




"""simple docstring"""

# Copyright (c) 2019 Uber Technologies, Inc.
#
# 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.

import argparse
import csv
import json
import math
import time

import numpy as np
import torch
import torch.optim as optim
import torch.utils.data as data
from nltk.tokenize.treebank import TreebankWordDetokenizer
from pplm_classification_head import ClassificationHead
from torch import nn
from torchtext import data as torchtext_data
from torchtext import datasets
from tqdm import tqdm, trange

from transformers import GPTaLMHeadModel, GPTaTokenizer


torch.manual_seed(0)
np.random.seed(0)
__A							=     1e-1_0
__A							=     "This is incredible! I love it, this is the best chicken I have ever had."
__A							=     100
class      lowerCamelCase__      (					nn.Module     ):


			def __init__(       self			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE="gpt2-medium"			,	SCREAMING_SNAKE_CASE=False			,	SCREAMING_SNAKE_CASE="cpu"							):



					"""simple docstring"""







					super().__init__()
					snake_case						:  Optional[Any]        =			GPTaTokenizer.from_pretrained(SCREAMING_SNAKE_CASE							)
					snake_case						:  Tuple        =			GPTaLMHeadModel.from_pretrained(SCREAMING_SNAKE_CASE							)
					snake_case						:  Optional[Any]        =			self.encoder.transformer.config.hidden_size
					snake_case						:  Optional[int]        =			ClassificationHead(class_size=SCREAMING_SNAKE_CASE			,	embed_size=self.embed_size							)
					snake_case						:  Optional[Any]        =			cached_mode
					snake_case						:  str        =			device


			def 							lowerCamelCase_			(       self							):



					"""simple docstring"""







					return self.classifier_head


			def 							lowerCamelCase_			(       self							):



					"""simple docstring"""







					for param in self.encoder.parameters():
							snake_case						:  Tuple        =			False
					self.classifier_head.train()


			def 							lowerCamelCase_			(       self			,	SCREAMING_SNAKE_CASE							):



					"""simple docstring"""







					snake_case						:  Any        =			x.ne(0							).unsqueeze(2							).repeat(1			,	1			,	self.embed_size							).float().to(self.device							).detach()
					snake_case						:  Optional[Any]        =			self.encoder.transformer(SCREAMING_SNAKE_CASE							)["last_hidden_state"]
					snake_case						:  Dict        =			hidden * mask
					snake_case						:  Optional[int]        =			torch.sum(SCREAMING_SNAKE_CASE			,	dim=1							) / (torch.sum(SCREAMING_SNAKE_CASE			,	dim=1							).detach() + EPSILON)
					return avg_hidden



			def 							lowerCamelCase_			(       self			,	SCREAMING_SNAKE_CASE							):



					"""simple docstring"""







					if self.cached_mode:
							snake_case						:  Any        =			x.to(self.device							)
					else:
							snake_case						:  str        =			self.avg_representation(x.to(self.device							)							)

					snake_case						:  Union[str, Any]        =			self.classifier_head(SCREAMING_SNAKE_CASE							)
					snake_case						:  Optional[Any]        =			nn.functional.log_softmax(SCREAMING_SNAKE_CASE			,	dim=-1							)

					return probs







class      lowerCamelCase__      (					data.Dataset     ):


			def __init__(       self			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE							):



					"""simple docstring"""







					snake_case						:  List[str]        =			X
					snake_case						:  Dict        =			y


			def __len__(       self							):



					"""simple docstring"""







					return len(self.X							)



			def __getitem__(       self			,	SCREAMING_SNAKE_CASE							):



