

'''simple docstring'''







from dataclasses import dataclass
from typing import Any, Dict, Optional

import torch
import torch.nn.functional as F
from torch import nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..models.embeddings import ImagePositionalEmbeddings
from ..utils import BaseOutput, deprecate
from .attention import BasicTransformerBlock
from .embeddings import PatchEmbed
from .modeling_utils import ModelMixin


@dataclass
class 				__lowerCamelCase				( a_ ):
    """simple docstring"""
    a									=					42




class 				__lowerCamelCase				( a_       ,      a_ ):
    """simple docstring"""


    @register_to_config
    def __init__(						self    :							List[Any]   ,							SCREAMING_SNAKE_CASE    :							int = 16   ,							SCREAMING_SNAKE_CASE    :							int = 88   ,							SCREAMING_SNAKE_CASE    :							Optional[int] = None   ,							SCREAMING_SNAKE_CASE    :							Optional[int] = None   ,							SCREAMING_SNAKE_CASE    :							int = 1   ,							SCREAMING_SNAKE_CASE    :							float = 0.0   ,							SCREAMING_SNAKE_CASE    :							int = 32   ,							SCREAMING_SNAKE_CASE    :							Optional[int] = None   ,							SCREAMING_SNAKE_CASE    :							bool = False   ,							SCREAMING_SNAKE_CASE    :							Optional[int] = None   ,							SCREAMING_SNAKE_CASE    :							Optional[int] = None   ,							SCREAMING_SNAKE_CASE    :							Optional[int] = None   ,							SCREAMING_SNAKE_CASE    :							str = "geglu"   ,							SCREAMING_SNAKE_CASE    :							Optional[int] = None   ,							SCREAMING_SNAKE_CASE    :							bool = False   ,							SCREAMING_SNAKE_CASE    :							bool = False   ,							SCREAMING_SNAKE_CASE    :							bool = False   ,							SCREAMING_SNAKE_CASE    :							str = "layer_norm"   ,							SCREAMING_SNAKE_CASE    :							bool = True   ,							):
           super().__init__()
           _A  :			Optional[int]											=      use_linear_projection
           _A  :			Union[str, Any]											=      num_attention_heads
           _A  :			Optional[int]											=      attention_head_dim
           _A  :			List[str]											=      num_attention_heads * attention_head_dim

           # 1. Transformer2DModel can process both standard continuous images of shape `(batch_size, num_channels, width, height)` as well as quantized image embeddings of shape `(batch_size, num_image_vectors)`
           # Define whether input is continuous or discrete depending on configuration
           _A  :			List[str]											=      (in_channels is not None) and (patch_size is None)
           _A  :			Dict											=      num_vector_embeds is not None
           _A  :			Tuple											=      in_channels is not None and patch_size is not None

           if norm_type == "layer_norm" and num_embeds_ada_norm is not None:
                  _A  :			Optional[int]											=      (
                      F'The configuration file of this model: {self.__class__} is outdated. `norm_type` is either not set or'
                      ' incorrectly set to `\'layer_norm\'`.Make sure to set `norm_type` to `\'ada_norm\'` in the config.'
                      ' Please make sure to update the config accordingly as leaving `norm_type` might led to incorrect'
                      ' results in future versions. If you have downloaded this checkpoint from the Hugging Face Hub, it'
                      ' would be very nice if you could open a Pull request for the `transformer/config.json` file'
                  )
                  deprecate('norm_type!=num_embeds_ada_norm'   ,							'1.0.0'   ,							SCREAMING_SNAKE_CASE   ,							standard_warn=SCREAMING_SNAKE_CASE)
                  _A  :			List[str]											=      'ada_norm'

