       snake_case_ =   self.prepare_inputs_dict(_UpperCAmelCase	,   _UpperCAmelCase	,   _UpperCAmelCase      )
       return config, input_dict



 def 		UpperCamelCase__  (			self      ):
       snake_case_ ,					snake_case_ =   self.prepare_config_and_inputs()
       return config, inputs_dict



 def 		UpperCamelCase__  (			self      ):
       return TaConfig(
           vocab_size=1_66	,   d_model=self.hidden_size	,   d_ff=self.d_ff	,   d_kv=self.hidden_size // self.num_attention_heads	,   num_layers=self.num_hidden_layers	,   num_decoder_layers=self.decoder_layers	,   num_heads=self.num_attention_heads	,   relative_attention_num_buckets=self.relative_attention_num_buckets	,   dropout_rate=self.dropout_rate	,   initializer_factor=self.initializer_factor	,   eos_token_id=self.eos_token_id	,   bos_token_id=self.pad_token_id	,   pad_token_id=self.pad_token_id	,   decoder_start_token_id=self.decoder_start_token_id	,   )



 def 		UpperCamelCase__  (			self      ):
       return TaConfig(
           vocab_size=self.vocab_size	,   d_model=self.hidden_size	,   d_ff=self.d_ff	,   d_kv=self.hidden_size // self.num_attention_heads	,   num_layers=self.num_hidden_layers	,   num_decoder_layers=self.decoder_layers	,   num_heads=self.num_attention_heads	,   relative_attention_num_buckets=self.relative_attention_num_buckets	,   dropout_rate=self.dropout_rate	,   initializer_factor=self.initializer_factor	,   eos_token_id=self.eos_token_id	,   bos_token_id=self.pad_token_id	,   pad_token_id=self.pad_token_id	,   decoder_start_token_id=self.decoder_start_token_id	,   )



 def 		UpperCamelCase__  (			self	,   _UpperCAmelCase	,   _UpperCAmelCase	,   _UpperCAmelCase	,   _UpperCAmelCase	,   _UpperCAmelCase	,   _UpperCAmelCase	,   ):
       snake_case_ =   UMTaModel(config=_UpperCAmelCase      )
       model.to(_UpperCAmelCase      )
       model.eval()
       snake_case_ =   model(
           input_ids=_UpperCAmelCase	,   decoder_input_ids=_UpperCAmelCase	,   attention_mask=_UpperCAmelCase	,   decoder_attention_mask=_UpperCAmelCase	,   )
       snake_case_ =   model(input_ids=_UpperCAmelCase	,   decoder_input_ids=_UpperCAmelCase      )
       snake_case_ =   result.last_hidden_state
       snake_case_ =   result.past_key_values
       snake_case_ =   result.encoder_last_hidden_state

       self.parent.assertEqual(encoder_output.size()	,   (self.batch_size, self.encoder_seq_length, self.hidden_size)      )
       self.parent.assertEqual(decoder_output.size()	,   (self.batch_size, self.decoder_seq_length, self.hidden_size)      )
       # There should be `num_layers` key value embeddings stored in decoder_past
       self.parent.assertEqual(len(_UpperCAmelCase      )	,   config.num_layers      )
       # There should be a self attn key, a self attn value, a cross attn key and a cross attn value stored in each decoder_past tuple
       self.parent.assertEqual(len(decoder_past[0]      )	,   4      )



 def 		UpperCamelCase__  (			self	,   _UpperCAmelCase	,   _UpperCAmelCase	,   _UpperCAmelCase	,   _UpperCAmelCase	,   _UpperCAmelCase	,   _UpperCAmelCase	,   ):
       snake_case_ =   UMTaModel(config=_UpperCAmelCase      ).get_decoder().to(_UpperCAmelCase      ).eval()
       # first forward pass
       snake_case_ =   model(_UpperCAmelCase	,   use_cache=_UpperCAmelCase      )
       snake_case_ =   model(_UpperCAmelCase      )
       snake_case_ =   model(_UpperCAmelCase	,   use_cache=_UpperCAmelCase      )

       self.parent.assertTrue(len(_UpperCAmelCase      ) == len(_UpperCAmelCase      )      )
       self.parent.assertTrue(len(_UpperCAmelCase      ) == len(_UpperCAmelCase      ) + 1      )

       snake_case_ ,					snake_case_ =   outputs.to_tuple()

