from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__lowercase							 =					{'''configuration_xglm''': ['''XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''XGLMConfig''']}

try:
    if not is_sentencepiece_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __lowercase							 =					['''XGLMTokenizer''']

try:
    if not is_tokenizers_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __lowercase							 =					['''XGLMTokenizerFast''']

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __lowercase							 =					[
        '''XGLM_PRETRAINED_MODEL_ARCHIVE_LIST''',
        '''XGLMForCausalLM''',
        '''XGLMModel''',
        '''XGLMPreTrainedModel''',
    ]


try:
    if not is_flax_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __lowercase							 =					[
        '''FlaxXGLMForCausalLM''',
        '''FlaxXGLMModel''',
        '''FlaxXGLMPreTrainedModel''',
    ]


try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __lowercase							 =					[
        '''TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST''',
        '''TFXGLMForCausalLM''',
        '''TFXGLMModel''',
        '''TFXGLMPreTrainedModel''',
    ]


if TYPE_CHECKING:
    from .configuration_xglm import XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XGLMConfig

    try:
        if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_xglm import XGLMTokenizer

    try:
        if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_xglm_fast import XGLMTokenizerFast

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_xglm import XGLM_PRETRAINED_MODEL_ARCHIVE_LIST, XGLMForCausalLM, XGLMModel, XGLMPreTrainedModel

    try:
        if not is_flax_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_flax_xglm import FlaxXGLMForCausalLM, FlaxXGLMModel, FlaxXGLMPreTrainedModel

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_xglm import (
            TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST,
            TFXGLMForCausalLM,
            TFXGLMModel,
            TFXGLMPreTrainedModel,
        )


else:
    import sys

    __lowercase							 =					_LazyModule(__name__, globals()['''__file__'''], _import_structure)









'''simple docstring'''

import inspect
import unittest
from typing import List

import numpy as np

from transformers import EfficientFormerConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
     import tensorflow as tf

     from transformers import (
         TFEfficientFormerForImageClassification,
         TFEfficientFormerForImageClassificationWithTeacher,
         TFEfficientFormerModel,
     )
     from transformers.models.efficientformer.modeling_tf_efficientformer import (
         TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
     )


if is_vision_available():
     from PIL import Image

     from transformers import EfficientFormerImageProcessor





class      snake_case    :





  """simple docstring"""




  def __init__(    self			,  UpperCamelCase			,  UpperCamelCase = 13			,  UpperCamelCase = 64			,  UpperCamelCase = 2			,  UpperCamelCase = 3			,  UpperCamelCase = 3			,  UpperCamelCase = True			,  UpperCamelCase = True			,  UpperCamelCase = 128			,  UpperCamelCase=[16, 32, 64, 128]			,  UpperCamelCase = 7			,  UpperCamelCase = 4			,  UpperCamelCase = 37			,  UpperCamelCase = "gelu"			,  UpperCamelCase = 0.1			,  UpperCamelCase = 0.1			,  UpperCamelCase = 10			,  UpperCamelCase = 0.02			,  UpperCamelCase = 2			,  UpperCamelCase = 1			,  UpperCamelCase = 128			,  UpperCamelCase = [2, 2, 2, 2]			,  UpperCamelCase = 2			,  UpperCamelCase = 2			,  ):







   """simple docstring"""
   lowerCamelCase_      =  parent
   lowerCamelCase_      =  batch_size
   lowerCamelCase_      =  image_size
   lowerCamelCase_      =  patch_size
   lowerCamelCase_      =  num_channels
   lowerCamelCase_      =  is_training
   lowerCamelCase_      =  use_labels
   lowerCamelCase_      =  hidden_size
   lowerCamelCase_      =  num_hidden_layers
   lowerCamelCase_      =  num_attention_heads
   lowerCamelCase_      =  intermediate_size
   lowerCamelCase_      =  hidden_act
   lowerCamelCase_      =  hidden_dropout_prob
   lowerCamelCase_      =  attention_probs_dropout_prob
   lowerCamelCase_      =  type_sequence_label_size
   lowerCamelCase_      =  initializer_range
   lowerCamelCase_      =  encoder_stride
   lowerCamelCase_      =  num_attention_outputs
   lowerCamelCase_      =  embed_dim
   lowerCamelCase_      =  embed_dim + 1
   lowerCamelCase_      =  resolution
   lowerCamelCase_      =  depths
   lowerCamelCase_      =  hidden_sizes
   lowerCamelCase_      =  dim
   lowerCamelCase_      =  mlp_expansion_ratio






  def   snake_case		(    self     ):







