'''simple docstring'''




import json
import os
import unittest

from transformers import AutoTokenizer, GPTaTokenizer, GPTaTokenizerFast
from transformers.models.gpta.tokenization_gpta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin






@require_tokenizers
class 							a__(   A__					,		unittest.TestCase      ):


			'''simple docstring'''






			UpperCAmelCase_     :	Any						=			GPTaTokenizer
			UpperCAmelCase_     :	Any						=			GPTaTokenizerFast
			UpperCAmelCase_     :	List[str]						=			True
			UpperCAmelCase_     :	int						=			{'''add_prefix_space''': True}
			UpperCAmelCase_     :	Dict						=			False



			def 				a_			(  self):




					"""simple docstring"""


					super().setUp()

					# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
					lowerCAmelCase     =    [
					    """l""",
					    """o""",
					    """w""",
					    """e""",
					    """r""",
					    """s""",
					    """t""",
					    """i""",
					    """d""",
					    """n""",
					    """\u0120""",
					    """\u0120l""",
					    """\u0120n""",
					    """\u0120lo""",
					    """\u0120low""",
					    """er""",
					    """\u0120lowest""",
					    """\u0120newer""",
					    """\u0120wider""",
					    """<unk>""",
					    """<|endoftext|>""",
					]
					lowerCAmelCase     =    dict(zip(__lowerCAmelCase			,       range(len(__lowerCAmelCase))))
					lowerCAmelCase     =    ["""#version: 0.2""", """\u0120 l""", """\u0120l o""", """\u0120lo w""", """e r""", """"""]
					lowerCAmelCase     =    {"""unk_token""": """<unk>"""}

					lowerCAmelCase     =    os.path.join(self.tmpdirname			,       VOCAB_FILES_NAMES["""vocab_file"""])
					lowerCAmelCase     =    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(__lowerCAmelCase) + """\n""")
					with open(self.merges_file			,       """w"""			,       encoding="""utf-8""") as fp:
							fp.write("""\n""".join(__lowerCAmelCase))



			def 				a_			(  self			,       **__lowerCAmelCase):




					"""simple docstring"""


					kwargs.update(self.special_tokens_map)
					return GPTaTokenizer.from_pretrained(self.tmpdirname			,       **__lowerCAmelCase)



			def 				a_			(  self			,       **__lowerCAmelCase):




					"""simple docstring"""


					kwargs.update(self.special_tokens_map)
					return GPTaTokenizerFast.from_pretrained(self.tmpdirname			,       **__lowerCAmelCase)



			def 				a_			(  self			,       __lowerCAmelCase):




					"""simple docstring"""


					lowerCAmelCase     =    """lower newer"""
					lowerCAmelCase     =    """lower newer"""
					return input_text, output_text



			def 				a_			(  self):




					"""simple docstring"""


					lowerCAmelCase     =    GPTaTokenizer(self.vocab_file			,       self.merges_file			,       **self.special_tokens_map)
					lowerCAmelCase     =    """lower newer"""
					lowerCAmelCase     =    ["""\u0120low""", """er""", """\u0120""", """n""", """e""", """w""", """er"""]
					lowerCAmelCase     =    tokenizer.tokenize(__lowerCAmelCase			,       add_prefix_space=__lowerCAmelCase)
					self.assertListEqual(__lowerCAmelCase			,       __lowerCAmelCase)

					lowerCAmelCase     =    tokens + [tokenizer.unk_token]
					lowerCAmelCase     =    [14, 15, 10, 9, 3, 2, 15, 19]
					self.assertListEqual(tokenizer.convert_tokens_to_ids(__lowerCAmelCase)			,       __lowerCAmelCase)



			def 				a_			(  self):




					"""simple docstring"""


					if not self.test_rust_tokenizer:
							return

					lowerCAmelCase     =    self.get_tokenizer()
					lowerCAmelCase     =    self.get_rust_tokenizer(add_prefix_space=__lowerCAmelCase)

					lowerCAmelCase     =    """lower newer"""

					# Testing tokenization
					lowerCAmelCase     =    tokenizer.tokenize(__lowerCAmelCase			,       add_prefix_space=__lowerCAmelCase)
					lowerCAmelCase     =    rust_tokenizer.tokenize(__lowerCAmelCase)
					self.assertListEqual(__lowerCAmelCase			,       __lowerCAmelCase)