       # create hypothetical next token and extent to next_input_ids
       snake_case_ =   ids_tensor((self.batch_size, 1)	,   config.vocab_size      )

       # append to next input_ids and
       snake_case_ =   torch.cat([input_ids, next_tokens]	,   dim=-1      )

       snake_case_ =   model(_UpperCAmelCase      )['''last_hidden_state''']
       snake_case_ =   model(_UpperCAmelCase	,   past_key_values=_UpperCAmelCase      )['''last_hidden_state''']

       # select random slice
       snake_case_ =   ids_tensor((1,)	,   output_from_past.shape[-1]      ).item()
       snake_case_ =   output_from_no_past[:, -1, random_slice_idx].detach()
       snake_case_ =   output_from_past[:, 0, random_slice_idx].detach()

       # test that outputs are equal for slice
       self.parent.assertTrue(torch.allclose(_UpperCAmelCase	,   _UpperCAmelCase	,   atol=1E-3      )      )





 def 		UpperCamelCase__  (			self	,   _UpperCAmelCase	,   _UpperCAmelCase	,   ):
       snake_case_ =   UMTaModel(config=_UpperCAmelCase      ).to(_UpperCAmelCase      ).half().eval()
       snake_case_ =   model(**_UpperCAmelCase      )['''last_hidden_state''']
       self.parent.assertFalse(torch.isnan(_UpperCAmelCase      ).any().item()      )







@require_torch
class lowerCAmelCase_		(				lowerCamelCase__      ,		lowerCamelCase__      ,		lowerCamelCase__      ,		unittest.TestCase ):

 '''simple docstring'''

 __snake_case				=						(
     (UMTaModel, UMTaForConditionalGeneration, UMTaForQuestionAnswering) if is_torch_available() else ()
 )
 __snake_case				=						(UMTaForConditionalGeneration,) if is_torch_available() else ()
 __snake_case				=						(
     {
         "conversational": UMTaForConditionalGeneration,
         "feature-extraction": UMTaModel,
         "summarization": UMTaForConditionalGeneration,
         "text2text-generation": UMTaForConditionalGeneration,
         "translation": UMTaForConditionalGeneration,
         "question-answering": UMTaForQuestionAnswering,
     }
     if is_torch_available()
     else {}
 )
 __snake_case				=						True
 __snake_case				=						False
 __snake_case				=						False
 __snake_case				=						True
 __snake_case				=						True
 # The small UMT5 model needs higher percentages for CPU/MP tests
 __snake_case				=						[0.8, 0.9]



 def 		UpperCamelCase__  (			self      ):
       snake_case_ =   UMTaModelTester(self      )



 @unittest.skip('''Test has a segmentation fault on torch 1.8.0'''      )
 def 		UpperCamelCase__  (			self      ):
       snake_case_ =   self.model_tester.prepare_config_and_inputs()
       snake_case_ =   UMTaModel(config_and_inputs[0]      ).to(_UpperCAmelCase      )
       with tempfile.TemporaryDirectory() as tmpdirname:
             torch.onnx.export(
                 _UpperCAmelCase	,   (config_and_inputs[1], config_and_inputs[3], config_and_inputs[2])	,   F'''{tmpdirname}/t5_test.onnx'''	,   export_params=_UpperCAmelCase	,   opset_version=9	,   input_names=['''input_ids''', '''decoder_input_ids''']	,   )



 @unittest.skipIf(torch_device == '''cpu'''	,   '''Cant do half precision'''      )
 def 		UpperCamelCase__  (			self      ):
       snake_case_ =   self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model_fpaa_forward(*_UpperCAmelCase      )



 def 		UpperCamelCase__  (			self      ):
       snake_case_ =   ['''encoder_attentions''', '''decoder_attentions''', '''cross_attentions''']
       snake_case_ =   self.model_tester.prepare_config_and_inputs()
       snake_case_ =   config_and_inputs[0]
       snake_case_ =   UMTaForConditionalGeneration(_UpperCAmelCase      ).eval()
       model.to(_UpperCAmelCase      )

