from __future__ import annotations

import unittest

from transformers import 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, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
				import tensorflow as tf

				from transformers import (
				    TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFXLMForMultipleChoice,
				    TFXLMForQuestionAnsweringSimple,
				    TFXLMForSequenceClassification,
				    TFXLMForTokenClassification,
				    TFXLMModel,
				    TFXLMWithLMHeadModel,
				    XLMConfig,
				)



class    lowercase_    :



							def __init__(						self      ,				lowercase_      ,				):
													_snake_case						:     Tuple				=    parent
													_snake_case						:     Tuple				=    13
													_snake_case						:     Any				=    7
													_snake_case						:     Tuple				=    True
													_snake_case						:     Union[str, Any]				=    True
													_snake_case						:     str				=    True
													_snake_case						:     Union[str, Any]				=    True
													_snake_case						:     Union[str, Any]				=    True
													_snake_case						:     Union[str, Any]				=    False
													_snake_case						:     Optional[int]				=    False
													_snake_case						:     List[str]				=    False
													_snake_case						:     Optional[Any]				=    2
													_snake_case						:     Tuple				=    99
													_snake_case						:     Dict				=    0
													_snake_case						:     List[Any]				=    32
													_snake_case						:     Dict				=    2
													_snake_case						:     Union[str, Any]				=    4
													_snake_case						:     int				=    0.1
													_snake_case						:     str				=    0.1
													_snake_case						:     int				=    512
													_snake_case						:     Optional[Any]				=    16
													_snake_case						:     int				=    2
													_snake_case						:     Tuple				=    0.02
													_snake_case						:     str				=    3
													_snake_case						:     Union[str, Any]				=    4
													_snake_case						:     Optional[Any]				=    "last"
													_snake_case						:     Any				=    True
													_snake_case						:     List[str]				=    None
													_snake_case						:     Dict				=    0



							def        UpperCamelCase       (						self				):
													_snake_case						:     int				=    ids_tensor([self.batch_size, self.seq_length]      ,				self.vocab_size				)
													_snake_case						:     str				=    random_attention_mask([self.batch_size, self.seq_length]      ,				dtype=tf.floataa				)

													_snake_case						:     List[Any]				=    None
													if self.use_input_lengths:
																			_snake_case						:     List[Any]				=    (
																			    ids_tensor([self.batch_size]      ,				vocab_size=2				) + self.seq_length - 2
																			)  # small variation of seq_length

													_snake_case						:     Any				=    None
													if self.use_token_type_ids:
																			_snake_case						:     Dict				=    ids_tensor([self.batch_size, self.seq_length]      ,				self.n_langs				)

													_snake_case						:     List[str]				=    None
													_snake_case						:     List[Any]				=    None
													_snake_case						:     Optional[Any]				=    None
													if self.use_labels:
																			_snake_case						:     Optional[Any]				=    ids_tensor([self.batch_size]      ,				self.type_sequence_label_size				)
																			_snake_case						:     Tuple				=    ids_tensor([self.batch_size, self.seq_length]      ,				self.num_labels				)
																			_snake_case						:     Any				=    ids_tensor([self.batch_size]      ,				2      ,				dtype=tf.floataa				)
																			_snake_case						:     int				=    ids_tensor([self.batch_size]      ,				self.num_choices				)

													_snake_case						:     Optional[Any]				=    XLMConfig(
													    vocab_size=self.vocab_size      ,				n_special=self.n_special      ,				emb_dim=self.hidden_size      ,				n_layers=self.num_hidden_layers      ,				n_heads=self.num_attention_heads      ,				dropout=self.hidden_dropout_prob      ,				attention_dropout=self.attention_probs_dropout_prob      ,				gelu_activation=self.gelu_activation      ,				sinusoidal_embeddings=self.sinusoidal_embeddings      ,				asm=self.asm      ,				causal=self.causal      ,				n_langs=self.n_langs      ,				max_position_embeddings=self.max_position_embeddings      ,				initializer_range=self.initializer_range      ,				summary_type=self.summary_type      ,				use_proj=self.use_proj      ,				bos_token_id=self.bos_token_id      ,				)

