

import argparse
import json
import sys
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import List

import torch
import torch.nn as nn
from huggingface_hub import cached_download, hf_hub_download
from torch import Tensor

from transformers import AutoImageProcessor, VanConfig, VanForImageClassification
from transformers.models.deprecated.van.modeling_van import VanLayerScaling
from transformers.utils import logging


logging.set_verbosity_info()
_a:      Union[str, Any]								=     logging.get_logger(__name__)

@dataclass
class   __UpperCamelCase      :
	SCREAMING_SNAKE_CASE__      			=       42
	SCREAMING_SNAKE_CASE__      			=       field(default_factory=_a  )
	SCREAMING_SNAKE_CASE__      			=       field(default_factory=_a  )




	def  __A   (						self		:  Tuple						,		lowerCAmelCase		:  Dict						,		lowerCAmelCase		:  Tensor						,		lowerCAmelCase		:  Tensor							):


		'''simple docstring'''







		UpperCAmelCase_    			=     len(list(m.modules()							)							) == 1 or isinstance(lowerCAmelCase						,		nn.Convad							) or isinstance(lowerCAmelCase						,		nn.BatchNormad							)
		if has_not_submodules:
			if not isinstance(lowerCAmelCase						,		lowerCAmelCase							):
				self.traced.append(lowerCAmelCase							)




	def __call__(						self		:  Optional[int]						,		lowerCAmelCase		:  Tensor							):


		'''simple docstring'''







		for m in self.module.modules():
			self.handles.append(m.register_forward_hook(self._forward_hook							)							)
		self.module(lowerCAmelCase							)
		[x.remove() for x in self.handles]
		return self




	@property
	def  __A   (						self		:  Tuple							):


		'''simple docstring'''







		return list(filter(lambda lowerCAmelCase							: len(list(x.state_dict().keys()							)							) > 0						,		self.traced							)							)


@dataclass
class   __UpperCamelCase      :
	SCREAMING_SNAKE_CASE__      			=       42
	SCREAMING_SNAKE_CASE__      			=       42
	SCREAMING_SNAKE_CASE__      			=       0
	SCREAMING_SNAKE_CASE__      			=       field(default_factory=_a  )
	SCREAMING_SNAKE_CASE__      			=       field(default_factory=_a  )




	def __call__(						self		:  Dict						,		lowerCAmelCase		:  Tensor							):


		'''simple docstring'''







		UpperCAmelCase_    			=     Tracker(self.dest							)(lowerCAmelCase							).parametrized
		UpperCAmelCase_    			=     Tracker(self.src							)(lowerCAmelCase							).parametrized

		UpperCAmelCase_    			=     list(filter(lambda lowerCAmelCase							: type(lowerCAmelCase							) not in self.src_skip						,		lowerCAmelCase							)							)
		UpperCAmelCase_    			=     list(filter(lambda lowerCAmelCase							: type(lowerCAmelCase							) not in self.dest_skip						,		lowerCAmelCase							)							)

		if len(lowerCAmelCase							) != len(lowerCAmelCase							):
			raise Exception(
			    F"Numbers of operations are different. Source module has {len(lowerCAmelCase							)} operations while"
			    F" destination module has {len(lowerCAmelCase							)}."							)

		for dest_m, src_m in zip(lowerCAmelCase						,		lowerCAmelCase							):
			dest_m.load_state_dict(src_m.state_dict()							)
			if self.verbose == 1:
				print(F"Transfered from={src_m} to={dest_m}"							)



def  __lowerCAmelCase (  A    ,       A      ):
	UpperCAmelCase_    			=     from_model.state_dict()
	UpperCAmelCase_    			=     our_model.state_dict()
	UpperCAmelCase_    			=     our_model.config
	UpperCAmelCase_    			=     []
	for stage_idx in range(len(config.hidden_sizes      )      ):
		for block_id in range(config.depths[stage_idx]      ):
			UpperCAmelCase_    			=     F"block{stage_idx + 1}.{block_id}.layer_scale_1"
			UpperCAmelCase_    			=     F"van.encoder.stages.{stage_idx}.layers.{block_id}.attention_scaling.weight"

