




"""simple docstring"""


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


_A													=      os.path.abspath(os.path.dirname(__file__))
with ExtendSysPath(f"""{bindir}/../../examples/pytorch/translation"""):
  from run_translation import main  # noqa


set_seed(4_2)
_A													=      """sshleifer/student_marian_en_ro_6_1"""
_A													=      """sshleifer/tiny-mbart"""


@require_torch
class _lowerCamelCase    (					a_						):






   def 	_lowerCAmelCase   (		self       :		int   ,     UpperCamelCase       :		Optional[Any]=False   ,     UpperCamelCase       :		Dict=None   ,     UpperCamelCase       :		int=True   ,     UpperCamelCase       :		Union[str, Any]=True   ,     UpperCamelCase       :		Union[str, Any]=True   ,     UpperCamelCase       :		str=True   ,     )			->						List[str]:

         """simple docstring"""
         lowerCAmelCase__      :			int   =	self.run_trainer(
             eval_steps=1   ,     max_len=12   ,     model_name=UpperCamelCase   ,     num_train_epochs=1   ,     distributed=UpperCamelCase   ,     extra_args_str=UpperCamelCase   ,     predict_with_generate=UpperCamelCase   ,     do_train=UpperCamelCase   ,     do_eval=UpperCamelCase   ,     do_predict=UpperCamelCase   ,     )
         lowerCAmelCase__      :			Union[str, Any]   =	TrainerState.load_from_json(os.path.join(UpperCamelCase   ,     """trainer_state.json"""   )   ).log_history

         if not do_eval:
               return

         lowerCAmelCase__      :			Optional[Any]   =	[log for log in logs if """eval_loss""" in log.keys()]

         lowerCAmelCase__      :			List[str]   =	eval_metrics[0]
         if predict_with_generate:
               assert "eval_bleu" in first_step_stats

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






   @require_torch_non_multi_gpu
   def 	_lowerCAmelCase   (		self       :		str   )			->						List[str]:

         """simple docstring"""
         self.run_seqaseq_quick()






   @require_torch_multi_gpu
   def 	_lowerCAmelCase   (		self       :		Union[str, Any]   )			->						List[str]:

         """simple docstring"""
         self.run_seqaseq_quick(distributed=UpperCamelCase   )






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

         """simple docstring"""
         self.run_seqaseq_quick(distributed=UpperCamelCase   )






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

         """simple docstring"""
         self.run_seqaseq_quick(distributed=UpperCamelCase   ,     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       :		List[Any]   )			->						str:

         """simple docstring"""
         self.run_seqaseq_quick(distributed=UpperCamelCase   ,     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       :		Tuple   )			->						Union[str, Any]:

         """simple docstring"""
         self.run_seqaseq_quick(distributed=UpperCamelCase   ,     extra_args_str="""--sharded_ddp zero_dp_2"""   ,     predict_with_generate=UpperCamelCase   )






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

         """simple docstring"""
         self.run_seqaseq_quick(
             distributed=UpperCamelCase   ,     extra_args_str="""--sharded_ddp zero_dp_2 --fp16"""   ,     predict_with_generate=UpperCamelCase   )






   @require_apex
   @require_torch_gpu
   def 	_lowerCAmelCase   (		self       :		List[Any]   )			->						List[str]:

         """simple docstring"""
         self.run_seqaseq_quick(distributed=UpperCamelCase   ,     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=UpperCamelCase   ,     extra_args_str="""--fp16 --fp16_backend=apex"""   )






   @parameterized.expand(["""base""", """low""", """high""", """mixed"""]   )
   @require_torch_multi_gpu
   def 	_lowerCAmelCase   (		self       :		Optional[int]   ,     UpperCamelCase       :		Any   )			->						int:

         """simple docstring"""
         lowerCAmelCase__      :			Tuple   =	{
             # 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},
         }

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






   @slow
   def 	_lowerCAmelCase   (		self       :		Optional[Any]   )			->						Optional[int]:

         """simple docstring"""
         lowerCAmelCase__      :			str   =	self.run_trainer(
             eval_steps=2   ,     max_len=1_28   ,     model_name=UpperCamelCase   ,     learning_rate=3E-4   ,     num_train_epochs=10   ,     distributed=UpperCamelCase   ,     )

