


import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

# Register SEW's fairseq modules
from sew_asapp import tasks  # noqa: F401

from transformers import (
    SEWConfig,
    SEWForCTC,
    SEWModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
__UpperCAmelCase : Tuple 			=				logging.get_logger(__name__)

__UpperCAmelCase : Dict 			=				{
    "post_extract_proj": "feature_projection",
    "encoder.pos_conv.0": "encoder.pos_conv_embed.conv",
    "self_attn.k_proj": "encoder.layers.*.attention.k_proj",
    "self_attn.v_proj": "encoder.layers.*.attention.v_proj",
    "self_attn.q_proj": "encoder.layers.*.attention.q_proj",
    "self_attn.out_proj": "encoder.layers.*.attention.out_proj",
    "self_attn_layer_norm": "encoder.layers.*.layer_norm",
    "fc1": "encoder.layers.*.feed_forward.intermediate_dense",
    "fc2": "encoder.layers.*.feed_forward.output_dense",
    "final_layer_norm": "encoder.layers.*.final_layer_norm",
    "encoder.upsample.0": "encoder.upsample.projection",
    "encoder.layer_norm": "encoder.layer_norm",
    "w2v_model.layer_norm": "layer_norm",
    "w2v_encoder.proj": "lm_head",
    "mask_emb": "masked_spec_embed",
}


def      a	(							SCREAMING_SNAKE_CASE_      :		int							,			SCREAMING_SNAKE_CASE_      :		Any							,			SCREAMING_SNAKE_CASE_      :		List[Any]							,			SCREAMING_SNAKE_CASE_      :		List[Any]							,			SCREAMING_SNAKE_CASE_      :		Tuple       ):






       """simple docstring"""





       for attribute in key.split('''.'''       ):
              UpperCamelCase    :	Any   	=		getattr(SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_       )

       if weight_type is not None:
              UpperCamelCase    :	List[Any]   	=		getattr(SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_       ).shape
       else:
              UpperCamelCase    :	Dict   	=		hf_pointer.shape

       assert hf_shape == value.shape, (
           F"""Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be"""
           F""" {value.shape} for {full_name}"""
       )

       if weight_type == "weight":
              UpperCamelCase    :	Optional[Any]   	=		value
       elif weight_type == "weight_g":
              UpperCamelCase    :	int   	=		value
       elif weight_type == "weight_v":
              UpperCamelCase    :	Tuple   	=		value
       elif weight_type == "bias":
              UpperCamelCase    :	str   	=		value
       else:
              UpperCamelCase    :	str   	=		value

       logger.info(F"""{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}."""       )


def      a	(							SCREAMING_SNAKE_CASE_      :		Optional[int]							,			SCREAMING_SNAKE_CASE_      :		Any							,			SCREAMING_SNAKE_CASE_      :		str       ):






       """simple docstring"""





       UpperCamelCase    :	Any   	=		[]
       UpperCamelCase    :	Optional[int]   	=		fairseq_model.state_dict()

       UpperCamelCase    :	int   	=		hf_model.sew.feature_extractor if is_finetuned else hf_model.feature_extractor

       for name, value in fairseq_dict.items():
              UpperCamelCase    :	Optional[int]   	=		False
              if "conv_layers" in name:
                     load_conv_layer(
                         SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_							,			hf_model.config.feat_extract_norm == '''group'''							,			)
                     UpperCamelCase    :	Optional[int]   	=		True
              else:
                     for key, mapped_key in MAPPING.items():
                            UpperCamelCase    :	List[str]   	=		'''sew.''' + mapped_key if (is_finetuned and mapped_key != '''lm_head''') else mapped_key

