




"""simple docstring"""


import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import BlenderbotConfig, is_flax_available
from transformers.testing_utils import jax_device, require_flax, slow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor


if is_flax_available():
     import os

     # The slow tests are often failing with OOM error on GPU
     # This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
     # but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
     A_						=				'''platform'''

     import jax
     import jax.numpy as jnp

     from transformers import BlenderbotTokenizer
     from transformers.models.blenderbot.modeling_flax_blenderbot import (
         FlaxBlenderbotForConditionalGeneration,
         FlaxBlenderbotModel,
         shift_tokens_right,
     )


def 					UpperCAmelCase__       (snake_case__							:					int		,   snake_case__							:					List[str]		,   snake_case__							:					str=None		,   snake_case__							:					Optional[int]=None		,   snake_case__							:					List[Any]=None		,   snake_case__							:					int=None		,   snake_case__							:					Union[str, Any]=None		,   snake_case__							:					Tuple=None		,   ):
  """simple docstring"""

  if attention_mask is None:
    _snake_case							:		Dict								=  np.where(input_ids != config.pad_token_id		,   1		,   0    )
  if decoder_attention_mask is None:
    _snake_case							:		Tuple								=  np.where(decoder_input_ids != config.pad_token_id		,   1		,   0    )
  if head_mask is None:
    _snake_case							:		Tuple								=  np.ones((config.encoder_layers, config.encoder_attention_heads)    )
  if decoder_head_mask is None:
    _snake_case							:		Dict								=  np.ones((config.decoder_layers, config.decoder_attention_heads)    )
  if cross_attn_head_mask is None:
    _snake_case							:		Any								=  np.ones((config.decoder_layers, config.decoder_attention_heads)    )
  return {
      "input_ids": input_ids,
      "decoder_input_ids": decoder_input_ids,
      "attention_mask": attention_mask,
      "decoder_attention_mask": attention_mask,
  }






class 					lowercase:







 '''simple docstring'''





 def __init__(				self:		Dict,      a_:		Any,      a_:		str=13,      a_:		int=7,      a_:		Any=True,      a_:		Union[str, Any]=False,      a_:		str=99,      a_:		List[Any]=16,      a_:		Optional[Any]=2,      a_:		int=4,      a_:		int=4,      a_:		Dict="gelu",      a_:		int=0.1,      a_:		Dict=0.1,      a_:		Tuple=32,      a_:		Optional[int]=2,      a_:		Any=1,      a_:		List[str]=0,      a_:		Union[str, Any]=0.02,      ):




   '''simple docstring'''



   _snake_case							:		Optional[int]								=  parent
   _snake_case							:		Tuple								=  batch_size
   _snake_case							:		int								=  seq_length
   _snake_case							:		Optional[int]								=  is_training
   _snake_case							:		Tuple								=  use_labels
   _snake_case							:		List[str]								=  vocab_size
   _snake_case							:		Dict								=  hidden_size
   _snake_case							:		int								=  num_hidden_layers
   _snake_case							:		Tuple								=  num_attention_heads
   _snake_case							:		Optional[int]								=  intermediate_size
   _snake_case							:		Any								=  hidden_act
   _snake_case							:		Optional[int]								=  hidden_dropout_prob
   _snake_case							:		List[Any]								=  attention_probs_dropout_prob
   _snake_case							:		Tuple								=  max_position_embeddings
   _snake_case							:		Any								=  eos_token_id
   _snake_case							:		List[str]								=  pad_token_id
   _snake_case							:		Dict								=  bos_token_id
   _snake_case							:		Tuple								=  initializer_range


 def  UpperCamelCase_   (				self:		Any		):




   '''simple docstring'''



   _snake_case							:		Any								=  np.clip(ids_tensor([self.batch_size, self.seq_length - 1],      self.vocab_size		),      3,      self.vocab_size		)
   _snake_case							:		Tuple								=  np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1),      dtype=np.intaa		)),      -1		)

   _snake_case							:		List[str]								=  shift_tokens_right(a_,      1,      2		)

