






'''simple docstring'''







from __future__ import annotations

import unittest

from transformers import AutoTokenizer, PegasusConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
			import tensorflow as tf

			from transformers import TFAutoModelForSeqaSeqLM, TFPegasusForConditionalGeneration, TFPegasusModel

@require_tf
class _lowercase			:
							a          = PegasusConfig
							a          = {}
							a          = """gelu"""




							def __init__(							self:       List[Any]    ,							UpperCamelCase__:       int    ,							UpperCamelCase__:       Optional[int]=13    ,							UpperCamelCase__:       Dict=7    ,							UpperCamelCase__:       Tuple=True    ,							UpperCamelCase__:       List[str]=False    ,							UpperCamelCase__:       Dict=99    ,							UpperCamelCase__:       Any=32    ,							UpperCamelCase__:       Union[str, Any]=2    ,							UpperCamelCase__:       str=4    ,							UpperCamelCase__:       Optional[int]=37    ,							UpperCamelCase__:       List[Any]=0.1    ,							UpperCamelCase__:       List[str]=0.1    ,							UpperCamelCase__:       Optional[int]=40    ,							UpperCamelCase__:       Any=2    ,							UpperCamelCase__:       Optional[int]=1    ,							UpperCamelCase__:       int=0    ,							):
														lowerCamelCase__      :			int 						= parent
														lowerCamelCase__      :			int 						= batch_size
														lowerCamelCase__      :			str 						= seq_length
														lowerCamelCase__      :			List[Any] 						= is_training
														lowerCamelCase__      :			List[Any] 						= use_labels
														lowerCamelCase__      :			Optional[Any] 						= vocab_size
														lowerCamelCase__      :			List[Any] 						= hidden_size
														lowerCamelCase__      :			Optional[Any] 						= num_hidden_layers
														lowerCamelCase__      :			Optional[int] 						= num_attention_heads
														lowerCamelCase__      :			Tuple 						= intermediate_size

														lowerCamelCase__      :			Optional[int] 						= hidden_dropout_prob
														lowerCamelCase__      :			Optional[Any] 						= attention_probs_dropout_prob
														lowerCamelCase__      :			Tuple 						= max_position_embeddings
														lowerCamelCase__      :			Union[str, Any] 						= eos_token_id
														lowerCamelCase__      :			Any 						= pad_token_id
														lowerCamelCase__      :			Tuple 						= bos_token_id




							def 	lowerCamelCase_       (							self:       Union[str, Any]  ):
														lowerCamelCase__      :			Optional[int] 						= ids_tensor([self.batch_size, self.seq_length - 1]    ,							self.vocab_size  )
														lowerCamelCase__      :			int 						= tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size  )    ,							1  )
														lowerCamelCase__      :			Dict 						= tf.concat([input_ids, eos_tensor]    ,							axis=1  )

														lowerCamelCase__      :			Union[str, Any] 						= ids_tensor([self.batch_size, self.seq_length]    ,							self.vocab_size  )

														lowerCamelCase__      :			List[str] 						= self.config_cls(
														    vocab_size=self.vocab_size    ,							d_model=self.hidden_size    ,							encoder_layers=self.num_hidden_layers    ,							decoder_layers=self.num_hidden_layers    ,							encoder_attention_heads=self.num_attention_heads    ,							decoder_attention_heads=self.num_attention_heads    ,							encoder_ffn_dim=self.intermediate_size    ,							decoder_ffn_dim=self.intermediate_size    ,							dropout=self.hidden_dropout_prob    ,							attention_dropout=self.attention_probs_dropout_prob    ,							max_position_embeddings=self.max_position_embeddings    ,							eos_token_ids=[2]    ,							bos_token_id=self.bos_token_id    ,							pad_token_id=self.pad_token_id    ,							decoder_start_token_id=self.pad_token_id    ,							**self.config_updates    ,							)
														lowerCamelCase__      :			int 						= prepare_pegasus_inputs_dict(lowercase_    ,							lowercase_    ,							lowercase_  )
														return config, inputs_dict