					"""simple docstring"""







					snake_case						:  Optional[Any]        =			{}
					snake_case						:  Optional[Any]        =			self.X[index]
					snake_case						:  Optional[int]        =			self.y[index]
					return data







def 					UpperCamelCase__       (						lowercase__					:       Optional[int]				):
		def pad_sequences(lowercase__					:       List[Any]				):
				snake_case						:  Union[str, Any]        =			[len(lowercase__				) for seq in sequences]

				snake_case						:  List[str]        =			torch.zeros(len(lowercase__				)    ,       max(lowercase__				)				).long()  # padding value = 0

				for i, seq in enumerate(lowercase__				):
						snake_case						:  Dict        =			lengths[i]
						snake_case						:  str        =			seq[:end]

				return padded_sequences, lengths

		snake_case						:  Any        =			{}
		for key in data[0].keys():
				snake_case						:  Union[str, Any]        =			[d[key] for d in data]

		snake_case     ,			snake_case						:  Tuple        =			pad_sequences(item_info["X"]				)
		snake_case						:  Any        =			torch.tensor(item_info["y"]    ,       dtype=torch.long				)

		return x_batch, y_batch



def 					UpperCamelCase__       (						lowercase__					:       Optional[Any]				):
		snake_case						:  Optional[Any]        =			{}
		for key in data[0].keys():
				snake_case						:  List[str]        =			[d[key] for d in data]

		snake_case						:  str        =			torch.cat(item_info["X"]    ,       0				)
		snake_case						:  Union[str, Any]        =			torch.tensor(item_info["y"]    ,       dtype=torch.long				)

		return x_batch, y_batch



def 					UpperCamelCase__       (						lowercase__					:       List[Any]    ,       lowercase__					:       Optional[int]    ,       lowercase__					:       int    ,       lowercase__					:       List[str]=0    ,       lowercase__					:       List[str]=10    ,       lowercase__					:       Optional[Any]="cpu"				):
		snake_case						:  int        =			0
		discriminator.train_custom()
		for batch_idx, (input_t, target_t) in enumerate(lowercase__				):
				snake_case     ,			snake_case						:  Union[str, Any]        =			input_t.to(lowercase__				), target_t.to(lowercase__				)

				optimizer.zero_grad()

				snake_case						:  List[Any]        =			discriminator(lowercase__				)
				snake_case						:  List[Any]        =			nn.functional.nll_loss(lowercase__    ,       lowercase__				)
				loss.backward(retain_graph=lowercase__				)
				optimizer.step()

				samples_so_far += len(lowercase__				)

				if batch_idx % log_interval == 0:
						print(
						    "Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}".format(
						        epoch + 1    ,       lowercase__    ,       len(data_loader.dataset				)    ,       100 * samples_so_far / len(data_loader.dataset				)    ,       loss.item()    ,       )				)



def 					UpperCamelCase__       (						lowercase__					:       Union[str, Any]    ,       lowercase__					:       List[str]    ,       lowercase__					:       List[str]="cpu"				):
		discriminator.eval()
		snake_case						:  List[str]        =			0
		snake_case						:  Tuple        =			0
		with torch.no_grad():
				for input_t, target_t in data_loader:
						snake_case     ,			snake_case						:  Optional[Any]        =			input_t.to(lowercase__				), target_t.to(lowercase__				)
						snake_case						:  List[Any]        =			discriminator(lowercase__				)
						# sum up batch loss
						test_loss += nn.functional.nll_loss(lowercase__    ,       lowercase__    ,       reduction="sum"				).item()
						# get the index of the max log-probability
						snake_case						:  Tuple        =			output_t.argmax(dim=1    ,       keepdim=lowercase__				)
						correct += pred_t.eq(target_t.view_as(lowercase__				)				).sum().item()

		test_loss /= len(data_loader.dataset				)

		print(
		    "Performance on test set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)".format(
		        lowercase__    ,       lowercase__    ,       len(data_loader.dataset				)    ,       1_00.0 * correct / len(data_loader.dataset				)				)				)



def 					UpperCamelCase__       (						lowercase__					:       Tuple    ,       lowercase__					:       str    ,       lowercase__					:       Any    ,       lowercase__					:       Tuple=False    ,       lowercase__					:       List[Any]="cpu"				):
		snake_case						:  int        =			model.tokenizer.encode(lowercase__				)
		snake_case						:  Optional[int]        =			torch.tensor([input_t]    ,       dtype=torch.long    ,       device=lowercase__				)
		if cached:
				snake_case						:  Tuple        =			model.avg_representation(lowercase__				)