       snake_case_ =   {
           '''head_mask''': torch.zeros(config.num_layers	,   config.num_heads	,   device=_UpperCAmelCase      ),
           '''decoder_head_mask''': torch.zeros(config.num_decoder_layers	,   config.num_heads	,   device=_UpperCAmelCase      ),
           '''cross_attn_head_mask''': torch.zeros(config.num_decoder_layers	,   config.num_heads	,   device=_UpperCAmelCase      ),
       }

       for attn_name, (name, mask) in zip(_UpperCAmelCase	,   head_masking.items()      ):
             snake_case_ =   {name: mask}
             # Explicitly pass decoder_head_mask as it is required from T5 model when head_mask specified
             if name == "head_mask":
                   snake_case_ =   torch.ones(
                       config.num_decoder_layers	,   config.num_heads	,   device=_UpperCAmelCase      )

             snake_case_ =   model.generate(
                 config_and_inputs[1]['''input_ids''']	,   num_beams=1	,   max_length=3	,   output_attentions=_UpperCAmelCase	,   return_dict_in_generate=_UpperCAmelCase	,   **_UpperCAmelCase	,   )
             # We check the state of decoder_attentions and cross_attentions just from the last step
             snake_case_ =   out[attn_name] if attn_name == attention_names[0] else out[attn_name][-1]
             self.assertEqual(sum([w.sum().item() for w in attn_weights]      )	,   0.0      )





 @unittest.skip('''Does not work on the tiny model as we keep hitting edge cases.'''      )
 def 		UpperCamelCase__  (			self      ):
       pass







@require_torch
@require_sentencepiece
@require_tokenizers
class lowerCAmelCase_		(				unittest.TestCase ):

 '''simple docstring'''

 @slow
 @unittest.skip(
     '''Unless we stop stripping left and right by default for all special tokens, the expected ids obtained here will not match the original ones. Wait for https://github.com/huggingface/transformers/pull/23909 to be merged'''      )
 def 		UpperCamelCase__  (			self      ):
       snake_case_ =   UMTaForConditionalGeneration.from_pretrained('''google/umt5-small'''	,   return_dict=_UpperCAmelCase      ).to(_UpperCAmelCase      )
       snake_case_ =   AutoTokenizer.from_pretrained('''google/umt5-small'''	,   use_fast=_UpperCAmelCase	,   legacy=_UpperCAmelCase      )
       snake_case_ =   [
           '''Bonjour monsieur <extra_id_0> bien <extra_id_1>.''',
           '''No se como puedo <extra_id_0>.''',
           '''This is the reason why we <extra_id_0> them.''',
           '''The <extra_id_0> walks in <extra_id_1>, seats''',
           '''A <extra_id_0> walks into a bar and orders a <extra_id_1> with <extra_id_2> pinch of <extra_id_3>.''',
       ]
       snake_case_ =   tokenizer(_UpperCAmelCase	,   return_tensors='''pt'''	,   padding=_UpperCAmelCase      ).input_ids
       # fmt: off
       snake_case_ =   torch.tensor(
           [
               [ 3_85_30, 21_07_03, 25_62_99, 14_10, 25_62_98, 2_74, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
               [   8_26, 3_21, 6_71, 2_59_22, 25_62_99, 2_74, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
               [  14_60, 3_39, 3_12, 1_90_14, 1_06_20, 7_58, 25_62_99, 23_55,2_74, 1, 0, 0, 0, 0, 0, 0,0, 0],
               [   5_17, 25_62_99, 1_48_69, 2_81, 3_01, 25_62_98, 2_75, 11_99_83,1, 0, 0, 0, 0, 0, 0, 0,0, 0],
               [   3_20, 25_62_99, 1_48_69, 2_81, 22_34, 2_89, 22_75, 3_33,6_13_91, 2_89, 25_62_98, 5_43, 25_62_97, 16_87_14, 3_29, 25_62_96,2_74, 1],
           ]      )
       # fmt: on
       torch.testing.assert_allclose(_UpperCAmelCase	,   _UpperCAmelCase      )

