'''simple docstring'''




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				:		int       			=		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				:		Tuple       			=		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   _lowercase:
   __lowerCamelCase    =  None
   __lowerCamelCase    =  ()
   __lowerCamelCase    =  ()
   __lowerCamelCase    =  True
   __lowerCamelCase    =  True
   __lowerCamelCase    =  True
   __lowerCamelCase    =  False





   def 			snake_case	(							self:    List[Any]			,a:    Optional[Any]			,a:    Any			,a:    int=False	):
    __UpperCAmelCase         =					copy.deepcopy(a	)

    if model_class in get_values(a	):
     __UpperCAmelCase         =					{
         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	):
      __UpperCAmelCase         =					tf.ones(self.model_tester.batch_size			,dtype=tf.intaa	)
     elif model_class in get_values(a	):
      __UpperCAmelCase         =					tf.zeros(self.model_tester.batch_size			,dtype=tf.intaa	)
      __UpperCAmelCase         =					tf.zeros(self.model_tester.batch_size			,dtype=tf.intaa	)
     elif model_class in [
         *get_values(a	),
         *get_values(a	),
     ]:
      __UpperCAmelCase         =					tf.zeros(self.model_tester.batch_size			,dtype=tf.intaa	)
     elif model_class in get_values(a	):
      __UpperCAmelCase         =					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	),
     ]:
      __UpperCAmelCase         =					tf.zeros(
          (self.model_tester.batch_size, self.model_tester.seq_length)			,dtype=tf.intaa	)
    return inputs_dict





   @slow
   def 			snake_case	(							self:    Dict	):
    __UpperCAmelCase         =					self.model_tester.prepare_config_and_inputs_for_common()
    for model_class in self.all_model_classes[:2]:
     __UpperCAmelCase         =					self._prepare_for_class(a			,a	)
     __UpperCAmelCase         =					model_class(a	)

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

     __UpperCAmelCase         =					run_in_graph_mode()
     self.assertIsNotNone(a	)





   @slow
   def 			snake_case	(							self:    Dict	):
    __UpperCAmelCase         =					self.model_tester.prepare_config_and_inputs_for_common()
    for model_class in self.all_model_classes[:2]:
     __UpperCAmelCase         =					self._prepare_for_class(a			,a	)
     __UpperCAmelCase         =					model_class(a	)

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

     __UpperCAmelCase         =					run_in_graph_mode()
     self.assertIsNotNone(a	)





   @slow
   def 			snake_case	(							self:    Tuple	):
    __UpperCAmelCase         =					self.model_tester.prepare_config_and_inputs_for_common()
    for model_class in self.all_model_classes[:2]:
     __UpperCAmelCase         =					model_class(a	)
     if getattr(a			,'hf_compute_loss'			,a	):
      # Test that model correctly compute the loss with kwargs
      __UpperCAmelCase         =					self._prepare_for_class(inputs_dict.copy()			,a			,return_labels=a	)
      # Is there a better way to remove these decoder inputs?
      __UpperCAmelCase         =					{
          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")
      }

      __UpperCAmelCase         =					{
          "labels",
          "label",
          "label_ids",
          "start_positions",
          "start_position",
          "end_positions",
          "end_position",
          "next_sentence_label",
      }
      __UpperCAmelCase         =					possible_label_cols.intersection(set(a	)	)
      self.assertGreater(len(a	)			,0			,msg='No matching label names found!'	)
      __UpperCAmelCase         =					{key: val for key, val in prepared_for_class.items() if key in label_names}
      __UpperCAmelCase         =					{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
      __UpperCAmelCase         =					model.fit(
          a			,validation_data=a			,steps_per_epoch=1			,validation_steps=1			,shuffle=a			,verbose=0			,)
      __UpperCAmelCase         =					history.history["loss"][0]
      self.assertTrue(not isnan(a	)	)
      __UpperCAmelCase         =					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.
      __UpperCAmelCase         =					model_class(a	)
      model.compile(optimizer=tf.keras.optimizers.SGD(0.0	)			,jit_compile=a	)
      __UpperCAmelCase         =					model.fit(
          a			,a			,validation_data=(inputs_minus_labels, labels)			,steps_per_epoch=1			,validation_steps=1			,shuffle=a			,verbose=0			,)

