



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		:		Tuple      ,				__lowercase		:		Dict      ,				):

											'''simple docstring'''
											__a       =		parent
											__a       =		13
											__a       =		7
											__a       =		30
											__a       =		self.seq_length + self.mem_len
											__a       =		15
											__a       =		True
											__a       =		True
											__a       =		99
											__a       =		[10, 50, 80]
											__a       =		32
											__a       =		32
											__a       =		4
											__a       =		8
											__a       =		128
											__a       =		2
											__a       =		2
											__a       =		None
											__a       =		1
											__a       =		0
											__a       =		3
											__a       =		self.vocab_size - 1
											__a       =		0.01





							def 						UpperCamelCase_					(   self		:		Any	):

											'''simple docstring'''
											__a       =		ids_tensor([self.batch_size, self.seq_length]      ,				self.vocab_size	)
											__a       =		ids_tensor([self.batch_size, self.seq_length]      ,				self.vocab_size	)

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

											__a       =		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 						UpperCamelCase_					(   self		:		Any	):

											'''simple docstring'''
											random.seed(self.seed	)
											tf.random.set_seed(self.seed	)





							def 						UpperCamelCase_					(   self		:		List[str]      ,				__lowercase		:		Optional[Any]      ,				__lowercase		:		str      ,				__lowercase		:		int      ,				__lowercase		:		str	):

											'''simple docstring'''
											__a       =		TFTransfoXLModel(UpperCamelCase__	)

											__a     ,					__a       =		model(UpperCamelCase__	).to_tuple()

											__a       =		{"""input_ids""": input_ids_a, """mems""": mems_a}

											__a     ,					__a       =		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 						UpperCamelCase_					(   self		:		Tuple      ,				__lowercase		:		Dict      ,				__lowercase		:		str      ,				__lowercase		:		Any      ,				__lowercase		:		Optional[Any]	):

											'''simple docstring'''
											__a       =		TFTransfoXLLMHeadModel(UpperCamelCase__	)

											__a     ,					__a       =		model(UpperCamelCase__	).to_tuple()

											__a       =		{"""input_ids""": input_ids_a, """labels""": lm_labels}
											__a     ,					__a       =		model(UpperCamelCase__	).to_tuple()

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

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

											__a     ,					__a       =		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 						UpperCamelCase_					(   self		:		str      ,				__lowercase		:		Tuple      ,				__lowercase		:		Optional[int]      ,				__lowercase		:		Union[str, Any]      ,				__lowercase		:		List[str]	):

											'''simple docstring'''
											__a       =		TFTransfoXLForSequenceClassification(UpperCamelCase__	)
											__a       =		model(UpperCamelCase__	)
											self.parent.assertEqual(result.logits.shape      ,				(self.batch_size, self.num_labels)	)







							def 						UpperCamelCase_					(   self		:		List[Any]	):

											'''simple docstring'''
											__a       =		self.prepare_config_and_inputs()
											((__a)     ,					(__a)     ,					(__a)     ,					(__a))       =		config_and_inputs
											__a       =		{"""input_ids""": input_ids_a}
											return config, inputs_dict






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





							def 						UpperCamelCase_					(   self		:		Optional[int]      ,				__lowercase		:		Any      ,				__lowercase		:		str      ,				__lowercase		:		Tuple      ,				__lowercase		:		str      ,				__lowercase		:		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 						UpperCamelCase_					(   self		:		List[Any]	):

											'''simple docstring'''
											__a       =		TFTransfoXLModelTester(self	)
											__a       =		ConfigTester(self      ,				config_class=UpperCamelCase__      ,				d_embed=37	)





							def 						UpperCamelCase_					(   self		:		Dict	):

											'''simple docstring'''
											self.config_tester.run_common_tests()





							def 						UpperCamelCase_					(   self		:		List[Any]	):

											'''simple docstring'''
											self.model_tester.set_seed()
											__a       =		self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_transfo_xl_model(*UpperCamelCase__	)





							def 						UpperCamelCase_					(   self		:		Any	):

											'''simple docstring'''
											self.model_tester.set_seed()
											__a       =		self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_transfo_xl_lm_head(*UpperCamelCase__	)





							def 						UpperCamelCase_					(   self		:		Any	):

											'''simple docstring'''
											__a       =		self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_transfo_xl_for_sequence_classification(*UpperCamelCase__	)





							def 						UpperCamelCase_					(   self		:		Any	):