         # Check metrics
         lowerCAmelCase__      :			int   =	TrainerState.load_from_json(os.path.join(UpperCamelCase   ,     """trainer_state.json"""   )   ).log_history
         lowerCAmelCase__      :			Optional[int]   =	[log for log in logs if """eval_loss""" in log.keys()]
         lowerCAmelCase__      :			int   =	eval_metrics[0]
         lowerCAmelCase__      :			Any   =	eval_metrics[-1]

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

         # test if do_predict saves generations and metrics
         lowerCAmelCase__      :			Tuple   =	os.listdir(UpperCamelCase   )
         lowerCAmelCase__      :			Union[str, Any]   =	{os.path.basename(UpperCamelCase   ) for p in contents}
         assert "generated_predictions.txt" in contents
         assert "predict_results.json" in contents






   @slow
   @require_bitsandbytes
   def 	_lowerCAmelCase   (		self       :		Tuple   )			->						Tuple:

         """simple docstring"""
         from transformers.training_args import OptimizerNames

         def train_and_return_metrics(UpperCamelCase       :		str   ) -> Tuple[int, float]:
               lowerCAmelCase__      :			Tuple   =	"""--skip_memory_metrics 0"""

               lowerCAmelCase__      :			List[Any]   =	self.run_trainer(
                   max_len=1_28   ,     model_name=UpperCamelCase   ,     learning_rate=3E-4   ,     num_train_epochs=1   ,     optim=UpperCamelCase   ,     distributed=UpperCamelCase   ,     extra_args_str=UpperCamelCase   ,     do_eval=UpperCamelCase   ,     do_predict=UpperCamelCase   ,     n_gpus_to_use=1   ,     )

               # Check metrics
               lowerCAmelCase__      :			List[Any]   =	TrainerState.load_from_json(Path(UpperCamelCase   ,     """trainer_state.json"""   )   ).log_history
               lowerCAmelCase__      :			Dict   =	int(logs[0]["""train_mem_gpu_peaked_delta"""] / 2**20   )
               lowerCAmelCase__      :			Dict   =	int(logs[0]["""train_mem_gpu_alloc_delta"""] / 2**20   )

               lowerCAmelCase__      :			List[Any]   =	logs[0]["""train_loss"""]
               return gpu_peak_mem_mb, gpu_alloc_mem_mb, loss

         lowerCAmelCase__      :			List[Any]   =	train_and_return_metrics(OptimizerNames.ADAMW_TORCH.value   )
         lowerCAmelCase__      :			Tuple   =	train_and_return_metrics(OptimizerNames.ADAMW_BNB.value   )

         lowerCAmelCase__      :			str   =	gpu_alloc_mem_orig - gpu_alloc_mem_bnb

         lowerCAmelCase__      :			Optional[Any]   =	gpu_peak_mem_orig + gpu_alloc_mem_orig
         lowerCAmelCase__      :			Any   =	gpu_peak_mem_bnb + gpu_alloc_mem_bnb
         lowerCAmelCase__      :			str   =	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
         lowerCAmelCase__      :			Union[str, Any]   =	1_20

         # 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(
             UpperCamelCase   ,     UpperCamelCase   ,     """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(
             UpperCamelCase   ,     UpperCamelCase   ,     """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(
             UpperCamelCase   ,     UpperCamelCase   ,     f"""loss should be the same, but got loss_orig={loss_orig}, loss_bnb={loss_bnb}"""   )






   def 	_lowerCAmelCase   (		self       :		Optional[Any]   ,     UpperCamelCase       :		int   ,     UpperCamelCase       :		str   ,     UpperCamelCase       :		int   ,     UpperCamelCase       :		float = 3E-3   ,     UpperCamelCase       :		str = "adafactor"   ,     UpperCamelCase       :		bool = False   ,     UpperCamelCase       :		str = None   ,     UpperCamelCase       :		int = 0   ,     UpperCamelCase       :		bool = True   ,     UpperCamelCase       :		bool = True   ,     UpperCamelCase       :		bool = True   ,     UpperCamelCase       :		bool = True   ,     UpperCamelCase       :		int = None   ,     )			->						List[str]:

         """simple docstring"""
         lowerCAmelCase__      :			Dict   =	self.test_file_dir / """../fixtures/tests_samples/wmt_en_ro"""
         lowerCAmelCase__      :			Any   =	self.get_auto_remove_tmp_dir()
         lowerCAmelCase__      :			List[str]   =	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(UpperCamelCase   )}
            --per_device_train_batch_size 4
            --learning_rate {learning_rate}
            --warmup_steps 8
            --logging_steps 0
            --logging_strategy no
            --save_steps {str(UpperCamelCase   )}
            --group_by_length
            --label_smoothing_factor 0.1
            --target_lang ro_RO
            --source_lang en_XX
        """.split()

         lowerCAmelCase__      :			Union[str, Any]   =	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(UpperCamelCase   )}
        """.split()

         lowerCAmelCase__      :			Optional[int]   =	"""
            --do_predict
        """.split()

         lowerCAmelCase__      :			Tuple   =	[]
         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:
                     lowerCAmelCase__      :			List[str]   =	get_gpu_count()
               lowerCAmelCase__      :			List[Any]   =	get_torch_dist_unique_port()
               lowerCAmelCase__      :			Optional[int]   =	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()
               lowerCAmelCase__      :			List[Any]   =	[sys.executable] + distributed_args + args
               # keep for quick debug
               # print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] +cmd)); die
               execute_subprocess_async(UpperCamelCase   ,     env=self.get_env()   )
         else:
               lowerCAmelCase__      :			str   =	["""run_translation.py"""] + args
               with patch.object(UpperCamelCase   ,     """argv"""   ,     UpperCamelCase   ):
                     main()

         return output_dir











"""simple docstring"""


from typing import Optional, Tuple

import jax
import jax.numpy as jnp
from flax import linen as nn
from flax.core.frozen_dict import FrozenDict
from transformers import CLIPConfig, FlaxPreTrainedModel
from transformers.models.clip.modeling_flax_clip import FlaxCLIPVisionModule



def 	lowercase_				(			__UpperCAmelCase    ,		__UpperCAmelCase    ,		__UpperCAmelCase=1E-12				)						->    Dict:
      lowerCAmelCase__      :			Optional[int]   =	jnp.divide(emb_a.T    ,		jnp.clip(jnp.linalg.norm(__UpperCAmelCase    ,		axis=1				)    ,		a_min=__UpperCAmelCase				)				).T
      lowerCAmelCase__      :			Any   =	jnp.divide(emb_a.T    ,		jnp.clip(jnp.linalg.norm(__UpperCAmelCase    ,		axis=1				)    ,		a_min=__UpperCAmelCase				)				).T
      return jnp.matmul(__UpperCAmelCase    ,		norm_emb_a.T				)


class _lowerCamelCase    (					nn.Module						):
   _lowerCamelCase    :CLIPConfig
   _lowerCamelCase    :jnp.dtype     				=     jnp.floataa






   def 	_lowerCAmelCase   (		self       :		List[str]   )			->						Tuple:

         """simple docstring"""
         lowerCAmelCase__      :			Optional[int]   =	FlaxCLIPVisionModule(self.config.vision_config   )
         lowerCAmelCase__      :			Dict   =	nn.Dense(self.config.projection_dim   ,     use_bias=UpperCamelCase   ,     dtype=self.dtype   )

         lowerCAmelCase__      :			Optional[int]   =	self.param("""concept_embeds"""   ,     jax.nn.initializers.ones   ,     (17, self.config.projection_dim)   )
         lowerCAmelCase__      :			Optional[int]   =	self.param(
             """special_care_embeds"""   ,     jax.nn.initializers.ones   ,     (3, self.config.projection_dim)   )

         lowerCAmelCase__      :			List[Any]   =	self.param("""concept_embeds_weights"""   ,     jax.nn.initializers.ones   ,     (17,)   )
         lowerCAmelCase__      :			List[Any]   =	self.param("""special_care_embeds_weights"""   ,     jax.nn.initializers.ones   ,     (3,)   )






   def __call__(		self       :		str   ,     UpperCamelCase       :		List[Any]   )			->						Union[str, Any]:

