






'''simple docstring'''







from __future__ import annotations

import random
import unittest

from transformers import TransfoXLConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

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 (
			    TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
			    TFTransfoXLForSequenceClassification,
			    TFTransfoXLLMHeadModel,
			    TFTransfoXLModel,
			)

class _lowercase			:




							def __init__(							self:       List[Any]    ,							UpperCamelCase__:       int    ,							):
														lowerCamelCase__      :			str 						= parent
														lowerCamelCase__      :			Tuple 						= 13
														lowerCamelCase__      :			int 						= 7
														lowerCamelCase__      :			Tuple 						= 30
														lowerCamelCase__      :			str 						= self.seq_length + self.mem_len
														lowerCamelCase__      :			Any 						= 15
														lowerCamelCase__      :			Union[str, Any] 						= True
														lowerCamelCase__      :			Optional[Any] 						= True
														lowerCamelCase__      :			str 						= 99
														lowerCamelCase__      :			str 						= [10, 50, 80]
														lowerCamelCase__      :			int 						= 32
														lowerCamelCase__      :			Dict 						= 32
														lowerCamelCase__      :			Tuple 						= 4
														lowerCamelCase__      :			str 						= 8
														lowerCamelCase__      :			Dict 						= 128
														lowerCamelCase__      :			Optional[int] 						= 2
														lowerCamelCase__      :			Optional[int] 						= 2
														lowerCamelCase__      :			Dict 						= None
														lowerCamelCase__      :			List[Any] 						= 1
														lowerCamelCase__      :			Dict 						= 0
														lowerCamelCase__      :			List[str] 						= 3
														lowerCamelCase__      :			Optional[Any] 						= self.vocab_size - 1
														lowerCamelCase__      :			List[Any] 						= 0.01




							def 	lowerCamelCase_       (							self:       List[Any]  ):
														lowerCamelCase__      :			Optional[int] 						= ids_tensor([self.batch_size, self.seq_length]    ,							self.vocab_size  )
														lowerCamelCase__      :			Union[str, Any] 						= ids_tensor([self.batch_size, self.seq_length]    ,							self.vocab_size  )

														lowerCamelCase__      :			Optional[int] 						= None
														if self.use_labels:
																					lowerCamelCase__      :			Optional[int] 						= ids_tensor([self.batch_size, self.seq_length]    ,							self.vocab_size  )

														lowerCamelCase__      :			Dict 						= TransfoXLConfig(
														    vocab_size=self.vocab_size    ,							mem_len=self.mem_len    ,							clamp_len=self.clamp_len    ,							cutoffs=self.cutoffs    ,							d_model=self.hidden_size    ,							d_embed=self.d_embed    ,							n_head=self.num_attention_heads    ,							d_head=self.d_head    ,							d_inner=self.d_inner    ,							div_val=self.div_val    ,							n_layer=self.num_hidden_layers    ,							eos_token_id=self.eos_token_id    ,							pad_token_id=self.vocab_size - 1    ,							init_range=self.init_range    ,							num_labels=self.num_labels    ,							)

														return (config, input_ids_a, input_ids_a, lm_labels)




							def 	lowerCamelCase_       (							self:       List[str]  ):
														random.seed(self.seed  )
														tf.random.set_seed(self.seed  )




							def 	lowerCamelCase_       (							self:       Optional[int]    ,							UpperCamelCase__:       Union[str, Any]    ,							UpperCamelCase__:       Any    ,							UpperCamelCase__:       Dict    ,							UpperCamelCase__:       Optional[int]  ):
														lowerCamelCase__      :			Union[str, Any] 						= TFTransfoXLModel(UpperCamelCase__  )

														lowerCamelCase__						,	lowerCamelCase__      :			Union[str, Any] 						= model(UpperCamelCase__  ).to_tuple()

														lowerCamelCase__      :			List[str] 						= {"""input_ids""": input_ids_a, """mems""": mems_a}

														lowerCamelCase__						,	lowerCamelCase__      :			List[Any] 						= model(UpperCamelCase__  ).to_tuple()

