


from collections import Counter
import datasets
import transformers
from transformers.convert_slow_tokenizer import SLOW_TO_FAST_CONVERTERS

from transformers.utils import logging

logging.set_verbosity_info()

A :       Union[str, Any]						=   {
    name: (getattr(transformers, name), getattr(transformers, name + "Fast")) for name in SLOW_TO_FAST_CONVERTERS
}

A :       str						=   datasets.load_dataset("xnli", split="test+validation")

A :       Tuple						=   0
A :       Dict						=   0
A :       str						=   0
A :       Union[str, Any]						=   0

def  lowercase_						(						_A     :       int       ,   _A     :       Union[str, Any]       ,   _A     :       str       ,   _A     :       int		):

      """simple docstring"""




      if spm_diff == list(reversed(_A		)		):
            # AAA -> AA+A vs A+AA case.
            return True
      elif len(_A		) == len(_A		) and fast.decode(_A		) == fast.decode(_A		):
            # Second order OK
            # Barrich -> Barr + ich vs Bar + rich
            return True
      lowerCamelCase__						:							List[str]       =    slow.encode(slow.decode(_A		)		)
      lowerCamelCase__						:							List[Any]       =    fast.encode(fast.decode(_A		)		)
      if spm_reencoded != spm_diff and spm_reencoded == tok_reencoded:
            # Type 3 error.
            # Snehagatha ->
            #       Sne, h, aga, th, a
            #       Sne, ha, gat, ha
            # Encoding the wrong with sp does not even recover what spm gave us
            # It fits tokenizer however...
            return True
      return False

def  lowercase_						(						_A     :       Union[str, Any]       ,   _A     :       List[Any]       ,   _A     :       Dict		):

      """simple docstring"""




      lowerCamelCase__						:							Optional[int]       =    fast.encode_plus(_A		)[0]
      lowerCamelCase__						:							Tuple       =    enc.offsets
      lowerCamelCase__       ,  lowerCamelCase__						:							Optional[int]       =    offsets[idx], offsets[idx - 1]
      if curr is not None and line[curr[0] : curr[1]] == "\u200f":
            return True
      if prev is not None and line[prev[0] : prev[1]] == "\u200f":
            return True

def  lowercase_						(						_A     :       int       ,   _A     :       Optional[Any]       ,   _A     :       Tuple       ,   _A     :       Any       ,   _A     :       Optional[int]		):

      """simple docstring"""




      for i, (spm_id, tok_id) in enumerate(zip(_A       ,   _A		)		):
            if spm_id != tok_id:
                  break
      lowerCamelCase__						:							Tuple       =    i
      for i, (spm_id, tok_id) in enumerate(zip(reversed(_A		)       ,   reversed(_A		)		)		):
            if spm_id != tok_id:
                  break
      lowerCamelCase__						:							List[str]       =    len(_A		) - i

      lowerCamelCase__						:							List[str]       =    spm_ids[first:last]
      lowerCamelCase__						:							List[str]       =    tok_ids[first:last]

      if check_diff(_A       ,   _A       ,   _A       ,   _A		):
            return True

      if check_LTR_mark(_A       ,   _A       ,   _A		):
            return True

      if last - first > 5:
            # We might have twice a single problem, attempt to subdivide the disjointed tokens into smaller problems
            lowerCamelCase__						:							Dict       =    Counter(spm_ids[first:last]		)
            lowerCamelCase__						:							Tuple       =    Counter(tok_ids[first:last]		)

            lowerCamelCase__						:							Any       =    {spm_ for (spm_, si) in spms.items() if toks.get(spm_       ,   0		) == si}
            lowerCamelCase__						:							Any       =    3
            for i in range(last - first - min_width		):
                  if all(spm_ids[first + i + j] in removable_tokens for j in range(_A		)		):
                        lowerCamelCase__						:							Optional[int]       =    [
                            k
                            for k in range(last - first - min_width		)
                            if tok_ids[first + k : first + k + min_width] == spm_ids[first + i : first + i + min_width]
                        ]
                        for j in possible_matches:
                              if check_diff(spm_ids[first : first + i]       ,   tok_ids[first : first + j]       ,   _A       ,   _A		) and check_details(
                                  _A       ,   spm_ids[first + i : last]       ,   tok_ids[first + j : last]       ,   _A       ,   _A       ,   ):
                                    return True

