
import json
import os
import unittest

from transformers import BartTokenizer, BartTokenizerFast, BatchEncoding
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin, filter_roberta_detectors





@require_tokenizers
class       __lowerCAmelCase				(  lowerCamelCase__ ,		unittest.TestCase ):
				__lowerCamelCase				=       BartTokenizer
				__lowerCamelCase				=       BartTokenizerFast
				__lowerCamelCase				=       True
				__lowerCamelCase				=       filter_roberta_detectors




				def 							snake_case     (							self	):



									"""simple docstring"""





									super().setUp()
									_lowerCAmelCase      			=					[
									    """l""",
									    """o""",
									    """w""",
									    """e""",
									    """r""",
									    """s""",
									    """t""",
									    """i""",
									    """d""",
									    """n""",
									    """\u0120""",
									    """\u0120l""",
									    """\u0120n""",
									    """\u0120lo""",
									    """\u0120low""",
									    """er""",
									    """\u0120lowest""",
									    """\u0120newer""",
									    """\u0120wider""",
									    """<unk>""",
									]
									_lowerCAmelCase      			=					dict(zip(_snake_case		,       range(len(_snake_case	)	)	)	)
									_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(_snake_case	) + """\n"""	)
									with open(self.merges_file		,       """w"""		,       encoding="""utf-8"""	) as fp:
														fp.write("""\n""".join(_snake_case	)	)




				def 							snake_case     (							self		,       **_snake_case	):



									"""simple docstring"""





									kwargs.update(self.special_tokens_map	)
									return self.tokenizer_class.from_pretrained(self.tmpdirname		,       **_snake_case	)




				def 							snake_case     (							self		,       **_snake_case	):



									"""simple docstring"""





									kwargs.update(self.special_tokens_map	)
									return self.rust_tokenizer_class.from_pretrained(self.tmpdirname		,       **_snake_case	)




				def 							snake_case     (							self		,       _snake_case	):



									"""simple docstring"""





									return "lower newer", "lower newer"




				@cached_property
				def 							snake_case     (							self	):



									"""simple docstring"""





									return BartTokenizer.from_pretrained("""facebook/bart-large"""	)




				@cached_property
				def 							snake_case     (							self	):



									"""simple docstring"""





									return BartTokenizerFast.from_pretrained("""facebook/bart-large"""	)




				@require_torch
				def 							snake_case     (							self	):



									"""simple docstring"""





									_lowerCAmelCase      			=					["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
									_lowerCAmelCase      			=					[0, 250, 251, 17818, 13, 39186, 1938, 4, 2]

									for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
														_lowerCAmelCase      			=					tokenizer(_snake_case		,       max_length=len(_snake_case	)		,       padding=_snake_case		,       return_tensors="""pt"""	)
														self.assertIsInstance(_snake_case		,       _snake_case	)

														self.assertEqual((2, 9)		,       batch.input_ids.shape	)
														self.assertEqual((2, 9)		,       batch.attention_mask.shape	)
														_lowerCAmelCase      			=					batch.input_ids.tolist()[0]
														self.assertListEqual(_snake_case		,       _snake_case	)
														# Test that special tokens are reset




				@require_torch
				def 							snake_case     (							self	):



									"""simple docstring"""





									_lowerCAmelCase      			=					["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
									for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
														_lowerCAmelCase      			=					tokenizer(_snake_case		,       padding=_snake_case		,       return_tensors="""pt"""	)
														# check if input_ids are returned and no labels
														self.assertIn("""input_ids"""		,       _snake_case	)
														self.assertIn("""attention_mask"""		,       _snake_case	)
														self.assertNotIn("""labels"""		,       _snake_case	)
														self.assertNotIn("""decoder_attention_mask"""		,       _snake_case	)




				@require_torch
				def 							snake_case     (							self	):



									"""simple docstring"""





									_lowerCAmelCase      			=					[
									    """Summary of the text.""",
									    """Another summary.""",
									]
									for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
														_lowerCAmelCase      			=					tokenizer(text_target=_snake_case		,       max_length=32		,       padding="""max_length"""		,       return_tensors="""pt"""	)
														self.assertEqual(32		,       targets["""input_ids"""].shape[1]	)




				@require_torch
				def 							snake_case     (							self	):



									"""simple docstring"""





									for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
														_lowerCAmelCase      			=					tokenizer(
														    ["""I am a small frog""" * 1024, """I am a small frog"""]		,       padding=_snake_case		,       truncation=_snake_case		,       return_tensors="""pt"""	)
														self.assertIsInstance(_snake_case		,       _snake_case	)
														self.assertEqual(batch.input_ids.shape		,       (2, 1024)	)




				@require_torch
				def 							snake_case     (							self	):



									"""simple docstring"""





									_lowerCAmelCase      			=					["""A long paragraph for summarization."""]
									_lowerCAmelCase      			=					[
									    """Summary of the text.""",
									]
									for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
														_lowerCAmelCase      			=					tokenizer(_snake_case		,       return_tensors="""pt"""	)
														_lowerCAmelCase      			=					tokenizer(text_target=_snake_case		,       return_tensors="""pt"""	)
														_lowerCAmelCase      			=					inputs["""input_ids"""]
														_lowerCAmelCase      			=					targets["""input_ids"""]
														self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item()	)
														self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item()	)
														self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item()	)
														self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item()	)




				def 							snake_case     (							self	):



