


'''simple docstring'''





import os
import re
import shutil
import sys
import tempfile
import unittest

import black


__lowerCamelCase												=						os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, '''utils'''))

import check_copies  # noqa: E402


# This is the reference code that will be used in the tests.
# If BertLMPredictionHead is changed in modeling_bert.py, this code needs to be manually updated.
__lowerCamelCase												=						'''    def __init__(self, config):
        super().__init__()
        self.transform = BertPredictionHeadTransform(config)

        # The output weights are the same as the input embeddings, but there is
        # an output-only bias for each token.
        self.decoder = nn.Linear(config.hidden_size, config.vocab_size, bias=False)

        self.bias = nn.Parameter(torch.zeros(config.vocab_size))

        # Need a link between the two variables so that the bias is correctly resized with `resize_token_embeddings`
        self.decoder.bias = self.bias

    def forward(self, hidden_states):
        hidden_states = self.transform(hidden_states)
        hidden_states = self.decoder(hidden_states)
        return hidden_states
'''




class 				UpperCAmelCase			(			unittest.TestCase						):


 def        __SCREAMING_SNAKE_CASE					(      self							:	int ):
    UpperCAmelCase__	:Dict      =      tempfile.mkdtemp()
    os.makedirs(os.path.join(self.transformer_dir			,       '''models/bert/''' ) )
    UpperCAmelCase__	:List[Any]      =      self.transformer_dir
    shutil.copy(
        os.path.join(__lowerCamelCase			,       '''src/transformers/models/bert/modeling_bert.py''' )			,       os.path.join(self.transformer_dir			,       '''models/bert/modeling_bert.py''' )			,       )


 def        __SCREAMING_SNAKE_CASE					(      self							:	List[Any] ):
    UpperCAmelCase__	:Optional[Any]      =      '''src/transformers'''
    shutil.rmtree(self.transformer_dir )


 def        __SCREAMING_SNAKE_CASE					(      self							:	int			,       __lowerCamelCase							:	Dict			,       __lowerCamelCase							:	Union[str, Any]			,       __lowerCamelCase							:	Tuple			,       __lowerCamelCase							:	Optional[int]=None ):
    UpperCAmelCase__	:Dict      =      comment + f'''\nclass {class_name}(nn.Module):\n''' + class_code
    if overwrite_result is not None:
       UpperCAmelCase__	:Dict      =      comment + f'''\nclass {class_name}(nn.Module):\n''' + overwrite_result
    UpperCAmelCase__	:str      =      black.Mode(target_versions={black.TargetVersion.PYaa}			,       line_length=1_1_9 )
    UpperCAmelCase__	:Any      =      black.format_str(__lowerCamelCase			,       mode=__lowerCamelCase )
    UpperCAmelCase__	:Any      =      os.path.join(self.transformer_dir			,       '''new_code.py''' )
    with open(__lowerCamelCase			,       '''w'''			,       newline='''\n''' ) as f:
       f.write(__lowerCamelCase )
    if overwrite_result is None:
       self.assertTrue(len(check_copies.is_copy_consistent(__lowerCamelCase ) ) == 0 )
    else:
       check_copies.is_copy_consistent(f.name			,       overwrite=__lowerCamelCase )
       with open(__lowerCamelCase			,       '''r''' ) as f:
          self.assertTrue(f.read()			,       __lowerCamelCase )


 def        __SCREAMING_SNAKE_CASE					(      self							:	List[str] ):
    UpperCAmelCase__	:str      =      check_copies.find_code_in_transformers('''models.bert.modeling_bert.BertLMPredictionHead''' )
    self.assertEqual(__lowerCamelCase			,       __lowerCamelCase )


 def        __SCREAMING_SNAKE_CASE					(      self							:	List[Any] ):
    # Base copy consistency
    self.check_copy_consistency(
        '''# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead'''			,       '''BertLMPredictionHead'''			,       REFERENCE_CODE + '''\n'''			,       )

    # With no empty line at the end
    self.check_copy_consistency(
        '''# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead'''			,       '''BertLMPredictionHead'''			,       __lowerCamelCase			,       )

    # Copy consistency with rename
    self.check_copy_consistency(
        '''# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead with Bert->TestModel'''			,       '''TestModelLMPredictionHead'''			,       re.sub('''Bert'''			,       '''TestModel'''			,       __lowerCamelCase )			,       )

    # Copy consistency with a really long name
    UpperCAmelCase__	:Any      =      '''TestModelWithAReallyLongNameBecauseSomePeopleLikeThatForSomeReason'''
    self.check_copy_consistency(
        f'''# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead with Bert->{long_class_name}'''			,       f'''{long_class_name}LMPredictionHead'''			,       re.sub('''Bert'''			,       __lowerCamelCase			,       __lowerCamelCase )			,       )