   _snake_case							:		List[str]								=  BlenderbotConfig(
       vocab_size=self.vocab_size,      d_model=self.hidden_size,      encoder_layers=self.num_hidden_layers,      decoder_layers=self.num_hidden_layers,      encoder_attention_heads=self.num_attention_heads,      decoder_attention_heads=self.num_attention_heads,      encoder_ffn_dim=self.intermediate_size,      decoder_ffn_dim=self.intermediate_size,      dropout=self.hidden_dropout_prob,      attention_dropout=self.attention_probs_dropout_prob,      max_position_embeddings=self.max_position_embeddings,      eos_token_id=self.eos_token_id,      bos_token_id=self.bos_token_id,      pad_token_id=self.pad_token_id,      initializer_range=self.initializer_range,      use_cache=a_,      )
   _snake_case							:		Tuple								=  prepare_blenderbot_inputs_dict(a_,      a_,      a_		)
   return config, inputs_dict


 def  UpperCamelCase_   (				self:		Any		):




   '''simple docstring'''



   _snake_case		,					_snake_case							:		Tuple								=  self.prepare_config_and_inputs()
   return config, inputs_dict


 def  UpperCamelCase_   (				self:		Union[str, Any],      a_:		Optional[Any],      a_:		Any,      a_:		Union[str, Any]		):




   '''simple docstring'''



   _snake_case							:		Optional[Any]								=  20
   _snake_case							:		List[str]								=  model_class_name(a_		)

   _snake_case							:		Optional[Any]								=  model.encode(inputs_dict["""input_ids"""]		)

   _snake_case		,					_snake_case							:		Optional[Any]								=  (
       inputs_dict["""decoder_input_ids"""],
       inputs_dict["""decoder_attention_mask"""],
   )

   _snake_case							:		Tuple								=  model.init_cache(decoder_input_ids.shape[0],      a_,      a_		)
   _snake_case							:		Optional[Any]								=  jnp.ones((decoder_input_ids.shape[0], max_decoder_length),      dtype="""i4"""		)

   _snake_case							:		Any								=  jnp.broadcast_to(
       jnp.arange(decoder_input_ids.shape[-1] - 1		)[None, :],      (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1),      )
   _snake_case							:		List[str]								=  model.decode(
       decoder_input_ids[:, :-1],      a_,      decoder_attention_mask=a_,      past_key_values=a_,      decoder_position_ids=a_,      )

   _snake_case							:		Union[str, Any]								=  jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]],      dtype="""i4"""		)
   _snake_case							:		Tuple								=  model.decode(
       decoder_input_ids[:, -1:],      a_,      decoder_attention_mask=a_,      past_key_values=outputs_cache.past_key_values,      decoder_position_ids=a_,      )

   _snake_case							:		List[str]								=  model.decode(a_,      a_		)

   _snake_case							:		Tuple								=  np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])		)		)
   self.parent.assertTrue(diff < 1E-3,      msg=f"Max diff is {diff}"		)


 def  UpperCamelCase_   (				self:		Union[str, Any],      a_:		Optional[Any],      a_:		List[Any],      a_:		Any		):




   '''simple docstring'''



   _snake_case							:		Dict								=  20
   _snake_case							:		Optional[int]								=  model_class_name(a_		)

   _snake_case							:		Optional[Any]								=  model.encode(inputs_dict["""input_ids"""]		)

   _snake_case		,					_snake_case							:		Dict								=  (
       inputs_dict["""decoder_input_ids"""],
       inputs_dict["""decoder_attention_mask"""],
   )

   _snake_case							:		Any								=  jnp.concatenate(
       [
           decoder_attention_mask,
           jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])		),
       ],      axis=-1,      )

   _snake_case							:		List[Any]								=  model.init_cache(decoder_input_ids.shape[0],      a_,      a_		)
   _snake_case							:		Union[str, Any]								=  jnp.broadcast_to(
       jnp.arange(decoder_input_ids.shape[-1] - 1		)[None, :],      (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1),      )

   _snake_case							:		str								=  model.decode(
       decoder_input_ids[:, :-1],      a_,      decoder_attention_mask=a_,      past_key_values=a_,      decoder_position_ids=a_,      )
   _snake_case							:		str								=  jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]],      dtype="""i4"""		)
   _snake_case							:		Optional[int]								=  model.decode(
       decoder_input_ids[:, -1:],      a_,      past_key_values=outputs_cache.past_key_values,      decoder_attention_mask=a_,      decoder_position_ids=a_,      )