											'''simple docstring'''
											__a     ,					__a       =		self.model_tester.prepare_config_and_inputs_for_common()
											__a       =		[TFTransfoXLForSequenceClassification]

											for model_class in self.all_model_classes:
															__a       =		model_class(UpperCamelCase__	)
															assert isinstance(model.get_input_embeddings()      ,				tf.keras.layers.Layer	)
															if model_class in list_other_models_with_output_ebd:
																			__a       =		model.get_output_embeddings()
																			assert isinstance(UpperCamelCase__      ,				tf.keras.layers.Layer	)
																			__a       =		model.get_bias()
																			assert name is None
															else:
																			__a       =		model.get_output_embeddings()
																			assert x is None
																			__a       =		model.get_bias()
																			assert name is None





							def 						UpperCamelCase_					(   self		:		Tuple	):

											'''simple docstring'''
											pass





							@slow
							def 						UpperCamelCase_					(   self		:		Dict	):

											'''simple docstring'''






											for model_name in TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
															__a       =		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 						UpperCamelCase_					(   self		:		Union[str, Any]	):

											'''simple docstring'''
											pass






@require_tf
class    SCREAMING_SNAKE_CASE			(							unittest.TestCase					):





							@unittest.skip("""Skip test until #12651 is resolved."""	)
							@slow
							def 						UpperCamelCase_					(   self		:		str	):

											'''simple docstring'''
											__a       =		TFTransfoXLLMHeadModel.from_pretrained("""transfo-xl-wt103"""	)
											# fmt: off
											__a       =		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
											__a       =		[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>

											__a       =		model.generate(UpperCamelCase__      ,				max_length=200      ,				do_sample=UpperCamelCase__	)
											self.assertListEqual(output_ids[0].numpy().tolist()      ,				UpperCamelCase__	)




from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import center_crop, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
						import PIL


lowerCamelCase__			= logging.get_logger(__name__)






class    SCREAMING_SNAKE_CASE			(							lowerCamelCase__					):
							__lowerCamelCase	:	str    =['pixel_values']





							def __init__(   self		:		str      ,				__lowercase		:		bool = True      ,				__lowercase		:		Dict[str, int] = None      ,				__lowercase		:		PILImageResampling = PIL.Image.BICUBIC      ,				__lowercase		:		bool = True      ,				__lowercase		:		Dict[str, int] = None      ,				__lowercase		:		Union[int, float] = 1 / 255      ,				__lowercase		:		bool = True      ,				__lowercase		:		bool = True      ,				__lowercase		:		Optional[Union[float, List[float]]] = None      ,				__lowercase		:		Optional[Union[float, List[float]]] = None      ,				**__lowercase		:		Any      ,				):

											'''simple docstring'''
											super().__init__(**__lowercase	)
											__a       =		size if size is not None else {"""height""": 256, """width""": 256}
											__a       =		get_size_dict(__lowercase	)
											__a       =		crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
											__a       =		get_size_dict(__lowercase      ,				param_name="""crop_size"""	)

											__a       =		do_resize
											__a       =		size
											__a       =		resample
											__a       =		do_center_crop
											__a       =		crop_size
											__a       =		do_rescale
											__a       =		rescale_factor
											__a       =		do_normalize
											__a       =		image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
											__a       =		image_std if image_std is not None else IMAGENET_STANDARD_STD





							def 						UpperCamelCase_					(   self		:		List[Any]      ,				__lowercase		:		np.ndarray      ,				__lowercase		:		Dict[str, int]      ,				__lowercase		:		PILImageResampling = PIL.Image.BICUBIC      ,				__lowercase		:		Optional[Union[str, ChannelDimension]] = None      ,				**__lowercase		:		Union[str, Any]      ,				):

											'''simple docstring'''
											__a       =		get_size_dict(__lowercase	)
											if "height" not in size or "width" not in size:
															raise ValueError(F"The size dictionary must have keys 'height' and 'width'. Got {size.keys()}"	)
											return resize(
											    __lowercase      ,				size=(size["""height"""], size["""width"""])      ,				resample=__lowercase      ,				data_format=__lowercase      ,				**__lowercase	)





							def 						UpperCamelCase_					(   self		:		Dict      ,				__lowercase		:		np.ndarray      ,				__lowercase		:		Dict[str, int]      ,				__lowercase		:		Optional[Union[str, ChannelDimension]] = None      ,				**__lowercase		:		str      ,				):