    # Copy consistency with overwrite
    self.check_copy_consistency(
        '''# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead with Bert->TestModel'''			,       '''TestModelLMPredictionHead'''			,       __lowerCamelCase			,       overwrite_result=re.sub('''Bert'''			,       '''TestModel'''			,       __lowerCamelCase )			,       )





 def        __SCREAMING_SNAKE_CASE					(      self							:	Optional[int] ):
    UpperCAmelCase__	:Any      =      check_copies.LOCALIZED_READMES['''README_zh-hans.md''']

    UpperCAmelCase__	:Union[str, Any]      =      (
        '''1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (from Google Research and the'''
        ''' Toyota Technological Institute at Chicago) released with the paper [ALBERT: A Lite BERT for'''
        ''' Self-supervised Learning of Language Representations](https://arxiv.org/abs/1909.11942), by Zhenzhong'''
        ''' Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut.\n1.'''
        ''' **[DistilBERT](https://huggingface.co/transformers/model_doc/distilbert.html)** (from HuggingFace),'''
        ''' released together with the paper [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and'''
        ''' lighter](https://arxiv.org/abs/1910.01108) by Victor Sanh, Lysandre Debut and Thomas Wolf. The same'''
        ''' method has been applied to compress GPT2 into'''
        ''' [DistilGPT2](https://github.com/huggingface/transformers/tree/main/examples/distillation), RoBERTa into'''
        ''' [DistilRoBERTa](https://github.com/huggingface/transformers/tree/main/examples/distillation),'''
        ''' Multilingual BERT into'''
        ''' [DistilmBERT](https://github.com/huggingface/transformers/tree/main/examples/distillation) and a German'''
        ''' version of DistilBERT.\n1. **[ELECTRA](https://huggingface.co/transformers/model_doc/electra.html)**'''
        ''' (from Google Research/Stanford University) released with the paper [ELECTRA: Pre-training text encoders'''
        ''' as discriminators rather than generators](https://arxiv.org/abs/2003.10555) by Kevin Clark, Minh-Thang'''
        ''' Luong, Quoc V. Le, Christopher D. Manning.'''
    )
    UpperCAmelCase__	:Optional[Any]      =      (
        '''1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (来自 Google Research and the'''
        ''' Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of'''
        ''' Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian'''
        ''' Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n'''
    )
    UpperCAmelCase__	:int      =      (
        '''1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (来自 Google Research and the'''
        ''' Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of'''
        ''' Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian'''
        ''' Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n1.'''
        ''' **[DistilBERT](https://huggingface.co/transformers/model_doc/distilbert.html)** (来自 HuggingFace) 伴随论文'''
        ''' [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and'''
        ''' lighter](https://arxiv.org/abs/1910.01108) 由 Victor Sanh, Lysandre Debut and Thomas Wolf 发布。 The same'''
        ''' method has been applied to compress GPT2 into'''
        ''' [DistilGPT2](https://github.com/huggingface/transformers/tree/main/examples/distillation), RoBERTa into'''
        ''' [DistilRoBERTa](https://github.com/huggingface/transformers/tree/main/examples/distillation),'''
        ''' Multilingual BERT into'''
        ''' [DistilmBERT](https://github.com/huggingface/transformers/tree/main/examples/distillation) and a German'''
        ''' version of DistilBERT.\n1. **[ELECTRA](https://huggingface.co/transformers/model_doc/electra.html)** (来自'''
        ''' Google Research/Stanford University) 伴随论文 [ELECTRA: Pre-training text encoders as discriminators rather'''
        ''' than generators](https://arxiv.org/abs/2003.10555) 由 Kevin Clark, Minh-Thang Luong, Quoc V. Le,'''
        ''' Christopher D. Manning 发布。\n'''
    )

    UpperCAmelCase__   ,						UpperCAmelCase__	:Optional[Any]      =      check_copies.convert_to_localized_md(
        __lowerCamelCase			,       __lowerCamelCase			,       localized_readme['''format_model_list'''] )

    self.assertFalse(__lowerCamelCase )
    self.assertEqual(__lowerCamelCase			,       __lowerCamelCase )

    UpperCAmelCase__   ,						UpperCAmelCase__	:List[Any]      =      check_copies.convert_to_localized_md(
        __lowerCamelCase			,       __lowerCamelCase			,       localized_readme['''format_model_list'''] )