			all_keys.append((from_key, to_key)      )
			UpperCAmelCase_    			=     F"block{stage_idx + 1}.{block_id}.layer_scale_2"
			UpperCAmelCase_    			=     F"van.encoder.stages.{stage_idx}.layers.{block_id}.mlp_scaling.weight"

			all_keys.append((from_key, to_key)      )

	for from_key, to_key in all_keys:
		UpperCAmelCase_    			=     from_state_dict.pop(snake_case__      )

	our_model.load_state_dict(snake_case__      )
	return our_model



def  __lowerCAmelCase (  A    ,       A    ,       A    ,       A    ,       A    ,       A = True    ,       ):
	print(F"Downloading weights for {name}..."      )
	UpperCAmelCase_    			=     cached_download(snake_case__      )
	print(F"Converting {name}..."      )
	UpperCAmelCase_    			=     torch.load(snake_case__      )["state_dict"]
	from_model.load_state_dict(snake_case__      )
	from_model.eval()
	with torch.no_grad():
		UpperCAmelCase_    			=     VanForImageClassification(snake_case__      ).eval()
		UpperCAmelCase_    			=     ModuleTransfer(src=snake_case__    ,       dest=snake_case__      )
		UpperCAmelCase_    			=     torch.randn((1, 3, 224, 224)      )
		module_transfer(snake_case__      )
		UpperCAmelCase_    			=     copy_parameters(snake_case__    ,       snake_case__      )

	if not torch.allclose(from_model(snake_case__      )    ,       our_model(snake_case__      ).logits      ):
		raise ValueError("The model logits don't match the original one."      )

	UpperCAmelCase_    			=     name
	print(snake_case__      )

	if push_to_hub:
		our_model.push_to_hub(
		    repo_path_or_name=save_directory / checkpoint_name    ,       commit_message="Add model"    ,       use_temp_dir=snake_case__    ,       )

		# we can use the convnext one
		UpperCAmelCase_    			=     AutoImageProcessor.from_pretrained("facebook/convnext-base-224-22k-1k"      )
		image_processor.push_to_hub(
		    repo_path_or_name=save_directory / checkpoint_name    ,       commit_message="Add image processor"    ,       use_temp_dir=snake_case__    ,       )

		print(F"Pushed {checkpoint_name}"      )



def  __lowerCAmelCase (  A    ,       A = None    ,       A = True      ):
	UpperCAmelCase_    			=     "imagenet-1k-id2label.json"
	UpperCAmelCase_    			=     1000

	UpperCAmelCase_    			=     "huggingface/label-files"
	UpperCAmelCase_    			=     num_labels
	UpperCAmelCase_    			=     json.load(open(hf_hub_download(snake_case__    ,       snake_case__    ,       repo_type="dataset"      )    ,       "r"      )      )
	UpperCAmelCase_    			=     {int(snake_case__      ): v for k, v in idalabel.items()}

	UpperCAmelCase_    			=     idalabel
	UpperCAmelCase_    			=     {v: k for k, v in idalabel.items()}

	UpperCAmelCase_    			=     partial(snake_case__    ,       num_labels=snake_case__    ,       idalabel=snake_case__    ,       labelaid=snake_case__      )

	UpperCAmelCase_    			=     {
	    "van-tiny": ImageNetPreTrainedConfig(
	        hidden_sizes=[32, 64, 160, 256]    ,       depths=[3, 3, 5, 2]    ,       mlp_ratios=[8, 8, 4, 4]    ,       ),
	    "van-small": ImageNetPreTrainedConfig(
	        hidden_sizes=[64, 128, 320, 512]    ,       depths=[2, 2, 4, 2]    ,       mlp_ratios=[8, 8, 4, 4]    ,       ),
	    "van-base": ImageNetPreTrainedConfig(
	        hidden_sizes=[64, 128, 320, 512]    ,       depths=[3, 3, 12, 3]    ,       mlp_ratios=[8, 8, 4, 4]    ,       ),
	    "van-large": ImageNetPreTrainedConfig(
	        hidden_sizes=[64, 128, 320, 512]    ,       depths=[3, 5, 27, 3]    ,       mlp_ratios=[8, 8, 4, 4]    ,       ),
	}

