



'''simple docstring'''





import subprocess
import sys

from transformers import BertConfig, BertModel, BertTokenizer, pipeline
from transformers.testing_utils import TestCasePlus, require_torch


class 				snake_case (UpperCamelCase   ):



       @require_torch
       def 							_a      (				self      )  ->							Dict:
           # this test is a bit tricky since TRANSFORMERS_OFFLINE can only be changed before
           # `transformers` is loaded, and it's too late for inside pytest - so we are changing it
           # while running an external program

           # python one-liner segments

           # this must be loaded before socket.socket is monkey-patched
           lowercase__		       =						"\nfrom transformers import BertConfig, BertModel, BertTokenizer, pipeline\n        "

           lowercase__		       =						"\nmname = \"hf-internal-testing/tiny-random-bert\"\nBertConfig.from_pretrained(mname)\nBertModel.from_pretrained(mname)\nBertTokenizer.from_pretrained(mname)\npipe = pipeline(task=\"fill-mask\", model=mname)\nprint(\"success\")\n        "

           lowercase__		       =						"\nimport socket\ndef offline_socket(*args, **kwargs): raise RuntimeError(\"Offline mode is enabled, we shouldn't access internet\")\nsocket.socket = offline_socket\n        "

           # Force fetching the files so that we can use the cache
           lowercase__		       =						"hf-internal-testing/tiny-random-bert"
           BertConfig.from_pretrained(UpperCAmelCase_      )
           BertModel.from_pretrained(UpperCAmelCase_      )
           BertTokenizer.from_pretrained(UpperCAmelCase_      )
           pipeline(task="fill-mask"     ,model=UpperCAmelCase_      )

           # baseline - just load from_pretrained with normal network
           lowercase__		       =						[sys.executable, "-c", "\n".join([load, run, mock]      )]

           # should succeed
           lowercase__		       =						self.get_env()
           # should succeed as TRANSFORMERS_OFFLINE=1 tells it to use local files
           lowercase__		       =						"1"
           lowercase__		       =						subprocess.run(UpperCAmelCase_     ,env=UpperCAmelCase_     ,check=UpperCAmelCase_     ,capture_output=UpperCAmelCase_      )
           self.assertEqual(result.returncode     ,0     ,result.stderr      )
           self.assertIn("success"     ,result.stdout.decode()      )



       @require_torch
       def 							_a      (				self      )  ->							List[Any]:
           # python one-liner segments
           # this must be loaded before socket.socket is monkey-patched
           lowercase__		       =						"\nfrom transformers import BertConfig, BertModel, BertTokenizer, pipeline\n        "

           lowercase__		       =						"\nmname = \"hf-internal-testing/tiny-random-bert\"\nBertConfig.from_pretrained(mname)\nBertModel.from_pretrained(mname)\nBertTokenizer.from_pretrained(mname)\npipe = pipeline(task=\"fill-mask\", model=mname)\nprint(\"success\")\n        "

           lowercase__		       =						"\nimport socket\ndef offline_socket(*args, **kwargs): raise socket.error(\"Faking flaky internet\")\nsocket.socket = offline_socket\n        "

           # Force fetching the files so that we can use the cache
           lowercase__		       =						"hf-internal-testing/tiny-random-bert"
           BertConfig.from_pretrained(UpperCAmelCase_      )
           BertModel.from_pretrained(UpperCAmelCase_      )
           BertTokenizer.from_pretrained(UpperCAmelCase_      )
           pipeline(task="fill-mask"     ,model=UpperCAmelCase_      )

           # baseline - just load from_pretrained with normal network
           lowercase__		       =						[sys.executable, "-c", "\n".join([load, run, mock]      )]

           # should succeed
           lowercase__		       =						self.get_env()
           lowercase__		       =						subprocess.run(UpperCAmelCase_     ,env=UpperCAmelCase_     ,check=UpperCAmelCase_     ,capture_output=UpperCAmelCase_      )
           self.assertEqual(result.returncode     ,0     ,result.stderr      )
           self.assertIn("success"     ,result.stdout.decode()      )



