


'''simple docstring'''

import gc
import importlib.metadata
import tempfile
import unittest

from packaging import version

from transformers import (
    AutoModel,
    AutoModelForCausalLM,
    AutoModelForSeqaSeqLM,
    AutoModelForSequenceClassification,
    AutoTokenizer,
    BitsAndBytesConfig,
    pipeline,
)
from transformers.testing_utils import (
    is_torch_available,
    require_accelerate,
    require_bitsandbytes,
    require_torch,
    require_torch_gpu,
    require_torch_multi_gpu,
    slow,
)

def _SCREAMING_SNAKE_CASE      (A       )       ->       str:






       """simple docstring"""







       if model.config.model_type == "gpt2":
              return model.transformer.h[0].mlp.c_fc
       return model.transformer.h[0].mlp.dense_ah_to_h


if is_torch_available():
 import torch
 import torch.nn as nn
 class   __lowerCAmelCase						(nn.Module     ):





       '''simple docstring'''






       def __init__(self    :    int   ,						UpperCamelCase    :    nn.Module   ,						UpperCamelCase    :    int ):

              '''simple docstring'''







              super().__init__()
              lowercase__      			= module
              lowercase__      			= nn.Sequential(
                  nn.Linear(module.in_features   ,						UpperCamelCase   ,						bias=UpperCamelCase )   ,						nn.Linear(UpperCamelCase   ,						module.out_features   ,						bias=UpperCamelCase )   ,						)
              lowercase__      			= (2.0 / (5 * min(module.in_features   ,						module.out_features ))) ** 0.5
              nn.init.normal_(self.adapter[0].weight   ,						std=UpperCamelCase )
              nn.init.zeros_(self.adapter[1].weight )
              self.adapter.to(module.weight.device )






       def       UpperCamelCase__		(self    :    Any   ,						UpperCamelCase    :    str   ,						*UpperCamelCase    :    str   ,						**UpperCamelCase    :    Optional[Any] ):

              '''simple docstring'''







              return self.module(UpperCamelCase   ,						*UpperCamelCase   ,						**UpperCamelCase ) + self.adapter(UpperCamelCase )
@require_bitsandbytes
@require_accelerate
@require_torch
@require_torch_gpu
@slow
class   __lowerCAmelCase						(unittest.TestCase     ):





      '''simple docstring'''






      lowerCAmelCase__					:     Optional[int]  					=							"""bigscience/bloom-1b7"""

      # Constant values
      lowerCAmelCase__					:     Optional[Any]  					=							2.109_659_552_692_574

      lowerCAmelCase__					:     Union[str, Any]  					=							"""Hello my name is"""
      lowerCAmelCase__					:     Tuple  					=							set()
      EXPECTED_OUTPUTS.add("""Hello my name is John and I am a professional photographer. I"""     )
      EXPECTED_OUTPUTS.add("""Hello my name is John.\nI am a friend of your father.\n"""     )
      EXPECTED_OUTPUTS.add("""Hello my name is John Doe, I am a student at the University"""     )
      lowerCAmelCase__					:     Union[str, Any]  					=							10



      def       UpperCamelCase__		(self    :    Tuple ):

             '''simple docstring'''







             lowercase__      			= AutoTokenizer.from_pretrained(self.model_name )






class   __lowerCAmelCase						(lowercase_     ):





      '''simple docstring'''






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

             '''simple docstring'''







             super().setUp()

             # Models and tokenizer
             lowercase__      			= AutoModelForCausalLM.from_pretrained(
                 self.model_name   ,						torch_dtype=torch.floataa   ,						device_map='''auto''' )
             lowercase__      			= AutoModelForCausalLM.from_pretrained(self.model_name   ,						load_in_abit=UpperCamelCase   ,						device_map='''auto''' )



      def       UpperCamelCase__		(self    :    int ):

             '''simple docstring'''







             del self.model_fpaa
             del self.model_abit

             gc.collect()
             torch.cuda.empty_cache()



      def       UpperCamelCase__		(self    :    Dict ):

             '''simple docstring'''







             lowercase__      			= self.model_abit.config

             self.assertTrue(hasattr(UpperCamelCase   ,						'''quantization_config''' ) )

             lowercase__      			= config.to_dict()
             lowercase__      			= config.to_diff_dict()

             lowercase__      			= config.to_json_string()



      def       UpperCamelCase__		(self    :    str ):