           if self.is_input_continuous and self.is_input_vectorized:
                  raise ValueError(
                      F'Cannot define both `in_channels`: {in_channels} and `num_vector_embeds`: {num_vector_embeds}. Make'
                      ' sure that either `in_channels` or `num_vector_embeds` is None.')
           elif self.is_input_vectorized and self.is_input_patches:
                  raise ValueError(
                      F'Cannot define both `num_vector_embeds`: {num_vector_embeds} and `patch_size`: {patch_size}. Make'
                      ' sure that either `num_vector_embeds` or `num_patches` is None.')
           elif not self.is_input_continuous and not self.is_input_vectorized and not self.is_input_patches:
                  raise ValueError(
                      F'Has to define `in_channels`: {in_channels}, `num_vector_embeds`: {num_vector_embeds}, or patch_size:'
                      F' {patch_size}. Make sure that `in_channels`, `num_vector_embeds` or `num_patches` is not None.')

           # 2. Define input layers
           if self.is_input_continuous:
                  _A  :			Optional[int]											=      in_channels

                  _A  :			Optional[Any]											=      torch.nn.GroupNorm(num_groups=SCREAMING_SNAKE_CASE   ,							num_channels=SCREAMING_SNAKE_CASE   ,							eps=1e-6   ,							affine=SCREAMING_SNAKE_CASE)
                  if use_linear_projection:
                         _A  :			str											=      nn.Linear(SCREAMING_SNAKE_CASE   ,							SCREAMING_SNAKE_CASE)
                  else:
                         _A  :			Dict											=      nn.Convad(SCREAMING_SNAKE_CASE   ,							SCREAMING_SNAKE_CASE   ,							kernel_size=1   ,							stride=1   ,							padding=0)
           elif self.is_input_vectorized:
                  assert sample_size is not None, "Transformer2DModel over discrete input must provide sample_size"
                  assert num_vector_embeds is not None, "Transformer2DModel over discrete input must provide num_embed"

                  _A  :			Dict											=      sample_size
                  _A  :			Any											=      sample_size
                  _A  :			List[str]											=      num_vector_embeds
                  _A  :			Optional[int]											=      self.height * self.width

                  _A  :			int											=      ImagePositionalEmbeddings(
                      num_embed=SCREAMING_SNAKE_CASE   ,							embed_dim=SCREAMING_SNAKE_CASE   ,							height=self.height   ,							width=self.width)
           elif self.is_input_patches:
                  assert sample_size is not None, "Transformer2DModel over patched input must provide sample_size"

                  _A  :			Union[str, Any]											=      sample_size
                  _A  :			Optional[Any]											=      sample_size

                  _A  :			Optional[int]											=      patch_size
                  _A  :			Any											=      PatchEmbed(
                      height=SCREAMING_SNAKE_CASE   ,							width=SCREAMING_SNAKE_CASE   ,							patch_size=SCREAMING_SNAKE_CASE   ,							in_channels=SCREAMING_SNAKE_CASE   ,							embed_dim=SCREAMING_SNAKE_CASE   ,							)

           # 3. Define transformers blocks
           _A  :			Dict											=      nn.ModuleList(
               [
                   BasicTransformerBlock(
                       SCREAMING_SNAKE_CASE   ,							SCREAMING_SNAKE_CASE   ,							SCREAMING_SNAKE_CASE   ,							dropout=SCREAMING_SNAKE_CASE   ,							cross_attention_dim=SCREAMING_SNAKE_CASE   ,							activation_fn=SCREAMING_SNAKE_CASE   ,							num_embeds_ada_norm=SCREAMING_SNAKE_CASE   ,							attention_bias=SCREAMING_SNAKE_CASE   ,							only_cross_attention=SCREAMING_SNAKE_CASE   ,							upcast_attention=SCREAMING_SNAKE_CASE   ,							norm_type=SCREAMING_SNAKE_CASE   ,							norm_elementwise_affine=SCREAMING_SNAKE_CASE   ,							)
                   for d in range(SCREAMING_SNAKE_CASE)
               ])