       snake_case_ =   model.generate(input_ids.to(_UpperCAmelCase      )      )
       snake_case_ =   [
           '''<pad><extra_id_0> et<extra_id_1> [eod] <extra_id_2><extra_id_55>.. [eod] 💐 💐 💐 💐 💐 💐 💐 💐 💐 💐 💐 <extra_id_56>ajšietosto<extra_id_56>lleux<extra_id_19><extra_id_6>ajšie</s>''',
           '''<pad><extra_id_0>.<extra_id_1>.,<0x0A>...spech <0x0A><extra_id_20> <extra_id_21></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>''',
           '''<pad><extra_id_0> are not going to be a part of the world. We are not going to be a part of<extra_id_1> and<extra_id_2><0x0A><extra_id_48>.<extra_id_48></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>''',
           '''<pad><extra_id_0> door<extra_id_1>, the door<extra_id_2> 피해[/</s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>''',
           '''<pad><extra_id_0>nyone who<extra_id_1> drink<extra_id_2> a<extra_id_3> alcohol<extra_id_4> A<extra_id_5> A. This<extra_id_6> I<extra_id_7><extra_id_52><extra_id_53></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>''',
       ]
       snake_case_ =   tokenizer.batch_decode(_UpperCAmelCase      )
       self.assertEqual(_UpperCAmelCase	,   _UpperCAmelCase      )



import inspect
import unittest
import warnings

from transformers import DeiTConfig
from transformers.models.auto import get_values
from transformers.testing_utils import (
    require_accelerate,
    require_torch,
    require_torch_gpu,
    require_vision,
    slow,
    torch_device,
)
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
   import torch
   from torch import nn

   from transformers import (
       MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
       MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
       MODEL_MAPPING,
       DeiTForImageClassification,
       DeiTForImageClassificationWithTeacher,
       DeiTForMaskedImageModeling,
       DeiTModel,
   )
   from transformers.models.deit.modeling_deit import DEIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
   from PIL import Image

   from transformers import DeiTImageProcessor







class lowerCAmelCase_		:

 '''simple docstring'''

 def __init__(			self	,   _UpperCAmelCase	,   _UpperCAmelCase=13	,   _UpperCAmelCase=30	,   _UpperCAmelCase=2	,   _UpperCAmelCase=3	,   _UpperCAmelCase=True	,   _UpperCAmelCase=True	,   _UpperCAmelCase=32	,   _UpperCAmelCase=5	,   _UpperCAmelCase=4	,   _UpperCAmelCase=37	,   _UpperCAmelCase="gelu"	,   _UpperCAmelCase=0.1	,   _UpperCAmelCase=0.1	,   _UpperCAmelCase=10	,   _UpperCAmelCase=0.02	,   _UpperCAmelCase=3	,   _UpperCAmelCase=None	,   _UpperCAmelCase=2	,   ):
       snake_case_ =   parent
       snake_case_ =   batch_size
       snake_case_ =   image_size
       snake_case_ =   patch_size
       snake_case_ =   num_channels
       snake_case_ =   is_training
       snake_case_ =   use_labels
       snake_case_ =   hidden_size
       snake_case_ =   num_hidden_layers
       snake_case_ =   num_attention_heads
       snake_case_ =   intermediate_size
       snake_case_ =   hidden_act
       snake_case_ =   hidden_dropout_prob
       snake_case_ =   attention_probs_dropout_prob
       snake_case_ =   type_sequence_label_size
       snake_case_ =   initializer_range
       snake_case_ =   scope
       snake_case_ =   encoder_stride

       # in DeiT, the seq length equals the number of patches + 2 (we add 2 for the [CLS] and distilation tokens)
       snake_case_ =   (image_size // patch_size) ** 2
       snake_case_ =   num_patches + 2



 def 		UpperCamelCase__  (			self      ):
       snake_case_ =   floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]      )

       snake_case_ =   None
       if self.use_labels:
             snake_case_ =   ids_tensor([self.batch_size]	,   self.type_sequence_label_size      )

       snake_case_ =   self.get_config()

       return config, pixel_values, labels



 def 		UpperCamelCase__  (			self      ):
       return DeiTConfig(
           image_size=self.image_size	,   patch_size=self.patch_size	,   num_channels=self.num_channels	,   hidden_size=self.hidden_size	,   num_hidden_layers=self.num_hidden_layers	,   num_attention_heads=self.num_attention_heads	,   intermediate_size=self.intermediate_size	,   hidden_act=self.hidden_act	,   hidden_dropout_prob=self.hidden_dropout_prob	,   attention_probs_dropout_prob=self.attention_probs_dropout_prob	,   is_decoder=_UpperCAmelCase	,   initializer_range=self.initializer_range	,   encoder_stride=self.encoder_stride	,   )