             '''simple docstring'''







             from bitsandbytes.nn import Paramsabit

             lowercase__      			= self.model_fpaa.get_memory_footprint()
             lowercase__      			= self.model_abit.get_memory_footprint()

             self.assertAlmostEqual(mem_fpaa / mem_abit   ,						self.EXPECTED_RELATIVE_DIFFERENCE )
             lowercase__      			= get_some_linear_layer(self.model_abit )
             self.assertTrue(linear.weight.__class__ == Paramsabit )



      def       UpperCamelCase__		(self    :    int ):

             '''simple docstring'''







             from transformers import TaPreTrainedModel

             self.model_fpaa.get_memory_footprint()
             self.model_abit.get_memory_footprint()

             for name, module in self.model_abit.named_modules():
                    if isinstance(UpperCamelCase   ,						torch.nn.Linear ):
                           if name not in ["lm_head"] + TaPreTrainedModel._keep_in_fpaa_modules:
                                  # 4-bit parameters are packed in uint8 variables
                                  self.assertTrue(module.weight.dtype == torch.uinta )



      def       UpperCamelCase__		(self    :    Tuple ):

             '''simple docstring'''







             lowercase__      			= self.tokenizer(self.input_text   ,						return_tensors='''pt''' )
             lowercase__      			= self.model_abit.generate(input_ids=encoded_input['''input_ids'''].to(0 )   ,						max_new_tokens=10 )

             self.assertIn(self.tokenizer.decode(output_sequences[0]   ,						skip_special_tokens=UpperCamelCase )   ,						self.EXPECTED_OUTPUTS )



      def       UpperCamelCase__		(self    :    str ):

             '''simple docstring'''







             lowercase__      			= BitsAndBytesConfig()
             lowercase__      			= True

             lowercase__      			= AutoModelForCausalLM.from_pretrained(
                 self.model_name   ,						quantization_config=UpperCamelCase   ,						device_map='''auto''' )

             lowercase__      			= self.tokenizer(self.input_text   ,						return_tensors='''pt''' )
             lowercase__      			= model_abit_from_config.generate(
                 input_ids=encoded_input['''input_ids'''].to(0 )   ,						max_new_tokens=10 )

             self.assertIn(self.tokenizer.decode(output_sequences[0]   ,						skip_special_tokens=UpperCamelCase )   ,						self.EXPECTED_OUTPUTS )



      def       UpperCamelCase__		(self    :    int ):

             '''simple docstring'''







             with self.assertRaises(UpperCamelCase ), tempfile.TemporaryDirectory() as tmpdirname:
                    self.model_abit.save_pretrained(UpperCamelCase )



      def       UpperCamelCase__		(self    :    Optional[Any] ):

             '''simple docstring'''







             lowercase__      			= BitsAndBytesConfig()

             with self.assertRaises(UpperCamelCase ):
                    lowercase__      			= AutoModelForCausalLM.from_pretrained(
                        self.model_name   ,						quantization_config=UpperCamelCase   ,						load_in_abit=UpperCamelCase   ,						device_map='''auto'''   ,						bnb_abit_quant_type='''nf4'''   ,						)



      def       UpperCamelCase__		(self    :    int ):

             '''simple docstring'''







             with self.assertRaises(UpperCamelCase ):
                    # Tries with `str`
                    self.model_abit.to('''cpu''' )

             with self.assertRaises(UpperCamelCase ):
                    # Tries with a `dtype``
                    self.model_abit.to(torch.floataa )

             with self.assertRaises(UpperCamelCase ):
                    # Tries with a `device`
                    self.model_abit.to(torch.device('''cuda:0''' ) )

             with self.assertRaises(UpperCamelCase ):
                    # Tries with a `device`
                    self.model_abit.float()

             with self.assertRaises(UpperCamelCase ):
                    # Tries with a `device`
                    self.model_abit.half()

             # Test if we did not break anything
             lowercase__      			= self.tokenizer(self.input_text   ,						return_tensors='''pt''' )

             lowercase__      			= self.model_fpaa.to(torch.floataa )
             lowercase__      			= self.model_fpaa.generate(input_ids=encoded_input['''input_ids'''].to(0 )   ,						max_new_tokens=10 )