    # Check whether the number of models is equal to README.md after conversion.
    self.assertTrue(__lowerCamelCase )

    UpperCAmelCase__	:List[Any]      =      (
        '''1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (from Google Research and the'''
        ''' Toyota Technological Institute at Chicago) released with the paper [ALBERT: A Lite BERT for'''
        ''' Self-supervised Learning of Language Representations](https://arxiv.org/abs/1909.11942), by Zhenzhong'''
        ''' Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut.'''
    )
    UpperCAmelCase__	:int      =      (
        '''1. **[ALBERT](https://huggingface.co/transformers/main/model_doc/albert.html)** (来自 Google Research and'''
        ''' the Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of'''
        ''' Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian'''
        ''' Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n'''
    )
    UpperCAmelCase__	:Union[str, Any]      =      (
        '''1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (来自 Google Research and the'''
        ''' Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of'''
        ''' Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian'''
        ''' Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n'''
    )

    UpperCAmelCase__   ,						UpperCAmelCase__	:Tuple      =      check_copies.convert_to_localized_md(
        __lowerCamelCase			,       __lowerCamelCase			,       localized_readme['''format_model_list'''] )

    # Check if the model link is synchronized.
    self.assertEqual(__lowerCamelCase			,       __lowerCamelCase )







'''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 				UpperCAmelCase			(			unittest.TestCase						):
 UpperCAmelCase				=					ViTImageProcessor if is_vision_available() else None


 @property
 def        __SCREAMING_SNAKE_CASE					(      self							:	List[Any] ):
    return self.image_processor_tester.prepare_image_processor_dict()


 def        __SCREAMING_SNAKE_CASE					(      self							:	List[str] ):
    UpperCAmelCase__	:Any      =      (3, 3_2, 1_2_8)
    UpperCAmelCase__	:Optional[int]      =      tempfile.mkdtemp()

    # fmt: off
    UpperCAmelCase__	: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
    UpperCAmelCase__	:str      =      dict(zip(__lowerCamelCase			,       range(len(__lowerCamelCase ) ) ) )

    UpperCAmelCase__	:str      =      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(__lowerCamelCase ) + '''\n''' )

    UpperCAmelCase__	:Dict      =      {
        '''do_normalize''': False,
        '''do_resize''': True,
        '''image_processor_type''': '''ViTImageProcessor''',
        '''resample''': 3,
        '''size''': {'''height''': 3_2, '''width''': 1_2_8},
    }
    UpperCAmelCase__	:Optional[Any]      =      os.path.join(self.tmpdirname			,       __lowerCamelCase )
    with open(self.image_processor_file			,       '''w'''			,       encoding='''utf-8''' ) as fp:
       json.dump(__lowerCamelCase			,       __lowerCamelCase )


 def        __SCREAMING_SNAKE_CASE					(      self							:	Tuple			,       **__lowerCamelCase							:	Any ):
    return MgpstrTokenizer.from_pretrained(self.tmpdirname			,       **__lowerCamelCase )


 def        __SCREAMING_SNAKE_CASE					(      self							:	int			,       **__lowerCamelCase							:	str ):
    return ViTImageProcessor.from_pretrained(self.tmpdirname			,       **__lowerCamelCase )


 def        __SCREAMING_SNAKE_CASE					(      self							:	int ):
    shutil.rmtree(self.tmpdirname )


 def        __SCREAMING_SNAKE_CASE					(      self							:	Union[str, Any] ):
    UpperCAmelCase__	:Optional[int]      =      np.random.randint(2_5_5			,       size=(3, 3_0, 4_0_0)			,       dtype=np.uinta )

    UpperCAmelCase__	:Union[str, Any]      =      Image.fromarray(np.moveaxis(__lowerCamelCase			,       0			,       -1 ) )

    return image_input


 def        __SCREAMING_SNAKE_CASE					(      self							:	int ):
    UpperCAmelCase__	:Optional[Any]      =      self.get_tokenizer()
    UpperCAmelCase__	:str      =      self.get_image_processor()

    UpperCAmelCase__	:List[str]      =      MgpstrProcessor(tokenizer=__lowerCamelCase			,       image_processor=__lowerCamelCase )
    processor.save_pretrained(self.tmpdirname )
    UpperCAmelCase__	:Optional[int]      =      MgpstrProcessor.from_pretrained(self.tmpdirname			,       use_fast=__lowerCamelCase )

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

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


 def        __SCREAMING_SNAKE_CASE					(      self							:	Union[str, Any] ):
    UpperCAmelCase__	:Any      =      self.get_tokenizer()
    UpperCAmelCase__	:int      =      self.get_image_processor()

    UpperCAmelCase__	:Union[str, Any]      =      MgpstrProcessor(tokenizer=__lowerCamelCase			,       image_processor=__lowerCamelCase )
    processor.save_pretrained(self.tmpdirname )

    UpperCAmelCase__	:List[Any]      =      self.get_tokenizer(bos_token='''(BOS)'''			,       eos_token='''(EOS)''' )
    UpperCAmelCase__	:List[Any]      =      self.get_image_processor(do_normalize=__lowerCamelCase			,       padding_value=1.0 )

