






import unittest

from transformers import (
    MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    TextaTextGenerationPipeline,
    pipeline,
)
from transformers.testing_utils import is_pipeline_test, require_tf, require_torch
from transformers.utils import is_torch_available

from .test_pipelines_common import ANY


if is_torch_available():
					import torch


@is_pipeline_test
class     lowercase (		unittest.TestCase       ):
		__lowercase	:	List[str]     					=    MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
		__lowercase	:	Optional[Any]     					=    TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING



		def __UpperCamelCase			(			self    ,				A_    ,				A_    ,				A_	)  -> Dict:
				"""simple docstring"""
				UpperCamelCase          =					TextaTextGenerationPipeline(model=__SCREAMING_SNAKE_CASE    ,				tokenizer=__SCREAMING_SNAKE_CASE	)
				return generator, ["Something to write", "Something else"]



		def __UpperCamelCase			(			self    ,				A_    ,				A_	)  -> Union[str, Any]:
				"""simple docstring"""
				UpperCamelCase          =					generator('Something there'	)
				self.assertEqual(__SCREAMING_SNAKE_CASE    ,				[{'generated_text': ANY(__SCREAMING_SNAKE_CASE	)}]	)
				# These are encoder decoder, they don't just append to incoming string
				self.assertFalse(outputs[0]['generated_text'].startswith('Something there'	)	)

				UpperCamelCase          =					generator(['This is great !', 'Something else']    ,				num_return_sequences=2    ,				do_sample=__SCREAMING_SNAKE_CASE	)
				self.assertEqual(
				    __SCREAMING_SNAKE_CASE    ,				[
				        [{'generated_text': ANY(__SCREAMING_SNAKE_CASE	)}, {'generated_text': ANY(__SCREAMING_SNAKE_CASE	)}],
				        [{'generated_text': ANY(__SCREAMING_SNAKE_CASE	)}, {'generated_text': ANY(__SCREAMING_SNAKE_CASE	)}],
				    ]    ,				)

				UpperCamelCase          =					generator(
				    ['This is great !', 'Something else']    ,				num_return_sequences=2    ,				batch_size=2    ,				do_sample=__SCREAMING_SNAKE_CASE	)
				self.assertEqual(
				    __SCREAMING_SNAKE_CASE    ,				[
				        [{'generated_text': ANY(__SCREAMING_SNAKE_CASE	)}, {'generated_text': ANY(__SCREAMING_SNAKE_CASE	)}],
				        [{'generated_text': ANY(__SCREAMING_SNAKE_CASE	)}, {'generated_text': ANY(__SCREAMING_SNAKE_CASE	)}],
				    ]    ,				)

				with self.assertRaises(__SCREAMING_SNAKE_CASE	):
						generator(4	)



		@require_torch
		def __UpperCamelCase			(			self	)  -> int:
				"""simple docstring"""
				UpperCamelCase          =					pipeline('text2text-generation'    ,				model='patrickvonplaten/t5-tiny-random'    ,				framework='pt'	)
				# do_sample=False necessary for reproducibility
				UpperCamelCase          =					generator('Something there'    ,				do_sample=__SCREAMING_SNAKE_CASE	)
				self.assertEqual(__SCREAMING_SNAKE_CASE    ,				[{'generated_text': ''}]	)

				UpperCamelCase          =					3
				UpperCamelCase          =					generator(
				    'Something there'    ,				num_return_sequences=__SCREAMING_SNAKE_CASE    ,				num_beams=__SCREAMING_SNAKE_CASE    ,				)
				UpperCamelCase          =					[
				    {'generated_text': 'Beide Beide Beide Beide Beide Beide Beide Beide Beide'},
				    {'generated_text': 'Beide Beide Beide Beide Beide Beide Beide Beide'},
				    {'generated_text': ''},
				]
				self.assertEqual(__SCREAMING_SNAKE_CASE    ,				__SCREAMING_SNAKE_CASE	)

