





import argparse
import collections

import jax
import jax.numpy as jnp
import torch
import torch.nn as nn
from clip.model import CLIP
from flax.training import checkpoints
from huggingface_hub import Repository

from transformers import (
    CLIPTokenizer,
    OwlViTConfig,
    OwlViTForObjectDetection,
    OwlViTImageProcessor,
    OwlViTModel,
    OwlViTProcessor,
)


__UpperCAmelCase			:  List[Any]										=				{
    "vit_b32": {
        "embed_dim": 512,
        "image_resolution": 768,
        "context_length": 16,
        "vocab_size": 49_408,
        "vision_layers": 12,
        "vision_width": 768,
        "vision_patch_size": 32,
        "transformer_width": 512,
        "transformer_heads": 8,
        "transformer_layers": 12,
    },
    "vit_b16": {
        "embed_dim": 512,
        "image_resolution": 768,
        "context_length": 16,
        "vocab_size": 49_408,
        "vision_layers": 12,
        "vision_width": 768,
        "vision_patch_size": 16,
        "transformer_width": 512,
        "transformer_heads": 8,
        "transformer_layers": 12,
    },
    "vit_l14": {
        "embed_dim": 768,
        "image_resolution": 840,
        "context_length": 16,
        "vocab_size": 49_408,
        "vision_layers": 24,
        "vision_width": 1_024,
        "vision_patch_size": 14,
        "transformer_width": 768,
        "transformer_heads": 12,
        "transformer_layers": 12,
    },
}







def      A__				(       SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__=""   ,   SCREAMING_SNAKE_CASE__="/")  ->       Union[str, Any]:
  __snake_case:		Optional[int]					    =		[]

  for k, v in params.items():
    __snake_case:		Dict					    =		parent_key + sep + k if parent_key else k

    if isinstance(SCREAMING_SNAKE_CASE__   ,   collections.MutableMapping):
      items.extend(flatten_nested_dict(SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__   ,   sep=SCREAMING_SNAKE_CASE__).items())
    else:
      items.append((new_key, v))
  return dict(SCREAMING_SNAKE_CASE__)







def      A__				(       SCREAMING_SNAKE_CASE__)  ->       str:
  return jax.tree_util.tree_map(lambda SCREAMING_SNAKE_CASE__: x.astype(jnp.floataa) if x.dtype == jnp.bfloataa else x   ,   SCREAMING_SNAKE_CASE__)







def      A__				(       SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__)  ->       str:
  __snake_case    ,  __snake_case    ,  __snake_case:		Optional[int]					    =		pt_attn_layer.in_proj_weight.chunk(3   ,   dim=0)
  __snake_case    ,  __snake_case    ,  __snake_case:		str					    =		pt_attn_layer.in_proj_bias.chunk(3   ,   dim=0)

  __snake_case:		List[Any]					    =		pt_attn_layer.out_proj.weight
  __snake_case:		List[Any]					    =		pt_attn_layer.out_proj.bias

  __snake_case:		int					    =		q_proj
  __snake_case:		int					    =		q_proj_bias

  __snake_case:		Dict					    =		k_proj
  __snake_case:		Optional[int]					    =		k_proj_bias

  __snake_case:		List[Any]					    =		v_proj
  __snake_case:		Optional[int]					    =		v_proj_bias

  __snake_case:		Dict					    =		out_proj_weights
  __snake_case:		Optional[Any]					    =		out_proj_bias







def      A__				(       SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__)  ->       List[str]:
  copy_linear(hf_mlp.fca   ,   pt_mlp.c_fc)
  copy_linear(hf_mlp.fca   ,   pt_mlp.c_proj)







def      A__				(       SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__)  ->       Dict:
  __snake_case:		Optional[Any]					    =		pt_linear.weight
  __snake_case:		List[str]					    =		pt_linear.bias







def      A__				(       SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__)  ->       Optional[int]:
  # copy layer norms
  copy_linear(hf_layer.layer_norma   ,   pt_layer.ln_a)
  copy_linear(hf_layer.layer_norma   ,   pt_layer.ln_a)

  # copy MLP
  copy_mlp(hf_layer.mlp   ,   pt_layer.mlp)

  # copy attn
  copy_attn_layer(hf_layer.self_attn   ,   pt_layer.attn)







def      A__				(       SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__)  ->       List[Any]:
  for hf_layer, pt_layer in zip(SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__):
    copy_layer(SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__)







def      A__				(       SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__)  ->       str:
  # copy  embeds
  __snake_case:		Optional[Any]					    =		pt_model.token_embedding.weight
  __snake_case:		Dict					    =		pt_model.positional_embedding

  # copy layer norm
  copy_linear(hf_encoder.final_layer_norm   ,   pt_model.ln_final)

  # copy hidden layers
  copy_layers(hf_encoder.encoder.layers   ,   pt_model.transformer.resblocks)







def      A__				(       SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__)  ->       Dict:
  # copy projection
  __snake_case:		int					    =		pt_model.text_projection.data.T