	UpperCAmelCase_    			=     {
	    "van-tiny": van_tiny,
	    "van-small": van_small,
	    "van-base": van_base,
	    "van-large": van_large,
	}

	UpperCAmelCase_    			=     {
	    "van-tiny": (
	        "https://huggingface.co/Visual-Attention-Network/VAN-Tiny-original/resolve/main/van_tiny_754.pth.tar"
	    ),
	    "van-small": (
	        "https://huggingface.co/Visual-Attention-Network/VAN-Small-original/resolve/main/van_small_811.pth.tar"
	    ),
	    "van-base": (
	        "https://huggingface.co/Visual-Attention-Network/VAN-Base-original/resolve/main/van_base_828.pth.tar"
	    ),
	    "van-large": (
	        "https://huggingface.co/Visual-Attention-Network/VAN-Large-original/resolve/main/van_large_839.pth.tar"
	    ),
	}

	if model_name:
		convert_weight_and_push(
		    snake_case__    ,       names_to_config[model_name]    ,       checkpoint=names_to_original_checkpoints[model_name]    ,       from_model=names_to_original_models[model_name]()    ,       save_directory=snake_case__    ,       push_to_hub=snake_case__    ,       )
	else:
		for model_name, config in names_to_config.items():
			convert_weight_and_push(
			    snake_case__    ,       snake_case__    ,       checkpoint=names_to_original_checkpoints[model_name]    ,       from_model=names_to_original_models[model_name]()    ,       save_directory=snake_case__    ,       push_to_hub=snake_case__    ,       )


if __name__ == "__main__":
				_a:      Any								=     argparse.ArgumentParser()
				# Required parameters
				parser.add_argument(
				    """--model-name""",
				    default=None,
				    type=str,
				    help=(
				        """The name of the model you wish to convert, it must be one of the supported resnet* architecture,"""
				        """ currently: van-tiny/small/base/large. If `None`, all of them will the converted."""
				    ),
				)
				parser.add_argument(
				    """--pytorch_dump_folder_path""",
				    default=None,
				    type=Path,
				    required=True,
				    help="""Path to the output PyTorch model directory.""",
				)
				parser.add_argument(
				    """--van_dir""",
				    required=True,
				    type=Path,
				    help=(
				        """A path to VAN\'s original implementation directory. You can download from here:"""
				        """ https://github.com/Visual-Attention-Network/VAN-Classification"""
				    ),
				)
				parser.add_argument(
				    """--push_to_hub""",
				    default=True,
				    type=bool,
				    required=False,
				    help="""If True, push model and image processor to the hub.""",
				)

				_a:      Optional[Any]								=     parser.parse_args()
				_a:      List[Any]								=     args.pytorch_dump_folder_path
				pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
				_a:      Union[str, Any]								=     args.van_dir
				# append the path to the parents to maskformer dir
				sys.path.append(str(van_dir.parent))
				from van.models.van import van_base, van_large, van_small, van_tiny

				convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)


import argparse
import logging
import os
import sys
import tempfile
from pathlib import Path

import lightning_base
import pytest
import pytorch_lightning as pl
import torch
from convert_pl_checkpoint_to_hf import convert_pl_to_hf
from distillation import distill_main
from finetune import SummarizationModule, main
from huggingface_hub import list_models
from parameterized import parameterized
from run_eval import generate_summaries_or_translations
from torch import nn

from transformers import AutoConfig, AutoModelForSeqaSeqLM
from transformers.testing_utils import CaptureStderr, CaptureStdout, TestCasePlus, require_torch_gpu, slow
from utils import label_smoothed_nll_loss, lmap, load_json


logging.basicConfig(level=logging.DEBUG)

