
"""simple docstring"""

import unittest
from queue import Empty
from threading import Thread

from transformers import AutoTokenizer, TextIteratorStreamer, TextStreamer, is_torch_available
from transformers.testing_utils import CaptureStdout, require_torch, torch_device

from ..test_modeling_common import ids_tensor


if is_torch_available():
  import torch

  from transformers import AutoModelForCausalLM

@require_torch
class __lowerCAmelCase		(				unittest.TestCase      ):





     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      __UpperCamelCase      =	AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-gpt2'				)
      __UpperCamelCase      =	AutoModelForCausalLM.from_pretrained('hf-internal-testing/tiny-random-gpt2'				).to(__UpperCAmelCase				)
      __UpperCamelCase      =	-1

      __UpperCamelCase      =	ids_tensor((1, 5)     ,					vocab_size=model.config.vocab_size				).to(__UpperCAmelCase				)
      __UpperCamelCase      =	model.generate(__UpperCAmelCase     ,					max_new_tokens=10     ,					do_sample=__UpperCAmelCase				)
      __UpperCamelCase      =	tokenizer.decode(greedy_ids[0]				)

      with CaptureStdout() as cs:
       __UpperCamelCase      =	TextStreamer(__UpperCAmelCase				)
       model.generate(__UpperCAmelCase     ,					max_new_tokens=10     ,					do_sample=__UpperCAmelCase     ,					streamer=__UpperCAmelCase				)
      # The greedy text should be printed to stdout, except for the final "\n" in the streamer
      __UpperCamelCase      =	cs.out[:-1]

      self.assertEqual(__UpperCAmelCase     ,					__UpperCAmelCase				)





     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      __UpperCamelCase      =	AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-gpt2'				)
      __UpperCamelCase      =	AutoModelForCausalLM.from_pretrained('hf-internal-testing/tiny-random-gpt2'				).to(__UpperCAmelCase				)
      __UpperCamelCase      =	-1

      __UpperCamelCase      =	ids_tensor((1, 5)     ,					vocab_size=model.config.vocab_size				).to(__UpperCAmelCase				)
      __UpperCamelCase      =	model.generate(__UpperCAmelCase     ,					max_new_tokens=10     ,					do_sample=__UpperCAmelCase				)
      __UpperCamelCase      =	tokenizer.decode(greedy_ids[0]				)

      __UpperCamelCase      =	TextIteratorStreamer(__UpperCAmelCase				)
      __UpperCamelCase      =	{'input_ids': input_ids, 'max_new_tokens': 10, 'do_sample': False, 'streamer': streamer}
      __UpperCamelCase      =	Thread(target=model.generate     ,					kwargs=__UpperCAmelCase				)
      thread.start()
      __UpperCamelCase      =	''
      for new_text in streamer:
       streamer_text += new_text

      self.assertEqual(__UpperCAmelCase     ,					__UpperCAmelCase				)





     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      __UpperCamelCase      =	AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-gpt2'				)
      __UpperCamelCase      =	AutoModelForCausalLM.from_pretrained('hf-internal-testing/tiny-random-gpt2'				).to(__UpperCAmelCase				)
      __UpperCamelCase      =	-1

      __UpperCamelCase      =	ids_tensor((1, 5)     ,					vocab_size=model.config.vocab_size				).to(__UpperCAmelCase				)
      __UpperCamelCase      =	model.generate(__UpperCAmelCase     ,					max_new_tokens=10     ,					do_sample=__UpperCAmelCase				)
      __UpperCamelCase      =	greedy_ids[:, input_ids.shape[1] :]
      __UpperCamelCase      =	tokenizer.decode(new_greedy_ids[0]				)

      with CaptureStdout() as cs:
       __UpperCamelCase      =	TextStreamer(__UpperCAmelCase     ,					skip_prompt=__UpperCAmelCase				)
       model.generate(__UpperCAmelCase     ,					max_new_tokens=10     ,					do_sample=__UpperCAmelCase     ,					streamer=__UpperCAmelCase				)
      # The greedy text should be printed to stdout, except for the final "\n" in the streamer
      __UpperCamelCase      =	cs.out[:-1]

      self.assertEqual(__UpperCAmelCase     ,					__UpperCAmelCase				)





     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      __UpperCamelCase      =	AutoTokenizer.from_pretrained('distilgpt2'				)
      __UpperCamelCase      =	AutoModelForCausalLM.from_pretrained('distilgpt2'				).to(__UpperCAmelCase				)
      __UpperCamelCase      =	-1