				UpperCamelCase          =					generator('This is a test'    ,				do_sample=__SCREAMING_SNAKE_CASE    ,				num_return_sequences=2    ,				return_tensors=__SCREAMING_SNAKE_CASE	)
				self.assertEqual(
				    __SCREAMING_SNAKE_CASE    ,				[
				        {'generated_token_ids': ANY(torch.Tensor	)},
				        {'generated_token_ids': ANY(torch.Tensor	)},
				    ]    ,				)
				UpperCamelCase          =					generator.model.config.eos_token_id
				UpperCamelCase          =					'<pad>'
				UpperCamelCase          =					generator(
				    ['This is a test', 'This is a second test']    ,				do_sample=__SCREAMING_SNAKE_CASE    ,				num_return_sequences=2    ,				batch_size=2    ,				return_tensors=__SCREAMING_SNAKE_CASE    ,				)
				self.assertEqual(
				    __SCREAMING_SNAKE_CASE    ,				[
				        [
				            {'generated_token_ids': ANY(torch.Tensor	)},
				            {'generated_token_ids': ANY(torch.Tensor	)},
				        ],
				        [
				            {'generated_token_ids': ANY(torch.Tensor	)},
				            {'generated_token_ids': ANY(torch.Tensor	)},
				        ],
				    ]    ,				)



		@require_tf
		def __UpperCamelCase			(			self	)  -> int:
				"""simple docstring"""
				UpperCamelCase          =					pipeline('text2text-generation'    ,				model='patrickvonplaten/t5-tiny-random'    ,				framework='tf'	)
				# do_sample=False necessary for reproducibility
				UpperCamelCase          =					generator('Something there'    ,				do_sample=__SCREAMING_SNAKE_CASE	)
				self.assertEqual(__SCREAMING_SNAKE_CASE    ,				[{'generated_text': ''}]	)


import json
import os
import unittest

from transformers.models.ctrl.tokenization_ctrl import VOCAB_FILES_NAMES, CTRLTokenizer

from ...test_tokenization_common import TokenizerTesterMixin


class     lowercase (		_SCREAMING_SNAKE_CASE  ,       unittest.TestCase       ):
		__lowercase	:	Any     					=    CTRLTokenizer
		__lowercase	:	Any     					=    False
		__lowercase	:	Union[str, Any]     					=    False



		def __UpperCamelCase			(			self	)  -> Any:
				"""simple docstring"""
				super().setUp()

				# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
				UpperCamelCase          =					['adapt', 're@@', 'a@@', 'apt', 'c@@', 't', '<unk>']
				UpperCamelCase          =					dict(zip(A_    ,				range(len(A_	)	)	)	)
				UpperCamelCase          =					['#version: 0.2', 'a p', 'ap t</w>', 'r e', 'a d', 'ad apt</w>', '']
				UpperCamelCase          =					{'unk_token': '<unk>'}

				UpperCamelCase          =					os.path.join(self.tmpdirname    ,				VOCAB_FILES_NAMES['vocab_file']	)
				UpperCamelCase          =					os.path.join(self.tmpdirname    ,				VOCAB_FILES_NAMES['merges_file']	)
				with open(self.vocab_file    ,				'w'    ,				encoding='utf-8'	) as fp:
						fp.write(json.dumps(A_	) + '\n'	)
				with open(self.merges_file    ,				'w'    ,				encoding='utf-8'	) as fp:
						fp.write('\n'.join(A_	)	)



		def __UpperCamelCase			(			self    ,				**A_	)  -> List[Any]:
				"""simple docstring"""
				kwargs.update(self.special_tokens_map	)
				return CTRLTokenizer.from_pretrained(self.tmpdirname    ,				**A_	)



		def __UpperCamelCase			(			self    ,				A_	)  -> Optional[Any]:
				"""simple docstring"""
				UpperCamelCase          =					'adapt react readapt apt'
				UpperCamelCase          =					'adapt react readapt apt'
				return input_text, output_text



		def __UpperCamelCase			(			self	)  -> str:
				"""simple docstring"""
				UpperCamelCase          =					CTRLTokenizer(self.vocab_file    ,				self.merges_file    ,				**self.special_tokens_map	)
				UpperCamelCase          =					'adapt react readapt apt'
				UpperCamelCase          =					'adapt re@@ a@@ c@@ t re@@ adapt apt'.split()
				UpperCamelCase          =					tokenizer.tokenize(A_	)
				self.assertListEqual(A_    ,				A_	)

				UpperCamelCase          =					tokens + [tokenizer.unk_token]

				UpperCamelCase          =					[0, 1, 2, 4, 5, 1, 0, 3, 6]
				self.assertListEqual(tokenizer.convert_tokens_to_ids(A_	)    ,				A_	)