   _snake_case							:		Optional[int]								=  model.decode(a_,      a_,      decoder_attention_mask=a_		)

   _snake_case							:		Optional[Any]								=  np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])		)		)
   self.parent.assertTrue(diff < 1E-3,      msg=f"Max diff is {diff}"		)







@require_flax
class 					lowercase(     unittest.TestCase ):







 '''simple docstring'''





 lowercase__ 					=					99


 def  UpperCamelCase_   (				self:		Optional[int]		):




   '''simple docstring'''



   _snake_case							:		List[Any]								=  np.array(
       [
           [71, 82, 18, 33, 46, 91, 2],
           [68, 34, 26, 58, 30, 82, 2],
           [5, 97, 17, 39, 94, 40, 2],
           [76, 83, 94, 25, 70, 78, 2],
           [87, 59, 41, 35, 48, 66, 2],
           [55, 13, 16, 58, 5, 2, 1],  # note padding
           [64, 27, 31, 51, 12, 75, 2],
           [52, 64, 86, 17, 83, 39, 2],
           [48, 61, 9, 24, 71, 82, 2],
           [26, 1, 60, 48, 22, 13, 2],
           [21, 5, 62, 28, 14, 76, 2],
           [45, 98, 37, 86, 59, 48, 2],
           [70, 70, 50, 9, 28, 0, 2],
       ],      dtype=np.intaa,      )

   _snake_case							:		Any								=  input_ids.shape[0]
   _snake_case							:		int								=  BlenderbotConfig(
       vocab_size=self.vocab_size,      d_model=24,      encoder_layers=2,      decoder_layers=2,      encoder_attention_heads=2,      decoder_attention_heads=2,      encoder_ffn_dim=32,      decoder_ffn_dim=32,      max_position_embeddings=48,      eos_token_id=2,      pad_token_id=1,      bos_token_id=0,      )
   return config, input_ids, batch_size


 def  UpperCamelCase_   (				self:		List[Any]		):




   '''simple docstring'''



   _snake_case		,					_snake_case		,					_snake_case							:		Dict								=  self._get_config_and_data()
   _snake_case							:		List[str]								=  FlaxBlenderbotForConditionalGeneration(a_		)
   _snake_case							:		str								=  lm_model(input_ids=a_		)
   _snake_case							:		Optional[int]								=  (batch_size, input_ids.shape[1], config.vocab_size)
   self.assertEqual(outputs["""logits"""].shape,      a_		)


 def  UpperCamelCase_   (				self:		Dict		):




   '''simple docstring'''



   _snake_case							:		Optional[Any]								=  BlenderbotConfig(
       vocab_size=self.vocab_size,      d_model=14,      encoder_layers=2,      decoder_layers=2,      encoder_attention_heads=2,      decoder_attention_heads=2,      encoder_ffn_dim=8,      decoder_ffn_dim=8,      max_position_embeddings=48,      )
   _snake_case							:		Union[str, Any]								=  FlaxBlenderbotForConditionalGeneration(a_		)
   _snake_case							:		str								=  np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]],      dtype=np.intaa		)
   _snake_case							:		Dict								=  np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]],      dtype=np.intaa		)
   _snake_case							:		Optional[Any]								=  lm_model(input_ids=a_,      decoder_input_ids=a_		)
   _snake_case							:		List[str]								=  (*summary.shape, config.vocab_size)
   self.assertEqual(outputs["""logits"""].shape,      a_		)


 def  UpperCamelCase_   (				self:		Optional[int]		):




   '''simple docstring'''



   _snake_case							:		str								=  np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]],      dtype=np.intaa		)
   _snake_case							:		List[str]								=  shift_tokens_right(a_,      1,      2		)
   _snake_case							:		int								=  np.equal(a_,      1		).astype(np.floataa		).sum()
   _snake_case							:		Optional[int]								=  np.equal(a_,      1		).astype(np.floataa		).sum()
   self.assertEqual(shifted.shape,      input_ids.shape		)
   self.assertEqual(a_,      n_pad_before - 1		)
   self.assertTrue(np.equal(shifted[:, 0],      2		).all()		)