      __UpperCamelCase      =	torch.ones((1, 5)     ,					device=__UpperCAmelCase				).long() * model.config.bos_token_id
      with CaptureStdout() as cs:
       __UpperCamelCase      =	TextStreamer(__UpperCAmelCase     ,					skip_special_tokens=__UpperCAmelCase				)
       model.generate(__UpperCAmelCase     ,					max_new_tokens=1     ,					do_sample=__UpperCAmelCase     ,					streamer=__UpperCAmelCase				)

      # The prompt contains a special token, so the streamer should not print it. As such, the output text, when
      # re-tokenized, must only contain one token
      __UpperCamelCase      =	cs.out[:-1]  # Remove the final "\n"
      __UpperCamelCase      =	tokenizer(__UpperCAmelCase     ,					return_tensors='pt'				)
      self.assertEqual(streamer_text_tokenized.input_ids.shape     ,					(1, 1)				)







     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      __UpperCamelCase      =	AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-gpt2'				)
      __UpperCamelCase      =	AutoModelForCausalLM.from_pretrained('hf-internal-testing/tiny-random-gpt2'				).to(__UpperCAmelCase				)
      __UpperCamelCase      =	-1

      __UpperCamelCase      =	ids_tensor((1, 5)     ,					vocab_size=model.config.vocab_size				).to(__UpperCAmelCase				)
      __UpperCamelCase      =	TextIteratorStreamer(__UpperCAmelCase     ,					timeout=0.0_0_1				)
      __UpperCamelCase      =	{'input_ids': input_ids, 'max_new_tokens': 10, 'do_sample': False, 'streamer': streamer}
      __UpperCamelCase      =	Thread(target=model.generate     ,					kwargs=__UpperCAmelCase				)
      thread.start()

      # The streamer will timeout after 0.001 seconds, so an exception will be raised
      with self.assertRaises(__UpperCAmelCase				):
       __UpperCamelCase      =	''
       for new_text in streamer:
        streamer_text += new_text





"""simple docstring"""

import functools
import operator

from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCamelCase     :    str       =			logging.get_logger(__name__)

UpperCamelCase     :    Dict       =			{
    "microsoft/wavlm-base": "https://huggingface.co/microsoft/wavlm-base/resolve/main/config.json",
    # See all WavLM models at https://huggingface.co/models?filter=wavlm
}

class __lowerCAmelCase		(				__SCREAMING_SNAKE_CASE      ):
     lowercase						       =						"wavlm"





     def __init__(     self     ,					__UpperCAmelCase=32     ,					__UpperCAmelCase=768     ,					__UpperCAmelCase=12     ,					__UpperCAmelCase=12     ,					__UpperCAmelCase=3072     ,					__UpperCAmelCase="gelu"     ,					__UpperCAmelCase=0.1     ,					__UpperCAmelCase=0.1     ,					__UpperCAmelCase=0.1     ,					__UpperCAmelCase=0.0     ,					__UpperCAmelCase=0.1     ,					__UpperCAmelCase=0.1     ,					__UpperCAmelCase=0.0_2     ,					__UpperCAmelCase=1E-5     ,					__UpperCAmelCase="group"     ,					__UpperCAmelCase="gelu"     ,					__UpperCAmelCase=(512, 512, 512, 512, 512, 512, 512)     ,					__UpperCAmelCase=(5, 2, 2, 2, 2, 2, 2)     ,					__UpperCAmelCase=(10, 3, 3, 3, 3, 2, 2)     ,					__UpperCAmelCase=False     ,					__UpperCAmelCase=128     ,					__UpperCAmelCase=16     ,					__UpperCAmelCase=320     ,					__UpperCAmelCase=800     ,					__UpperCAmelCase=False     ,					__UpperCAmelCase=True     ,					__UpperCAmelCase=0.0_5     ,					__UpperCAmelCase=10     ,					__UpperCAmelCase=2     ,					__UpperCAmelCase=0.0     ,					__UpperCAmelCase=10     ,					__UpperCAmelCase=320     ,					__UpperCAmelCase=2     ,					__UpperCAmelCase=0.1     ,					__UpperCAmelCase=100     ,					__UpperCAmelCase=256     ,					__UpperCAmelCase=256     ,					__UpperCAmelCase=0.1     ,					__UpperCAmelCase="mean"     ,					__UpperCAmelCase=False     ,					__UpperCAmelCase=False     ,					__UpperCAmelCase=256     ,					__UpperCAmelCase=(512, 512, 512, 512, 1500)     ,					__UpperCAmelCase=(5, 3, 3, 1, 1)     ,					__UpperCAmelCase=(1, 2, 3, 1, 1)     ,					__UpperCAmelCase=512     ,					__UpperCAmelCase=80     ,					__UpperCAmelCase=0     ,					__UpperCAmelCase=1     ,					__UpperCAmelCase=2     ,					__UpperCAmelCase=False     ,					__UpperCAmelCase=3     ,					__UpperCAmelCase=2     ,					__UpperCAmelCase=3     ,					__UpperCAmelCase=None     ,					**__UpperCAmelCase     ,					):

