


"""simple docstring"""
import unittest

from transformers import PegasusConfig, PegasusTokenizer, is_flax_available
from transformers.testing_utils import require_flax, slow

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


if is_flax_available():
      import os

      # The slow tests are often failing with OOM error on GPU
      # This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
      # but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
      lowercase_       :						Any     					=	"platform"
      import jax
      import jax.numpy as jnp
      import numpy as np

      from transformers import FlaxPegasusForConditionalGeneration, FlaxPegasusModel
@require_flax
class 			UpperCamelCase      :
     A__      =			PegasusConfig
     A__      =			{}
     A__      =			"""gelu"""
     def __init__(     self  ,     snake_case__  ,     snake_case__=13  ,     snake_case__=7  ,     snake_case__=True  ,     snake_case__=False  ,     snake_case__=99  ,     snake_case__=32  ,     snake_case__=5  ,     snake_case__=4  ,     snake_case__=37  ,     snake_case__=0.1  ,     snake_case__=0.1  ,     snake_case__=20  ,     snake_case__=2  ,     snake_case__=1  ,     snake_case__=0  ,     ):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				List[str]							    = parent
       _SCREAMING_SNAKE_CASE   :				Tuple							    = batch_size
       _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = seq_length
       _SCREAMING_SNAKE_CASE   :				List[str]							    = is_training
       _SCREAMING_SNAKE_CASE   :				Optional[int]							    = use_labels
       _SCREAMING_SNAKE_CASE   :				List[Any]							    = vocab_size
       _SCREAMING_SNAKE_CASE   :				List[Any]							    = hidden_size
       _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = num_hidden_layers
       _SCREAMING_SNAKE_CASE   :				str							    = num_attention_heads
       _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = intermediate_size

       _SCREAMING_SNAKE_CASE   :				List[str]							    = hidden_dropout_prob
       _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = attention_probs_dropout_prob
       _SCREAMING_SNAKE_CASE   :				List[str]							    = max_position_embeddings
       _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = eos_token_id
       _SCREAMING_SNAKE_CASE   :				List[str]							    = pad_token_id
       _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = bos_token_id
     def 				__SCREAMING_SNAKE_CASE							(     self				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				List[Any]							    = ids_tensor([self.batch_size, self.seq_length - 1]  ,     self.vocab_size				).clip(3  ,     self.vocab_size				)
       _SCREAMING_SNAKE_CASE   :				List[str]							    = np.expand_dims(np.array([self.eos_token_id] * self.batch_size				)  ,     1				)
       _SCREAMING_SNAKE_CASE   :				Any							    = np.concatenate([input_ids, eos_tensor]  ,     axis=1				)

       _SCREAMING_SNAKE_CASE   :				Optional[int]							    = ids_tensor([self.batch_size, self.seq_length]  ,     self.vocab_size				)

       _SCREAMING_SNAKE_CASE   :				str							    = self.config_cls(
           vocab_size=self.vocab_size  ,     d_model=self.hidden_size  ,     encoder_layers=self.num_hidden_layers  ,     decoder_layers=self.num_hidden_layers  ,     encoder_attention_heads=self.num_attention_heads  ,     decoder_attention_heads=self.num_attention_heads  ,     encoder_ffn_dim=self.intermediate_size  ,     decoder_ffn_dim=self.intermediate_size  ,     dropout=self.hidden_dropout_prob  ,     attention_dropout=self.attention_probs_dropout_prob  ,     max_position_embeddings=self.max_position_embeddings  ,     eos_token_ids=[2]  ,     bos_token_id=self.bos_token_id  ,     pad_token_id=self.pad_token_id  ,     decoder_start_token_id=self.pad_token_id  ,     **self.config_updates  ,     )
       _SCREAMING_SNAKE_CASE   :				Optional[int]							    = prepare_pegasus_inputs_dict(__UpperCamelCase  ,     __UpperCamelCase  ,     __UpperCamelCase				)
       return config, inputs_dict
     def 				__SCREAMING_SNAKE_CASE							(     self  ,     snake_case__  ,     snake_case__  ,     snake_case__				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = 20
       _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = model_class_name(__UpperCamelCase				)

