


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 		_A		(		_lowercase )					-> List[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						):







           def __init__(			self: Union[str, Any],A_: nn.Module,A_: int					):






                '''simple docstring'''




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







           def 		snake_case_	(			self: Optional[Any],A_: int,*A_: Union[str, Any],**A_: str					):






                '''simple docstring'''




                return self.module(A_,*A_,**A_					) + self.adapter(A_					)



@require_bitsandbytes
@require_accelerate
@require_torch
@require_torch_gpu
@slow
class 	__lowerCamelCase			(unittest.TestCase						):
       # We keep the constants inside the init function and model loading inside setUp function

       # We need to test on relatively large models (aka >1b parameters otherwise the quantiztion may not work as expected)
       # Therefore here we use only bloom-1b3 to test our module
       _lowercase											=     """bigscience/bloom-1b7"""

       # Constant values
       _lowercase											=     2.109659552692574

       _lowercase											=     """Hello my name is"""
       _lowercase											=     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"""						)
       _lowercase											=     10







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






            '''simple docstring'''




            __UpperCamelCase =			AutoTokenizer.from_pretrained(self.model_name					)



class 	__lowerCamelCase			(_a						):







       def 		snake_case_	(			self: int					):






            '''simple docstring'''




            super().setUp()

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







       def 		snake_case_	(			self: Optional[Any]					):






            '''simple docstring'''




            del self.model_fpaa
            del self.model_abit

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







       def 		snake_case_	(			self: str					):






            '''simple docstring'''




            __UpperCamelCase =			self.model_abit.config

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

            __UpperCamelCase =			config.to_dict()
            __UpperCamelCase =			config.to_diff_dict()

            __UpperCamelCase =			config.to_json_string()







       def 		snake_case_	(			self: Optional[int]					):






            '''simple docstring'''




            from bitsandbytes.nn import Paramsabit

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

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







       def 		snake_case_	(			self: str					):






            '''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(A_,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 		snake_case_	(			self: str					):






            '''simple docstring'''




            __UpperCamelCase =			self.tokenizer(self.input_text,return_tensors='pt'					)
            __UpperCamelCase =			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=A_					),self.EXPECTED_OUTPUTS					)







       def 		snake_case_	(			self: str					):






            '''simple docstring'''




            __UpperCamelCase =			BitsAndBytesConfig()
            __UpperCamelCase =			True

            __UpperCamelCase =			AutoModelForCausalLM.from_pretrained(
                self.model_name,quantization_config=A_,device_map='auto'					)

            __UpperCamelCase =			self.tokenizer(self.input_text,return_tensors='pt'					)
            __UpperCamelCase =			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=A_					),self.EXPECTED_OUTPUTS					)







       def 		snake_case_	(			self: List[str]					):






            '''simple docstring'''




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







       def 		snake_case_	(			self: Dict					):






            '''simple docstring'''




            __UpperCamelCase =			BitsAndBytesConfig()

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







       def 		snake_case_	(			self: Tuple					):






            '''simple docstring'''




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

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

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

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

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

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

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

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

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

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







       def 		snake_case_	(			self: Optional[int]					):






            '''simple docstring'''




            __UpperCamelCase =			AutoModelForSeqaSeqLM.from_pretrained('t5-small',load_in_abit=A_,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						):







       @classmethod
       def 		snake_case_	(			cls: Optional[Any]					):






            '''simple docstring'''




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







       def 		snake_case_	(			self: Dict					):






            '''simple docstring'''




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







       def 		snake_case_	(			self: List[str]					):






            '''simple docstring'''




            from transformers import TaForConditionalGeneration

            __UpperCamelCase =			TaForConditionalGeneration._keep_in_fpaa_modules
            __UpperCamelCase =			None

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

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







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






            '''simple docstring'''




            import bitsandbytes as bnb

            from transformers import TaForConditionalGeneration

            # test with `t5-small`
            __UpperCamelCase =			TaForConditionalGeneration.from_pretrained(self.model_name,load_in_abit=A_,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					)					)

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

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



class 	__lowerCamelCase			(_a						):







       def 		snake_case_	(			self: Tuple					):






            '''simple docstring'''




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

            # Different types of model

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







       def 		snake_case_	(			self: 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 		snake_case_	(			self: str					):