                            if key in name or key.split('''w2v_model.'''       )[-1] == name.split('''.'''       )[0]:
                                   UpperCamelCase    :	str   	=		True
                                   if "*" in mapped_key:
                                          UpperCamelCase    :	Optional[Any]   	=		name.split(SCREAMING_SNAKE_CASE_       )[0].split('''.'''       )[-2]
                                          UpperCamelCase    :	str   	=		mapped_key.replace('''*'''							,			SCREAMING_SNAKE_CASE_       )
                                   if "weight_g" in name:
                                          UpperCamelCase    :	Optional[int]   	=		'''weight_g'''
                                   elif "weight_v" in name:
                                          UpperCamelCase    :	Optional[int]   	=		'''weight_v'''
                                   elif "weight" in name:
                                          UpperCamelCase    :	Tuple   	=		'''weight'''
                                   elif "bias" in name:
                                          UpperCamelCase    :	Dict   	=		'''bias'''
                                   else:
                                          UpperCamelCase    :	int   	=		None
                                   set_recursively(SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_       )
                            continue
              if not is_used:
                     unused_weights.append(SCREAMING_SNAKE_CASE_       )

       logger.warning(F"""Unused weights: {unused_weights}"""       )


def      a	(							SCREAMING_SNAKE_CASE_      :		Optional[int]							,			SCREAMING_SNAKE_CASE_      :		Tuple							,			SCREAMING_SNAKE_CASE_      :		Optional[int]							,			SCREAMING_SNAKE_CASE_      :		Dict							,			SCREAMING_SNAKE_CASE_      :		Optional[int]       ):






       """simple docstring"""





       UpperCamelCase    :	int   	=		full_name.split('''conv_layers.'''       )[-1]
       UpperCamelCase    :	Optional[int]   	=		name.split('''.'''       )
       UpperCamelCase    :	Optional[int]   	=		int(items[0]       )
       UpperCamelCase    :	Union[str, Any]   	=		int(items[1]       )

       if type_id == 0:
              if "bias" in name:
                     assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
                         F"""{full_name} has size {value.shape}, but"""
                         F""" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found."""
                     )
                     UpperCamelCase    :	Any   	=		value
                     logger.info(F"""Feat extract conv layer {layer_id} was initialized from {full_name}."""       )
              elif "weight" in name:
                     assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
                         F"""{full_name} has size {value.shape}, but"""
                         F""" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found."""
                     )
                     UpperCamelCase    :	List[Any]   	=		value
                     logger.info(F"""Feat extract conv layer {layer_id} was initialized from {full_name}."""       )
       elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
              if "bias" in name:
                     assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
                         F"""{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was"""
                         " found."
                     )
                     UpperCamelCase    :	List[Any]   	=		value
                     logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""       )
              elif "weight" in name:
                     assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
                         F"""{full_name} has size {value.shape}, but"""
                         F""" {feature_extractor[layer_id].layer_norm.weight.data.shape} was found."""
                     )
                     UpperCamelCase    :	Optional[int]   	=		value
                     logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""       )
       else:
              unused_weights.append(SCREAMING_SNAKE_CASE_       )


def      a	(							SCREAMING_SNAKE_CASE_      :		List[Any]							,			SCREAMING_SNAKE_CASE_      :		List[str]       ):






       """simple docstring"""





       UpperCamelCase    :	Union[str, Any]   	=		SEWConfig()
       if is_finetuned:
              UpperCamelCase    :	Any   	=		model.wav_encoder.wav_model.cfg
       else:
              UpperCamelCase    :	List[str]   	=		model.cfg

