





import argparse
import json
import logging
import os
import shutil
import sys
import tempfile
import unittest
from unittest import mock

import torch
from accelerate.utils import write_basic_config

from transformers.testing_utils import TestCasePlus, get_gpu_count, run_command, slow, torch_device
from transformers.utils import is_apex_available


logging.basicConfig(level=logging.DEBUG)

_lowerCAmelCase							:					Optional[int]		    =		logging.getLogger()

def    __snake_case  (    )       ->  Optional[Any]:
   A_    : List[Any]								=	argparse.ArgumentParser()
   parser.add_argument("-f"		)
   A_    : int								=	parser.parse_args()
   return args.f

def    __snake_case  (    _lowerCAmelCase       :       Union[str, Any]		)       ->  int:
   A_    : Optional[int]								=	{}
   A_    : Optional[int]								=	os.path.join(lowerCAmelCase__ , "all_results.json"		)
   if os.path.exists(lowerCAmelCase__		):
      with open(lowerCAmelCase__ , "r"		) as f:
         A_    : Any								=	json.load(lowerCAmelCase__		)
   else:
      raise ValueError(f"can\'t find {path}"		)
   return results

def    __snake_case  (    )       ->  Optional[Any]:
   A_    : List[Any]								=	torch.cuda.is_available() and torch_device == "cuda"
   return is_using_cuda and is_apex_available()


_lowerCAmelCase							:					int		    =		logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)





class     __magic_name__     (							SCREAMING_SNAKE_CASE__							):





     """simple docstring"""



     @classmethod
     def   SCREAMING_SNAKE_CASE						(							cls				:Any     ):





        '''simple docstring'''




        A_    : Optional[int]								=	tempfile.mkdtemp()
        A_    : Tuple								=	os.path.join(cls.tmpdir	,					"default_config.yml"     )
        write_basic_config(save_location=cls.configPath     )
        A_    : Any								=	["accelerate", "launch", "--config_file", cls.configPath]



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





        '''simple docstring'''




        shutil.rmtree(cls.tmpdir     )



     @mock.patch.dict(os.environ	,					{"WANDB_MODE": "offline"}     )
     def   SCREAMING_SNAKE_CASE						(							self				:Tuple     ):





        '''simple docstring'''




        A_    : List[str]								=	self.get_auto_remove_tmp_dir()
        A_    : Optional[int]								=	f"\n            {self.examples_dir}/pytorch/text-classification/run_glue_no_trainer.py\n            --model_name_or_path distilbert-base-uncased\n            --output_dir {tmp_dir}\n            --train_file ./tests/fixtures/tests_samples/MRPC/train.csv\n            --validation_file ./tests/fixtures/tests_samples/MRPC/dev.csv\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n            --learning_rate=1e-4\n            --seed=42\n            --checkpointing_steps epoch\n            --with_tracking\n        ".split()

        if is_cuda_and_apex_available():
           testargs.append("--fp16"     )

        run_command(self._launch_args + testargs     )
        A_    : Tuple								=	get_results(A__     )
        self.assertGreaterEqual(result["eval_accuracy"]	,					0.75     )
        self.assertTrue(os.path.exists(os.path.join(A__	,					"epoch_0"     )     )     )
        self.assertTrue(os.path.exists(os.path.join(A__	,					"glue_no_trainer"     )     )     )



     @mock.patch.dict(os.environ	,					{"WANDB_MODE": "offline"}     )
     def   SCREAMING_SNAKE_CASE						(							self				:Dict     ):





        '''simple docstring'''




        A_    : int								=	self.get_auto_remove_tmp_dir()
        A_    : Tuple								=	f"\n            {self.examples_dir}/pytorch/language-modeling/run_clm_no_trainer.py\n            --model_name_or_path distilgpt2\n            --train_file ./tests/fixtures/sample_text.txt\n            --validation_file ./tests/fixtures/sample_text.txt\n            --block_size 128\n            --per_device_train_batch_size 5\n            --per_device_eval_batch_size 5\n            --num_train_epochs 2\n            --output_dir {tmp_dir}\n            --checkpointing_steps epoch\n            --with_tracking\n        ".split()

        if torch.cuda.device_count() > 1:
           # Skipping because there are not enough batches to train the model + would need a drop_last to work.
           return

        run_command(self._launch_args + testargs     )
        A_    : Optional[Any]								=	get_results(A__     )
        self.assertLess(result["perplexity"]	,					100     )
        self.assertTrue(os.path.exists(os.path.join(A__	,					"epoch_0"     )     )     )
        self.assertTrue(os.path.exists(os.path.join(A__	,					"clm_no_trainer"     )     )     )