 def 		UpperCamelCase__  (			self	,   _UpperCAmelCase	,   _UpperCAmelCase	,   _UpperCAmelCase      ):
       snake_case_ =   DeiTModel(config=_UpperCAmelCase      )
       model.to(_UpperCAmelCase      )
       model.eval()
       snake_case_ =   model(_UpperCAmelCase      )
       self.parent.assertEqual(result.last_hidden_state.shape	,   (self.batch_size, self.seq_length, self.hidden_size)      )



 def 		UpperCamelCase__  (			self	,   _UpperCAmelCase	,   _UpperCAmelCase	,   _UpperCAmelCase      ):
       snake_case_ =   DeiTForMaskedImageModeling(config=_UpperCAmelCase      )
       model.to(_UpperCAmelCase      )
       model.eval()
       snake_case_ =   model(_UpperCAmelCase      )
       self.parent.assertEqual(
           result.reconstruction.shape	,   (self.batch_size, self.num_channels, self.image_size, self.image_size)      )

       # test greyscale images
       snake_case_ =   1
       snake_case_ =   DeiTForMaskedImageModeling(_UpperCAmelCase      )
       model.to(_UpperCAmelCase      )
       model.eval()

       snake_case_ =   floats_tensor([self.batch_size, 1, self.image_size, self.image_size]      )
       snake_case_ =   model(_UpperCAmelCase      )
       self.parent.assertEqual(result.reconstruction.shape	,   (self.batch_size, 1, self.image_size, self.image_size)      )



 def 		UpperCamelCase__  (			self	,   _UpperCAmelCase	,   _UpperCAmelCase	,   _UpperCAmelCase      ):
       snake_case_ =   self.type_sequence_label_size
       snake_case_ =   DeiTForImageClassification(_UpperCAmelCase      )
       model.to(_UpperCAmelCase      )
       model.eval()
       snake_case_ =   model(_UpperCAmelCase	,   labels=_UpperCAmelCase      )
       self.parent.assertEqual(result.logits.shape	,   (self.batch_size, self.type_sequence_label_size)      )

       # test greyscale images
       snake_case_ =   1
       snake_case_ =   DeiTForImageClassification(_UpperCAmelCase      )
       model.to(_UpperCAmelCase      )
       model.eval()

       snake_case_ =   floats_tensor([self.batch_size, 1, self.image_size, self.image_size]      )
       snake_case_ =   model(_UpperCAmelCase	,   labels=_UpperCAmelCase      )
       self.parent.assertEqual(result.logits.shape	,   (self.batch_size, self.type_sequence_label_size)      )





 def 		UpperCamelCase__  (			self      ):
       snake_case_ =   self.prepare_config_and_inputs()
       (
           (
           snake_case_
       ) ,					(
           snake_case_
       ) ,					(
           snake_case_
       ) ,					
       ) =   config_and_inputs
       snake_case_ =   {'''pixel_values''': pixel_values}
       return config, inputs_dict







@require_torch
class lowerCAmelCase_		(				lowerCamelCase__      ,		lowerCamelCase__      ,		unittest.TestCase ):

 '''simple docstring'''

 __snake_case				=						(
     (
         DeiTModel,
         DeiTForImageClassification,
         DeiTForImageClassificationWithTeacher,
         DeiTForMaskedImageModeling,
     )
     if is_torch_available()
     else ()
 )
 __snake_case				=						(
     {
         "feature-extraction": DeiTModel,
         "image-classification": (DeiTForImageClassification, DeiTForImageClassificationWithTeacher),
     }
     if is_torch_available()
     else {}
 )

 __snake_case				=						False
 __snake_case				=						False
 __snake_case				=						False



 def 		UpperCamelCase__  (			self      ):
       snake_case_ =   DeiTModelTester(self      )
       snake_case_ =   ConfigTester(self	,   config_class=_UpperCAmelCase	,   has_text_modality=_UpperCAmelCase	,   hidden_size=37      )



