





"""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    :







		'''simple docstring'''


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

		def    __SCREAMING_SNAKE_CASE				(		self   :       str	)		->				int:
								_UpperCamelCase      :	Any						=       ids_tensor([self.batch_size, self.seq_length]					,   self.vocab_size	)
								_UpperCamelCase      :	List[Any]						=       ids_tensor([self.batch_size, self.seq_length]					,   self.vocab_size	)

								_UpperCamelCase      :	Union[str, Any]						=       None
								if self.use_labels:
														_UpperCamelCase      :	List[Any]						=       ids_tensor([self.batch_size, self.seq_length]					,   self.vocab_size	)

								_UpperCamelCase      :	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    __SCREAMING_SNAKE_CASE				(		self   :       Tuple	)		->				Tuple:
								random.seed(self.seed	)
								tf.random.set_seed(self.seed	)

		def    __SCREAMING_SNAKE_CASE				(		self   :       Union[str, Any]					,   __a   :       int					,   __a   :       int					,   __a   :       Dict					,   __a   :       Tuple	)		->				List[str]:
								_UpperCamelCase      :	str						=       TFTransfoXLModel(__a	)

								_UpperCamelCase,  _UpperCamelCase      :	Tuple						=       model(__a	).to_tuple()

								_UpperCamelCase      :	Optional[int]						=       {"input_ids": input_ids_a, "mems": mems_a}

								_UpperCamelCase,  _UpperCamelCase      :	List[Any]						=       model(__a	).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    __SCREAMING_SNAKE_CASE				(		self   :       Any					,   __a   :       str					,   __a   :       Dict					,   __a   :       List[Any]					,   __a   :       List[str]	)		->				List[Any]:
								_UpperCamelCase      :	Tuple						=       TFTransfoXLLMHeadModel(__a	)

								_UpperCamelCase,  _UpperCamelCase      :	int						=       model(__a	).to_tuple()

								_UpperCamelCase      :	List[Any]						=       {"input_ids": input_ids_a, "labels": lm_labels}
								_UpperCamelCase,  _UpperCamelCase      :	List[Any]						=       model(__a	).to_tuple()

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

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

								_UpperCamelCase,  _UpperCamelCase      :	Any						=       model(__a	).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    __SCREAMING_SNAKE_CASE				(		self   :       int					,   __a   :       Any					,   __a   :       Optional[Any]					,   __a   :       Tuple					,   __a   :       List[str]	)		->				Optional[int]:
								_UpperCamelCase      :	List[Any]						=       TFTransfoXLForSequenceClassification(__a	)
								_UpperCamelCase      :	Optional[Any]						=       model(__a	)
								self.parent.assertEqual(result.logits.shape					,   (self.batch_size, self.num_labels)	)


		def    __SCREAMING_SNAKE_CASE				(		self   :       Optional[Any]	)		->				Union[str, Any]:
								_UpperCamelCase      :	List[str]						=       self.prepare_config_and_inputs()
								((_UpperCamelCase),  (_UpperCamelCase),  (_UpperCamelCase),  (_UpperCamelCase))      :	Optional[Any]						=       config_and_inputs
								_UpperCamelCase      :	List[str]						=       {"input_ids": input_ids_a}
								return config, inputs_dict





@require_tf
class      __SCREAMING_SNAKE_CASE    (						_UpperCamelCase		,  _UpperCamelCase		,  unittest.TestCase   ):







		'''simple docstring'''


		SCREAMING_SNAKE_CASE__							:List[Any]	     =					(
		    (TFTransfoXLModel, TFTransfoXLLMHeadModel, TFTransfoXLForSequenceClassification) if is_tf_available() else ()
		)
		SCREAMING_SNAKE_CASE__							:Tuple	     =					() 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__							:Union[str, Any]	     =					False
		SCREAMING_SNAKE_CASE__							:Tuple	     =					False
		SCREAMING_SNAKE_CASE__							:Any	     =					False
		SCREAMING_SNAKE_CASE__							:str	     =					False

		def    __SCREAMING_SNAKE_CASE				(		self   :       Union[str, Any]					,   __a   :       Optional[Any]					,   __a   :       Dict					,   __a   :       int					,   __a   :       Tuple					,   __a   :       Dict	)		->				int:
								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    __SCREAMING_SNAKE_CASE				(		self   :       Dict	)		->				Optional[Any]:
								_UpperCamelCase      :	int						=       TFTransfoXLModelTester(self	)
								_UpperCamelCase      :	List[Any]						=       ConfigTester(self					,   config_class=__a					,   d_embed=37	)