     @mock.patch.dict(os.environ	,					{"WANDB_MODE": "offline"}     )
     def   SCREAMING_SNAKE_CASE						(							self				:Union[str, Any]     ):





        '''simple docstring'''




        A_    : Dict								=	self.get_auto_remove_tmp_dir()
        A_    : Optional[Any]								=	f"\n            {self.examples_dir}/pytorch/language-modeling/run_mlm_no_trainer.py\n            --model_name_or_path distilroberta-base\n            --train_file ./tests/fixtures/sample_text.txt\n            --validation_file ./tests/fixtures/sample_text.txt\n            --output_dir {tmp_dir}\n            --num_train_epochs=1\n            --checkpointing_steps epoch\n            --with_tracking\n        ".split()

        run_command(self._launch_args + testargs     )
        A_    : List[str]								=	get_results(A__     )
        self.assertLess(result["perplexity"]	,					42     )
        self.assertTrue(os.path.exists(os.path.join(A__	,					"epoch_0"     )     )     )
        self.assertTrue(os.path.exists(os.path.join(A__	,					"mlm_no_trainer"     )     )     )



     @mock.patch.dict(os.environ	,					{"WANDB_MODE": "offline"}     )
     def   SCREAMING_SNAKE_CASE						(							self				:Optional[int]     ):





        '''simple docstring'''




        A_    : Union[str, Any]								=	7 if get_gpu_count() > 1 else 2

        A_    : Optional[Any]								=	self.get_auto_remove_tmp_dir()
        A_    : Any								=	f"\n            {self.examples_dir}/pytorch/token-classification/run_ner_no_trainer.py\n            --model_name_or_path bert-base-uncased\n            --train_file tests/fixtures/tests_samples/conll/sample.json\n            --validation_file tests/fixtures/tests_samples/conll/sample.json\n            --output_dir {tmp_dir}\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=2\n            --num_train_epochs={epochs}\n            --seed 7\n            --checkpointing_steps epoch\n            --with_tracking\n        ".split()

        run_command(self._launch_args + testargs     )
        A_    : Tuple								=	get_results(A__     )
        self.assertGreaterEqual(result["eval_accuracy"]	,					0.75     )
        self.assertLess(result["train_loss"]	,					0.5     )
        self.assertTrue(os.path.exists(os.path.join(A__	,					"epoch_0"     )     )     )
        self.assertTrue(os.path.exists(os.path.join(A__	,					"ner_no_trainer"     )     )     )



     @unittest.skip(reason="Fix me @muellerzr"     )
     @mock.patch.dict(os.environ	,					{"WANDB_MODE": "offline"}     )
     def   SCREAMING_SNAKE_CASE						(							self				:List[Any]     ):





        '''simple docstring'''




        A_    : Dict								=	self.get_auto_remove_tmp_dir()
        A_    : Optional[int]								=	f"\n            {self.examples_dir}/pytorch/question-answering/run_qa_no_trainer.py\n            --model_name_or_path bert-base-uncased\n            --version_2_with_negative\n            --train_file tests/fixtures/tests_samples/SQUAD/sample.json\n            --validation_file tests/fixtures/tests_samples/SQUAD/sample.json\n            --output_dir {tmp_dir}\n            --seed=42\n            --max_train_steps=10\n            --num_warmup_steps=2\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n            --checkpointing_steps epoch\n            --with_tracking\n        ".split()

        run_command(self._launch_args + testargs     )
        A_    : str								=	get_results(A__     )
        # Because we use --version_2_with_negative the testing script uses SQuAD v2 metrics.
        self.assertGreaterEqual(result["eval_f1"]	,					28     )
        self.assertGreaterEqual(result["eval_exact"]	,					28     )
        self.assertTrue(os.path.exists(os.path.join(A__	,					"epoch_0"     )     )     )
        self.assertTrue(os.path.exists(os.path.join(A__	,					"qa_no_trainer"     )     )     )



     @mock.patch.dict(os.environ	,					{"WANDB_MODE": "offline"}     )
     def   SCREAMING_SNAKE_CASE						(							self				:Dict     ):





        '''simple docstring'''