           # 4. Define output layers
           _A  :			Union[str, Any]											=      in_channels if out_channels is None else out_channels
           if self.is_input_continuous:
                  # TODO: should use out_channels for continuous projections
                  if use_linear_projection:
                         _A  :			Union[str, Any]											=      nn.Linear(SCREAMING_SNAKE_CASE   ,							SCREAMING_SNAKE_CASE)
                  else:
                         _A  :			int											=      nn.Convad(SCREAMING_SNAKE_CASE   ,							SCREAMING_SNAKE_CASE   ,							kernel_size=1   ,							stride=1   ,							padding=0)
           elif self.is_input_vectorized:
                  _A  :			Dict											=      nn.LayerNorm(SCREAMING_SNAKE_CASE)
                  _A  :			Dict											=      nn.Linear(SCREAMING_SNAKE_CASE   ,							self.num_vector_embeds - 1)
           elif self.is_input_patches:
                  _A  :			Union[str, Any]											=      nn.LayerNorm(SCREAMING_SNAKE_CASE   ,							elementwise_affine=SCREAMING_SNAKE_CASE   ,							eps=1e-6)
                  _A  :			Any											=      nn.Linear(SCREAMING_SNAKE_CASE   ,							2 * inner_dim)
                  _A  :			Optional[int]											=      nn.Linear(SCREAMING_SNAKE_CASE   ,							patch_size * patch_size * self.out_channels)


    def      A					(						self    :							Tuple   ,							SCREAMING_SNAKE_CASE    :							torch.Tensor   ,							SCREAMING_SNAKE_CASE    :							Optional[torch.Tensor] = None   ,							SCREAMING_SNAKE_CASE    :							Optional[torch.LongTensor] = None   ,							SCREAMING_SNAKE_CASE    :							Optional[torch.LongTensor] = None   ,							SCREAMING_SNAKE_CASE    :							Dict[str, Any] = None   ,							SCREAMING_SNAKE_CASE    :							Optional[torch.Tensor] = None   ,							SCREAMING_SNAKE_CASE    :							Optional[torch.Tensor] = None   ,							SCREAMING_SNAKE_CASE    :							bool = True   ,							):
           # ensure attention_mask is a bias, and give it a singleton query_tokens dimension.
           #   we may have done this conversion already, e.g. if we came here via UNet2DConditionModel#forward.
           #   we can tell by counting dims; if ndim == 2: it's a mask rather than a bias.
           # expects mask of shape:
           #   [batch, key_tokens]
           # adds singleton query_tokens dimension:
           #   [batch,                    1, key_tokens]
           # this helps to broadcast it as a bias over attention scores, which will be in one of the following shapes:
           #   [batch,  heads, query_tokens, key_tokens] (e.g. torch sdp attn)
           #   [batch * heads, query_tokens, key_tokens] (e.g. xformers or classic attn)
           if attention_mask is not None and attention_mask.ndim == 2:
                  # assume that mask is expressed as:
                  #   (1 = keep,      0 = discard)
                  # convert mask into a bias that can be added to attention scores:
                  #       (keep = +0,     discard = -10000.0)
                  _A  :			Optional[int]											=      (1 - attention_mask.to(hidden_states.dtype)) * -10000.0
                  _A  :			Any											=      attention_mask.unsqueeze(1)

           # convert encoder_attention_mask to a bias the same way we do for attention_mask
           if encoder_attention_mask is not None and encoder_attention_mask.ndim == 2:
                  _A  :			Dict											=      (1 - encoder_attention_mask.to(hidden_states.dtype)) * -10000.0
                  _A  :			Optional[Any]											=      encoder_attention_mask.unsqueeze(1)

           # 1. Input
           if self.is_input_continuous:
                  _A  :			Union[str, Any]											=      hidden_states.shape
                  _A  :			Any											=      hidden_states