					# Testing conversion to ids without special tokens
					lowerCAmelCase     =    tokenizer.encode(__lowerCAmelCase			,       add_special_tokens=__lowerCAmelCase			,       add_prefix_space=__lowerCAmelCase)
					lowerCAmelCase     =    rust_tokenizer.encode(__lowerCAmelCase			,       add_special_tokens=__lowerCAmelCase)
					self.assertListEqual(__lowerCAmelCase			,       __lowerCAmelCase)

					# Testing conversion to ids with special tokens
					lowerCAmelCase     =    self.get_rust_tokenizer(add_prefix_space=__lowerCAmelCase)
					lowerCAmelCase     =    tokenizer.encode(__lowerCAmelCase			,       add_prefix_space=__lowerCAmelCase)
					lowerCAmelCase     =    rust_tokenizer.encode(__lowerCAmelCase)
					self.assertListEqual(__lowerCAmelCase			,       __lowerCAmelCase)

					# Testing the unknown token
					lowerCAmelCase     =    tokens + [rust_tokenizer.unk_token]
					lowerCAmelCase     =    [14, 15, 10, 9, 3, 2, 15, 19]
					self.assertListEqual(rust_tokenizer.convert_tokens_to_ids(__lowerCAmelCase)			,       __lowerCAmelCase)



			def 				a_			(  self			,       *__lowerCAmelCase			,       **__lowerCAmelCase):




					"""simple docstring"""


					pass



			def 				a_			(  self			,       __lowerCAmelCase=15):




					"""simple docstring"""


					for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
							with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"):
									lowerCAmelCase     =    self.rust_tokenizer_class.from_pretrained(__lowerCAmelCase			,       **__lowerCAmelCase)

									# Simple input
									lowerCAmelCase     =    """This is a simple input"""
									lowerCAmelCase     =    ["""This is a simple input 1""", """This is a simple input 2"""]
									lowerCAmelCase     =    ("""This is a simple input""", """This is a pair""")
									lowerCAmelCase     =    [
									    ("""This is a simple input 1""", """This is a simple input 2"""),
									    ("""This is a simple pair 1""", """This is a simple pair 2"""),
									]

									# Simple input tests
									self.assertRaises(__lowerCAmelCase			,       tokenizer_r.encode			,       __lowerCAmelCase			,       max_length=__lowerCAmelCase			,       padding="""max_length""")

									# Simple input
									self.assertRaises(__lowerCAmelCase			,       tokenizer_r.encode_plus			,       __lowerCAmelCase			,       max_length=__lowerCAmelCase			,       padding="""max_length""")

									# Simple input
									self.assertRaises(
									    __lowerCAmelCase			,       tokenizer_r.batch_encode_plus			,       __lowerCAmelCase			,       max_length=__lowerCAmelCase			,       padding="""max_length"""			,       )

									# Pair input
									self.assertRaises(__lowerCAmelCase			,       tokenizer_r.encode			,       __lowerCAmelCase			,       max_length=__lowerCAmelCase			,       padding="""max_length""")

									# Pair input
									self.assertRaises(__lowerCAmelCase			,       tokenizer_r.encode_plus			,       __lowerCAmelCase			,       max_length=__lowerCAmelCase			,       padding="""max_length""")

									# Pair input
									self.assertRaises(
									    __lowerCAmelCase			,       tokenizer_r.batch_encode_plus			,       __lowerCAmelCase			,       max_length=__lowerCAmelCase			,       padding="""max_length"""			,       )



			def 				a_			(  self):




					"""simple docstring"""


					lowerCAmelCase     =    GPTaTokenizer.from_pretrained(self.tmpdirname			,       pad_token="""<pad>""")

					# Simple input
					lowerCAmelCase     =    """This is a simple input"""
					lowerCAmelCase     =    ["""This is a simple input looooooooong""", """This is a simple input"""]
					lowerCAmelCase     =    ("""This is a simple input""", """This is a pair""")
					lowerCAmelCase     =    [
					    ("""This is a simple input loooooong""", """This is a simple input"""),
					    ("""This is a simple pair loooooong""", """This is a simple pair"""),
					]

					lowerCAmelCase     =    tokenizer.pad_token_id

					lowerCAmelCase     =    tokenizer(__lowerCAmelCase			,       padding="""max_length"""			,       max_length=30			,       return_tensors="""np""")
					lowerCAmelCase     =    tokenizer(__lowerCAmelCase			,       padding=__lowerCAmelCase			,       truncate=__lowerCAmelCase			,       return_tensors="""np""")
					lowerCAmelCase     =    tokenizer(*__lowerCAmelCase			,       padding="""max_length"""			,       max_length=60			,       return_tensors="""np""")
					lowerCAmelCase     =    tokenizer(__lowerCAmelCase			,       padding=__lowerCAmelCase			,       truncate=__lowerCAmelCase			,       return_tensors="""np""")