         """simple docstring"""
         lowerCAmelCase__      :			Union[str, Any]   =	self.vision_model(UpperCamelCase   )[1]
         lowerCAmelCase__      :			List[Any]   =	self.visual_projection(UpperCamelCase   )

         lowerCAmelCase__      :			str   =	jax_cosine_distance(UpperCamelCase   ,     self.special_care_embeds   )
         lowerCAmelCase__      :			Optional[int]   =	jax_cosine_distance(UpperCamelCase   ,     self.concept_embeds   )

         # increase this value to create a stronger `nfsw` filter
         # at the cost of increasing the possibility of filtering benign image inputs
         lowerCAmelCase__      :			Any   =	0.0

         lowerCAmelCase__      :			List[Any]   =	special_cos_dist - self.special_care_embeds_weights[None, :] + adjustment
         lowerCAmelCase__      :			Any   =	jnp.round(UpperCamelCase   ,     3   )
         lowerCAmelCase__      :			List[str]   =	jnp.any(special_scores > 0   ,     axis=1   ,     keepdims=UpperCamelCase   )
         # Use a lower threshold if an image has any special care concept
         lowerCAmelCase__      :			Tuple   =	is_special_care * 0.01

         lowerCAmelCase__      :			List[Any]   =	cos_dist - self.concept_embeds_weights[None, :] + special_adjustment
         lowerCAmelCase__      :			Dict   =	jnp.round(UpperCamelCase   ,     3   )
         lowerCAmelCase__      :			List[str]   =	jnp.any(concept_scores > 0   ,     axis=1   )

         return has_nsfw_concepts






class _lowerCamelCase    (					a_						):
   _lowerCamelCase    :Optional[Any]     				=     CLIPConfig
   _lowerCamelCase    :Dict     				=     "clip_input"
   _lowerCamelCase    :int     				=     FlaxStableDiffusionSafetyCheckerModule






   def __init__(		self       :		int   ,     UpperCamelCase       :		CLIPConfig   ,     UpperCamelCase       :		Optional[Tuple] = None   ,     UpperCamelCase       :		int = 0   ,     UpperCamelCase       :		jnp.dtype = jnp.floataa   ,     UpperCamelCase       :		bool = True   ,     **UpperCamelCase       :		int   ,     )			->						str:

         """simple docstring"""
         if input_shape is None:
               lowerCAmelCase__      :			str   =	(1, 2_24, 2_24, 3)
         lowerCAmelCase__      :			Union[str, Any]   =	self.module_class(config=UpperCamelCase   ,     dtype=UpperCamelCase   ,     **UpperCamelCase   )
         super().__init__(UpperCamelCase   ,     UpperCamelCase   ,     input_shape=UpperCamelCase   ,     seed=UpperCamelCase   ,     dtype=UpperCamelCase   ,     _do_init=_do_init   )






   def 	_lowerCAmelCase   (		self       :		Dict   ,     UpperCamelCase       :		jax.random.KeyArray   ,     UpperCamelCase       :		Tuple   ,     UpperCamelCase       :		FrozenDict = None   )			->						FrozenDict:

         """simple docstring"""
         # init input tensor
         lowerCAmelCase__      :			Union[str, Any]   =	jax.random.normal(UpperCamelCase   ,     UpperCamelCase   )

         lowerCAmelCase__ ,   lowerCAmelCase__      :			Union[str, Any]   =	jax.random.split(UpperCamelCase   )
         lowerCAmelCase__      :			List[Any]   =	{"""params""": params_rng, """dropout""": dropout_rng}

         lowerCAmelCase__      :			str   =	self.module.init(UpperCamelCase   ,     UpperCamelCase   )["""params"""]

         return random_params






   def __call__(		self       :		Dict   ,     UpperCamelCase       :		Tuple   ,     UpperCamelCase       :		dict = None   ,     )			->						List[Any]:

         """simple docstring"""
         lowerCAmelCase__      :			Union[str, Any]   =	jnp.transpose(UpperCamelCase   ,     (0, 2, 3, 1)   )

         return self.module.apply(
             {"""params""": params or self.params}   ,     jnp.array(UpperCamelCase   ,     dtype=jnp.floataa   )   ,     rngs={}   ,     )





