



import copy
import os
from typing import Union

from ...configuration_utils import PretrainedConfig
from ...utils import logging


A_		:     str        =     logging.get_logger(__name__)

A_		:     Dict        =     {
    '''microsoft/git-base''': '''https://huggingface.co/microsoft/git-base/resolve/main/config.json''',
}



class 		_lowercase			(       a__	):
   _UpperCAmelCase     	=			"""git_vision_model"""

   def __init__(      self			:       Tuple							,   __lowerCAmelCase			:       List[Any]=768							,   __lowerCAmelCase			:       Union[str, Any]=3072							,   __lowerCAmelCase			:       Any=12							,   __lowerCAmelCase			:       Optional[int]=12							,   __lowerCAmelCase			:       Optional[int]=3							,   __lowerCAmelCase			:       str=224							,   __lowerCAmelCase			:       List[str]=16							,   __lowerCAmelCase			:       Any="quick_gelu"							,   __lowerCAmelCase			:       Tuple=1E-5							,   __lowerCAmelCase			:       Any=0.0							,   __lowerCAmelCase			:       Tuple=0.0_2							,   **__lowerCAmelCase			:       int							,   )       ->				str:


    """simple docstring"""






    super().__init__(**lowerCAmelCase__							)

    a						=		hidden_size
    a						=		intermediate_size
    a						=		num_hidden_layers
    a						=		num_attention_heads
    a						=		num_channels
    a						=		patch_size
    a						=		image_size
    a						=		initializer_range
    a						=		attention_dropout
    a						=		layer_norm_eps
    a						=		hidden_act




   @classmethod
   def 		A							(      cls			:       int							,   __lowerCAmelCase			:       Tuple							,   **__lowerCAmelCase			:       int							)       ->				"PretrainedConfig":


    """simple docstring"""






    cls._set_token_in_kwargs(lowerCAmelCase__							)

    a						=		cls.get_config_dict(lowerCAmelCase__							,   **lowerCAmelCase__							)

    # get the vision config dict if we are loading from GITConfig
    if config_dict.get("model_type"							) == "git":
     a						=		config_dict["vision_config"]

    if "model_type" in config_dict and hasattr(cls							,   "model_type"							) and config_dict["model_type"] != cls.model_type:
     logger.warning(
         f"""You are using a model of type {config_dict["model_type"]} to instantiate a model of type """
         f"""{cls.model_type}. This is not supported for all configurations of models and can yield errors."""							)

    return cls.from_dict(lowerCAmelCase__							,   **lowerCAmelCase__							)



class 		_lowercase			(       a__	):
   _UpperCAmelCase     	=			"""git"""

   def __init__(      self			:       Dict							,   __lowerCAmelCase			:       Tuple=None							,   __lowerCAmelCase			:       List[str]=3_0522							,   __lowerCAmelCase			:       List[str]=768							,   __lowerCAmelCase			:       List[Any]=6							,   __lowerCAmelCase			:       Any=12							,   __lowerCAmelCase			:       Tuple=3072							,   __lowerCAmelCase			:       Optional[Any]="gelu"							,   __lowerCAmelCase			:       str=0.1							,   __lowerCAmelCase			:       List[Any]=0.1							,   __lowerCAmelCase			:       int=1024							,   __lowerCAmelCase			:       Any=0.0_2							,   __lowerCAmelCase			:       List[str]=1E-12							,   __lowerCAmelCase			:       Any=0							,   __lowerCAmelCase			:       Union[str, Any]="absolute"							,   __lowerCAmelCase			:       Dict=True							,   __lowerCAmelCase			:       Any=False							,   __lowerCAmelCase			:       List[Any]=101							,   __lowerCAmelCase			:       List[str]=102							,   __lowerCAmelCase			:       Optional[Any]=None							,   **__lowerCAmelCase			:       List[str]							,   )       ->				List[Any]:


    """simple docstring"""






    super().__init__(bos_token_id=lowerCAmelCase__							,   eos_token_id=lowerCAmelCase__							,   pad_token_id=lowerCAmelCase__							,   **lowerCAmelCase__							)

    if vision_config is None:
     a						=		{}
     logger.info("vision_config is None. initializing the GitVisionConfig with default values."							)

    a						=		GitVisionConfig(**lowerCAmelCase__							)
    a						=		vocab_size
    a						=		hidden_size
    a						=		num_hidden_layers
    a						=		num_attention_heads
    a						=		hidden_act
    a						=		intermediate_size
    a						=		hidden_dropout_prob
    a						=		attention_probs_dropout_prob
    a						=		max_position_embeddings
    a						=		initializer_range
    a						=		layer_norm_eps
    a						=		position_embedding_type
    a						=		use_cache
    a						=		tie_word_embeddings
    a						=		num_image_with_embedding

    a						=		bos_token_id
    a						=		eos_token_id




   def 		A							(      self			:       Union[str, Any]							)       ->				str:


    """simple docstring"""






    a						=		copy.deepcopy(self.__dict__							)
    a						=		self.vision_config.to_dict()
    a						=		self.__class__.model_type
    return output






import re
from pathlib import Path
from unittest import TestCase

import pytest



@pytest.mark.integration
class 		_lowercase		(       UpperCAmelCase__	):

   def 		A							(      self			:       Optional[int]							,   __lowerCAmelCase			:       str							)       ->				Union[str, Any]:


    """simple docstring"""






    with open(__lowerCAmelCase							,   encoding="utf-8"							) as input_file:
     a						=		re.compile(R"(?!.*\b(?:encoding|rb|w|wb|w+|wb+|ab|ab+)\b)(?<=\s)(open)\((.*)\)"							)
     a						=		input_file.read()
     a						=		regexp.search(__lowerCAmelCase							)

    return match

   def 		A							(      self			:       List[Any]							,   __lowerCAmelCase			:       str							)       ->				Dict:


    """simple docstring"""






    with open(__lowerCAmelCase							,   encoding="utf-8"							) as input_file:
     a						=		re.compile(R"#[^\r\n]*print\(|\"[^\r\n]*print\(|\"\"\".*?print\(.*?\"\"\"|(print\()"							,   re.DOTALL							)
     a						=		input_file.read()
     # use `re.finditer` to handle the case where the ignored groups would be matched first by `re.search`
     a						=		regexp.finditer(__lowerCAmelCase							)

    a						=		[match for match in matches if match is not None and match.group(1							) is not None]
    return matches[0] if matches else None

   def 		A							(      self			:       List[str]							)       ->				List[Any]:


    """simple docstring"""






    a						=		Path("./datasets"							)
    a						=		list(dataset_paths.absolute().glob("**/*.py"							)							)

    for dataset in dataset_files:
     if self._no_encoding_on_file_open(str(__lowerCAmelCase							)							):
      raise AssertionError(f"""open(...) must use utf-8 encoding in {dataset}"""							)




   def 		A							(      self			:       Tuple							)       ->				Union[str, Any]:


    """simple docstring"""






    a						=		Path("./datasets"							)
    a						=		list(dataset_paths.absolute().glob("**/*.py"							)							)

    for dataset in dataset_files:
     if self._no_print_statements(str(__lowerCAmelCase							)							):
      raise AssertionError(f"""print statement found in {dataset}. Use datasets.logger/logging instead."""							)