		snake_case						:  Any        =			model(lowercase__				).data.cpu().numpy().flatten().tolist()
		print("Input sentence:"    ,       lowercase__				)
		print(
		    "Predictions:"    ,       ", ".join("{}: {:.4f}".format(lowercase__    ,       math.exp(lowercase__				)				) for c, log_prob in zip(lowercase__    ,       lowercase__				)				)    ,       )



def 					UpperCamelCase__       (						lowercase__					:       str    ,       lowercase__					:       List[Any]    ,       lowercase__					:       Any    ,       lowercase__					:       List[str]=False    ,       lowercase__					:       List[str]="cpu"				):
		snake_case						:  str        =			torch.utils.data.DataLoader(dataset=lowercase__    ,       batch_size=lowercase__    ,       collate_fn=lowercase__				)

		snake_case						:  Union[str, Any]        =			[]
		snake_case						:  Dict        =			[]
		for batch_idx, (x, y) in enumerate(tqdm(lowercase__    ,       ascii=lowercase__				)				):
				with torch.no_grad():
						snake_case						:  Tuple        =			x.to(lowercase__				)
						snake_case						:  Any        =			discriminator.avg_representation(lowercase__				).cpu().detach()
						snake_case						:  List[str]        =			torch.unbind(avg_rep.unsqueeze(1				)				)
						xs += avg_rep_list
						ys += y.cpu().numpy().tolist()

		snake_case						:  Tuple        =			torch.utils.data.DataLoader(
		    dataset=Dataset(lowercase__    ,       lowercase__				)    ,       batch_size=lowercase__    ,       shuffle=lowercase__    ,       collate_fn=lowercase__				)

		return data_loader



def 					UpperCamelCase__       (						lowercase__					:       Optional[Any]    ,       lowercase__					:       Any=None    ,       lowercase__					:       Dict="gpt2-medium"    ,       lowercase__					:       str=10    ,       lowercase__					:       Tuple=64    ,       lowercase__					:       List[Any]=10    ,       lowercase__					:       str=False    ,       lowercase__					:       int=False    ,       lowercase__					:       Optional[int]=False    ,       ):
		snake_case						:  List[str]        =			"cuda" if torch.cuda.is_available() and not no_cuda else "cpu"

		print("Preprocessing {} dataset...".format(lowercase__				)				)
		snake_case						:  str        =			time.time()

		if dataset == "SST":
				snake_case						:  Optional[int]        =			["positive", "negative", "very positive", "very negative", "neutral"]
				snake_case						:  str        =			{c: i for i, c in enumerate(lowercase__				)}

				snake_case						:  str        =			Discriminator(
				    class_size=len(lowercase__				)    ,       pretrained_model=lowercase__    ,       cached_mode=lowercase__    ,       device=lowercase__				).to(lowercase__				)

				snake_case						:  Dict        =			torchtext_data.Field()
				snake_case						:  int        =			torchtext_data.Field(sequential=lowercase__				)
				snake_case     ,			snake_case     ,			snake_case						:  Dict        =			datasets.SST.splits(
				    lowercase__    ,       lowercase__    ,       fine_grained=lowercase__    ,       train_subtrees=lowercase__    ,       )

				snake_case						:  Tuple        =			[]
				snake_case						:  Dict        =			[]
				for i in trange(len(lowercase__				)    ,       ascii=lowercase__				):
						snake_case						:  int        =			TreebankWordDetokenizer().detokenize(vars(train_data[i]				)["text"]				)
						snake_case						:  Dict        =			discriminator.tokenizer.encode(lowercase__				)
						snake_case						:  Optional[Any]        =			torch.tensor([5_0256] + seq    ,       device=lowercase__    ,       dtype=torch.long				)
						x.append(lowercase__				)
						y.append(classaidx[vars(train_data[i]				)["label"]]				)
				snake_case						:  int        =			Dataset(lowercase__    ,       lowercase__				)