        A_    : str								=	self.get_auto_remove_tmp_dir()
        A_    : List[Any]								=	f"\n            {self.examples_dir}/pytorch/multiple-choice/run_swag_no_trainer.py\n            --model_name_or_path bert-base-uncased\n            --train_file tests/fixtures/tests_samples/swag/sample.json\n            --validation_file tests/fixtures/tests_samples/swag/sample.json\n            --output_dir {tmp_dir}\n            --max_train_steps=20\n            --num_warmup_steps=2\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n            --with_tracking\n        ".split()

        run_command(self._launch_args + testargs     )
        A_    : Tuple								=	get_results(A__     )
        self.assertGreaterEqual(result["eval_accuracy"]	,					0.8     )
        self.assertTrue(os.path.exists(os.path.join(A__	,					"swag_no_trainer"     )     )     )



     @slow
     @mock.patch.dict(os.environ	,					{"WANDB_MODE": "offline"}     )
     def   SCREAMING_SNAKE_CASE						(							self				:Any     ):





        '''simple docstring'''




        A_    : Any								=	self.get_auto_remove_tmp_dir()
        A_    : List[Any]								=	f"\n            {self.examples_dir}/pytorch/summarization/run_summarization_no_trainer.py\n            --model_name_or_path t5-small\n            --train_file tests/fixtures/tests_samples/xsum/sample.json\n            --validation_file tests/fixtures/tests_samples/xsum/sample.json\n            --output_dir {tmp_dir}\n            --max_train_steps=50\n            --num_warmup_steps=8\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n            --checkpointing_steps epoch\n            --with_tracking\n        ".split()

        run_command(self._launch_args + testargs     )
        A_    : Optional[int]								=	get_results(A__     )
        self.assertGreaterEqual(result["eval_rouge1"]	,					10     )
        self.assertGreaterEqual(result["eval_rouge2"]	,					2     )
        self.assertGreaterEqual(result["eval_rougeL"]	,					7     )
        self.assertGreaterEqual(result["eval_rougeLsum"]	,					7     )
        self.assertTrue(os.path.exists(os.path.join(A__	,					"epoch_0"     )     )     )
        self.assertTrue(os.path.exists(os.path.join(A__	,					"summarization_no_trainer"     )     )     )



     @slow
     @mock.patch.dict(os.environ	,					{"WANDB_MODE": "offline"}     )
     def   SCREAMING_SNAKE_CASE						(							self				:Optional[int]     ):





        '''simple docstring'''




        A_    : Tuple								=	self.get_auto_remove_tmp_dir()
        A_    : Union[str, Any]								=	f"\n            {self.examples_dir}/pytorch/translation/run_translation_no_trainer.py\n            --model_name_or_path sshleifer/student_marian_en_ro_6_1\n            --source_lang en\n            --target_lang ro\n            --train_file tests/fixtures/tests_samples/wmt16/sample.json\n            --validation_file tests/fixtures/tests_samples/wmt16/sample.json\n            --output_dir {tmp_dir}\n            --max_train_steps=50\n            --num_warmup_steps=8\n            --num_beams=6\n            --learning_rate=3e-3\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n            --source_lang en_XX\n            --target_lang ro_RO\n            --checkpointing_steps epoch\n            --with_tracking\n        ".split()

        run_command(self._launch_args + testargs     )
        A_    : Optional[int]								=	get_results(A__     )
        self.assertGreaterEqual(result["eval_bleu"]	,					30     )
        self.assertTrue(os.path.exists(os.path.join(A__	,					"epoch_0"     )     )     )
        self.assertTrue(os.path.exists(os.path.join(A__	,					"translation_no_trainer"     )     )     )



     @slow
     def   SCREAMING_SNAKE_CASE						(							self				:Union[str, Any]     ):





        '''simple docstring'''




        A_    : List[Any]								=	logging.StreamHandler(sys.stdout     )
        logger.addHandler(A__     )

        A_    : Optional[Any]								=	self.get_auto_remove_tmp_dir()
        A_    : Tuple								=	f"\n            {self.examples_dir}/pytorch/semantic-segmentation/run_semantic_segmentation_no_trainer.py\n            --dataset_name huggingface/semantic-segmentation-test-sample\n            --output_dir {tmp_dir}\n            --max_train_steps=10\n            --num_warmup_steps=2\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n            --checkpointing_steps epoch\n        ".split()

        run_command(self._launch_args + testargs     )
        A_    : Optional[Any]								=	get_results(A__     )
        self.assertGreaterEqual(result["eval_overall_accuracy"]	,					0.10     )