  # copy text encoder
  copy_encoder(hf_model.text_model   ,   SCREAMING_SNAKE_CASE__)







def      A__				(       SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__)  ->       Tuple:
  # copy projection
  __snake_case:		Tuple					    =		pt_model.visual.proj.data.T

  # copy layer norms
  copy_linear(hf_model.vision_model.pre_layernorm   ,   pt_model.visual.ln_pre)
  copy_linear(hf_model.vision_model.post_layernorm   ,   pt_model.visual.ln_post)

  # copy embeds
  __snake_case:		List[Any]					    =		pt_model.visual.conva.weight.data
  __snake_case:		Optional[int]					    =		pt_model.visual.class_embedding
  __snake_case:		Dict					    =		pt_model.visual.positional_embedding.data

  # copy encoder
  copy_layers(hf_model.vision_model.encoder.layers   ,   pt_model.visual.transformer.resblocks)







def      A__				(       SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__)  ->       Optional[int]:
  __snake_case:		Union[str, Any]					    =		flatten_nested_dict(flax_params["""backbone"""]["""merged_class_token"""])

  __snake_case:		Dict					    =		torch.from_numpy(flax_class_token_params["""scale"""])
  __snake_case:		int					    =		torch.from_numpy(flax_class_token_params["""bias"""])
  __snake_case:		Optional[Any]					    =		nn.Parameter(SCREAMING_SNAKE_CASE__)
  __snake_case:		Tuple					    =		nn.Parameter(SCREAMING_SNAKE_CASE__)







def      A__				(       SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__)  ->       Tuple:
  __snake_case:		Any					    =		hf_model.state_dict()
  __snake_case:		List[Any]					    =		{}

  # Rename class prediction head flax params to pytorch HF
  __snake_case:		Union[str, Any]					    =		flatten_nested_dict(flax_params["""class_head"""])

  for flax_key, v in flax_class_params.items():
    __snake_case:		Any					    =		flax_key.replace("""/"""   ,   """.""")
    __snake_case:		Dict					    =		torch_key.replace(""".kernel"""   ,   """.weight""")
    __snake_case:		Tuple					    =		torch_key.replace("""Dense_0"""   ,   """dense0""")
    __snake_case:		Optional[int]					    =		"""class_head.""" + torch_key

    if "weight" in torch_key and v.ndim == 2:
      __snake_case:		Union[str, Any]					    =		v.T

    __snake_case:		int					    =		nn.Parameter(torch.from_numpy(SCREAMING_SNAKE_CASE__))

  # Rename box prediction box flax params to pytorch HF
  __snake_case:		Any					    =		flatten_nested_dict(flax_params["""obj_box_head"""])

  for flax_key, v in flax_box_params.items():
    __snake_case:		Any					    =		flax_key.replace("""/"""   ,   """.""")
    __snake_case:		List[Any]					    =		torch_key.replace(""".kernel"""   ,   """.weight""")
    __snake_case:		List[Any]					    =		torch_key.replace("""_"""   ,   """""").lower()
    __snake_case:		Dict					    =		"""box_head.""" + torch_key

    if "weight" in torch_key and v.ndim == 2:
      __snake_case:		Dict					    =		v.T

    __snake_case:		str					    =		nn.Parameter(torch.from_numpy(SCREAMING_SNAKE_CASE__))

  # Copy flax params to PyTorch params
  for name, param in new_params.items():
    if name in pt_params.keys():
      pt_params[name].copy_(SCREAMING_SNAKE_CASE__)







def      A__				(       SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__)  ->       int:
  for k, v in flax_attn_params.items():
    if k.startswith("""transformer"""):
      __snake_case:		Optional[Any]					    =		k.replace("""transformer.resblocks"""   ,   """text_model.encoder.layers""")
    else:
      __snake_case:		List[str]					    =		k.replace("""visual.transformer.resblocks"""   ,   """vision_model.encoder.layers""")

    __snake_case:		Any					    =		torch_key.replace("""attn"""   ,   """self_attn""")
    __snake_case:		Any					    =		torch_key.replace("""key"""   ,   """k_proj""")
    __snake_case:		int					    =		torch_key.replace("""value"""   ,   """v_proj""")
    __snake_case:		Tuple					    =		torch_key.replace("""query"""   ,   """q_proj""")
    __snake_case:		Any					    =		torch_key.replace("""out"""   ,   """out_proj""")

    if "bias" in torch_key and v.ndim == 2:
      __snake_case:		Optional[Any]					    =		v.shape[0] * v.shape[1]
      __snake_case:		Union[str, Any]					    =		v.reshape(SCREAMING_SNAKE_CASE__)

    if "weight" in torch_key and "out" in torch_key:
      __snake_case:		Dict					    =		(v.shape[0] * v.shape[1], v.shape[2])
      __snake_case:		Any					    =		v.reshape(SCREAMING_SNAKE_CASE__).T

    if "weight" in torch_key and "out" not in torch_key:
      __snake_case:		List[Any]					    =		(v.shape[0], v.shape[1] * v.shape[2])
      __snake_case:		Dict					    =		v.reshape(SCREAMING_SNAKE_CASE__).T

