




from __future__ import annotations

import copy
import os
import tempfile
from importlib import import_module
from math import isnan

from transformers import is_tf_available
from transformers.models.auto import get_values
from transformers.testing_utils import _tf_gpu_memory_limit, require_tf, slow

from ..test_modeling_tf_common import ids_tensor


if is_tf_available():
   import numpy as np
   import tensorflow as tf

   from transformers import (
       TF_MODEL_FOR_CAUSAL_LM_MAPPING,
       TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
       TF_MODEL_FOR_MASKED_LM_MAPPING,
       TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
       TF_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING,
       TF_MODEL_FOR_PRETRAINING_MAPPING,
       TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
       TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
       TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
       TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
       TFSharedEmbeddings,
   )

   if _tf_gpu_memory_limit is not None:
      UpperCAmelCase__						:       List[Any]							=    tf.config.list_physical_devices('GPU')
      for gpu in gpus:
         # Restrict TensorFlow to only allocate x GB of memory on the GPUs
         try:
            tf.config.set_logical_device_configuration(
                gpu, [tf.config.LogicalDeviceConfiguration(memory_limit=_tf_gpu_memory_limit)]
            )
            UpperCAmelCase__						:       Optional[Any]							=    tf.config.list_logical_devices('GPU')
            print('Logical GPUs', logical_gpus)
         except RuntimeError as e:
            # Virtual devices must be set before GPUs have been initialized
            print(e)

@require_tf
class      UpperCAmelCase						:
   __SCREAMING_SNAKE_CASE		:							Any							=					None
   __SCREAMING_SNAKE_CASE		:							str							=					()
   __SCREAMING_SNAKE_CASE		:							List[Any]							=					()
   __SCREAMING_SNAKE_CASE		:							List[Any]							=					True
   __SCREAMING_SNAKE_CASE		:							Optional[int]							=					True
   __SCREAMING_SNAKE_CASE		:							Any							=					True
   __SCREAMING_SNAKE_CASE		:							List[str]							=					False






   def  _a						(	self   :       Optional[Any]						,					UpperCamelCase_   :       Union[str, Any]						,					UpperCamelCase_   :       Union[str, Any]						,					UpperCamelCase_   :       Dict=False     )->			Optional[Any]:
     _lowercase: int						     =    copy.deepcopy(a_     )

     if model_class in get_values(a_     ):
       _lowercase: Union[str, Any]						     =    {
           k: tf.tile(tf.expand_dims(a_						,					1     )						,					(1, self.model_tester.num_choices) + (1,) * (v.ndim - 1)     )
           if isinstance(a_						,					tf.Tensor     ) and v.ndim > 0
           else v
           for k, v in inputs_dict.items()
       }

     if return_labels:
       if model_class in get_values(a_     ):
         _lowercase: Tuple						     =    tf.ones(self.model_tester.batch_size						,					dtype=tf.intaa     )
       elif model_class in get_values(a_     ):
         _lowercase: Optional[Any]						     =    tf.zeros(self.model_tester.batch_size						,					dtype=tf.intaa     )
         _lowercase: Tuple						     =    tf.zeros(self.model_tester.batch_size						,					dtype=tf.intaa     )
       elif model_class in [
           *get_values(a_     ),
           *get_values(a_     ),
       ]:
         _lowercase: str						     =    tf.zeros(self.model_tester.batch_size						,					dtype=tf.intaa     )
       elif model_class in get_values(a_     ):
         _lowercase: Optional[Any]						     =    tf.zeros(self.model_tester.batch_size						,					dtype=tf.intaa     )
       elif model_class in [
           *get_values(a_     ),
           *get_values(a_     ),
           *get_values(a_     ),
           *get_values(a_     ),
           *get_values(a_     ),
       ]:
         _lowercase: Optional[Any]						     =    tf.zeros(
             (self.model_tester.batch_size, self.model_tester.seq_length)						,					dtype=tf.intaa     )
     return inputs_dict






