

import argparse
import os
import re

import packaging.version


_lowerCAmelCase						=		'examples/'
_lowerCAmelCase						=		{
    'examples': (re.compile(r"""^check_min_version\(\"[^\"]+\"\)\s*$""", re.MULTILINE), 'check_min_version("VERSION")\n'),
    'init': (re.compile(r"""^__version__\s+=\s+\"([^\"]+)\"\s*$""", re.MULTILINE), '__version__ = "VERSION"\n'),
    'setup': (re.compile(r"""^(\s*)version\s*=\s*\"[^\"]+\",""", re.MULTILINE), R'\1version="VERSION",'),
    'doc': (re.compile(r"""^(\s*)release\s*=\s*\"[^\"]+\"$""", re.MULTILINE), 'release = "VERSION"\n'),
}
_lowerCAmelCase						=		{
    'init': 'src/transformers/__init__.py',
    'setup': 'setup.py',
}
_lowerCAmelCase						=		'README.md'

def 		lowerCamelCase__		(					_lowerCamelCase ,   _lowerCamelCase ,   _lowerCamelCase   ):

    '''simple docstring'''



    with open(_SCREAMING_SNAKE_CASE ,   'r' ,   encoding='utf-8' ,   newline='\n'   ) as f:
        _lowerCAmelCase       :   Tuple  =    f.read()
    _lowerCAmelCase,					_lowerCAmelCase       :   Tuple  =    REPLACE_PATTERNS[pattern]
    _lowerCAmelCase       :   Tuple  =    replace.replace('VERSION' ,   _SCREAMING_SNAKE_CASE   )
    _lowerCAmelCase       :   str  =    re_pattern.sub(_SCREAMING_SNAKE_CASE ,   _SCREAMING_SNAKE_CASE   )
    with open(_SCREAMING_SNAKE_CASE ,   'w' ,   encoding='utf-8' ,   newline='\n'   ) as f:
        f.write(_SCREAMING_SNAKE_CASE   )

def 		lowerCamelCase__		(					_lowerCamelCase   ):

    '''simple docstring'''



    for folder, directories, fnames in os.walk(_SCREAMING_SNAKE_CASE   ):
        # Removing some of the folders with non-actively maintained examples from the walk
        if "research_projects" in directories:
            directories.remove('research_projects'   )
        if "legacy" in directories:
            directories.remove('legacy'   )
        for fname in fnames:
            if fname.endswith('.py'   ):
                update_version_in_file(os.path.join(_SCREAMING_SNAKE_CASE ,   _SCREAMING_SNAKE_CASE   ) ,   _SCREAMING_SNAKE_CASE ,   pattern='examples'   )

def 		lowerCamelCase__		(					_lowerCamelCase ,   _lowerCamelCase=False   ):

    '''simple docstring'''



    for pattern, fname in REPLACE_FILES.items():
        update_version_in_file(_SCREAMING_SNAKE_CASE ,   _SCREAMING_SNAKE_CASE ,   _SCREAMING_SNAKE_CASE   )
    if not patch:
        update_version_in_examples(_SCREAMING_SNAKE_CASE   )

def 		lowerCamelCase__		(					):

    '''simple docstring'''



    _lowerCAmelCase       :   Union[str, Any]  =    '🤗 Transformers currently provides the following architectures'
    _lowerCAmelCase       :   List[str]  =    '1. Want to contribute a new model?'
    with open(_SCREAMING_SNAKE_CASE ,   'r' ,   encoding='utf-8' ,   newline='\n'   ) as f:
        _lowerCAmelCase       :   int  =    f.readlines()

    # Find the start of the list.
    _lowerCAmelCase       :   Optional[Any]  =    0
    while not lines[start_index].startswith(_start_prompt   ):
        start_index += 1
    start_index += 1

    _lowerCAmelCase       :   Optional[int]  =    start_index
    # Update the lines in the model list.
    while not lines[index].startswith(_end_prompt   ):
        if lines[index].startswith('1.'   ):
            _lowerCAmelCase       :   Union[str, Any]  =    lines[index].replace(
                'https://huggingface.co/docs/transformers/main/model_doc' ,   'https://huggingface.co/docs/transformers/model_doc' ,   )
        index += 1

    with open(_SCREAMING_SNAKE_CASE ,   'w' ,   encoding='utf-8' ,   newline='\n'   ) as f:
        f.writelines(_SCREAMING_SNAKE_CASE   )

def 		lowerCamelCase__		(					):