       _SCREAMING_SNAKE_CASE   :				Dict							    = model.encode(inputs_dict["input_ids"]				)

       _SCREAMING_SNAKE_CASE    ,		_SCREAMING_SNAKE_CASE   :				int							    = (
           inputs_dict["decoder_input_ids"],
           inputs_dict["decoder_attention_mask"],
       )

       _SCREAMING_SNAKE_CASE   :				Any							    = model.init_cache(decoder_input_ids.shape[0]  ,     __UpperCamelCase  ,     __UpperCamelCase				)
       _SCREAMING_SNAKE_CASE   :				Tuple							    = jnp.ones((decoder_input_ids.shape[0], max_decoder_length)  ,     dtype="i4"				)

       _SCREAMING_SNAKE_CASE   :				Dict							    = jnp.broadcast_to(
           jnp.arange(decoder_input_ids.shape[-1] - 1				)[None, :]  ,     (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)  ,     )
       _SCREAMING_SNAKE_CASE   :				str							    = model.decode(
           decoder_input_ids[:, :-1]  ,     __UpperCamelCase  ,     decoder_attention_mask=__UpperCamelCase  ,     past_key_values=__UpperCamelCase  ,     decoder_position_ids=__UpperCamelCase  ,     )

       _SCREAMING_SNAKE_CASE   :				Optional[int]							    = jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]  ,     dtype="i4"				)
       _SCREAMING_SNAKE_CASE   :				List[Any]							    = model.decode(
           decoder_input_ids[:, -1:]  ,     __UpperCamelCase  ,     decoder_attention_mask=__UpperCamelCase  ,     past_key_values=outputs_cache.past_key_values  ,     decoder_position_ids=__UpperCamelCase  ,     )

       _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = model.decode(__UpperCamelCase  ,     __UpperCamelCase				)

       _SCREAMING_SNAKE_CASE   :				List[str]							    = np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])				)				)
       self.parent.assertTrue(diff < 1E-3  ,     msg=F'''Max diff is {diff}'''				)




     def 				__SCREAMING_SNAKE_CASE							(     self  ,     snake_case__  ,     snake_case__  ,     snake_case__				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				int							    = 20
       _SCREAMING_SNAKE_CASE   :				Tuple							    = model_class_name(__UpperCamelCase				)

       _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = model.encode(inputs_dict["input_ids"]				)

       _SCREAMING_SNAKE_CASE    ,		_SCREAMING_SNAKE_CASE   :				Optional[int]							    = (
           inputs_dict["decoder_input_ids"],
           inputs_dict["decoder_attention_mask"],
       )

       _SCREAMING_SNAKE_CASE   :				int							    = jnp.concatenate(
           [
               decoder_attention_mask,
               jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])				),
           ]  ,     axis=-1  ,     )

       _SCREAMING_SNAKE_CASE   :				Any							    = model.init_cache(decoder_input_ids.shape[0]  ,     __UpperCamelCase  ,     __UpperCamelCase				)
       _SCREAMING_SNAKE_CASE   :				str							    = jnp.broadcast_to(
           jnp.arange(decoder_input_ids.shape[-1] - 1				)[None, :]  ,     (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)  ,     )

       _SCREAMING_SNAKE_CASE   :				str							    = model.decode(
           decoder_input_ids[:, :-1]  ,     __UpperCamelCase  ,     decoder_attention_mask=__UpperCamelCase  ,     past_key_values=__UpperCamelCase  ,     decoder_position_ids=__UpperCamelCase  ,     )
       _SCREAMING_SNAKE_CASE   :				Dict							    = jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]  ,     dtype="i4"				)
       _SCREAMING_SNAKE_CASE   :				str							    = model.decode(
           decoder_input_ids[:, -1:]  ,     __UpperCamelCase  ,     past_key_values=outputs_cache.past_key_values  ,     decoder_attention_mask=__UpperCamelCase  ,     decoder_position_ids=__UpperCamelCase  ,     )

       _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = model.decode(__UpperCamelCase  ,     __UpperCamelCase  ,     decoder_attention_mask=__UpperCamelCase				)