@require_flax
class 					lowercase(     __a       ,			unittest.TestCase       ,			__a ):







 '''simple docstring'''





 lowercase__ 					=					True
 lowercase__ 					=					(
     (
         FlaxBlenderbotModel,
         FlaxBlenderbotForConditionalGeneration,
     )
     if is_flax_available()
     else ()
 )
 lowercase__ 					=					(FlaxBlenderbotForConditionalGeneration,) if is_flax_available() else ()


 def  UpperCamelCase_   (				self:		Any		):




   '''simple docstring'''



   _snake_case							:		Union[str, Any]								=  FlaxBlenderbotModelTester(self		)


 def  UpperCamelCase_   (				self:		Optional[int]		):




   '''simple docstring'''



   _snake_case		,					_snake_case							:		List[str]								=  self.model_tester.prepare_config_and_inputs()
   for model_class in self.all_model_classes:
     self.model_tester.check_use_cache_forward(a_,      a_,      a_		)


 def  UpperCamelCase_   (				self:		Tuple		):




   '''simple docstring'''



   _snake_case		,					_snake_case							:		Optional[Any]								=  self.model_tester.prepare_config_and_inputs()
   for model_class in self.all_model_classes:
     self.model_tester.check_use_cache_forward_with_attn_mask(a_,      a_,      a_		)


 def  UpperCamelCase_   (				self:		Dict		):




   '''simple docstring'''



   _snake_case		,					_snake_case							:		Optional[Any]								=  self.model_tester.prepare_config_and_inputs_for_common()

   for model_class in self.all_model_classes:
     with self.subTest(model_class.__name__		):
       _snake_case							:		List[Any]								=  self._prepare_for_class(a_,      a_		)
       _snake_case							:		List[Any]								=  model_class(a_		)

       @jax.jit
       def encode_jitted(a_:		str,      a_:		Optional[Any]=None,      **a_:		List[str]		):
         return model.encode(input_ids=a_,      attention_mask=a_		)

       with self.subTest("""JIT Enabled"""		):
         _snake_case							:		Optional[Any]								=  encode_jitted(**a_		).to_tuple()

       with self.subTest("""JIT Disabled"""		):
         with jax.disable_jit():
           _snake_case							:		int								=  encode_jitted(**a_		).to_tuple()

       self.assertEqual(len(a_		),      len(a_		)		)
       for jitted_output, output in zip(a_,      a_		):
         self.assertEqual(jitted_output.shape,      output.shape		)


 def  UpperCamelCase_   (				self:		Tuple		):




   '''simple docstring'''



   _snake_case		,					_snake_case							:		List[Any]								=  self.model_tester.prepare_config_and_inputs_for_common()

   for model_class in self.all_model_classes:
     with self.subTest(model_class.__name__		):
       _snake_case							:		int								=  model_class(a_		)
       _snake_case							:		int								=  model.encode(inputs_dict["""input_ids"""],      inputs_dict["""attention_mask"""]		)

       _snake_case							:		Optional[Any]								=  {
           """decoder_input_ids""": inputs_dict["""decoder_input_ids"""],
           """decoder_attention_mask""": inputs_dict["""decoder_attention_mask"""],
           """encoder_outputs""": encoder_outputs,
       }

       @jax.jit
       def decode_jitted(a_:		Any,      a_:		Tuple,      a_:		int		):
         return model.decode(
             decoder_input_ids=a_,      decoder_attention_mask=a_,      encoder_outputs=a_,      )

       with self.subTest("""JIT Enabled"""		):
         _snake_case							:		Any								=  decode_jitted(**a_		).to_tuple()

       with self.subTest("""JIT Disabled"""		):
         with jax.disable_jit():
           _snake_case							:		List[str]								=  decode_jitted(**a_		).to_tuple()

       self.assertEqual(len(a_		),      len(a_		)		)
       for jitted_output, output in zip(a_,      a_		):
         self.assertEqual(jitted_output.shape,      output.shape		)


 @slow
 def  UpperCamelCase_   (				self:		Optional[Any]		):