      '''simple docstring'''




      super().__init__(**__UpperCAmelCase     ,					pad_token_id=__UpperCAmelCase     ,					bos_token_id=__UpperCAmelCase     ,					eos_token_id=__UpperCAmelCase				)
      __UpperCamelCase      =	hidden_size
      __UpperCamelCase      =	feat_extract_norm
      __UpperCamelCase      =	feat_extract_activation
      __UpperCamelCase      =	list(__UpperCAmelCase				)
      __UpperCamelCase      =	list(__UpperCAmelCase				)
      __UpperCamelCase      =	list(__UpperCAmelCase				)
      __UpperCamelCase      =	conv_bias
      __UpperCamelCase      =	num_buckets
      __UpperCamelCase      =	max_bucket_distance
      __UpperCamelCase      =	num_conv_pos_embeddings
      __UpperCamelCase      =	num_conv_pos_embedding_groups
      __UpperCamelCase      =	len(self.conv_dim				)
      __UpperCamelCase      =	num_hidden_layers
      __UpperCamelCase      =	intermediate_size
      __UpperCamelCase      =	hidden_act
      __UpperCamelCase      =	num_attention_heads
      __UpperCamelCase      =	hidden_dropout
      __UpperCamelCase      =	attention_dropout
      __UpperCamelCase      =	activation_dropout
      __UpperCamelCase      =	feat_proj_dropout
      __UpperCamelCase      =	final_dropout
      __UpperCamelCase      =	layerdrop
      __UpperCamelCase      =	layer_norm_eps
      __UpperCamelCase      =	initializer_range
      __UpperCamelCase      =	num_ctc_classes
      __UpperCamelCase      =	vocab_size
      __UpperCamelCase      =	do_stable_layer_norm
      __UpperCamelCase      =	use_weighted_layer_sum
      __UpperCamelCase      =	classifier_proj_size

      if (
          (len(self.conv_stride				) != self.num_feat_extract_layers)
          or (len(self.conv_kernel				) != self.num_feat_extract_layers)
          or (len(self.conv_dim				) != self.num_feat_extract_layers)
      ):
       raise ValueError(
           'Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =='
           ' `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ='
           F' {len(self.conv_dim				)}`, `len(config.conv_stride) = {len(self.conv_stride				)}`,'
           F' `len(config.conv_kernel) = {len(self.conv_kernel				)}`.'				)

      # fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
      __UpperCamelCase      =	apply_spec_augment
      __UpperCamelCase      =	mask_time_prob
      __UpperCamelCase      =	mask_time_length
      __UpperCamelCase      =	mask_time_min_masks
      __UpperCamelCase      =	mask_feature_prob
      __UpperCamelCase      =	mask_feature_length

      # parameters for pretraining with codevector quantized representations
      __UpperCamelCase      =	num_codevectors_per_group
      __UpperCamelCase      =	num_codevector_groups
      __UpperCamelCase      =	contrastive_logits_temperature
      __UpperCamelCase      =	num_negatives
      __UpperCamelCase      =	codevector_dim
      __UpperCamelCase      =	proj_codevector_dim
      __UpperCamelCase      =	diversity_loss_weight

      # ctc loss
      __UpperCamelCase      =	ctc_loss_reduction
      __UpperCamelCase      =	ctc_zero_infinity

      # adapter
      __UpperCamelCase      =	add_adapter
      __UpperCamelCase      =	adapter_kernel_size
      __UpperCamelCase      =	adapter_stride
      __UpperCamelCase      =	num_adapter_layers
      __UpperCamelCase      =	output_hidden_size or hidden_size

      # SequenceClassification-specific parameter. Feel free to ignore for other classes.
      __UpperCamelCase      =	classifier_proj_size

      # XVector-specific parameters. Feel free to ignore for other classes.
      __UpperCamelCase      =	list(__UpperCAmelCase				)
      __UpperCamelCase      =	list(__UpperCAmelCase				)
      __UpperCamelCase      =	list(__UpperCAmelCase				)
      __UpperCamelCase      =	xvector_output_dim







     @property
     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      return functools.reduce(operator.mul     ,					self.conv_stride     ,					1				)