                  _A  :			Optional[int]											=      self.norm(SCREAMING_SNAKE_CASE)
                  if not self.use_linear_projection:
                         _A  :			str											=      self.proj_in(SCREAMING_SNAKE_CASE)
                         _A  :			Tuple											=      hidden_states.shape[1]
                         _A  :			Optional[Any]											=      hidden_states.permute(0   ,							2   ,							3   ,							1).reshape(SCREAMING_SNAKE_CASE   ,							height * width   ,							SCREAMING_SNAKE_CASE)
                  else:
                         _A  :			int											=      hidden_states.shape[1]
                         _A  :			List[Any]											=      hidden_states.permute(0   ,							2   ,							3   ,							1).reshape(SCREAMING_SNAKE_CASE   ,							height * width   ,							SCREAMING_SNAKE_CASE)
                         _A  :			int											=      self.proj_in(SCREAMING_SNAKE_CASE)
           elif self.is_input_vectorized:
                  _A  :			Any											=      self.latent_image_embedding(SCREAMING_SNAKE_CASE)
           elif self.is_input_patches:
                  _A  :			Optional[Any]											=      self.pos_embed(SCREAMING_SNAKE_CASE)

           # 2. Blocks
           for block in self.transformer_blocks:
                  _A  :			Any											=      block(
                      SCREAMING_SNAKE_CASE   ,							attention_mask=SCREAMING_SNAKE_CASE   ,							encoder_hidden_states=SCREAMING_SNAKE_CASE   ,							encoder_attention_mask=SCREAMING_SNAKE_CASE   ,							timestep=SCREAMING_SNAKE_CASE   ,							cross_attention_kwargs=SCREAMING_SNAKE_CASE   ,							class_labels=SCREAMING_SNAKE_CASE   ,							)

           # 3. Output
           if self.is_input_continuous:
                  if not self.use_linear_projection:
                         _A  :			Dict											=      hidden_states.reshape(SCREAMING_SNAKE_CASE   ,							SCREAMING_SNAKE_CASE   ,							SCREAMING_SNAKE_CASE   ,							SCREAMING_SNAKE_CASE).permute(0   ,							3   ,							1   ,							2).contiguous()
                         _A  :			List[Any]											=      self.proj_out(SCREAMING_SNAKE_CASE)
                  else:
                         _A  :			List[Any]											=      self.proj_out(SCREAMING_SNAKE_CASE)
                         _A  :			List[str]											=      hidden_states.reshape(SCREAMING_SNAKE_CASE   ,							SCREAMING_SNAKE_CASE   ,							SCREAMING_SNAKE_CASE   ,							SCREAMING_SNAKE_CASE).permute(0   ,							3   ,							1   ,							2).contiguous()

                  _A  :			List[str]											=      hidden_states + residual
           elif self.is_input_vectorized:
                  _A  :			Optional[int]											=      self.norm_out(SCREAMING_SNAKE_CASE)
                  _A  :			Optional[int]											=      self.out(SCREAMING_SNAKE_CASE)
                  # (batch, self.num_vector_embeds - 1, self.num_latent_pixels)
                  _A  :			List[Any]											=      logits.permute(0   ,							2   ,							1)

                  # log(p(x_0))
                  _A  :			Any											=      F.log_softmax(logits.double()   ,							dim=1).float()
           elif self.is_input_patches:
                  # TODO: cleanup!
                  _A  :			Any											=      self.transformer_blocks[0].norma.emb(
                      SCREAMING_SNAKE_CASE   ,							SCREAMING_SNAKE_CASE   ,							hidden_dtype=hidden_states.dtype)
                  _A  :			List[Any]											=      self.proj_out_a(F.silu(SCREAMING_SNAKE_CASE)).chunk(2   ,							dim=1)
                  _A  :			Optional[Any]											=      self.norm_out(SCREAMING_SNAKE_CASE) * (1 + scale[:, None]) + shift[:, None]
                  _A  :			Optional[int]											=      self.proj_out_a(SCREAMING_SNAKE_CASE)