							def 	lowerCamelCase_       (							self:       int    ,							UpperCamelCase__:       Any    ,							UpperCamelCase__:       Union[str, Any]  ):
														lowerCamelCase__      :			Any 						= TFPegasusModel(config=lowercase_  ).get_decoder()
														lowerCamelCase__      :			Optional[Any] 						= inputs_dict["""input_ids"""]

														lowerCamelCase__      :			Dict 						= input_ids[:1, :]
														lowerCamelCase__      :			int 						= inputs_dict["""attention_mask"""][:1, :]
														lowerCamelCase__      :			Dict 						= inputs_dict["""head_mask"""]
														lowerCamelCase__      :			str 						= 1

														# first forward pass
														lowerCamelCase__      :			Optional[Any] 						= model(lowercase_    ,							attention_mask=lowercase_    ,							head_mask=lowercase_    ,							use_cache=lowercase_  )

														lowerCamelCase__      :			Dict 						= outputs.to_tuple()

														# create hypothetical next token and extent to next_input_ids
														lowerCamelCase__      :			Tuple 						= ids_tensor((self.batch_size, 3)    ,							config.vocab_size  )
														lowerCamelCase__      :			Optional[int] 						= tf.cast(ids_tensor((self.batch_size, 3)    ,							2  )    ,							tf.inta  )

														# append to next input_ids and
														lowerCamelCase__      :			List[Any] 						= tf.concat([input_ids, next_tokens]    ,							axis=-1  )
														lowerCamelCase__      :			Optional[Any] 						= tf.concat([attention_mask, next_attn_mask]    ,							axis=-1  )

														lowerCamelCase__      :			int 						= model(lowercase_    ,							attention_mask=lowercase_  )[0]
														lowerCamelCase__      :			int 						= model(lowercase_    ,							attention_mask=lowercase_    ,							past_key_values=lowercase_  )[0]

														self.parent.assertEqual(next_tokens.shape[1]    ,							output_from_past.shape[1]  )

														# select random slice
														lowerCamelCase__      :			str 						= int(ids_tensor((1,)    ,							output_from_past.shape[-1]  )  )
														lowerCamelCase__      :			int 						= output_from_no_past[:, -3:, random_slice_idx]
														lowerCamelCase__      :			Dict 						= output_from_past[:, :, random_slice_idx]

														# test that outputs are equal for slice
														tf.debugging.assert_near(lowercase_    ,							lowercase_    ,							rtol=1e-3  )





def 				SCREAMING_SNAKE_CASE_ (UpperCamelCase       ,    UpperCamelCase       ,    UpperCamelCase       ,    UpperCamelCase=None       ,    UpperCamelCase=None       ,    UpperCamelCase=None       ,    UpperCamelCase=None       ,    UpperCamelCase=None       ,    )   -> Dict:
							if attention_mask is None:
														lowerCamelCase__      :			List[Any] 						= tf.cast(tf.math.not_equal(UpperCAmelCase__       ,    config.pad_token_id   )       ,    tf.inta   )
							if decoder_attention_mask is None:
														lowerCamelCase__      :			int 						= tf.concat(
														    [
														        tf.ones(decoder_input_ids[:, :1].shape       ,    dtype=tf.inta   ),
														        tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:]       ,    config.pad_token_id   )       ,    tf.inta   ),
														    ]       ,    axis=-1       ,    )
							if head_mask is None:
														lowerCamelCase__      :			Optional[int] 						= tf.ones((config.encoder_layers, config.encoder_attention_heads)   )
							if decoder_head_mask is None:
														lowerCamelCase__      :			Any 						= tf.ones((config.decoder_layers, config.decoder_attention_heads)   )
							if cross_attn_head_mask is None:
														lowerCamelCase__      :			Optional[Any] 						= tf.ones((config.decoder_layers, config.decoder_attention_heads)   )
							return {
							    "input_ids": input_ids,
							    "decoder_input_ids": decoder_input_ids,
							    "attention_mask": attention_mask,
							    "decoder_attention_mask": decoder_attention_mask,
							    "head_mask": head_mask,
							    "decoder_head_mask": decoder_head_mask,
							    "cross_attn_head_mask": cross_attn_head_mask,
							}

