



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   __lowerCAmelCase  :
					"""simple docstring"""






					def __init__(  self       :							List[str]					,       _snake_case       :							Any					,       ):






								"""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__	  =     1_28
								A__	  =     2
								A__	  =     2
								A__	  =     None
								A__	  =     1
								A__	  =     0
								A__	  =     3
								A__	  =     self.vocab_size - 1
								A__	  =     0.01



					def      _a				(  self       :							int       ):






								"""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      _a				(  self       :							Tuple       ):






								"""simple docstring"""


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



					def      _a				(  self       :							int					,       _snake_case       :							List[str]					,       _snake_case       :							Tuple					,       _snake_case       :							str					,       _snake_case       :							Union[str, Any]       ):






								"""simple docstring"""


								A__	  =     TFTransfoXLModel(_snake_case       )

								A__  ,			A__	  =     model(_snake_case       ).to_tuple()

								A__	  =     {'input_ids': input_ids_a, 'mems': mems_a}

								A__  ,			A__	  =     model(_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      _a				(  self       :							int					,       _snake_case       :							Dict					,       _snake_case       :							Dict					,       _snake_case       :							Dict					,       _snake_case       :							Tuple       ):






								"""simple docstring"""


								A__	  =     TFTransfoXLLMHeadModel(_snake_case       )

								A__  ,			A__	  =     model(_snake_case       ).to_tuple()

								A__	  =     {'input_ids': input_ids_a, 'labels': lm_labels}
								A__  ,			A__	  =     model(_snake_case       ).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(_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      _a				(  self       :							Optional[Any]					,       _snake_case       :							Tuple					,       _snake_case       :							Dict					,       _snake_case       :							int					,       _snake_case       :							Tuple       ):






								"""simple docstring"""


								A__	  =     TFTransfoXLForSequenceClassification(_snake_case       )
								A__	  =     model(_snake_case       )
								self.parent.assertEqual(result.logits.shape					,       (self.batch_size, self.num_labels)       )



					def      _a				(  self       :							Optional[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   __lowerCAmelCase  (		UpperCAmelCase_				,  UpperCAmelCase_				,  unittest.TestCase   ):
					"""simple docstring"""






					A__					:		Optional[Any]      							=     (
					    (TFTransfoXLModel, TFTransfoXLLMHeadModel, TFTransfoXLForSequenceClassification) if is_tf_available() else ()
					)
					A__					:		str      							=     () if is_tf_available() else ()
					A__					:		Optional[int]      							=     (
					    {
					        "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__					:		List[str]      							=     False
					A__					:		int      							=     False
					A__					:		Tuple      							=     False
					A__					:		Dict      							=     False



					def      _a				(  self       :							Dict					,       _snake_case       :							List[Any]					,       _snake_case       :							int					,       _snake_case       :							Dict					,       _snake_case       :							List[str]					,       _snake_case       :							str       ):






								"""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      _a				(  self       :							Union[str, Any]       ):






								"""simple docstring"""


								A__	  =     TFTransfoXLModelTester(self       )
								A__	  =     ConfigTester(self					,       config_class=_snake_case					,       d_embed=37       )



					def      _a				(  self       :							Tuple       ):






								"""simple docstring"""


								self.config_tester.run_common_tests()



					def      _a				(  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_model(*_snake_case       )



					def      _a				(  self       :							Dict       ):






								"""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(*_snake_case       )



					def      _a				(  self       :							int       ):






								"""simple docstring"""


								A__	  =     self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_transfo_xl_for_sequence_classification(*_snake_case       )



					def      _a				(  self       :							Union[str, 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(_snake_case       )
											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(_snake_case					,       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      _a				(  self       :							Union[str, Any]       ):






								"""simple docstring"""


								pass



					@slow
					def      _a				(  self       :							str       ):






								"""simple docstring"""


								for model_name in TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
											A__	  =     TFTransfoXLModel.from_pretrained(_snake_case       )
											self.assertIsNotNone(_snake_case       )



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






								"""simple docstring"""


								pass







@require_tf
class   __lowerCAmelCase  (		unittest.TestCase   ):
					"""simple docstring"""






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






								"""simple docstring"""