   @slow
   def  _a						(	self   :       Optional[Any]     )->			Optional[Any]:
     _lowercase     ,					_lowercase: Any						     =    self.model_tester.prepare_config_and_inputs_for_common()
     for model_class in self.all_model_classes[:2]:
       _lowercase: str						     =    self._prepare_for_class(a_						,					a_     )
       _lowercase: int						     =    model_class(a_     )

       @tf.function
       def run_in_graph_mode():
         return model(a_     )

       _lowercase: Optional[int]						     =    run_in_graph_mode()
       self.assertIsNotNone(a_     )






   @slow
   def  _a						(	self   :       Optional[Any]     )->			Dict:
     _lowercase     ,					_lowercase: Any						     =    self.model_tester.prepare_config_and_inputs_for_common()
     for model_class in self.all_model_classes[:2]:
       _lowercase: List[str]						     =    self._prepare_for_class(a_						,					a_     )
       _lowercase: int						     =    model_class(a_     )

       @tf.function(experimental_compile=a_     )
       def run_in_graph_mode():
         return model(a_     )

       _lowercase: Optional[Any]						     =    run_in_graph_mode()
       self.assertIsNotNone(a_     )






   @slow
   def  _a						(	self   :       Optional[Any]     )->			List[str]:
     _lowercase     ,					_lowercase: Union[str, Any]						     =    self.model_tester.prepare_config_and_inputs_for_common()
     for model_class in self.all_model_classes[:2]:
       _lowercase: Optional[Any]						     =    model_class(a_     )
       if getattr(a_						,					'hf_compute_loss'						,					a_     ):
         # Test that model correctly compute the loss with kwargs
         _lowercase: int						     =    self._prepare_for_class(inputs_dict.copy()						,					a_						,					return_labels=a_     )
         # Is there a better way to remove these decoder inputs?
         _lowercase: Dict						     =    {
             key: val
             for key, val in prepared_for_class.items()
             if key not in ('head_mask', 'decoder_head_mask', 'cross_attn_head_mask', 'decoder_input_ids')
         }

         _lowercase: List[Any]						     =    {
             'labels',
             'label',
             'label_ids',
             'start_positions',
             'start_position',
             'end_positions',
             'end_position',
             'next_sentence_label',
         }
         _lowercase: Tuple						     =    possible_label_cols.intersection(set(a_     )     )
         self.assertGreater(len(a_     )						,					0						,					msg='No matching label names found!'     )
         _lowercase: Union[str, Any]						     =    {key: val for key, val in prepared_for_class.items() if key in label_names}
         _lowercase: List[str]						     =    {key: val for key, val in prepared_for_class.items() if key not in label_names}
         self.assertGreater(len(a_     )						,					0     )

         # Make sure it works with XLA!
         model.compile(optimizer=tf.keras.optimizers.SGD(0.0     )						,					jit_compile=a_     )
         # Make sure the model fits without crashing regardless of where we pass the labels
         _lowercase: Dict						     =    model.fit(
             a_						,					validation_data=a_						,					steps_per_epoch=1						,					validation_steps=1						,					shuffle=a_						,					verbose=0						,					)
         _lowercase: Dict						     =    history.history['loss'][0]
         self.assertTrue(not isnan(a_     )     )
         _lowercase: Tuple						     =    history.history['val_loss'][0]
         self.assertTrue(not isnan(a_     )     )

         # Now test it with separate labels, to make sure that path works in XLA too.
         _lowercase: Union[str, Any]						     =    model_class(a_     )
         model.compile(optimizer=tf.keras.optimizers.SGD(0.0     )						,					jit_compile=a_     )
         _lowercase: List[str]						     =    model.fit(
             a_						,					a_						,					validation_data=(inputs_minus_labels, labels)						,					steps_per_epoch=1						,					validation_steps=1						,					shuffle=a_						,					verbose=0						,					)