_a:      Tuple								=     logging.getLogger()
_a:      Union[str, Any]								=     torch.cuda.is_available()
_a:      Dict								=     {
    """max_tokens_per_batch""": None,
    """supervise_forward""": True,
    """normalize_hidden""": True,
    """label_smoothing""": 0.2,
    """eval_max_gen_length""": None,
    """eval_beams""": 1,
    """val_metric""": """loss""",
    """save_top_k""": 1,
    """adafactor""": True,
    """early_stopping_patience""": 2,
    """logger_name""": """default""",
    """length_penalty""": 0.5,
    """cache_dir""": """""",
    """task""": """summarization""",
    """num_workers""": 2,
    """alpha_hid""": 0,
    """freeze_embeds""": True,
    """enc_only""": False,
    """tgt_suffix""": """""",
    """resume_from_checkpoint""": None,
    """sortish_sampler""": True,
    """student_decoder_layers""": 1,
    """val_check_interval""": 1.0,
    """output_dir""": """""",
    """fp16""": False,  # TODO(SS): set this to CUDA_AVAILABLE if ci installs apex or start using native amp
    """no_teacher""": False,
    """fp16_opt_level""": """O1""",
    """gpus""": 1 if CUDA_AVAILABLE else 0,
    """n_tpu_cores""": 0,
    """max_grad_norm""": 1.0,
    """do_train""": True,
    """do_predict""": True,
    """accumulate_grad_batches""": 1,
    """server_ip""": """""",
    """server_port""": """""",
    """seed""": 42,
    """model_name_or_path""": """sshleifer/bart-tiny-random""",
    """config_name""": """""",
    """tokenizer_name""": """facebook/bart-large""",
    """do_lower_case""": False,
    """learning_rate""": 0.3,
    """lr_scheduler""": """linear""",
    """weight_decay""": 0.0,
    """adam_epsilon""": 1e-08,
    """warmup_steps""": 0,
    """max_epochs""": 1,
    """train_batch_size""": 2,
    """eval_batch_size""": 2,
    """max_source_length""": 12,
    """max_target_length""": 12,
    """val_max_target_length""": 12,
    """test_max_target_length""": 12,
    """fast_dev_run""": False,
    """no_cache""": False,
    """n_train""": -1,
    """n_val""": -1,
    """n_test""": -1,
    """student_encoder_layers""": 1,
    """freeze_encoder""": False,
    """auto_scale_batch_size""": False,
    """overwrite_output_dir""": False,
    """student""": None,
}



def  __lowerCAmelCase (  A    ,       A      ):
	UpperCAmelCase_    			=     "\n".join(A      )
	Path(A      ).open("w"      ).writelines(A      )


_a:      Dict								=     [""" Sam ate lunch today.""", """Sams lunch ingredients."""]
_a:      Optional[Any]								=     ["""A very interesting story about what I ate for lunch.""", """Avocado, celery, turkey, coffee"""]
_a:      Any								=     """patrickvonplaten/t5-tiny-random"""
_a:      List[Any]								=     """sshleifer/t5-tinier-random"""
_a:      Optional[Any]								=     """sshleifer/bart-tiny-random"""
_a:      List[Any]								=     """sshleifer/tiny-mbart"""
_a:      Optional[int]								=     """sshleifer/tiny-marian-en-de"""
_a:      Tuple								=     """stas/tiny-wmt19-en-de"""


_a:      Optional[int]								=     logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
logging.disable(logging.CRITICAL)  # remove noisy download output from tracebacks



def  __lowerCAmelCase (  A      ):
	for split in ["train", "val", "test"]:
		_dump_articles(os.path.join(A    ,       F"{split}.source"      )    ,       A      )
		_dump_articles(os.path.join(A    ,       F"{split}.target"      )    ,       A      )
	return tmp_dir

class   __UpperCamelCase      (   lowercase  ):




	@classmethod
	def  __A   (						cls		:  Dict							):


		'''simple docstring'''







		logging.disable(logging.CRITICAL							)  # remove noisy download output from tracebacks
		return cls




	@slow
	@require_torch_gpu
	def  __A   (						self		:  List[Any]							):


		'''simple docstring'''







		UpperCAmelCase_    			=     list_models()
		UpperCAmelCase_    			=     "sshleifer"
		UpperCAmelCase_    			=     [x.modelId for x in model_list if x.modelId.startswith(lowerCAmelCase							)]
		UpperCAmelCase_    			=     ["sshleifer/blenderbot-3B", "sshleifer/blenderbot-90M"]
		UpperCAmelCase_    			=     []
		for m in model_ids:
			if m in allowed_to_be_broken:
				continue
			try:
				AutoConfig.from_pretrained(lowerCAmelCase							)
			except Exception:
				failures.append(lowerCAmelCase							)
		assert not failures, F"The following models could not be loaded through AutoConfig: {failures}"




	def  __A   (						self		:  Any							):