    # Copy flax CLIP attn params to HF PyTorch params
    __snake_case:		Optional[Any]					    =		torch.from_numpy(SCREAMING_SNAKE_CASE__)
    hf_backbone.state_dict()[torch_key].copy_(SCREAMING_SNAKE_CASE__)







def      A__				(       SCREAMING_SNAKE_CASE__)  ->       Union[str, Any]:
  __snake_case:		Dict					    =		{}
  __snake_case:		Tuple					    =		[]

  for k, v in params.items():
    if "attn." in k:
      __snake_case:		List[Any]					    =		k[: k.rindex("""attn.""") + 5]
      if base in processed_attn_layers:
        continue

      processed_attn_layers.append(SCREAMING_SNAKE_CASE__)
      __snake_case:		List[Any]					    =		params[base + """out.weight"""].shape[-1]
      __snake_case:		Dict					    =		params[base + """out.weight"""].reshape(SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__).T
      __snake_case:		Any					    =		params[base + """out.bias"""]
    else:
      __snake_case:		Any					    =		v
  return new_params







def      A__				(       SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__)  ->       Tuple:
  __snake_case:		str					    =		CLIP(**SCREAMING_SNAKE_CASE__)
  torch_model.eval()
  __snake_case:		Union[str, Any]					    =		torch_model.state_dict()

  __snake_case:		Tuple					    =		flatten_nested_dict(flax_params["""backbone"""]["""clip"""])
  __snake_case:		Any					    =		{}

  for flax_key, v in flax_clip_params.items():
    __snake_case:		Tuple					    =		flax_key.replace("""/"""   ,   """.""")
    __snake_case:		Tuple					    =		torch_key.replace("""text.token_embedding.embedding"""   ,   """token_embedding.kernel""")

    if (
        torch_key.startswith("""text.transformer""")
        or torch_key.startswith("""text.text_projection""")
        or torch_key.startswith("""text.ln_final""")
        or torch_key.startswith("""text.positional_embedding""")
    ):
      __snake_case:		Optional[Any]					    =		torch_key[5:]

    __snake_case:		Union[str, Any]					    =		torch_key.replace("""text_projection.kernel"""   ,   """text_projection""")
    __snake_case:		Optional[Any]					    =		torch_key.replace("""visual.proj.kernel"""   ,   """visual.proj""")
    __snake_case:		Dict					    =		torch_key.replace(""".scale"""   ,   """.weight""")
    __snake_case:		List[Any]					    =		torch_key.replace(""".kernel"""   ,   """.weight""")

    if "conv" in torch_key or "downsample.0.weight" in torch_key:
      __snake_case:		str					    =		v.transpose(3   ,   2   ,   0   ,   1)

    elif "weight" in torch_key and v.ndim == 2 and "embedding" not in torch_key:
      # Fully connected layers are transposed, embeddings are not
      __snake_case:		List[Any]					    =		v.T

    __snake_case:		Dict					    =		v

  __snake_case:		str					    =		_convert_attn_layers(SCREAMING_SNAKE_CASE__)
  new_torch_params.update(SCREAMING_SNAKE_CASE__)
  __snake_case:		Tuple					    =		{}

  # Copy flax CLIP backbone params to PyTorch params
  for name, param in new_torch_params.items():
    if name in torch_clip_params.keys():
      __snake_case:		Dict					    =		torch.from_numpy(new_torch_params[name])
      torch_clip_params[name].copy_(SCREAMING_SNAKE_CASE__)
    else:
      __snake_case:		Tuple					    =		param

  return torch_clip_params, torch_model, attn_params







@torch.no_grad()
def      A__				(       SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__=None)  ->       Union[str, Any]:
  __snake_case:		Optional[Any]					    =		Repository(SCREAMING_SNAKE_CASE__   ,   clone_from=F'''google/{pytorch_dump_folder_path}''')
  repo.git_pull()

  if config_path is not None:
    __snake_case:		Union[str, Any]					    =		OwlViTConfig.from_pretrained(SCREAMING_SNAKE_CASE__)
  else:
    __snake_case:		List[str]					    =		OwlViTConfig()

  __snake_case:		str					    =		OwlViTModel(SCREAMING_SNAKE_CASE__).eval()
  __snake_case:		Any					    =		OwlViTForObjectDetection(SCREAMING_SNAKE_CASE__).eval()

  copy_text_model_and_projection(SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__)
  copy_vision_model_and_projection(SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__)
  __snake_case:		Dict					    =		pt_backbone.logit_scale
  copy_flax_attn_params(SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__)

  __snake_case:		Union[str, Any]					    =		hf_backbone
  copy_class_merge_token(SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__)
  copy_class_box_heads(SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__)