         _lowercase: Optional[int]						     =    history.history['loss'][0]
         self.assertTrue(not isnan(a_     )     )
         _lowercase: Optional[int]						     =    history.history['val_loss'][0]
         self.assertTrue(not isnan(a_     )     )






   @slow
   def  _a						(	self   :       Tuple     )->			Union[str, Any]:
     _lowercase     ,					_lowercase: List[str]						     =    self.model_tester.prepare_config_and_inputs_for_common()
     _lowercase: List[str]						     =    True
     _lowercase: Dict						     =    True

     if hasattr(a_						,					'use_cache'     ):
       _lowercase: Union[str, Any]						     =    True

     _lowercase: Optional[Any]						     =    getattr(self.model_tester						,					'encoder_seq_length'						,					self.model_tester.seq_length     )
     _lowercase: Union[str, Any]						     =    getattr(self.model_tester						,					'key_length'						,					a_     )

     for model_class in self.all_model_classes[:2]:
       _lowercase: Any						     =    self._prepare_for_class(a_						,					a_     )
       _lowercase: int						     =    model_class(a_     )
       model.build()
       _lowercase: int						     =    len(model(a_     )     )

       for key in list(class_inputs_dict.keys()     ):
         # Remove keys not in the serving signature, as the SavedModel will not be compiled to deal with them
         if key not in model.input_signature:
           del class_inputs_dict[key]
         # Check it's a tensor, in case the inputs dict has some bools in it too
         elif isinstance(class_inputs_dict[key]						,					tf.Tensor     ) and class_inputs_dict[key].dtype.is_integer:
           _lowercase: Union[str, Any]						     =    tf.cast(class_inputs_dict[key]						,					tf.intaa     )

       if set(class_inputs_dict.keys()     ) != set(model.input_signature.keys()     ):
         continue  # Some models have inputs that the preparation functions don't create, we skip those

       with tempfile.TemporaryDirectory() as tmpdirname:
         model.save_pretrained(a_						,					saved_model=a_     )
         _lowercase: int						     =    os.path.join(a_						,					'saved_model'						,					'1'     )
         _lowercase: Tuple						     =    tf.keras.models.load_model(a_     )
         _lowercase: Optional[int]						     =    model(a_     )

         if self.is_encoder_decoder:
           _lowercase: Optional[Any]						     =    outputs['encoder_hidden_states']
           _lowercase: Union[str, Any]						     =    outputs['encoder_attentions']
         else:
           _lowercase: List[str]						     =    outputs['hidden_states']
           _lowercase: int						     =    outputs['attentions']

         self.assertEqual(len(a_     )						,					a_     )

         _lowercase: List[str]						     =    getattr(
             self.model_tester						,					'expected_num_hidden_layers'						,					self.model_tester.num_hidden_layers + 1     )

         self.assertEqual(len(a_     )						,					a_     )
         self.assertListEqual(
             list(output_hidden_states[0].shape[-2:]     )						,					[self.model_tester.seq_length, self.model_tester.hidden_size]						,					)

         self.assertEqual(len(a_     )						,					self.model_tester.num_hidden_layers     )
         self.assertListEqual(
             list(output_attentions[0].shape[-3:]     )						,					[self.model_tester.num_attention_heads, encoder_seq_length, encoder_key_length]						,					)






   @slow
   def  _a						(	self   :       Optional[Any]     )->			Optional[Any]:
     tf.keras.mixed_precision.set_global_policy('mixed_float16'     )

     # try/finally block to ensure subsequent tests run in float32
     try:
       _lowercase     ,					_lowercase: str						     =    self.model_tester.prepare_config_and_inputs_for_common()
       for model_class in self.all_model_classes[:2]:
         _lowercase: Any						     =    self._prepare_for_class(a_						,					a_     )
         _lowercase: Tuple						     =    model_class(a_     )
         _lowercase: Dict						     =    model(a_     )

         self.assertIsNotNone(a_     )
     finally:
       tf.keras.mixed_precision.set_global_policy('float32'     )






