






from __future__ import annotations

import unittest

from transformers import AutoTokenizer, GPTJConfig, is_tf_available
from transformers.testing_utils import require_tf, slow, tooslow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
from ...utils.test_modeling_tf_core import TFCoreModelTesterMixin


if is_tf_available():
 import tensorflow as tf

 from transformers.models.gptj.modeling_tf_gptj import (
     TFGPTJForCausalLM,
     TFGPTJForQuestionAnswering,
     TFGPTJForSequenceClassification,
     TFGPTJModel,
     shape_list,
 )
class        __snake_case:



 def __init__(	self   :   str ,		UpperCAmelCase__   :   str	):

    """simple docstring"""






    lowercase__						:	List[Any]         = parent
    lowercase__						:	List[str]         = 13
    lowercase__						:	Optional[int]         = 7
    lowercase__						:	str         = True
    lowercase__						:	Any         = True
    lowercase__						:	Optional[Any]         = True
    lowercase__						:	Any         = True
    lowercase__						:	Dict         = True
    lowercase__						:	Dict         = 99
    lowercase__						:	List[str]         = 32
    lowercase__						:	List[Any]         = 4
    lowercase__						:	Optional[Any]         = 2
    lowercase__						:	Any         = 4
    lowercase__						:	int         = 37
    lowercase__						:	Any         = """gelu"""
    lowercase__						:	List[str]         = 0.1
    lowercase__						:	Dict         = 0.1
    lowercase__						:	Union[str, Any]         = 512
    lowercase__						:	List[str]         = 16
    lowercase__						:	int         = 2
    lowercase__						:	Any         = 0.02
    lowercase__						:	Optional[Any]         = 3
    lowercase__						:	int         = 4
    lowercase__						:	Optional[int]         = None
    lowercase__						:	List[str]         = self.vocab_size - 1
    lowercase__						:	List[Any]         = self.vocab_size - 1
    lowercase__						:	str         = self.vocab_size - 1



 def 						UpperCAmelCase    (	self   :   Optional[int]	):

    """simple docstring"""






    lowercase__						:	List[Any]         = ids_tensor([self.batch_size, self.seq_length] ,		self.vocab_size	)

    lowercase__						:	str         = None
    if self.use_input_mask:
       lowercase__						:	Optional[Any]         = random_attention_mask([self.batch_size, self.seq_length]	)

    lowercase__						:	int         = None
    if self.use_token_type_ids:
       lowercase__						:	Union[str, Any]         = ids_tensor([self.batch_size, self.seq_length] ,		self.type_vocab_size	)

    lowercase__						:	Tuple         = None
    if self.use_mc_token_ids:
       lowercase__						:	Union[str, Any]         = ids_tensor([self.batch_size, self.num_choices] ,		self.seq_length	)

    lowercase__						:	Tuple         = None
    lowercase__						:	List[Any]         = None
    lowercase__						:	Union[str, Any]         = None
    if self.use_labels:
       lowercase__						:	Tuple         = ids_tensor([self.batch_size] ,		self.type_sequence_label_size	)
       lowercase__						:	str         = ids_tensor([self.batch_size, self.seq_length] ,		self.num_labels	)
       lowercase__						:	Tuple         = ids_tensor([self.batch_size] ,		self.num_choices	)

    lowercase__						:	Dict         = GPTJConfig(
        vocab_size=self.vocab_size ,		n_embd=self.hidden_size ,		n_layer=self.num_hidden_layers ,		n_head=self.num_attention_heads ,		intermediate_size=self.intermediate_size ,		hidden_act=self.hidden_act ,		hidden_dropout_prob=self.hidden_dropout_prob ,		attention_probs_dropout_prob=self.attention_probs_dropout_prob ,		n_positions=self.max_position_embeddings ,		type_vocab_size=self.type_vocab_size ,		initializer_range=self.initializer_range ,		bos_token_id=self.bos_token_id ,		eos_token_id=self.eos_token_id ,		pad_token_id=self.pad_token_id ,		rotary_dim=self.rotary_dim ,		return_dict=UpperCAmelCase__ ,		)

    lowercase__						:	List[Any]         = ids_tensor([self.num_hidden_layers, self.num_attention_heads] ,		2	)

    return (
        config,
        input_ids,
        input_mask,
        head_mask,
        token_type_ids,
        mc_token_ids,
        sequence_labels,
        token_labels,
        choice_labels,
    )



 def 						UpperCAmelCase    (	self   :   Dict ,		UpperCAmelCase__   :   int ,		UpperCAmelCase__   :   str ,		UpperCAmelCase__   :   List[str] ,		UpperCAmelCase__   :   int ,		UpperCAmelCase__   :   List[Any] ,		*UpperCAmelCase__   :   Optional[Any]	):

