

'''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 		__SCREAMING_SNAKE_CASE       :







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







				def    lowerCamelCase_  (					self    :		Optional[int]      ):
										'''simple docstring'''
										lowercase   :       Any								=ids_tensor([self.batch_size, self.seq_length]		,				self.vocab_size      )
										lowercase   :       int								=ids_tensor([self.batch_size, self.seq_length]		,				self.vocab_size      )

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

										lowercase   :       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[Any]      ):
										'''simple docstring'''
										random.seed(self.seed      )
										tf.random.set_seed(self.seed      )







				def    lowerCamelCase_  (					self    :		Optional[int]		,				UpperCAmelCase__    :		Union[str, Any]		,				UpperCAmelCase__    :		Any		,				UpperCAmelCase__    :		Tuple		,				UpperCAmelCase__    :		Any      ):
										'''simple docstring'''
										lowercase   :       Any								=TFTransfoXLModel(UpperCAmelCase__      )

										lowercase		,  lowercase   :       Optional[Any]								=model(UpperCAmelCase__      ).to_tuple()

										lowercase   :       Dict								={'''input_ids''': input_ids_a, '''mems''': mems_a}

										lowercase		,  lowercase   :       Optional[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    :		Tuple		,				UpperCAmelCase__    :		int		,				UpperCAmelCase__    :		str		,				UpperCAmelCase__    :		Any		,				UpperCAmelCase__    :		Tuple      ):
										'''simple docstring'''
										lowercase   :       List[Any]								=TFTransfoXLLMHeadModel(UpperCAmelCase__      )

										lowercase		,  lowercase   :       Union[str, Any]								=model(UpperCAmelCase__      ).to_tuple()

										lowercase   :       List[str]								={'''input_ids''': input_ids_a, '''labels''': lm_labels}
										lowercase		,  lowercase   :       Optional[int]								=model(UpperCAmelCase__      ).to_tuple()

										lowercase		,  lowercase   :       Dict								=model([input_ids_a, mems_a]      ).to_tuple()

										lowercase   :       Optional[int]								={'''input_ids''': input_ids_a, '''mems''': mems_a, '''labels''': lm_labels}

										lowercase		,  lowercase   :       Optional[Any]								=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    :		int		,				UpperCAmelCase__    :		str		,				UpperCAmelCase__    :		List[Any]		,				UpperCAmelCase__    :		Optional[int]		,				UpperCAmelCase__    :		int      ):
										'''simple docstring'''
										lowercase   :       List[Any]								=TFTransfoXLForSequenceClassification(UpperCAmelCase__      )
										lowercase   :       Union[str, Any]								=model(UpperCAmelCase__      )
										self.parent.assertEqual(result.logits.shape		,				(self.batch_size, self.num_labels)      )







				def    lowerCamelCase_  (					self    :		str      ):
										'''simple docstring'''
										lowercase   :       Optional[Any]								=self.prepare_config_and_inputs()
										((lowercase)		,  (lowercase)		,  (lowercase)		,  (lowercase))   :       int								=config_and_inputs
										lowercase   :       Any								={'''input_ids''': input_ids_a}
										return config, inputs_dict





@require_tf
class 		__SCREAMING_SNAKE_CASE       ( lowercase__	,       lowercase__	,       unittest.TestCase			):
				lowerCamelCase_							=						(
				    (TFTransfoXLModel, TFTransfoXLLMHeadModel, TFTransfoXLForSequenceClassification) if is_tf_available() else ()
				)
				lowerCamelCase_							=						() if is_tf_available() else ()
				lowerCamelCase_							=						(
				    {
				        '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
				lowerCamelCase_							=						False
				lowerCamelCase_							=						False
				lowerCamelCase_							=						False
				lowerCamelCase_							=						False







				def    lowerCamelCase_  (					self    :		Optional[int]		,				UpperCAmelCase__    :		List[str]		,				UpperCAmelCase__    :		Union[str, Any]		,				UpperCAmelCase__    :		Any		,				UpperCAmelCase__    :		Tuple		,				UpperCAmelCase__    :		Union[str, Any]      ):
										'''simple docstring'''
										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    :		List[Any]      ):
										'''simple docstring'''
										lowercase   :       Any								=TFTransfoXLModelTester(self      )
										lowercase   :       Optional[Any]								=ConfigTester(self		,				config_class=UpperCAmelCase__		,				d_embed=37      )







				def    lowerCamelCase_  (					self    :		int      ):
										'''simple docstring'''
										self.config_tester.run_common_tests()







				def    lowerCamelCase_  (					self    :		int      ):
										'''simple docstring'''
										self.model_tester.set_seed()
										lowercase   :       Union[str, Any]								=self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_transfo_xl_model(*UpperCAmelCase__      )