											'''simple docstring'''
											__a       =		get_size_dict(__lowercase	)
											if "height" not in size or "width" not in size:
															raise ValueError(F"The size dictionary must have keys 'height' and 'width'. Got {size.keys()}"	)
											return center_crop(__lowercase      ,				size=(size["""height"""], size["""width"""])      ,				data_format=__lowercase      ,				**__lowercase	)





							def 						UpperCamelCase_					(   self		:		int      ,				__lowercase		:		np.ndarray      ,				__lowercase		:		Union[int, float]      ,				__lowercase		:		Optional[Union[str, ChannelDimension]] = None      ,				**__lowercase		:		List[str]      ,				):

											'''simple docstring'''
											return rescale(__lowercase      ,				scale=__lowercase      ,				data_format=__lowercase      ,				**__lowercase	)





							def 						UpperCamelCase_					(   self		:		Tuple      ,				__lowercase		:		np.ndarray      ,				__lowercase		:		Union[float, List[float]]      ,				__lowercase		:		Union[float, List[float]]      ,				__lowercase		:		Optional[Union[str, ChannelDimension]] = None      ,				**__lowercase		:		str      ,				):

											'''simple docstring'''
											return normalize(__lowercase      ,				mean=__lowercase      ,				std=__lowercase      ,				data_format=__lowercase      ,				**__lowercase	)







							def 						UpperCamelCase_					(   self		:		Optional[Any]      ,				__lowercase		:		ImageInput      ,				__lowercase		:		bool = None      ,				__lowercase		:		Dict[str, int] = None      ,				__lowercase		:		Dict=None      ,				__lowercase		:		bool = None      ,				__lowercase		:		Dict[str, int] = None      ,				__lowercase		:		bool = None      ,				__lowercase		:		float = None      ,				__lowercase		:		bool = None      ,				__lowercase		:		Optional[Union[float, List[float]]] = None      ,				__lowercase		:		Optional[Union[float, List[float]]] = None      ,				__lowercase		:		Optional[Union[str, TensorType]] = None      ,				__lowercase		:		ChannelDimension = ChannelDimension.FIRST      ,				**__lowercase		:		Optional[Any]      ,				):

											'''simple docstring'''
											__a       =		do_resize if do_resize is not None else self.do_resize
											__a       =		resample if resample is not None else self.resample
											__a       =		do_center_crop if do_center_crop is not None else self.do_center_crop
											__a       =		do_rescale if do_rescale is not None else self.do_rescale
											__a       =		rescale_factor if rescale_factor is not None else self.rescale_factor
											__a       =		do_normalize if do_normalize is not None else self.do_normalize
											__a       =		image_mean if image_mean is not None else self.image_mean
											__a       =		image_std if image_std is not None else self.image_std

											__a       =		size if size is not None else self.size
											__a       =		get_size_dict(__lowercase	)
											__a       =		crop_size if crop_size is not None else self.crop_size
											__a       =		get_size_dict(__lowercase      ,				param_name="""crop_size"""	)

											__a       =		make_list_of_images(__lowercase	)

											if not valid_images(__lowercase	):
															raise ValueError(
															    """Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
															    """torch.Tensor, tf.Tensor or jax.ndarray."""	)

											if do_resize and size is None or resample is None:
															raise ValueError("""Size and resample must be specified if do_resize is True."""	)

											if do_center_crop and crop_size is None:
															raise ValueError("""Crop size must be specified if do_center_crop is True."""	)

											if do_rescale and rescale_factor is None:
															raise ValueError("""Rescale factor must be specified if do_rescale is True."""	)

											if do_normalize and (image_mean is None or image_std is None):
															raise ValueError("""Image mean and std must be specified if do_normalize is True."""	)

											# All transformations expect numpy arrays.
											__a       =		[to_numpy_array(__lowercase	) for image in images]

											if do_resize:
															__a       =		[self.resize(image=__lowercase      ,				size=__lowercase      ,				resample=__lowercase	) for image in images]

											if do_center_crop:
															__a       =		[self.center_crop(image=__lowercase      ,				size=__lowercase	) for image in images]

											if do_rescale:
															__a       =		[self.rescale(image=__lowercase      ,				scale=__lowercase	) for image in images]

											if do_normalize:
															__a       =		[self.normalize(image=__lowercase      ,				mean=__lowercase      ,				std=__lowercase	) for image in images]

											__a       =		[to_channel_dimension_format(__lowercase      ,				__lowercase	) for image in images]

											__a       =		{"""pixel_values""": images}
											return BatchFeature(data=__lowercase      ,				tensor_type=__lowercase	)
