
from typing import TYPE_CHECKING

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


a_											=						{
    "configuration_clip": [
        "CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "CLIPConfig",
        "CLIPOnnxConfig",
        "CLIPTextConfig",
        "CLIPVisionConfig",
    ],
    "processing_clip": ["CLIPProcessor"],
    "tokenization_clip": ["CLIPTokenizer"],
}

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a_											=						["CLIPTokenizerFast"]

try:
  if not is_vision_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a_											=						["CLIPFeatureExtractor"]
  a_											=						["CLIPImageProcessor"]

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a_											=						[
      "CLIP_PRETRAINED_MODEL_ARCHIVE_LIST",
      "CLIPModel",
      "CLIPPreTrainedModel",
      "CLIPTextModel",
      "CLIPTextModelWithProjection",
      "CLIPVisionModel",
      "CLIPVisionModelWithProjection",
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a_											=						[
      "TF_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST",
      "TFCLIPModel",
      "TFCLIPPreTrainedModel",
      "TFCLIPTextModel",
      "TFCLIPVisionModel",
  ]

try:
  if not is_flax_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a_											=						[
      "FlaxCLIPModel",
      "FlaxCLIPPreTrainedModel",
      "FlaxCLIPTextModel",
      "FlaxCLIPTextPreTrainedModel",
      "FlaxCLIPVisionModel",
      "FlaxCLIPVisionPreTrainedModel",
  ]


if TYPE_CHECKING:
  from .configuration_clip import (
      CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
      CLIPConfig,
      CLIPOnnxConfig,
      CLIPTextConfig,
      CLIPVisionConfig,
  )
  from .processing_clip import CLIPProcessor
  from .tokenization_clip import CLIPTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_clip_fast import CLIPTokenizerFast

  try:
    if not is_vision_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .feature_extraction_clip import CLIPFeatureExtractor
    from .image_processing_clip import CLIPImageProcessor

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_clip import (
        CLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
        CLIPModel,
        CLIPPreTrainedModel,
        CLIPTextModel,
        CLIPTextModelWithProjection,
        CLIPVisionModel,
        CLIPVisionModelWithProjection,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_clip import (
        TF_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFCLIPModel,
        TFCLIPPreTrainedModel,
        TFCLIPTextModel,
        TFCLIPVisionModel,
    )

  try:
    if not is_flax_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_flax_clip import (
        FlaxCLIPModel,
        FlaxCLIPPreTrainedModel,
        FlaxCLIPTextModel,
        FlaxCLIPTextPreTrainedModel,
        FlaxCLIPVisionModel,
        FlaxCLIPVisionPreTrainedModel,
    )


else:
  import sys

  a_											=						_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)






'''simple docstring'''
import unittest
from pathlib import Path
from tempfile import TemporaryDirectory

from transformers import AutoConfig, TFAutoModel, is_tensorflow_text_available, is_tf_available
from transformers.models.bert.tokenization_bert import BertTokenizer
from transformers.testing_utils import require_tensorflow_text, require_tf, slow


if is_tf_available():
  import tensorflow as tf

if is_tensorflow_text_available():
  from transformers.models.bert import TFBertTokenizer


a_											=						["bert-base-uncased", "bert-base-cased"]
a_											=						"hf-internal-testing/tiny-bert-tf-only"

if is_tf_available():






  class    UpperCAmelCase_				(	tf.keras.Model					):





   def __init__(    self ,       lowercase_):
       super().__init__()
       snake_case_ :    Union[str, Any]     			=						tokenizer
       snake_case_ :    List[str]     			=						AutoConfig.from_pretrained(lowercase_)
       snake_case_ :    List[str]     			=						TFAutoModel.from_config(lowercase_)





   def        snake_case__		(    self ,       lowercase_):
       snake_case_ :    List[str]     			=						self.tokenizer(lowercase_)
       snake_case_ :    str     			=						self.bert(**lowercase_)
       return out["pooler_output"]