				def    lowerCamelCase_  (					self    :		Optional[Any]      ):
										'''simple docstring'''
										self.model_tester.set_seed()
										lowercase   :       Any								=self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_transfo_xl_lm_head(*UpperCAmelCase__      )







				def    lowerCamelCase_  (					self    :		Any      ):
										'''simple docstring'''
										lowercase   :       Optional[Any]								=self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_transfo_xl_for_sequence_classification(*UpperCAmelCase__      )







				def    lowerCamelCase_  (					self    :		Optional[int]      ):
										'''simple docstring'''
										lowercase		,  lowercase   :       Optional[int]								=self.model_tester.prepare_config_and_inputs_for_common()
										lowercase   :       List[Any]								=[TFTransfoXLForSequenceClassification]

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







				def    lowerCamelCase_  (					self    :		Union[str, Any]      ):
										'''simple docstring'''
										# TODO JP: Make TransfoXL XLA compliant
										pass







				@slow
				def    lowerCamelCase_  (					self    :		int      ):
										'''simple docstring'''
										for model_name in TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																lowercase   :       int								=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    :		List[Any]      ):
										'''simple docstring'''
										pass





@require_tf
class 		__SCREAMING_SNAKE_CASE       ( unittest.TestCase			):







				@unittest.skip('''Skip test until #12651 is resolved.'''      )
				@slow
				def    lowerCamelCase_  (					self    :		int      ):
										'''simple docstring'''
										lowercase   :       Optional[int]								=TFTransfoXLLMHeadModel.from_pretrained('''transfo-xl-wt103'''      )
										# fmt: off
										lowercase   :       Optional[int]								=tf.convert_to_tensor([[33,1297,2,1,1009,4,1109,11739,4762,358,5,25,245,22,1706,17,20098,5,3215,21,37,1110,3,13,1041,4,24,603,490,2,71477,20098,104447,2,20961,1,2604,4,1,329,3,6224,831,16002,2,8,603,78967,29546,23,803,20,25,416,5,8,232,4,277,6,1855,4601,3,29546,54,8,3609,5,57211,49,4,1,277,18,8,1755,15691,3,341,25,416,693,42573,71,17,401,94,31,17919,2,29546,7873,18,1,435,23,11011,755,5,5167,3,7983,98,84,2,29546,3267,8,3609,4,1,4865,1075,2,6087,71,6,346,8,5854,3,29546,824,1400,1868,2,19,160,2,311,8,5496,2,20920,17,25,15097,3,24,24,0]]		,				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
										lowercase   :       Tuple								=[33,1297,2,1,1009,4,1109,11739,4762,358,5,25,245,22,1706,17,20098,5,3215,21,37,1110,3,13,1041,4,24,603,490,2,71477,20098,104447,2,20961,1,2604,4,1,329,3,6224,831,16002,2,8,603,78967,29546,23,803,20,25,416,5,8,232,4,277,6,1855,4601,3,29546,54,8,3609,5,57211,49,4,1,277,18,8,1755,15691,3,341,25,416,693,42573,71,17,401,94,31,17919,2,29546,7873,18,1,435,23,11011,755,5,5167,3,7983,98,84,2,29546,3267,8,3609,4,1,4865,1075,2,6087,71,6,346,8,5854,3,29546,824,1400,1868,2,19,160,2,311,8,5496,2,20920,17,25,15097,3,24,24,0,33,1,1857,2,1,1009,4,1109,11739,4762,358,5,25,245,28,1110,3,13,1041,4,24,603,490,2,71477,20098,104447,2,20961,1,2604,4,1,329,3,0]  # 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>

										lowercase   :       Dict								=model.generate(UpperCAmelCase__		,				max_length=200		,				do_sample=UpperCAmelCase__      )
										self.assertListEqual(output_ids[0].numpy().tolist()		,				UpperCAmelCase__      )








'''simple docstring'''





# coding=utf-8
# Copyright 2023 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

# this script dumps information about the environment

import os
import platform
import sys


UpperCamelCase_							=					"""3"""

print("""Python version:""", sys.version)

print("""OS platform:""", platform.platform())
print("""OS architecture:""", platform.machine())

try:
							import torch

							print("""Torch version:""", torch.__version__)
							print("""Cuda available:""", torch.cuda.is_available())
							print("""Cuda version:""", torch.version.cuda)
							print("""CuDNN version:""", torch.backends.cudnn.version())
							print("""Number of GPUs available:""", torch.cuda.device_count())
except ImportError:
							print("""Torch version:""", None)

try:
							import transformers

							print("""transformers version:""", transformers.__version__)
except ImportError:
							print("""transformers version:""", None)