    """simple docstring"""






    lowercase__						:	int         = TFGPTJModel(config=UpperCAmelCase__	)
    lowercase__						:	Dict         = {
        """input_ids""": input_ids,
        """attention_mask""": input_mask,
        """token_type_ids""": token_type_ids,
    }
    lowercase__						:	Tuple         = model(UpperCAmelCase__	)

    lowercase__						:	Any         = [input_ids, None, input_mask]  # None is the input for 'past'
    lowercase__						:	str         = model(UpperCAmelCase__	)

    lowercase__						:	str         = model(UpperCAmelCase__	)

    self.parent.assertEqual(result.last_hidden_state.shape ,		(self.batch_size, self.seq_length, self.hidden_size)	)



 def 						UpperCAmelCase    (	self   :   Optional[Any] ,		UpperCAmelCase__   :   Optional[Any] ,		UpperCAmelCase__   :   Tuple ,		UpperCAmelCase__   :   Union[str, Any] ,		UpperCAmelCase__   :   int ,		UpperCAmelCase__   :   int ,		*UpperCAmelCase__   :   Optional[Any]	):

    """simple docstring"""






    lowercase__						:	List[Any]         = TFGPTJModel(config=UpperCAmelCase__	)

    # first forward pass
    lowercase__						:	Union[str, Any]         = model(UpperCAmelCase__ ,		token_type_ids=UpperCAmelCase__ ,		use_cache=UpperCAmelCase__	)
    lowercase__						:	Dict         = model(UpperCAmelCase__ ,		token_type_ids=UpperCAmelCase__	)
    lowercase__						:	int         = model(UpperCAmelCase__ ,		token_type_ids=UpperCAmelCase__ ,		use_cache=UpperCAmelCase__	)

    self.parent.assertTrue(len(UpperCAmelCase__	) == len(UpperCAmelCase__	)	)
    self.parent.assertTrue(len(UpperCAmelCase__	) == len(UpperCAmelCase__	) + 1	)

    lowercase__     ,					lowercase__						:	Tuple         = outputs.to_tuple()

    # create hypothetical next token and extent to next_input_ids
    lowercase__						:	Dict         = ids_tensor((self.batch_size, 1) ,		config.vocab_size	)
    lowercase__						:	Union[str, Any]         = ids_tensor([self.batch_size, 1] ,		self.type_vocab_size	)

    # append to next input_ids and token_type_ids
    lowercase__						:	Any         = tf.concat([input_ids, next_tokens] ,		axis=-1	)
    lowercase__						:	Union[str, Any]         = tf.concat([token_type_ids, next_token_types] ,		axis=-1	)

    lowercase__						:	str         = model(UpperCAmelCase__ ,		token_type_ids=UpperCAmelCase__	)["""last_hidden_state"""]
    lowercase__						:	List[Any]         = model(UpperCAmelCase__ ,		token_type_ids=UpperCAmelCase__ ,		past_key_values=UpperCAmelCase__	)[
        """last_hidden_state"""
    ]

    # select random slice
    lowercase__						:	Tuple         = int(ids_tensor((1,) ,		shape_list(UpperCAmelCase__	)[-1]	)	)
    lowercase__						:	Union[str, Any]         = output_from_no_past[:, -1, random_slice_idx]
    lowercase__						:	int         = output_from_past[:, 0, random_slice_idx]

    # test that outputs are equal for slice
    tf.debugging.assert_near(UpperCAmelCase__ ,		UpperCAmelCase__ ,		rtol=1e-6	)



 def 						UpperCAmelCase    (	self   :   Union[str, Any] ,		UpperCAmelCase__   :   Tuple ,		UpperCAmelCase__   :   List[str] ,		UpperCAmelCase__   :   List[Any] ,		UpperCAmelCase__   :   Tuple ,		UpperCAmelCase__   :   Tuple ,		*UpperCAmelCase__   :   Tuple	):

    """simple docstring"""






    lowercase__						:	str         = TFGPTJModel(config=UpperCAmelCase__	)