														self.parent.assertEqual(hidden_states_a.shape    ,							(self.batch_size, self.seq_length, self.hidden_size)  )
														self.parent.assertEqual(hidden_states_a.shape    ,							(self.batch_size, self.seq_length, self.hidden_size)  )
														self.parent.assertListEqual(
														    [mem.shape for mem in mems_a]    ,							[(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers    ,							)
														self.parent.assertListEqual(
														    [mem.shape for mem in mems_a]    ,							[(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers    ,							)




							def 	lowerCamelCase_       (							self:       Dict    ,							UpperCamelCase__:       Dict    ,							UpperCamelCase__:       Optional[int]    ,							UpperCamelCase__:       List[Any]    ,							UpperCamelCase__:       Optional[int]  ):
														lowerCamelCase__      :			Tuple 						= TFTransfoXLLMHeadModel(UpperCamelCase__  )

														lowerCamelCase__						,	lowerCamelCase__      :			List[str] 						= model(UpperCamelCase__  ).to_tuple()

														lowerCamelCase__      :			Optional[int] 						= {"""input_ids""": input_ids_a, """labels""": lm_labels}
														lowerCamelCase__						,	lowerCamelCase__      :			str 						= model(UpperCamelCase__  ).to_tuple()

														lowerCamelCase__						,	lowerCamelCase__      :			Any 						= model([input_ids_a, mems_a]  ).to_tuple()

														lowerCamelCase__      :			List[str] 						= {"""input_ids""": input_ids_a, """mems""": mems_a, """labels""": lm_labels}

														lowerCamelCase__						,	lowerCamelCase__      :			Dict 						= model(UpperCamelCase__  ).to_tuple()

														self.parent.assertEqual(lm_logits_a.shape    ,							(self.batch_size, self.seq_length, self.vocab_size)  )
														self.parent.assertListEqual(
														    [mem.shape for mem in mems_a]    ,							[(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers    ,							)

														self.parent.assertEqual(lm_logits_a.shape    ,							(self.batch_size, self.seq_length, self.vocab_size)  )
														self.parent.assertListEqual(
														    [mem.shape for mem in mems_a]    ,							[(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers    ,							)




							def 	lowerCamelCase_       (							self:       List[str]    ,							UpperCamelCase__:       Dict    ,							UpperCamelCase__:       Union[str, Any]    ,							UpperCamelCase__:       List[str]    ,							UpperCamelCase__:       Optional[Any]  ):
														lowerCamelCase__      :			Dict 						= TFTransfoXLForSequenceClassification(UpperCamelCase__  )
														lowerCamelCase__      :			List[Any] 						= model(UpperCamelCase__  )
														self.parent.assertEqual(result.logits.shape    ,							(self.batch_size, self.num_labels)  )




							def 	lowerCamelCase_       (							self:       Any  ):
														lowerCamelCase__      :			Tuple 						= self.prepare_config_and_inputs()
														((lowerCamelCase__)						,	(lowerCamelCase__)						,	(lowerCamelCase__)						,	(lowerCamelCase__))      :			Optional[Any] 						= config_and_inputs
														lowerCamelCase__      :			Dict 						= {"""input_ids""": input_ids_a}
														return config, inputs_dict



@require_tf
class _lowercase			(       _lowercase       ,   _lowercase       ,   unittest.TestCase	):
							a          = (
							    (TFTransfoXLModel, TFTransfoXLLMHeadModel, TFTransfoXLForSequenceClassification) if is_tf_available() else ()
							)
							a          = () if is_tf_available() else ()
							a          = (
							    {
							        """feature-extraction""": TFTransfoXLModel,
							        """text-classification""": TFTransfoXLForSequenceClassification,
							        """text-generation""": TFTransfoXLLMHeadModel,
							        """zero-shot""": TFTransfoXLForSequenceClassification,
							    }
							    if is_tf_available()
							    else {}
							)
							# TODO: add this test when TFTransfoXLLMHead has a linear output layer implemented
							a          = False
							a          = False
							a          = False
							a          = False




