




import copy
import os
from typing import Union

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


lowercase__				:      Optional[int]	   = logging.get_logger(__name__)

lowercase__				:      Optional[int]	   = {
    "BridgeTower/bridgetower-base": "https://huggingface.co/BridgeTower/bridgetower-base/blob/main/config.json",
    "BridgeTower/bridgetower-base-itm-mlm": (
        "https://huggingface.co/BridgeTower/bridgetower-base-itm-mlm/blob/main/config.json"
    ),
}




class       a__	( UpperCamelCase__				):
     a		:      Optional[int]		 =     """bridgetower_vision_model"""







     def __init__(     self  ,		A=768  ,		A=12  ,		A=3  ,		A=16  ,		A=288  ,		A=1  ,		A=1e-05  ,		A=False  ,		A=True  ,		A=False  ,		**A  ,		)  ->   Optional[Any]:



          '''simple docstring'''



          super().__init__(**A			)
          a     =				hidden_size
          a     =				num_hidden_layers
          a     =				num_channels
          a     =				patch_size
          a     =				image_size
          a     =				initializer_factor
          a     =				layer_norm_eps
          a     =				stop_gradient
          a     =				share_layernorm
          a     =				remove_last_layer







     @classmethod
     def       lowerCAmelCase_    (     cls  ,		A  ,		**A			)  ->   "PretrainedConfig":



          '''simple docstring'''



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

          if config_dict.get("model_type"			) == "bridgetower":
               a     =				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(A  ,		**A			)







class       a__	( UpperCamelCase__				):
     a		:      List[Any]		 =     """bridgetower_text_model"""







     def __init__(     self  ,		A=50265  ,		A=768  ,		A=12  ,		A=12  ,		A=1  ,		A=3072  ,		A="gelu"  ,		A=0.1  ,		A=0.1  ,		A=514  ,		A=1  ,		A=1e-05  ,		A=1  ,		A=0  ,		A=2  ,		A="absolute"  ,		A=True  ,		**A  ,		)  ->   int:



          '''simple docstring'''



          super().__init__(**A			)

          a     =				vocab_size
          a     =				hidden_size
          a     =				num_hidden_layers
          a     =				num_attention_heads
          a     =				hidden_act
          a     =				initializer_factor
          a     =				intermediate_size
          a     =				hidden_dropout_prob
          a     =				attention_probs_dropout_prob
          a     =				max_position_embeddings
          a     =				type_vocab_size
          a     =				layer_norm_eps
          a     =				position_embedding_type
          a     =				use_cache
          a     =				pad_token_id
          a     =				bos_token_id
          a     =				eos_token_id







     @classmethod
     def       lowerCAmelCase_    (     cls  ,		A  ,		**A			)  ->   "PretrainedConfig":



          '''simple docstring'''



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

          if config_dict.get("model_type"			) == "bridgetower":
               a     =				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(A  ,		**A			)







class       a__	( UpperCamelCase__				):
     a		:      str		 =     """bridgetower"""







     def __init__(     self  ,		A=True  ,		A="gelu"  ,		A=768  ,		A=1  ,		A=1e-05  ,		A=False  ,		A="add"  ,		A=12  ,		A=6  ,		A=False  ,		A=False  ,		A=None  ,		A=None  ,		**A  ,		)  ->   Dict:



          '''simple docstring'''



          a     =				kwargs.pop("text_config_dict"  ,		A			)
          a     =				kwargs.pop("vision_config_dict"  ,		A			)

          super().__init__(**A			)
          a     =				share_cross_modal_transformer_layers
          a     =				hidden_act
          a     =				hidden_size
          a     =				initializer_factor
          a     =				layer_norm_eps
          a     =				share_link_tower_layers
          a     =				link_tower_type
          a     =				num_attention_heads
          a     =				num_hidden_layers
          a     =				tie_word_embeddings
          a     =				init_layernorm_from_vision_encoder

          if text_config is None:
               a     =				{}
               logger.info("`text_config` is `None`. Initializing the `BridgeTowerTextConfig` with default values."			)

          if vision_config is None:
               a     =				{}
               logger.info("`vision_config` is `None`. Initializing the `BridgeTowerVisionConfig` with default values."			)

          a     =				BridgeTowerTextConfig(**A			)
          a     =				BridgeTowerVisionConfig(**A			)







     @classmethod
     def       lowerCAmelCase_    (     cls  ,		A  ,		A  ,		**A			)  ->   str:



          '''simple docstring'''



          return cls(text_config=text_config.to_dict()  ,		vision_config=vision_config.to_dict()  ,		**A			)







     def       lowerCAmelCase_    (     self			)  ->   List[Any]:



          '''simple docstring'''



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







import math
import os
import re
import sys
import unittest
from pathlib import Path
from typing import Tuple
from unittest.mock import patch

from parameterized import parameterized

from transformers.testing_utils import (
    CaptureStderr,
    ExtendSysPath,
    TestCasePlus,
    execute_subprocess_async,
    get_gpu_count,
    get_torch_dist_unique_port,
    require_apex,
    require_bitsandbytes,
    require_fairscale,
    require_torch,
    require_torch_gpu,
    require_torch_multi_gpu,
    require_torch_non_multi_gpu,
    slow,
)
from transformers.trainer_callback import TrainerState
from transformers.trainer_utils import set_seed


lowercase__				:      Optional[int]	   = os.path.abspath(os.path.dirname(__file__))
with ExtendSysPath(F'{bindir}/../../examples/pytorch/translation'):
   from run_translation import main  # noqa


set_seed(42)
lowercase__				:      List[str]	   = "sshleifer/student_marian_en_ro_6_1"
lowercase__				:      List[Any]	   = "sshleifer/tiny-mbart"




@require_torch
class       a__	( UpperCamelCase__				):







     def       lowerCAmelCase_    (     self  ,		A=False  ,		A=None  ,		A=True  ,		A=True  ,		A=True  ,		A=True  ,		)  ->   List[Any]:



          '''simple docstring'''



          a     =				self.run_trainer(
              eval_steps=1  ,		max_len=12  ,		model_name=A  ,		num_train_epochs=1  ,		distributed=A  ,		extra_args_str=A  ,		predict_with_generate=A  ,		do_train=A  ,		do_eval=A  ,		do_predict=A  ,		)
          a     =				TrainerState.load_from_json(os.path.join(A  ,		"trainer_state.json"			)			).log_history

          if not do_eval:
               return

          a     =				[log for log in logs if "eval_loss" in log.keys()]

          a     =				eval_metrics[0]
          if predict_with_generate:
               assert "eval_bleu" in first_step_stats

               a     =				eval_metrics[-1]
               assert isinstance(last_step_stats["eval_bleu"]  ,		A			)
               assert not math.isnan(float(last_step_stats["eval_loss"]			)			), "eval_loss must not be `nan`"







     @require_torch_non_multi_gpu
     def       lowerCAmelCase_    (     self			)  ->   Union[str, Any]:



          '''simple docstring'''



          self.run_seqaseq_quick()







     @require_torch_multi_gpu
     def       lowerCAmelCase_    (     self			)  ->   Dict:



          '''simple docstring'''



          self.run_seqaseq_quick(distributed=A			)







     @require_torch_multi_gpu
     def       lowerCAmelCase_    (     self			)  ->   Optional[int]:



          '''simple docstring'''



          self.run_seqaseq_quick(distributed=A			)







     @unittest.skip("Requires an update of the env running those tests"			)
     @require_torch_multi_gpu
     @require_fairscale
     def       lowerCAmelCase_    (     self			)  ->   str:



          '''simple docstring'''



          self.run_seqaseq_quick(distributed=A  ,		extra_args_str="--sharded_ddp simple"			)







     @unittest.skip("Requires an update of the env running those tests"			)
     @require_torch_multi_gpu
     @require_fairscale
     def       lowerCAmelCase_    (     self			)  ->   Tuple:



          '''simple docstring'''



          self.run_seqaseq_quick(distributed=A  ,		extra_args_str="--sharded_ddp simple --fp16"			)







     @unittest.skip("Requires an update of the env running those tests"			)
     @require_torch_multi_gpu
     @require_fairscale
     def       lowerCAmelCase_    (     self			)  ->   List[Any]:



          '''simple docstring'''



          self.run_seqaseq_quick(distributed=A  ,		extra_args_str="--sharded_ddp zero_dp_2"  ,		predict_with_generate=A			)







     @unittest.skip("Requires an update of the env running those tests"			)
     @require_torch_multi_gpu
     @require_fairscale
     def       lowerCAmelCase_    (     self			)  ->   Any:



          '''simple docstring'''



          self.run_seqaseq_quick(
              distributed=A  ,		extra_args_str="--sharded_ddp zero_dp_2 --fp16"  ,		predict_with_generate=A			)







     @require_apex
     @require_torch_gpu
     def       lowerCAmelCase_    (     self			)  ->   str:



          '''simple docstring'''



          self.run_seqaseq_quick(distributed=A  ,		extra_args_str="--fp16 --fp16_backend=apex"			)
          # test 2nd time - was getting eval_loss': nan'
          # to reproduce the problem set distributed=False
          self.run_seqaseq_quick(distributed=A  ,		extra_args_str="--fp16 --fp16_backend=apex"			)