									"""simple docstring"""





									pass




				def 							snake_case     (							self	):



									"""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(_snake_case		,       **_snake_case	)
																			_lowerCAmelCase      			=					self.tokenizer_class.from_pretrained(_snake_case		,       **_snake_case	)
																			_lowerCAmelCase      			=					"""A, <mask> AllenNLP sentence."""
																			_lowerCAmelCase      			=					tokenizer_r.encode_plus(_snake_case		,       add_special_tokens=_snake_case		,       return_token_type_ids=_snake_case	)
																			_lowerCAmelCase      			=					tokenizer_p.encode_plus(_snake_case		,       add_special_tokens=_snake_case		,       return_token_type_ids=_snake_case	)

																			# token_type_ids should put 0 everywhere
																			self.assertEqual(sum(tokens_r["""token_type_ids"""]	)		,       sum(tokens_p["""token_type_ids"""]	)	)

																			# attention_mask should put 1 everywhere, so sum over length should be 1
																			self.assertEqual(
																			    sum(tokens_r["""attention_mask"""]	) / len(tokens_r["""attention_mask"""]	)		,       sum(tokens_p["""attention_mask"""]	) / len(tokens_p["""attention_mask"""]	)		,       )

																			_lowerCAmelCase      			=					tokenizer_r.convert_ids_to_tokens(tokens_r["""input_ids"""]	)
																			_lowerCAmelCase      			=					tokenizer_p.convert_ids_to_tokens(tokens_p["""input_ids"""]	)

																			# Rust correctly handles the space before the mask while python doesnt
																			self.assertSequenceEqual(tokens_p["""input_ids"""]		,       [0, 250, 6, 50264, 3823, 487, 21992, 3645, 4, 2]	)
																			self.assertSequenceEqual(tokens_r["""input_ids"""]		,       [0, 250, 6, 50264, 3823, 487, 21992, 3645, 4, 2]	)

																			self.assertSequenceEqual(
																			    _snake_case		,       ["""<s>""", """A""", """,""", """<mask>""", """ĠAllen""", """N""", """LP""", """Ġsentence""", """.""", """</s>"""]	)
																			self.assertSequenceEqual(
																			    _snake_case		,       ["""<s>""", """A""", """,""", """<mask>""", """ĠAllen""", """N""", """LP""", """Ġsentence""", """.""", """</s>"""]	)








import unittest

from transformers import SpeechaTextaConfig
from transformers.testing_utils import is_torch_available, require_torch, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
	import torch

	from transformers.models.speech_to_text_a.modeling_speech_to_text_a import (
	    SpeechaTextaDecoder,
	    SpeechaTextaForCausalLM,
	)





@require_torch
class       __lowerCAmelCase				:




				def __init__(							self		,       _snake_case		,       _snake_case=99		,       _snake_case=13		,       _snake_case=16		,       _snake_case=7		,       _snake_case=True		,       _snake_case=True		,       _snake_case=True		,       _snake_case=False		,       _snake_case=True		,       _snake_case=2		,       _snake_case=32		,       _snake_case=4		,       _snake_case=4		,       _snake_case=30		,       _snake_case=0		,       _snake_case=1		,       _snake_case=2		,       _snake_case=None		,       ):



									"""simple docstring"""





									_lowerCAmelCase      			=					parent
									_lowerCAmelCase      			=					batch_size
									_lowerCAmelCase      			=					decoder_seq_length
									# For common tests
									_lowerCAmelCase      			=					self.decoder_seq_length
									_lowerCAmelCase      			=					is_training
									_lowerCAmelCase      			=					use_attention_mask
									_lowerCAmelCase      			=					use_labels

									_lowerCAmelCase      			=					vocab_size
									_lowerCAmelCase      			=					d_model
									_lowerCAmelCase      			=					d_model
									_lowerCAmelCase      			=					decoder_layers
									_lowerCAmelCase      			=					decoder_layers
									_lowerCAmelCase      			=					decoder_ffn_dim
									_lowerCAmelCase      			=					decoder_attention_heads
									_lowerCAmelCase      			=					decoder_attention_heads
									_lowerCAmelCase      			=					eos_token_id
									_lowerCAmelCase      			=					bos_token_id
									_lowerCAmelCase      			=					pad_token_id
									_lowerCAmelCase      			=					decoder_start_token_id
									_lowerCAmelCase      			=					use_cache
									_lowerCAmelCase      			=					max_position_embeddings

									_lowerCAmelCase      			=					None
									_lowerCAmelCase      			=					decoder_seq_length
									_lowerCAmelCase      			=					2
									_lowerCAmelCase      			=					1




				def 							snake_case     (							self	):



									"""simple docstring"""





									_lowerCAmelCase      			=					ids_tensor([self.batch_size, self.decoder_seq_length]		,       self.vocab_size	)