       _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])				)				)
       self.parent.assertTrue(diff < 1E-3  ,     msg=F'''Max diff is {diff}'''				)





def    _lowerCAmelCase    (     lowerCamelCase__							:							Optional[int],						lowerCamelCase__							:							int,						lowerCamelCase__							:							Tuple,						lowerCamelCase__							:							Any=None,						lowerCamelCase__							:							Dict=None,						)  ->    str:



  if attention_mask is None:
    _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = np.not_equal(_SCREAMING_SNAKE_CASE,						config.pad_token_id				).astype(np.inta				)
  if decoder_attention_mask is None:
    _SCREAMING_SNAKE_CASE   :				Any							    = np.concatenate(
        [
            np.ones(decoder_input_ids[:, :1].shape,						dtype=np.inta				),
            np.not_equal(decoder_input_ids[:, 1:],						config.pad_token_id				).astype(np.inta				),
        ],						axis=-1,						)
  return {
      "input_ids": input_ids,
      "decoder_input_ids": decoder_input_ids,
      "attention_mask": attention_mask,
      "decoder_attention_mask": decoder_attention_mask,
  }





@require_flax
class 			UpperCamelCase      (							__SCREAMING_SNAKE_CASE      ,       unittest.TestCase  ):
     A__      =			(
         (
             FlaxPegasusForConditionalGeneration,
             FlaxPegasusModel,
         )
         if is_flax_available()
         else ()
     )
     A__      =			(FlaxPegasusForConditionalGeneration,) if is_flax_available() else ()
     A__      =			True
     A__      =			False
     A__      =			False
     A__      =			False
     def 				__SCREAMING_SNAKE_CASE							(     self				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = FlaxPegasusModelTester(self				)
       _SCREAMING_SNAKE_CASE   :				Optional[int]							    = ConfigTester(self  ,     config_class=__UpperCamelCase				)
     def 				__SCREAMING_SNAKE_CASE							(     self				):




       """simple docstring"""


       self.config_tester.run_common_tests()
     def 				__SCREAMING_SNAKE_CASE							(     self				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE    ,		_SCREAMING_SNAKE_CASE   :				List[str]							    = self.model_tester.prepare_config_and_inputs_for_common()
       for model_class in self.all_model_classes:
         self.model_tester.check_use_cache_forward(__UpperCamelCase  ,     __UpperCamelCase  ,     __UpperCamelCase				)
     def 				__SCREAMING_SNAKE_CASE							(     self				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE    ,		_SCREAMING_SNAKE_CASE   :				str							    = self.model_tester.prepare_config_and_inputs_for_common()
       for model_class in self.all_model_classes:
         self.model_tester.check_use_cache_forward_with_attn_mask(__UpperCamelCase  ,     __UpperCamelCase  ,     __UpperCamelCase				)
     def 				__SCREAMING_SNAKE_CASE							(     self				):




       """simple docstring"""


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

       for model_class in self.all_model_classes:
         with self.subTest(model_class.__name__				):
           _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = self._prepare_for_class(__UpperCamelCase  ,     __UpperCamelCase				)
           _SCREAMING_SNAKE_CASE   :				str							    = model_class(__UpperCamelCase				)

           @jax.jit
           def encode_jitted(snake_case__  ,     snake_case__=None  ,     **snake_case__				):
             return model.encode(input_ids=__UpperCamelCase  ,     attention_mask=__UpperCamelCase				)

           with self.subTest("JIT Enabled"				):
             _SCREAMING_SNAKE_CASE   :				Optional[int]							    = encode_jitted(**__UpperCamelCase				).to_tuple()

           with self.subTest("JIT Disabled"				):
             with jax.disable_jit():
               _SCREAMING_SNAKE_CASE   :				List[Any]							    = encode_jitted(**__UpperCamelCase				).to_tuple()

           self.assertEqual(len(__UpperCamelCase				)  ,     len(__UpperCamelCase				)				)
           for jitted_output, output in zip(__UpperCamelCase  ,     __UpperCamelCase				):
             self.assertEqual(jitted_output.shape  ,     output.shape				)
     def 				__SCREAMING_SNAKE_CASE							(     self				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE    ,		_SCREAMING_SNAKE_CASE   :				List[str]							    = self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
         with self.subTest(model_class.__name__				):
           _SCREAMING_SNAKE_CASE   :				Tuple							    = model_class(__UpperCamelCase				)
           _SCREAMING_SNAKE_CASE   :				Dict							    = model.encode(inputs_dict["input_ids"]  ,     inputs_dict["attention_mask"]				)