   @slow
   def  _a						(	self   :       List[Any]     )->			List[Any]:
     _lowercase     ,					_lowercase: List[Any]						     =    self.model_tester.prepare_config_and_inputs_for_common()
     # head_mask and decoder_head_mask has different shapes than other input args
     if "head_mask" in inputs_dict:
       del inputs_dict["head_mask"]
     if "decoder_head_mask" in inputs_dict:
       del inputs_dict["decoder_head_mask"]
     if "cross_attn_head_mask" in inputs_dict:
       del inputs_dict["cross_attn_head_mask"]
     _lowercase: Union[str, Any]						     =    {
         module_member
         for model_class in self.all_model_classes
         for module in (import_module(model_class.__module__     ),)
         for module_member_name in dir(a_     )
         if module_member_name.endswith('MainLayer'     )
         for module_member in (getattr(a_						,					a_     ),)
         if isinstance(a_						,					a_     )
         and tf.keras.layers.Layer in module_member.__bases__
         and getattr(a_						,					'_keras_serializable'						,					a_     )
     }

     for main_layer_class in tf_main_layer_classes:
       # T5MainLayer needs an embed_tokens parameter when called without the inputs_embeds parameter
       if "T5" in main_layer_class.__name__:
         # Take the same values than in TFT5ModelTester for this shared layer
         _lowercase: Optional[int]						     =    TFSharedEmbeddings(self.model_tester.vocab_size						,					self.model_tester.hidden_size						,					name='shared'     )
         _lowercase: str						     =    False
         _lowercase: Dict						     =    main_layer_class(a_						,					embed_tokens=a_     )
       else:
         _lowercase: Optional[Any]						     =    main_layer_class(a_     )

       _lowercase: Optional[int]						     =    {
           name: tf.keras.Input(tensor.shape[1:]						,					dtype=tensor.dtype     ) for name, tensor in inputs_dict.items()
       }

       if hasattr(self.model_tester						,					'num_labels'     ):
         _lowercase: List[str]						     =    self.model_tester.num_labels
       else:
         _lowercase: Any						     =    2

       _lowercase: Optional[int]						     =    tf.data.Dataset.from_tensor_slices(
           (inputs_dict, np.ones((self.model_tester.batch_size, self.model_tester.seq_length, num_labels, 1)     ))     ).batch(1     )

       _lowercase: Optional[Any]						     =    main_layer(a_     )[0]
       _lowercase: Any						     =    tf.keras.layers.Dense(a_						,					activation='softmax'						,					name='outputs'     )(a_     )
       _lowercase: Dict						     =    tf.keras.models.Model(inputs=a_						,					outputs=[outputs]     )

       model.compile(loss='binary_crossentropy'						,					optimizer='adam'						,					metrics=['binary_accuracy']     )
       model.fit(a_						,					epochs=1     )

       with tempfile.TemporaryDirectory() as tmpdirname:
         _lowercase: Optional[Any]						     =    os.path.join(a_						,					'keras_model.h5'     )
         model.save(a_     )
         if "T5" in main_layer_class.__name__:
           _lowercase: Union[str, Any]						     =    tf.keras.models.load_model(
               a_						,					custom_objects={
                   main_layer_class.__name__: main_layer_class,
                   'TFSharedEmbeddings': TFSharedEmbeddings,
               }						,					)
         else:
           _lowercase: Optional[int]						     =    tf.keras.models.load_model(
               a_						,					custom_objects={main_layer_class.__name__: main_layer_class}     )
         assert isinstance(a_						,					tf.keras.Model     )
         model(a_     )






   @slow
   def  _a						(	self   :       Optional[int]     )->			Tuple:
     _lowercase     ,					_lowercase: int						     =    self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes[:2]:
       _lowercase: Union[str, Any]						     =    model_class(a_     )