													return (
													    config,
													    input_ids,
													    token_type_ids,
													    input_lengths,
													    sequence_labels,
													    token_labels,
													    is_impossible_labels,
													    choice_labels,
													    input_mask,
													)



							def        UpperCamelCase       (						self      ,				lowercase_      ,				lowercase_      ,				lowercase_      ,				lowercase_      ,				lowercase_      ,				lowercase_      ,				lowercase_      ,				lowercase_      ,				lowercase_      ,				):
													_snake_case						:     List[str]				=    TFXLMModel(config=lowercase_				)
													_snake_case						:     Optional[int]				=    {"input_ids": input_ids, "lengths": input_lengths, "langs": token_type_ids}
													_snake_case						:     List[Any]				=    model(lowercase_				)

													_snake_case						:     Tuple				=    [input_ids, input_mask]
													_snake_case						:     List[str]				=    model(lowercase_				)
													self.parent.assertEqual(result.last_hidden_state.shape      ,				(self.batch_size, self.seq_length, self.hidden_size)				)



							def        UpperCamelCase       (						self      ,				lowercase_      ,				lowercase_      ,				lowercase_      ,				lowercase_      ,				lowercase_      ,				lowercase_      ,				lowercase_      ,				lowercase_      ,				lowercase_      ,				):
													_snake_case						:     str				=    TFXLMWithLMHeadModel(lowercase_				)

													_snake_case						:     Optional[int]				=    {"input_ids": input_ids, "lengths": input_lengths, "langs": token_type_ids}
													_snake_case						:     Any				=    model(lowercase_				)

													_snake_case						:     Any				=    outputs

													self.parent.assertEqual(result.logits.shape      ,				(self.batch_size, self.seq_length, self.vocab_size)				)



							def        UpperCamelCase       (						self      ,				lowercase_      ,				lowercase_      ,				lowercase_      ,				lowercase_      ,				lowercase_      ,				lowercase_      ,				lowercase_      ,				lowercase_      ,				lowercase_      ,				):
													_snake_case						:     int				=    TFXLMForQuestionAnsweringSimple(lowercase_				)

													_snake_case						:     List[str]				=    {"input_ids": input_ids, "lengths": input_lengths}

													_snake_case						:     List[str]				=    model(lowercase_				)

													self.parent.assertEqual(result.start_logits.shape      ,				(self.batch_size, self.seq_length)				)
													self.parent.assertEqual(result.end_logits.shape      ,				(self.batch_size, self.seq_length)				)



							def        UpperCamelCase       (						self      ,				lowercase_      ,				lowercase_      ,				lowercase_      ,				lowercase_      ,				lowercase_      ,				lowercase_      ,				lowercase_      ,				lowercase_      ,				lowercase_      ,				):
													_snake_case						:     str				=    TFXLMForSequenceClassification(lowercase_				)

													_snake_case						:     Optional[Any]				=    {"input_ids": input_ids, "lengths": input_lengths}

													_snake_case						:     str				=    model(lowercase_				)

													self.parent.assertEqual(result.logits.shape      ,				(self.batch_size, self.type_sequence_label_size)				)



							def        UpperCamelCase       (						self      ,				lowercase_      ,				lowercase_      ,				lowercase_      ,				lowercase_      ,				lowercase_      ,				lowercase_      ,				lowercase_      ,				lowercase_      ,				lowercase_      ,				):
													_snake_case						:     Optional[int]				=    self.num_labels
													_snake_case						:     Any				=    TFXLMForTokenClassification(config=lowercase_				)
													_snake_case						:     int				=    {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
													_snake_case						:     Dict				=    model(lowercase_				)
													self.parent.assertEqual(result.logits.shape      ,				(self.batch_size, self.seq_length, self.num_labels)				)