    '''simple docstring'''



    with open(REPLACE_FILES['init'] ,   'r'   ) as f:
        _lowerCAmelCase       :   Tuple  =    f.read()
    _lowerCAmelCase       :   Tuple  =    REPLACE_PATTERNS['init'][0].search(_SCREAMING_SNAKE_CASE   ).groups()[0]
    return packaging.version.parse(_SCREAMING_SNAKE_CASE   )

def 		lowerCamelCase__		(					_lowerCamelCase=False   ):

    '''simple docstring'''



    _lowerCAmelCase       :   Dict  =    get_version()
    if patch and default_version.is_devrelease:
        raise ValueError('Can\'t create a patch version from the dev branch, checkout a released version!'   )
    if default_version.is_devrelease:
        _lowerCAmelCase       :   str  =    default_version.base_version
    elif patch:
        _lowerCAmelCase       :   Dict  =    f"""{default_version.major}.{default_version.minor}.{default_version.micro + 1}"""
    else:
        _lowerCAmelCase       :   Tuple  =    f"""{default_version.major}.{default_version.minor + 1}.0"""

    # Now let's ask nicely if that's the right one.
    _lowerCAmelCase       :   int  =    input(f"""Which version are you releasing? [{default_version}]"""   )
    if len(_SCREAMING_SNAKE_CASE   ) == 0:
        _lowerCAmelCase       :   Dict  =    default_version

    print(f"""Updating version to {version}."""   )
    global_version_update(_SCREAMING_SNAKE_CASE ,   patch=_SCREAMING_SNAKE_CASE   )
    if not patch:
        print('Cleaning main README, don\'t forget to run `make fix-copies`.'   )
        clean_main_ref_in_model_list()

def 		lowerCamelCase__		(					):

    '''simple docstring'''



    _lowerCAmelCase       :   Optional[Any]  =    get_version()
    _lowerCAmelCase       :   str  =    f"""{current_version.major}.{current_version.minor + 1}.0.dev0"""
    _lowerCAmelCase       :   List[str]  =    current_version.base_version

    # Check with the user we got that right.
    _lowerCAmelCase       :   str  =    input(f"""Which version are we developing now? [{dev_version}]"""   )
    if len(_SCREAMING_SNAKE_CASE   ) == 0:
        _lowerCAmelCase       :   Dict  =    dev_version

    print(f"""Updating version to {version}."""   )
    global_version_update(_SCREAMING_SNAKE_CASE   )
    print('Cleaning main README, don\'t forget to run `make fix-copies`.'   )
    clean_main_ref_in_model_list()


if __name__ == "__main__":
 _lowerCAmelCase						=		argparse.ArgumentParser()
 parser.add_argument("""--post_release""", action="""store_true""", help="""Whether this is pre or post release.""")
 parser.add_argument("""--patch""", action="""store_true""", help="""Whether or not this is a patch release.""")
 _lowerCAmelCase						=		parser.parse_args()
 if not args.post_release:
  pre_release_work(patch=args.patch)
 elif args.patch:
  print("""Nothing to do after a patch :-)""")
 else:
  post_release_work()








"""simple docstring"""






import math

import torch
from torch import nn

from ..configuration_utils import ConfigMixin, register_to_config
from .attention_processor import Attention
from .embeddings import get_timestep_embedding
from .modeling_utils import ModelMixin







class 	__UpperCamelCase    (    a__							,							a__						):

  @register_to_config
  def __init__(		self    ,_A = 128    ,_A = 256    ,_A = 2_0_0_0.0    ,_A = 768    ,_A = 12    ,_A = 12    ,_A = 64    ,_A = 2048    ,_A = 0.1    ,):
      '''simple docstring'''

      super().__init__()

      _lowerCAmelCase       :   int  =    nn.Sequential(
          nn.Linear(_A    ,d_model * 4    ,bias=_A      )    ,nn.SiLU()    ,nn.Linear(d_model * 4    ,d_model * 4    ,bias=_A      )    ,nn.SiLU()    ,)