    # create attention mask
    lowercase__						:	Any         = self.seq_length // 2
    lowercase__						:	Optional[Any]         = tf.ones((self.batch_size, half_seq_length) ,		dtype=tf.intaa	)
    lowercase__						:	List[Any]         = tf.zeros((self.batch_size, self.seq_length - half_seq_length) ,		dtype=tf.intaa	)
    lowercase__						:	Dict         = tf.concat([attn_mask_begin, attn_mask_end] ,		axis=1	)

    # first forward pass
    lowercase__     ,					lowercase__						:	int         = model(UpperCAmelCase__ ,		attention_mask=UpperCAmelCase__	).to_tuple()

    # create hypothetical next token and extent to next_input_ids
    lowercase__						:	Dict         = ids_tensor((self.batch_size, 1) ,		config.vocab_size	)

    # change a random masked slice from input_ids
    lowercase__						:	Any         = ids_tensor((1,) ,		UpperCAmelCase__	).numpy() + 1
    lowercase__						:	int         = ids_tensor((self.batch_size, self.seq_length) ,		config.vocab_size	)
    lowercase__						:	Tuple         = tf.range(self.seq_length	) == (self.seq_length - random_seq_idx_to_change)
    lowercase__						:	int         = tf.transpose(
        tf.broadcast_to(tf.expand_dims(UpperCAmelCase__ ,		-1	) ,		(self.seq_length, self.batch_size)	)	)
    lowercase__						:	Union[str, Any]         = tf.where(UpperCAmelCase__ ,		UpperCAmelCase__ ,		UpperCAmelCase__	)

    # append to next input_ids and attn_mask
    lowercase__						:	List[str]         = tf.concat([input_ids, next_tokens] ,		axis=-1	)
    lowercase__						:	int         = tf.concat([attn_mask, tf.ones((shape_list(UpperCAmelCase__	)[0], 1) ,		dtype=tf.intaa	)] ,		axis=1	)

    # get two different outputs
    lowercase__						:	Any         = model(UpperCAmelCase__ ,		attention_mask=UpperCAmelCase__	)["""last_hidden_state"""]
    lowercase__						:	Optional[Any]         = model(UpperCAmelCase__ ,		past_key_values=UpperCAmelCase__ ,		attention_mask=UpperCAmelCase__	)[
        """last_hidden_state"""
    ]

    # select random slice
    lowercase__						:	Dict         = int(ids_tensor((1,) ,		shape_list(UpperCAmelCase__	)[-1]	)	)
    lowercase__						:	int         = output_from_no_past[:, -1, random_slice_idx]
    lowercase__						:	Any         = output_from_past[:, 0, random_slice_idx]

    # test that outputs are equal for slice
    tf.debugging.assert_near(UpperCAmelCase__ ,		UpperCAmelCase__ ,		rtol=1e-12	)



 def 						UpperCAmelCase    (	self   :   Union[str, Any] ,		UpperCAmelCase__   :   Tuple ,		UpperCAmelCase__   :   List[Any] ,		UpperCAmelCase__   :   Optional[Any] ,		UpperCAmelCase__   :   List[Any] ,		UpperCAmelCase__   :   Dict ,		*UpperCAmelCase__   :   int	):

    """simple docstring"""






    lowercase__						:	Any         = TFGPTJModel(config=UpperCAmelCase__	)

    lowercase__						:	str         = input_ids[:1, :]
    lowercase__						:	Any         = input_mask[:1, :]
    lowercase__						:	Optional[int]         = token_type_ids[:1, :]
    lowercase__						:	Any         = 1

    # first forward pass
    lowercase__						:	str         = model(UpperCAmelCase__ ,		attention_mask=UpperCAmelCase__ ,		token_type_ids=UpperCAmelCase__ ,		use_cache=UpperCAmelCase__	)

    lowercase__     ,					lowercase__						:	Any         = outputs.to_tuple()

    # create hypothetical next token and extent to next_input_ids
    lowercase__						:	Union[str, Any]         = ids_tensor((self.batch_size, 3) ,		config.vocab_size	)
    lowercase__						:	List[str]         = ids_tensor((self.batch_size, 3) ,		2	)
    lowercase__						:	Optional[Any]         = ids_tensor((self.batch_size, 3) ,		self.type_vocab_size	)