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



 @unittest.skip(reason='''DeiT does not use inputs_embeds'''      )
 def 		UpperCamelCase__  (			self      ):
       pass



 def 		UpperCamelCase__  (			self      ):
       snake_case_ ,					snake_case_ =   self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
             snake_case_ =   model_class(_UpperCAmelCase      )
             self.assertIsInstance(model.get_input_embeddings()	,   (nn.Module)      )
             snake_case_ =   model.get_output_embeddings()
             self.assertTrue(x is None or isinstance(_UpperCAmelCase	,   nn.Linear      )      )



 def 		UpperCamelCase__  (			self      ):
       snake_case_ ,					snake_case_ =   self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
             snake_case_ =   model_class(_UpperCAmelCase      )
             snake_case_ =   inspect.signature(model.forward      )
             # signature.parameters is an OrderedDict => so arg_names order is deterministic
             snake_case_ =   [*signature.parameters.keys()]

             snake_case_ =   ['''pixel_values''']
             self.assertListEqual(arg_names[:1]	,   _UpperCAmelCase      )



 def 		UpperCamelCase__  (			self      ):
       snake_case_ =   self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*_UpperCAmelCase      )



 def 		UpperCamelCase__  (			self      ):
       snake_case_ =   self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_masked_image_modeling(*_UpperCAmelCase      )



 def 		UpperCamelCase__  (			self      ):
       snake_case_ =   self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_image_classification(*_UpperCAmelCase      )



 def 		UpperCamelCase__  (			self	,   _UpperCAmelCase	,   _UpperCAmelCase	,   _UpperCAmelCase=False      ):
       snake_case_ =   super()._prepare_for_class(_UpperCAmelCase	,   _UpperCAmelCase	,   return_labels=_UpperCAmelCase      )

       if return_labels:
             if model_class.__name__ == "DeiTForImageClassificationWithTeacher":
                   del inputs_dict["labels"]

       return inputs_dict



 def 		UpperCamelCase__  (			self      ):
       if not self.model_tester.is_training:
             return

       snake_case_ ,					snake_case_ =   self.model_tester.prepare_config_and_inputs_for_common()
       snake_case_ =   True

       for model_class in self.all_model_classes:
             # DeiTForImageClassificationWithTeacher supports inference-only
             if (
                 model_class in get_values(_UpperCAmelCase      )
                 or model_class.__name__ == "DeiTForImageClassificationWithTeacher"
             ):
                   continue
             snake_case_ =   model_class(_UpperCAmelCase      )
             model.to(_UpperCAmelCase      )
             model.train()
             snake_case_ =   self._prepare_for_class(_UpperCAmelCase	,   _UpperCAmelCase	,   return_labels=_UpperCAmelCase      )
             snake_case_ =   model(**_UpperCAmelCase      ).loss
             loss.backward()



 def 		UpperCamelCase__  (			self      ):
       snake_case_ ,					snake_case_ =   self.model_tester.prepare_config_and_inputs_for_common()
       if not self.model_tester.is_training:
             return

       snake_case_ =   False
       snake_case_ =   True

       for model_class in self.all_model_classes:
             if model_class in get_values(_UpperCAmelCase      ) or not model_class.supports_gradient_checkpointing:
                   continue
             # DeiTForImageClassificationWithTeacher supports inference-only
             if model_class.__name__ == "DeiTForImageClassificationWithTeacher":
                   continue
             snake_case_ =   model_class(_UpperCAmelCase      )
             model.gradient_checkpointing_enable()
             model.to(_UpperCAmelCase      )
             model.train()
             snake_case_ =   self._prepare_for_class(_UpperCAmelCase	,   _UpperCAmelCase	,   return_labels=_UpperCAmelCase      )
             snake_case_ =   model(**_UpperCAmelCase      ).loss
             loss.backward()



 def 		UpperCamelCase__  (			self      ):
       snake_case_ ,					snake_case_ =   self.model_tester.prepare_config_and_inputs_for_common()

       snake_case_ =   [
           {'''title''': '''multi_label_classification''', '''num_labels''': 2, '''dtype''': torch.float},
           {'''title''': '''single_label_classification''', '''num_labels''': 1, '''dtype''': torch.long},
           {'''title''': '''regression''', '''num_labels''': 1, '''dtype''': torch.float},
       ]

       for model_class in self.all_model_classes:
             if (
                 model_class
                 not in [
                     *get_values(_UpperCAmelCase      ),
                     *get_values(_UpperCAmelCase      ),
                 ]
                 or model_class.__name__ == "DeiTForImageClassificationWithTeacher"
             ):
                   continue

             for problem_type in problem_types:
                   with self.subTest(msg=F'''Testing {model_class} with {problem_type["title"]}'''      ):
                         snake_case_ =   problem_type['''title''']
                         snake_case_ =   problem_type['''num_labels''']

