





'''simple docstring'''







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 				__SCREAMING_SNAKE_CASE  (unittest.TestCase      ):






				"""simple docstring"""







				__a            =MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
				__a            =TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING




				def   UpperCamelCase__   (  self     :					Optional[int]					,				__a     :					Optional[Any]					,				__a     :					Dict					,				__a     :					List[str]					):
								_a								= TextaTextGenerationPipeline(model=__a					,				tokenizer=__a					)
								return generator, ["Something to write", "Something else"]




				def   UpperCamelCase__   (  self     :					List[str]					,				__a     :					List[Any]					,				__a     :					Any					):
								_a								= generator("Something there"					)
								self.assertEqual(__a					,				[{"generated_text": ANY(__a					)}]					)
								# These are encoder decoder, they don't just append to incoming string
								self.assertFalse(outputs[0]["generated_text"].startswith("Something there"					)					)

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

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

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




				@require_torch
				def   UpperCamelCase__   (  self     :					Optional[int]					):
								_a								= pipeline("text2text-generation"					,				model="patrickvonplaten/t5-tiny-random"					,				framework="pt"					)
								# do_sample=False necessary for reproducibility
								_a								= generator("Something there"					,				do_sample=__a					)
								self.assertEqual(__a					,				[{"generated_text": ""}]					)

								_a								= 3
								_a								= generator(
								    "Something there"					,				num_return_sequences=__a					,				num_beams=__a					,				)
								_a								= [
								    {"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(__a					,				__a					)

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








"""simple docstring"""


import unittest

import numpy as np

from transformers import RoFormerConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
       import jax.numpy as jnp

       from transformers.models.roformer.modeling_flax_roformer import (
           FlaxRoFormerForMaskedLM,
           FlaxRoFormerForMultipleChoice,
           FlaxRoFormerForQuestionAnswering,
           FlaxRoFormerForSequenceClassification,
           FlaxRoFormerForTokenClassification,
           FlaxRoFormerModel,
       )


class 					snake_case		(							unittest.TestCase						):


   '''simple docstring'''

   def __init__(	self :     Optional[Any],       _lowerCamelCase :     Tuple,       _lowerCamelCase :     List[str]=13,       _lowerCamelCase :     Optional[Any]=7,       _lowerCamelCase :     Optional[int]=True,       _lowerCamelCase :     int=True,       _lowerCamelCase :     List[str]=True,       _lowerCamelCase :     Optional[Any]=True,       _lowerCamelCase :     int=99,       _lowerCamelCase :     Optional[int]=32,       _lowerCamelCase :     Tuple=5,       _lowerCamelCase :     Tuple=4,       _lowerCamelCase :     str=37,       _lowerCamelCase :     Union[str, Any]="gelu",       _lowerCamelCase :     int=0.1,       _lowerCamelCase :     List[Any]=0.1,       _lowerCamelCase :     Dict=5_12,       _lowerCamelCase :     List[Any]=16,       _lowerCamelCase :     Any=2,       _lowerCamelCase :     Any=0.02,       _lowerCamelCase :     Dict=4,       ):

     '''simple docstring'''
     __A									= parent
     __A									= batch_size
     __A									= seq_length
     __A									= is_training
     __A									= use_attention_mask
     __A									= use_token_type_ids
     __A									= use_labels
     __A									= vocab_size
     __A									= hidden_size
     __A									= num_hidden_layers
     __A									= num_attention_heads
     __A									= intermediate_size
     __A									= hidden_act
     __A									= hidden_dropout_prob
     __A									= attention_probs_dropout_prob
     __A									= max_position_embeddings
     __A									= type_vocab_size
     __A									= type_sequence_label_size
     __A									= initializer_range
     __A									= num_choices


   def 				_SCREAMING_SNAKE_CASE				(	self :     Any  ):

     '''simple docstring'''
     __A									= ids_tensor([self.batch_size, self.seq_length],       self.vocab_size  )

     __A									= None
     if self.use_attention_mask:
       __A									= random_attention_mask([self.batch_size, self.seq_length]  )

     __A									= None
     if self.use_token_type_ids:
       __A									= ids_tensor([self.batch_size, self.seq_length],       self.type_vocab_size  )

     __A									= RoFormerConfig(
         vocab_size=self.vocab_size,       hidden_size=self.hidden_size,       num_hidden_layers=self.num_hidden_layers,       num_attention_heads=self.num_attention_heads,       intermediate_size=self.intermediate_size,       hidden_act=self.hidden_act,       hidden_dropout_prob=self.hidden_dropout_prob,       attention_probs_dropout_prob=self.attention_probs_dropout_prob,       max_position_embeddings=self.max_position_embeddings,       type_vocab_size=self.type_vocab_size,       is_decoder=_lowerCamelCase,       initializer_range=self.initializer_range,       )

     return config, input_ids, token_type_ids, attention_mask






   def 				_SCREAMING_SNAKE_CASE				(	self :     Optional[int]  ):

     '''simple docstring'''
     __A									= self.prepare_config_and_inputs()
     __A			,    __A			,    __A			,    __A									= config_and_inputs
     __A									= {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': attention_mask}
     return config, inputs_dict





@require_flax
class 					snake_case		(							_lowerCAmelCase   ,	unittest.TestCase						):


   '''simple docstring'''

   A_						:	Dict						 = True

   A_						:	Tuple						 = (
       (
           FlaxRoFormerModel,
           FlaxRoFormerForMaskedLM,
           FlaxRoFormerForSequenceClassification,
           FlaxRoFormerForTokenClassification,
           FlaxRoFormerForMultipleChoice,
           FlaxRoFormerForQuestionAnswering,
       )
       if is_flax_available()
       else ()
   )


   def 				_SCREAMING_SNAKE_CASE				(	self :     Union[str, Any]  ):

     '''simple docstring'''
     __A									= FlaxRoFormerModelTester(self  )






   @slow
   def 				_SCREAMING_SNAKE_CASE				(	self :     Any  ):

     '''simple docstring'''
     for model_class_name in self.all_model_classes:
       __A									= model_class_name.from_pretrained('''junnyu/roformer_chinese_small''',       from_pt=_lowerCamelCase  )
       __A									= model(np.ones((1, 1)  )  )
       self.assertIsNotNone(_lowerCamelCase  )





@require_flax
class 					snake_case		(							unittest.TestCase						):


   '''simple docstring'''

   @slow
   def 				_SCREAMING_SNAKE_CASE				(	self :     Dict  ):

     '''simple docstring'''
     __A									= FlaxRoFormerForMaskedLM.from_pretrained('''junnyu/roformer_chinese_base'''  )
     __A									= jnp.array([[0, 1, 2, 3, 4, 5]]  )
     __A									= model(_lowerCamelCase  )[0]

     __A									= 5_00_00

     __A									= (1, 6, vocab_size)
     self.assertEqual(output.shape,       _lowerCamelCase  )

     __A									= jnp.array(
         [[[-0.12_05, -1.02_65, 0.29_22], [-1.51_34, 0.19_74, 0.15_19], [-5.01_35, -3.90_03, -0.84_04]]]  )

     self.assertTrue(jnp.allclose(output[:, :3, :3],       _lowerCamelCase,       atol=1e-4  )  )