           _SCREAMING_SNAKE_CASE   :				Dict							    = {
               "decoder_input_ids": inputs_dict["decoder_input_ids"],
               "decoder_attention_mask": inputs_dict["decoder_attention_mask"],
               "encoder_outputs": encoder_outputs,
           }

           @jax.jit
           def decode_jitted(snake_case__  ,     snake_case__  ,     snake_case__				):
             return model.decode(
                 decoder_input_ids=__UpperCamelCase  ,     decoder_attention_mask=__UpperCamelCase  ,     encoder_outputs=__UpperCamelCase  ,     )

           with self.subTest("JIT Enabled"				):
             _SCREAMING_SNAKE_CASE   :				str							    = decode_jitted(**__UpperCamelCase				).to_tuple()

           with self.subTest("JIT Disabled"				):
             with jax.disable_jit():
               _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = decode_jitted(**__UpperCamelCase				).to_tuple()

           self.assertEqual(len(__UpperCamelCase				)  ,     len(__UpperCamelCase				)				)
           for jitted_output, output in zip(__UpperCamelCase  ,     __UpperCamelCase				):
             self.assertEqual(jitted_output.shape  ,     output.shape				)
     @slow
     def 				__SCREAMING_SNAKE_CASE							(     self				):




       """simple docstring"""


       for model_class_name in self.all_model_classes:
         _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = model_class_name.from_pretrained("google/pegasus-large"  ,     from_pt=__UpperCamelCase				)
         _SCREAMING_SNAKE_CASE   :				List[Any]							    = np.ones((1, 1)				)
         _SCREAMING_SNAKE_CASE   :				int							    = model(__UpperCamelCase				)
         self.assertIsNotNone(__UpperCamelCase				)




     @slow
     def 				__SCREAMING_SNAKE_CASE							(     self				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				Optional[int]							    = FlaxPegasusForConditionalGeneration.from_pretrained("google/pegasus-xsum"				)
       _SCREAMING_SNAKE_CASE   :				int							    = PegasusTokenizer.from_pretrained("google/pegasus-xsum"				)

       _SCREAMING_SNAKE_CASE   :				Dict							    = [
           " PG&E stated it scheduled the blackouts in response to forecasts for high winds amid dry conditions. The aim is to reduce the risk of wildfires. Nearly 800 thousand customers were scheduled to be affected by the shutoffs which were expected to last through at least midday tomorrow.",
           " The London trio are up for best UK act and best album, as well as getting two nominations in the best song category.\"We got told like this morning \'Oh I think you\'re nominated\'\", said Dappy.\"And I was like \'Oh yeah, which one?\' And now we\'ve got nominated for four awards. I mean, wow!\"Bandmate Fazer added: \"We thought it\'s best of us to come down and mingle with everyone and say hello to the cameras. And now we find we\'ve got four nominations.\"The band have two shots at the best song prize, getting the nod for their Tynchy Stryder collaboration Number One, and single Strong Again.Their album Uncle B will also go up against records by the likes of Beyonce and Kanye West.N-Dubz picked up the best newcomer Mobo in 2007, but female member Tulisa said they wouldn\'t be too disappointed if they didn\'t win this time around.\"At the end of the day we\'re grateful to be where we are in our careers.\"If it don\'t happen then it don\'t happen - live to fight another day and keep on making albums and hits for the fans.\"Dappy also revealed they could be performing live several times on the night.The group will be doing Number One and also a possible rendition of the War Child single, I Got Soul.The charity song is a  re-working of The Killers\' All These Things That I\'ve Done and is set to feature artists like Chipmunk, Ironik and Pixie Lott.This year\'s Mobos will be held outside of London for the first time, in Glasgow on 30 September.N-Dubz said they were looking forward to performing for their Scottish fans and boasted about their recent shows north of the border.\"We just done Edinburgh the other day,\" said Dappy.\"We smashed up an N-Dubz show over there. We done Aberdeen about three or four months ago - we smashed up that show over there! Everywhere we go we smash it up!\" ",
       ]