     @mock.patch.dict(os.environ	,					{"WANDB_MODE": "offline"}     )
     def   SCREAMING_SNAKE_CASE						(							self				:List[str]     ):





        '''simple docstring'''




        A_    : Any								=	self.get_auto_remove_tmp_dir()
        A_    : str								=	f"\n            {self.examples_dir}/pytorch/image-classification/run_image_classification_no_trainer.py\n            --model_name_or_path google/vit-base-patch16-224-in21k\n            --dataset_name hf-internal-testing/cats_vs_dogs_sample\n            --learning_rate 1e-4\n            --per_device_train_batch_size 2\n            --per_device_eval_batch_size 1\n            --max_train_steps 2\n            --train_val_split 0.1\n            --seed 42\n            --output_dir {tmp_dir}\n            --with_tracking\n            --checkpointing_steps 1\n        ".split()

        if is_cuda_and_apex_available():
           testargs.append("--fp16"     )

        run_command(self._launch_args + testargs     )
        A_    : List[Any]								=	get_results(A__     )
        # The base model scores a 25%
        self.assertGreaterEqual(result["eval_accuracy"]	,					0.6     )
        self.assertTrue(os.path.exists(os.path.join(A__	,					"step_1"     )     )     )
        self.assertTrue(os.path.exists(os.path.join(A__	,					"image_classification_no_trainer"     )     )     )


import copy
import os
from typing import Union

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


lowercase__		:Dict   =    logging.get_logger(__name__)

lowercase__		:Optional[int]   =    {
    "BAAI/AltCLIP": "https://huggingface.co/BAAI/AltCLIP/resolve/main/config.json",
    # See all AltCLIP models at https://huggingface.co/models?filter=altclip
}







class 				lowercase		(					SCREAMING_SNAKE_CASE__		):
							lowercase_       :       Any       					='''altclip_text_model'''




							def __init__(				self   ,A__=2_5_0_0_0_2   ,A__=1_0_2_4   ,A__=2_4   ,A__=1_6   ,A__=4_0_9_6   ,A__="gelu"   ,A__=0.1   ,A__=0.1   ,A__=5_1_4   ,A__=1   ,A__=0.02   ,A__=0.02   ,A__=1E-05   ,A__=1   ,A__=0   ,A__=2   ,A__="absolute"   ,A__=True   ,A__=7_6_8   ,**A__   ,):
												super().__init__(pad_token_id=A__   ,bos_token_id=A__   ,eos_token_id=A__   ,**A__)

												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							=						initializer_factor
												lowercase							=						layer_norm_eps
												lowercase							=						position_embedding_type
												lowercase							=						use_cache
												lowercase							=						project_dim







class 				lowercase		(					SCREAMING_SNAKE_CASE__		):
							lowercase_       :       Optional[int]       					='''altclip_vision_model'''




							def __init__(				self   ,A__=7_6_8   ,A__=3_0_7_2   ,A__=5_1_2   ,A__=1_2   ,A__=1_2   ,A__=3   ,A__=2_2_4   ,A__=3_2   ,A__="quick_gelu"   ,A__=1E-5   ,A__=0.0   ,A__=0.02   ,A__=1.0   ,**A__   ,):
												super().__init__(**A__)

												lowercase							=						hidden_size
												lowercase							=						intermediate_size
												lowercase							=						projection_dim
												lowercase							=						num_hidden_layers
												lowercase							=						num_attention_heads
												lowercase							=						num_channels
												lowercase							=						patch_size
												lowercase							=						image_size
												lowercase							=						initializer_range
												lowercase							=						initializer_factor
												lowercase							=						attention_dropout
												lowercase							=						layer_norm_eps
												lowercase							=						hidden_act





							@classmethod
							def  A__      (				cls   ,A__   ,**A__):
												cls._set_token_in_kwargs(A__)

												lowercase	,						lowercase							=						cls.get_config_dict(A__   ,**A__)