             # Check this does not throw an error
             lowercase__      			= self.model_fpaa.to('''cpu''' )

             # Check this does not throw an error
             lowercase__      			= self.model_fpaa.half()

             # Check this does not throw an error
             lowercase__      			= self.model_fpaa.float()






      def       UpperCamelCase__		(self    :    Tuple ):

             '''simple docstring'''







             lowercase__      			= AutoModelForSeqaSeqLM.from_pretrained('''t5-small'''   ,						load_in_abit=UpperCamelCase   ,						device_map='''auto''' )
             self.assertTrue(model.decoder.block[0].layer[2].DenseReluDense.wo.weight.dtype == torch.floataa )






@require_bitsandbytes
@require_accelerate
@require_torch
@require_torch_gpu
@slow
class   __lowerCAmelCase						(unittest.TestCase     ):





      '''simple docstring'''






      @classmethod
      def       UpperCamelCase__		(cls    :    Dict ):

             '''simple docstring'''







             lowercase__      			= '''t5-small'''
             lowercase__      			= '''google/flan-t5-small'''  # flan-t5 uses dense-act instead of dense-relu-dense
             lowercase__      			= AutoTokenizer.from_pretrained(cls.model_name )
             lowercase__      			= '''Translate in German: Hello, my dog is cute'''



      def       UpperCamelCase__		(self    :    Optional[int] ):

             '''simple docstring'''







             gc.collect()
             torch.cuda.empty_cache()



      def       UpperCamelCase__		(self    :    Optional[Any] ):

             '''simple docstring'''







             from transformers import TaForConditionalGeneration

             lowercase__      			= TaForConditionalGeneration._keep_in_fpaa_modules
             lowercase__      			= None

             # test with `t5-small`
             lowercase__      			= TaForConditionalGeneration.from_pretrained(self.model_name   ,						load_in_abit=UpperCamelCase   ,						device_map='''auto''' )
             lowercase__      			= self.tokenizer(self.input_text   ,						return_tensors='''pt''' ).to(0 )
             lowercase__      			= model.generate(**UpperCamelCase )

             # test with `flan-t5-small`
             lowercase__      			= TaForConditionalGeneration.from_pretrained(
                 self.dense_act_model_name   ,						load_in_abit=UpperCamelCase   ,						device_map='''auto''' )
             lowercase__      			= self.tokenizer(self.input_text   ,						return_tensors='''pt''' ).to(0 )
             lowercase__      			= model.generate(**UpperCamelCase )
             lowercase__      			= modules






      def       UpperCamelCase__		(self    :    Optional[int] ):

             '''simple docstring'''







             import bitsandbytes as bnb

             from transformers import TaForConditionalGeneration

             # test with `t5-small`
             lowercase__      			= TaForConditionalGeneration.from_pretrained(self.model_name   ,						load_in_abit=UpperCamelCase   ,						device_map='''auto''' )

             # there was a bug with decoders - this test checks that it is fixed
             self.assertTrue(isinstance(model.decoder.block[0].layer[0].SelfAttention.q   ,						bnb.nn.Linearabit ) )

             lowercase__      			= self.tokenizer(self.input_text   ,						return_tensors='''pt''' ).to(0 )
             lowercase__      			= model.generate(**UpperCamelCase )

             # test with `flan-t5-small`
             lowercase__      			= TaForConditionalGeneration.from_pretrained(
                 self.dense_act_model_name   ,						load_in_abit=UpperCamelCase   ,						device_map='''auto''' )
             lowercase__      			= self.tokenizer(self.input_text   ,						return_tensors='''pt''' ).to(0 )
             lowercase__      			= model.generate(**UpperCamelCase )






class   __lowerCAmelCase						(lowercase_     ):





      '''simple docstring'''






      def       UpperCamelCase__		(self    :    Dict ):

             '''simple docstring'''







             super().setUp()
             # model_name
             lowercase__      			= '''bigscience/bloom-560m'''
             lowercase__      			= '''t5-small'''