            '''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			(_a						):







       def 		snake_case_	(			self: Any					):






            '''simple docstring'''




            super().setUp()







       def 		snake_case_	(			self: List[str]					):






            '''simple docstring'''




            del self.pipe

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







       def 		snake_case_	(			self: str					):






            '''simple docstring'''




            __UpperCamelCase =			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
            __UpperCamelCase =			self.pipe(self.input_text					)
            self.assertIn(pipeline_output[0]['generated_text'],self.EXPECTED_OUTPUTS					)



@require_torch_multi_gpu
class 	__lowerCamelCase			(_a						):







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






            '''simple docstring'''




            super().setUp()







       def 		snake_case_	(			self: List[str]					):






            '''simple docstring'''




            __UpperCamelCase =			AutoModelForCausalLM.from_pretrained(
                self.model_name,load_in_abit=A_,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
            __UpperCamelCase =			self.tokenizer(self.input_text,return_tensors='pt'					)

            # Second real batch
            __UpperCamelCase =			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=A_					),self.EXPECTED_OUTPUTS					)



class 	__lowerCamelCase			(_a						):







       def 		snake_case_	(			self: List[str]					):






            '''simple docstring'''




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







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






            '''simple docstring'''




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

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

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

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

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

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

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

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



class 	__lowerCamelCase			(_a						):
       _lowercase											=     """gpt2-xl"""
       _lowercase											=     3.3191854854152187










'''simple docstring'''







import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_torch_available, is_vision_available


if is_torch_available():
		import torch


if is_vision_available():
		from PIL import Image

		from transformers import MgpstrProcessor, ViTImageProcessor





@require_torch
@require_vision
class 						SCREAMING_SNAKE_CASE       (       unittest.TestCase							):
			snake_case__	=					ViTImageProcessor if is_vision_available() else None






			@property
			def       SCREAMING_SNAKE_CASE   (			self   :      List[str]       )      ->      List[str]:
							return self.image_processor_tester.prepare_image_processor_dict()






			def       SCREAMING_SNAKE_CASE   (			self   :      Tuple       )      ->      Optional[int]:
							a_		:	Optional[Any]									=   (3, 32, 128)
							a_		:	Optional[Any]									=   tempfile.mkdtemp()

							# fmt: off
							a_		:	Union[str, Any]									=   ['''[GO]''', '''[s]''', '''0''', '''1''', '''2''', '''3''', '''4''', '''5''', '''6''', '''7''', '''8''', '''9''', '''a''', '''b''', '''c''', '''d''', '''e''', '''f''', '''g''', '''h''', '''i''', '''j''', '''k''', '''l''', '''m''', '''n''', '''o''', '''p''', '''q''', '''r''', '''s''', '''t''', '''u''', '''v''', '''w''', '''x''', '''y''', '''z''']
							# fmt: on
							a_		:	List[Any]									=   dict(zip(__SCREAMING_SNAKE_CASE	,    range(len(__SCREAMING_SNAKE_CASE       )       )       )       )

							a_		:	Union[str, Any]									=   os.path.join(self.tmpdirname	,    VOCAB_FILES_NAMES['''vocab_file''']       )
							with open(self.vocab_file	,    '''w'''	,    encoding='''utf-8'''       ) as fp:
											fp.write(json.dumps(__SCREAMING_SNAKE_CASE       ) + '''\n'''       )

							a_		:	Union[str, Any]									=   {
							    '''do_normalize''': False,
							    '''do_resize''': True,
							    '''image_processor_type''': '''ViTImageProcessor''',
							    '''resample''': 3,
							    '''size''': {'''height''': 32, '''width''': 128},
							}
							a_		:	Any									=   os.path.join(self.tmpdirname	,    __SCREAMING_SNAKE_CASE       )
							with open(self.image_processor_file	,    '''w'''	,    encoding='''utf-8'''       ) as fp:
											json.dump(__SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE       )






			def       SCREAMING_SNAKE_CASE   (			self   :      List[Any]	,    **__SCREAMING_SNAKE_CASE   :      Union[str, Any]       )      ->      Dict:
							return MgpstrTokenizer.from_pretrained(self.tmpdirname	,    **__SCREAMING_SNAKE_CASE       )