      _lowerCAmelCase       :   Any  =    nn.Embedding(_A    ,_A      )
      _lowerCAmelCase       :   Tuple  =    False

      _lowerCAmelCase       :   Union[str, Any]  =    nn.Linear(_A    ,_A    ,bias=_A      )

      _lowerCAmelCase       :   int  =    nn.Dropout(p=_A      )

      _lowerCAmelCase       :   int  =    nn.ModuleList()
      for lyr_num in range(_A      ):
          # FiLM conditional T5 decoder
          _lowerCAmelCase       :   Any  =    DecoderLayer(d_model=_A    ,d_kv=_A    ,num_heads=_A    ,d_ff=_A    ,dropout_rate=_A      )
          self.decoders.append(_A      )

      _lowerCAmelCase       :   Optional[Any]  =    TaLayerNorm(_A      )

      _lowerCAmelCase       :   List[str]  =    nn.Dropout(p=_A      )
      _lowerCAmelCase       :   Optional[Any]  =    nn.Linear(_A    ,_A    ,bias=_A      )

  def __lowerCamelCase		(		self    ,_A    ,_A      ):
      '''simple docstring'''

      _lowerCAmelCase       :   Dict  =    torch.mul(query_input.unsqueeze(-1      )    ,key_input.unsqueeze(-2      )      )
      return mask.unsqueeze(-3      )







  def __lowerCamelCase		(		self    ,_A    ,_A    ,_A      ):
      '''simple docstring'''

      _lowerCAmelCase,					_lowerCAmelCase,					_lowerCAmelCase       :   Dict  =    decoder_input_tokens.shape
      assert decoder_noise_time.shape == (batch,)

      # decoder_noise_time is in [0, 1), so rescale to expected timing range.
      _lowerCAmelCase       :   Any  =    get_timestep_embedding(
          decoder_noise_time * self.config.max_decoder_noise_time    ,embedding_dim=self.config.d_model    ,max_period=self.config.max_decoder_noise_time    ,).to(dtype=self.dtype      )

      _lowerCAmelCase       :   Union[str, Any]  =    self.conditioning_emb(_A      ).unsqueeze(1      )

      assert conditioning_emb.shape == (batch, 1, self.config.d_model * 4)

      _lowerCAmelCase       :   str  =    decoder_input_tokens.shape[1]

      # If we want to use relative positions for audio context, we can just offset
      # this sequence by the length of encodings_and_masks.
      _lowerCAmelCase       :   Union[str, Any]  =    torch.broadcast_to(
          torch.arange(_A    ,device=decoder_input_tokens.device      )    ,(batch, seq_length)    ,)

      _lowerCAmelCase       :   Any  =    self.position_encoding(_A      )

      _lowerCAmelCase       :   str  =    self.continuous_inputs_projection(_A      )
      inputs += position_encodings
      _lowerCAmelCase       :   int  =    self.dropout(_A      )

      # decoder: No padding present.
      _lowerCAmelCase       :   Union[str, Any]  =    torch.ones(
          decoder_input_tokens.shape[:2]    ,device=decoder_input_tokens.device    ,dtype=inputs.dtype      )

      # Translate encoding masks to encoder-decoder masks.
      _lowerCAmelCase       :   Optional[Any]  =    [(x, self.encoder_decoder_mask(_A    ,_A      )) for x, y in encodings_and_masks]

      # cross attend style: concat encodings
      _lowerCAmelCase       :   Dict  =    torch.cat([x[0] for x in encodings_and_encdec_masks]    ,dim=1      )
      _lowerCAmelCase       :   Tuple  =    torch.cat([x[1] for x in encodings_and_encdec_masks]    ,dim=-1      )

      for lyr in self.decoders:
          _lowerCAmelCase       :   Tuple  =    lyr(
              _A    ,conditioning_emb=_A    ,encoder_hidden_states=_A    ,encoder_attention_mask=_A    ,)[0]

      _lowerCAmelCase       :   Any  =    self.decoder_norm(_A      )
      _lowerCAmelCase       :   List[Any]  =    self.post_dropout(_A      )

      _lowerCAmelCase       :   int  =    self.spec_out(_A      )
      return spec_out







class 	__UpperCamelCase    (    nn.Module						):

  def __init__(		self    ,_A    ,_A    ,_A    ,_A    ,_A    ,_A=1E-6      ):
      '''simple docstring'''

      super().__init__()
      _lowerCAmelCase       :   Optional[Any]  =    nn.ModuleList()