                         snake_case_ =   model_class(_UpperCAmelCase      )
                         model.to(_UpperCAmelCase      )
                         model.train()

                         snake_case_ =   self._prepare_for_class(_UpperCAmelCase	,   _UpperCAmelCase	,   return_labels=_UpperCAmelCase      )

                         if problem_type["num_labels"] > 1:
                               snake_case_ =   inputs['''labels'''].unsqueeze(1      ).repeat(1	,   problem_type['''num_labels''']      )

                         snake_case_ =   inputs['''labels'''].to(problem_type['''dtype''']      )

                         # This tests that we do not trigger the warning form PyTorch "Using a target size that is different
                         # to the input size. This will likely lead to incorrect results due to broadcasting. Please ensure
                         # they have the same size." which is a symptom something in wrong for the regression problem.
                         # See https://github.com/huggingface/transformers/issues/11780
                         with warnings.catch_warnings(record=_UpperCAmelCase      ) as warning_list:
                               snake_case_ =   model(**_UpperCAmelCase      ).loss
                         for w in warning_list:
                               if "Using a target size that is different to the input size" in str(w.message      ):
                                     raise ValueError(
                                         F'''Something is going wrong in the regression problem: intercepted {w.message}'''      )

                         loss.backward()





 @slow
 def 		UpperCamelCase__  (			self      ):
       for model_name in DEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             snake_case_ =   DeiTModel.from_pretrained(_UpperCAmelCase      )
             self.assertIsNotNone(_UpperCAmelCase      )






def  __lowerCAmelCase					()->						Optional[int]:




      """simple docstring"""

      snake_case_ =   Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''      )
      return image







@require_torch
@require_vision
class lowerCAmelCase_		(				unittest.TestCase ):

 '''simple docstring'''

 @cached_property
 def 		UpperCamelCase__  (			self      ):
       return (
           DeiTImageProcessor.from_pretrained('''facebook/deit-base-distilled-patch16-224'''      )
           if is_vision_available()
           else None
       )



 @slow
 def 		UpperCamelCase__  (			self      ):
       snake_case_ =   DeiTForImageClassificationWithTeacher.from_pretrained('''facebook/deit-base-distilled-patch16-224'''      ).to(
           _UpperCAmelCase      )

       snake_case_ =   self.default_image_processor
       snake_case_ =   prepare_img()
       snake_case_ =   image_processor(images=_UpperCAmelCase	,   return_tensors='''pt'''      ).to(_UpperCAmelCase      )

       # forward pass
       with torch.no_grad():
             snake_case_ =   model(**_UpperCAmelCase      )

       # verify the logits
       snake_case_ =   torch.Size((1, 10_00)      )
       self.assertEqual(outputs.logits.shape	,   _UpperCAmelCase      )

       snake_case_ =   torch.tensor([-1.0_266, 0.1_912, -1.2_861]      ).to(_UpperCAmelCase      )

       self.assertTrue(torch.allclose(outputs.logits[0, :3]	,   _UpperCAmelCase	,   atol=1E-4      )      )





 @slow
 @require_accelerate
 @require_torch_gpu
 def 		UpperCamelCase__  (			self      ):
       snake_case_ =   DeiTModel.from_pretrained(
           '''facebook/deit-base-distilled-patch16-224'''	,   torch_dtype=torch.floataa	,   device_map='''auto'''      )
       snake_case_ =   self.default_image_processor

       snake_case_ =   prepare_img()
       snake_case_ =   image_processor(images=_UpperCAmelCase	,   return_tensors='''pt'''      )
       snake_case_ =   inputs.pixel_values.to(_UpperCAmelCase      )

       # forward pass to make sure inference works in fp16
       with torch.no_grad():
             snake_case_ =   model(_UpperCAmelCase      )