					# s
					# test single string max_length padding
					self.assertEqual(out_s["""input_ids"""].shape[-1]			,       30)
					self.assertTrue(pad_token_id in out_s["""input_ids"""])
					self.assertTrue(0 in out_s["""attention_mask"""])

					# s2
					# test automatic padding
					self.assertEqual(out_sa["""input_ids"""].shape[-1]			,       33)
					# long slice doesn't have padding
					self.assertFalse(pad_token_id in out_sa["""input_ids"""][0])
					self.assertFalse(0 in out_sa["""attention_mask"""][0])
					# short slice does have padding
					self.assertTrue(pad_token_id in out_sa["""input_ids"""][1])
					self.assertTrue(0 in out_sa["""attention_mask"""][1])

					# p
					# test single pair max_length padding
					self.assertEqual(out_p["""input_ids"""].shape[-1]			,       60)
					self.assertTrue(pad_token_id in out_p["""input_ids"""])
					self.assertTrue(0 in out_p["""attention_mask"""])

					# p2
					# test automatic padding pair
					self.assertEqual(out_pa["""input_ids"""].shape[-1]			,       52)
					# long slice pair doesn't have padding
					self.assertFalse(pad_token_id in out_pa["""input_ids"""][0])
					self.assertFalse(0 in out_pa["""attention_mask"""][0])
					# short slice pair does have padding
					self.assertTrue(pad_token_id in out_pa["""input_ids"""][1])
					self.assertTrue(0 in out_pa["""attention_mask"""][1])



			def 				a_			(  self):




					"""simple docstring"""


					lowerCAmelCase     =    """$$$"""
					lowerCAmelCase     =    GPTaTokenizer.from_pretrained(self.tmpdirname			,       bos_token=__lowerCAmelCase			,       add_bos_token=__lowerCAmelCase)

					lowerCAmelCase     =    """This is a simple input"""
					lowerCAmelCase     =    ["""This is a simple input 1""", """This is a simple input 2"""]

					lowerCAmelCase     =    tokenizer.bos_token_id

					lowerCAmelCase     =    tokenizer(__lowerCAmelCase)
					lowerCAmelCase     =    tokenizer(__lowerCAmelCase)

					self.assertEqual(out_s.input_ids[0]			,       __lowerCAmelCase)
					self.assertTrue(all(o[0] == bos_token_id for o in out_sa.input_ids))

					lowerCAmelCase     =    tokenizer.decode(out_s.input_ids)
					lowerCAmelCase     =    tokenizer.batch_decode(out_sa.input_ids)

					self.assertEqual(decode_s.split()[0]			,       __lowerCAmelCase)
					self.assertTrue(all(d.split()[0] == bos_token for d in decode_sa))



			def 				a_			(  self):




					"""simple docstring"""


					pass







			def 				a_			(  self):




					"""simple docstring"""


					lowerCAmelCase     =    [self.get_tokenizer(do_lower_case=__lowerCAmelCase			,       add_bos_token=__lowerCAmelCase)]
					for tokenizer in tokenizers:
							with self.subTest(f"{tokenizer.__class__.__name__}"):
									lowerCAmelCase     =    """Encode this."""
									lowerCAmelCase     =    """This one too please."""
									lowerCAmelCase     =    tokenizer.encode(__lowerCAmelCase			,       add_special_tokens=__lowerCAmelCase)
									encoded_sequence += tokenizer.encode(__lowerCAmelCase			,       add_special_tokens=__lowerCAmelCase)
									lowerCAmelCase     =    tokenizer.encode_plus(
									    __lowerCAmelCase			,       __lowerCAmelCase			,       add_special_tokens=__lowerCAmelCase			,       return_special_tokens_mask=__lowerCAmelCase			,       )
									lowerCAmelCase     =    encoded_sequence_dict["""input_ids"""]
									lowerCAmelCase     =    encoded_sequence_dict["""special_tokens_mask"""]
									self.assertEqual(len(__lowerCAmelCase)			,       len(__lowerCAmelCase))

									lowerCAmelCase     =    [
									    (x if not special_tokens_mask[i] else None) for i, x in enumerate(__lowerCAmelCase)
									]
									lowerCAmelCase     =    [x for x in filtered_sequence if x is not None]
									self.assertEqual(__lowerCAmelCase			,       __lowerCAmelCase)