				snake_case						:  int        =			[]
				snake_case						:  Dict        =			[]
				for i in trange(len(lowercase__				)    ,       ascii=lowercase__				):
						snake_case						:  List[Any]        =			TreebankWordDetokenizer().detokenize(vars(test_data[i]				)["text"]				)
						snake_case						:  int        =			discriminator.tokenizer.encode(lowercase__				)
						snake_case						:  Union[str, Any]        =			torch.tensor([5_0256] + seq    ,       device=lowercase__    ,       dtype=torch.long				)
						test_x.append(lowercase__				)
						test_y.append(classaidx[vars(test_data[i]				)["label"]]				)
				snake_case						:  Optional[Any]        =			Dataset(lowercase__    ,       lowercase__				)

				snake_case						:  Dict        =			{
				    "class_size": len(lowercase__				),
				    "embed_size": discriminator.embed_size,
				    "pretrained_model": pretrained_model,
				    "class_vocab": classaidx,
				    "default_class": 2,
				}

		elif dataset == "clickbait":
				snake_case						:  Tuple        =			["non_clickbait", "clickbait"]
				snake_case						:  Dict        =			{c: i for i, c in enumerate(lowercase__				)}

				snake_case						:  Dict        =			Discriminator(
				    class_size=len(lowercase__				)    ,       pretrained_model=lowercase__    ,       cached_mode=lowercase__    ,       device=lowercase__				).to(lowercase__				)

				with open("datasets/clickbait/clickbait_train_prefix.txt"				) as f:
						snake_case						:  List[str]        =			[]
						for i, line in enumerate(lowercase__				):
								try:
										data.append(eval(lowercase__				)				)
								except Exception:
										print("Error evaluating line {}: {}".format(lowercase__    ,       lowercase__				)				)
										continue
				snake_case						:  Tuple        =			[]
				snake_case						:  Optional[int]        =			[]
				with open("datasets/clickbait/clickbait_train_prefix.txt"				) as f:
						for i, line in enumerate(tqdm(lowercase__    ,       ascii=lowercase__				)				):
								try:
										snake_case						:  Optional[Any]        =			eval(lowercase__				)
										snake_case						:  List[Any]        =			discriminator.tokenizer.encode(d["text"]				)

										if len(lowercase__				) < max_length_seq:
												snake_case						:  Union[str, Any]        =			torch.tensor([5_0256] + seq    ,       device=lowercase__    ,       dtype=torch.long				)
										else:
												print("Line {} is longer than maximum length {}".format(lowercase__    ,       lowercase__				)				)
												continue
										x.append(lowercase__				)
										y.append(d["label"]				)
								except Exception:
										print("Error evaluating / tokenizing line {}, skipping it".format(lowercase__				)				)
										pass

				snake_case						:  Dict        =			Dataset(lowercase__    ,       lowercase__				)
				snake_case						:  Dict        =			int(0.9 * len(lowercase__				)				)
				snake_case						:  Any        =			len(lowercase__				) - train_size
				snake_case     ,			snake_case						:  int        =			torch.utils.data.random_split(lowercase__    ,       [train_size, test_size]				)

				snake_case						:  str        =			{
				    "class_size": len(lowercase__				),
				    "embed_size": discriminator.embed_size,
				    "pretrained_model": pretrained_model,
				    "class_vocab": classaidx,
				    "default_class": 1,
				}

		elif dataset == "toxic":
				snake_case						:  List[Any]        =			["non_toxic", "toxic"]
				snake_case						:  Union[str, Any]        =			{c: i for i, c in enumerate(lowercase__				)}

				snake_case						:  str        =			Discriminator(
				    class_size=len(lowercase__				)    ,       pretrained_model=lowercase__    ,       cached_mode=lowercase__    ,       device=lowercase__				).to(lowercase__				)

				snake_case						:  Optional[int]        =			[]
				snake_case						:  str        =			[]
				with open("datasets/toxic/toxic_train.txt"				) as f:
						for i, line in enumerate(tqdm(lowercase__    ,       ascii=lowercase__				)				):
								try:
										snake_case						:  Any        =			eval(lowercase__				)
										snake_case						:  Dict        =			discriminator.tokenizer.encode(d["text"]				)