		def    __SCREAMING_SNAKE_CASE				(		self   :       Dict	)		->				Union[str, Any]:
								self.config_tester.run_common_tests()

		def    __SCREAMING_SNAKE_CASE				(		self   :       Optional[int]	)		->				Optional[Any]:
								self.model_tester.set_seed()
								_UpperCamelCase      :	List[Any]						=       self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_transfo_xl_model(*__a	)

		def    __SCREAMING_SNAKE_CASE				(		self   :       str	)		->				str:
								self.model_tester.set_seed()
								_UpperCamelCase      :	List[str]						=       self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_transfo_xl_lm_head(*__a	)

		def    __SCREAMING_SNAKE_CASE				(		self   :       List[str]	)		->				Optional[int]:
								_UpperCamelCase      :	List[Any]						=       self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_transfo_xl_for_sequence_classification(*__a	)

		def    __SCREAMING_SNAKE_CASE				(		self   :       Dict	)		->				Union[str, Any]:
								_UpperCamelCase,  _UpperCamelCase      :	Optional[int]						=       self.model_tester.prepare_config_and_inputs_for_common()
								_UpperCamelCase      :	Tuple						=       [TFTransfoXLForSequenceClassification]

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

		def    __SCREAMING_SNAKE_CASE				(		self   :       Union[str, Any]	)		->				List[Any]:
								# TODO JP: Make TransfoXL XLA compliant
								pass

		@slow
		def    __SCREAMING_SNAKE_CASE				(		self   :       Optional[Any]	)		->				int:
								for model_name in TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
														_UpperCamelCase      :	Dict						=       TFTransfoXLModel.from_pretrained(__a	)
														self.assertIsNotNone(__a	)


		@unittest.skip(reason="This model doesn't play well with fit() due to not returning a single loss."	)
		def    __SCREAMING_SNAKE_CASE				(		self   :       Optional[Any]	)		->				Union[str, Any]:
								pass





@require_tf
class      __SCREAMING_SNAKE_CASE    (						unittest.TestCase   ):







		'''simple docstring'''


		@unittest.skip("Skip test until #12651 is resolved."	)
		@slow
		def    __SCREAMING_SNAKE_CASE				(		self   :       List[str]	)		->				str:
								_UpperCamelCase      :	Union[str, Any]						=       TFTransfoXLLMHeadModel.from_pretrained("transfo-xl-wt103"	)
								# fmt: off
								_UpperCamelCase      :	Tuple						=       tf.convert_to_tensor([[33,1297,2,1,1009,4,1109,1_1739,4762,358,5,25,245,22,1706,17,2_0098,5,3215,21,37,1110,3,13,1041,4,24,603,490,2,7_1477,2_0098,10_4447,2,2_0961,1,2604,4,1,329,3,6224,831,1_6002,2,8,603,7_8967,2_9546,23,803,20,25,416,5,8,232,4,277,6,1855,4601,3,2_9546,54,8,3609,5,5_7211,49,4,1,277,18,8,1755,1_5691,3,341,25,416,693,4_2573,71,17,401,94,31,1_7919,2,2_9546,7873,18,1,435,23,1_1011,755,5,5167,3,7983,98,84,2,2_9546,3267,8,3609,4,1,4865,1075,2,6087,71,6,346,8,5854,3,2_9546,824,1400,1868,2,19,160,2,311,8,5496,2,2_0920,17,25,1_5097,3,24,24,0]]					,   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
								_UpperCamelCase      :	str						=       [33,1297,2,1,1009,4,1109,1_1739,4762,358,5,25,245,22,1706,17,2_0098,5,3215,21,37,1110,3,13,1041,4,24,603,490,2,7_1477,2_0098,10_4447,2,2_0961,1,2604,4,1,329,3,6224,831,1_6002,2,8,603,7_8967,2_9546,23,803,20,25,416,5,8,232,4,277,6,1855,4601,3,2_9546,54,8,3609,5,5_7211,49,4,1,277,18,8,1755,1_5691,3,341,25,416,693,4_2573,71,17,401,94,31,1_7919,2,2_9546,7873,18,1,435,23,1_1011,755,5,5167,3,7983,98,84,2,2_9546,3267,8,3609,4,1,4865,1075,2,6087,71,6,346,8,5854,3,2_9546,824,1400,1868,2,19,160,2,311,8,5496,2,2_0920,17,25,1_5097,3,24,24,0,33,1,1857,2,1,1009,4,1109,1_1739,4762,358,5,25,245,28,1110,3,13,1041,4,24,603,490,2,7_1477,2_0098,10_4447,2,2_0961,1,2604,4,1,329,3,0]  # 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>