       _lowercase: str						     =    copy.deepcopy(a_     )

       if not self.is_encoder_decoder:
         _lowercase: str						     =    inputs['input_ids']
         del inputs["input_ids"]
       else:
         _lowercase: Any						     =    inputs['input_ids']
         _lowercase: Optional[int]						     =    inputs.get('decoder_input_ids'						,					a_     )
         del inputs["input_ids"]
         inputs.pop('decoder_input_ids'						,					a_     )

       if not self.is_encoder_decoder:
         _lowercase: str						     =    model.get_input_embeddings()(a_     )
       else:
         _lowercase: Any						     =    model.get_input_embeddings()(a_     )
         _lowercase: Any						     =    model.get_input_embeddings()(a_     )

       _lowercase: int						     =    self._prepare_for_class(a_						,					a_     )

       @tf.function
       def run_in_graph_mode():
         return model(a_     )

       _lowercase: Dict						     =    run_in_graph_mode()
       self.assertIsNotNone(a_     )






   def  _a						(	self   :       Optional[Any]						,					UpperCamelCase_   :       str						,					UpperCamelCase_   :       Optional[Any]     )->			str:
     _lowercase: Optional[int]						     =    []
     if model.config.bos_token_id is not None:
       special_tokens.append(model.config.bos_token_id     )
     if model.config.pad_token_id is not None:
       special_tokens.append(model.config.pad_token_id     )
     if model.config.eos_token_id is not None:
       special_tokens.append(model.config.eos_token_id     )

     # create random bad tokens that are not special tokens
     _lowercase: str						     =    []
     while len(a_     ) < num_bad_tokens:
       _lowercase: Union[str, Any]						     =    tf.squeeze(ids_tensor((1, 1)						,					self.model_tester.vocab_size     )						,					0     ).numpy()[0]
       if token not in special_tokens:
         bad_tokens.append(a_     )
     return bad_tokens






   def  _a						(	self   :       List[Any]						,					UpperCamelCase_   :       List[Any]     )->			Dict:



     for token_id in output_ids[0].numpy().tolist():
       self.assertGreaterEqual(a_						,					0     )
       self.assertLess(a_						,					self.model_tester.vocab_size     )






   def  _a						(	self   :       Optional[Any]						,					UpperCamelCase_   :       List[Any]						,					UpperCamelCase_   :       int     )->			Optional[int]:



     for bad_word_ids in bad_words_ids:
       # for all slices in batch
       for generated_ids_slice in generated_ids:
         # for all word idx
         for i in range(len(a_     )						,					len(a_     )     ):
           # if tokens match
           if generated_ids_slice[i - len(a_     ) : i] == bad_word_ids:
             return True
     return False










import argparse

import requests
import torch
from PIL import Image

from transformers import (
    CLIPSegConfig,
    CLIPSegForImageSegmentation,
    CLIPSegProcessor,
    CLIPSegTextConfig,
    CLIPSegVisionConfig,
    CLIPTokenizer,
    ViTImageProcessor,
)


def 		_lowerCamelCase				(					UpperCamelCase							)			->		str:





  """simple docstring"""





  _lowercase: Any						     =    CLIPSegTextConfig()
  _lowercase: Optional[Any]						     =    CLIPSegVisionConfig(patch_size=16							)

  _lowercase: Union[str, Any]						     =    True if 'refined' in model_name else False
  _lowercase: str						     =    16 if 'rd16' in model_name else 64

  _lowercase: Any						     =    CLIPSegConfig.from_text_vision_configs(
      UpperCamelCase					,     UpperCamelCase					,     use_complex_transposed_convolution=UpperCamelCase					,     reduce_dim=UpperCamelCase					,     )
  return config


def 		_lowerCamelCase				(					UpperCamelCase							)			->		Any:





  """simple docstring"""





  if "clip_model" in name:
    _lowercase: Tuple						     =    name.replace('clip_model'					,     'clip'							)
  if "transformer" in name:
    if "visual" in name:
      _lowercase: Union[str, Any]						     =    name.replace('visual.transformer'					,     'vision_model'							)
    else:
      _lowercase: Optional[int]						     =    name.replace('transformer'					,     'text_model'							)
  if "resblocks" in name:
    _lowercase: List[Any]						     =    name.replace('resblocks'					,     'encoder.layers'							)
  if "ln_1" in name:
    _lowercase: Union[str, Any]						     =    name.replace('ln_1'					,     'layer_norm1'							)
  if "ln_2" in name:
    _lowercase: Union[str, Any]						     =    name.replace('ln_2'					,     'layer_norm2'							)
  if "c_fc" in name:
    _lowercase: str						     =    name.replace('c_fc'					,     'fc1'							)
  if "c_proj" in name:
    _lowercase: List[Any]						     =    name.replace('c_proj'					,     'fc2'							)
  if "attn" in name and "self" not in name:
    _lowercase: Dict						     =    name.replace('attn'					,     'self_attn'							)
  # text encoder
  if "token_embedding" in name:
    _lowercase: Optional[int]						     =    name.replace('token_embedding'					,     'text_model.embeddings.token_embedding'							)
  if "positional_embedding" in name and "visual" not in name:
    _lowercase: int						     =    name.replace('positional_embedding'					,     'text_model.embeddings.position_embedding.weight'							)
  if "ln_final" in name:
    _lowercase: Optional[Any]						     =    name.replace('ln_final'					,     'text_model.final_layer_norm'							)
  # vision encoder
  if "visual.class_embedding" in name:
    _lowercase: Union[str, Any]						     =    name.replace('visual.class_embedding'					,     'vision_model.embeddings.class_embedding'							)
  if "visual.conv1" in name:
    _lowercase: int						     =    name.replace('visual.conv1'					,     'vision_model.embeddings.patch_embedding'							)
  if "visual.positional_embedding" in name:
    _lowercase: List[Any]						     =    name.replace('visual.positional_embedding'					,     'vision_model.embeddings.position_embedding.weight'							)
  if "visual.ln_pre" in name:
    _lowercase: Dict						     =    name.replace('visual.ln_pre'					,     'vision_model.pre_layrnorm'							)
  if "visual.ln_post" in name:
    _lowercase: Dict						     =    name.replace('visual.ln_post'					,     'vision_model.post_layernorm'							)
  # projection layers
  if "visual.proj" in name:
    _lowercase: List[str]						     =    name.replace('visual.proj'					,     'visual_projection.weight'							)
  if "text_projection" in name:
    _lowercase: Any						     =    name.replace('text_projection'					,     'text_projection.weight'							)
  # decoder
  if "trans_conv" in name:
    _lowercase: List[Any]						     =    name.replace('trans_conv'					,     'transposed_convolution'							)
  if "film_mul" in name or "film_add" in name or "reduce" in name or "transposed_convolution" in name:
    _lowercase: Any						     =    'decoder.' + name
  if "blocks" in name:
    _lowercase: Optional[int]						     =    name.replace('blocks'					,     'decoder.layers'							)
  if "linear1" in name:
    _lowercase: Optional[Any]						     =    name.replace('linear1'					,     'mlp.fc1'							)
  if "linear2" in name:
    _lowercase: int						     =    name.replace('linear2'					,     'mlp.fc2'							)
  if "norm1" in name and "layer_" not in name:
    _lowercase: Optional[int]						     =    name.replace('norm1'					,     'layer_norm1'							)
  if "norm2" in name and "layer_" not in name:
    _lowercase: Tuple						     =    name.replace('norm2'					,     'layer_norm2'							)

  return name


def 		_lowerCamelCase				(					UpperCamelCase					,     UpperCamelCase							)			->		Optional[Any]:





  """simple docstring"""





  for key in orig_state_dict.copy().keys():
    _lowercase: int						     =    orig_state_dict.pop(UpperCamelCase							)

    if key.startswith('clip_model'							) and "attn.in_proj" in key:
      _lowercase: Union[str, Any]						     =    key.split('.'							)
      if "visual" in key:
        _lowercase: List[str]						     =    int(key_split[4]							)
        _lowercase: Dict						     =    config.vision_config.hidden_size
        _lowercase: Union[str, Any]						     =    'vision_model'
      else:
        _lowercase: str						     =    int(key_split[3]							)
        _lowercase: Optional[Any]						     =    config.text_config.hidden_size
        _lowercase: List[str]						     =    'text_model'

      if "weight" in key:
        _lowercase: Optional[int]						     =    val[:dim, :]
        _lowercase: List[Any]						     =    val[
            dim : dim * 2, :
        ]
        _lowercase: Tuple						     =    val[-dim:, :]
      else:
        _lowercase: Any						     =    val[:dim]
        _lowercase: List[str]						     =    val[dim : dim * 2]
        _lowercase: Optional[int]						     =    val[-dim:]
    elif "self_attn" in key and "out_proj" not in key:
      _lowercase: str						     =    key.split('.'							)
      _lowercase: Union[str, Any]						     =    int(key_split[1]							)
      _lowercase: List[str]						     =    config.reduce_dim
      if "weight" in key:
        _lowercase: Union[str, Any]						     =    val[:dim, :]
        _lowercase: Tuple						     =    val[dim : dim * 2, :]
        _lowercase: Union[str, Any]						     =    val[-dim:, :]
      else:
        _lowercase: Union[str, Any]						     =    val[:dim]
        _lowercase: Dict						     =    val[dim : dim * 2]
        _lowercase: Any						     =    val[-dim:]
    else:
      _lowercase: Dict						     =    rename_key(UpperCamelCase							)
      if "visual_projection" in new_name or "text_projection" in new_name:
        _lowercase: str						     =    val.T
      _lowercase: Tuple						     =    val

  return orig_state_dict


def 		_lowerCamelCase				(					)			->		Optional[Any]:





  """simple docstring"""





  _lowercase: Any						     =    'http://images.cocodataset.org/val2017/000000039769.jpg'
  _lowercase: str						     =    Image.open(requests.get(UpperCamelCase					,     stream=UpperCamelCase							).raw							)
  return image


def 		_lowerCamelCase				(					UpperCamelCase					,     UpperCamelCase					,     UpperCamelCase					,     UpperCamelCase							)			->		Optional[int]:





  """simple docstring"""





  _lowercase: Optional[Any]						     =    get_clipseg_config(UpperCamelCase							)
  _lowercase: Optional[Any]						     =    CLIPSegForImageSegmentation(UpperCamelCase							)
  model.eval()

  _lowercase: Dict						     =    torch.load(UpperCamelCase					,     map_location='cpu'							)

  # remove some keys
  for key in state_dict.copy().keys():
    if key.startswith('model'							):
      state_dict.pop(UpperCamelCase					,     UpperCamelCase							)

    # rename some keys
  _lowercase: List[str]						     =    convert_state_dict(UpperCamelCase					,     UpperCamelCase							)
  _lowercase     ,					_lowercase: List[Any]						     =    model.load_state_dict(UpperCamelCase					,     strict=UpperCamelCase							)

  if missing_keys != ["clip.text_model.embeddings.position_ids", "clip.vision_model.embeddings.position_ids"]:
    raise ValueError('Missing keys that are not expected: {}'.format(UpperCamelCase							)							)
  if unexpected_keys != ["decoder.reduce.weight", "decoder.reduce.bias"]:
    raise ValueError(F'Unexpected keys: {unexpected_keys}'							)

