






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 			_UpperCamelCase:

		def __init__(					self		:    Any    ,       SCREAMING_SNAKE_CASE__		:    List[Any]    ,       ):



					'''simple docstring'''



					__a						:  Optional[int]									=     parent
					__a						:  int									=     1_3
					__a						:  Union[str, Any]									=     7
					__a						:  Dict									=     3_0
					__a						:  Tuple									=     self.seq_length + self.mem_len
					__a						:  Optional[int]									=     1_5
					__a						:  Tuple									=     True
					__a						:  Optional[Any]									=     True
					__a						:  str									=     9_9
					__a						:  int									=     [1_0, 5_0, 8_0]
					__a						:  Union[str, Any]									=     3_2
					__a						:  Dict									=     3_2
					__a						:  Tuple									=     4
					__a						:  Union[str, Any]									=     8
					__a						:  Optional[Any]									=     1_2_8
					__a						:  str									=     2
					__a						:  str									=     2
					__a						:  Dict									=     None
					__a						:  List[str]									=     1
					__a						:  Union[str, Any]									=     0
					__a						:  Optional[Any]									=     3
					__a						:  List[str]									=     self.vocab_size - 1
					__a						:  int									=     0.01

		def    __lowerCAmelCase  (					self		:    Optional[int]    ):



					'''simple docstring'''



					__a						:  str									=     ids_tensor([self.batch_size, self.seq_length]    ,       self.vocab_size    )
					__a						:  str									=     ids_tensor([self.batch_size, self.seq_length]    ,       self.vocab_size    )

					__a						:  Tuple									=     None
					if self.use_labels:
								__a						:  Tuple									=     ids_tensor([self.batch_size, self.seq_length]    ,       self.vocab_size    )

					__a						:  Optional[int]									=     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		:    Optional[int]    ):



					'''simple docstring'''



					random.seed(self.seed    )
					tf.random.set_seed(self.seed    )

		def    __lowerCAmelCase  (					self		:    Union[str, Any]    ,       SCREAMING_SNAKE_CASE__		:    Any    ,       SCREAMING_SNAKE_CASE__		:    Optional[int]    ,       SCREAMING_SNAKE_CASE__		:    Union[str, Any]    ,       SCREAMING_SNAKE_CASE__		:    Any    ):



					'''simple docstring'''



					__a						:  Optional[int]									=     TFTransfoXLModel(SCREAMING_SNAKE_CASE__    )

					__a						,					__a						:  Dict									=     model(SCREAMING_SNAKE_CASE__    ).to_tuple()

					__a						:  str									=     {'input_ids': input_ids_a, 'mems': mems_a}

					__a						,					__a						:  Optional[Any]									=     model(SCREAMING_SNAKE_CASE__    ).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		:    int    ,       SCREAMING_SNAKE_CASE__		:    Tuple    ,       SCREAMING_SNAKE_CASE__		:    Tuple    ,       SCREAMING_SNAKE_CASE__		:    Dict    ,       SCREAMING_SNAKE_CASE__		:    str    ):



					'''simple docstring'''



					__a						:  str									=     TFTransfoXLLMHeadModel(SCREAMING_SNAKE_CASE__    )

					__a						,					__a						:  Dict									=     model(SCREAMING_SNAKE_CASE__    ).to_tuple()

					__a						:  List[str]									=     {'input_ids': input_ids_a, 'labels': lm_labels}
					__a						,					__a						:  List[str]									=     model(SCREAMING_SNAKE_CASE__    ).to_tuple()

					__a						,					__a						:  int									=     model([input_ids_a, mems_a]    ).to_tuple()

					__a						:  Dict									=     {'input_ids': input_ids_a, 'mems': mems_a, 'labels': lm_labels}

					__a						,					__a						:  Tuple									=     model(SCREAMING_SNAKE_CASE__    ).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		:    Union[str, Any]    ,       SCREAMING_SNAKE_CASE__		:    List[Any]    ,       SCREAMING_SNAKE_CASE__		:    List[Any]    ,       SCREAMING_SNAKE_CASE__		:    Optional[int]    ,       SCREAMING_SNAKE_CASE__		:    str    ):



					'''simple docstring'''



					__a						:  Any									=     TFTransfoXLForSequenceClassification(SCREAMING_SNAKE_CASE__    )
					__a						:  Tuple									=     model(SCREAMING_SNAKE_CASE__    )
					self.parent.assertEqual(result.logits.shape    ,       (self.batch_size, self.num_labels)    )



		def    __lowerCAmelCase  (					self		:    List[str]    ):



					'''simple docstring'''



					__a						:  str									=     self.prepare_config_and_inputs()
					((__a)						,					(__a)						,					(__a)						,					(__a))						:  Union[str, Any]									=     config_and_inputs
					__a						:  str									=     {'input_ids': input_ids_a}
					return config, inputs_dict