								_UpperCamelCase      :	Union[str, Any]						=       model.generate(__a					,   max_length=200					,   do_sample=__a	)
								self.assertListEqual(output_ids[0].numpy().tolist()					,   __a	)







"""simple docstring"""


from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowerCamelCase__				  =   logging.get_logger(__name__)

lowerCamelCase__				  =   {
    "hustvl/yolos-small": "https://huggingface.co/hustvl/yolos-small/resolve/main/config.json",
    # See all YOLOS models at https://huggingface.co/models?filter=yolos
}



class      __SCREAMING_SNAKE_CASE    (						_UpperCamelCase   ):







  '''simple docstring'''


  SCREAMING_SNAKE_CASE__							:Tuple	     =					"yolos"

  def __init__(		self   :       Dict					,   __a   :       Optional[Any]=768					,   __a   :       List[Any]=12					,   __a   :       Any=12					,   __a   :       List[Any]=3072					,   __a   :       Optional[int]="gelu"					,   __a   :       Dict=0.0					,   __a   :       Optional[Any]=0.0					,   __a   :       Any=0.02					,   __a   :       Optional[int]=1e-1_2					,   __a   :       List[Any]=[512, 864]					,   __a   :       List[str]=16					,   __a   :       str=3					,   __a   :       Optional[Any]=True					,   __a   :       Optional[Any]=100					,   __a   :       List[str]=True					,   __a   :       Any=False					,   __a   :       List[str]=1					,   __a   :       str=5					,   __a   :       Optional[Any]=2					,   __a   :       Tuple=5					,   __a   :       Any=2					,   __a   :       Union[str, Any]=0.1					,   **__a   :       List[str]					,   )		->				List[str]:
        super().__init__(**__a	)

        _UpperCamelCase      :	Dict						=       hidden_size
        _UpperCamelCase      :	Any						=       num_hidden_layers
        _UpperCamelCase      :	str						=       num_attention_heads
        _UpperCamelCase      :	Dict						=       intermediate_size
        _UpperCamelCase      :	List[str]						=       hidden_act
        _UpperCamelCase      :	List[str]						=       hidden_dropout_prob
        _UpperCamelCase      :	str						=       attention_probs_dropout_prob
        _UpperCamelCase      :	Tuple						=       initializer_range
        _UpperCamelCase      :	List[str]						=       layer_norm_eps
        _UpperCamelCase      :	Tuple						=       image_size
        _UpperCamelCase      :	Tuple						=       patch_size
        _UpperCamelCase      :	Dict						=       num_channels
        _UpperCamelCase      :	Any						=       qkv_bias
        _UpperCamelCase      :	str						=       num_detection_tokens
        _UpperCamelCase      :	str						=       use_mid_position_embeddings
        _UpperCamelCase      :	List[str]						=       auxiliary_loss
        # Hungarian matcher
        _UpperCamelCase      :	List[Any]						=       class_cost
        _UpperCamelCase      :	int						=       bbox_cost
        _UpperCamelCase      :	Optional[int]						=       giou_cost
        # Loss coefficients
        _UpperCamelCase      :	List[Any]						=       bbox_loss_coefficient
        _UpperCamelCase      :	str						=       giou_loss_coefficient
        _UpperCamelCase      :	Dict						=       eos_coefficient





class      __SCREAMING_SNAKE_CASE    (						_UpperCamelCase   ):







  '''simple docstring'''


  SCREAMING_SNAKE_CASE__							:List[str]	     =					version.parse("1.11"   )

  @property
  def    __SCREAMING_SNAKE_CASE				(		self   :       str	)		->				Mapping[str, Mapping[int, str]]:
        return OrderedDict(
            [
                ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
            ]	)

  @property
  def    __SCREAMING_SNAKE_CASE				(		self   :       Optional[Any]	)		->				float:
        return 1e-4


  @property
  def    __SCREAMING_SNAKE_CASE				(		self   :       Dict	)		->				int:
        return 12