		'''simple docstring'''







		UpperCAmelCase_    			=     {"student_encoder_layers": 2, "student_decoder_layers": 1, "no_teacher": True}
		self._test_distiller_cli(lowerCAmelCase							)




	def  __A   (						self		:  List[Any]							):


		'''simple docstring'''







		UpperCAmelCase_    			=     {
		    "student_encoder_layers": 2,
		    "student_decoder_layers": 1,
		    "max_epochs": 4,
		    "val_check_interval": 0.25,
		    "alpha_hid": 2.0,
		    "model_name_or_path": "IGNORE_THIS_IT_DOESNT_GET_USED",
		}
		UpperCAmelCase_    			=     self._test_distiller_cli(lowerCAmelCase						,		check_contents=lowerCAmelCase							)

		UpperCAmelCase_    			=     list(Path(model.output_dir							).glob("*.ckpt"							)							)
		self.assertEqual(1						,		len(lowerCAmelCase							)							)
		UpperCAmelCase_    			=     list(Path(model.output_dir							).glob("**/*.bin"							)							)
		self.assertEqual(len(lowerCAmelCase							)						,		2							)
		UpperCAmelCase_    			=     lmap(str.strip						,		Path(model.hparams.data_dir							).joinpath("test.source"							).open().readlines()							)
		UpperCAmelCase_    			=     tempfile.mktemp()  # XXX: not being cleaned up
		generate_summaries_or_translations(lowerCAmelCase						,		lowerCAmelCase						,		str(model.output_dir / "best_tfmr"							)							)
		self.assertTrue(Path(lowerCAmelCase							).exists()							)

		UpperCAmelCase_    			=     self.get_auto_remove_tmp_dir()
		convert_pl_to_hf(ckpts[0]						,		transformer_ckpts[0].parent						,		lowerCAmelCase							)
		assert os.path.exists(os.path.join(lowerCAmelCase						,		"pytorch_model.bin"							)							)




	def  __A   (						self		:  List[Any]							):


		'''simple docstring'''







		UpperCAmelCase_    			=     AutoModelForSeqaSeqLM.from_pretrained(lowerCAmelCase							)
		UpperCAmelCase_			,				UpperCAmelCase_    			=     model.dummy_inputs["input_ids"], model.dummy_inputs["attention_mask"]
		UpperCAmelCase_    			=     torch.tensor([[0, 4, 8, 2], [0, 8, 2, 1]]						,		dtype=torch.long						,		device=model.device							)
		UpperCAmelCase_    			=     target_ids[:, :-1].contiguous()  # Why this line?
		UpperCAmelCase_    			=     target_ids[:, 1:].clone()  # why clone?
		UpperCAmelCase_    			=     model(
		    lowerCAmelCase						,		attention_mask=lowerCAmelCase						,		decoder_input_ids=lowerCAmelCase						,		labels=lowerCAmelCase						,		use_cache=lowerCAmelCase							).loss

		UpperCAmelCase_    			=     model(lowerCAmelCase						,		attention_mask=lowerCAmelCase						,		decoder_input_ids=lowerCAmelCase						,		use_cache=lowerCAmelCase							).logits

		UpperCAmelCase_    			=     nn.functional.log_softmax(lowerCAmelCase						,		dim=-1							)
		UpperCAmelCase_			,				UpperCAmelCase_    			=     label_smoothed_nll_loss(
		    lowerCAmelCase						,		lowerCAmelCase						,		0.1						,		ignore_index=model.config.pad_token_id							)
		with self.assertRaises(lowerCAmelCase							):
			# TODO: understand why this breaks
			self.assertEqual(lowerCAmelCase						,		lowerCAmelCase							)




	def  __A   (						self		:  Optional[int]							):


		'''simple docstring'''