										if len(lowercase__				) < max_length_seq:
												snake_case						:  Tuple        =			torch.tensor([5_0256] + seq    ,       device=lowercase__    ,       dtype=torch.long				)
										else:
												print("Line {} is longer than maximum length {}".format(lowercase__    ,       lowercase__				)				)
												continue
										x.append(lowercase__				)
										y.append(int(np.sum(d["label"]				) > 0				)				)
								except Exception:
										print("Error evaluating / tokenizing line {}, skipping it".format(lowercase__				)				)
										pass

				snake_case						:  Dict        =			Dataset(lowercase__    ,       lowercase__				)
				snake_case						:  Optional[int]        =			int(0.9 * len(lowercase__				)				)
				snake_case						:  List[Any]        =			len(lowercase__				) - train_size
				snake_case     ,			snake_case						:  Dict        =			torch.utils.data.random_split(lowercase__    ,       [train_size, test_size]				)

				snake_case						:  Optional[Any]        =			{
				    "class_size": len(lowercase__				),
				    "embed_size": discriminator.embed_size,
				    "pretrained_model": pretrained_model,
				    "class_vocab": classaidx,
				    "default_class": 0,
				}

		else:  # if dataset == "generic":
				# This assumes the input dataset is a TSV with the following structure:
				# class \t text

				if dataset_fp is None:
						raise ValueError("When generic dataset is selected, dataset_fp needs to be specified aswell."				)

				snake_case						:  Any        =			set()
				with open(lowercase__				) as f:
						snake_case						:  List[str]        =			csv.reader(lowercase__    ,       delimiter="\t"				)
						for row in tqdm(lowercase__    ,       ascii=lowercase__				):
								if row:
										classes.add(row[0]				)

				snake_case						:  List[Any]        =			sorted(lowercase__				)
				snake_case						:  Optional[int]        =			{c: i for i, c in enumerate(lowercase__				)}

				snake_case						:  int        =			Discriminator(
				    class_size=len(lowercase__				)    ,       pretrained_model=lowercase__    ,       cached_mode=lowercase__    ,       device=lowercase__				).to(lowercase__				)

				snake_case						:  Dict        =			[]
				snake_case						:  List[Any]        =			[]
				with open(lowercase__				) as f:
						snake_case						:  List[str]        =			csv.reader(lowercase__    ,       delimiter="\t"				)
						for i, row in enumerate(tqdm(lowercase__    ,       ascii=lowercase__				)				):
								if row:
										snake_case						:  Optional[int]        =			row[0]
										snake_case						:  Optional[Any]        =			row[1]

										try:
												snake_case						:  Union[str, Any]        =			discriminator.tokenizer.encode(lowercase__				)
												if len(lowercase__				) < max_length_seq:
														snake_case						:  Union[str, Any]        =			torch.tensor([5_0256] + seq    ,       device=lowercase__    ,       dtype=torch.long				)

												else:
														print("Line {} is longer than maximum length {}".format(lowercase__    ,       lowercase__				)				)
														continue

												x.append(lowercase__				)
												y.append(classaidx[label]				)

										except Exception:
												print("Error tokenizing line {}, skipping it".format(lowercase__				)				)
												pass

				snake_case						:  List[Any]        =			Dataset(lowercase__    ,       lowercase__				)
				snake_case						:  int        =			int(0.9 * len(lowercase__				)				)
				snake_case						:  List[str]        =			len(lowercase__				) - train_size
				snake_case     ,			snake_case						:  Dict        =			torch.utils.data.random_split(lowercase__    ,       [train_size, test_size]				)

				snake_case						:  List[str]        =			{
				    "class_size": len(lowercase__				),
				    "embed_size": discriminator.embed_size,
				    "pretrained_model": pretrained_model,
				    "class_vocab": classaidx,
				    "default_class": 0,
				}

		snake_case						:  Tuple        =			time.time()
		print("Preprocessed {} data points".format(len(lowercase__				) + len(lowercase__				)				)				)
		print("Data preprocessing took: {:.3f}s".format(end - start				)				)

		if cached:
				print("Building representation cache..."				)

				snake_case						:  int        =			time.time()