   '''simple docstring'''



   for model_class_name in self.all_model_classes:
     _snake_case							:		List[Any]								=  model_class_name.from_pretrained("""facebook/blenderbot-400M-distill"""		)
     # FlaxBlenderbotForSequenceClassification expects eos token in input_ids
     _snake_case							:		Optional[int]								=  np.ones((1, 1)		) * model.config.eos_token_id
     _snake_case							:		Union[str, Any]								=  model(a_		)
     self.assertIsNotNone(a_		)


 @unittest.skipUnless(jax_device != """cpu""",      """3B test too slow on CPU."""		)
 @slow
 def  UpperCamelCase_   (				self:		Optional[int]		):




   '''simple docstring'''



   _snake_case							:		Dict								=  {"""num_beams""": 1, """early_stopping""": True, """min_length""": 15, """max_length""": 25}
   _snake_case							:		str								=  {"""skip_special_tokens""": True, """clean_up_tokenization_spaces""": True}

   _snake_case							:		List[str]								=  FlaxBlenderbotForConditionalGeneration.from_pretrained("""facebook/blenderbot-3B""",      from_pt=a_		)
   _snake_case							:		Union[str, Any]								=  BlenderbotTokenizer.from_pretrained("""facebook/blenderbot-3B"""		)

   _snake_case							:		str								=  ["""Sam"""]
   _snake_case							:		Tuple								=  tokenizer(a_,      return_tensors="""jax"""		)

   _snake_case							:		str								=  model.generate(**a_,      **a_		)
   _snake_case							:		List[Any]								=  """Sam is a great name. It means \"sun\" in Gaelic."""

   _snake_case							:		int								=  tokenizer.batch_decode(a_,      **a_		)
   assert generated_txt[0].strip() == tgt_text








"""simple docstring"""


import argparse
import json

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import ViTImageProcessor, ViTMSNConfig, ViTMSNModel
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD


torch.set_grad_enabled(False)


def 					UpperCAmelCase__       (snake_case__							:					str		,   snake_case__							:					List[str]=False    ):
  """simple docstring"""

  _snake_case							:		Optional[Any]								=  []
  for i in range(config.num_hidden_layers    ):
    # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
    rename_keys.append((F"module.blocks.{i}.norm1.weight", F"vit.encoder.layer.{i}.layernorm_before.weight")    )
    rename_keys.append((F"module.blocks.{i}.norm1.bias", F"vit.encoder.layer.{i}.layernorm_before.bias")    )
    rename_keys.append(
        (F"module.blocks.{i}.attn.proj.weight", F"vit.encoder.layer.{i}.attention.output.dense.weight")    )
    rename_keys.append((F"module.blocks.{i}.attn.proj.bias", F"vit.encoder.layer.{i}.attention.output.dense.bias")    )
    rename_keys.append((F"module.blocks.{i}.norm2.weight", F"vit.encoder.layer.{i}.layernorm_after.weight")    )
    rename_keys.append((F"module.blocks.{i}.norm2.bias", F"vit.encoder.layer.{i}.layernorm_after.bias")    )
    rename_keys.append((F"module.blocks.{i}.mlp.fc1.weight", F"vit.encoder.layer.{i}.intermediate.dense.weight")    )
    rename_keys.append((F"module.blocks.{i}.mlp.fc1.bias", F"vit.encoder.layer.{i}.intermediate.dense.bias")    )
    rename_keys.append((F"module.blocks.{i}.mlp.fc2.weight", F"vit.encoder.layer.{i}.output.dense.weight")    )
    rename_keys.append((F"module.blocks.{i}.mlp.fc2.bias", F"vit.encoder.layer.{i}.output.dense.bias")    )

  # projection layer + position embeddings
  rename_keys.extend(
      [
          ("""module.cls_token""", """vit.embeddings.cls_token"""),
          ("""module.patch_embed.proj.weight""", """vit.embeddings.patch_embeddings.projection.weight"""),
          ("""module.patch_embed.proj.bias""", """vit.embeddings.patch_embeddings.projection.bias"""),
          ("""module.pos_embed""", """vit.embeddings.position_embeddings"""),
      ]    )

  if base_model:
    # layernorm + pooler
    rename_keys.extend(
        [
            ("""module.norm.weight""", """layernorm.weight"""),
            ("""module.norm.bias""", """layernorm.bias"""),
        ]    )