       UpperCamelCase    :	str   	=		fs_config.conv_bias
       UpperCamelCase    :	Any   	=		eval(fs_config.conv_feature_layers       )
       UpperCamelCase    :	Optional[int]   	=		[x[0] for x in conv_layers]
       UpperCamelCase    :	Optional[int]   	=		[x[1] for x in conv_layers]
       UpperCamelCase    :	str   	=		[x[2] for x in conv_layers]
       UpperCamelCase    :	Union[str, Any]   	=		'''gelu'''
       UpperCamelCase    :	int   	=		'''layer''' if fs_config.extractor_mode == '''layer_norm''' else '''group'''
       UpperCamelCase    :	Optional[Any]   	=		0.0
       UpperCamelCase    :	Union[str, Any]   	=		fs_config.activation_fn.name
       UpperCamelCase    :	int   	=		fs_config.encoder_embed_dim
       UpperCamelCase    :	Union[str, Any]   	=		0.02
       UpperCamelCase    :	Union[str, Any]   	=		fs_config.encoder_ffn_embed_dim
       UpperCamelCase    :	str   	=		1E-5
       UpperCamelCase    :	int   	=		fs_config.encoder_layerdrop
       UpperCamelCase    :	List[str]   	=		fs_config.encoder_attention_heads
       UpperCamelCase    :	Union[str, Any]   	=		fs_config.conv_pos_groups
       UpperCamelCase    :	Union[str, Any]   	=		fs_config.conv_pos
       UpperCamelCase    :	Dict   	=		len(SCREAMING_SNAKE_CASE_       )
       UpperCamelCase    :	Tuple   	=		fs_config.encoder_layers
       UpperCamelCase    :	int   	=		fs_config.squeeze_factor

       # take care of any params that are overridden by the Wav2VecCtc model
       if is_finetuned:
              UpperCamelCase    :	List[Any]   	=		model.cfg
              UpperCamelCase    :	Optional[int]   	=		fs_config.final_dropout
              UpperCamelCase    :	Optional[Any]   	=		fs_config.layerdrop
       UpperCamelCase    :	Any   	=		fs_config.activation_dropout
       UpperCamelCase    :	Union[str, Any]   	=		fs_config.mask_prob > 0 or fs_config.mask_channel_prob > 0
       UpperCamelCase    :	Tuple   	=		fs_config.attention_dropout
       UpperCamelCase    :	Optional[Any]   	=		fs_config.dropout_input
       UpperCamelCase    :	Optional[int]   	=		fs_config.dropout
       UpperCamelCase    :	List[str]   	=		fs_config.mask_channel_length
       UpperCamelCase    :	Union[str, Any]   	=		fs_config.mask_channel_prob
       UpperCamelCase    :	List[Any]   	=		fs_config.mask_length
       UpperCamelCase    :	List[str]   	=		fs_config.mask_prob

       UpperCamelCase    :	Any   	=		'''Wav2Vec2FeatureExtractor'''
       UpperCamelCase    :	List[str]   	=		'''Wav2Vec2CTCTokenizer'''

       return config


@torch.no_grad()
def      a	(							SCREAMING_SNAKE_CASE_      :		Tuple							,			SCREAMING_SNAKE_CASE_      :		int							,			SCREAMING_SNAKE_CASE_      :		List[Any]=None							,			SCREAMING_SNAKE_CASE_      :		Union[str, Any]=None							,			SCREAMING_SNAKE_CASE_      :		str=True       ):






       """simple docstring"""





       if is_finetuned:
              UpperCamelCase	, UpperCamelCase	, UpperCamelCase    :	Any   	=		fairseq.checkpoint_utils.load_model_ensemble_and_task(
                  [checkpoint_path]							,			arg_overrides={'''data''': '''/'''.join(dict_path.split('''/'''       )[:-1]       )}       )
       else:
              UpperCamelCase	, UpperCamelCase	, UpperCamelCase    :	Optional[int]   	=		fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]       )

       if config_path is not None:
              UpperCamelCase    :	Union[str, Any]   	=		SEWConfig.from_pretrained(SCREAMING_SNAKE_CASE_       )
       else:
              UpperCamelCase    :	Dict   	=		convert_config(model[0]							,			SCREAMING_SNAKE_CASE_       )
       UpperCamelCase    :	Any   	=		model[0].eval()