@require_tf
class _lowercase			(       _UpperCAmelCase       ,   _UpperCAmelCase       ,   unittest.TestCase	):
							a          = (TFPegasusForConditionalGeneration, TFPegasusModel) if is_tf_available() else ()
							a          = (TFPegasusForConditionalGeneration,) if is_tf_available() else ()
							a          = (
							    {
							        """conversational""": TFPegasusForConditionalGeneration,
							        """feature-extraction""": TFPegasusModel,
							        """summarization""": TFPegasusForConditionalGeneration,
							        """text2text-generation""": TFPegasusForConditionalGeneration,
							        """translation""": TFPegasusForConditionalGeneration,
							    }
							    if is_tf_available()
							    else {}
							)
							a          = True
							a          = False
							a          = False




							def 	lowerCamelCase_       (							self:       List[str]  ):
														lowerCamelCase__      :			Dict 						= TFPegasusModelTester(self  )
														lowerCamelCase__      :			Any 						= ConfigTester(self    ,							config_class=lowercase_  )




							def 	lowerCamelCase_       (							self:       Dict  ):
														self.config_tester.run_common_tests()




							def 	lowerCamelCase_       (							self:       List[Any]  ):
														lowerCamelCase__      :			int 						= self.model_tester.prepare_config_and_inputs_for_common()
														self.model_tester.check_decoder_model_past_large_inputs(*lowercase_  )



@require_sentencepiece
@require_tokenizers
@require_tf
class _lowercase			(       unittest.TestCase	):
							a          = [
							    """ PG&E stated it scheduled the blackouts in response to forecasts for high winds amid dry conditions. The aim is to reduce the risk of wildfires. Nearly 800 thousand customers were scheduled to be affected by the shutoffs which were expected to last through at least midday tomorrow.""",
							    """ The London trio are up for best UK act and best album, as well as getting two nominations in the best song category.\"We got told like this morning \'Oh I think you\'re nominated\'\", said Dappy.\"And I was like \'Oh yeah, which one?\' And now we\'ve got nominated for four awards. I mean, wow!\"Bandmate Fazer added: \"We thought it\'s best of us to come down and mingle with everyone and say hello to the cameras. And now we find we\'ve got four nominations.\"The band have two shots at the best song prize, getting the nod for their Tynchy Stryder collaboration Number One, and single Strong Again.Their album Uncle B will also go up against records by the likes of Beyonce and Kanye West.N-Dubz picked up the best newcomer Mobo in 2007, but female member Tulisa said they wouldn\'t be too disappointed if they didn\'t win this time around.\"At the end of the day we\'re grateful to be where we are in our careers.\"If it don\'t happen then it don\'t happen - live to fight another day and keep on making albums and hits for the fans.\"Dappy also revealed they could be performing live several times on the night.The group will be doing Number One and also a possible rendition of the War Child single, I Got Soul.The charity song is a  re-working of The Killers\' All These Things That I\'ve Done and is set to feature artists like Chipmunk, Ironik and Pixie Lott.This year\'s Mobos will be held outside of London for the first time, in Glasgow on 30 September.N-Dubz said they were looking forward to performing for their Scottish fans and boasted about their recent shows north of the border.\"We just done Edinburgh the other day,\" said Dappy.\"We smashed up an N-Dubz show over there. We done Aberdeen about three or four months ago - we smashed up that show over there! Everywhere we go we smash it up!\" """,
							]
							a          = [
							    """California\'s largest electricity provider has cut power to hundreds of thousands of customers in an effort to"""
							    """ reduce the risk of wildfires.""",
							    """N-Dubz have revealed they\'re \"grateful\" to have been nominated for four Mobo Awards.""",
							]  # differs slightly from pytorch, likely due to numerical differences in linear layers
							a          = """google/pegasus-xsum"""