      __UpperCAmelCase         =					history.history["loss"][0]
      self.assertTrue(not isnan(a	)	)
      __UpperCAmelCase         =					history.history["val_loss"][0]
      self.assertTrue(not isnan(a	)	)





   @slow
   def 			snake_case	(							self:    str	):
    __UpperCAmelCase         =					self.model_tester.prepare_config_and_inputs_for_common()
    __UpperCAmelCase         =					True
    __UpperCAmelCase         =					True

    if hasattr(a			,'use_cache'	):
     __UpperCAmelCase         =					True

    __UpperCAmelCase         =					getattr(self.model_tester			,'encoder_seq_length'			,self.model_tester.seq_length	)
    __UpperCAmelCase         =					getattr(self.model_tester			,'key_length'			,a	)

    for model_class in self.all_model_classes[:2]:
     __UpperCAmelCase         =					self._prepare_for_class(a			,a	)
     __UpperCAmelCase         =					model_class(a	)
     model.build()
     __UpperCAmelCase         =					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:
       __UpperCAmelCase         =					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	)
      __UpperCAmelCase         =					os.path.join(a			,'saved_model'			,'1'	)
      __UpperCAmelCase         =					tf.keras.models.load_model(a	)
      __UpperCAmelCase         =					model(a	)

      if self.is_encoder_decoder:
       __UpperCAmelCase         =					outputs["encoder_hidden_states"]
       __UpperCAmelCase         =					outputs["encoder_attentions"]
      else:
       __UpperCAmelCase         =					outputs["hidden_states"]
       __UpperCAmelCase         =					outputs["attentions"]

      self.assertEqual(len(a	)			,a	)

      __UpperCAmelCase         =					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 			snake_case	(							self:    int	):
    tf.keras.mixed_precision.set_global_policy('mixed_float16'	)

    # try/finally block to ensure subsequent tests run in float32
    try:
     __UpperCAmelCase         =					self.model_tester.prepare_config_and_inputs_for_common()
     for model_class in self.all_model_classes[:2]:
      __UpperCAmelCase         =					self._prepare_for_class(a			,a	)
      __UpperCAmelCase         =					model_class(a	)
      __UpperCAmelCase         =					model(a	)

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





   @slow
   def 			snake_case	(							self:    int	):
    __UpperCAmelCase         =					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"]
    __UpperCAmelCase         =					{
        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
      __UpperCAmelCase         =					TFSharedEmbeddings(self.model_tester.vocab_size			,self.model_tester.hidden_size			,name='shared'	)
      __UpperCAmelCase         =					False
      __UpperCAmelCase         =					main_layer_class(a			,embed_tokens=a	)
     else:
      __UpperCAmelCase         =					main_layer_class(a	)

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

     if hasattr(self.model_tester			,'num_labels'	):
      __UpperCAmelCase         =					self.model_tester.num_labels
     else:
      __UpperCAmelCase         =					2

     __UpperCAmelCase         =					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	)

     __UpperCAmelCase         =					main_layer(a	)[0]
     __UpperCAmelCase         =					tf.keras.layers.Dense(a			,activation='softmax'			,name='outputs'	)(a	)
     __UpperCAmelCase         =					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:
      __UpperCAmelCase         =					os.path.join(a			,'keras_model.h5'	)
      model.save(a	)
      if "T5" in main_layer_class.__name__:
       __UpperCAmelCase         =					tf.keras.models.load_model(
           a			,custom_objects={
               main_layer_class.__name__: main_layer_class,
               'TFSharedEmbeddings': TFSharedEmbeddings,
           }			,)
      else:
       __UpperCAmelCase         =					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 			snake_case	(							self:    List[Any]	):
    __UpperCAmelCase         =					self.model_tester.prepare_config_and_inputs_for_common()

    for model_class in self.all_model_classes[:2]:
     __UpperCAmelCase         =					model_class(a	)