       UpperCamelCase    :	Optional[Any]   	=		True if config.feat_extract_norm == '''layer''' else False
       UpperCamelCase    :	Optional[int]   	=		WavaVecaFeatureExtractor(
           feature_size=1							,			sampling_rate=1_6_0_0_0							,			padding_value=0							,			do_normalize=SCREAMING_SNAKE_CASE_							,			return_attention_mask=SCREAMING_SNAKE_CASE_							,			)

       if is_finetuned:
              if dict_path:
                     UpperCamelCase    :	List[str]   	=		Dictionary.load(SCREAMING_SNAKE_CASE_       )

                     # important change bos & pad token id since CTC symbol is <pad> and
                     # not <s> as in fairseq
                     UpperCamelCase    :	List[Any]   	=		target_dict.pad_index
                     UpperCamelCase    :	List[str]   	=		target_dict.bos_index
                     UpperCamelCase    :	Optional[Any]   	=		target_dict.pad_index
                     UpperCamelCase    :	List[Any]   	=		target_dict.bos_index
                     UpperCamelCase    :	Optional[Any]   	=		target_dict.eos_index
                     UpperCamelCase    :	List[Any]   	=		len(target_dict.symbols       )
                     UpperCamelCase    :	Union[str, Any]   	=		os.path.join(SCREAMING_SNAKE_CASE_							,			'''vocab.json'''       )
                     if not os.path.isdir(SCREAMING_SNAKE_CASE_       ):
                            logger.error('''--pytorch_dump_folder_path ({}) should be a directory'''.format(SCREAMING_SNAKE_CASE_       )       )
                            return
                     os.makedirs(SCREAMING_SNAKE_CASE_							,			exist_ok=SCREAMING_SNAKE_CASE_       )
                     with open(SCREAMING_SNAKE_CASE_							,			'''w'''							,			encoding='''utf-8'''       ) as vocab_handle:
                            json.dump(target_dict.indices							,			SCREAMING_SNAKE_CASE_       )
                     UpperCamelCase    :	Union[str, Any]   	=		WavaVecaCTCTokenizer(
                         SCREAMING_SNAKE_CASE_							,			unk_token=target_dict.unk_word							,			pad_token=target_dict.pad_word							,			bos_token=target_dict.bos_word							,			eos_token=target_dict.eos_word							,			word_delimiter_token='''|'''							,			do_lower_case=SCREAMING_SNAKE_CASE_							,			)
                     UpperCamelCase    :	List[str]   	=		WavaVecaProcessor(feature_extractor=SCREAMING_SNAKE_CASE_							,			tokenizer=SCREAMING_SNAKE_CASE_       )
                     processor.save_pretrained(SCREAMING_SNAKE_CASE_       )

              UpperCamelCase    :	List[str]   	=		SEWForCTC(SCREAMING_SNAKE_CASE_       )
       else:
              UpperCamelCase    :	Any   	=		SEWModel(SCREAMING_SNAKE_CASE_       )
              feature_extractor.save_pretrained(SCREAMING_SNAKE_CASE_       )

       recursively_load_weights(SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_       )

       hf_model.save_pretrained(SCREAMING_SNAKE_CASE_       )


if __name__ == "__main__":
  __UpperCAmelCase : Union[str, Any] 			=				argparse.ArgumentParser()
  parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
  parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to fairseq checkpoint")
  parser.add_argument("--dict_path", default=None, type=str, help="Path to dict of fine-tuned model")
  parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
  parser.add_argument(
      "--is_finetuned", action="store_true", help="Whether the model to convert is a fine-tuned model or not"
  )
  __UpperCAmelCase : List[str] 			=				parser.parse_args()
  convert_sew_checkpoint(
      args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_finetuned
  )





from __future__ import annotations

import unittest

from transformers import XGLMConfig, XGLMTokenizer, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
  import tensorflow as tf

  from transformers.models.xglm.modeling_tf_xglm import (
      TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFXGLMForCausalLM,
      TFXGLMModel,
  )