  # Save HF model
  hf_model.save_pretrained(repo.local_dir)

  # Initialize image processor
  __snake_case:		Optional[Any]					    =		OwlViTImageProcessor(
      size=config.vision_config.image_size   ,   crop_size=config.vision_config.image_size)
  # Initialize tokenizer
  __snake_case:		Tuple					    =		CLIPTokenizer.from_pretrained("""openai/clip-vit-base-patch32"""   ,   pad_token="""!"""   ,   model_max_length=16)

  # Initialize processor
  __snake_case:		Union[str, Any]					    =		OwlViTProcessor(image_processor=SCREAMING_SNAKE_CASE__   ,   tokenizer=SCREAMING_SNAKE_CASE__)
  image_processor.save_pretrained(repo.local_dir)
  processor.save_pretrained(repo.local_dir)

  repo.git_add()
  repo.git_commit("""Upload model and processor""")
  repo.git_push()


if __name__ == "__main__":
      __UpperCAmelCase			:  Optional[Any]										=				argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          "--owlvit_version",
          default=None,
          type=str,
          required=True,
          help="OWL-ViT model name [clip_b16, clip_b32, clip_l14].",
      )
      parser.add_argument(
          "--owlvit_checkpoint", default=None, type=str, required=True, help="Path to flax model checkpoint."
      )
      parser.add_argument("--hf_config", default=None, type=str, required=True, help="Path to HF model config.")
      parser.add_argument(
          "--pytorch_dump_folder_path", default="hf_model", type=str, help="Path to the output PyTorch model."
      )
      __UpperCAmelCase			:  List[Any]										=				parser.parse_args()

      # Initialize PyToch clip model
      __UpperCAmelCase			:  Any										=				args.owlvit_version
      if model_name == "clip_b16":
            __UpperCAmelCase			:  Optional[Any]										=				CONFIGS["vit_b16"]
      elif model_name == "clip_b32":
            __UpperCAmelCase			:  str										=				CONFIGS["vit_b32"]
      elif model_name == "clip_l14":
            __UpperCAmelCase			:  Union[str, Any]										=				CONFIGS["vit_l14"]

      # Load from checkpoint and convert params to float-32
      __UpperCAmelCase			:  Any										=				checkpoints.restore_checkpoint(args.owlvit_checkpoint, target=None)["optimizer"]["target"]
      __UpperCAmelCase			:  Optional[Any]										=				jax.tree_util.tree_map(lambda x: x.astype(jnp.floataa) if x.dtype == jnp.bfloataa else x, variables)
      del variables

      # Convert CLIP backbone
      __UpperCAmelCase			,    __UpperCAmelCase			,    __UpperCAmelCase			:  Optional[int]										=				convert_clip_backbone(flax_params, torch_config)

      convert_owlvit_checkpoint(clip_pt, flax_params, attn_params, args.pytorch_dump_folder_path, args.hf_config)













import unittest

from transformers import FunnelConfig, FunnelTokenizer, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
      import torch

      from transformers import (
          MODEL_FOR_PRETRAINING_MAPPING,
          FunnelBaseModel,
          FunnelForMaskedLM,
          FunnelForMultipleChoice,
          FunnelForPreTraining,
          FunnelForQuestionAnswering,
          FunnelForSequenceClassification,
          FunnelForTokenClassification,
          FunnelModel,
      )







class        __snake_case			:





       '''simple docstring'''







       def __init__(    self					:       str				,							A					:       int				,							A					:       List[Any]=13				,							A					:       Dict=7				,							A					:       Optional[int]=True				,							A					:       Dict=True				,							A					:       str=True				,							A					:       Union[str, Any]=True				,							A					:       Union[str, Any]=99				,							A					:       Dict=[1, 1, 2]				,							A					:       List[Any]=1				,							A					:       List[str]=32				,							A					:       Dict=4				,							A					:       Tuple=8				,							A					:       List[Any]=37				,							A					:       Dict="gelu_new"				,							A					:       Dict=0.1				,							A					:       List[Any]=0.1				,							A					:       Optional[int]=0.0				,							A					:       Tuple=512				,							A					:       Tuple=3				,							A					:       List[str]=0.02				,							A					:       str=3				,							A					:       Optional[Any]=4				,							A					:       str=None				,							A					:       Union[str, Any]=False				,							):
         __snake_case:		int					    =		parent
         __snake_case:		Optional[Any]					    =		batch_size
         __snake_case:		Optional[Any]					    =		seq_length
         __snake_case:		List[Any]					    =		is_training
         __snake_case:		List[str]					    =		use_input_mask
         __snake_case:		Any					    =		use_token_type_ids
         __snake_case:		Union[str, Any]					    =		use_labels
         __snake_case:		Any					    =		vocab_size
         __snake_case:		int					    =		block_sizes
         __snake_case:		Optional[int]					    =		num_decoder_layers
         __snake_case:		Union[str, Any]					    =		d_model
         __snake_case:		List[str]					    =		n_head
         __snake_case:		Optional[Any]					    =		d_head
         __snake_case:		str					    =		d_inner
         __snake_case:		Optional[Any]					    =		hidden_act
         __snake_case:		List[Any]					    =		hidden_dropout
         __snake_case:		List[Any]					    =		attention_dropout
         __snake_case:		Tuple					    =		activation_dropout
         __snake_case:		Optional[int]					    =		max_position_embeddings
         __snake_case:		Optional[int]					    =		type_vocab_size
         __snake_case:		Any					    =		2
         __snake_case:		Dict					    =		num_labels
         __snake_case:		List[Any]					    =		num_choices
         __snake_case:		Any					    =		scope
         __snake_case:		Optional[Any]					    =		initializer_std