		UpperCAmelCase_    			=     {
		    "student_encoder_layers": 2,
		    "student_decoder_layers": 1,
		    "num_train_epochs": 4,
		    "val_check_interval": 0.25,
		    "alpha_hid": 2.0,
		    "task": "translation",
		    "model_name_or_path": "IGNORE_THIS_IT_DOESNT_GET_USED",
		    "tokenizer_name": MBART_TINY,
		    "teacher": MBART_TINY,
		    "src_lang": "en_XX",
		    "tgt_lang": "ro_RO",
		}
		UpperCAmelCase_    			=     self._test_distiller_cli(lowerCAmelCase						,		check_contents=lowerCAmelCase							)
		assert model.model.config.model_type == "mbart"

		UpperCAmelCase_    			=     list(Path(model.output_dir							).glob("*.ckpt"							)							)
		self.assertEqual(1						,		len(lowerCAmelCase							)							)
		UpperCAmelCase_    			=     list(Path(model.output_dir							).glob("**/*.bin"							)							)
		UpperCAmelCase_    			=     list(Path(model.output_dir							).glob("best_tfmr/*"							)							)
		assert len(lowerCAmelCase							) > 2
		self.assertEqual(len(lowerCAmelCase							)						,		2							)




	def  __A   (						self		:  int							):


		'''simple docstring'''







		UpperCAmelCase_    			=     {
		    "student_encoder_layers": 1,
		    "student_decoder_layers": 1,
		    "alpha_hid": 2.0,
		    "teacher": T5_TINY,
		    "model_name_or_path": T5_TINY,
		    "tokenizer_name": T5_TINY,
		}
		self._test_distiller_cli(lowerCAmelCase							)




	def  __A   (						self		:  int							):


		'''simple docstring'''







		UpperCAmelCase_    			=     {
		    "teacher": T5_TINY,
		    "student": T5_TINIER,
		    "model_name_or_path": T5_TINIER,
		    "tokenizer_name": T5_TINIER,
		}
		self._test_distiller_cli(lowerCAmelCase							)




	def  __A   (						self		:  Optional[int]						,		lowerCAmelCase		:  Tuple						,		lowerCAmelCase		:  Any=True							):


		'''simple docstring'''







		UpperCAmelCase_    			=     {
		    "label_smoothing": 0.0,
		    "early_stopping_patience": -1,
		    "train_batch_size": 1,
		    "eval_batch_size": 2,
		    "max_epochs": 2,
		    "alpha_mlm": 0.2,
		    "alpha_ce": 0.8,
		    "do_predict": True,
		    "model_name_or_path": "sshleifer/tinier_bart",
		    "teacher": CHEAP_ARGS["model_name_or_path"],
		    "val_check_interval": 0.5,
		}
		default_updates.update(lowerCAmelCase							)
		UpperCAmelCase_    			=     CHEAP_ARGS.copy()
		UpperCAmelCase_    			=     make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()							)
		UpperCAmelCase_    			=     self.get_auto_remove_tmp_dir()

		args_d.update(data_dir=lowerCAmelCase						,		output_dir=lowerCAmelCase						,		**lowerCAmelCase							)
		UpperCAmelCase_    			=     distill_main(argparse.Namespace(**lowerCAmelCase							)							)
		if not check_contents:
			return model
		UpperCAmelCase_    			=     os.listdir(lowerCAmelCase							)
		UpperCAmelCase_    			=     {os.path.basename(lowerCAmelCase							) for p in contents}
		UpperCAmelCase_    			=     [p for p in contents if p.endswith("ckpt"							)]
		assert len(lowerCAmelCase							) > 0

		self.assertIn("test_generations.txt"						,		lowerCAmelCase							)
		self.assertIn("test_results.txt"						,		lowerCAmelCase							)

		UpperCAmelCase_    			=     load_json(model.metrics_save_path							)
		UpperCAmelCase_    			=     metrics["val"][-1]
		self.assertGreaterEqual(last_step_stats["val_avg_gen_time"]						,		0.01							)
		self.assertGreaterEqual(1.0						,		last_step_stats["val_avg_gen_time"]							)
		self.assertIsInstance(last_step_stats[F"val_avg_{model.val_metric}"]						,		lowerCAmelCase							)
		UpperCAmelCase_    			=     int(args_d["max_epochs"] * (1 / args_d["val_check_interval"]) + 1							)
		self.assertEqual(len(metrics["val"]							)						,		lowerCAmelCase							)
		self.assertEqual(len(metrics["test"]							)						,		1							)
		return model


class   __UpperCamelCase      (   lowercase  ):