							def        UpperCamelCase       (						self      ,				lowercase_      ,				lowercase_      ,				lowercase_      ,				lowercase_      ,				lowercase_      ,				lowercase_      ,				lowercase_      ,				lowercase_      ,				lowercase_      ,				):
													_snake_case						:     str				=    self.num_choices
													_snake_case						:     List[Any]				=    TFXLMForMultipleChoice(config=lowercase_				)
													_snake_case						:     int				=    tf.tile(tf.expand_dims(lowercase_      ,				1				)      ,				(1, self.num_choices, 1)				)
													_snake_case						:     str				=    tf.tile(tf.expand_dims(lowercase_      ,				1				)      ,				(1, self.num_choices, 1)				)
													_snake_case						:     str				=    tf.tile(tf.expand_dims(lowercase_      ,				1				)      ,				(1, self.num_choices, 1)				)
													_snake_case						:     Union[str, Any]				=    {
													    "input_ids": multiple_choice_inputs_ids,
													    "attention_mask": multiple_choice_input_mask,
													    "token_type_ids": multiple_choice_token_type_ids,
													}
													_snake_case						:     Optional[Any]				=    model(lowercase_				)
													self.parent.assertEqual(result.logits.shape      ,				(self.batch_size, self.num_choices)				)



							def        UpperCamelCase       (						self				):
													_snake_case						:     int				=    self.prepare_config_and_inputs()
													(
													    (
													    _snake_case
													)				,(
													    _snake_case
													)				,(
													    _snake_case
													)				,(
													    _snake_case
													)				,(
													    _snake_case
													)				,(
													    _snake_case
													)				,(
													    _snake_case
													)				,(
													    _snake_case
													)				,(
													    _snake_case
													)				,
													)						:     str				=    config_and_inputs
													_snake_case						:     List[str]				=    {
													    "input_ids": input_ids,
													    "token_type_ids": token_type_ids,
													    "langs": token_type_ids,
													    "lengths": input_lengths,
													}
													return config, inputs_dict




@require_tf
class    lowercase_    (     __snake_case						, __snake_case						, unittest.TestCase		):
							_lowerCamelCase						=		(
							    (
							        TFXLMModel,
							        TFXLMWithLMHeadModel,
							        TFXLMForSequenceClassification,
							        TFXLMForQuestionAnsweringSimple,
							        TFXLMForTokenClassification,
							        TFXLMForMultipleChoice,
							    )
							    if is_tf_available()
							    else ()
							)
							_lowerCamelCase						=		(
							    (TFXLMWithLMHeadModel,) if is_tf_available() else ()
							)  # TODO (PVP): Check other models whether language generation is also applicable
							_lowerCamelCase						=		(
							    {
							        'feature-extraction': TFXLMModel,
							        'fill-mask': TFXLMWithLMHeadModel,
							        'question-answering': TFXLMForQuestionAnsweringSimple,
							        'text-classification': TFXLMForSequenceClassification,
							        'text-generation': TFXLMWithLMHeadModel,
							        'token-classification': TFXLMForTokenClassification,
							        'zero-shot': TFXLMForSequenceClassification,
							    }
							    if is_tf_available()
							    else {}
							)
							_lowerCamelCase						=		False
							_lowerCamelCase						=		False



							def        UpperCamelCase       (						self      ,				lowercase_      ,				lowercase_      ,				lowercase_      ,				lowercase_      ,				lowercase_				):
													if (
													    pipeline_test_casse_name == "QAPipelineTests"
													    and tokenizer_name is not None
													    and not tokenizer_name.endswith("Fast"				)
													):
																			# `QAPipelineTests` fails for a few models when the slower tokenizer are used.
																			# (The slower tokenizers were never used for pipeline tests before the pipeline testing rework)
																			# TODO: check (and possibly fix) the `QAPipelineTests` with slower tokenizer
																			return True

													return False



							def        UpperCamelCase       (						self				):
													_snake_case						:     str				=    TFXLMModelTester(self				)
													_snake_case						:     Optional[int]				=    ConfigTester(self      ,				config_class=lowercase_      ,				emb_dim=37				)



							def        UpperCamelCase       (						self				):
													self.config_tester.run_common_tests()



							def        UpperCamelCase       (						self				):
													_snake_case						:     Tuple				=    self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_xlm_model(*lowercase_				)



							def        UpperCamelCase       (						self				):
													_snake_case						:     Dict				=    self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_xlm_lm_head(*lowercase_				)



							def        UpperCamelCase       (						self				):
													_snake_case						:     Dict				=    self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_xlm_qa(*lowercase_				)



							def        UpperCamelCase       (						self				):
													_snake_case						:     Optional[int]				=    self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_xlm_sequence_classif(*lowercase_				)



							def        UpperCamelCase       (						self				):
													_snake_case						:     Union[str, Any]				=    self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_xlm_for_token_classification(*lowercase_				)



							def        UpperCamelCase       (						self				):
													_snake_case						:     Optional[Any]				=    self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_xlm_for_multiple_choice(*lowercase_				)