												# get the vision config dict if we are loading from AltCLIPConfig
												if config_dict.get('''model_type''') == "altclip":
																	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(A__   ,**A__)







class 				lowercase		(					SCREAMING_SNAKE_CASE__		):
							lowercase_       :       Any       					='''altclip'''
							lowercase_       :       List[Any]       					=True




							def __init__(				self   ,A__=None   ,A__=None   ,A__=7_6_8   ,A__=2.6592   ,**A__):
												# If `_config_dict` exist, we use them for the backward compatibility.
												# We pop out these 2 attributes before calling `super().__init__` to avoid them being saved (which causes a lot
												# of confusion!).
												lowercase							=						kwargs.pop('''text_config_dict'''   ,A__)
												lowercase							=						kwargs.pop('''vision_config_dict'''   ,A__)

												super().__init__(**A__)

												# Instead of simply assigning `[text|vision]_config_dict` to `[text|vision]_config`, we use the values in
												# `[text|vision]_config_dict` to update the values in `[text|vision]_config`. The values should be same in most
												# cases, but we don't want to break anything regarding `_config_dict` that existed before commit `8827e1b2`.
												if text_config_dict is not None:
																	if text_config is None:
																						lowercase							=						{}

																	# This is the complete result when using `text_config_dict`.
																	lowercase							=						AltCLIPTextConfig(**A__).to_dict()

																	# Give a warning if the values exist in both `_text_config_dict` and `text_config` but being different.
																	for key, value in _text_config_dict.items():
																						if key in text_config and value != text_config[key] and key not in ["transformers_version"]:
																											# If specified in `text_config_dict`
																											if key in text_config_dict:
																																lowercase							=						(
																																    f'`{key}` is found in both `text_config_dict` and `text_config` but with different values. '
																																    f'The value `text_config_dict["{key}"]` will be used instead.'
																																)
																											# If inferred from default argument values (just to be super careful)
																											else:
																																lowercase							=						(
																																    f'`text_config_dict` is provided which will be used to initialize `AltCLIPTextConfig`. The '
																																    f'value `text_config["{key}"]` will be overriden.'
																																)
																											logger.warning(A__)

            # Update all values in `text_config` with the ones in `_text_config_dict`.
																	text_config.update(_text_config_dict)

												if vision_config_dict is not None:
																	if vision_config is None:
																						lowercase							=						{}

																	# This is the complete result when using `vision_config_dict`.
																	lowercase							=						AltCLIPVisionConfig(**A__).to_dict()
																	# convert keys to string instead of integer
																	if "id2label" in _vision_config_dict:
																						lowercase							=						{
																						    str(A__): value for key, value in _vision_config_dict['''id2label'''].items()
																						}

																	# Give a warning if the values exist in both `_vision_config_dict` and `vision_config` but being different.
																	for key, value in _vision_config_dict.items():
																						if key in vision_config and value != vision_config[key] and key not in ["transformers_version"]:
																											# If specified in `vision_config_dict`
																											if key in vision_config_dict:
																																lowercase							=						(
																																    f'`{key}` is found in both `vision_config_dict` and `vision_config` but with different '
																																    f'values. The value `vision_config_dict["{key}"]` will be used instead.'
																																)
																											# If inferred from default argument values (just to be super careful)
																											else:
																																lowercase							=						(
																																    f'`vision_config_dict` is provided which will be used to initialize `AltCLIPVisionConfig`. '
																																    f'The value `vision_config["{key}"]` will be overriden.'
																																)
																											logger.warning(A__)

            # Update all values in `vision_config` with the ones in `_vision_config_dict`.
																	vision_config.update(_vision_config_dict)

												if text_config is None:
																	lowercase							=						{}
																	logger.info('''`text_config` is `None`. Initializing the `AltCLIPTextConfig` with default values.''')

												if vision_config is None:
																	lowercase							=						{}
																	logger.info('''`vision_config` is `None`. initializing the `AltCLIPVisionConfig` with default values.''')

												lowercase							=						AltCLIPTextConfig(**A__)
												lowercase							=						AltCLIPVisionConfig(**A__)

												lowercase							=						projection_dim
												lowercase							=						logit_scale_init_value
												lowercase							=						1.0




							@classmethod
							def  A__      (				cls   ,A__   ,A__   ,**A__):
												return cls(text_config=text_config.to_dict()   ,vision_config=vision_config.to_dict()   ,**A__)





							def  A__      (				self):
												lowercase							=						copy.deepcopy(self.__dict__)
												lowercase							=						self.text_config.to_dict()
												lowercase							=						self.vision_config.to_dict()
												lowercase							=						self.__class__.model_type
												return output