      # cond self attention: layer 0
      self.layer.append(
          TaLayerSelfAttentionCond(d_model=_A    ,d_kv=_A    ,num_heads=_A    ,dropout_rate=_A      )      )

      # cross attention: layer 1
      self.layer.append(
          TaLayerCrossAttention(
              d_model=_A    ,d_kv=_A    ,num_heads=_A    ,dropout_rate=_A    ,layer_norm_epsilon=_A    ,)      )

      # Film Cond MLP + dropout: last layer
      self.layer.append(
          TaLayerFFCond(d_model=_A    ,d_ff=_A    ,dropout_rate=_A    ,layer_norm_epsilon=_A      )      )







  def __lowerCamelCase		(		self    ,_A    ,_A=None    ,_A=None    ,_A=None    ,_A=None    ,_A=None    ,):
      '''simple docstring'''

      _lowerCAmelCase       :   Any  =    self.layer[0](
          _A    ,conditioning_emb=_A    ,attention_mask=_A    ,)

      if encoder_hidden_states is not None:
          _lowerCAmelCase       :   Any  =    torch.where(encoder_attention_mask > 0    ,0    ,-1E10      ).to(
              encoder_hidden_states.dtype      )

          _lowerCAmelCase       :   str  =    self.layer[1](
              _A    ,key_value_states=_A    ,attention_mask=_A    ,)

      # Apply Film Conditional Feed Forward layer
      _lowerCAmelCase       :   Optional[Any]  =    self.layer[-1](_A    ,_A      )

      return (hidden_states,)







class 	__UpperCamelCase    (    nn.Module						):

  def __init__(		self    ,_A    ,_A    ,_A    ,_A      ):
      '''simple docstring'''

      super().__init__()
      _lowerCAmelCase       :   Union[str, Any]  =    TaLayerNorm(_A      )
      _lowerCAmelCase       :   Any  =    TaFiLMLayer(in_features=d_model * 4    ,out_features=_A      )
      _lowerCAmelCase       :   Dict  =    Attention(query_dim=_A    ,heads=_A    ,dim_head=_A    ,out_bias=_A    ,scale_qk=_A      )
      _lowerCAmelCase       :   Tuple  =    nn.Dropout(_A      )







  def __lowerCamelCase		(		self    ,_A    ,_A=None    ,_A=None    ,):
      '''simple docstring'''

      _lowerCAmelCase       :   int  =    self.layer_norm(_A      )

      if conditioning_emb is not None:
          _lowerCAmelCase       :   Union[str, Any]  =    self.FiLMLayer(_A    ,_A      )

      # Self-attention block
      _lowerCAmelCase       :   Union[str, Any]  =    self.attention(_A      )

      _lowerCAmelCase       :   Optional[Any]  =    hidden_states + self.dropout(_A      )

      return hidden_states







class 	__UpperCamelCase    (    nn.Module						):

  def __init__(		self    ,_A    ,_A    ,_A    ,_A    ,_A      ):
      '''simple docstring'''

      super().__init__()
      _lowerCAmelCase       :   List[str]  =    Attention(query_dim=_A    ,heads=_A    ,dim_head=_A    ,out_bias=_A    ,scale_qk=_A      )
      _lowerCAmelCase       :   Optional[int]  =    TaLayerNorm(_A    ,eps=_A      )
      _lowerCAmelCase       :   Tuple  =    nn.Dropout(_A      )







  def __lowerCamelCase		(		self    ,_A    ,_A=None    ,_A=None    ,):
      '''simple docstring'''

      _lowerCAmelCase       :   Union[str, Any]  =    self.layer_norm(_A      )
      _lowerCAmelCase       :   str  =    self.attention(
          _A    ,encoder_hidden_states=_A    ,attention_mask=attention_mask.squeeze(1      )    ,)
      _lowerCAmelCase       :   Any  =    hidden_states + self.dropout(_A      )
      return layer_output







class 	__UpperCamelCase    (    nn.Module						):

  def __init__(		self    ,_A    ,_A    ,_A    ,_A      ):
      '''simple docstring'''

      super().__init__()
      _lowerCAmelCase       :   Optional[int]  =    TaDenseGatedActDense(d_model=_A    ,d_ff=_A    ,dropout_rate=_A      )
      _lowerCAmelCase       :   Tuple  =    TaFiLMLayer(in_features=d_model * 4    ,out_features=_A      )
      _lowerCAmelCase       :   Any  =    TaLayerNorm(_A    ,eps=_A      )
      _lowerCAmelCase       :   Union[str, Any]  =    nn.Dropout(_A      )







  def __lowerCamelCase		(		self    ,_A    ,_A=None      ):
      '''simple docstring'''