    UpperCAmelCase__	:Any      =      MgpstrProcessor.from_pretrained(
        self.tmpdirname			,       bos_token='''(BOS)'''			,       eos_token='''(EOS)'''			,       do_normalize=__lowerCamelCase			,       padding_value=1.0 )

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

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


 def        __SCREAMING_SNAKE_CASE					(      self							:	List[Any] ):
    UpperCAmelCase__	:Tuple      =      self.get_image_processor()
    UpperCAmelCase__	:Any      =      self.get_tokenizer()

    UpperCAmelCase__	:Any      =      MgpstrProcessor(tokenizer=__lowerCamelCase			,       image_processor=__lowerCamelCase )

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

    UpperCAmelCase__	:str      =      image_processor(__lowerCamelCase			,       return_tensors='''np''' )
    UpperCAmelCase__	:Tuple      =      processor(images=__lowerCamelCase			,       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							:	Optional[int] ):
    UpperCAmelCase__	:Optional[Any]      =      self.get_image_processor()
    UpperCAmelCase__	:List[Any]      =      self.get_tokenizer()

    UpperCAmelCase__	:List[str]      =      MgpstrProcessor(tokenizer=__lowerCamelCase			,       image_processor=__lowerCamelCase )

    UpperCAmelCase__	:Union[str, Any]      =      '''test'''

    UpperCAmelCase__	:Tuple      =      processor(text=__lowerCamelCase )

    UpperCAmelCase__	:Tuple      =      tokenizer(__lowerCamelCase )
    for key in encoded_tok.keys():
       self.assertListEqual(encoded_tok[key]			,       encoded_processor[key] )


 def        __SCREAMING_SNAKE_CASE					(      self							:	str ):
    UpperCAmelCase__	:int      =      self.get_image_processor()
    UpperCAmelCase__	:Dict      =      self.get_tokenizer()

    UpperCAmelCase__	:int      =      MgpstrProcessor(tokenizer=__lowerCamelCase			,       image_processor=__lowerCamelCase )

    UpperCAmelCase__	:List[Any]      =      '''test'''
    UpperCAmelCase__	:List[Any]      =      self.prepare_image_inputs()

    UpperCAmelCase__	:Any      =      processor(text=__lowerCamelCase			,       images=__lowerCamelCase )

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

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


 def        __SCREAMING_SNAKE_CASE					(      self							:	List[Any] ):
    UpperCAmelCase__	:int      =      self.get_image_processor()
    UpperCAmelCase__	:Optional[int]      =      self.get_tokenizer()

    UpperCAmelCase__	:Union[str, Any]      =      MgpstrProcessor(tokenizer=__lowerCamelCase			,       image_processor=__lowerCamelCase )

    UpperCAmelCase__	:Any      =      [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9], [3, 4, 3, 1, 1, 8, 9]]

    UpperCAmelCase__	:Optional[Any]      =      processor.char_decode(__lowerCamelCase )
    UpperCAmelCase__	:int      =      tokenizer.batch_decode(__lowerCamelCase )
    UpperCAmelCase__	:int      =      [seq.replace(''' '''			,       '''''' ) for seq in decoded_tok]

    self.assertListEqual(__lowerCamelCase			,       __lowerCamelCase )


 def        __SCREAMING_SNAKE_CASE					(      self							:	Union[str, Any] ):
    UpperCAmelCase__	:int      =      self.get_image_processor()
    UpperCAmelCase__	:Optional[int]      =      self.get_tokenizer()

    UpperCAmelCase__	:Dict      =      MgpstrProcessor(tokenizer=__lowerCamelCase			,       image_processor=__lowerCamelCase )

    UpperCAmelCase__	:List[str]      =      None
    UpperCAmelCase__	:Optional[int]      =      self.prepare_image_inputs()

    UpperCAmelCase__	:Tuple      =      processor(text=__lowerCamelCase			,       images=__lowerCamelCase )

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





 def        __SCREAMING_SNAKE_CASE					(      self							:	Tuple ):
    UpperCAmelCase__	:Dict      =      self.get_image_processor()
    UpperCAmelCase__	:int      =      self.get_tokenizer()

    UpperCAmelCase__	:Any      =      MgpstrProcessor(tokenizer=__lowerCamelCase			,       image_processor=__lowerCamelCase )

    UpperCAmelCase__	:Union[str, Any]      =      torch.randn(1			,       2_7			,       3_8 )
    UpperCAmelCase__	:Any      =      torch.randn(1			,       2_7			,       5_0_2_5_7 )
    UpperCAmelCase__	:Union[str, Any]      =      torch.randn(1			,       2_7			,       3_0_5_2_2 )

    UpperCAmelCase__	:Optional[Any]      =      processor.batch_decode([char_input, bpe_input, wp_input] )

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