				snake_case						:  Union[str, Any]        =			get_cached_data_loader(lowercase__    ,       lowercase__    ,       lowercase__    ,       shuffle=lowercase__    ,       device=lowercase__				)

				snake_case						:  List[Any]        =			get_cached_data_loader(lowercase__    ,       lowercase__    ,       lowercase__    ,       device=lowercase__				)

				snake_case						:  Optional[int]        =			time.time()
				print("Building representation cache took: {:.3f}s".format(end - start				)				)

		else:
				snake_case						:  Union[str, Any]        =			torch.utils.data.DataLoader(
				    dataset=lowercase__    ,       batch_size=lowercase__    ,       shuffle=lowercase__    ,       collate_fn=lowercase__				)
				snake_case						:  int        =			torch.utils.data.DataLoader(dataset=lowercase__    ,       batch_size=lowercase__    ,       collate_fn=lowercase__				)

		if save_model:
				with open("{}_classifier_head_meta.json".format(lowercase__				)    ,       "w"				) as meta_file:
						json.dump(lowercase__    ,       lowercase__				)

		snake_case						:  Union[str, Any]        =			optim.Adam(discriminator.parameters()    ,       lr=0.00_01				)

		for epoch in range(lowercase__				):
				snake_case						:  Union[str, Any]        =			time.time()
				print("\nEpoch"    ,       epoch + 1				)

				train_epoch(
				    discriminator=lowercase__    ,       data_loader=lowercase__    ,       optimizer=lowercase__    ,       epoch=lowercase__    ,       log_interval=lowercase__    ,       device=lowercase__    ,       )
				evaluate_performance(data_loader=lowercase__    ,       discriminator=lowercase__    ,       device=lowercase__				)

				snake_case						:  List[Any]        =			time.time()
				print("Epoch took: {:.3f}s".format(end - start				)				)

				print("\nExample prediction"				)
				predict(lowercase__    ,       lowercase__    ,       lowercase__    ,       cached=lowercase__    ,       device=lowercase__				)

				if save_model:
						# torch.save(discriminator.state_dict(),
						#           "{}_discriminator_{}.pt".format(
						#               args.dataset, epoch + 1
						#               ))
						torch.save(
						    discriminator.get_classifier().state_dict()    ,       "{}_classifier_head_epoch_{}.pt".format(lowercase__    ,       epoch + 1				)    ,       )


if __name__ == "__main__":
							__A							=     argparse.ArgumentParser(description="Train a discriminator on top of GPT-2 representations")
							parser.add_argument(
							    "--dataset",
							    type=str,
							    default="SST",
							    choices=("SST", "clickbait", "toxic", "generic"),
							    help=(
							        "dataset to train the discriminator on."
							        "In case of generic, the dataset is expected"
							        "to be a TSBV file with structure: class \\t text"
							    ),
							)
							parser.add_argument(
							    "--dataset_fp",
							    type=str,
							    default="",
							    help="File path of the dataset to use. Needed only in case of generic datadset",
							)
							parser.add_argument(
							    "--pretrained_model", type=str, default="gpt2-medium", help="Pretrained model to use as encoder"
							)
							parser.add_argument("--epochs", type=int, default=10, metavar="N", help="Number of training epochs")
							parser.add_argument(
							    "--batch_size", type=int, default=64, metavar="N", help="input batch size for training (default: 64)"
							)
							parser.add_argument(
							    "--log_interval",
							    type=int,
							    default=10,
							    metavar="N",
							    help="how many batches to wait before logging training status",
							)
							parser.add_argument("--save_model", action="store_true", help="whether to save the model")
							parser.add_argument("--cached", action="store_true", help="whether to cache the input representations")
							parser.add_argument("--no_cuda", action="store_true", help="use to turn off cuda")
							__A							=     parser.parse_args()

							train_discriminator(**(vars(args)))