    # if just the base model, we should remove "vit" from all keys that start with "vit"
    _snake_case							:		Any								=  [(pair[0], pair[1][4:]) if pair[1].startswith("""vit"""    ) else pair for pair in rename_keys]
  else:
    # layernorm + classification head
    rename_keys.extend(
        [
            ("""norm.weight""", """vit.layernorm.weight"""),
            ("""norm.bias""", """vit.layernorm.bias"""),
            ("""head.weight""", """classifier.weight"""),
            ("""head.bias""", """classifier.bias"""),
        ]    )

  return rename_keys


def 					UpperCAmelCase__       (snake_case__							:					Dict		,   snake_case__							:					Dict		,   snake_case__							:					List[str]=False    ):
  """simple docstring"""

  for i in range(config.num_hidden_layers    ):
    if base_model:
      _snake_case							:		List[Any]								=  """"""
    else:
      _snake_case							:		List[Any]								=  """vit."""
    # read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
    _snake_case							:		Optional[Any]								=  state_dict.pop(F"module.blocks.{i}.attn.qkv.weight"    )
    _snake_case							:		Optional[Any]								=  state_dict.pop(F"module.blocks.{i}.attn.qkv.bias"    )
    # next, add query, keys and values (in that order) to the state dict
    _snake_case							:		Optional[Any]								=  in_proj_weight[
        : config.hidden_size, :
    ]
    _snake_case							:		Union[str, Any]								=  in_proj_bias[: config.hidden_size]
    _snake_case							:		Union[str, Any]								=  in_proj_weight[
        config.hidden_size : config.hidden_size * 2, :
    ]
    _snake_case							:		Optional[Any]								=  in_proj_bias[
        config.hidden_size : config.hidden_size * 2
    ]
    _snake_case							:		Union[str, Any]								=  in_proj_weight[
        -config.hidden_size :, :
    ]
    _snake_case							:		List[str]								=  in_proj_bias[-config.hidden_size :]


def 					UpperCAmelCase__       (snake_case__							:					str    ):
  """simple docstring"""

  _snake_case							:		Tuple								=  ["""head.weight""", """head.bias"""]
  for k in ignore_keys:
    state_dict.pop(snake_case__		,   snake_case__    )


def 					UpperCAmelCase__       (snake_case__							:					int    ):
  """simple docstring"""

  _snake_case							:		List[str]								=  [
      """module.fc.fc1.weight""",
      """module.fc.fc1.bias""",
      """module.fc.bn1.weight""",
      """module.fc.bn1.bias""",
      """module.fc.bn1.running_mean""",
      """module.fc.bn1.running_var""",
      """module.fc.bn1.num_batches_tracked""",
      """module.fc.fc2.weight""",
      """module.fc.fc2.bias""",
      """module.fc.bn2.weight""",
      """module.fc.bn2.bias""",
      """module.fc.bn2.running_mean""",
      """module.fc.bn2.running_var""",
      """module.fc.bn2.num_batches_tracked""",
      """module.fc.fc3.weight""",
      """module.fc.fc3.bias""",
  ]
  for k in ignore_keys:
    state_dict.pop(snake_case__		,   snake_case__    )


def 					UpperCAmelCase__       (snake_case__							:					List[Any]		,   snake_case__							:					Tuple		,   snake_case__							:					int    ):
  """simple docstring"""

  _snake_case							:		Optional[Any]								=  dct.pop(snake_case__    )
  _snake_case							:		Union[str, Any]								=  val


def 					UpperCAmelCase__       (snake_case__							:					List[Any]		,   snake_case__							:					str    ):
  """simple docstring"""

  _snake_case							:		str								=  ViTMSNConfig()
  _snake_case							:		Any								=  10_00