			def       SCREAMING_SNAKE_CASE   (			self   :      List[str]	,    **__SCREAMING_SNAKE_CASE   :      Union[str, Any]       )      ->      List[Any]:
							return ViTImageProcessor.from_pretrained(self.tmpdirname	,    **__SCREAMING_SNAKE_CASE       )






			def       SCREAMING_SNAKE_CASE   (			self   :      Optional[Any]       )      ->      Union[str, Any]:
							shutil.rmtree(self.tmpdirname       )






			def       SCREAMING_SNAKE_CASE   (			self   :      str       )      ->      str:
							a_		:	Optional[Any]									=   np.random.randint(255	,    size=(3, 30, 400)	,    dtype=np.uinta       )

							a_		:	Optional[int]									=   Image.fromarray(np.moveaxis(__SCREAMING_SNAKE_CASE	,    0	,    -1       )       )

							return image_input






			def       SCREAMING_SNAKE_CASE   (			self   :      int       )      ->      int:
							a_		:	str									=   self.get_tokenizer()
							a_		:	List[Any]									=   self.get_image_processor()

							a_		:	Union[str, Any]									=   MgpstrProcessor(tokenizer=__SCREAMING_SNAKE_CASE	,    image_processor=__SCREAMING_SNAKE_CASE       )
							processor.save_pretrained(self.tmpdirname       )
							a_		:	Tuple									=   MgpstrProcessor.from_pretrained(self.tmpdirname	,    use_fast=__SCREAMING_SNAKE_CASE       )

							self.assertEqual(processor.char_tokenizer.get_vocab()	,    tokenizer.get_vocab()       )
							self.assertIsInstance(processor.char_tokenizer	,    __SCREAMING_SNAKE_CASE       )

							self.assertEqual(processor.image_processor.to_json_string()	,    image_processor.to_json_string()       )
							self.assertIsInstance(processor.image_processor	,    __SCREAMING_SNAKE_CASE       )






			def       SCREAMING_SNAKE_CASE   (			self   :      Optional[int]       )      ->      int:
							a_		:	Any									=   self.get_tokenizer()
							a_		:	Dict									=   self.get_image_processor()

							a_		:	Optional[Any]									=   MgpstrProcessor(tokenizer=__SCREAMING_SNAKE_CASE	,    image_processor=__SCREAMING_SNAKE_CASE       )
							processor.save_pretrained(self.tmpdirname       )

							a_		:	str									=   self.get_tokenizer(bos_token='''(BOS)'''	,    eos_token='''(EOS)'''       )
							a_		:	str									=   self.get_image_processor(do_normalize=__SCREAMING_SNAKE_CASE	,    padding_value=1.0       )

							a_		:	Optional[int]									=   MgpstrProcessor.from_pretrained(
							    self.tmpdirname	,    bos_token='''(BOS)'''	,    eos_token='''(EOS)'''	,    do_normalize=__SCREAMING_SNAKE_CASE	,    padding_value=1.0       )

							self.assertEqual(processor.char_tokenizer.get_vocab()	,    tokenizer_add_kwargs.get_vocab()       )
							self.assertIsInstance(processor.char_tokenizer	,    __SCREAMING_SNAKE_CASE       )

							self.assertEqual(processor.image_processor.to_json_string()	,    image_processor_add_kwargs.to_json_string()       )
							self.assertIsInstance(processor.image_processor	,    __SCREAMING_SNAKE_CASE       )






			def       SCREAMING_SNAKE_CASE   (			self   :      str       )      ->      Optional[Any]:
							a_		:	Dict									=   self.get_image_processor()
							a_		:	Tuple									=   self.get_tokenizer()

							a_		:	Any									=   MgpstrProcessor(tokenizer=__SCREAMING_SNAKE_CASE	,    image_processor=__SCREAMING_SNAKE_CASE       )

							a_		:	Optional[Any]									=   self.prepare_image_inputs()

							a_		:	List[Any]									=   image_processor(__SCREAMING_SNAKE_CASE	,    return_tensors='''np'''       )
							a_		:	int									=   processor(images=__SCREAMING_SNAKE_CASE	,    return_tensors='''np'''       )

							for key in input_image_proc.keys():
											self.assertAlmostEqual(input_image_proc[key].sum()	,    input_processor[key].sum()	,    delta=1e-2       )






