


'''simple docstring'''


import functools
import operator

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


lowerCamelCase__    	=     logging.get_logger(__name__)

lowerCamelCase__    	=     {
    'microsoft/unispeech-large-1500h-cv': (
        'https://huggingface.co/microsoft/unispeech-large-1500h-cv/resolve/main/config.json'
    ),
    # See all UniSpeech models at https://huggingface.co/models?filter=unispeech
}






class 	lowerCAmelCase__			(			UpperCAmelCase__	):
			lowerCAmelCase :	List[str]          =     "unispeech"




			def __init__(		self  :     List[str]	,  lowerCamelCase__  :     Dict=32	,  lowerCamelCase__  :     List[str]=7_68	,  lowerCamelCase__  :     str=12	,  lowerCamelCase__  :     int=12	,  lowerCamelCase__  :     int=30_72	,  lowerCamelCase__  :     Any="gelu"	,  lowerCamelCase__  :     List[Any]=0.1	,  lowerCamelCase__  :     List[Any]=0.1	,  lowerCamelCase__  :     Optional[int]=0.1	,  lowerCamelCase__  :     str=0.0	,  lowerCamelCase__  :     int=0.0	,  lowerCamelCase__  :     Dict=0.1	,  lowerCamelCase__  :     int=0.1	,  lowerCamelCase__  :     List[str]=0.0_2	,  lowerCamelCase__  :     Tuple=1E-5	,  lowerCamelCase__  :     Optional[int]="group"	,  lowerCamelCase__  :     List[str]="gelu"	,  lowerCamelCase__  :     Optional[Any]=(5_12, 5_12, 5_12, 5_12, 5_12, 5_12, 5_12)	,  lowerCamelCase__  :     Optional[int]=(5, 2, 2, 2, 2, 2, 2)	,  lowerCamelCase__  :     List[Any]=(10, 3, 3, 3, 3, 2, 2)	,  lowerCamelCase__  :     int=False	,  lowerCamelCase__  :     Union[str, Any]=1_28	,  lowerCamelCase__  :     List[Any]=16	,  lowerCamelCase__  :     List[str]=False	,  lowerCamelCase__  :     List[Any]=True	,  lowerCamelCase__  :     List[Any]=0.0_5	,  lowerCamelCase__  :     Dict=10	,  lowerCamelCase__  :     Union[str, Any]=2	,  lowerCamelCase__  :     List[Any]=0.0	,  lowerCamelCase__  :     Tuple=10	,  lowerCamelCase__  :     Optional[Any]=0	,  lowerCamelCase__  :     str=3_20	,  lowerCamelCase__  :     Dict=2	,  lowerCamelCase__  :     int=0.1	,  lowerCamelCase__  :     Optional[int]=1_00	,  lowerCamelCase__  :     Optional[Any]=2_56	,  lowerCamelCase__  :     Any=2_56	,  lowerCamelCase__  :     Any=0.1	,  lowerCamelCase__  :     str="mean"	,  lowerCamelCase__  :     Optional[Any]=False	,  lowerCamelCase__  :     Tuple=False	,  lowerCamelCase__  :     str=2_56	,  lowerCamelCase__  :     List[str]=80	,  lowerCamelCase__  :     Tuple=0	,  lowerCamelCase__  :     Optional[Any]=1	,  lowerCamelCase__  :     Optional[Any]=2	,  lowerCamelCase__  :     str=0.5	,  **lowerCamelCase__  :     Union[str, Any]	,  )   ->Any:



					'''simple docstring'''