       @require_torch
       def 							_a      (				self      )  ->							Tuple:
           # this test is a bit tricky since TRANSFORMERS_OFFLINE can only be changed before
           # `transformers` is loaded, and it's too late for inside pytest - so we are changing it
           # while running an external program

           # python one-liner segments

           # this must be loaded before socket.socket is monkey-patched
           lowercase__		       =						"\nfrom transformers import BertConfig, BertModel, BertTokenizer\n        "

           lowercase__		       =						"\nmname = \"hf-internal-testing/tiny-random-bert-sharded\"\nBertConfig.from_pretrained(mname)\nBertModel.from_pretrained(mname)\nprint(\"success\")\n        "

           lowercase__		       =						"\nimport socket\ndef offline_socket(*args, **kwargs): raise ValueError(\"Offline mode is enabled\")\nsocket.socket = offline_socket\n        "

           # baseline - just load from_pretrained with normal network
           lowercase__		       =						[sys.executable, "-c", "\n".join([load, run]      )]

           # should succeed
           lowercase__		       =						self.get_env()
           lowercase__		       =						subprocess.run(UpperCAmelCase_     ,env=UpperCAmelCase_     ,check=UpperCAmelCase_     ,capture_output=UpperCAmelCase_      )
           self.assertEqual(result.returncode     ,0     ,result.stderr      )
           self.assertIn("success"     ,result.stdout.decode()      )

           # next emulate no network
           lowercase__		       =						[sys.executable, "-c", "\n".join([load, mock, run]      )]

           # Doesn't fail anymore since the model is in the cache due to other tests, so commenting this.
           # env["TRANSFORMERS_OFFLINE"] = "0"
           # result = subprocess.run(cmd, env=env, check=False, capture_output=True)
           # self.assertEqual(result.returncode, 1, result.stderr)

           # should succeed as TRANSFORMERS_OFFLINE=1 tells it to use local files
           lowercase__		       =						"1"
           lowercase__		       =						subprocess.run(UpperCAmelCase_     ,env=UpperCAmelCase_     ,check=UpperCAmelCase_     ,capture_output=UpperCAmelCase_      )
           self.assertEqual(result.returncode     ,0     ,result.stderr      )
           self.assertIn("success"     ,result.stdout.decode()      )



       @require_torch
       def 							_a      (				self      )  ->							str:
           lowercase__		       =						"\nfrom transformers import pipeline\n        "
           lowercase__		       =						"\nmname = \"hf-internal-testing/tiny-random-bert\"\npipe = pipeline(model=mname)\n        "

           lowercase__		       =						"\nimport socket\ndef offline_socket(*args, **kwargs): raise socket.error(\"Offline mode is enabled\")\nsocket.socket = offline_socket\n        "
           lowercase__		       =						self.get_env()
           lowercase__		       =						"1"
           lowercase__		       =						[sys.executable, "-c", "\n".join([load, mock, run]      )]
           lowercase__		       =						subprocess.run(UpperCAmelCase_     ,env=UpperCAmelCase_     ,check=UpperCAmelCase_     ,capture_output=UpperCAmelCase_      )
           self.assertEqual(result.returncode     ,1     ,result.stderr      )
           self.assertIn(
               "You cannot infer task automatically within `pipeline` when using offline mode"     ,result.stderr.decode().replace("\n"     ,""      )     ,)







       @require_torch
       def 							_a      (				self      )  ->							Union[str, Any]:
           lowercase__		       =						"\nfrom transformers import AutoModel\n        "
           lowercase__		       =						"\nmname = \"hf-internal-testing/test_dynamic_model\"\nAutoModel.from_pretrained(mname, trust_remote_code=True)\nprint(\"success\")\n        "

           # baseline - just load from_pretrained with normal network
           lowercase__		       =						[sys.executable, "-c", "\n".join([load, run]      )]