							def 	lowerCamelCase_       (							self:       str    ,							UpperCamelCase__:       str    ,							UpperCamelCase__:       Tuple    ,							UpperCamelCase__:       int    ,							UpperCamelCase__:       Union[str, Any]    ,							UpperCamelCase__:       str  ):
														if pipeline_test_casse_name == "TextGenerationPipelineTests":
																					# Get `ValueError: AttributeError: 'NoneType' object has no attribute 'new_ones'` or `AssertionError`.
																					# `TransfoXLConfig` was never used in pipeline tests: cannot create a simple
																					# tokenizer.
																					return True

														return False




							def 	lowerCamelCase_       (							self:       str  ):
														lowerCamelCase__      :			List[str] 						= TFTransfoXLModelTester(self  )
														lowerCamelCase__      :			Union[str, Any] 						= ConfigTester(self    ,							config_class=UpperCamelCase__    ,							d_embed=37  )




							def 	lowerCamelCase_       (							self:       List[Any]  ):
														self.config_tester.run_common_tests()




							def 	lowerCamelCase_       (							self:       Dict  ):
														self.model_tester.set_seed()
														lowerCamelCase__      :			Optional[Any] 						= self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_transfo_xl_model(*UpperCamelCase__  )




							def 	lowerCamelCase_       (							self:       List[str]  ):
														self.model_tester.set_seed()
														lowerCamelCase__      :			List[str] 						= self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_transfo_xl_lm_head(*UpperCamelCase__  )




							def 	lowerCamelCase_       (							self:       Any  ):
														lowerCamelCase__      :			Dict 						= self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_transfo_xl_for_sequence_classification(*UpperCamelCase__  )




							def 	lowerCamelCase_       (							self:       Optional[Any]  ):
														lowerCamelCase__						,	lowerCamelCase__      :			str 						= self.model_tester.prepare_config_and_inputs_for_common()
														lowerCamelCase__      :			int 						= [TFTransfoXLForSequenceClassification]

														for model_class in self.all_model_classes:
																					lowerCamelCase__      :			int 						= model_class(UpperCamelCase__  )
																					assert isinstance(model.get_input_embeddings()    ,							tf.keras.layers.Layer  )
																					if model_class in list_other_models_with_output_ebd:
																												lowerCamelCase__      :			Union[str, Any] 						= model.get_output_embeddings()
																												assert isinstance(UpperCamelCase__    ,							tf.keras.layers.Layer  )
																												lowerCamelCase__      :			Any 						= model.get_bias()
																												assert name is None
																					else:
																												lowerCamelCase__      :			List[str] 						= model.get_output_embeddings()
																												assert x is None
																												lowerCamelCase__      :			int 						= model.get_bias()
																												assert name is None




							def 	lowerCamelCase_       (							self:       str  ):
														# TODO JP: Make TransfoXL XLA compliant
														pass




							@slow
							def 	lowerCamelCase_       (							self:       List[Any]  ):
														for model_name in TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																					lowerCamelCase__      :			Tuple 						= TFTransfoXLModel.from_pretrained(UpperCamelCase__  )
																					self.assertIsNotNone(UpperCamelCase__  )




							@unittest.skip(reason="""This model doesn't play well with fit() due to not returning a single loss."""  )
							def 	lowerCamelCase_       (							self:       Tuple  ):
														pass



@require_tf
class _lowercase			(       unittest.TestCase	):