    # append to next input_ids and token_type_ids
    lowercase__						:	Optional[int]         = tf.concat([input_ids, next_tokens] ,		axis=-1	)
    lowercase__						:	str         = tf.concat([input_mask, next_attn_mask] ,		axis=-1	)
    lowercase__						:	Any         = tf.concat([token_type_ids, next_token_types] ,		axis=-1	)

    lowercase__						:	Dict         = model(
        UpperCAmelCase__ ,		token_type_ids=UpperCAmelCase__ ,		attention_mask=UpperCAmelCase__	)["""last_hidden_state"""]
    lowercase__						:	str         = model(
        UpperCAmelCase__ ,		token_type_ids=UpperCAmelCase__ ,		attention_mask=UpperCAmelCase__ ,		past_key_values=UpperCAmelCase__ ,		)["""last_hidden_state"""]
    self.parent.assertTrue(output_from_past.shape[1] == next_tokens.shape[1]	)

    # select random slice
    lowercase__						:	Optional[int]         = int(ids_tensor((1,) ,		shape_list(UpperCAmelCase__	)[-1]	)	)
    lowercase__						:	Optional[Any]         = output_from_no_past[:, -3:, random_slice_idx]
    lowercase__						:	Any         = output_from_past[:, :, random_slice_idx]

    # test that outputs are equal for slice
    tf.debugging.assert_near(UpperCAmelCase__ ,		UpperCAmelCase__ ,		rtol=1e-3	)



 def 						UpperCAmelCase    (	self   :   int ,		UpperCAmelCase__   :   Optional[int] ,		UpperCAmelCase__   :   Optional[Any] ,		UpperCAmelCase__   :   int ,		UpperCAmelCase__   :   Any ,		UpperCAmelCase__   :   Optional[int] ,		*UpperCAmelCase__   :   List[str]	):

    """simple docstring"""






    lowercase__						:	Optional[int]         = TFGPTJForCausalLM(config=UpperCAmelCase__	)
    lowercase__						:	Optional[int]         = {
        """input_ids""": input_ids,
        """attention_mask""": input_mask,
        """token_type_ids""": token_type_ids,
    }
    lowercase__						:	List[Any]         = model(UpperCAmelCase__	)
    self.parent.assertEqual(result.logits.shape ,		(self.batch_size, self.seq_length, self.vocab_size)	)







 def 						UpperCAmelCase    (	self   :   Tuple	):

    """simple docstring"""






    lowercase__						:	Dict         = self.prepare_config_and_inputs()

    (
        (
        lowercase__
    )     ,					(
        lowercase__
    )     ,					(
        lowercase__
    )     ,					(
        lowercase__
    )     ,					(
        lowercase__
    )     ,					(
        lowercase__
    )     ,					(
        lowercase__
    )     ,					(
        lowercase__
    )     ,					(
        lowercase__
    )     ,					
    )						:	List[Any]         = config_and_inputs

    lowercase__						:	str         = {
        """input_ids""": input_ids,
        """token_type_ids""": token_type_ids,
        """attention_mask""": input_mask,
    }
    return config, inputs_dict
@require_tf
class        __snake_case(					__UpperCamelCase     ,       __UpperCamelCase     ,       __UpperCamelCase     ,       unittest.TestCase      ):
 lowercase :       str		      =		(
     (TFGPTJForCausalLM, TFGPTJForSequenceClassification, TFGPTJForQuestionAnswering, TFGPTJModel)
     if is_tf_available()
     else ()
 )

 lowercase :       Union[str, Any]		      =		(TFGPTJForCausalLM,) if is_tf_available() else ()
 lowercase :       List[str]		      =		(
     {
         '''feature-extraction''': TFGPTJModel,
         '''question-answering''': TFGPTJForQuestionAnswering,
         '''text-classification''': TFGPTJForSequenceClassification,
         '''text-generation''': TFGPTJForCausalLM,
         '''zero-shot''': TFGPTJForSequenceClassification,
     }
     if is_tf_available()
     else {}
 )
 lowercase :       Tuple		      =		False
 lowercase :       Dict		      =		False
 lowercase :       Tuple		      =		False
 lowercase :       Any		      =		False



 def 						UpperCAmelCase    (	self   :   List[str] ,		UpperCAmelCase__   :   str ,		UpperCAmelCase__   :   List[str] ,		UpperCAmelCase__   :   Dict ,		UpperCAmelCase__   :   Dict ,		UpperCAmelCase__   :   Dict	):