	@parameterized.expand(
	    [T5_TINY, BART_TINY, MBART_TINY, MARIAN_TINY, FSMT_TINY]						,		)
	def  __A   (						self		:  Any						,		lowerCAmelCase		:  Tuple							):


		'''simple docstring'''







		UpperCAmelCase_    			=     CHEAP_ARGS.copy()
		UpperCAmelCase_    			=     "translation" if model in [MBART_TINY, MARIAN_TINY, FSMT_TINY] else "summarization"
		UpperCAmelCase_    			=     0.1 if task == "translation" else 0

		UpperCAmelCase_    			=     make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()							)
		UpperCAmelCase_    			=     self.get_auto_remove_tmp_dir()
		args_d.update(
		    data_dir=lowerCAmelCase						,		model_name_or_path=lowerCAmelCase						,		tokenizer_name=lowerCAmelCase						,		train_batch_size=2						,		eval_batch_size=2						,		output_dir=lowerCAmelCase						,		do_predict=lowerCAmelCase						,		task=lowerCAmelCase						,		src_lang="en_XX"						,		tgt_lang="ro_RO"						,		freeze_encoder=lowerCAmelCase						,		freeze_embeds=lowerCAmelCase						,		)
		assert "n_train" in args_d
		UpperCAmelCase_    			=     argparse.Namespace(**lowerCAmelCase							)
		UpperCAmelCase_    			=     main(lowerCAmelCase							)

		UpperCAmelCase_    			=     module.model.get_input_embeddings()
		assert not input_embeds.weight.requires_grad
		if model == T5_TINY:
			UpperCAmelCase_    			=     module.model.lm_head
			assert not lm_head.weight.requires_grad
			assert (lm_head.weight == input_embeds.weight).all().item()
		elif model == FSMT_TINY:
			UpperCAmelCase_    			=     module.model.model
			UpperCAmelCase_    			=     fsmt.decoder.embed_positions
			assert not embed_pos.weight.requires_grad
			assert not fsmt.decoder.embed_tokens.weight.requires_grad
			# check that embeds are not the same
			assert fsmt.decoder.embed_tokens != fsmt.encoder.embed_tokens
		else:
			UpperCAmelCase_    			=     module.model.model
			UpperCAmelCase_    			=     bart.decoder.embed_positions
			assert not embed_pos.weight.requires_grad
			assert not bart.shared.weight.requires_grad
			# check that embeds are the same
			assert bart.decoder.embed_tokens == bart.encoder.embed_tokens
			assert bart.decoder.embed_tokens == bart.shared

		UpperCAmelCase_    			=     load_json(module.output_dir / "text_batch.json"							)
		assert isinstance(lowerCAmelCase						,		lowerCAmelCase							)
		assert len(lowerCAmelCase							) >= 4




	def  __A   (						self		:  Tuple							):


		'''simple docstring'''







		UpperCAmelCase_    			=     CHEAP_ARGS.copy()

		UpperCAmelCase_    			=     "summarization"
		UpperCAmelCase_    			=     make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()							)

		args_d.update(
		    data_dir=lowerCAmelCase						,		tokenizer_name=lowerCAmelCase						,		train_batch_size=2						,		eval_batch_size=2						,		do_predict=lowerCAmelCase						,		task=lowerCAmelCase						,		src_lang="en_XX"						,		tgt_lang="ro_RO"						,		freeze_encoder=lowerCAmelCase						,		freeze_embeds=lowerCAmelCase						,		)

		# test models whose config includes the extra_model_args
		UpperCAmelCase_    			=     BART_TINY
		UpperCAmelCase_    			=     self.get_auto_remove_tmp_dir()
		UpperCAmelCase_    			=     args_d.copy()
		args_da.update(
		    model_name_or_path=lowerCAmelCase						,		output_dir=lowerCAmelCase						,		)
		UpperCAmelCase_    			=     ("encoder_layerdrop", "decoder_layerdrop", "dropout", "attention_dropout")
		for p in extra_model_params:
			UpperCAmelCase_    			=     0.5
		UpperCAmelCase_    			=     argparse.Namespace(**lowerCAmelCase							)
		UpperCAmelCase_    			=     main(lowerCAmelCase							)
		for p in extra_model_params:
			assert getattr(model.config						,		lowerCAmelCase							) == 0.5, F"failed to override the model config for param {p}"