							@unittest.skip("""Skip test until #12651 is resolved."""  )
							@slow
							def 	lowerCamelCase_       (							self:       Tuple  ):
														lowerCamelCase__      :			Any 						= TFTransfoXLLMHeadModel.from_pretrained("""transfo-xl-wt103"""  )
														# fmt: off
														lowerCamelCase__      :			Optional[Any] 						= tf.convert_to_tensor([[33,1_297,2,1,1_009,4,1_109,11_739,4_762,358,5,25,245,22,1_706,17,20_098,5,3_215,21,37,1_110,3,13,1_041,4,24,603,490,2,71_477,20_098,104_447,2,20_961,1,2_604,4,1,329,3,6_224,831,16_002,2,8,603,78_967,29_546,23,803,20,25,416,5,8,232,4,277,6,1_855,4_601,3,29_546,54,8,3_609,5,57_211,49,4,1,277,18,8,1_755,15_691,3,341,25,416,693,42_573,71,17,401,94,31,17_919,2,29_546,7_873,18,1,435,23,11_011,755,5,5_167,3,7_983,98,84,2,29_546,3_267,8,3_609,4,1,4_865,1_075,2,6_087,71,6,346,8,5_854,3,29_546,824,1_400,1_868,2,19,160,2,311,8,5_496,2,20_920,17,25,15_097,3,24,24,0]]    ,							dtype=tf.intaa  )  # noqa: E231
														# fmt: on
														#  In 1991 , the remains of Russian Tsar Nicholas II and his family
														#  ( except for Alexei and Maria ) are discovered .
														#  The voice of Nicholas's young son , Tsarevich Alexei Nikolaevich , narrates the
														#  remainder of the story . 1883 Western Siberia ,
														#  a young Grigori Rasputin is asked by his father and a group of men to perform magic .
														#  Rasputin has a vision and denounces one of the men as a horse thief . Although his
														#  father initially slaps him for making such an accusation , Rasputin watches as the
														#  man is chased outside and beaten . Twenty years later , Rasputin sees a vision of
														#  the Virgin Mary , prompting him to become a priest . Rasputin quickly becomes famous ,
														#  with people , even a bishop , begging for his blessing . <eod> </s> <eos>

														# fmt: off
														lowerCamelCase__      :			Optional[int] 						= [33,1_297,2,1,1_009,4,1_109,11_739,4_762,358,5,25,245,22,1_706,17,20_098,5,3_215,21,37,1_110,3,13,1_041,4,24,603,490,2,71_477,20_098,104_447,2,20_961,1,2_604,4,1,329,3,6_224,831,16_002,2,8,603,78_967,29_546,23,803,20,25,416,5,8,232,4,277,6,1_855,4_601,3,29_546,54,8,3_609,5,57_211,49,4,1,277,18,8,1_755,15_691,3,341,25,416,693,42_573,71,17,401,94,31,17_919,2,29_546,7_873,18,1,435,23,11_011,755,5,5_167,3,7_983,98,84,2,29_546,3_267,8,3_609,4,1,4_865,1_075,2,6_087,71,6,346,8,5_854,3,29_546,824,1_400,1_868,2,19,160,2,311,8,5_496,2,20_920,17,25,15_097,3,24,24,0,33,1,1_857,2,1,1_009,4,1_109,11_739,4_762,358,5,25,245,28,1_110,3,13,1_041,4,24,603,490,2,71_477,20_098,104_447,2,20_961,1,2_604,4,1,329,3,0]  # noqa: E231
														# fmt: on
														#  In 1991, the remains of Russian Tsar Nicholas II and his family (
														#  except for Alexei and Maria ) are discovered. The voice of young son,
														#  Tsarevich Alexei Nikolaevich, narrates the remainder of the story.
														#  1883 Western Siberia, a young Grigori Rasputin is asked by his father
														#  and a group of men to perform magic. Rasputin has a vision and
														#  denounces one of the men as a horse thief. Although his father initially
														#  slaps him for making such an accusation, Rasputin watches as the man
														#  is chased outside and beaten. Twenty years later, Rasputin sees a vision
														#  of the Virgin Mary, prompting him to become a priest.
														#  Rasputin quickly becomes famous, with people, even a bishop, begging for
														#  his blessing. <unk> <unk> <eos> In the 1990s, the remains of Russian Tsar
														# Nicholas II and his family were discovered. The voice of <unk> young son,
														# Tsarevich Alexei Nikolaevich, narrates the remainder of the story.<eos>

														lowerCamelCase__      :			List[Any] 						= model.generate(UpperCamelCase__    ,							max_length=200    ,							do_sample=UpperCamelCase__  )
														self.assertListEqual(output_ids[0].numpy().tolist()    ,							UpperCamelCase__  )









'''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