     @parameterized.expand(["base", "low", "high", "mixed"]			)
     @require_torch_multi_gpu
     def       lowerCAmelCase_    (     self  ,		A			)  ->   Dict:



          '''simple docstring'''



          a     =				{
              # test with the default log_level - should be info and thus log info once
              "base": {"extra_args_str": "", "n_matches": 1},
              # test with low log_level and log_level_replica - should be noisy on all processes
              # now the info string should appear twice on 2 processes
              "low": {"extra_args_str": "--log_level debug --log_level_replica debug", "n_matches": 2},
              # test with high log_level and low log_level_replica
              # now the info string should appear once only on the replica
              "high": {"extra_args_str": "--log_level error --log_level_replica debug", "n_matches": 1},
              # test with high log_level and log_level_replica - should be quiet on all processes
              "mixed": {"extra_args_str": "--log_level error --log_level_replica error", "n_matches": 0},
          }

          a     =				experiments[experiment_id]
          a     =				{"distributed": True, "predict_with_generate": False, "do_eval": False, "do_predict": False}
          a     =				"Running training"
          with CaptureStderr() as cl:
               self.run_seqaseq_quick(**A  ,		extra_args_str=data["extra_args_str"]			)
          a     =				len(re.findall(A  ,		cl.err			)			)
          self.assertEqual(A  ,		data["n_matches"]			)







     @slow
     def       lowerCAmelCase_    (     self			)  ->   str:



          '''simple docstring'''



          a     =				self.run_trainer(
              eval_steps=2  ,		max_len=128  ,		model_name=A  ,		learning_rate=3e-4  ,		num_train_epochs=10  ,		distributed=A  ,		)

          # Check metrics
          a     =				TrainerState.load_from_json(os.path.join(A  ,		"trainer_state.json"			)			).log_history
          a     =				[log for log in logs if "eval_loss" in log.keys()]
          a     =				eval_metrics[0]
          a     =				eval_metrics[-1]

          assert first_step_stats["eval_loss"] > last_step_stats["eval_loss"], "model learned nothing"
          assert isinstance(last_step_stats["eval_bleu"]  ,		A			)

          # test if do_predict saves generations and metrics
          a     =				os.listdir(A			)
          a     =				{os.path.basename(A			) for p in contents}
          assert "generated_predictions.txt" in contents
          assert "predict_results.json" in contents







     @slow
     @require_bitsandbytes
     def       lowerCAmelCase_    (     self			)  ->   List[Any]:



          '''simple docstring'''



          from transformers.training_args import OptimizerNames

          def train_and_return_metrics(A			) -> Tuple[int, float]:
               a     =				"--skip_memory_metrics 0"

               a     =				self.run_trainer(
                   max_len=128  ,		model_name=A  ,		learning_rate=3e-4  ,		num_train_epochs=1  ,		optim=A  ,		distributed=A  ,		extra_args_str=A  ,		do_eval=A  ,		do_predict=A  ,		n_gpus_to_use=1  ,		)

               # Check metrics
               a     =				TrainerState.load_from_json(Path(A  ,		"trainer_state.json"			)			).log_history
               a     =				int(logs[0]["train_mem_gpu_peaked_delta"] / 2**20			)
               a     =				int(logs[0]["train_mem_gpu_alloc_delta"] / 2**20			)

               a     =				logs[0]["train_loss"]
               return gpu_peak_mem_mb, gpu_alloc_mem_mb, loss

          a  ,				a  ,				a     =				train_and_return_metrics(OptimizerNames.ADAMW_TORCH.value			)
          a  ,				a  ,				a     =				train_and_return_metrics(OptimizerNames.ADAMW_BNB.value			)

          a     =				gpu_alloc_mem_orig - gpu_alloc_mem_bnb

          a     =				gpu_peak_mem_orig + gpu_alloc_mem_orig
          a     =				gpu_peak_mem_bnb + gpu_alloc_mem_bnb
          a     =				gpu_total_mem_orig - gpu_total_mem_bnb

          # sshleifer/student_marian_en_ro_6_1 has 54M parameter, 29M of which is `nn.Embedding` which
          # doesn't get quantized and remains in fp32. Therefore we only have 25M parameters quantized
          # in 2 bytes and the diff in optim memory usage is derived as so:
          #
          # - normal 25*8=~200MB (8 bytes per param)
          # - bnb    25*2= ~50MB (2 bytes per param)
          #
          # Thus we should expect ~150MB total memory saved.
          #
          # Peak memory should be the same - the total should be different by about that same margin
          #
          # After leaving a small margin to accommodate for differences between gpus let's check
          # that we have at least 120MB in savings
          a     =				120