@require_tokenizers
class 							a__(   unittest.TestCase      ):


			'''simple docstring'''






			def 				a_			(  self):




					"""simple docstring"""


					lowerCAmelCase     =    AutoTokenizer.from_pretrained("""facebook/opt-350m"""			,       from_slow=__lowerCAmelCase)
					lowerCAmelCase     =    """A photo of a cat"""

					lowerCAmelCase     =    tokenizer.encode(
					    __lowerCAmelCase			,       )
					self.assertEqual(__lowerCAmelCase			,       [2, 250, 1345, 9, 10, 4758])
					tokenizer.save_pretrained("""test_opt""")

					lowerCAmelCase     =    AutoTokenizer.from_pretrained("""./test_opt""")
					lowerCAmelCase     =    tokenizer.encode(
					    __lowerCAmelCase			,       )
					self.assertEqual(__lowerCAmelCase			,       [2, 250, 1345, 9, 10, 4758])



			def 				a_			(  self):




					"""simple docstring"""


					lowerCAmelCase     =    AutoTokenizer.from_pretrained("""facebook/opt-350m"""			,       use_slow=__lowerCAmelCase)
					lowerCAmelCase     =    """A photo of a cat"""

					lowerCAmelCase     =    tokenizer.encode(
					    __lowerCAmelCase			,       )
					# Same as above
					self.assertEqual(__lowerCAmelCase			,       [2, 250, 1345, 9, 10, 4758])







			@unittest.skip("""This test is failing because of a bug in the fast tokenizer""")
			def 				a_			(  self):




					"""simple docstring"""


					lowerCAmelCase     =    AutoTokenizer.from_pretrained("""facebook/opt-350m"""			,       from_slow=__lowerCAmelCase)

					lowerCAmelCase     =    """bos"""
					lowerCAmelCase     =    tokenizer.get_vocab()["""bos"""]

					lowerCAmelCase     =    """A photo of a cat"""
					lowerCAmelCase     =    tokenizer.encode(
					    __lowerCAmelCase			,       )
					# We changed the bos token
					self.assertEqual(__lowerCAmelCase			,       [31957, 250, 1345, 9, 10, 4758])
					tokenizer.save_pretrained("""./tok""")
					lowerCAmelCase     =    AutoTokenizer.from_pretrained("""./tok""")
					self.assertTrue(tokenizer.is_fast)
					lowerCAmelCase     =    tokenizer.encode(
					    __lowerCAmelCase			,       )
					self.assertEqual(__lowerCAmelCase			,       [31957, 250, 1345, 9, 10, 4758])












import unittest

import numpy as np

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

from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
							import jax.numpy as jnp

							from transformers.models.roberta_prelayernorm.modeling_flax_roberta_prelayernorm import (
							    FlaxRobertaPreLayerNormForCausalLM,
							    FlaxRobertaPreLayerNormForMaskedLM,
							    FlaxRobertaPreLayerNormForMultipleChoice,
							    FlaxRobertaPreLayerNormForQuestionAnswering,
							    FlaxRobertaPreLayerNormForSequenceClassification,
							    FlaxRobertaPreLayerNormForTokenClassification,
							    FlaxRobertaPreLayerNormModel,
							)
class  _UpperCAmelCase   (			unittest.TestCase      ):

				"""simple docstring"""




				def __init__(	self		:       List[Any],    lowerCamelCase		:       Dict,    lowerCamelCase		:       Dict=13,    lowerCamelCase		:       Optional[int]=7,    lowerCamelCase		:       Optional[Any]=True,    lowerCamelCase		:       Union[str, Any]=True,    lowerCamelCase		:       Optional[Any]=True,    lowerCamelCase		:       Optional[Any]=True,    lowerCamelCase		:       List[Any]=99,    lowerCamelCase		:       Any=32,    lowerCamelCase		:       List[str]=5,    lowerCamelCase		:       Union[str, Any]=4,    lowerCamelCase		:       Union[str, Any]=37,    lowerCamelCase		:       Optional[int]="gelu",    lowerCamelCase		:       Tuple=0.1,    lowerCamelCase		:       List[str]=0.1,    lowerCamelCase		:       Optional[Any]=512,    lowerCamelCase		:       int=16,    lowerCamelCase		:       str=2,    lowerCamelCase		:       int=0.02,    lowerCamelCase		:       int=4,    ):