@require_tf
class 			_UpperCamelCase(       __lowerCamelCase		,				__lowerCamelCase		,				unittest.TestCase		):
		__SCREAMING_SNAKE_CASE  :       List[str]							=							(
		    (TFTransfoXLModel, TFTransfoXLLMHeadModel, TFTransfoXLForSequenceClassification) if is_tf_available() else ()
		)
		__SCREAMING_SNAKE_CASE  :       int							=							() if is_tf_available() else ()
		__SCREAMING_SNAKE_CASE  :       Dict							=							(
		    {
		        '''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
		__SCREAMING_SNAKE_CASE  :       List[Any]							=							False
		__SCREAMING_SNAKE_CASE  :       str							=							False
		__SCREAMING_SNAKE_CASE  :       Optional[Any]							=							False
		__SCREAMING_SNAKE_CASE  :       Any							=							False

		def    __lowerCAmelCase  (					self		:    List[str]    ,       SCREAMING_SNAKE_CASE__		:    Any    ,       SCREAMING_SNAKE_CASE__		:    str    ,       SCREAMING_SNAKE_CASE__		:    List[str]    ,       SCREAMING_SNAKE_CASE__		:    List[str]    ,       SCREAMING_SNAKE_CASE__		:    Tuple    ):



					'''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		:    Optional[int]    ):



					'''simple docstring'''



					__a						:  List[str]									=     TFTransfoXLModelTester(self    )
					__a						:  List[str]									=     ConfigTester(self    ,       config_class=SCREAMING_SNAKE_CASE__    ,       d_embed=3_7    )

		def    __lowerCAmelCase  (					self		:    Optional[int]    ):



					'''simple docstring'''



					self.config_tester.run_common_tests()

		def    __lowerCAmelCase  (					self		:    Any    ):



					'''simple docstring'''



					self.model_tester.set_seed()
					__a						:  str									=     self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_transfo_xl_model(*SCREAMING_SNAKE_CASE__    )

		def    __lowerCAmelCase  (					self		:    Tuple    ):



					'''simple docstring'''



					self.model_tester.set_seed()
					__a						:  List[Any]									=     self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_transfo_xl_lm_head(*SCREAMING_SNAKE_CASE__    )

		def    __lowerCAmelCase  (					self		:    Tuple    ):



					'''simple docstring'''



					__a						:  int									=     self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_transfo_xl_for_sequence_classification(*SCREAMING_SNAKE_CASE__    )

		def    __lowerCAmelCase  (					self		:    Tuple    ):



					'''simple docstring'''



					__a						,					__a						:  List[Any]									=     self.model_tester.prepare_config_and_inputs_for_common()
					__a						:  Any									=     [TFTransfoXLForSequenceClassification]

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

		def    __lowerCAmelCase  (					self		:    Dict    ):



					'''simple docstring'''



					pass

		@slow
		def    __lowerCAmelCase  (					self		:    Dict    ):



					'''simple docstring'''



					for model_name in TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
								__a						:  Optional[int]									=     TFTransfoXLModel.from_pretrained(SCREAMING_SNAKE_CASE__    )
								self.assertIsNotNone(SCREAMING_SNAKE_CASE__    )



		@unittest.skip(reason='This model doesn\'t play well with fit() due to not returning a single loss.'    )
		def    __lowerCAmelCase  (					self		:    List[str]    ):



					'''simple docstring'''



					pass






@require_tf
class 			_UpperCamelCase(       unittest.TestCase		):

		@unittest.skip('Skip test until #12651 is resolved.'    )
		@slow
		def    __lowerCAmelCase  (					self		:    int    ):



					'''simple docstring'''