  _snake_case							:		Tuple								=  """datasets/huggingface/label-files"""
  _snake_case							:		Dict								=  """imagenet-1k-id2label.json"""
  _snake_case							:		int								=  json.load(open(hf_hub_download(snake_case__		,   snake_case__    )		,   """r"""    )    )
  _snake_case							:		Any								=  {int(snake_case__    ): v for k, v in idalabel.items()}
  _snake_case							:		List[Any]								=  idalabel
  _snake_case							:		str								=  {v: k for k, v in idalabel.items()}

  if "s16" in checkpoint_url:
    _snake_case							:		Tuple								=  3_84
    _snake_case							:		Dict								=  15_36
    _snake_case							:		Tuple								=  6
  elif "l16" in checkpoint_url:
    _snake_case							:		Any								=  10_24
    _snake_case							:		int								=  40_96
    _snake_case							:		str								=  24
    _snake_case							:		Optional[int]								=  16
    _snake_case							:		List[Any]								=  0.1
  elif "b4" in checkpoint_url:
    _snake_case							:		Tuple								=  4
  elif "l7" in checkpoint_url:
    _snake_case							:		int								=  7
    _snake_case							:		Dict								=  10_24
    _snake_case							:		Optional[Any]								=  40_96
    _snake_case							:		Any								=  24
    _snake_case							:		Union[str, Any]								=  16
    _snake_case							:		Optional[int]								=  0.1

  _snake_case							:		int								=  ViTMSNModel(snake_case__    )

  _snake_case							:		Optional[int]								=  torch.hub.load_state_dict_from_url(snake_case__		,   map_location="""cpu"""    )["""target_encoder"""]

  _snake_case							:		List[str]								=  ViTImageProcessor(size=config.image_size    )

  remove_projection_head(snake_case__    )
  _snake_case							:		List[str]								=  create_rename_keys(snake_case__		,   base_model=snake_case__    )

  for src, dest in rename_keys:
    rename_key(snake_case__		,   snake_case__		,   snake_case__    )
  read_in_q_k_v(snake_case__		,   snake_case__		,   base_model=snake_case__    )

  model.load_state_dict(snake_case__    )
  model.eval()

  _snake_case							:		Union[str, Any]								=  """http://images.cocodataset.org/val2017/000000039769.jpg"""

  _snake_case							:		Tuple								=  Image.open(requests.get(snake_case__		,   stream=snake_case__    ).raw    )
  _snake_case							:		str								=  ViTImageProcessor(
      size=config.image_size		,   image_mean=snake_case__		,   image_std=snake_case__    )
  _snake_case							:		Any								=  image_processor(images=snake_case__		,   return_tensors="""pt"""    )

  # forward pass
  torch.manual_seed(2    )
  _snake_case							:		int								=  model(**snake_case__    )
  _snake_case							:		List[Any]								=  outputs.last_hidden_state

  # The following Colab Notebook was used to generate these outputs:
  # https://colab.research.google.com/gist/sayakpaul/3672419a04f5997827503fd84079bdd1/scratchpad.ipynb
  if "s16" in checkpoint_url:
    _snake_case							:		Optional[Any]								=  torch.tensor([[-1.09_15, -1.48_76, -1.18_09]]    )
  elif "b16" in checkpoint_url:
    _snake_case							:		str								=  torch.tensor([[14.28_89, -18.90_45, 11.72_81]]    )
  elif "l16" in checkpoint_url:
    _snake_case							:		Optional[int]								=  torch.tensor([[41.50_28, -22.86_81, 45.64_75]]    )
  elif "b4" in checkpoint_url:
    _snake_case							:		List[Any]								=  torch.tensor([[-4.38_68, 5.29_32, -0.41_37]]    )
  else:
    _snake_case							:		Optional[int]								=  torch.tensor([[-0.17_92, -0.64_65, 2.42_63]]    )

  # verify logits
  assert torch.allclose(last_hidden_state[:, 0, :3]		,   snake_case__		,   atol=1e-4    )

  print(F"Saving model to {pytorch_dump_folder_path}"    )
  model.save_pretrained(snake_case__    )

  print(F"Saving image processor to {pytorch_dump_folder_path}"    )
  image_processor.save_pretrained(snake_case__    )


if __name__ == "__main__":
     A_						=				argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         '''--checkpoint_url''',
         default='''https://dl.fbaipublicfiles.com/msn/vits16_800ep.pth.tar''',
         type=str,
         help='''URL of the checkpoint you\'d like to convert.''',
     )
     parser.add_argument(
         '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
     )

     A_						=				parser.parse_args()
     convert_vit_msn_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)