   """simple docstring"""
   lowerCamelCase_      =  floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]     )

   lowerCamelCase_      =  None
   if self.use_labels:
    lowerCamelCase_      =  ids_tensor([self.batch_size]			,  self.type_sequence_label_size     )

   lowerCamelCase_      =  self.get_config()

   return config, pixel_values, labels






  def   snake_case		(    self     ):







   """simple docstring"""
   return EfficientFormerConfig(
       image_size=self.image_size			,  patch_size=self.patch_size			,  num_channels=self.num_channels			,  hidden_size=self.hidden_size			,  num_hidden_layers=self.num_hidden_layers			,  num_attention_heads=self.num_attention_heads			,  intermediate_size=self.intermediate_size			,  hidden_act=self.hidden_act			,  hidden_dropout_prob=self.hidden_dropout_prob			,  attention_probs_dropout_prob=self.attention_probs_dropout_prob			,  is_decoder=UpperCamelCase			,  initializer_range=self.initializer_range			,  encoder_stride=self.encoder_stride			,  resolution=self.resolution			,  depths=self.depths			,  hidden_sizes=self.hidden_sizes			,  dim=self.dim			,  mlp_expansion_ratio=self.mlp_expansion_ratio			,  )






  def   snake_case		(    self			,  UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase     ):







   """simple docstring"""
   lowerCamelCase_      =  TFEfficientFormerModel(config=UpperCamelCase     )
   lowerCamelCase_      =  model(UpperCamelCase			,  training=UpperCamelCase     )
   self.parent.assertEqual(result.last_hidden_state.shape			,  (self.batch_size, self.seq_length, self.hidden_size)     )






  def   snake_case		(    self			,  UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase     ):







   """simple docstring"""
   lowerCamelCase_      =  self.type_sequence_label_size
   lowerCamelCase_      =  TFEfficientFormerForImageClassification(UpperCamelCase     )
   lowerCamelCase_      =  model(UpperCamelCase			,  labels=UpperCamelCase			,  training=UpperCamelCase     )
   self.parent.assertEqual(result.logits.shape			,  (self.batch_size, self.type_sequence_label_size)     )

   # test greyscale images
   lowerCamelCase_      =  1
   lowerCamelCase_      =  TFEfficientFormerForImageClassification(UpperCamelCase     )

   lowerCamelCase_      =  floats_tensor([self.batch_size, 1, self.image_size, self.image_size]     )
   lowerCamelCase_      =  model(UpperCamelCase			,  labels=UpperCamelCase     )
   self.parent.assertEqual(result.logits.shape			,  (self.batch_size, self.type_sequence_label_size)     )







  def   snake_case		(    self     ):







   """simple docstring"""
   lowerCamelCase_      =  self.prepare_config_and_inputs()
   lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_      =  config_and_inputs
   lowerCamelCase_      =  {"pixel_values": pixel_values}
   return config, inputs_dict





@require_tf
class      snake_case    (    lowercase			,						lowercase			,						unittest.TestCase			):





  """simple docstring"""




  _lowerCamelCase		    =  (
      (
          TFEfficientFormerModel,
          TFEfficientFormerForImageClassificationWithTeacher,
          TFEfficientFormerForImageClassification,
      )
      if is_tf_available()
      else ()
  )
  _lowerCamelCase		    =  (
      {
          "feature-extraction": TFEfficientFormerModel,
          "image-classification": (
              TFEfficientFormerForImageClassification,
              TFEfficientFormerForImageClassificationWithTeacher,
          ),
      }
      if is_tf_available()
      else {}
  )

  _lowerCamelCase		    =  False

  _lowerCamelCase		    =  False
  _lowerCamelCase		    =  False
  _lowerCamelCase		    =  False
  _lowerCamelCase		    =  False






  def   snake_case		(    self     ):







   """simple docstring"""
   lowerCamelCase_      =  TFEfficientFormerModelTester(self     )
   lowerCamelCase_      =  ConfigTester(
       self			,  config_class=UpperCamelCase			,  has_text_modality=UpperCamelCase			,  hidden_size=37     )






  def   snake_case		(    self     ):







   """simple docstring"""
   self.config_tester.run_common_tests()






  @unittest.skip(reason="EfficientFormer does not use inputs_embeds"     )
  def   snake_case		(    self     ):







   """simple docstring"""
   pass






  @unittest.skip(reason="EfficientFormer does not support input and output embeddings"     )
  def   snake_case		(    self     ):







   """simple docstring"""
   pass






  def   snake_case		(    self     ):