         # Used in the tests to check the size of the first attention layer
         __snake_case:		Union[str, Any]					    =		n_head
         # Used in the tests to check the size of the first hidden state
         __snake_case:		Union[str, Any]					    =		self.d_model
         # Used in the tests to check the number of output hidden states/attentions
         __snake_case:		Optional[int]					    =		sum(self.block_sizes						) + (0 if base else self.num_decoder_layers)
         # FunnelModel adds two hidden layers: input embeddings and the sum of the upsampled encoder hidden state with
         # the last hidden state of the first block (which is the first hidden state of the decoder).
         if not base:
           __snake_case:		Any					    =		self.num_hidden_layers + 2




       def 				UpperCAmelCase__					(    self					:       Any						):
         __snake_case:		Tuple					    =		ids_tensor([self.batch_size, self.seq_length]				,							self.vocab_size						)

         __snake_case:		Optional[int]					    =		None
         if self.use_input_mask:
           __snake_case:		Optional[Any]					    =		ids_tensor([self.batch_size, self.seq_length]				,							vocab_size=2						)

         __snake_case:		Dict					    =		None
         if self.use_token_type_ids:
           __snake_case:		List[str]					    =		ids_tensor([self.batch_size, self.seq_length]				,							self.type_vocab_size						)

         __snake_case:		Optional[Any]					    =		None
         __snake_case:		Optional[int]					    =		None
         __snake_case:		Any					    =		None
         if self.use_labels:
           __snake_case:		List[Any]					    =		ids_tensor([self.batch_size]				,							self.type_sequence_label_size						)
           __snake_case:		Any					    =		ids_tensor([self.batch_size, self.seq_length]				,							self.num_labels						)
           __snake_case:		Union[str, Any]					    =		ids_tensor([self.batch_size]				,							self.num_choices						)
           __snake_case:		Tuple					    =		ids_tensor([self.batch_size, self.seq_length]				,							1						)

         __snake_case:		Dict					    =		self.get_config()

         return (
             config,
             input_ids,
             token_type_ids,
             input_mask,
             sequence_labels,
             token_labels,
             choice_labels,
             fake_token_labels,
         )




       def 				UpperCAmelCase__					(    self					:       Dict						):
         return FunnelConfig(
             vocab_size=self.vocab_size				,							block_sizes=self.block_sizes				,							num_decoder_layers=self.num_decoder_layers				,							d_model=self.d_model				,							n_head=self.n_head				,							d_head=self.d_head				,							d_inner=self.d_inner				,							hidden_act=self.hidden_act				,							hidden_dropout=self.hidden_dropout				,							attention_dropout=self.attention_dropout				,							activation_dropout=self.activation_dropout				,							max_position_embeddings=self.max_position_embeddings				,							type_vocab_size=self.type_vocab_size				,							initializer_std=self.initializer_std				,							)




       def 				UpperCAmelCase__					(    self					:       List[Any]				,							A					:       Tuple				,							A					:       Any				,							A					:       int				,							A					:       Optional[Any]				,							A					:       List[str]				,							A					:       Optional[int]				,							A					:       Tuple				,							A					:       Optional[Any]				,							):
         __snake_case:		int					    =		FunnelModel(config=A						)
         model.to(A						)
         model.eval()
         __snake_case:		Union[str, Any]					    =		model(A				,							attention_mask=A				,							token_type_ids=A						)
         __snake_case:		Dict					    =		model(A				,							token_type_ids=A						)
         __snake_case:		Optional[int]					    =		model(A						)
         self.parent.assertEqual(result.last_hidden_state.shape				,							(self.batch_size, self.seq_length, self.d_model)						)

         __snake_case:		Any					    =		False
         __snake_case:		List[Any]					    =		model(A						)
         self.parent.assertEqual(result.last_hidden_state.shape				,							(self.batch_size, self.seq_length, self.d_model)						)