@require_tf
class    UpperCAmelCase_	:
       '''simple docstring'''
       __UpperCamelCase      :     Any							=					XGLMConfig
       __UpperCamelCase      :     Dict							=					{}
       __UpperCamelCase      :     List[str]							=					"gelu"






       def __init__(     self ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE=14 ,					__SCREAMING_SNAKE_CASE=7 ,					__SCREAMING_SNAKE_CASE=True ,					__SCREAMING_SNAKE_CASE=True ,					__SCREAMING_SNAKE_CASE=True ,					__SCREAMING_SNAKE_CASE=99 ,					__SCREAMING_SNAKE_CASE=32 ,					__SCREAMING_SNAKE_CASE=2 ,					__SCREAMING_SNAKE_CASE=4 ,					__SCREAMING_SNAKE_CASE=37 ,					__SCREAMING_SNAKE_CASE="gelu" ,					__SCREAMING_SNAKE_CASE=0.1 ,					__SCREAMING_SNAKE_CASE=0.1 ,					__SCREAMING_SNAKE_CASE=512 ,					__SCREAMING_SNAKE_CASE=0.02 ,					):




              """simple docstring"""




              UpperCamelCase    :	Any   	=		parent
              UpperCamelCase    :	Optional[int]   	=		batch_size
              UpperCamelCase    :	str   	=		seq_length
              UpperCamelCase    :	List[str]   	=		is_training
              UpperCamelCase    :	Tuple   	=		use_input_mask
              UpperCamelCase    :	Union[str, Any]   	=		use_labels
              UpperCamelCase    :	int   	=		vocab_size
              UpperCamelCase    :	Optional[int]   	=		d_model
              UpperCamelCase    :	Any   	=		num_hidden_layers
              UpperCamelCase    :	List[str]   	=		num_attention_heads
              UpperCamelCase    :	Optional[Any]   	=		ffn_dim
              UpperCamelCase    :	Optional[int]   	=		activation_function
              UpperCamelCase    :	List[str]   	=		activation_dropout
              UpperCamelCase    :	Any   	=		attention_dropout
              UpperCamelCase    :	str   	=		max_position_embeddings
              UpperCamelCase    :	Union[str, Any]   	=		initializer_range
              UpperCamelCase    :	int   	=		None
              UpperCamelCase    :	Dict   	=		0
              UpperCamelCase    :	int   	=		2
              UpperCamelCase    :	Any   	=		1






       def  _lowercase   (     self						):




              """simple docstring"""




              return XGLMConfig.from_pretrained('''facebook/xglm-564M'''						)






       def  _lowercase   (     self						):




              """simple docstring"""




              UpperCamelCase    :	Optional[int]   	=		tf.clip_by_value(
                  ids_tensor([self.batch_size, self.seq_length] ,					self.vocab_size						) ,					clip_value_min=0 ,					clip_value_max=3						)

              UpperCamelCase    :	int   	=		None
              if self.use_input_mask:
                     UpperCamelCase    :	Any   	=		random_attention_mask([self.batch_size, self.seq_length]						)

              UpperCamelCase    :	Tuple   	=		self.get_config()

              UpperCamelCase    :	str   	=		floats_tensor([self.num_hidden_layers, self.num_attention_heads] ,					2						)

              return (
                  config,
                  input_ids,
                  input_mask,
                  head_mask,
              )






       def  _lowercase   (     self						):




              """simple docstring"""




              return XGLMConfig(
                  vocab_size=self.vocab_size ,					d_model=self.hidden_size ,					num_layers=self.num_hidden_layers ,					attention_heads=self.num_attention_heads ,					ffn_dim=self.ffn_dim ,					activation_function=self.activation_function ,					activation_dropout=self.activation_dropout ,					attention_dropout=self.attention_dropout ,					max_position_embeddings=self.max_position_embeddings ,					initializer_range=self.initializer_range ,					use_cache=__SCREAMING_SNAKE_CASE ,					bos_token_id=self.bos_token_id ,					eos_token_id=self.eos_token_id ,					pad_token_id=self.pad_token_id ,					return_dict=__SCREAMING_SNAKE_CASE ,					)






       def  _lowercase   (     self						):




