

import argparse
import os
import sys
from unittest.mock import patch

import pytorch_lightning as pl
import timeout_decorator
import torch
from distillation import SummarizationDistiller, distill_main
from finetune import SummarizationModule, main

from transformers import MarianMTModel
from transformers.file_utils import cached_path
from transformers.testing_utils import TestCasePlus, require_torch_gpu, slow
from utils import load_json


a_     =			'sshleifer/mar_enro_6_3_student'






class _UpperCamelCase							(							__A      ):
			'''simple docstring'''







			def 							__UpperCamelCase       (	self							:       int		)							->				Optional[Any]:

				"""simple docstring"""

				super().setUp()

				SCREAMING_SNAKE_CASE      :					Optional[int]    =				cached_path(
				    "https://cdn-datasets.huggingface.co/translation/wmt_en_ro-tr40k-va0.5k-te0.5k.tar.gz"					,					extract_compressed_file=__UpperCAmelCase					,					)
				SCREAMING_SNAKE_CASE      :					Optional[Any]    =				F"{data_cached}/wmt_en_ro-tr40k-va0.5k-te0.5k"
			@slow
			@require_torch_gpu
			def 							__UpperCamelCase       (	self							:       Optional[int]		)							->				Optional[Any]:

				"""simple docstring"""

				MarianMTModel.from_pretrained(__UpperCAmelCase		)






			@slow
			@require_torch_gpu
			def 							__UpperCamelCase       (	self							:       int		)							->				Dict:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE      :					List[Any]    =				{
				    "$MAX_LEN": 64,
				    "$BS": 64,
				    "$GAS": 1,
				    "$ENRO_DIR": self.data_dir,
				    "facebook/mbart-large-cc25": MARIAN_MODEL,
				    # "val_check_interval=0.25": "val_check_interval=1.0",
				    "--learning_rate=3e-5": "--learning_rate 3e-4",
				    "--num_train_epochs 6": "--num_train_epochs 1",
				}

				# Clean up bash script
				SCREAMING_SNAKE_CASE      :					int    =				(self.test_file_dir / "train_mbart_cc25_enro.sh").open().read().split("finetune.py"		)[1].strip()
				SCREAMING_SNAKE_CASE      :					List[str]    =				bash_script.replace("\\\n"					,					""		).strip().replace("\"$@\""					,					""		)
				for k, v in env_vars_to_replace.items():
					SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				bash_script.replace(__UpperCAmelCase					,					str(__UpperCAmelCase		)		)
				SCREAMING_SNAKE_CASE      :					List[Any]    =				self.get_auto_remove_tmp_dir()

				# bash_script = bash_script.replace("--fp16 ", "")
				SCREAMING_SNAKE_CASE      :					Optional[Any]    =				F"\n            --output_dir {output_dir}\n            --tokenizer_name Helsinki-NLP/opus-mt-en-ro\n            --sortish_sampler\n            --do_predict\n            --gpus 1\n            --freeze_encoder\n            --n_train 40000\n            --n_val 500\n            --n_test 500\n            --fp16_opt_level O1\n            --num_sanity_val_steps 0\n            --eval_beams 2\n        ".split()
				# XXX: args.gpus > 1 : handle multi_gpu in the future

				SCREAMING_SNAKE_CASE      :					int    =				["finetune.py"] + bash_script.split() + args
				with patch.object(__UpperCAmelCase					,					"argv"					,					__UpperCAmelCase		):
					SCREAMING_SNAKE_CASE      :					int    =				argparse.ArgumentParser()
					SCREAMING_SNAKE_CASE      :					List[str]    =				pl.Trainer.add_argparse_args(__UpperCAmelCase		)
					SCREAMING_SNAKE_CASE      :					Optional[int]    =				SummarizationModule.add_model_specific_args(__UpperCAmelCase					,					os.getcwd()		)
					SCREAMING_SNAKE_CASE      :					Optional[Any]    =				parser.parse_args()
					SCREAMING_SNAKE_CASE      :					str    =				main(__UpperCAmelCase		)