    """simple docstring"""






    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   :   Dict	):

    """simple docstring"""






    lowercase__						:	Union[str, Any]         = TFGPTJModelTester(self	)
    lowercase__						:	List[str]         = ConfigTester(self ,		config_class=UpperCAmelCase__ ,		n_embd=37	)



 def 						UpperCAmelCase    (	self   :   Optional[Any]	):

    """simple docstring"""






    self.config_tester.run_common_tests()



 def 						UpperCAmelCase    (	self   :   Any	):

    """simple docstring"""






    lowercase__						:	Union[str, Any]         = self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_gptj_model(*UpperCAmelCase__	)



 def 						UpperCAmelCase    (	self   :   List[str]	):

    """simple docstring"""






    lowercase__						:	int         = self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_gptj_model_past(*UpperCAmelCase__	)



 def 						UpperCAmelCase    (	self   :   Union[str, Any]	):

    """simple docstring"""






    lowercase__						:	Dict         = self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_gptj_model_attention_mask_past(*UpperCAmelCase__	)



 def 						UpperCAmelCase    (	self   :   List[Any]	):

    """simple docstring"""






    lowercase__						:	Union[str, Any]         = self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_gptj_model_past_large_inputs(*UpperCAmelCase__	)



 def 						UpperCAmelCase    (	self   :   Union[str, Any]	):

    """simple docstring"""






    lowercase__						:	Union[str, Any]         = self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_gptj_lm_head_model(*UpperCAmelCase__	)



 @slow
 @unittest.skipIf(
     not is_tf_available() or len(tf.config.list_physical_devices("""GPU"""	)	) > 0 ,		"""skip testing on GPU for now to avoid GPU OOM.""" ,		)
 def 						UpperCAmelCase    (	self   :   int	):

    """simple docstring"""






    lowercase__						:	Tuple         = TFGPTJModel.from_pretrained("""EleutherAI/gpt-j-6B""" ,		from_pt=UpperCAmelCase__	)
    self.assertIsNotNone(UpperCAmelCase__	)







 @unittest.skip(reason="""Currently, model embeddings are going to undergo a major refactor."""	)
 def 						UpperCAmelCase    (	self   :   Any	):

    """simple docstring"""






    super().test_resize_token_embeddings()
@require_tf
@tooslow
# Marked as @tooslow due to GPU OOM -- but still useful to run locally. Requires ~39GB of RAM.
class        __snake_case(					unittest.TestCase      ):



 def 						UpperCAmelCase    (	self   :   Optional[Any]	):

    """simple docstring"""






    lowercase__						:	Any         = TFGPTJForCausalLM.from_pretrained("""EleutherAI/gpt-j-6B""" ,		from_pt=UpperCAmelCase__	)
    lowercase__						:	str         = tf.convert_to_tensor([[464, 3_290]] ,		dtype=tf.intaa	)  # The dog
    # fmt: off
    # The dog is a man's best friend. It is a loyal companion, and it is a friend
    lowercase__						:	Any         = [464, 3_290, 318, 257, 582, 338, 1_266, 1_545, 13, 632, 318, 257, 9_112, 15_185, 11, 290, 340, 318, 257, 1_545]
    # fmt: on
    lowercase__						:	List[str]         = model.generate(UpperCAmelCase__ ,		do_sample=UpperCAmelCase__	)
    self.assertListEqual(output_ids[0].numpy().tolist() ,		UpperCAmelCase__	)



 def 						UpperCAmelCase    (	self   :   Dict	):

    """simple docstring"""






    lowercase__						:	Any         = AutoTokenizer.from_pretrained("""EleutherAI/gpt-j-6B""" ,		revision="""float16"""	)
    lowercase__						:	Union[str, Any]         = TFGPTJForCausalLM.from_pretrained("""EleutherAI/gpt-j-6B""" ,		revision="""float16""" ,		from_pt=UpperCAmelCase__	)

    lowercase__						:	int         = tokenizer("""Today is a nice day and""" ,		return_tensors="""tf"""	)
    # forces the generation to happen on CPU, to avoid GPU-related quirks
    with tf.device(""":/CPU:0"""	):
       lowercase__						:	int         = model.generate(**UpperCAmelCase__ ,		do_sample=UpperCAmelCase__ ,		seed=[42, 0]	)
    lowercase__						:	Optional[Any]         = tokenizer.decode(output_ids[0] ,		skip_special_tokens=UpperCAmelCase__	)

    lowercase__						:	Union[str, Any]         = """Today is a nice day and I’m going to go for a walk. I’"""
    self.assertEqual(UpperCAmelCase__ ,		UpperCAmelCase__	)



 def 						UpperCAmelCase    (	self   :   List[str]	):