              """simple docstring"""




              UpperCamelCase    :	List[str]   	=		self.prepare_config_and_inputs()

              (
                  (
                  UpperCamelCase
              )	, (
                  UpperCamelCase
              )	, (
                  UpperCamelCase
              )	, (
                  UpperCamelCase
              )	, 
              )    :	Dict   	=		config_and_inputs

              UpperCamelCase    :	List[str]   	=		{
                  '''input_ids''': input_ids,
                  '''head_mask''': head_mask,
              }

              return config, inputs_dict




@require_tf
class    UpperCAmelCase_	(							_a,     _a,     unittest.TestCase):
       '''simple docstring'''
       __UpperCamelCase      :     Optional[Any]							=					(TFXGLMModel, TFXGLMForCausalLM) if is_tf_available() else ()
       __UpperCamelCase      :     Union[str, Any]							=					(TFXGLMForCausalLM,) if is_tf_available() else ()
       __UpperCamelCase      :     Any							=					(
           {"feature-extraction": TFXGLMModel, "text-generation": TFXGLMForCausalLM} if is_tf_available() else {}
       )
       __UpperCamelCase      :     Optional[int]							=					False
       __UpperCamelCase      :     List[Any]							=					False
       __UpperCamelCase      :     List[Any]							=					False






       def  _lowercase   (     self						):




              """simple docstring"""




              UpperCamelCase    :	List[Any]   	=		TFXGLMModelTester(self						)
              UpperCamelCase    :	Any   	=		ConfigTester(self ,					config_class=__SCREAMING_SNAKE_CASE ,					n_embd=37						)






       def  _lowercase   (     self						):




              """simple docstring"""




              self.config_tester.run_common_tests()






       @slow
       def  _lowercase   (     self						):




              """simple docstring"""




              for model_name in TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                     UpperCamelCase    :	List[Any]   	=		TFXGLMModel.from_pretrained(__SCREAMING_SNAKE_CASE						)
                     self.assertIsNotNone(__SCREAMING_SNAKE_CASE						)






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




              """simple docstring"""




              super().test_resize_token_embeddings()




@require_tf
class    UpperCAmelCase_	(							unittest.TestCase):
       '''simple docstring'''






       @slow
       def  _lowercase   (     self ,					__SCREAMING_SNAKE_CASE=True						):




              """simple docstring"""




              UpperCamelCase    :	List[str]   	=		TFXGLMForCausalLM.from_pretrained('''facebook/xglm-564M'''						)
              UpperCamelCase    :	List[Any]   	=		tf.convert_to_tensor([[2, 268, 9_865]] ,					dtype=tf.intaa						)  # The dog
              # </s> The dog is a very friendly dog. He is very affectionate and loves to play with other
              # fmt: off
              UpperCamelCase    :	str   	=		[2, 268, 9_865, 67, 11, 1_988, 57_252, 9_865, 5, 984, 67, 1_988, 213_838, 1_658, 53, 70_446, 33, 6_657, 278, 1_581]
              # fmt: on
              UpperCamelCase    :	Union[str, Any]   	=		model.generate(__SCREAMING_SNAKE_CASE ,					do_sample=__SCREAMING_SNAKE_CASE ,					num_beams=1						)
              if verify_outputs:
                     self.assertListEqual(output_ids[0].numpy().tolist() ,					__SCREAMING_SNAKE_CASE						)






       @slow
       def  _lowercase   (     self						):




              """simple docstring"""