				# Check metrics
				SCREAMING_SNAKE_CASE      :					List[Any]    =				load_json(model.metrics_save_path		)
				SCREAMING_SNAKE_CASE      :					List[Any]    =				metrics["val"][0]
				SCREAMING_SNAKE_CASE      :					Tuple    =				metrics["val"][-1]
				self.assertEqual(len(metrics["val"]		)					,					(args.max_epochs / args.val_check_interval)		)
				assert isinstance(last_step_stats[F"val_avg_{model.val_metric}"]					,					__UpperCAmelCase		)

				self.assertGreater(last_step_stats["val_avg_gen_time"]					,					0.01		)
				# model hanging on generate. Maybe bad config was saved. (XXX: old comment/assert?)
				self.assertLessEqual(last_step_stats["val_avg_gen_time"]					,					1.0		)

				# test learning requirements:

				# 1. BLEU improves over the course of training by more than 2 pts
				self.assertGreater(last_step_stats["val_avg_bleu"] - first_step_stats["val_avg_bleu"]					,					2		)

				# 2. BLEU finishes above 17
				self.assertGreater(last_step_stats["val_avg_bleu"]					,					17		)

				# 3. test BLEU and val BLEU within ~1.1 pt.
				self.assertLess(abs(metrics["val"][-1]["val_avg_bleu"] - metrics["test"][-1]["test_avg_bleu"]		)					,					1.1		)

				# check lightning ckpt can be loaded and has a reasonable statedict
				SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				os.listdir(__UpperCAmelCase		)
				SCREAMING_SNAKE_CASE      :					Dict    =				[x for x in contents if x.endswith(".ckpt"		)][0]
				SCREAMING_SNAKE_CASE      :					List[str]    =				os.path.join(args.output_dir					,					__UpperCAmelCase		)
				SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				torch.load(__UpperCAmelCase					,					map_location="cpu"		)
				SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				"model.model.decoder.layers.0.encoder_attn_layer_norm.weight"
				assert expected_key in ckpt["state_dict"]
				assert ckpt["state_dict"]["model.model.decoder.layers.0.encoder_attn_layer_norm.weight"].dtype == torch.floataa

				# TODO: turn on args.do_predict when PL bug fixed.
				if args.do_predict:
					SCREAMING_SNAKE_CASE      :					List[Any]    =				{os.path.basename(__UpperCAmelCase		) for p in contents}
					assert "test_generations.txt" in contents
					assert "test_results.txt" in contents
					# assert len(metrics["val"]) ==  desired_n_evals
					assert len(metrics["test"]		) == 1






class _UpperCamelCase							(							__A      ):
			'''simple docstring'''







			@timeout_decorator.timeout(600		)
			@slow
			@require_torch_gpu
			def 							__UpperCamelCase       (	self							:       Dict		)							->				str:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE      :					List[Any]    =				F"{self.test_file_dir_str}/test_data/wmt_en_ro"
				SCREAMING_SNAKE_CASE      :					str    =				{
				    "--fp16_opt_level=O1": "",
				    "$MAX_LEN": 128,
				    "$BS": 16,
				    "$GAS": 1,
				    "$ENRO_DIR": data_dir,
				    "$m": "sshleifer/student_marian_en_ro_6_1",
				    "val_check_interval=0.25": "val_check_interval=1.0",
				}

				# Clean up bash script
				SCREAMING_SNAKE_CASE      :					Any    =				(
				    (self.test_file_dir / "distil_marian_no_teacher.sh").open().read().split("distillation.py"		)[1].strip()
				)
				SCREAMING_SNAKE_CASE      :					Tuple    =				bash_script.replace("\\\n"					,					""		).strip().replace("\"$@\""					,					""		)
				SCREAMING_SNAKE_CASE      :					Optional[int]    =				bash_script.replace("--fp16 "					,					" "		)

