

"""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 __lowerCAmelCase				:




			"""simple docstring"""







			def __init__(				self						:					int       ,   _snake_case						:					Tuple       ,   )      -> Tuple:







								"""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_				 =						0.0_1




			def      lowerCamelCase__     (				self						:					Optional[int]	)      -> Dict:







								"""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      lowerCamelCase__     (				self						:					List[Any]	)      -> Optional[int]:







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




			def      lowerCamelCase__     (				self						:					Optional[int]       ,   _snake_case						:					Union[str, Any]       ,   _snake_case						:					Any       ,   _snake_case						:					Tuple       ,   _snake_case						:					Any	)      -> List[Any]:







								"""simple docstring"""
								A_				 =						TFTransfoXLModel(UpperCAmelCase__	)

								A_				 =						model(UpperCAmelCase__	).to_tuple()

								A_				 =						{'''input_ids''': input_ids_a, '''mems''': mems_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      lowerCamelCase__     (				self						:					Tuple       ,   _snake_case						:					int       ,   _snake_case						:					str       ,   _snake_case						:					Any       ,   _snake_case						:					Tuple	)      -> int:







								"""simple docstring"""
								A_				 =						TFTransfoXLLMHeadModel(UpperCAmelCase__	)

								A_				 =						model(UpperCAmelCase__	).to_tuple()

								A_				 =						{'''input_ids''': input_ids_a, '''labels''': lm_labels}
								A_				 =						model(UpperCAmelCase__	).to_tuple()

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

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

								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      lowerCamelCase__     (				self						:					int       ,   _snake_case						:					str       ,   _snake_case						:					List[Any]       ,   _snake_case						:					Optional[int]       ,   _snake_case						:					int	)      -> Union[str, Any]:







								"""simple docstring"""
								A_				 =						TFTransfoXLForSequenceClassification(UpperCAmelCase__	)
								A_				 =						model(UpperCAmelCase__	)
								self.parent.assertEqual(result.logits.shape       ,   (self.batch_size, self.num_labels)	)






			def      lowerCamelCase__     (				self						:					str	)      -> List[Any]:







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





@require_tf
class __lowerCAmelCase				(					lowercase__     ,				lowercase__     ,				unittest.TestCase      ):




			"""simple docstring"""







			snake_case	      =				(
			    (TFTransfoXLModel, TFTransfoXLLMHeadModel, TFTransfoXLForSequenceClassification) if is_tf_available() else ()
			)
			snake_case	      =				() if is_tf_available() else ()
			snake_case	      =				(
			    {
			        "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
			snake_case	      =				False
			snake_case	      =				False
			snake_case	      =				False
			snake_case	      =				False




			def      lowerCamelCase__     (				self						:					Optional[int]       ,   _snake_case						:					List[str]       ,   _snake_case						:					Union[str, Any]       ,   _snake_case						:					Any       ,   _snake_case						:					Tuple       ,   _snake_case						:					Union[str, Any]	)      -> 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						:					List[Any]	)      -> Optional[Any]:







								"""simple docstring"""
								A_				 =						TFTransfoXLModelTester(self	)
								A_				 =						ConfigTester(self       ,   config_class=UpperCAmelCase__       ,   d_embed=37	)




			def      lowerCamelCase__     (				self						:					int	)      -> Tuple:







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




			def      lowerCamelCase__     (				self						:					int	)      -> Optional[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      lowerCamelCase__     (				self						:					Optional[Any]	)      -> 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(*UpperCAmelCase__	)




			def      lowerCamelCase__     (				self						:					Any	)      -> Union[str, Any]:







								"""simple docstring"""
								A_				 =						self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_transfo_xl_for_sequence_classification(*UpperCAmelCase__	)




			def      lowerCamelCase__     (				self						:					Optional[int]	)      -> Any:







								"""simple docstring"""
								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      lowerCamelCase__     (				self						:					Union[str, Any]	)      -> Dict:







								"""simple docstring"""
								pass




			@slow
			def      lowerCamelCase__     (				self						:					int	)      -> Optional[Any]:







								"""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      lowerCamelCase__     (				self						:					List[Any]	)      -> Tuple:







								"""simple docstring"""
								pass





@require_tf
class __lowerCAmelCase				(					unittest.TestCase      ):




			"""simple docstring"""







			@unittest.skip("Skip test until #12651 is resolved."	)
			@slow
			def      lowerCamelCase__     (				self						:					int	)      -> List[str]:







								"""simple docstring"""
								A_				 =						TFTransfoXLLMHeadModel.from_pretrained("transfo-xl-wt103"	)
								# fmt: off
								A_				 =						tf.convert_to_tensor([[33,1_297,2,1,1_009,4,1_109,11_739,4_762,358,5,25,245,22,1_706,17,20_098,5,3_215,21,37,1_110,3,13,1_041,4,24,603,490,2,71_477,20_098,104_447,2,20_961,1,2_604,4,1,329,3,6_224,831,16_002,2,8,603,78_967,29_546,23,803,20,25,416,5,8,232,4,277,6,1_855,4_601,3,29_546,54,8,3_609,5,57_211,49,4,1,277,18,8,1_755,15_691,3,341,25,416,693,42_573,71,17,401,94,31,17_919,2,29_546,7_873,18,1,435,23,11_011,755,5,5_167,3,7_983,98,84,2,29_546,3_267,8,3_609,4,1,4_865,1_075,2,6_087,71,6,346,8,5_854,3,29_546,824,1_400,1_868,2,19,160,2,311,8,5_496,2,20_920,17,25,15_097,3,24,24,0]]       ,   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_				 =						[33,1_297,2,1,1_009,4,1_109,11_739,4_762,358,5,25,245,22,1_706,17,20_098,5,3_215,21,37,1_110,3,13,1_041,4,24,603,490,2,71_477,20_098,104_447,2,20_961,1,2_604,4,1,329,3,6_224,831,16_002,2,8,603,78_967,29_546,23,803,20,25,416,5,8,232,4,277,6,1_855,4_601,3,29_546,54,8,3_609,5,57_211,49,4,1,277,18,8,1_755,15_691,3,341,25,416,693,42_573,71,17,401,94,31,17_919,2,29_546,7_873,18,1,435,23,11_011,755,5,5_167,3,7_983,98,84,2,29_546,3_267,8,3_609,4,1,4_865,1_075,2,6_087,71,6,346,8,5_854,3,29_546,824,1_400,1_868,2,19,160,2,311,8,5_496,2,20_920,17,25,15_097,3,24,24,0,33,1,1_857,2,1,1_009,4,1_109,11_739,4_762,358,5,25,245,28,1_110,3,13,1_041,4,24,603,490,2,71_477,20_098,104_447,2,20_961,1,2_604,4,1,329,3,0]  # 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__	)






"""simple docstring"""





from argparse import ArgumentParser, Namespace

from ..utils import logging
from . import BaseTransformersCLICommand
def 							A_     (__a				):

					'''simple docstring'''





					return ConvertCommand(
					    args.model_type				,   args.tf_checkpoint				,   args.pytorch_dump_output				,   args.config				,   args.finetuning_task_name				)


UpperCamelCase_					:						Tuple				= '''
transformers can only be used from the commandline to convert TensorFlow models in PyTorch, In that case, it requires
TensorFlow to be installed. Please see https://www.tensorflow.org/install/ for installation instructions.
'''




class __lowerCAmelCase				(					_lowercase      ):




			"""simple docstring"""







			@staticmethod
			def      lowerCamelCase__     (				_snake_case						:					ArgumentParser	)      -> Union[str, Any]:







								"""simple docstring"""
								A_				 =						parser.add_parser(
								    "convert"       ,   help="CLI tool to run convert model from original author checkpoints to Transformers PyTorch checkpoints."       ,   )
								train_parser.add_argument("--model_type"       ,   type=_snake_case       ,   required=_snake_case       ,   help="Model's type."	)
								train_parser.add_argument(
								    "--tf_checkpoint"       ,   type=_snake_case       ,   required=_snake_case       ,   help="TensorFlow checkpoint path or folder."	)
								train_parser.add_argument(
								    "--pytorch_dump_output"       ,   type=_snake_case       ,   required=_snake_case       ,   help="Path to the PyTorch saved model output."	)
								train_parser.add_argument("--config"       ,   type=_snake_case       ,   default=""       ,   help="Configuration file path or folder."	)
								train_parser.add_argument(
								    "--finetuning_task_name"       ,   type=_snake_case       ,   default=_snake_case       ,   help="Optional fine-tuning task name if the TF model was a finetuned model."       ,   )
								train_parser.set_defaults(func=_snake_case	)




			def __init__(				self						:					str       ,   _snake_case						:					str       ,   _snake_case						:					str       ,   _snake_case						:					str       ,   _snake_case						:					str       ,   _snake_case						:					str       ,   *_snake_case						:					Optional[int]       ,   )      -> Union[str, Any]:







								"""simple docstring"""
								A_				 =						logging.get_logger("transformers-cli/converting"	)

								self._logger.info(F'Loading model {model_type}'	)
								A_				 =						model_type
								A_				 =						tf_checkpoint
								A_				 =						pytorch_dump_output
								A_				 =						config
								A_				 =						finetuning_task_name






			def      lowerCamelCase__     (				self						:					Tuple	)      -> Dict:







								"""simple docstring"""
								if self._model_type == "albert":
													try:
																		from ..models.albert.convert_albert_original_tf_checkpoint_to_pytorch import (
																		    convert_tf_checkpoint_to_pytorch,
																		)
													except ImportError:
																		raise ImportError(_snake_case	)

													convert_tf_checkpoint_to_pytorch(self._tf_checkpoint       ,   self._config       ,   self._pytorch_dump_output	)
								elif self._model_type == "bert":
													try:
																		from ..models.bert.convert_bert_original_tf_checkpoint_to_pytorch import (
																		    convert_tf_checkpoint_to_pytorch,
																		)
													except ImportError:
																		raise ImportError(_snake_case	)

													convert_tf_checkpoint_to_pytorch(self._tf_checkpoint       ,   self._config       ,   self._pytorch_dump_output	)
								elif self._model_type == "funnel":
													try:
																		from ..models.funnel.convert_funnel_original_tf_checkpoint_to_pytorch import (
																		    convert_tf_checkpoint_to_pytorch,
																		)
													except ImportError:
																		raise ImportError(_snake_case	)

													convert_tf_checkpoint_to_pytorch(self._tf_checkpoint       ,   self._config       ,   self._pytorch_dump_output	)
								elif self._model_type == "t5":
													try:
																		from ..models.ta.convert_ta_original_tf_checkpoint_to_pytorch import convert_tf_checkpoint_to_pytorch
													except ImportError:
																		raise ImportError(_snake_case	)

													convert_tf_checkpoint_to_pytorch(self._tf_checkpoint       ,   self._config       ,   self._pytorch_dump_output	)
								elif self._model_type == "gpt":
													from ..models.openai.convert_openai_original_tf_checkpoint_to_pytorch import (
													    convert_openai_checkpoint_to_pytorch,
													)

													convert_openai_checkpoint_to_pytorch(self._tf_checkpoint       ,   self._config       ,   self._pytorch_dump_output	)
								elif self._model_type == "transfo_xl":
													try:
																		from ..models.transfo_xl.convert_transfo_xl_original_tf_checkpoint_to_pytorch import (
																		    convert_transfo_xl_checkpoint_to_pytorch,
																		)
													except ImportError:
																		raise ImportError(_snake_case	)

													if "ckpt" in self._tf_checkpoint.lower():
																		A_				 =						self._tf_checkpoint
																		A_				 =						""
													else:
																		A_				 =						self._tf_checkpoint
																		A_				 =						""
													convert_transfo_xl_checkpoint_to_pytorch(
													    _snake_case       ,   self._config       ,   self._pytorch_dump_output       ,   _snake_case	)
								elif self._model_type == "gpt2":
													try:
																		from ..models.gpta.convert_gpta_original_tf_checkpoint_to_pytorch import (
																		    convert_gpta_checkpoint_to_pytorch,
																		)
													except ImportError:
																		raise ImportError(_snake_case	)

													convert_gpta_checkpoint_to_pytorch(self._tf_checkpoint       ,   self._config       ,   self._pytorch_dump_output	)
								elif self._model_type == "xlnet":
													try:
																		from ..models.xlnet.convert_xlnet_original_tf_checkpoint_to_pytorch import (
																		    convert_xlnet_checkpoint_to_pytorch,
																		)
													except ImportError:
																		raise ImportError(_snake_case	)

													convert_xlnet_checkpoint_to_pytorch(
													    self._tf_checkpoint       ,   self._config       ,   self._pytorch_dump_output       ,   self._finetuning_task_name	)
								elif self._model_type == "xlm":
													from ..models.xlm.convert_xlm_original_pytorch_checkpoint_to_pytorch import (
													    convert_xlm_checkpoint_to_pytorch,
													)

													convert_xlm_checkpoint_to_pytorch(self._tf_checkpoint       ,   self._pytorch_dump_output	)
								elif self._model_type == "lxmert":
													from ..models.lxmert.convert_lxmert_original_tf_checkpoint_to_pytorch import (
													    convert_lxmert_checkpoint_to_pytorch,
													)

													convert_lxmert_checkpoint_to_pytorch(self._tf_checkpoint       ,   self._pytorch_dump_output	)
								elif self._model_type == "rembert":
													from ..models.rembert.convert_rembert_tf_checkpoint_to_pytorch import (
													    convert_rembert_tf_checkpoint_to_pytorch,
													)

													convert_rembert_tf_checkpoint_to_pytorch(self._tf_checkpoint       ,   self._config       ,   self._pytorch_dump_output	)
								else:
													raise ValueError(
													    "--model_type should be selected in the list [bert, gpt, gpt2, t5, transfo_xl, xlnet, xlm, lxmert]"	)