     __UpperCAmelCase         =					copy.deepcopy(a	)

     if not self.is_encoder_decoder:
      __UpperCAmelCase         =					inputs["input_ids"]
      del inputs["input_ids"]
     else:
      __UpperCAmelCase         =					inputs["input_ids"]
      __UpperCAmelCase         =					inputs.get('decoder_input_ids'			,a	)
      del inputs["input_ids"]
      inputs.pop('decoder_input_ids'			,a	)

     if not self.is_encoder_decoder:
      __UpperCAmelCase         =					model.get_input_embeddings()(a	)
     else:
      __UpperCAmelCase         =					model.get_input_embeddings()(a	)
      __UpperCAmelCase         =					model.get_input_embeddings()(a	)

     __UpperCAmelCase         =					self._prepare_for_class(a			,a	)

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

     __UpperCAmelCase         =					run_in_graph_mode()
     self.assertIsNotNone(a	)





   def 			snake_case	(							self:    Union[str, Any]			,a:    List[str]			,a:    List[str]	):
    __UpperCAmelCase         =					[]
    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
    __UpperCAmelCase         =					[]
    while len(a	) < num_bad_tokens:
     __UpperCAmelCase         =					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 			snake_case	(							self:    List[Any]			,a:    Dict	):







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





   def 			snake_case	(							self:    Any			,a:    Optional[int]			,a:    Dict	):







    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




'''simple docstring'''




from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_UpperCamelCase				:		Any       			=		logging.get_logger(__name__)

_UpperCamelCase				:		Any       			=		{
    'apple/mobilevitv2-1.0': 'https://huggingface.co/apple/mobilevitv2-1.0/resolve/main/config.json',
}

class   _lowercase(     _lowerCamelCase				):



   """simple docstring"""





   __lowerCamelCase    =  '''mobilevitv2'''





   def __init__(							self:    Union[str, Any]			,a:    Optional[int]=3			,a:    Any=256			,a:    str=2			,a:    Any=2.0			,a:    Union[str, Any]="swish"			,a:    List[str]=3			,a:    List[str]=32			,a:    int=0.1			,a:    List[Any]=0.02			,a:    List[Any]=1e-5			,a:    Tuple=512			,a:    Optional[Any]=[6, 12, 18]			,a:    Dict=0.1			,a:    List[Any]=255			,a:    Union[str, Any]=[2, 4, 3]			,a:    str=[128, 192, 256]			,a:    int=1.0			,a:    Optional[int]=2			,a:    Optional[Any]=0.0			,a:    int=0.0			,**a:    Optional[Any]			,):
    super().__init__(**a	)

    __UpperCAmelCase         =					num_channels
    __UpperCAmelCase         =					image_size
    __UpperCAmelCase         =					patch_size
    __UpperCAmelCase         =					expand_ratio
    __UpperCAmelCase         =					hidden_act
    __UpperCAmelCase         =					conv_kernel_size
    __UpperCAmelCase         =					output_stride
    __UpperCAmelCase         =					initializer_range
    __UpperCAmelCase         =					layer_norm_eps
    __UpperCAmelCase         =					n_attn_blocks
    __UpperCAmelCase         =					base_attn_unit_dims
    __UpperCAmelCase         =					width_multiplier
    __UpperCAmelCase         =					ffn_multiplier
    __UpperCAmelCase         =					ffn_dropout
    __UpperCAmelCase         =					attn_dropout
    __UpperCAmelCase         =					classifier_dropout_prob

    # decode head attributes for semantic segmentation
    __UpperCAmelCase         =					aspp_out_channels
    __UpperCAmelCase         =					atrous_rates
    __UpperCAmelCase         =					aspp_dropout_prob
    __UpperCAmelCase         =					semantic_loss_ignore_index




class   _lowercase(     _lowerCamelCase				):



   """simple docstring"""





   __lowerCamelCase    =  version.parse('''1.11'''				)





   @property
   def 			snake_case	(							self:    Optional[int]	):
    return OrderedDict([('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'})]	)





   @property
   def 			snake_case	(							self:    Tuple	):
    if self.task == "image-classification":
     return OrderedDict([('logits', {0: 'batch'})]	)
    else:
     return OrderedDict([('last_hidden_state', {0: 'batch'}), ('pooler_output', {0: 'batch'})]	)





   @property
   def 			snake_case	(							self:    Union[str, Any]	):
    return 1e-4