                  # unpatchify
                  _A  :			Tuple											=      int(hidden_states.shape[1] ** 0.5)
                  _A  :			Optional[int]											=      hidden_states.reshape(
                      shape=(-1, height, width, self.patch_size, self.patch_size, self.out_channels))
                  _A  :			Union[str, Any]											=      torch.einsum('nhwpqc->nchpwq'   ,							SCREAMING_SNAKE_CASE)
                  _A  :			Union[str, Any]											=      hidden_states.reshape(
                      shape=(-1, self.out_channels, height * self.patch_size, width * self.patch_size))

           if not return_dict:
                  return (output,)

           return TransformeraDModelOutput(sample=SCREAMING_SNAKE_CASE)










'''simple docstring'''







import argparse
import copy
import json
import multiprocessing
import os
import time

from create_dummy_models import COMPOSITE_MODELS, create_tiny_models
from huggingface_hub import ModelFilter, hf_api

import transformers
from transformers import AutoFeatureExtractor, AutoImageProcessor, AutoTokenizer
from transformers.image_processing_utils import BaseImageProcessor






def    lowerCAmelCase__ (       ):
       _A  :			Union[str, Any]											=      set()
       # Each auto modeling files contains multiple mappings. Let's get them in a dynamic way.
       for module_name in ["modeling_auto", "modeling_tf_auto", "modeling_flax_auto"]:
              _A  :			Optional[Any]											=      getattr(transformers.models.auto						,lowerCamelCase						,lowerCamelCase     )
              if module is None:
                     continue
              # all mappings in a single auto modeling file
              _A  :			Any											=      [
                  x
                  for x in dir(lowerCamelCase     )
                  if x.endswith('_MAPPING_NAMES'     )
                  and (x.startswith('MODEL_'     ) or x.startswith('TF_MODEL_'     ) or x.startswith('FLAX_MODEL_'     ))
              ]
              for name in mapping_names:
                     _A  :			Tuple											=      getattr(lowerCamelCase						,lowerCamelCase     )
                     if mapping is not None:
                            for v in mapping.values():
                                   if isinstance(lowerCamelCase						,(list, tuple)     ):
                                          model_names.update(lowerCamelCase     )
                                   elif isinstance(lowerCamelCase						,lowerCamelCase     ):
                                          model_names.add(lowerCamelCase     )

       return sorted(lowerCamelCase     )






def    lowerCAmelCase__ (       ):
       # All model names defined in auto mappings
       _A  :			Union[str, Any]											=      set(get_all_model_names()     )

       with open('tests/utils/tiny_model_summary.json'     ) as fp:
              _A  :			Any											=      json.load(lowerCamelCase     )
       _A  :			Union[str, Any]											=      set()
       for model_base_name in tiny_model_info:
              tiny_models_names.update(tiny_model_info[model_base_name]['model_classes']     )

       # Remove a tiny model name if one of its framework implementation hasn't yet a tiny version on the Hub.
       _A  :			List[Any]											=      model_names.difference(lowerCamelCase     )
       for model_name in copy.copy(lowerCamelCase     ):
              if not model_name.startswith('TF'     ) and F'TF{model_name}' in not_on_hub:
                     tiny_models_names.remove(lowerCamelCase     )
              elif model_name.startswith('TF'     ) and model_name[2:] in not_on_hub:
                     tiny_models_names.remove(lowerCamelCase     )

       return sorted(lowerCamelCase     )






def    lowerCAmelCase__ (       lowerCamelCase  :    Union[str, Any]     ):
       _A  :			Optional[int]											=      COMPOSITE_MODELS.values()

       # All tiny model base names on Hub
       _A  :			Optional[int]											=      get_all_model_names()
       _A  :			Tuple											=      hf_api.list_models(
           filter=ModelFilter(
               author='hf-internal-testing'						,)     )
       _A  :			Any											=      set()
       for x in models:
              _A  :			List[Any]											=      x.modelId
              _A						,				_A  :			Any											=      model.split('/'     )
              if not model.startswith('tiny-random-'     ):
                     continue
              _A  :			Dict											=      model.replace('tiny-random-'						,''     )
              if not model[0].isupper():
                     continue
              if model not in model_names and model not in special_models:
                     continue
              _models.add(lowerCamelCase     )