      print(F"Spm: {[fast.decode([spm_ids[i]]		) for i in range(_A       ,   _A		)]}"		)
      try:
            print(F"Tok: {[fast.decode([tok_ids[i]]		) for i in range(_A       ,   _A		)]}"		)
      except Exception:
            pass

      lowerCamelCase__						:							Optional[Any]       =    fast.decode(spm_ids[:first]		)
      lowerCamelCase__						:							Optional[int]       =    fast.decode(spm_ids[last:]		)
      lowerCamelCase__						:							int       =    fast.decode(spm_ids[first:last]		)
      print()
      print(_A		)
      return False

def  lowercase_						(						_A     :       str       ,   _A     :       Any       ,   _A     :       Optional[int]		):

      """simple docstring"""




      global perfect
      global imperfect
      global wrong
      global total

      lowerCamelCase__						:							Optional[Any]       =    slow.encode(_A		)
      lowerCamelCase__						:							int       =    fast.encode(_A		)

      lowerCamelCase__						:							int       =    False
      total += 1

      if slow_ids != fast_ids:
            if check_details(_A       ,   _A       ,   _A       ,   _A       ,   _A		):
                  lowerCamelCase__						:							Optional[int]       =    True
                  imperfect += 1
            else:
                  wrong += 1
      else:
            perfect += 1

      if total % 10000 == 0:
            print(F"({perfect} / {imperfect} / {wrong} ----- {perfect + imperfect + wrong})"		)

      if skip_assert:
            return

      assert (
          slow_ids == fast_ids
      ), F"line {text} : \n\n{slow_ids}\n{fast_ids}\n\n{slow.tokenize(_A		)}\n{fast.tokenize(_A		)}"

def  lowercase_						(						_A     :       Tuple       ,   _A     :       str		):

      """simple docstring"""




      global batch_total
      for i in range(len(_A		)		):
            # premise, all languages
            for text in dataset[i]["premise"].values():
                  test_string(_A       ,   _A       ,   _A		)

            # hypothesis, all languages
            for text in dataset[i]["hypothesis"]["translation"]:
                  test_string(_A       ,   _A       ,   _A		)


if __name__ == "__main__":
    for name, (slow_class, fast_class) in TOKENIZER_CLASSES.items():
        A :       List[Any]						=   list(slow_class.max_model_input_sizes.keys())
        for checkpoint in checkpoint_names:
            A :       List[Any]						=   0
            A :       Tuple						=   0
            A :       Dict						=   0
            A :       Any						=   0

            print(f'========================== Checking {name}: {checkpoint} ==========================')
            A :       Dict						=   slow_class.from_pretrained(checkpoint, force_download=True)
            A :       Dict						=   fast_class.from_pretrained(checkpoint, force_download=True)
            test_tokenizer(slow, fast)
            print(f'Accuracy {perfect * 100 / total:.2f}')






import inspect
import unittest

from transformers import ResNetConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from transformers.utils import cached_property, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor


if is_flax_available():
    import jax
    import jax.numpy as jnp

    from transformers.models.resnet.modeling_flax_resnet import FlaxResNetForImageClassification, FlaxResNetModel

if is_vision_available():
    from PIL import Image

    from transformers import AutoImageProcessor







class      _lowercase					(				unittest.TestCase):


       """simple docstring"""




       def __init__(  self				:     Dict   , __lowerCamelCase				:     int   , __lowerCamelCase				:     List[Any]=3   , __lowerCamelCase				:     Dict=32   , __lowerCamelCase				:     Optional[Any]=3   , __lowerCamelCase				:     List[str]=10   , __lowerCamelCase				:     List[str]=[10, 20, 30, 40]   , __lowerCamelCase				:     List[Any]=[1, 1, 2, 1]   , __lowerCamelCase				:     Any=True   , __lowerCamelCase				:     Optional[int]=True   , __lowerCamelCase				:     int="relu"   , __lowerCamelCase				:     List[str]=3   , __lowerCamelCase				:     Any=None   , ):


             '''simple docstring'''



             lowerCamelCase__						:							int       =    parent
             lowerCamelCase__						:							Optional[int]       =    batch_size
             lowerCamelCase__						:							List[Any]       =    image_size
             lowerCamelCase__						:							Union[str, Any]       =    num_channels
             lowerCamelCase__						:							Any       =    embeddings_size
             lowerCamelCase__						:							Tuple       =    hidden_sizes
             lowerCamelCase__						:							Tuple       =    depths
             lowerCamelCase__						:							List[str]       =    is_training
             lowerCamelCase__						:							Optional[Any]       =    use_labels
             lowerCamelCase__						:							str       =    hidden_act
             lowerCamelCase__						:							Any       =    num_labels
             lowerCamelCase__						:							List[str]       =    scope
             lowerCamelCase__						:							Any       =    len(__lowerCamelCase			)





       def 		lowerCAmelCase	(  self				:     List[str]			):