             # Different types of model

             lowercase__      			= AutoModel.from_pretrained(self.model_name   ,						load_in_abit=UpperCamelCase   ,						device_map='''auto''' )
             # Sequence classification model
             lowercase__      			= AutoModelForSequenceClassification.from_pretrained(
                 self.model_name   ,						load_in_abit=UpperCamelCase   ,						device_map='''auto''' )
             # CausalLM model
             lowercase__      			= AutoModelForCausalLM.from_pretrained(self.model_name   ,						load_in_abit=UpperCamelCase   ,						device_map='''auto''' )
             # Seq2seq model
             lowercase__      			= AutoModelForSeqaSeqLM.from_pretrained(
                 self.seq_to_seq_name   ,						load_in_abit=UpperCamelCase   ,						device_map='''auto''' )



      def       UpperCamelCase__		(self    :    Optional[Any] ):

             '''simple docstring'''







             del self.base_model
             del self.sequence_model
             del self.model_abit
             del self.seq_to_seq_model

             gc.collect()
             torch.cuda.empty_cache()






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

             '''simple docstring'''







             from bitsandbytes.nn import Paramsabit

             self.assertTrue(self.base_model.h[-1].mlp.dense_ah_to_h.weight.__class__ == Paramsabit )

             # Other heads should be nn.Parameter
             self.assertTrue(self.model_abit.lm_head.weight.__class__ == torch.nn.Parameter )
             self.assertTrue(self.sequence_model.score.weight.__class__ == torch.nn.Parameter )
             self.assertTrue(self.seq_to_seq_model.lm_head.weight.__class__ == torch.nn.Parameter )






class   __lowerCAmelCase						(lowercase_     ):





      '''simple docstring'''






      def       UpperCamelCase__		(self    :    List[str] ):

             '''simple docstring'''







             super().setUp()



      def       UpperCamelCase__		(self    :    Optional[Any] ):

             '''simple docstring'''







             del self.pipe

             gc.collect()
             torch.cuda.empty_cache()






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

             '''simple docstring'''







             lowercase__      			= pipeline(
                 '''text-generation'''   ,						model=self.model_name   ,						model_kwargs={'''device_map''': '''auto''', '''load_in_4bit''': True, '''torch_dtype''': torch.floataa}   ,						max_new_tokens=self.MAX_NEW_TOKENS   ,						)

             # Real second forward pass
             lowercase__      			= self.pipe(self.input_text )
             self.assertIn(pipeline_output[0]['''generated_text''']   ,						self.EXPECTED_OUTPUTS )






@require_torch_multi_gpu
class   __lowerCAmelCase						(lowercase_     ):





      '''simple docstring'''






      def       UpperCamelCase__		(self    :    Tuple ):

             '''simple docstring'''







             super().setUp()






      def       UpperCamelCase__		(self    :    str ):

             '''simple docstring'''







             lowercase__      			= AutoModelForCausalLM.from_pretrained(
                 self.model_name   ,						load_in_abit=UpperCamelCase   ,						device_map='''balanced''' )

             # Check correct device map
             self.assertEqual(set(model_parallel.hf_device_map.values() )   ,						{0, 1} )

             # Check that inference pass works on the model
             lowercase__      			= self.tokenizer(self.input_text   ,						return_tensors='''pt''' )

             # Second real batch
             lowercase__      			= model_parallel.generate(input_ids=encoded_input['''input_ids'''].to(0 )   ,						max_new_tokens=10 )
             self.assertIn(self.tokenizer.decode(output_parallel[0]   ,						skip_special_tokens=UpperCamelCase )   ,						self.EXPECTED_OUTPUTS )






class   __lowerCAmelCase						(lowercase_     ):





      '''simple docstring'''






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

             '''simple docstring'''







             lowercase__      			= '''facebook/opt-350m'''
             super().setUp()






      def       UpperCamelCase__		(self    :    Optional[int] ):

             '''simple docstring'''







             if version.parse(importlib.metadata.version('''bitsandbytes''' ) ) < version.parse('''0.37.0''' ):
                    return

             # Step 1: freeze all parameters
             lowercase__      			= AutoModelForCausalLM.from_pretrained(self.model_name   ,						load_in_abit=UpperCamelCase )

             self.assertEqual(set(model.hf_device_map.values() )   ,						{torch.cuda.current_device()} )

             for param in model.parameters():
                    lowercase__      			= False  # freeze the model - train adapters later
                    if param.ndim == 1:
                           # cast the small parameters (e.g. layernorm) to fp32 for stability
                           lowercase__      			= param.data.to(torch.floataa )