					__a						:  str									=     TFTransfoXLLMHeadModel.from_pretrained('transfo-xl-wt103'    )
					# fmt: off
					__a						:  Optional[int]									=     tf.convert_to_tensor([[3_3,1_2_9_7,2,1,1_0_0_9,4,1_1_0_9,1_1_7_3_9,4_7_6_2,3_5_8,5,2_5,2_4_5,2_2,1_7_0_6,1_7,2_0_0_9_8,5,3_2_1_5,2_1,3_7,1_1_1_0,3,1_3,1_0_4_1,4,2_4,6_0_3,4_9_0,2,7_1_4_7_7,2_0_0_9_8,1_0_4_4_4_7,2,2_0_9_6_1,1,2_6_0_4,4,1,3_2_9,3,6_2_2_4,8_3_1,1_6_0_0_2,2,8,6_0_3,7_8_9_6_7,2_9_5_4_6,2_3,8_0_3,2_0,2_5,4_1_6,5,8,2_3_2,4,2_7_7,6,1_8_5_5,4_6_0_1,3,2_9_5_4_6,5_4,8,3_6_0_9,5,5_7_2_1_1,4_9,4,1,2_7_7,1_8,8,1_7_5_5,1_5_6_9_1,3,3_4_1,2_5,4_1_6,6_9_3,4_2_5_7_3,7_1,1_7,4_0_1,9_4,3_1,1_7_9_1_9,2,2_9_5_4_6,7_8_7_3,1_8,1,4_3_5,2_3,1_1_0_1_1,7_5_5,5,5_1_6_7,3,7_9_8_3,9_8,8_4,2,2_9_5_4_6,3_2_6_7,8,3_6_0_9,4,1,4_8_6_5,1_0_7_5,2,6_0_8_7,7_1,6,3_4_6,8,5_8_5_4,3,2_9_5_4_6,8_2_4,1_4_0_0,1_8_6_8,2,1_9,1_6_0,2,3_1_1,8,5_4_9_6,2,2_0_9_2_0,1_7,2_5,1_5_0_9_7,3,2_4,2_4,0]]    ,       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
					__a						:  List[Any]									=     [3_3,1_2_9_7,2,1,1_0_0_9,4,1_1_0_9,1_1_7_3_9,4_7_6_2,3_5_8,5,2_5,2_4_5,2_2,1_7_0_6,1_7,2_0_0_9_8,5,3_2_1_5,2_1,3_7,1_1_1_0,3,1_3,1_0_4_1,4,2_4,6_0_3,4_9_0,2,7_1_4_7_7,2_0_0_9_8,1_0_4_4_4_7,2,2_0_9_6_1,1,2_6_0_4,4,1,3_2_9,3,6_2_2_4,8_3_1,1_6_0_0_2,2,8,6_0_3,7_8_9_6_7,2_9_5_4_6,2_3,8_0_3,2_0,2_5,4_1_6,5,8,2_3_2,4,2_7_7,6,1_8_5_5,4_6_0_1,3,2_9_5_4_6,5_4,8,3_6_0_9,5,5_7_2_1_1,4_9,4,1,2_7_7,1_8,8,1_7_5_5,1_5_6_9_1,3,3_4_1,2_5,4_1_6,6_9_3,4_2_5_7_3,7_1,1_7,4_0_1,9_4,3_1,1_7_9_1_9,2,2_9_5_4_6,7_8_7_3,1_8,1,4_3_5,2_3,1_1_0_1_1,7_5_5,5,5_1_6_7,3,7_9_8_3,9_8,8_4,2,2_9_5_4_6,3_2_6_7,8,3_6_0_9,4,1,4_8_6_5,1_0_7_5,2,6_0_8_7,7_1,6,3_4_6,8,5_8_5_4,3,2_9_5_4_6,8_2_4,1_4_0_0,1_8_6_8,2,1_9,1_6_0,2,3_1_1,8,5_4_9_6,2,2_0_9_2_0,1_7,2_5,1_5_0_9_7,3,2_4,2_4,0,3_3,1,1_8_5_7,2,1,1_0_0_9,4,1_1_0_9,1_1_7_3_9,4_7_6_2,3_5_8,5,2_5,2_4_5,2_8,1_1_1_0,3,1_3,1_0_4_1,4,2_4,6_0_3,4_9_0,2,7_1_4_7_7,2_0_0_9_8,1_0_4_4_4_7,2,2_0_9_6_1,1,2_6_0_4,4,1,3_2_9,3,0]  # 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>

					__a						:  List[str]									=     model.generate(SCREAMING_SNAKE_CASE__    ,       max_length=2_0_0    ,       do_sample=SCREAMING_SNAKE_CASE__    )
					self.assertListEqual(output_ids[0].numpy().tolist()    ,       SCREAMING_SNAKE_CASE__    )









import numpy as np
import torch
from imwatermark import WatermarkEncoder


# Copied from https://github.com/Stability-AI/generative-models/blob/613af104c6b85184091d42d374fef420eddb356d/scripts/demo/streamlit_helpers.py#L66
lowerCAmelCase__			:      Optional[int]								=0B1011_0011_1110_1100_1001_0000_0111_1011_1011_0001_1001_1110
# bin(x)[2:] gives bits of x as str, use int to convert them to 0/1
lowerCAmelCase__			:      str								=[int(bit) for bit in bin(WATERMARK_MESSAGE)[2:]]







class 	__lowercase    :





							"""simple docstring"""







							def __init__(      self       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     List[Any]					=					WATERMARK_BITS
										SCREAMING_SNAKE_CASE_		:     int					=					WatermarkEncoder()

										self.encoder.set_watermark('bits'			,						self.watermark       )






							def 					UpperCamelCase__     (      self			,						lowerCAmelCase__       ):





										"""simple docstring"""




										if images.shape[-1] < 2_5_6:
													return images

										SCREAMING_SNAKE_CASE_		:     Tuple					=					(2_5_5 * (images / 2 + 0.5)).cpu().permute(0			,						2			,						3			,						1       ).float().numpy()

										SCREAMING_SNAKE_CASE_		:     Optional[int]					=					[self.encoder.encode(lowerCAmelCase__			,						'dwtDct'       ) for image in images]

										SCREAMING_SNAKE_CASE_		:     List[str]					=					torch.from_numpy(np.array(lowerCAmelCase__       )       ).permute(0			,						3			,						1			,						2       )

										SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					torch.clamp(2 * (images / 2_5_5 - 0.5)			,						min=-1.0			,						max=1.0       )
										return images