             '''simple docstring'''



             lowerCamelCase__						:							Any       =    floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]			)

             lowerCamelCase__						:							Any       =    self.get_config()

             return config, pixel_values





       def 		lowerCAmelCase	(  self				:     Optional[int]			):


             '''simple docstring'''



             return ResNetConfig(
                 num_channels=self.num_channels   , embeddings_size=self.embeddings_size   , hidden_sizes=self.hidden_sizes   , depths=self.depths   , hidden_act=self.hidden_act   , num_labels=self.num_labels   , image_size=self.image_size   , )





       def 		lowerCAmelCase	(  self				:     str   , __lowerCamelCase				:     List[Any]   , __lowerCamelCase				:     Tuple			):


             '''simple docstring'''



             lowerCamelCase__						:							Any       =    FlaxResNetModel(config=__lowerCamelCase			)
             lowerCamelCase__						:							str       =    model(__lowerCamelCase			)

             # Output shape (b, c, h, w)
             self.parent.assertEqual(
                 result.last_hidden_state.shape   , (self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)   , )





       def 		lowerCAmelCase	(  self				:     int   , __lowerCamelCase				:     Optional[Any]   , __lowerCamelCase				:     List[str]			):


             '''simple docstring'''



             lowerCamelCase__						:							Optional[Any]       =    self.num_labels
             lowerCamelCase__						:							int       =    FlaxResNetForImageClassification(config=__lowerCamelCase			)
             lowerCamelCase__						:							Optional[int]       =    model(__lowerCamelCase			)
             self.parent.assertEqual(result.logits.shape   , (self.batch_size, self.num_labels)			)





       def 		lowerCAmelCase	(  self				:     Dict			):


             '''simple docstring'''



             lowerCamelCase__						:							Tuple       =    self.prepare_config_and_inputs()
             lowerCamelCase__       ,  lowerCamelCase__						:							Tuple       =    config_and_inputs
             lowerCamelCase__						:							Optional[int]       =    {"pixel_values": pixel_values}
             return config, inputs_dict







@require_flax
class      _lowercase					(				lowercase__				,    unittest.TestCase):


       """simple docstring"""




       A__   		=     (FlaxResNetModel, FlaxResNetForImageClassification) if is_flax_available() else ()

       A__   		=     False
       A__   		=     False
       A__   		=     False





       def 		lowerCAmelCase	(  self				:     List[str]			):


             '''simple docstring'''



             lowerCamelCase__						:							List[str]       =    FlaxResNetModelTester(self			)
             lowerCamelCase__						:							Dict       =    ConfigTester(self   , config_class=__lowerCamelCase   , has_text_modality=__lowerCamelCase			)





       def 		lowerCAmelCase	(  self				:     Optional[Any]			):


             '''simple docstring'''



             self.create_and_test_config_common_properties()
             self.config_tester.create_and_test_config_to_json_string()
             self.config_tester.create_and_test_config_to_json_file()
             self.config_tester.create_and_test_config_from_and_save_pretrained()
             self.config_tester.create_and_test_config_with_num_labels()
             self.config_tester.check_config_can_be_init_without_params()
             self.config_tester.check_config_arguments_init()





       def 		lowerCAmelCase	(  self				:     Dict			):


             '''simple docstring'''



             return





       def 		lowerCAmelCase	(  self				:     Dict			):


             '''simple docstring'''



             lowerCamelCase__						:							str       =    self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_model(*__lowerCamelCase			)





       def 		lowerCAmelCase	(  self				:     List[Any]			):


             '''simple docstring'''



             lowerCamelCase__						:							str       =    self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_for_image_classification(*__lowerCamelCase			)





       @unittest.skip(reason="ResNet does not use inputs_embeds"			)
       def 		lowerCAmelCase	(  self				:     Dict			):


             '''simple docstring'''



             pass





       @unittest.skip(reason="ResNet does not support input and output embeddings"			)
       def 		lowerCAmelCase	(  self				:     int			):