         __snake_case:		List[str]					    =		False
         __snake_case:		Optional[int]					    =		model(A						)
         self.parent.assertEqual(result.last_hidden_state.shape				,							(self.batch_size, self.seq_length, self.d_model)						)




       def 				UpperCAmelCase__					(    self					:       Optional[Any]				,							A					:       Union[str, Any]				,							A					:       str				,							A					:       Any				,							A					:       str				,							A					:       Any				,							A					:       str				,							A					:       List[str]				,							A					:       Dict				,							):
         __snake_case:		Dict					    =		FunnelBaseModel(config=A						)
         model.to(A						)
         model.eval()
         __snake_case:		List[str]					    =		model(A				,							attention_mask=A				,							token_type_ids=A						)
         __snake_case:		Union[str, Any]					    =		model(A				,							token_type_ids=A						)
         __snake_case:		List[Any]					    =		model(A						)
         self.parent.assertEqual(result.last_hidden_state.shape				,							(self.batch_size, 2, self.d_model)						)

         __snake_case:		Optional[Any]					    =		False
         __snake_case:		Tuple					    =		model(A						)
         self.parent.assertEqual(result.last_hidden_state.shape				,							(self.batch_size, 3, self.d_model)						)

         __snake_case:		Union[str, Any]					    =		False
         __snake_case:		List[Any]					    =		model(A						)
         self.parent.assertEqual(result.last_hidden_state.shape				,							(self.batch_size, 2, self.d_model)						)




       def 				UpperCAmelCase__					(    self					:       Tuple				,							A					:       Union[str, Any]				,							A					:       Optional[int]				,							A					:       Optional[int]				,							A					:       Any				,							A					:       str				,							A					:       Optional[int]				,							A					:       Dict				,							A					:       str				,							):
         __snake_case:		Optional[int]					    =		self.num_labels
         __snake_case:		Optional[Any]					    =		FunnelForPreTraining(config=A						)
         model.to(A						)
         model.eval()
         __snake_case:		Dict					    =		model(A				,							attention_mask=A				,							token_type_ids=A				,							labels=A						)
         self.parent.assertEqual(result.logits.shape				,							(self.batch_size, self.seq_length)						)




       def 				UpperCAmelCase__					(    self					:       List[str]				,							A					:       Optional[int]				,							A					:       List[str]				,							A					:       Tuple				,							A					:       Any				,							A					:       str				,							A					:       Optional[Any]				,							A					:       Optional[Any]				,							A					:       Union[str, Any]				,							):
         __snake_case:		List[str]					    =		FunnelForMaskedLM(config=A						)
         model.to(A						)
         model.eval()
         __snake_case:		Any					    =		model(A				,							attention_mask=A				,							token_type_ids=A				,							labels=A						)
         self.parent.assertEqual(result.logits.shape				,							(self.batch_size, self.seq_length, self.vocab_size)						)




       def 				UpperCAmelCase__					(    self					:       Dict				,							A					:       str				,							A					:       Dict				,							A					:       List[str]				,							A					:       Any				,							A					:       Optional[Any]				,							A					:       Optional[Any]				,							A					:       Optional[Any]				,							A					:       Any				,							):
         __snake_case:		Dict					    =		self.num_labels
         __snake_case:		Dict					    =		FunnelForSequenceClassification(A						)
         model.to(A						)
         model.eval()
         __snake_case:		str					    =		model(A				,							attention_mask=A				,							token_type_ids=A				,							labels=A						)
         self.parent.assertEqual(result.logits.shape				,							(self.batch_size, self.num_labels)						)




       def 				UpperCAmelCase__					(    self					:       List[str]				,							A					:       Optional[int]				,							A					:       str				,							A					:       Optional[int]				,							A					:       str				,							A					:       Tuple				,							A					:       int				,							A					:       int				,							A					:       Dict				,							):
         __snake_case:		List[Any]					    =		self.num_choices
         __snake_case:		Optional[Any]					    =		FunnelForMultipleChoice(config=A						)
         model.to(A						)
         model.eval()
         __snake_case:		int					    =		input_ids.unsqueeze(1						).expand(-1				,							self.num_choices				,							-1						).contiguous()
         __snake_case:		Tuple					    =		token_type_ids.unsqueeze(1						).expand(-1				,							self.num_choices				,							-1						).contiguous()
         __snake_case:		List[str]					    =		input_mask.unsqueeze(1						).expand(-1				,							self.num_choices				,							-1						).contiguous()
         __snake_case:		Tuple					    =		model(
             A				,							attention_mask=A				,							token_type_ids=A				,							labels=A				,							)
         self.parent.assertEqual(result.logits.shape				,							(self.batch_size, self.num_choices)						)




       def 				UpperCAmelCase__					(    self					:       List[Any]				,							A					:       Optional[int]				,							A					:       str				,							A					:       Union[str, Any]				,							A					:       Optional[int]				,							A					:       Union[str, Any]				,							A					:       Optional[int]				,							A					:       Union[str, Any]				,							A					:       Union[str, Any]				,							):
         __snake_case:		Dict					    =		self.num_labels
         __snake_case:		List[Any]					    =		FunnelForTokenClassification(config=A						)
         model.to(A						)
         model.eval()
         __snake_case:		Tuple					    =		model(A				,							attention_mask=A				,							token_type_ids=A				,							labels=A						)
         self.parent.assertEqual(result.logits.shape				,							(self.batch_size, self.seq_length, self.num_labels)						)