   """simple docstring"""
   lowerCamelCase_ ,lowerCamelCase_      =  self.model_tester.prepare_config_and_inputs_for_common()

   for model_class in self.all_model_classes:
    lowerCamelCase_      =  model_class(UpperCamelCase     )
    lowerCamelCase_      =  inspect.signature(model.call     )
    # signature.parameters is an OrderedDict => so arg_names order is deterministic
    lowerCamelCase_      =  [*signature.parameters.keys()]

    lowerCamelCase_      =  ["pixel_values"]
    self.assertListEqual(arg_names[:1]			,  UpperCamelCase     )






  def   snake_case		(    self     ):







   """simple docstring"""
   def check_hidden_states_output(UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase     ):
    lowerCamelCase_      =  model_class(UpperCamelCase     )

    lowerCamelCase_      =  model(**self._prepare_for_class(UpperCamelCase			,  UpperCamelCase     )			,  training=UpperCamelCase     )
    lowerCamelCase_      =  outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

    lowerCamelCase_      =  getattr(
        self.model_tester			,  "expected_num_hidden_layers"			,  self.model_tester.num_hidden_layers + 1     )
    self.assertEqual(len(UpperCamelCase     )			,  UpperCamelCase     )

    if hasattr(self.model_tester			,  "encoder_seq_length"     ):
     lowerCamelCase_      =  self.model_tester.encoder_seq_length
     if hasattr(self.model_tester			,  "chunk_length"     ) and self.model_tester.chunk_length > 1:
      lowerCamelCase_      =  seq_length * self.model_tester.chunk_length
    else:
     lowerCamelCase_      =  self.model_tester.seq_length

    self.assertListEqual(
        list(hidden_states[-1].shape[-2:]     )			,  [seq_length, self.model_tester.hidden_size]			,  )

    if config.is_encoder_decoder:
     lowerCamelCase_      =  outputs.decoder_hidden_states

     self.asseretIsInstance(UpperCamelCase			,  (list, tuple)     )
     self.assertEqual(len(UpperCamelCase     )			,  UpperCamelCase     )
     lowerCamelCase_      =  getattr(self.model_tester			,  "seq_length"			,  UpperCamelCase     )
     lowerCamelCase_      =  getattr(self.model_tester			,  "decoder_seq_length"			,  UpperCamelCase     )

     self.assertListEqual(
         list(hidden_states[-1].shape[-2:]     )			,  [decoder_seq_length, self.model_tester.hidden_size]			,  )

   lowerCamelCase_ ,lowerCamelCase_      =  self.model_tester.prepare_config_and_inputs_for_common()

   for model_class in self.all_model_classes:
    lowerCamelCase_      =  True
    check_hidden_states_output(UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase     )

    # check that output_hidden_states also work using config
    del inputs_dict["output_hidden_states"]
    lowerCamelCase_      =  True

    check_hidden_states_output(UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase     )






  def   snake_case		(    self			,  UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase=False     ):







   """simple docstring"""
   lowerCamelCase_      =  super()._prepare_for_class(UpperCamelCase			,  UpperCamelCase			,  return_labels=UpperCamelCase     )

   if return_labels:
    if model_class.__name__ == "TFEfficientFormerForImageClassificationWithTeacher":
     del inputs_dict["labels"]

   return inputs_dict






  def   snake_case		(    self     ):







   """simple docstring"""
   lowerCamelCase_      =  self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_model(*UpperCamelCase     )






  @unittest.skip(reason="EfficientFormer does not implement masked image modeling yet"     )
  def   snake_case		(    self     ):







   """simple docstring"""
   lowerCamelCase_      =  self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_for_masked_image_modeling(*UpperCamelCase     )






  def   snake_case		(    self     ):







   """simple docstring"""
   lowerCamelCase_      =  self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_for_image_classification(*UpperCamelCase     )






  @slow
  def   snake_case		(    self     ):







   """simple docstring"""
   for model_name in TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
    lowerCamelCase_      =  TFEfficientFormerModel.from_pretrained(UpperCamelCase     )
    self.assertIsNotNone(UpperCamelCase     )






  def   snake_case		(    self     ):







   """simple docstring"""
   lowerCamelCase_ ,lowerCamelCase_      =  self.model_tester.prepare_config_and_inputs_for_common()
   lowerCamelCase_      =  True

   lowerCamelCase_      =  getattr(self.model_tester			,  "seq_length"			,  UpperCamelCase     )
   lowerCamelCase_      =  getattr(self.model_tester			,  "encoder_seq_length"			,  UpperCamelCase     )
   lowerCamelCase_      =  getattr(self.model_tester			,  "key_length"			,  UpperCamelCase     )
   lowerCamelCase_      =  getattr(self.model_tester			,  "chunk_length"			,  UpperCamelCase     )

   if chunk_length is not None and hasattr(self.model_tester			,  "num_hashes"     ):
    lowerCamelCase_      =  encoder_seq_length * self.model_tester.num_hashes

   for model_class in self.all_model_classes:
    lowerCamelCase_      =  True
    lowerCamelCase_      =  False
    lowerCamelCase_      =  True
    lowerCamelCase_      =  model_class(UpperCamelCase     )

    lowerCamelCase_      =  model(**self._prepare_for_class(UpperCamelCase			,  UpperCamelCase     )			,  training=UpperCamelCase     )
    lowerCamelCase_      =  outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
    self.assertEqual(len(UpperCamelCase     )			,  self.model_tester.num_attention_outputs     )