             '''simple docstring'''



             pass





       def 		lowerCAmelCase	(  self				:     Any			):


             '''simple docstring'''



             lowerCamelCase__       ,  lowerCamelCase__						:							Tuple       =    self.model_tester.prepare_config_and_inputs_for_common()

             for model_class in self.all_model_classes:
                   lowerCamelCase__						:							Tuple       =    model_class(__lowerCamelCase			)
                   lowerCamelCase__						:							Tuple       =    inspect.signature(model.__call__			)
                   # signature.parameters is an OrderedDict => so arg_names order is deterministic
                   lowerCamelCase__						:							Optional[Any]       =    [*signature.parameters.keys()]

                   lowerCamelCase__						:							List[Any]       =    ["pixel_values"]
                   self.assertListEqual(arg_names[:1]   , __lowerCamelCase			)





       def 		lowerCAmelCase	(  self				:     Any			):


             '''simple docstring'''



             def check_hidden_states_output(__lowerCamelCase				:     List[str]   , __lowerCamelCase				:     Optional[int]   , __lowerCamelCase				:     str			):
                   lowerCamelCase__						:							Any       =    model_class(__lowerCamelCase			)

                   lowerCamelCase__						:							Dict       =    model(**self._prepare_for_class(__lowerCamelCase   , __lowerCamelCase			)			)

                   lowerCamelCase__						:							str       =    outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

                   lowerCamelCase__						:							Tuple       =    self.model_tester.num_stages
                   self.assertEqual(len(__lowerCamelCase			)   , expected_num_stages + 1			)





       @unittest.skip(reason="ResNet does not use feedforward chunking"			)
       def 		lowerCAmelCase	(  self				:     Any			):


             '''simple docstring'''



             pass





       def 		lowerCAmelCase	(  self				:     int			):


             '''simple docstring'''



             lowerCamelCase__       ,  lowerCamelCase__						:							Dict       =    self.model_tester.prepare_config_and_inputs_for_common()

             for model_class in self.all_model_classes:
                   with self.subTest(model_class.__name__			):
                         lowerCamelCase__						:							Any       =    self._prepare_for_class(__lowerCamelCase   , __lowerCamelCase			)
                         lowerCamelCase__						:							Optional[int]       =    model_class(__lowerCamelCase			)

                         @jax.jit
                         def model_jitted(__lowerCamelCase				:     Optional[Any]   , **__lowerCamelCase				:     List[Any]			):
                               return model(pixel_values=__lowerCamelCase   , **__lowerCamelCase			)

                         with self.subTest("JIT Enabled"			):
                               lowerCamelCase__						:							Any       =    model_jitted(**__lowerCamelCase			).to_tuple()

                         with self.subTest("JIT Disabled"			):
                               with jax.disable_jit():
                                     lowerCamelCase__						:							Union[str, Any]       =    model_jitted(**__lowerCamelCase			).to_tuple()

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



def  lowercase_						(						):

      """simple docstring"""




      lowerCamelCase__						:							Optional[Any]       =    Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"		)
      return image







@require_flax
class      _lowercase					(				unittest.TestCase):


       """simple docstring"""




       @cached_property
       def 		lowerCAmelCase	(  self				:     Tuple			):


             '''simple docstring'''



             return AutoImageProcessor.from_pretrained("microsoft/resnet-50"			) if is_vision_available() else None





       @slow
       def 		lowerCAmelCase	(  self				:     List[str]			):


             '''simple docstring'''



             lowerCamelCase__						:							List[Any]       =    FlaxResNetForImageClassification.from_pretrained("microsoft/resnet-50"			)

             lowerCamelCase__						:							List[str]       =    self.default_image_processor
             lowerCamelCase__						:							Optional[Any]       =    prepare_img()
             lowerCamelCase__						:							str       =    image_processor(images=__lowerCamelCase   , return_tensors="np"			)

             lowerCamelCase__						:							str       =    model(**__lowerCamelCase			)

             # verify the logits
             lowerCamelCase__						:							int       =    (1, 1000)
             self.assertEqual(outputs.logits.shape   , __lowerCamelCase			)

             lowerCamelCase__						:							Optional[int]       =    jnp.array([-1_1.1_0_6_9, -9.7_8_7_7, -8.3_7_7_7]			)

             self.assertTrue(jnp.allclose(outputs.logits[0, :3]   , __lowerCamelCase   , atol=1E-4			)			)