      _lowerCAmelCase       :   int  =    self.layer_norm(_A      )
      if conditioning_emb is not None:
          _lowerCAmelCase       :   Union[str, Any]  =    self.film(_A    ,_A      )

      _lowerCAmelCase       :   str  =    self.DenseReluDense(_A      )
      _lowerCAmelCase       :   Tuple  =    hidden_states + self.dropout(_A      )
      return hidden_states







class 	__UpperCamelCase    (    nn.Module						):

  def __init__(		self    ,_A    ,_A    ,_A      ):
      '''simple docstring'''

      super().__init__()
      _lowerCAmelCase       :   Union[str, Any]  =    nn.Linear(_A    ,_A    ,bias=_A      )
      _lowerCAmelCase       :   Any  =    nn.Linear(_A    ,_A    ,bias=_A      )
      _lowerCAmelCase       :   Union[str, Any]  =    nn.Linear(_A    ,_A    ,bias=_A      )
      _lowerCAmelCase       :   Union[str, Any]  =    nn.Dropout(_A      )
      _lowerCAmelCase       :   int  =    NewGELUActivation()







  def __lowerCamelCase		(		self    ,_A      ):
      '''simple docstring'''

      _lowerCAmelCase       :   Tuple  =    self.act(self.wi_a(_A      )      )
      _lowerCAmelCase       :   Optional[int]  =    self.wi_a(_A      )
      _lowerCAmelCase       :   Union[str, Any]  =    hidden_gelu * hidden_linear
      _lowerCAmelCase       :   Dict  =    self.dropout(_A      )

      _lowerCAmelCase       :   Dict  =    self.wo(_A      )
      return hidden_states







class 	__UpperCamelCase    (    nn.Module						):

  def __init__(		self    ,_A    ,_A=1E-6      ):
      '''simple docstring'''

      super().__init__()
      _lowerCAmelCase       :   Union[str, Any]  =    nn.Parameter(torch.ones(_A      )      )
      _lowerCAmelCase       :   Optional[int]  =    eps







  def __lowerCamelCase		(		self    ,_A      ):
      '''simple docstring'''

      _lowerCAmelCase       :   Optional[int]  =    hidden_states.to(torch.floataa      ).pow(2      ).mean(-1    ,keepdim=_A      )
      _lowerCAmelCase       :   List[Any]  =    hidden_states * torch.rsqrt(variance + self.variance_epsilon      )

      # convert into half-precision if necessary
      if self.weight.dtype in [torch.floataa, torch.bfloataa]:
          _lowerCAmelCase       :   Optional[int]  =    hidden_states.to(self.weight.dtype      )

      return self.weight * hidden_states







class 	__UpperCamelCase    (    nn.Module						):

  def __lowerCamelCase		(		self    ,_A      ):
      '''simple docstring'''

      return 0.5 * input * (1.0 + torch.tanh(math.sqrt(2.0 / math.pi      ) * (input + 0.0_4_4_7_1_5 * torch.pow(_A    ,3.0      ))      ))







class 	__UpperCamelCase    (    nn.Module						):

  def __init__(		self    ,_A    ,_A      ):
      '''simple docstring'''

      super().__init__()
      _lowerCAmelCase       :   List[str]  =    nn.Linear(_A    ,out_features * 2    ,bias=_A      )







  def __lowerCamelCase		(		self    ,_A    ,_A      ):
      '''simple docstring'''

      _lowerCAmelCase       :   List[Any]  =    self.scale_bias(_A      )
      _lowerCAmelCase,					_lowerCAmelCase       :   List[Any]  =    torch.chunk(_A    ,2    ,-1      )
      _lowerCAmelCase       :   List[Any]  =    x * (1 + scale) + shift
      return x