			def       SCREAMING_SNAKE_CASE   (			self   :      str       )      ->      Optional[Any]:
							a_		:	Dict									=   self.get_image_processor()
							a_		:	List[Any]									=   self.get_tokenizer()

							a_		:	int									=   MgpstrProcessor(tokenizer=__SCREAMING_SNAKE_CASE	,    image_processor=__SCREAMING_SNAKE_CASE       )

							a_		:	Tuple									=   '''test'''

							a_		:	Any									=   processor(text=__SCREAMING_SNAKE_CASE       )

							a_		:	Optional[int]									=   tokenizer(__SCREAMING_SNAKE_CASE       )
							for key in encoded_tok.keys():
											self.assertListEqual(encoded_tok[key]	,    encoded_processor[key]       )






			def       SCREAMING_SNAKE_CASE   (			self   :      int       )      ->      int:
							a_		:	Union[str, Any]									=   self.get_image_processor()
							a_		:	Tuple									=   self.get_tokenizer()

							a_		:	Optional[Any]									=   MgpstrProcessor(tokenizer=__SCREAMING_SNAKE_CASE	,    image_processor=__SCREAMING_SNAKE_CASE       )

							a_		:	Union[str, Any]									=   '''test'''
							a_		:	str									=   self.prepare_image_inputs()

							a_		:	Tuple									=   processor(text=__SCREAMING_SNAKE_CASE	,    images=__SCREAMING_SNAKE_CASE       )

							self.assertListEqual(list(inputs.keys()       )	,    ['''pixel_values''', '''labels''']       )

							# test if it raises when no input is passed
							with pytest.raises(__SCREAMING_SNAKE_CASE       ):
											processor()






			def       SCREAMING_SNAKE_CASE   (			self   :      List[str]       )      ->      int:
							a_		:	int									=   self.get_image_processor()
							a_		:	List[Any]									=   self.get_tokenizer()

							a_		:	Dict									=   MgpstrProcessor(tokenizer=__SCREAMING_SNAKE_CASE	,    image_processor=__SCREAMING_SNAKE_CASE       )

							a_		:	Tuple									=   [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9], [3, 4, 3, 1, 1, 8, 9]]

							a_		:	List[Any]									=   processor.char_decode(__SCREAMING_SNAKE_CASE       )
							a_		:	Optional[int]									=   tokenizer.batch_decode(__SCREAMING_SNAKE_CASE       )
							a_		:	Union[str, Any]									=   [seq.replace(''' '''	,    ''''''       ) for seq in decoded_tok]

							self.assertListEqual(__SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE       )






			def       SCREAMING_SNAKE_CASE   (			self   :      Any       )      ->      List[Any]:
							a_		:	int									=   self.get_image_processor()
							a_		:	Tuple									=   self.get_tokenizer()

							a_		:	Any									=   MgpstrProcessor(tokenizer=__SCREAMING_SNAKE_CASE	,    image_processor=__SCREAMING_SNAKE_CASE       )

							a_		:	Dict									=   None
							a_		:	Tuple									=   self.prepare_image_inputs()

							a_		:	Optional[int]									=   processor(text=__SCREAMING_SNAKE_CASE	,    images=__SCREAMING_SNAKE_CASE       )

							self.assertListEqual(list(inputs.keys()       )	,    processor.model_input_names       )







			def       SCREAMING_SNAKE_CASE   (			self   :      Tuple       )      ->      Union[str, Any]:
							a_		:	List[str]									=   self.get_image_processor()
							a_		:	str									=   self.get_tokenizer()

							a_		:	Any									=   MgpstrProcessor(tokenizer=__SCREAMING_SNAKE_CASE	,    image_processor=__SCREAMING_SNAKE_CASE       )

							a_		:	str									=   torch.randn(1	,    27	,    38       )
							a_		:	int									=   torch.randn(1	,    27	,    5_0257       )
							a_		:	Union[str, Any]									=   torch.randn(1	,    27	,    3_0522       )

							a_		:	Tuple									=   processor.batch_decode([char_input, bpe_input, wp_input]       )

							self.assertListEqual(list(results.keys()       )	,    ['''generated_text''', '''scores''', '''char_preds''', '''bpe_preds''', '''wp_preds''']       )