							@cached_property
							def 	lowerCamelCase_       (							self:       Dict  ):
														return AutoTokenizer.from_pretrained(self.model_name  )




							@cached_property
							def 	lowerCamelCase_       (							self:       List[str]  ):
														lowerCamelCase__      :			Any 						= TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name  )
														return model




							def 	lowerCamelCase_       (							self:       Union[str, Any]    ,							**UpperCamelCase__:       Any  ):
														lowerCamelCase__      :			List[Any] 						= self.translate_src_text(**lowercase_  )
														assert self.expected_text == generated_words




							def 	lowerCamelCase_       (							self:       Union[str, Any]    ,							**UpperCamelCase__:       List[Any]  ):
														lowerCamelCase__      :			Optional[Any] 						= self.tokenizer(self.src_text    ,							**lowercase_    ,							padding=lowercase_    ,							return_tensors="""tf"""  )
														lowerCamelCase__      :			int 						= self.model.generate(
														    model_inputs.input_ids    ,							attention_mask=model_inputs.attention_mask    ,							num_beams=2    ,							use_cache=lowercase_    ,							)
														lowerCamelCase__      :			Union[str, Any] 						= self.tokenizer.batch_decode(generated_ids.numpy()    ,							skip_special_tokens=lowercase_  )
														return generated_words




							@slow
							def 	lowerCamelCase_       (							self:       Any  ):
														self._assert_generated_batch_equal_expected()









'''simple docstring'''







import os
from typing import Optional

import fsspec
from fsspec.archive import AbstractArchiveFileSystem
from fsspec.utils import DEFAULT_BLOCK_SIZE

class _lowercase			(       _lowercase	):
							a          = """"""
							a          = (
							    None  # protocol passed in prefix to the url. ex: "gzip", for gzip://file.txt::http://foo.bar/file.txt.gz
							)
							a          = None  # compression type in fsspec. ex: "gzip"
							a          = None  # extension of the filename to strip. ex: "".gz" to get file.txt from file.txt.gz




							def __init__(							self:       str    ,							UpperCamelCase__:       str = ""    ,							UpperCamelCase__:       Optional[str] = None    ,							UpperCamelCase__:       Optional[dict] = None    ,							**UpperCamelCase__:       List[Any]  ):
														super().__init__(self    ,							**UpperCamelCase__  )
														# always open as "rb" since fsspec can then use the TextIOWrapper to make it work for "r" mode
														lowerCamelCase__      :			List[Any] 						= fsspec.open(
														    UpperCamelCase__    ,							mode="""rb"""    ,							protocol=UpperCamelCase__    ,							compression=self.compression    ,							client_kwargs={
														        """requote_redirect_url""": False,  # see https://github.com/huggingface/datasets/pull/5459
														        """trust_env""": True,  # Enable reading proxy env variables.
														        **(target_options or {}).pop("""client_kwargs"""    ,							{}  ),  # To avoid issues if it was already passed.
														    }    ,							**(target_options or {})    ,							)
														lowerCamelCase__      :			str 						= os.path.basename(self.file.path.split("""::"""  )[0]  )
														lowerCamelCase__      :			Union[str, Any] 						= (
														    self.compressed_name[: self.compressed_name.rindex("""."""  )]
														    if """.""" in self.compressed_name
														    else self.compressed_name
														)
														lowerCamelCase__      :			Tuple 						= None