           # should succeed
           lowercase__		       =						self.get_env()
           lowercase__		       =						subprocess.run(UpperCAmelCase_     ,env=UpperCAmelCase_     ,check=UpperCAmelCase_     ,capture_output=UpperCAmelCase_      )
           self.assertEqual(result.returncode     ,0     ,result.stderr      )
           self.assertIn("success"     ,result.stdout.decode()      )

           # should succeed as TRANSFORMERS_OFFLINE=1 tells it to use local files
           lowercase__		       =						"1"
           lowercase__		       =						subprocess.run(UpperCAmelCase_     ,env=UpperCAmelCase_     ,check=UpperCAmelCase_     ,capture_output=UpperCAmelCase_      )
           self.assertEqual(result.returncode     ,0     ,result.stderr      )
           self.assertIn("success"     ,result.stdout.decode()      )









'''simple docstring'''





import copy
import os
from typing import TYPE_CHECKING, List, Union


if TYPE_CHECKING:
 pass

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


SCREAMING_SNAKE_CASE__      					=     logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__      					=     {
    "kakaobrain/align-base": "https://huggingface.co/kakaobrain/align-base/resolve/main/config.json",
}


class 				snake_case (UpperCamelCase   ):
       lowerCAmelCase__  :Optional[int]						   =					"align_text_model"



       def __init__(				self     ,UpperCAmelCase_=30_522     ,UpperCAmelCase_=768     ,UpperCAmelCase_=12     ,UpperCAmelCase_=12     ,UpperCAmelCase_=3_072     ,UpperCAmelCase_="gelu"     ,UpperCAmelCase_=0.1     ,UpperCAmelCase_=0.1     ,UpperCAmelCase_=512     ,UpperCAmelCase_=2     ,UpperCAmelCase_=0.02     ,UpperCAmelCase_=1E-1_2     ,UpperCAmelCase_=0     ,UpperCAmelCase_="absolute"     ,UpperCAmelCase_=True     ,**UpperCAmelCase_     ,)  ->							List[str]:
           super().__init__(**UpperCAmelCase_      )

           lowercase__		       =						vocab_size
           lowercase__		       =						hidden_size
           lowercase__		       =						num_hidden_layers
           lowercase__		       =						num_attention_heads
           lowercase__		       =						hidden_act
           lowercase__		       =						intermediate_size
           lowercase__		       =						hidden_dropout_prob
           lowercase__		       =						attention_probs_dropout_prob
           lowercase__		       =						max_position_embeddings
           lowercase__		       =						type_vocab_size
           lowercase__		       =						initializer_range
           lowercase__		       =						layer_norm_eps
           lowercase__		       =						position_embedding_type
           lowercase__		       =						use_cache
           lowercase__		       =						pad_token_id







       @classmethod
       def 							_a      (				cls     ,UpperCAmelCase_     ,**UpperCAmelCase_      )  ->							"PretrainedConfig":
           cls._set_token_in_kwargs(UpperCAmelCase_      )

           lowercase__       ,       lowercase__		       =						cls.get_config_dict(UpperCAmelCase_     ,**UpperCAmelCase_      )

           # get the text config dict if we are loading from AlignConfig
           if config_dict.get("model_type"      ) == "align":
               lowercase__		       =						config_dict["text_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(UpperCAmelCase_     ,**UpperCAmelCase_      )





class 				snake_case (UpperCamelCase   ):
       lowerCAmelCase__  :Tuple						   =					"align_vision_model"