									_lowerCAmelCase      			=					None
									if self.use_attention_mask:
														_lowerCAmelCase      			=					ids_tensor([self.batch_size, self.decoder_seq_length]		,       vocab_size=2	)

									_lowerCAmelCase      			=					None
									if self.use_labels:
														_lowerCAmelCase      			=					ids_tensor([self.batch_size, self.decoder_seq_length]		,       self.vocab_size	)

									_lowerCAmelCase      			=					SpeechaTextaConfig(
									    vocab_size=self.vocab_size		,       d_model=self.d_model		,       decoder_layers=self.decoder_layers		,       decoder_ffn_dim=self.decoder_ffn_dim		,       decoder_attention_heads=self.decoder_attention_heads		,       eos_token_id=self.eos_token_id		,       bos_token_id=self.bos_token_id		,       use_cache=self.use_cache		,       pad_token_id=self.pad_token_id		,       decoder_start_token_id=self.decoder_start_token_id		,       max_position_embeddings=self.max_position_embeddings		,       )

									return (
									    config,
									    input_ids,
									    attention_mask,
									    lm_labels,
									)




				def 							snake_case     (							self		,       _snake_case		,       _snake_case		,       _snake_case		,       _snake_case		,       ):



									"""simple docstring"""





									_lowerCAmelCase      			=					True
									_lowerCAmelCase      			=					SpeechaTextaDecoder(config=_snake_case	).to(_snake_case	).eval()
									_lowerCAmelCase      			=					input_ids[:2]

									input_ids[input_ids == 0] += 1
									# first forward pass
									_lowerCAmelCase      			=					model(_snake_case		,       use_cache=_snake_case	)
									_lowerCAmelCase      			=					model(_snake_case	)
									_lowerCAmelCase      			=					model(_snake_case		,       use_cache=_snake_case	)

									self.parent.assertTrue(len(_snake_case	) == len(_snake_case	)	)
									self.parent.assertTrue(len(_snake_case	) == len(_snake_case	) + 1	)

									_lowerCAmelCase      			=					outputs["""past_key_values"""]

									# create hypothetical next token and extent to next_input_ids
									_lowerCAmelCase      			=					ids_tensor((2, 1)		,       config.vocab_size - 1	) + 1

									# append to next input_ids and
									_lowerCAmelCase      			=					torch.cat([input_ids, next_tokens]		,       dim=-1	)

									print(_snake_case	)

									_lowerCAmelCase      			=					model(_snake_case	)["""last_hidden_state"""]
									_lowerCAmelCase      			=					model(_snake_case		,       past_key_values=_snake_case	)["""last_hidden_state"""]

									# select random slice
									_lowerCAmelCase      			=					ids_tensor((1,)		,       output_from_past.shape[-1]	).item()
									_lowerCAmelCase      			=					output_from_no_past[:, next_input_ids.shape[-1] - 1, random_slice_idx].detach()
									_lowerCAmelCase      			=					output_from_past[:, 0, random_slice_idx].detach()

									# test that outputs are equal for slice
									assert torch.allclose(_snake_case		,       _snake_case		,       atol=1e-3	)




				def 							snake_case     (							self	):



									"""simple docstring"""





									_lowerCAmelCase      			=					self.prepare_config_and_inputs()
									(
									    (
									    _lowerCAmelCase
									)			,			(
									    _lowerCAmelCase
									)			,			(
									    _lowerCAmelCase
									)			,			(
									    _lowerCAmelCase
									)			,			
									)      			=					config_and_inputs

									_lowerCAmelCase      			=					{
									    """input_ids""": input_ids,
									    """attention_mask""": attention_mask,
									}
									return config, inputs_dict





@require_torch
class       __lowerCAmelCase				(  lowerCamelCase__ ,		lowerCamelCase__ ,		lowerCamelCase__ ,		unittest.TestCase ):
				__lowerCamelCase				=       (SpeechaTextaDecoder, SpeechaTextaForCausalLM) if is_torch_available() else ()
				__lowerCamelCase				=       (SpeechaTextaForCausalLM,) if is_torch_available() else ()
				__lowerCamelCase				=       {'''text-generation''': SpeechaTextaForCausalLM} if is_torch_available() else {}
				__lowerCamelCase				=       True
				__lowerCamelCase				=       False




				def 							snake_case     (							self		,       ):



									"""simple docstring"""





									_lowerCAmelCase      			=					SpeechaTextaStandaloneDecoderModelTester(self		,       is_training=_snake_case	)
									_lowerCAmelCase      			=					ConfigTester(self		,       config_class=_snake_case	)




				def 							snake_case     (							self	):



									"""simple docstring"""





									pass




				def 							snake_case     (							self	):



									"""simple docstring"""





									pass




				def 							snake_case     (							self	):



									"""simple docstring"""





									pass




				def 							snake_case     (							self	):



									"""simple docstring"""





									self.config_tester.run_common_tests()




				def 							snake_case     (							self	):



									"""simple docstring"""





									_lowerCAmelCase      			=					self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_decoder_model_past(*_snake_case	)




				def 							snake_case     (							self	):



									"""simple docstring"""





									return