    """simple docstring"""






    lowercase__						:	int         = TFGPTJForCausalLM.from_pretrained("""EleutherAI/gpt-j-6B""" ,		revision="""float16""" ,		from_pt=UpperCAmelCase__	)
    lowercase__						:	str         = AutoTokenizer.from_pretrained("""EleutherAI/gpt-j-6B""" ,		revision="""float16"""	)

    lowercase__						:	str         = """left"""

    # Define PAD Token = EOS Token = 50256
    lowercase__						:	int         = tokenizer.eos_token
    lowercase__						:	str         = model.config.eos_token_id

    # use different length sentences to test batching
    lowercase__						:	Union[str, Any]         = [
        """Hello, my dog is a little""",
        """Today, I""",
    ]
    lowercase__						:	List[Any]         = [
        """Hello, my dog is a little over a year old and has been diagnosed with hip dysplasia""",
        """Today, I’m going to be talking about a topic that’""",
    ]
    return model, tokenizer, sentences, expected_output_sentences



 def 						UpperCAmelCase    (	self   :   str	):

    """simple docstring"""






    lowercase__     ,					lowercase__     ,					lowercase__     ,					lowercase__						:	Optional[Any]         = self._get_beam_search_test_objects()

    lowercase__						:	Optional[int]         = tokenizer(UpperCAmelCase__ ,		return_tensors="""tf""" ,		padding=UpperCAmelCase__	)
    lowercase__						:	Dict         = model.generate(**UpperCAmelCase__ ,		do_sample=UpperCAmelCase__ ,		num_beams=2	)
    lowercase__						:	Optional[Any]         = tokenizer.batch_decode(UpperCAmelCase__ ,		skip_special_tokens=UpperCAmelCase__	)
    self.assertListEqual(UpperCAmelCase__ ,		UpperCAmelCase__	)



 def 						UpperCAmelCase    (	self   :   Optional[Any]	):

    """simple docstring"""






    lowercase__     ,					lowercase__     ,					lowercase__     ,					lowercase__						:	Dict         = self._get_beam_search_test_objects()

    lowercase__						:	Dict         = tokenizer(UpperCAmelCase__ ,		return_tensors="""tf""" ,		padding=UpperCAmelCase__	)
    lowercase__						:	Tuple         = tokenizer(sentences[0] ,		return_tensors="""tf"""	)
    lowercase__						:	Any         = model.generate(**UpperCAmelCase__ ,		do_sample=UpperCAmelCase__ ,		num_beams=2	)
    lowercase__						:	str         = (
        shape_list(inputs_non_padded["""input_ids"""]	)[-1]
        - tf.reduce_sum(tf.cast(inputs["""attention_mask"""][-1] ,		tf.intaa	)	).numpy()
    )
    lowercase__						:	Optional[Any]         = tokenizer(sentences[1] ,		return_tensors="""tf"""	)
    lowercase__						:	Tuple         = model.generate(
        **UpperCAmelCase__ ,		do_sample=UpperCAmelCase__ ,		num_beams=2 ,		max_length=model.config.max_length - num_paddings	)
    lowercase__						:	List[str]         = tokenizer.decode(output_non_padded[0] ,		skip_special_tokens=UpperCAmelCase__	)
    lowercase__						:	List[str]         = tokenizer.decode(output_padded[0] ,		skip_special_tokens=UpperCAmelCase__	)
    self.assertListEqual(UpperCAmelCase__ ,		[non_padded_sentence, padded_sentence]	)







 def 						UpperCAmelCase    (	self   :   Any	):