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




				def 							lowercase__  (	self		:       Dict    ):

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

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

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

								lowercase__  					=     RobertaPreLayerNormConfig(
								    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 							lowercase__  (	self		:       List[str]    ):

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




				def 							lowercase__  (	self		:       Optional[int]    ):

								'''simple docstring'''
								lowercase__  					=     self.prepare_config_and_inputs()
								lowercase__					,		lowercase__					,		lowercase__					,		lowercase__  					=     config_and_inputs

								lowercase__  					=     True
								lowercase__  					=     floats_tensor([self.batch_size, self.seq_length, self.hidden_size]    )
								lowercase__  					=     ids_tensor([self.batch_size, self.seq_length],    vocab_size=2    )

								return (
								    config,
								    input_ids,
								    token_type_ids,
								    encoder_hidden_states,
								    encoder_attention_mask,
								)



@require_flax
# Copied from tests.models.roberta.test_modelling_flax_roberta.FlaxRobertaPreLayerNormModelTest with ROBERTA->ROBERTA_PRELAYERNORM,Roberta->RobertaPreLayerNorm,roberta-base->andreasmadsen/efficient_mlm_m0.40
class  _UpperCAmelCase   (			A__						,unittest.TestCase      ):

				"""simple docstring"""




				lowercase__											=			True

				lowercase__											=			(
				    (
				        FlaxRobertaPreLayerNormModel,
				        FlaxRobertaPreLayerNormForCausalLM,
				        FlaxRobertaPreLayerNormForMaskedLM,
				        FlaxRobertaPreLayerNormForSequenceClassification,
				        FlaxRobertaPreLayerNormForTokenClassification,
				        FlaxRobertaPreLayerNormForMultipleChoice,
				        FlaxRobertaPreLayerNormForQuestionAnswering,
				    )
				    if is_flax_available()
				    else ()
				)




				def 							lowercase__  (	self		:       int    ):

								'''simple docstring'''
								lowercase__  					=     FlaxRobertaPreLayerNormModelTester(self    )




				@slow
				def 							lowercase__  (	self		:       List[Any]    ):

								'''simple docstring'''
								for model_class_name in self.all_model_classes:
												lowercase__  					=     model_class_name.from_pretrained('''andreasmadsen/efficient_mlm_m0.40''',    from_pt=lowerCamelCase    )
												lowercase__  					=     model(np.ones((1, 1)    )    )
												self.assertIsNotNone(lowerCamelCase    )



@require_flax
class  _UpperCAmelCase   (			unittest.TestCase      ):

				"""simple docstring"""




				@slow
				def 							lowercase__  (	self		:       Union[str, Any]    ):

								'''simple docstring'''
								lowercase__  					=     FlaxRobertaPreLayerNormForMaskedLM.from_pretrained('''andreasmadsen/efficient_mlm_m0.40''',    from_pt=lowerCamelCase    )

								lowercase__  					=     np.array([[0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]],    dtype=jnp.intaa    )
								lowercase__  					=     model(lowerCamelCase    )[0]
								lowercase__  					=     [1, 11, 50_265]
								self.assertEqual(list(output.shape    ),    lowerCamelCase    )
								# compare the actual values for a slice.
								lowercase__  					=     np.array(
								    [[[40.4880, 18.0199, -5.2367], [-1.8877, -4.0885, 10.7085], [-2.2613, -5.6110, 7.2665]]],    dtype=np.floataa    )
								self.assertTrue(np.allclose(output[:, :3, :3],    lowerCamelCase,    atol=1E-4    )    )




				@slow
				def 							lowercase__  (	self		:       Any    ):

								'''simple docstring'''
								lowercase__  					=     FlaxRobertaPreLayerNormModel.from_pretrained('''andreasmadsen/efficient_mlm_m0.40''',    from_pt=lowerCamelCase    )

								lowercase__  					=     np.array([[0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]],    dtype=jnp.intaa    )
								lowercase__  					=     model(lowerCamelCase    )[0]
								# compare the actual values for a slice.
								lowercase__  					=     np.array(
								    [[[0.0208, -0.0356, 0.0237], [-0.1569, -0.0411, -0.2626], [0.1879, 0.0125, -0.0089]]],    dtype=np.floataa    )
								self.assertTrue(np.allclose(output[:, :3, :3],    lowerCamelCase,    atol=1E-4    )    )