       def __init__(				self     ,UpperCAmelCase_ = 3     ,UpperCAmelCase_ = 600     ,UpperCAmelCase_ = 2.0     ,UpperCAmelCase_ = 3.1     ,UpperCAmelCase_ = 8     ,UpperCAmelCase_ = [3, 3, 5, 3, 5, 5, 3]     ,UpperCAmelCase_ = [32, 16, 24, 40, 80, 112, 192]     ,UpperCAmelCase_ = [16, 24, 40, 80, 112, 192, 320]     ,UpperCAmelCase_ = []     ,UpperCAmelCase_ = [1, 2, 2, 2, 1, 2, 1]     ,UpperCAmelCase_ = [1, 2, 2, 3, 3, 4, 1]     ,UpperCAmelCase_ = [1, 6, 6, 6, 6, 6, 6]     ,UpperCAmelCase_ = 0.25     ,UpperCAmelCase_ = "swish"     ,UpperCAmelCase_ = 2_560     ,UpperCAmelCase_ = "mean"     ,UpperCAmelCase_ = 0.02     ,UpperCAmelCase_ = 0.0_01     ,UpperCAmelCase_ = 0.99     ,UpperCAmelCase_ = 0.2     ,**UpperCAmelCase_     ,)  ->							Union[str, Any]:
           super().__init__(**UpperCAmelCase_      )

           lowercase__		       =						num_channels
           lowercase__		       =						image_size
           lowercase__		       =						width_coefficient
           lowercase__		       =						depth_coefficient
           lowercase__		       =						depth_divisor
           lowercase__		       =						kernel_sizes
           lowercase__		       =						in_channels
           lowercase__		       =						out_channels
           lowercase__		       =						depthwise_padding
           lowercase__		       =						strides
           lowercase__		       =						num_block_repeats
           lowercase__		       =						expand_ratios
           lowercase__		       =						squeeze_expansion_ratio
           lowercase__		       =						hidden_act
           lowercase__		       =						hidden_dim
           lowercase__		       =						pooling_type
           lowercase__		       =						initializer_range
           lowercase__		       =						batch_norm_eps
           lowercase__		       =						batch_norm_momentum
           lowercase__		       =						drop_connect_rate
           lowercase__		       =						sum(UpperCAmelCase_      ) * 4







       @classmethod
       def 							_a      (				cls     ,UpperCAmelCase_     ,**UpperCAmelCase_      )  ->							"PretrainedConfig":
           cls._set_token_in_kwargs(UpperCAmelCase_      )

           lowercase__       ,       lowercase__		       =						cls.get_config_dict(UpperCAmelCase_     ,**UpperCAmelCase_      )

           # get the vision config dict if we are loading from AlignConfig
           if config_dict.get("model_type"      ) == "align":
               lowercase__		       =						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(UpperCAmelCase_     ,**UpperCAmelCase_      )





class 				snake_case (UpperCamelCase   ):
       lowerCAmelCase__  :Union[str, Any]						   =					"align"
       lowerCAmelCase__  :Tuple						   =					True



       def __init__(				self     ,UpperCAmelCase_=None     ,UpperCAmelCase_=None     ,UpperCAmelCase_=640     ,UpperCAmelCase_=1.0     ,UpperCAmelCase_=0.02     ,**UpperCAmelCase_     ,)  ->							Union[str, Any]:
           super().__init__(**UpperCAmelCase_      )

           if text_config is None:
               lowercase__		       =						{}
               logger.info("text_config is None. Initializing the AlignTextConfig with default values."      )

           if vision_config is None:
               lowercase__		       =						{}
               logger.info("vision_config is None. Initializing the AlignVisionConfig with default values."      )

           lowercase__		       =						AlignTextConfig(**UpperCAmelCase_      )
           lowercase__		       =						AlignVisionConfig(**UpperCAmelCase_      )

           lowercase__		       =						projection_dim
           lowercase__		       =						temperature_init_value
           lowercase__		       =						initializer_range



       @classmethod
       def 							_a      (				cls     ,UpperCAmelCase_     ,UpperCAmelCase_     ,**UpperCAmelCase_      )  ->							List[str]:
           return cls(text_config=text_config.to_dict()     ,vision_config=vision_config.to_dict()     ,**UpperCAmelCase_      )







       def 							_a      (				self      )  ->							List[Any]:
           lowercase__		       =						copy.deepcopy(self.__dict__      )
           lowercase__		       =						self.text_config.to_dict()
           lowercase__		       =						self.vision_config.to_dict()
           lowercase__		       =						self.__class__.model_type
           return output