       _A  :			Dict											=      sorted(_models     )
       # All tiny model names on Hub
       _A  :			List[Any]											=      {}
       for model in models:
              _A  :			Optional[Any]											=      F'hf-internal-testing/tiny-random-{model}'
              _A  :			Optional[Any]											=      model.split('-'     )[0]
              try:
                     _A  :			Any											=      hf_api.repo_info(lowerCamelCase     )
                     _A  :			Any											=      {
                         'tokenizer_classes': set(),
                         'processor_classes': set(),
                         'model_classes': set(),
                         'sha': repo_info.sha,
                     }
              except Exception:
                     continue
              try:
                     time.sleep(1     )
                     _A  :			Optional[Any]											=      AutoTokenizer.from_pretrained(lowerCamelCase     )
                     content["tokenizer_classes"].add(tokenizer_fast.__class__.__name__     )
              except Exception:
                     pass
              try:
                     time.sleep(1     )
                     _A  :			str											=      AutoTokenizer.from_pretrained(lowerCamelCase						,use_fast=lowerCamelCase     )
                     content["tokenizer_classes"].add(tokenizer_slow.__class__.__name__     )
              except Exception:
                     pass
              try:
                     time.sleep(1     )
                     _A  :			Dict											=      AutoImageProcessor.from_pretrained(lowerCamelCase     )
                     content["processor_classes"].add(img_p.__class__.__name__     )
              except Exception:
                     pass
              try:
                     time.sleep(1     )
                     _A  :			Tuple											=      AutoFeatureExtractor.from_pretrained(lowerCamelCase     )
                     if not isinstance(lowerCamelCase						,lowerCamelCase     ):
                            content["processor_classes"].add(feat_p.__class__.__name__     )
              except Exception:
                     pass
              try:
                     time.sleep(1     )
                     _A  :			Optional[Any]											=      getattr(lowerCamelCase						,lowerCamelCase     )
                     _A  :			List[Any]											=      model_class.from_pretrained(lowerCamelCase     )
                     content["model_classes"].add(m.__class__.__name__     )
              except Exception:
                     pass
              try:
                     time.sleep(1     )
                     _A  :			Tuple											=      getattr(lowerCamelCase						,F'TF{model}'     )
                     _A  :			Optional[int]											=      model_class.from_pretrained(lowerCamelCase     )
                     content["model_classes"].add(m.__class__.__name__     )
              except Exception:
                     pass

              _A  :			Any											=      sorted(content['tokenizer_classes']     )
              _A  :			Tuple											=      sorted(content['processor_classes']     )
              _A  :			List[str]											=      sorted(content['model_classes']     )

              _A  :			int											=      content
              with open(os.path.join(lowerCamelCase						,'hub_tiny_model_summary.json'     )						,'w'     ) as fp:
                     json.dump(lowerCamelCase						,lowerCamelCase						,ensure_ascii=lowerCamelCase						,indent=4     )


if __name__ == "__main__":
   A   :  Dict       		=				argparse.ArgumentParser()
   parser.add_argument('''--num_workers''', default=1, type=int, help='''The number of workers to run.''')
   A   :  Optional[int]       		=				parser.parse_args()

   # This has to be `spawn` to avoid hanging forever!
   multiprocessing.set_start_method('''spawn''')

   A   :  Any       		=				'''tiny_models'''
   A   :  Union[str, Any]       		=				True
   A   :  List[Any]       		=				None
   A   :  Any       		=				get_tiny_model_names_from_repo()
   A   :  List[str]       		=				True
   A   :  Union[str, Any]       		=				True
   A   :  Tuple       		=				'''hf-internal-testing'''

   create_tiny_models(
       output_path,
       all,
       model_types,
       models_to_skip,
       no_check,
       upload,
       organization,
       token=os.environ.get('''TOKEN''', None),
       num_workers=args.num_workers,
   )