       def 				UpperCAmelCase__					(    self					:       Dict				,							A					:       Optional[Any]				,							A					:       int				,							A					:       Dict				,							A					:       Optional[int]				,							A					:       Tuple				,							A					:       str				,							A					:       List[Any]				,							A					:       Optional[Any]				,							):
         __snake_case:		List[str]					    =		FunnelForQuestionAnswering(config=A						)
         model.to(A						)
         model.eval()
         __snake_case:		str					    =		model(
             A				,							attention_mask=A				,							token_type_ids=A				,							start_positions=A				,							end_positions=A				,							)
         self.parent.assertEqual(result.start_logits.shape				,							(self.batch_size, self.seq_length)						)
         self.parent.assertEqual(result.end_logits.shape				,							(self.batch_size, self.seq_length)						)






       def 				UpperCAmelCase__					(    self					:       str						):
         __snake_case:		Optional[int]					    =		self.prepare_config_and_inputs()
         (
             (
             __snake_case
         )    ,  (
             __snake_case
         )    ,  (
             __snake_case
         )    ,  (
             __snake_case
         )    ,  (
             __snake_case
         )    ,  (
             __snake_case
         )    ,  (
             __snake_case
         )    ,  (
             __snake_case
         )    ,  
         ):		Dict					    =		config_and_inputs
         __snake_case:		Union[str, Any]					    =		{"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": input_mask}
         return config, inputs_dict







@require_torch
class        __snake_case			(				__lowerCamelCase  ,     __lowerCamelCase  ,     unittest.TestCase     ):





       '''simple docstring'''







       lowerCAmelCase__ 						=	False
       lowerCAmelCase__ 						=	False
       lowerCAmelCase__ 						=	(
           (
               FunnelModel,
               FunnelForMaskedLM,
               FunnelForPreTraining,
               FunnelForQuestionAnswering,
               FunnelForTokenClassification,
           )
           if is_torch_available()
           else ()
       )
       lowerCAmelCase__ 						=	(
           {
               """feature-extraction""": (FunnelBaseModel, FunnelModel),
               """fill-mask""": FunnelForMaskedLM,
               """question-answering""": FunnelForQuestionAnswering,
               """text-classification""": FunnelForSequenceClassification,
               """token-classification""": FunnelForTokenClassification,
               """zero-shot""": FunnelForSequenceClassification,
           }
           if is_torch_available()
           else {}
       )




       def 				UpperCAmelCase__					(    self					:       int				,							A					:       Optional[Any]				,							A					:       List[str]				,							A					:       int=False						):
         __snake_case:		str					    =		super()._prepare_for_class(A				,							A				,							return_labels=A						)

         if return_labels:
           if model_class in get_values(A						):
             __snake_case:		str					    =		torch.zeros(
                 (self.model_tester.batch_size, self.model_tester.seq_length)				,							dtype=torch.long				,							device=A						)
         return inputs_dict




       def 				UpperCAmelCase__					(    self					:       List[str]						):
         __snake_case:		Union[str, Any]					    =		FunnelModelTester(self						)
         __snake_case:		List[str]					    =		ConfigTester(self				,							config_class=A						)




       def 				UpperCAmelCase__					(    self					:       Dict						):
         self.config_tester.run_common_tests()




       def 				UpperCAmelCase__					(    self					:       Optional[Any]						):
         __snake_case:		int					    =		self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*A						)




       def 				UpperCAmelCase__					(    self					:       Optional[int]						):
         __snake_case:		Optional[int]					    =		self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_pretraining(*A						)




       def 				UpperCAmelCase__					(    self					:       str						):
         __snake_case:		Optional[int]					    =		self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_masked_lm(*A						)




       def 				UpperCAmelCase__					(    self					:       Optional[int]						):
         __snake_case:		Any					    =		self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_token_classification(*A						)




       def 				UpperCAmelCase__					(    self					:       str						):
         __snake_case:		str					    =		self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_question_answering(*A						)






       def 				UpperCAmelCase__					(    self					:       Union[str, Any]				,							A					:       List[Any]						):
         if hasattr(A				,							"""weight"""						) and module.weight is not None:
           module.weight.data.fill_(3						)
         if hasattr(A				,							"""bias"""						) and module.bias is not None:
           module.bias.data.fill_(3						)

         for param in ["r_w_bias", "r_r_bias", "r_kernel", "r_s_bias", "seg_embed"]:
           if hasattr(A				,							A						) and getattr(A				,							A						) is not None:
             __snake_case:		str					    =		getattr(A				,							A						)
             weight.data.fill_(3						)







@require_torch
class        __snake_case			(				__lowerCamelCase  ,     unittest.TestCase     ):