        # Step 2: add adapters
             for _, module in model.named_modules():
                    if "OPTAttention" in repr(type(UpperCamelCase ) ):
                           lowercase__      			= LoRALayer(module.q_proj   ,						rank=16 )
                           lowercase__      			= LoRALayer(module.k_proj   ,						rank=16 )
                           lowercase__      			= LoRALayer(module.v_proj   ,						rank=16 )

        # Step 3: dummy batch
             lowercase__      			= self.tokenizer('''Test batch '''   ,						return_tensors='''pt''' ).to(0 )

             # Step 4: Check if the gradient is not None
             with torch.cuda.amp.autocast():
                    lowercase__      			= model.forward(**UpperCamelCase )
                    out.logits.norm().backward()

             for module in model.modules():
                    if isinstance(UpperCamelCase   ,						UpperCamelCase ):
                           self.assertTrue(module.adapter[1].weight.grad is not None )
                           self.assertTrue(module.adapter[1].weight.grad.norm().item() > 0 )
                    elif isinstance(UpperCamelCase   ,						nn.Embedding ):
                           self.assertTrue(module.weight.grad is None )






class   __lowerCAmelCase						(lowercase_     ):





      '''simple docstring'''






      lowerCAmelCase__					:     Optional[Any]  					=							"""gpt2-xl"""
      lowerCAmelCase__					:     Dict  					=							3.3_191_854_854_152_187








'''simple docstring'''

import unittest

from transformers import TrOCRConfig
from transformers.testing_utils import is_torch_available, require_torch, torch_device

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


if is_torch_available():
 import torch

 from transformers.models.trocr.modeling_trocr import TrOCRDecoder, TrOCRForCausalLM
@require_torch
class   __lowerCAmelCase						:





      '''simple docstring'''






      def __init__(self    :    str   ,						UpperCamelCase    :    Tuple   ,						UpperCamelCase    :    Optional[int]=99   ,						UpperCamelCase    :    Optional[int]=13   ,						UpperCamelCase    :    Tuple=16   ,						UpperCamelCase    :    Union[str, Any]=7   ,						UpperCamelCase    :    List[Any]=True   ,						UpperCamelCase    :    List[str]=True   ,						UpperCamelCase    :    str=True   ,						UpperCamelCase    :    Tuple=False   ,						UpperCamelCase    :    str=True   ,						UpperCamelCase    :    Tuple=2   ,						UpperCamelCase    :    Optional[int]=32   ,						UpperCamelCase    :    Any=4   ,						UpperCamelCase    :    Optional[int]=4   ,						UpperCamelCase    :    Tuple=30   ,						UpperCamelCase    :    str=0   ,						UpperCamelCase    :    Tuple=1   ,						UpperCamelCase    :    List[Any]=2   ,						UpperCamelCase    :    str=None   ,						):

             '''simple docstring'''







             lowercase__      			= parent
             lowercase__      			= batch_size
             lowercase__      			= decoder_seq_length
             # For common tests
             lowercase__      			= self.decoder_seq_length
             lowercase__      			= is_training
             lowercase__      			= use_attention_mask
             lowercase__      			= use_labels

             lowercase__      			= vocab_size
             lowercase__      			= d_model
             lowercase__      			= d_model
             lowercase__      			= decoder_layers
             lowercase__      			= decoder_layers
             lowercase__      			= decoder_ffn_dim
             lowercase__      			= decoder_attention_heads
             lowercase__      			= decoder_attention_heads
             lowercase__      			= eos_token_id
             lowercase__      			= bos_token_id
             lowercase__      			= pad_token_id
             lowercase__      			= decoder_start_token_id
             lowercase__      			= use_cache
             lowercase__      			= max_position_embeddings

             lowercase__      			= None
             lowercase__      			= decoder_seq_length
             lowercase__      			= 2
             lowercase__      			= 1



      def       UpperCamelCase__		(self    :    str ):