    # check that output_attentions also work using config
    del inputs_dict["output_attentions"]
    lowerCamelCase_      =  True
    lowerCamelCase_      =  model_class(UpperCamelCase     )
    lowerCamelCase_      =  model(**self._prepare_for_class(UpperCamelCase			,  UpperCamelCase     )			,  training=UpperCamelCase     )

    lowerCamelCase_      =  outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
    self.assertEqual(len(UpperCamelCase     )			,  self.model_tester.num_attention_outputs     )

    if chunk_length is not None:
     self.assertListEqual(
         list(attentions[0].shape[-4:]     )			,  [self.model_tester.num_attention_heads, encoder_seq_length, chunk_length, encoder_key_length]			,  )
    else:
     self.assertListEqual(
         list(attentions[0].shape[-3:]     )			,  [self.model_tester.num_attention_heads, encoder_seq_length, encoder_key_length]			,  )







  def   snake_case		(    self     ):







   """simple docstring"""
   # We use a simplified version of this test for EfficientFormer because it requires training=False
   # and Keras refuses to let us force that during functional construction
   lowerCamelCase_ ,lowerCamelCase_      =  self.model_tester.prepare_config_and_inputs_for_common()

   for model_class in self.all_model_classes:
    # Prepare our model
    lowerCamelCase_      =  model_class(UpperCamelCase     )
    # These are maximally general inputs for the model, with multiple None dimensions
    # Hopefully this will catch any conditionals that fail for flexible shapes
    lowerCamelCase_      =  {
        key: tf.keras.Input(shape=val.shape[1:]			,  dtype=val.dtype			,  name=UpperCamelCase     )
        for key, val in model.input_signature.items()
        if key in model.dummy_inputs
    }
    lowerCamelCase_      =  model(UpperCamelCase     )
    self.assertTrue(outputs_dict is not None     )





def __snake_case					(      ):
 lowerCamelCase_      =  Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"		)
 return image







@require_tf
@require_vision
class      snake_case    (    unittest.TestCase			):





  """simple docstring"""




  @cached_property
  def   snake_case		(    self     ):







   """simple docstring"""
   return (
       EfficientFormerImageProcessor.from_pretrained("snap-research/efficientformer-l1-300"     )
       if is_vision_available()
       else None
   )






  @slow
  def   snake_case		(    self     ):







   """simple docstring"""
   lowerCamelCase_      =  TFEfficientFormerForImageClassification.from_pretrained("snap-research/efficientformer-l1-300"     )
   lowerCamelCase_      =  self.default_image_processor
   lowerCamelCase_      =  prepare_img()
   lowerCamelCase_      =  image_processor(images=UpperCamelCase			,  return_tensors="tf"     )
   # forward pass
   lowerCamelCase_      =  model(**UpperCamelCase			,  training=UpperCamelCase     )
   # verify the logits
   lowerCamelCase_      =  tf.TensorShape((1, 1000)     )
   self.assertEqual(outputs.logits.shape			,  UpperCamelCase     )
   lowerCamelCase_      =  tf.constant([-0.0_555, 0.4_825, -0.0_852]     )
   self.assertTrue(np.allclose(outputs.logits[0, :3]			,  UpperCamelCase			,  atol=1e-4     )     )







  @slow
  def   snake_case		(    self     ):







   """simple docstring"""
   lowerCamelCase_      =  TFEfficientFormerForImageClassificationWithTeacher.from_pretrained(
       "snap-research/efficientformer-l1-300"     )
   lowerCamelCase_      =  self.default_image_processor
   lowerCamelCase_      =  prepare_img()
   lowerCamelCase_      =  image_processor(images=UpperCamelCase			,  return_tensors="tf"     )
   # forward pass
   lowerCamelCase_      =  model(**UpperCamelCase			,  training=UpperCamelCase     )
   # verify the logits
   lowerCamelCase_      =  tf.TensorShape((1, 1000)     )
   self.assertEqual(outputs.logits.shape			,  UpperCamelCase     )
   lowerCamelCase_      =  tf.constant([-0.1_312, 0.4_353, -1.0_499]     )
   self.assertTrue(np.allclose(outputs.logits[0, :3]			,  UpperCamelCase			,  atol=1e-4     )     )