		# test models whose config doesn't include the extra_model_args
		UpperCAmelCase_    			=     T5_TINY
		UpperCAmelCase_    			=     self.get_auto_remove_tmp_dir()
		UpperCAmelCase_    			=     args_d.copy()
		args_da.update(
		    model_name_or_path=lowerCAmelCase						,		output_dir=lowerCAmelCase						,		)
		UpperCAmelCase_    			=     "encoder_layerdrop"
		UpperCAmelCase_    			=     0.5
		UpperCAmelCase_    			=     argparse.Namespace(**lowerCAmelCase							)
		with pytest.raises(lowerCAmelCase							) as excinfo:
			UpperCAmelCase_    			=     main(lowerCAmelCase							)
		assert str(excinfo.value							) == F"model config doesn't have a `{unsupported_param}` attribute"




	def  __A   (						self		:  Dict							):


		'''simple docstring'''







		UpperCAmelCase_    			=     CHEAP_ARGS.copy()

		UpperCAmelCase_    			=     "summarization"
		UpperCAmelCase_    			=     make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()							)

		UpperCAmelCase_    			=     BART_TINY
		UpperCAmelCase_    			=     self.get_auto_remove_tmp_dir()

		args_d.update(
		    data_dir=lowerCAmelCase						,		model_name_or_path=lowerCAmelCase						,		output_dir=lowerCAmelCase						,		tokenizer_name=lowerCAmelCase						,		train_batch_size=2						,		eval_batch_size=2						,		do_predict=lowerCAmelCase						,		task=lowerCAmelCase						,		src_lang="en_XX"						,		tgt_lang="ro_RO"						,		freeze_encoder=lowerCAmelCase						,		freeze_embeds=lowerCAmelCase						,		)

		# emulate finetune.py
		UpperCAmelCase_    			=     argparse.ArgumentParser()
		UpperCAmelCase_    			=     pl.Trainer.add_argparse_args(lowerCAmelCase							)
		UpperCAmelCase_    			=     SummarizationModule.add_model_specific_args(lowerCAmelCase						,		os.getcwd()							)
		UpperCAmelCase_    			=     {"--help": True}

		# --help test
		with pytest.raises(lowerCAmelCase							) as excinfo:
			with CaptureStdout() as cs:
				UpperCAmelCase_    			=     parser.parse_args(lowerCAmelCase							)
			assert False, "--help is expected to sys.exit"
		assert excinfo.type == SystemExit
		UpperCAmelCase_    			=     lightning_base.arg_to_scheduler_metavar
		assert expected in cs.out, "--help is expected to list the supported schedulers"

		# --lr_scheduler=non_existing_scheduler test
		UpperCAmelCase_    			=     "non_existing_scheduler"
		UpperCAmelCase_    			=     {F"--lr_scheduler={unsupported_param}"}
		with pytest.raises(lowerCAmelCase							) as excinfo:
			with CaptureStderr() as cs:
				UpperCAmelCase_    			=     parser.parse_args(lowerCAmelCase							)
			assert False, "invalid argument is expected to sys.exit"
		assert excinfo.type == SystemExit
		UpperCAmelCase_    			=     F"invalid choice: '{unsupported_param}'"
		assert expected in cs.err, F"should have bailed on invalid choice of scheduler {unsupported_param}"

		# --lr_scheduler=existing_scheduler test
		UpperCAmelCase_    			=     "cosine"
		UpperCAmelCase_    			=     args_d.copy()
		UpperCAmelCase_    			=     supported_param
		UpperCAmelCase_    			=     argparse.Namespace(**lowerCAmelCase							)
		UpperCAmelCase_    			=     main(lowerCAmelCase							)
		assert (
		    getattr(model.hparams						,		"lr_scheduler"							) == supported_param
		), F"lr_scheduler={supported_param} shouldn't fail"