  _lowercase: Tuple						     =    ViTImageProcessor(size=352							)
  _lowercase: Union[str, Any]						     =    CLIPTokenizer.from_pretrained('openai/clip-vit-base-patch32'							)
  _lowercase: Optional[Any]						     =    CLIPSegProcessor(image_processor=UpperCamelCase					,     tokenizer=UpperCamelCase							)

  _lowercase: Union[str, Any]						     =    prepare_img()
  _lowercase: List[Any]						     =    ['a glass', 'something to fill', 'wood', 'a jar']

  _lowercase: str						     =    processor(text=UpperCamelCase					,     images=[image] * len(UpperCamelCase							)					,     padding='max_length'					,     return_tensors='pt'							)

  with torch.no_grad():
    _lowercase: Optional[Any]						     =    model(**UpperCamelCase							)

  # verify values
  _lowercase: int						     =    torch.tensor([0.1_110, -0.1_882, 0.1_645]							)
  _lowercase: Tuple						     =    torch.tensor([0.2_692, -0.7_197, -0.1_328]							)
  if model_name == "clipseg-rd64-refined":
    _lowercase: str						     =    torch.tensor(
        [[-10.0_407, -9.9_431, -10.2_646], [-9.9_751, -9.7_064, -9.9_586], [-9.6_891, -9.5_645, -9.9_618]]							)
  elif model_name == "clipseg-rd64":
    _lowercase: Dict						     =    torch.tensor(
        [[-7.2_877, -7.2_711, -7.2_463], [-7.2_652, -7.2_780, -7.2_520], [-7.2_239, -7.2_204, -7.2_001]]							)
  elif model_name == "clipseg-rd16":
    _lowercase: Optional[int]						     =    torch.tensor(
        [[-6.3_955, -6.4_055, -6.4_151], [-6.3_911, -6.4_033, -6.4_100], [-6.3_474, -6.3_702, -6.3_762]]							)
  else:
    raise ValueError(F'Model name {model_name} not supported.'							)

  assert torch.allclose(outputs.logits[0, :3, :3]					,     UpperCamelCase					,     atol=1E-3							)
  assert torch.allclose(outputs.conditional_embeddings[0, :3]					,     UpperCamelCase					,     atol=1E-3							)
  assert torch.allclose(outputs.pooled_output[0, :3]					,     UpperCamelCase					,     atol=1E-3							)
  print('Looks ok!'							)

  if pytorch_dump_folder_path is not None:
    print(F'Saving model and processor to {pytorch_dump_folder_path}'							)
    model.save_pretrained(UpperCamelCase							)
    processor.save_pretrained(UpperCamelCase							)

  if push_to_hub:
    print(F'Pushing model and processor for {model_name} to the hub'							)
    model.push_to_hub(F'CIDAS/{model_name}'							)
    processor.push_to_hub(F'CIDAS/{model_name}'							)


if __name__ == "__main__":
   UpperCAmelCase__						:       int							=    argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       '--model_name',
       default='clipseg-rd64',
       type=str,
       choices=['clipseg-rd16', 'clipseg-rd64', 'clipseg-rd64-refined'],
       help=(
           'Name of the model. Supported models are: clipseg-rd64, clipseg-rd16 and clipseg-rd64-refined (rd meaning'
           ' reduce dimension)'
       ),
   )
   parser.add_argument(
       '--checkpoint_path',
       default='/Users/nielsrogge/Documents/CLIPSeg/clip_plus_rd64-uni.pth',
       type=str,
       help=(
           'Path to the original checkpoint. Note that the script assumes that the checkpoint includes both CLIP and'
           ' the decoder weights.'
       ),
   )
   parser.add_argument(
       '--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
   )
   parser.add_argument(
       '--push_to_hub', action='store_true', help='Whether or not to push the converted model to the 🤗 hub.'
   )

   UpperCAmelCase__						:       str							=    parser.parse_args()
   convert_clipseg_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)