          # uncomment the following if this test starts failing - requires py38 for a new print feature
          # gpu_peak_mem_diff = gpu_peak_mem_orig - gpu_peak_mem_bnb
          # print(f"{gpu_alloc_mem_orig=}MB {gpu_peak_mem_orig=}MB {gpu_alloc_mem_orig+gpu_peak_mem_orig=}MB")
          # print(f" {gpu_alloc_mem_bnb=}MB  {gpu_peak_mem_bnb=}MB  {gpu_alloc_mem_bnb+gpu_peak_mem_bnb=}MB")
          # print(f"{gpu_alloc_mem_diff=}MB")
          # print(f"{gpu_peak_mem_diff=}MB")
          # print(f"{gpu_total_mem_orig=}MB, {gpu_total_mem_bnb=}MB")
          # print(f"{gpu_total_mem_diff=}MB, {gpu_total_mem_diff=}MB")

          self.assertGreater(
              A  ,		A  ,		"should use ~150MB less alloc gpu memory with BNB, compared to without it for this model but got"
              F''' a difference of {gpu_alloc_mem_diff}MB, with gpu_alloc_mem_orig={gpu_alloc_mem_orig}MB and'''
              F''' gpu_alloc_mem_bnb={gpu_alloc_mem_bnb}MB'''  ,		)

          self.assertGreater(
              A  ,		A  ,		"should use ~150MB less total gpu memory with BNB, compared to without it for this model but got"
              F''' a difference of {gpu_total_mem_diff}MB, with gpu_total_mem_orig={gpu_total_mem_orig}MB and'''
              F''' gpu_total_mem_bnb={gpu_total_mem_bnb}MB'''  ,		)

          self.assertEqual(
              A  ,		A  ,		F'''loss should be the same, but got loss_orig={loss_orig}, loss_bnb={loss_bnb}'''			)







     def       lowerCAmelCase_    (     self  ,		A  ,		A  ,		A  ,		A = 3e-3  ,		A = "adafactor"  ,		A = False  ,		A = None  ,		A = 0  ,		A = True  ,		A = True  ,		A = True  ,		A = True  ,		A = None  ,		)  ->   Tuple:



          '''simple docstring'''



          a     =				self.test_file_dir / "../fixtures/tests_samples/wmt_en_ro"
          a     =				self.get_auto_remove_tmp_dir()
          a     =				F'''
            --model_name_or_path {model_name}
            --train_file {data_dir}/train.json
            --validation_file {data_dir}/val.json
            --test_file {data_dir}/test.json
            --output_dir {output_dir}
            --overwrite_output_dir
            --max_train_samples 8
            --max_source_length {max_len}
            --max_target_length {max_len}
            --do_train
            --num_train_epochs {str(A			)}
            --per_device_train_batch_size 4
            --learning_rate {learning_rate}
            --warmup_steps 8
            --logging_steps 0
            --logging_strategy no
            --save_steps {str(A			)}
            --group_by_length
            --label_smoothing_factor 0.1
            --target_lang ro_RO
            --source_lang en_XX
        '''.split()

          a     =				F'''
            --do_eval
            --per_device_eval_batch_size 4
            --max_eval_samples 8
            --val_max_target_length {max_len}
            --evaluation_strategy steps
            --eval_steps {str(A			)}
        '''.split()

          a     =				"\n            --do_predict\n        ".split()

          a     =				[]
          if do_train:
               args += args_train

          if do_eval:
               args += args_eval

          if do_predict:
               args += args_predict

          if predict_with_generate:
               args += "--predict_with_generate".split()

          if do_train:
               if optim == "adafactor":
                    args += "--adafactor".split()
               else:
                    args += F'''--optim {optim}'''.split()

          if extra_args_str is not None:
               args += extra_args_str.split()

          if distributed:
               if n_gpus_to_use is None:
                    a     =				get_gpu_count()
               a     =				get_torch_dist_unique_port()
               a     =				F'''
                -m torch.distributed.run
                --nproc_per_node={n_gpus_to_use}
                --master_port={master_port}
                {self.examples_dir_str}/pytorch/translation/run_translation.py
            '''.split()
               a     =				[sys.executable] + distributed_args + args
               # keep for quick debug
               # print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] +cmd)); die
               execute_subprocess_async(A  ,		env=self.get_env()			)
          else:
               a     =				["run_translation.py"] + args
               with patch.object(A  ,		"argv"  ,		A			):
                    main()

          return output_dir