							@slow
							def        UpperCamelCase       (						self				):
													for model_name in TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																			_snake_case						:     List[str]				=    TFXLMModel.from_pretrained(lowercase_				)
																			self.assertIsNotNone(lowercase_				)




@require_tf
class    lowercase_    (     unittest.TestCase		):



							@slow
							def        UpperCamelCase       (						self				):
													_snake_case						:     Any				=    TFXLMWithLMHeadModel.from_pretrained("xlm-mlm-en-2048"				)
													_snake_case						:     Union[str, Any]				=    tf.convert_to_tensor([[14, 447]]      ,				dtype=tf.intaa				)  # the president
													_snake_case						:     List[Any]				=    [
													    14,
													    447,
													    14,
													    447,
													    14,
													    447,
													    14,
													    447,
													    14,
													    447,
													    14,
													    447,
													    14,
													    447,
													    14,
													    447,
													    14,
													    447,
													    14,
													    447,
													]  # the president the president the president the president the president the president the president the president the president the president
													# TODO(PVP): this and other input_ids I tried for generation give pretty bad results. Not sure why. Model might just not be made for auto-regressive inference
													_snake_case						:     Union[str, Any]				=    model.generate(lowercase_      ,				do_sample=lowercase_				)
													self.assertListEqual(output_ids[0].numpy().tolist()      ,				lowercase_				)
import argparse

import torch

from transformers import HubertConfig, HubertForSequenceClassification, WavaVecaFeatureExtractor, logging


logging.set_verbosity_info()
__SCREAMING_SNAKE_CASE						:    Dict     = logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE						:    List[Any]     = ['UtteranceLevel']
@torch.no_grad()
def 					snake_case						(__lowercase					,			__lowercase					,			__lowercase					,			__lowercase						)       ->       Tuple:

						'''simple docstring'''



						_snake_case						:     Optional[int]				=    torch.load(__lowercase					,			map_location="cpu"						)
						if checkpoint["Config"]["downstream_expert"]["modelrc"]["select"] not in SUPPORTED_MODELS:
												raise NotImplementedError(F"""The supported s3prl models are {SUPPORTED_MODELS}"""						)

						_snake_case						:     List[str]				=    checkpoint["Downstream"]

						_snake_case						:     List[str]				=    HubertConfig.from_pretrained(__lowercase						)
						_snake_case						:     Union[str, Any]				=    HubertForSequenceClassification.from_pretrained(__lowercase					,			config=__lowercase						)
						_snake_case						:     Union[str, Any]				=    WavaVecaFeatureExtractor.from_pretrained(
						    __lowercase					,			return_attention_mask=__lowercase					,			do_normalize=__lowercase						)

						if hf_congfig.use_weighted_layer_sum:
												_snake_case						:     Optional[int]				=    checkpoint["Featurizer"]["weights"]

						_snake_case						:     str				=    downstream_dict["projector.weight"]
						_snake_case						:     Tuple				=    downstream_dict["projector.bias"]
						_snake_case						:     List[str]				=    downstream_dict["model.post_net.linear.weight"]
						_snake_case						:     str				=    downstream_dict["model.post_net.linear.bias"]

						hf_feature_extractor.save_pretrained(__lowercase						)
						hf_model.save_pretrained(__lowercase						)


if __name__ == "__main__":
				__SCREAMING_SNAKE_CASE						:    Optional[int]     = argparse.ArgumentParser()
				parser.add_argument(
				    '--base_model_name', default=None, type=str, help='Name of the huggingface pretrained base model.'
				)
				parser.add_argument('--config_path', default=None, type=str, help='Path to the huggingface classifier config.')
				parser.add_argument('--checkpoint_path', default=None, type=str, help='Path to the s3prl checkpoint.')
				parser.add_argument('--model_dump_path', default=None, type=str, help='Path to the final converted model.')
				__SCREAMING_SNAKE_CASE						:    str     = parser.parse_args()
				convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)