								A__	  =     TFTransfoXLLMHeadModel.from_pretrained('transfo-xl-wt103'       )
								# fmt: off
								A__	  =     tf.convert_to_tensor([[33,12_97,2,1,10_09,4,11_09,1_17_39,47_62,3_58,5,25,2_45,22,17_06,17,2_00_98,5,32_15,21,37,11_10,3,13,10_41,4,24,6_03,4_90,2,7_14_77,2_00_98,10_44_47,2,2_09_61,1,26_04,4,1,3_29,3,62_24,8_31,1_60_02,2,8,6_03,7_89_67,2_95_46,23,8_03,20,25,4_16,5,8,2_32,4,2_77,6,18_55,46_01,3,2_95_46,54,8,36_09,5,5_72_11,49,4,1,2_77,18,8,17_55,1_56_91,3,3_41,25,4_16,6_93,4_25_73,71,17,4_01,94,31,1_79_19,2,2_95_46,78_73,18,1,4_35,23,1_10_11,7_55,5,51_67,3,79_83,98,84,2,2_95_46,32_67,8,36_09,4,1,48_65,10_75,2,60_87,71,6,3_46,8,58_54,3,2_95_46,8_24,14_00,18_68,2,19,1_60,2,3_11,8,54_96,2,2_09_20,17,25,1_50_97,3,24,24,0]]					,       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__	  =     [33,12_97,2,1,10_09,4,11_09,1_17_39,47_62,3_58,5,25,2_45,22,17_06,17,2_00_98,5,32_15,21,37,11_10,3,13,10_41,4,24,6_03,4_90,2,7_14_77,2_00_98,10_44_47,2,2_09_61,1,26_04,4,1,3_29,3,62_24,8_31,1_60_02,2,8,6_03,7_89_67,2_95_46,23,8_03,20,25,4_16,5,8,2_32,4,2_77,6,18_55,46_01,3,2_95_46,54,8,36_09,5,5_72_11,49,4,1,2_77,18,8,17_55,1_56_91,3,3_41,25,4_16,6_93,4_25_73,71,17,4_01,94,31,1_79_19,2,2_95_46,78_73,18,1,4_35,23,1_10_11,7_55,5,51_67,3,79_83,98,84,2,2_95_46,32_67,8,36_09,4,1,48_65,10_75,2,60_87,71,6,3_46,8,58_54,3,2_95_46,8_24,14_00,18_68,2,19,1_60,2,3_11,8,54_96,2,2_09_20,17,25,1_50_97,3,24,24,0,33,1,18_57,2,1,10_09,4,11_09,1_17_39,47_62,3_58,5,25,2_45,28,11_10,3,13,10_41,4,24,6_03,4_90,2,7_14_77,2_00_98,10_44_47,2,2_09_61,1,26_04,4,1,3_29,3,0]  # 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(_snake_case					,       max_length=2_00					,       do_sample=_snake_case       )
								self.assertListEqual(output_ids[0].numpy().tolist()					,       _snake_case       )









import argparse

import OmegaConf
import torch

from diffusers import DDIMScheduler, LDMPipeline, UNetLDMModel, VQModel


def A    (							__UpperCamelCase			,						__UpperCamelCase			,						__UpperCamelCase       )    ->   int:
			A__	  =     OmegaConf.load(__UpperCamelCase       )
			A__	  =     torch.load(__UpperCamelCase			,						map_location='cpu'       )['model']
			A__	  =     list(state_dict.keys()       )

			# extract state_dict for VQVAE
			A__	  =     {}
			A__	  =     'first_stage_model.'
			for key in keys:
						if key.startswith(__UpperCamelCase       ):
									A__	  =     state_dict[key]

    # extract state_dict for UNetLDM
			A__	  =     {}
			A__	  =     'model.diffusion_model.'
			for key in keys:
						if key.startswith(__UpperCamelCase       ):
									A__	  =     state_dict[key]

			A__	  =     config.model.params.first_stage_config.params
			A__	  =     config.model.params.unet_config.params

			A__	  =     VQModel(**__UpperCamelCase       ).eval()
			vqvae.load_state_dict(__UpperCamelCase       )

			A__	  =     UNetLDMModel(**__UpperCamelCase       ).eval()
			unet.load_state_dict(__UpperCamelCase       )

			A__	  =     DDIMScheduler(
			    timesteps=config.model.params.timesteps			,						beta_schedule='scaled_linear'			,						beta_start=config.model.params.linear_start			,						beta_end=config.model.params.linear_end			,						clip_sample=__UpperCamelCase			,						)

			A__	  =     LDMPipeline(__UpperCamelCase			,						__UpperCamelCase			,						__UpperCamelCase       )
			pipeline.save_pretrained(__UpperCamelCase       )


if __name__ == "__main__":
							SCREAMING_SNAKE_CASE__   			=   argparse.ArgumentParser()
							parser.add_argument('''--checkpoint_path''', type=str, required=True)
							parser.add_argument('''--config_path''', type=str, required=True)
							parser.add_argument('''--output_path''', type=str, required=True)
							SCREAMING_SNAKE_CASE__   			=   parser.parse_args()

							convert_ldm_original(args.checkpoint_path, args.config_path, args.output_path)