             '''simple docstring'''







             lowercase__      			= ids_tensor([self.batch_size, self.decoder_seq_length]   ,						self.vocab_size )

             lowercase__      			= None
             if self.use_attention_mask:
                    lowercase__      			= ids_tensor([self.batch_size, self.decoder_seq_length]   ,						vocab_size=2 )

             lowercase__      			= None
             if self.use_labels:
                    lowercase__      			= ids_tensor([self.batch_size, self.decoder_seq_length]   ,						self.vocab_size )

             lowercase__      			= TrOCRConfig(
                 vocab_size=self.vocab_size   ,						d_model=self.d_model   ,						decoder_layers=self.decoder_layers   ,						decoder_ffn_dim=self.decoder_ffn_dim   ,						decoder_attention_heads=self.decoder_attention_heads   ,						eos_token_id=self.eos_token_id   ,						bos_token_id=self.bos_token_id   ,						use_cache=self.use_cache   ,						pad_token_id=self.pad_token_id   ,						decoder_start_token_id=self.decoder_start_token_id   ,						max_position_embeddings=self.max_position_embeddings   ,						)

             return (config, input_ids, attention_mask, lm_labels)



      def       UpperCamelCase__		(self    :    Tuple   ,						UpperCamelCase    :    List[Any]   ,						UpperCamelCase    :    Optional[Any]   ,						UpperCamelCase    :    Tuple   ,						UpperCamelCase    :    List[str]   ,						):

             '''simple docstring'''







             lowercase__      			= True
             lowercase__      			= TrOCRDecoder(config=UpperCamelCase ).to(UpperCamelCase ).eval()
             lowercase__      			= input_ids[:2]

             input_ids[input_ids == 0] += 1
             # first forward pass
             lowercase__      			= model(UpperCamelCase   ,						use_cache=UpperCamelCase )
             lowercase__      			= model(UpperCamelCase )
             lowercase__      			= model(UpperCamelCase   ,						use_cache=UpperCamelCase )

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

             lowercase__      			= outputs['''past_key_values''']

             # create hypothetical next token and extent to next_input_ids
             lowercase__      			= ids_tensor((2, 1)   ,						config.vocab_size - 1 ) + 1

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

             lowercase__      			= model(UpperCamelCase )['''last_hidden_state''']
             lowercase__      			= model(UpperCamelCase   ,						past_key_values=UpperCamelCase )['''last_hidden_state''']

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

             # test that outputs are equal for slice
             assert torch.allclose(UpperCamelCase   ,						UpperCamelCase   ,						atol=1E-3 )






      def       UpperCamelCase__		(self    :    Optional[Any] ):

             '''simple docstring'''







             lowercase__      			= self.prepare_config_and_inputs()
             lowercase__			,lowercase__			,lowercase__			,lowercase__      			= config_and_inputs

             lowercase__      			= {'''input_ids''': input_ids, '''attention_mask''': attention_mask}
             return config, inputs_dict






@require_torch
class   __lowerCAmelCase						(lowercase_  ,     lowercase_  ,     lowercase_  ,     unittest.TestCase     ):





      '''simple docstring'''






      lowerCAmelCase__					:     List[str]  					=							(TrOCRDecoder, TrOCRForCausalLM) if is_torch_available() else ()
      lowerCAmelCase__					:     List[Any]  					=							(TrOCRForCausalLM,) if is_torch_available() else ()
      lowerCAmelCase__					:     Optional[Any]  					=							{"""text-generation""": TrOCRForCausalLM} if is_torch_available() else {}
      lowerCAmelCase__					:     Optional[Any]  					=							True
      lowerCAmelCase__					:     List[str]  					=							False



      def       UpperCamelCase__		(self    :    Any ):

             '''simple docstring'''







             lowercase__      			= TrOCRStandaloneDecoderModelTester(self   ,						is_training=UpperCamelCase )
             lowercase__      			= ConfigTester(self   ,						config_class=UpperCamelCase )



      def       UpperCamelCase__		(self    :    List[str] ):

             '''simple docstring'''







             pass



      def       UpperCamelCase__		(self    :    Optional[int] ):

             '''simple docstring'''







             pass



      def       UpperCamelCase__		(self    :    Any ):

             '''simple docstring'''







             pass



      def       UpperCamelCase__		(self    :    Any ):

             '''simple docstring'''







             self.config_tester.run_common_tests()



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

             '''simple docstring'''







             lowercase__      			= self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_decoder_model_past(*UpperCamelCase )



      def       UpperCamelCase__		(self    :    Optional[int] ):

             '''simple docstring'''







             return






      @unittest.skip('''The model doesn\'t support left padding''' )  # and it's not used enough to be worth fixing :)
      def       UpperCamelCase__		(self    :    List[str] ):

             '''simple docstring'''







             pass