    """simple docstring"""






    lowercase__     ,					lowercase__     ,					lowercase__     ,					lowercase__						:	int         = self._get_beam_search_test_objects()

    lowercase__						:	Tuple         = tokenizer(UpperCAmelCase__ ,		return_tensors="""tf""" ,		padding=UpperCAmelCase__	)
    lowercase__						:	Optional[Any]         = tf.function(model.generate ,		jit_compile=UpperCAmelCase__	)
    lowercase__						:	Union[str, Any]         = xla_generate(**UpperCAmelCase__ ,		do_sample=UpperCAmelCase__ ,		num_beams=2	)
    lowercase__						:	Dict         = tokenizer.batch_decode(UpperCAmelCase__ ,		skip_special_tokens=UpperCAmelCase__	)
    self.assertListEqual(UpperCAmelCase__ ,		UpperCAmelCase__	)










import argparse

import torch

from transformers import (
    WavaVecaFeatureExtractor,
    WavLMConfig,
    WavLMForAudioFrameClassification,
    WavLMForSequenceClassification,
    WavLMForXVector,
    logging,
)


logging.set_verbosity_info()
a    =       logging.get_logger(__name__)
def       a__						(  A		,				A		,				A					):
   lowercase__						:	int         = WavLMForSequenceClassification.from_pretrained(A		,				config=A					)
   lowercase__						:	Dict         = downstream_dict["""projector.weight"""]
   lowercase__						:	int         = downstream_dict["""projector.bias"""]
   lowercase__						:	Optional[Any]         = downstream_dict["""model.post_net.linear.weight"""]
   lowercase__						:	List[str]         = downstream_dict["""model.post_net.linear.bias"""]
   return model
def       a__						(  A		,				A		,				A					):
   lowercase__						:	Tuple         = WavLMForAudioFrameClassification.from_pretrained(A		,				config=A					)
   lowercase__						:	List[Any]         = downstream_dict["""model.linear.weight"""]
   lowercase__						:	Optional[int]         = downstream_dict["""model.linear.bias"""]
   return model
def       a__						(  A		,				A		,				A					):
   lowercase__						:	Optional[int]         = WavLMForXVector.from_pretrained(A		,				config=A					)
   lowercase__						:	List[str]         = downstream_dict["""connector.weight"""]
   lowercase__						:	int         = downstream_dict["""connector.bias"""]
   for i, kernel_size in enumerate(hf_config.tdnn_kernel					):
      lowercase__						:	Optional[Any]         = downstream_dict[
          F'''model.framelevel_feature_extractor.module.{i}.kernel.weight'''
      ]
      lowercase__						:	List[str]         = downstream_dict[F'''model.framelevel_feature_extractor.module.{i}.kernel.bias''']

   lowercase__						:	Union[str, Any]         = downstream_dict["""model.utterancelevel_feature_extractor.linear1.weight"""]
   lowercase__						:	Optional[Any]         = downstream_dict["""model.utterancelevel_feature_extractor.linear1.bias"""]
   lowercase__						:	Any         = downstream_dict["""model.utterancelevel_feature_extractor.linear2.weight"""]
   lowercase__						:	List[Any]         = downstream_dict["""model.utterancelevel_feature_extractor.linear2.bias"""]
   lowercase__						:	List[Any]         = downstream_dict["""objective.W"""]
   return model
@torch.no_grad()
def       a__						(  A		,				A		,				A		,				A					):
   lowercase__						:	List[Any]         = torch.load(A		,				map_location="""cpu"""					)

   lowercase__						:	Tuple         = checkpoint["""Downstream"""]

   lowercase__						:	List[str]         = WavLMConfig.from_pretrained(A					)
   lowercase__						:	Dict         = WavaVecaFeatureExtractor.from_pretrained(
       A		,				return_attention_mask=A		,				do_normalize=A					)

   lowercase__						:	Union[str, Any]         = hf_config.architectures[0]
   if arch.endswith("""ForSequenceClassification"""					):
      lowercase__						:	Union[str, Any]         = convert_classification(A		,				A		,				A					)
   elif arch.endswith("""ForAudioFrameClassification"""					):
      lowercase__						:	Dict         = convert_diarization(A		,				A		,				A					)
   elif arch.endswith("""ForXVector"""					):
      lowercase__						:	Dict         = convert_xvector(A		,				A		,				A					)
   else:
      raise NotImplementedError(F'''S3PRL weights conversion is not supported for {arch}'''					)

   if hf_config.use_weighted_layer_sum:
      lowercase__						:	Union[str, Any]         = checkpoint["""Featurizer"""]["""weights"""]

   hf_feature_extractor.save_pretrained(A					)
   hf_model.save_pretrained(A					)


if __name__ == "__main__":
 a    =       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.')
 a    =       parser.parse_args()
 convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)