"""simple docstring"""

import unittest

import pytest

from transformers import (
    MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    MBartaaTokenizerFast,
    MBartConfig,
    MBartForConditionalGeneration,
    TranslationPipeline,
    pipeline,
)
from transformers.testing_utils import is_pipeline_test, require_tf, require_torch, slow

from .test_pipelines_common import ANY
@is_pipeline_test
class      lowerCamelCase__      (					unittest.TestCase     ):
			a__	: List[str]        =       MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
			a__	: List[Any]        =       TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING


			def 							lowerCamelCase_			(       self			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE							):



					"""simple docstring"""







					if isinstance(model.config			,	SCREAMING_SNAKE_CASE							):
							snake_case     ,			snake_case						:  Optional[Any]        =			list(tokenizer.lang_code_to_id.keys()							)[:2]
							snake_case						:  Tuple        =			TranslationPipeline(model=SCREAMING_SNAKE_CASE			,	tokenizer=SCREAMING_SNAKE_CASE			,	src_lang=SCREAMING_SNAKE_CASE			,	tgt_lang=SCREAMING_SNAKE_CASE							)
					else:
							snake_case						:  List[Any]        =			TranslationPipeline(model=SCREAMING_SNAKE_CASE			,	tokenizer=SCREAMING_SNAKE_CASE							)
					return translator, ["Some string", "Some other text"]


			def 							lowerCamelCase_			(       self			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE							):



					"""simple docstring"""







					snake_case						:  List[str]        =			translator("Some string"							)
					self.assertEqual(SCREAMING_SNAKE_CASE			,	[{"translation_text": ANY(SCREAMING_SNAKE_CASE							)}]							)

					snake_case						:  Optional[Any]        =			translator(["Some string"]							)
					self.assertEqual(SCREAMING_SNAKE_CASE			,	[{"translation_text": ANY(SCREAMING_SNAKE_CASE							)}]							)

					snake_case						:  List[Any]        =			translator(["Some string", "other string"]							)
					self.assertEqual(SCREAMING_SNAKE_CASE			,	[{"translation_text": ANY(SCREAMING_SNAKE_CASE							)}, {"translation_text": ANY(SCREAMING_SNAKE_CASE							)}]							)


			@require_torch
			def 							lowerCamelCase_			(       self							):



					"""simple docstring"""







					snake_case						:  Optional[int]        =			pipeline("translation_en_to_ro"			,	model="patrickvonplaten/t5-tiny-random"			,	framework="pt"							)
					snake_case						:  Union[str, Any]        =			translator("This is a test string"			,	max_length=20							)
					self.assertEqual(
					    SCREAMING_SNAKE_CASE			,	[
					        {
					            "translation_text": (
					                "Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide"
					                " Beide Beide"
					            )
					        }
					    ]			,	)


			@require_tf
			def 							lowerCamelCase_			(       self							):



					"""simple docstring"""







					snake_case						:  int        =			pipeline("translation_en_to_ro"			,	model="patrickvonplaten/t5-tiny-random"			,	framework="tf"							)
					snake_case						:  int        =			translator("This is a test string"			,	max_length=20							)
					self.assertEqual(
					    SCREAMING_SNAKE_CASE			,	[
					        {
					            "translation_text": (
					                "Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide"
					                " Beide Beide"
					            )
					        }
					    ]			,	)


			@require_torch
			def 							lowerCamelCase_			(       self							):



					"""simple docstring"""







					snake_case						:  List[Any]        =			pipeline("translation_en_to_de"			,	model="patrickvonplaten/t5-tiny-random"			,	framework="pt"							)
					snake_case						:  List[str]        =			translator("This is a test string"			,	max_length=20							)
					self.assertEqual(
					    SCREAMING_SNAKE_CASE			,	[
					        {
					            "translation_text": (
					                "monoton monoton monoton monoton monoton monoton monoton monoton monoton monoton urine urine"
					                " urine urine urine urine urine urine urine"
					            )
					        }
					    ]			,	)



			@require_tf
			def 							lowerCamelCase_			(       self							):