				for k, v in env_vars_to_replace.items():
					SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				bash_script.replace(__UpperCAmelCase					,					str(__UpperCAmelCase		)		)
				SCREAMING_SNAKE_CASE      :					Any    =				self.get_auto_remove_tmp_dir()
				SCREAMING_SNAKE_CASE      :					Any    =				bash_script.replace("--fp16"					,					""		)
				SCREAMING_SNAKE_CASE      :					Optional[int]    =				6
				SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				(
				    ["distillation.py"]
				    + bash_script.split()
				    + [
				        F"--output_dir={output_dir}",
				        "--gpus=1",
				        "--learning_rate=1e-3",
				        F"--num_train_epochs={epochs}",
				        "--warmup_steps=10",
				        "--val_check_interval=1.0",
				        "--do_predict",
				    ]
				)
				with patch.object(__UpperCAmelCase					,					"argv"					,					__UpperCAmelCase		):
					SCREAMING_SNAKE_CASE      :					Tuple    =				argparse.ArgumentParser()
					SCREAMING_SNAKE_CASE      :					Optional[int]    =				pl.Trainer.add_argparse_args(__UpperCAmelCase		)
					SCREAMING_SNAKE_CASE      :					List[str]    =				SummarizationDistiller.add_model_specific_args(__UpperCAmelCase					,					os.getcwd()		)
					SCREAMING_SNAKE_CASE      :					Optional[Any]    =				parser.parse_args()
					# assert args.gpus == gpus THIS BREAKS for multi_gpu

					SCREAMING_SNAKE_CASE      :					Optional[Any]    =				distill_main(__UpperCAmelCase		)

				# Check metrics
				SCREAMING_SNAKE_CASE      :					Tuple    =				load_json(model.metrics_save_path		)
				SCREAMING_SNAKE_CASE      :					int    =				metrics["val"][0]
				SCREAMING_SNAKE_CASE      :					str    =				metrics["val"][-1]
				assert len(metrics["val"]		) >= (args.max_epochs / args.val_check_interval)  # +1 accounts for val_sanity_check

				assert last_step_stats["val_avg_gen_time"] >= 0.01

				assert first_step_stats["val_avg_bleu"] < last_step_stats["val_avg_bleu"]  # model learned nothing
				assert 1.0 >= last_step_stats["val_avg_gen_time"]  # model hanging on generate. Maybe bad config was saved.
				assert isinstance(last_step_stats[F"val_avg_{model.val_metric}"]					,					__UpperCAmelCase		)

				# check lightning ckpt can be loaded and has a reasonable statedict
				SCREAMING_SNAKE_CASE      :					str    =				os.listdir(__UpperCAmelCase		)
				SCREAMING_SNAKE_CASE      :					Dict    =				[x for x in contents if x.endswith(".ckpt"		)][0]
				SCREAMING_SNAKE_CASE      :					int    =				os.path.join(args.output_dir					,					__UpperCAmelCase		)
				SCREAMING_SNAKE_CASE      :					Tuple    =				torch.load(__UpperCAmelCase					,					map_location="cpu"		)
				SCREAMING_SNAKE_CASE      :					Dict    =				"model.model.decoder.layers.0.encoder_attn_layer_norm.weight"
				assert expected_key in ckpt["state_dict"]
				assert ckpt["state_dict"]["model.model.decoder.layers.0.encoder_attn_layer_norm.weight"].dtype == torch.floataa

				# TODO: turn on args.do_predict when PL bug fixed.
				if args.do_predict:
					SCREAMING_SNAKE_CASE      :					Optional[int]    =				{os.path.basename(__UpperCAmelCase		) for p in contents}
					assert "test_generations.txt" in contents
					assert "test_results.txt" in contents
					# assert len(metrics["val"]) ==  desired_n_evals
					assert len(metrics["test"]		) == 1
"""simple docstring"""




# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


__lowerCamelCase							 =     {
    "configuration_vivit": ["VIVIT_PRETRAINED_CONFIG_ARCHIVE_MAP", "VivitConfig"],
}
try:
				if not is_vision_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__lowerCamelCase							 =     ["VivitImageProcessor"]


try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__lowerCamelCase							 =     [
				    "VIVIT_PRETRAINED_MODEL_ARCHIVE_LIST",
				    "VivitModel",
				    "VivitPreTrainedModel",
				    "VivitForVideoClassification",
				]


if TYPE_CHECKING:
				from .configuration_vivit import VIVIT_PRETRAINED_CONFIG_ARCHIVE_MAP, VivitConfig

				try:
								if not is_vision_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .image_processing_vivit import VivitImageProcessor

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_vivit import (
								    VIVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
								    VivitForVideoClassification,
								    VivitModel,
								    VivitPreTrainedModel,
								)


else:
				import sys

				__lowerCamelCase							 =     _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