@require_tf
@require_tensorflow_text
class    UpperCAmelCase_				(	unittest.TestCase					):





 def        snake_case__		(    self):
     super().setUp()

     snake_case_ :    Dict     			=						[
         BertTokenizer.from_pretrained(lowercase_) for checkpoint in (TOKENIZER_CHECKPOINTS * 2)
     ]  # repeat for when fast_bert_tokenizer=false
     snake_case_ :    Tuple     			=						[TFBertTokenizer.from_pretrained(lowercase_) for checkpoint in TOKENIZER_CHECKPOINTS] + [
         TFBertTokenizer.from_pretrained(lowercase_ ,       use_fast_bert_tokenizer=lowercase_)
         for checkpoint in TOKENIZER_CHECKPOINTS
     ]
     assert len(self.tokenizers) == len(self.tf_tokenizers)

     snake_case_ :    Dict     			=						[
         "This is a straightforward English test sentence.",
         "This one has some weird characters\rto\nsee\r\nif  those\u00E9break things.",
         "Now we're going to add some Chinese: 一 二 三 一二三",
         "And some much more rare Chinese: 齉 堃 齉堃",
         "Je vais aussi écrire en français pour tester les accents",
         "Classical Irish also has some unusual characters, so in they go: Gaelaċ, ꝼ",
     ]
     snake_case_ :    str     			=						list(zip(self.test_sentences ,       self.test_sentences[::-1]))





 def        snake_case__		(    self):
     for tokenizer, tf_tokenizer in zip(self.tokenizers ,       self.tf_tokenizers):
         for test_inputs in (self.test_sentences, self.paired_sentences):
             snake_case_ :    Any     			=						tokenizer(lowercase_ ,       return_tensors="tf" ,       padding="longest")
             snake_case_ :    Union[str, Any]     			=						tf_tokenizer(lowercase_)

             for key in python_outputs.keys():
                 self.assertTrue(tf.reduce_all(python_outputs[key].shape == tf_outputs[key].shape))
                 self.assertTrue(tf.reduce_all(tf.cast(python_outputs[key] ,       tf.intaa) == tf_outputs[key]))





 @slow
 def        snake_case__		(    self):
     for tf_tokenizer in self.tf_tokenizers:
         snake_case_ :    Union[str, Any]     			=						tf_tokenizer(self.paired_sentences)
         snake_case_ :    str     			=						tf_tokenizer(
             text=[sentence[0] for sentence in self.paired_sentences] ,       text_pair=[sentence[1] for sentence in self.paired_sentences] ,       )
         for key in merged_outputs.keys():
             self.assertTrue(tf.reduce_all(tf.cast(merged_outputs[key] ,       tf.intaa) == separated_outputs[key]))





 @slow
 def        snake_case__		(    self):
     for tf_tokenizer in self.tf_tokenizers:
         snake_case_ :    int     			=						tf.function(lowercase_)
         for test_inputs in (self.test_sentences, self.paired_sentences):
             snake_case_ :    Optional[int]     			=						tf.constant(lowercase_)
             snake_case_ :    Tuple     			=						compiled_tokenizer(lowercase_)
             snake_case_ :    Any     			=						tf_tokenizer(lowercase_)

             for key in eager_outputs.keys():
                 self.assertTrue(tf.reduce_all(eager_outputs[key] == compiled_outputs[key]))





 @slow
 def        snake_case__		(    self):
     for tf_tokenizer in self.tf_tokenizers:
         snake_case_ :    List[Any]     			=						ModelToSave(tokenizer=lowercase_)
         snake_case_ :    Any     			=						tf.convert_to_tensor(self.test_sentences)
         snake_case_ :    List[Any]     			=						model(lowercase_)  # Build model with some sample inputs
         with TemporaryDirectory() as tempdir:
             snake_case_ :    Optional[Any]     			=						Path(lowercase_) / "saved.model"
             model.save(lowercase_)
             snake_case_ :    Tuple     			=						tf.keras.models.load_model(lowercase_)
         snake_case_ :    str     			=						loaded_model(lowercase_)
         # We may see small differences because the loaded model is compiled, so we need an epsilon for the test
         self.assertLessEqual(tf.reduce_max(tf.abs(out - loaded_output)) ,       1E-5)