							@classmethod
							def 	lowerCamelCase_       (							cls:       Optional[int]    ,							UpperCamelCase__:       Optional[int]  ):
														# compressed file paths are always relative to the archive root
														return super()._strip_protocol(UpperCamelCase__  ).lstrip("""/"""  )




							def 	lowerCamelCase_       (							self:       Tuple  ):
														if self.dir_cache is None:
																					lowerCamelCase__      :			Dict 						= {**self.file.fs.info(self.file.path  ), """name""": self.uncompressed_name}
																					lowerCamelCase__      :			int 						= {f["""name"""]: f}




							def 	lowerCamelCase_       (							self:       List[str]    ,							UpperCamelCase__:       str  ):
														return self.file.open().read()




							def 	lowerCamelCase_       (							self:       Optional[int]    ,							UpperCamelCase__:       str    ,							UpperCamelCase__:       str = "rb"    ,							UpperCamelCase__:       Optional[int]=None    ,							UpperCamelCase__:       Tuple=True    ,							UpperCamelCase__:       Tuple=None    ,							**UpperCamelCase__:       Optional[Any]    ,							):
														lowerCamelCase__      :			Union[str, Any] 						= self._strip_protocol(UpperCamelCase__  )
														if mode != "rb":
																					raise ValueError(F'''Tried to read with mode {mode} on file {self.file.path} opened with mode \'rb\''''  )
														return self.file.open()



class _lowercase			(       _lowercase	):
							a          = """bz2"""
							a          = """bz2"""
							a          = """.bz2"""



class _lowercase			(       _lowercase	):
							a          = """gzip"""
							a          = """gzip"""
							a          = """.gz"""



class _lowercase			(       _lowercase	):
							a          = """lz4"""
							a          = """lz4"""
							a          = """.lz4"""



class _lowercase			(       _lowercase	):
							a          = """xz"""
							a          = """xz"""
							a          = """.xz"""



class _lowercase			(       _lowercase	):
							a          = """zstd"""
							a          = """zstd"""
							a          = """.zst"""




							def __init__(							self:       int    ,							UpperCamelCase__:       str    ,							UpperCamelCase__:       str = "rb"    ,							UpperCamelCase__:       Optional[str] = None    ,							UpperCamelCase__:       Optional[dict] = None    ,							UpperCamelCase__:       int = DEFAULT_BLOCK_SIZE    ,							**UpperCamelCase__:       Dict    ,							):
														super().__init__(
														    fo=UpperCamelCase__    ,							mode=UpperCamelCase__    ,							target_protocol=UpperCamelCase__    ,							target_options=UpperCamelCase__    ,							block_size=UpperCamelCase__    ,							**UpperCamelCase__    ,							)
														# We need to wrap the zstd decompressor to avoid this error in fsspec==2021.7.0 and zstandard==0.15.2:
														#
														# File "/Users/user/.virtualenvs/hf-datasets/lib/python3.7/site-packages/fsspec/core.py", line 145, in open
														#     out.close = close
														# AttributeError: 'zstd.ZstdDecompressionReader' object attribute 'close' is read-only
														#
														# see https://github.com/intake/filesystem_spec/issues/725
														lowerCamelCase__      :			Tuple 						= self.file.__enter__

														class _lowercase			:




																					def __init__(							self:       Optional[int]    ,							UpperCamelCase__:       Any  ):
																												lowerCamelCase__      :			Optional[int] 						= file_




																					def __enter__(							self:       List[Any]  ):
																												self._file.__enter__()
																												return self




																					def __exit__(							self:       Any    ,							*UpperCamelCase__:       str    ,							**UpperCamelCase__:       Any  ):
																												self._file.__exit__(*UpperCamelCase__    ,							**UpperCamelCase__  )




																					def __iter__(							self:       Any  ):
																												return iter(self._file  )




																					def 	lowerCamelCase_       (							self:       List[Any]  ):
																												return next(self._file  )




																					def __getattr__(							self:       List[str]    ,							UpperCamelCase__:       Dict  ):
																												return getattr(self._file    ,							UpperCamelCase__  )

														def fixed_enter(*UpperCamelCase__:       Union[str, Any]    ,							**UpperCamelCase__:       List[str]  ):
																					return WrappedFile(_enter(*UpperCamelCase__    ,							**UpperCamelCase__  )  )

														lowerCamelCase__      :			Optional[Any] 						= fixed_enter