       '''simple docstring'''







       lowerCAmelCase__ 						=	False
       lowerCAmelCase__ 						=	False
       lowerCAmelCase__ 						=	(
           (FunnelBaseModel, FunnelForMultipleChoice, FunnelForSequenceClassification) if is_torch_available() else ()
       )




       def 				UpperCAmelCase__					(    self					:       Any						):
         __snake_case:		Optional[int]					    =		FunnelModelTester(self				,							base=A						)
         __snake_case:		int					    =		ConfigTester(self				,							config_class=A						)




       def 				UpperCAmelCase__					(    self					:       Dict						):
         self.config_tester.run_common_tests()




       def 				UpperCAmelCase__					(    self					:       Optional[int]						):
         __snake_case:		List[str]					    =		self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_base_model(*A						)




       def 				UpperCAmelCase__					(    self					:       str						):
         __snake_case:		Dict					    =		self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_sequence_classification(*A						)




       def 				UpperCAmelCase__					(    self					:       Optional[Any]						):
         __snake_case:		List[str]					    =		self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_multiple_choice(*A						)




       def 				UpperCAmelCase__					(    self					:       Optional[Any]						):
         __snake_case    ,  __snake_case:		Optional[Any]					    =		self.model_tester.prepare_config_and_inputs_for_common()
         __snake_case:		List[str]					    =		True

         for model_class in self.all_model_classes:
           if model_class.__name__ == "FunnelBaseModel":
             continue
           __snake_case:		List[Any]					    =		model_class(A						)
           model.to(A						)
           model.train()
           __snake_case:		Optional[int]					    =		self._prepare_for_class(A				,							A				,							return_labels=A						)
           __snake_case:		Optional[int]					    =		model(**A						).loss
           loss.backward()






       def 				UpperCAmelCase__					(    self					:       Union[str, Any]				,							A					:       Optional[Any]						):
         if hasattr(A				,							"""weight"""						) and module.weight is not None:
           module.weight.data.fill_(3						)
         if hasattr(A				,							"""bias"""						) and module.bias is not None:
           module.bias.data.fill_(3						)

         for param in ["r_w_bias", "r_r_bias", "r_kernel", "r_s_bias", "seg_embed"]:
           if hasattr(A				,							A						) and getattr(A				,							A						) is not None:
             __snake_case:		int					    =		getattr(A				,							A						)
             weight.data.fill_(3						)







@require_torch
@require_sentencepiece
@require_tokenizers
class        __snake_case			(				unittest.TestCase     ):





       '''simple docstring'''







       def 				UpperCAmelCase__					(    self					:       str						):
         __snake_case:		List[str]					    =		13
         __snake_case:		List[Any]					    =		7
         __snake_case:		List[Any]					    =		torch.arange(0				,							batch_size * sequence_length						).long().reshape(A				,							A						)
         __snake_case:		Tuple					    =		[0, 1, 2, 3, 4, 5, 6, 4, 1, 3, 5, 0, 1]
         __snake_case:		List[str]					    =		torch.tensor([[2] + [0] * a + [1] * (sequence_length - a - 1) for a in lengths]						)

         __snake_case:		Optional[int]					    =		FunnelModel.from_pretrained("""sgugger/funnel-random-tiny"""						)
         __snake_case:		List[Any]					    =		model(A				,							token_type_ids=A						)[0].abs()

         __snake_case:		str					    =		torch.tensor(2344.8352						)
         __snake_case:		Union[str, Any]					    =		torch.tensor(0.8052						)
         self.assertTrue(torch.allclose(output.sum()				,							A				,							atol=1E-4						)						)
         self.assertTrue(torch.allclose(output.mean()				,							A				,							atol=1E-4						)						)

         __snake_case:		str					    =		torch.tensor([[1] * 7, [1] * 4 + [0] * 3] * 6 + [[0, 1, 1, 0, 0, 1, 1]]						)
         __snake_case:		Tuple					    =		model(A				,							attention_mask=A				,							token_type_ids=A						)[0].abs()

         __snake_case:		List[str]					    =		torch.tensor(2343.8425						)
         __snake_case:		List[Any]					    =		torch.tensor(0.8049						)
         self.assertTrue(torch.allclose(output.sum()				,							A				,							atol=1E-4						)						)
         self.assertTrue(torch.allclose(output.mean()				,							A				,							atol=1E-4						)						)






       @slow
       def 				UpperCAmelCase__					(    self					:       Optional[Any]						):
         __snake_case:		Union[str, Any]					    =		FunnelTokenizer.from_pretrained("""huggingface/funnel-small"""						)
         __snake_case:		Tuple					    =		FunnelModel.from_pretrained("""huggingface/funnel-small"""						)
         __snake_case:		Tuple					    =		tokenizer("""Hello! I am the Funnel Transformer model."""				,							return_tensors="""pt"""						)
         __snake_case:		Union[str, Any]					    =		model(**A						)[0]

         __snake_case:		int					    =		torch.tensor(235.7246						)
         __snake_case:		Dict					    =		torch.tensor(0.0256						)
         self.assertTrue(torch.allclose(output.sum()				,							A				,							atol=1E-4						)						)
         self.assertTrue(torch.allclose(output.mean()				,							A				,							atol=1E-4						)						)






