





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      lowerCAmelCase							(      unittest.TestCase    ):
	UpperCAmelCase__									=	MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
	UpperCAmelCase__									=	TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
	def   A_			(   self						:   List[str]    ,  UpperCAmelCase						:   Optional[Any]    ,  UpperCAmelCase						:   List[str]    ,  UpperCAmelCase						:   Dict			)    ->			Dict:
					lowerCamelCase__					:   Tuple      				=	TextaTextGenerationPipeline(model=UpperCAmelCase    ,  tokenizer=UpperCAmelCase			)
					return generator, ["Something to write", "Something else"]
	def   A_			(   self						:   Union[str, Any]    ,  UpperCAmelCase						:   Dict    ,  UpperCAmelCase						:   List[str]			)    ->			str:
					lowerCamelCase__					:   Optional[Any]      				=	generator('Something there'			)
					self.assertEqual(UpperCAmelCase    ,  [{'generated_text': ANY(UpperCAmelCase			)}]			)
					# These are encoder decoder, they don't just append to incoming string
					self.assertFalse(outputs[0]['generated_text'].startswith('Something there'			)			)

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

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

					with self.assertRaises(UpperCAmelCase			):
									generator(4			)
	@require_torch
	def   A_			(   self						:   List[str]			)    ->			str:
					lowerCamelCase__					:   Union[str, Any]      				=	pipeline('text2text-generation'    ,  model='patrickvonplaten/t5-tiny-random'    ,  framework='pt'			)
					# do_sample=False necessary for reproducibility
					lowerCamelCase__					:   int      				=	generator('Something there'    ,  do_sample=UpperCAmelCase			)
					self.assertEqual(UpperCAmelCase    ,  [{'generated_text': ''}]			)

					lowerCamelCase__					:   Union[str, Any]      				=	3
					lowerCamelCase__					:   Dict      				=	generator(
					    'Something there'    ,  num_return_sequences=UpperCAmelCase    ,  num_beams=UpperCAmelCase    ,  )
					lowerCamelCase__					:   Optional[Any]      				=	[
					    {'''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(UpperCAmelCase    ,  UpperCAmelCase			)

					lowerCamelCase__					:   List[Any]      				=	generator('This is a test'    ,  do_sample=UpperCAmelCase    ,  num_return_sequences=2    ,  return_tensors=UpperCAmelCase			)
					self.assertEqual(
					    UpperCAmelCase    ,  [
					        {'generated_token_ids': ANY(torch.Tensor			)},
					        {'generated_token_ids': ANY(torch.Tensor			)},
					    ]    ,  )
					lowerCamelCase__					:   Optional[Any]      				=	generator.model.config.eos_token_id
					lowerCamelCase__					:   Optional[Any]      				=	'''<pad>'''
					lowerCamelCase__					:   int      				=	generator(
					    ['This is a test', 'This is a second test']    ,  do_sample=UpperCAmelCase    ,  num_return_sequences=2    ,  batch_size=2    ,  return_tensors=UpperCAmelCase    ,  )
					self.assertEqual(
					    UpperCAmelCase    ,  [
					        [
					            {'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   A_			(   self						:   str			)    ->			List[Any]:
					lowerCamelCase__					:   int      				=	pipeline('text2text-generation'    ,  model='patrickvonplaten/t5-tiny-random'    ,  framework='tf'			)
					# do_sample=False necessary for reproducibility
					lowerCamelCase__					:   str      				=	generator('Something there'    ,  do_sample=UpperCAmelCase			)
					self.assertEqual(UpperCAmelCase    ,  [{'generated_text': ''}]			)












import socket


def  SCREAMING_SNAKE_CASE   ( )    ->  Any:
    lowerCamelCase__					:   int      				=	socket.socket(socket.AF_INET		,			socket.SOCK_STREAM				)
    lowerCamelCase__					:   Optional[int]      				=	socket.gethostname()
    lowerCamelCase__					:   Optional[int]      				=	1_2312

    sock.connect((host, port)				)
    sock.send(b'Hello server!'				)

    with open('Received_file'		,			'wb'				) as out_file:
        print('File opened'				)
        print('Receiving data...'				)
        while True:
            lowerCamelCase__					:   str      				=	sock.recv(1024				)
            if not data:
                break
            out_file.write(_UpperCAmelCase				)

    print('Successfully received the file'				)
    sock.close()
    print('Connection closed'				)


if __name__ == "__main__":
    main()