					"""simple docstring"""







					snake_case						:  List[str]        =			pipeline("translation_en_to_de"			,	model="patrickvonplaten/t5-tiny-random"			,	framework="tf"							)
					snake_case						:  Tuple        =			translator("This is a test string"			,	max_length=20							)
					self.assertEqual(
					    SCREAMING_SNAKE_CASE			,	[
					        {
					            "translation_text": (
					                "monoton monoton monoton monoton monoton monoton monoton monoton monoton monoton urine urine"
					                " urine urine urine urine urine urine urine"
					            )
					        }
					    ]			,	)







class      lowerCamelCase__      (					unittest.TestCase     ):


			@require_torch
			@slow
			def 							lowerCamelCase_			(       self							):



					"""simple docstring"""







					with self.assertRaises(SCREAMING_SNAKE_CASE							):
							pipeline(task="translation_cn_to_ar"							)

					# but we do for this one
					snake_case						:  List[str]        =			pipeline(task="translation_en_to_de"							)
					self.assertEqual(translator._preprocess_params["src_lang"]			,	"en"							)
					self.assertEqual(translator._preprocess_params["tgt_lang"]			,	"de"							)


			@require_torch
			@slow
			def 							lowerCamelCase_			(       self							):



					"""simple docstring"""







					snake_case						:  Any        =			MBartForConditionalGeneration.from_pretrained("facebook/mbart-large-50-many-to-many-mmt"							)
					snake_case						:  Union[str, Any]        =			MBartaaTokenizerFast.from_pretrained("facebook/mbart-large-50-many-to-many-mmt"							)

					snake_case						:  Dict        =			pipeline(task="translation"			,	model=SCREAMING_SNAKE_CASE			,	tokenizer=SCREAMING_SNAKE_CASE							)
					# Missing src_lang, tgt_lang
					with self.assertRaises(SCREAMING_SNAKE_CASE							):
							translator("This is a test"							)

					snake_case						:  Optional[int]        =			translator("This is a test"			,	src_lang="en_XX"			,	tgt_lang="ar_AR"							)
					self.assertEqual(SCREAMING_SNAKE_CASE			,	[{"translation_text": "هذا إختبار"}]							)

					snake_case						:  Dict        =			translator("This is a test"			,	src_lang="en_XX"			,	tgt_lang="hi_IN"							)
					self.assertEqual(SCREAMING_SNAKE_CASE			,	[{"translation_text": "यह एक परीक्षण है"}]							)

					# src_lang, tgt_lang can be defined at pipeline call time
					snake_case						:  Tuple        =			pipeline(task="translation"			,	model=SCREAMING_SNAKE_CASE			,	tokenizer=SCREAMING_SNAKE_CASE			,	src_lang="en_XX"			,	tgt_lang="ar_AR"							)
					snake_case						:  int        =			translator("This is a test"							)
					self.assertEqual(SCREAMING_SNAKE_CASE			,	[{"translation_text": "هذا إختبار"}]							)


			@require_torch
			def 							lowerCamelCase_			(       self							):



					"""simple docstring"""







					snake_case						:  int        =			"patrickvonplaten/t5-tiny-random"
					snake_case						:  Tuple        =			pipeline(task="translation_cn_to_ar"			,	model=SCREAMING_SNAKE_CASE							)
					self.assertEqual(translator._preprocess_params["src_lang"]			,	"cn"							)
					self.assertEqual(translator._preprocess_params["tgt_lang"]			,	"ar"							)


			@require_torch
			def 							lowerCamelCase_			(       self							):



					"""simple docstring"""







					snake_case						:  str        =			"patrickvonplaten/t5-tiny-random"
					with pytest.warns(SCREAMING_SNAKE_CASE			,	match=r".*translation_en_to_de.*"							):
							snake_case						:  Dict        =			pipeline(task="translation"			,	model=SCREAMING_SNAKE_CASE							)
					self.assertEqual(translator.task			,	"translation_en_to_de"							)
					self.assertEqual(translator._preprocess_params["src_lang"]			,	"en"							)
					self.assertEqual(translator._preprocess_params["tgt_lang"]			,	"de"							)



			@require_torch
			def 							lowerCamelCase_			(       self							):



					"""simple docstring"""







					with self.assertRaises(SCREAMING_SNAKE_CASE							):
							pipeline(task="translation"							)