					super().__init__(**lowerCamelCase__	,  pad_token_id=lowerCamelCase__	,  bos_token_id=lowerCamelCase__	,  eos_token_id=lowerCamelCase__ )
					_UpperCAmelCase		:       Any								=      hidden_size
					_UpperCAmelCase		:       Any								=      feat_extract_norm
					_UpperCAmelCase		:       Optional[int]								=      feat_extract_activation
					_UpperCAmelCase		:       Tuple								=      list(lowerCamelCase__ )
					_UpperCAmelCase		:       Union[str, Any]								=      list(lowerCamelCase__ )
					_UpperCAmelCase		:       Tuple								=      list(lowerCamelCase__ )
					_UpperCAmelCase		:       List[str]								=      conv_bias
					_UpperCAmelCase		:       int								=      num_conv_pos_embeddings
					_UpperCAmelCase		:       Dict								=      num_conv_pos_embedding_groups
					_UpperCAmelCase		:       Union[str, Any]								=      len(self.conv_dim )
					_UpperCAmelCase		:       Optional[int]								=      num_hidden_layers
					_UpperCAmelCase		:       Tuple								=      intermediate_size
					_UpperCAmelCase		:       List[Any]								=      hidden_act
					_UpperCAmelCase		:       Optional[int]								=      num_attention_heads
					_UpperCAmelCase		:       Dict								=      hidden_dropout
					_UpperCAmelCase		:       List[str]								=      attention_dropout
					_UpperCAmelCase		:       int								=      activation_dropout
					_UpperCAmelCase		:       Union[str, Any]								=      feat_proj_dropout
					_UpperCAmelCase		:       List[Any]								=      final_dropout
					_UpperCAmelCase		:       List[str]								=      layerdrop
					_UpperCAmelCase		:       Union[str, Any]								=      layer_norm_eps
					_UpperCAmelCase		:       Tuple								=      initializer_range
					_UpperCAmelCase		:       int								=      num_ctc_classes
					_UpperCAmelCase		:       Tuple								=      vocab_size
					_UpperCAmelCase		:       List[str]								=      do_stable_layer_norm
					_UpperCAmelCase		:       Optional[Any]								=      use_weighted_layer_sum
					_UpperCAmelCase		:       Dict								=      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		:       List[str]								=      apply_spec_augment
					_UpperCAmelCase		:       int								=      mask_time_prob
					_UpperCAmelCase		:       Union[str, Any]								=      mask_time_length
					_UpperCAmelCase		:       str								=      mask_time_min_masks
					_UpperCAmelCase		:       Dict								=      mask_feature_prob
					_UpperCAmelCase		:       Any								=      mask_feature_length
					_UpperCAmelCase		:       Union[str, Any]								=      mask_feature_min_masks

					# parameters for pretraining with codevector quantized representations
					_UpperCAmelCase		:       List[str]								=      num_codevectors_per_group
					_UpperCAmelCase		:       str								=      num_codevector_groups
					_UpperCAmelCase		:       Optional[Any]								=      contrastive_logits_temperature
					_UpperCAmelCase		:       Union[str, Any]								=      feat_quantizer_dropout
					_UpperCAmelCase		:       Any								=      num_negatives
					_UpperCAmelCase		:       str								=      codevector_dim
					_UpperCAmelCase		:       List[str]								=      proj_codevector_dim
					_UpperCAmelCase		:       Dict								=      diversity_loss_weight

					# ctc loss
					_UpperCAmelCase		:       Optional[int]								=      ctc_loss_reduction
					_UpperCAmelCase		:       Tuple								=      ctc_zero_infinity

					# pretraining loss
					_UpperCAmelCase		:       List[str]								=      replace_prob






			@property
			def 							lowerCAmelCase__    (		self  :     str )   ->Union[str, Any]:



					'''simple docstring'''







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







'''simple docstring'''


import os
import re
import shutil
import sys
import tempfile
import unittest

import black


lowerCamelCase__    	=     os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, 'utils'))

import check_copies  # noqa: E402


# This is the reference code that will be used in the tests.
# If DDPMSchedulerOutput is changed in scheduling_ddpm.py, this code needs to be manually updated.
lowerCamelCase__    	=     '    \"""\n    Output class for the scheduler\'s step function output.\n\n    Args:\n        prev_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):\n            Computed sample (x_{t-1}) of previous timestep. `prev_sample` should be used as next model input in the\n            denoising loop.\n        pred_original_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):\n            The predicted denoised sample (x_{0}) based on the model output from the current timestep.\n            `pred_original_sample` can be used to preview progress or for guidance.\n    \"""\n\n    prev_sample: torch.FloatTensor\n    pred_original_sample: Optional[torch.FloatTensor] = None\n'






class 	lowerCAmelCase__			(			unittest.TestCase	):




			def 							lowerCAmelCase__    (		self  :     Any )   ->Any:



					'''simple docstring'''







					_UpperCAmelCase		:       Optional[int]								=      tempfile.mkdtemp()
					os.makedirs(os.path.join(self.diffusers_dir	,  "schedulers/" ) )
					_UpperCAmelCase		:       Optional[Any]								=      self.diffusers_dir
					shutil.copy(
					    os.path.join(lowerCamelCase__	,  "src/diffusers/schedulers/scheduling_ddpm.py" )	,  os.path.join(self.diffusers_dir	,  "schedulers/scheduling_ddpm.py" )	,  )




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



					'''simple docstring'''







					_UpperCAmelCase		:       int								=      "src/diffusers"
					shutil.rmtree(self.diffusers_dir )




			def 							lowerCAmelCase__    (		self  :     str	,  lowerCamelCase__  :     List[str]	,  lowerCamelCase__  :     Optional[Any]	,  lowerCamelCase__  :     Union[str, Any]	,  lowerCamelCase__  :     Any=None )   ->List[str]:



					'''simple docstring'''







					_UpperCAmelCase		:       Union[str, Any]								=      comment + F"""\nclass {class_name}(nn.Module):\n""" + class_code
					if overwrite_result is not None:
							_UpperCAmelCase		:       Tuple								=      comment + F"""\nclass {class_name}(nn.Module):\n""" + overwrite_result
					_UpperCAmelCase		:       Optional[Any]								=      black.Mode(target_versions={black.TargetVersion.PYaa}	,  line_length=1_19 )
					_UpperCAmelCase		:       Tuple								=      black.format_str(lowerCamelCase__	,  mode=lowerCamelCase__ )
					_UpperCAmelCase		:       Optional[int]								=      os.path.join(self.diffusers_dir	,  "new_code.py" )
					with open(lowerCamelCase__	,  "w"	,  newline="\n" ) as f:
							f.write(lowerCamelCase__ )
					if overwrite_result is None:
							self.assertTrue(len(check_copies.is_copy_consistent(lowerCamelCase__ ) ) == 0 )
					else:
							check_copies.is_copy_consistent(f.name	,  overwrite=lowerCamelCase__ )
							with open(lowerCamelCase__	,  "r" ) as f:
									self.assertTrue(f.read()	,  lowerCamelCase__ )




			def 							lowerCAmelCase__    (		self  :     str )   ->Tuple:



					'''simple docstring'''







					_UpperCAmelCase		:       Tuple								=      check_copies.find_code_in_diffusers("schedulers.scheduling_ddpm.DDPMSchedulerOutput" )
					self.assertEqual(lowerCamelCase__	,  lowerCamelCase__ )






			def 							lowerCAmelCase__    (		self  :     Union[str, Any] )   ->Optional[int]:



					'''simple docstring'''







					self.check_copy_consistency(
					    "# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput"	,  "DDPMSchedulerOutput"	,  REFERENCE_CODE + "\n"	,  )

					# With no empty line at the end
					self.check_copy_consistency(
					    "# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput"	,  "DDPMSchedulerOutput"	,  lowerCamelCase__	,  )

					# Copy consistency with rename
					self.check_copy_consistency(
					    "# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->Test"	,  "TestSchedulerOutput"	,  re.sub("DDPM"	,  "Test"	,  lowerCamelCase__ )	,  )

					# Copy consistency with a really long name
					_UpperCAmelCase		:       int								=      "TestClassWithAReallyLongNameBecauseSomePeopleLikeThatForSomeReason"
					self.check_copy_consistency(
					    F"""# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->{long_class_name}"""	,  F"""{long_class_name}SchedulerOutput"""	,  re.sub("Bert"	,  lowerCamelCase__	,  lowerCamelCase__ )	,  )

					# Copy consistency with overwrite
					self.check_copy_consistency(
					    "# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->Test"	,  "TestSchedulerOutput"	,  lowerCamelCase__	,  overwrite_result=re.sub("DDPM"	,  "Test"	,  lowerCamelCase__ )	,  )