              UpperCamelCase    :	str   	=		XGLMTokenizer.from_pretrained('''facebook/xglm-564M'''						)
              UpperCamelCase    :	List[str]   	=		TFXGLMForCausalLM.from_pretrained('''facebook/xglm-564M'''						)

              tf.random.set_seed(0						)
              UpperCamelCase    :	Tuple   	=		tokenizer('''Today is a nice day and''' ,					return_tensors='''tf'''						)
              UpperCamelCase    :	int   	=		tokenized.input_ids
              # forces the generation to happen on CPU, to avoid GPU-related quirks (and assure same output regardless of the available devices)
              with tf.device(''':/CPU:0'''						):
                     UpperCamelCase    :	str   	=		model.generate(__SCREAMING_SNAKE_CASE ,					do_sample=__SCREAMING_SNAKE_CASE ,					seed=[7, 0]						)
              UpperCamelCase    :	Dict   	=		tokenizer.decode(output_ids[0] ,					skip_special_tokens=__SCREAMING_SNAKE_CASE						)

              UpperCamelCase    :	Optional[int]   	=		(
                  '''Today is a nice day and warm evening here over Southern Alberta!! Today when they closed schools due'''
              )
              self.assertEqual(__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE						)






       @slow
       def  _lowercase   (     self						):




              """simple docstring"""




              UpperCamelCase    :	Dict   	=		TFXGLMForCausalLM.from_pretrained('''facebook/xglm-564M'''						)
              UpperCamelCase    :	Tuple   	=		XGLMTokenizer.from_pretrained('''facebook/xglm-564M'''						)

              UpperCamelCase    :	Tuple   	=		'''left'''

              # use different length sentences to test batching
              UpperCamelCase    :	Any   	=		[
                  '''This is an extremelly long sentence that only exists to test the ability of the model to cope with '''
                  '''left-padding, such as in batched generation. The output for the sequence below should be the same '''
                  '''regardless of whether left padding is applied or not. When''',
                  '''Hello, my dog is a little''',
              ]

              UpperCamelCase    :	List[Any]   	=		tokenizer(__SCREAMING_SNAKE_CASE ,					return_tensors='''tf''' ,					padding=__SCREAMING_SNAKE_CASE						)
              UpperCamelCase    :	List[str]   	=		inputs['''input_ids''']

              UpperCamelCase    :	Optional[int]   	=		model.generate(input_ids=__SCREAMING_SNAKE_CASE ,					attention_mask=inputs['''attention_mask'''] ,					max_new_tokens=12						)

              UpperCamelCase    :	Optional[int]   	=		tokenizer(sentences[0] ,					return_tensors='''tf'''						).input_ids
              UpperCamelCase    :	Optional[Any]   	=		model.generate(input_ids=__SCREAMING_SNAKE_CASE ,					max_new_tokens=12						)

              UpperCamelCase    :	str   	=		tokenizer(sentences[1] ,					return_tensors='''tf'''						).input_ids
              UpperCamelCase    :	List[Any]   	=		model.generate(input_ids=__SCREAMING_SNAKE_CASE ,					max_new_tokens=12						)

              UpperCamelCase    :	Any   	=		tokenizer.batch_decode(__SCREAMING_SNAKE_CASE ,					skip_special_tokens=__SCREAMING_SNAKE_CASE						)
              UpperCamelCase    :	List[str]   	=		tokenizer.decode(output_non_padded[0] ,					skip_special_tokens=__SCREAMING_SNAKE_CASE						)
              UpperCamelCase    :	Tuple   	=		tokenizer.decode(output_padded[0] ,					skip_special_tokens=__SCREAMING_SNAKE_CASE						)

              UpperCamelCase    :	Optional[int]   	=		[
                  '''This is an extremelly long sentence that only exists to test the ability of the model to cope with '''
                  '''left-padding, such as in batched generation. The output for the sequence below should be the same '''
                  '''regardless of whether left padding is applied or not. When left padding is applied, the sequence will be '''
                  '''a single''',
                  '''Hello, my dog is a little bit of a shy one, but he is very friendly''',
              ]
              self.assertListEqual(__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE						)
              self.assertListEqual(__SCREAMING_SNAKE_CASE ,					[non_padded_sentence, padded_sentence]						)