       _SCREAMING_SNAKE_CASE   :				Any							    = [
           "California\'s largest electricity provider has turned off power to hundreds of thousands of customers.",
           "Pop group N-Dubz have revealed they were surprised to get four nominations for this year\'s Mobo Awards.",
       ]

       _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = tokenizer(__UpperCamelCase  ,     return_tensors="np"  ,     truncation=__UpperCamelCase  ,     max_length=512  ,     padding=__UpperCamelCase				)
       _SCREAMING_SNAKE_CASE   :				List[str]							    = model.generate(**__UpperCamelCase  ,     num_beams=2				).sequences
       _SCREAMING_SNAKE_CASE   :				str							    = tokenizer.batch_decode(__UpperCamelCase  ,     skip_special_tokens=__UpperCamelCase				)
       assert tgt_text == decoded





"""simple docstring"""
import collections
import os
import re
from pathlib import Path


lowercase_       :						Union[str, Any]     					=	'''src/transformers'''


# Matches is_xxx_available()
lowercase_       :						str     					=	re.compile(R'''is\_([a-z_]*)_available()''')
# Catches a one-line _import_struct = {xxx}
lowercase_       :						List[Any]     					=	re.compile(R'''^_import_structure\s+=\s+\{([^\}]+)\}''')
# Catches a line with a key-values pattern: "bla": ["foo", "bar"]
lowercase_       :						Dict     					=	re.compile(R'''\s+"\S*":\s+\[([^\]]*)\]''')
# Catches a line if not is_foo_available
lowercase_       :						int     					=	re.compile(R'''^\s*if\s+not\s+is\_[a-z_]*\_available\(\)''')
# Catches a line _import_struct["bla"].append("foo")
lowercase_       :						Optional[Any]     					=	re.compile(R'''^\s*_import_structure\["\S*"\]\.append\("(\S*)"\)''')
# Catches a line _import_struct["bla"].extend(["foo", "bar"]) or _import_struct["bla"] = ["foo", "bar"]
lowercase_       :						List[Any]     					=	re.compile(R'''^\s*_import_structure\[\S*\](?:\.extend\(|\s*=\s+)\[([^\]]*)\]''')
# Catches a line with an object between quotes and a comma:     "MyModel",
lowercase_       :						Union[str, Any]     					=	re.compile(R'''^\s+"([^"]+)",''')
# Catches a line with objects between brackets only:    ["foo", "bar"],
lowercase_       :						Tuple     					=	re.compile(R'''^\s+\[([^\]]+)\]''')
# Catches a line with from foo import bar, bla, boo
lowercase_       :						List[Any]     					=	re.compile(R'''\s+from\s+\S*\s+import\s+([^\(\s].*)\n''')
# Catches a line with try:
lowercase_       :						Optional[Any]     					=	re.compile(R'''^\s*try:''')
# Catches a line with else:
lowercase_       :						List[Any]     					=	re.compile(R'''^\s*else:''')





def    _lowerCAmelCase    (     lowerCamelCase__							:							str				)  ->    Dict:
  if _re_test_backend.search(lowerCamelCase__				) is None:
    return None
  _SCREAMING_SNAKE_CASE   :				Any							    = [b[0] for b in _re_backend.findall(lowerCamelCase__				)]
  backends.sort()
  return "_and_".join(lowerCamelCase__				)





def    _lowerCAmelCase    (     lowerCamelCase__							:							List[str]				)  ->    Any:
  with open(lowerCamelCase__,						"r",						encoding="utf-8",						newline="\n"				) as f:
    _SCREAMING_SNAKE_CASE   :				str							    = f.readlines()

  _SCREAMING_SNAKE_CASE   :				List[str]							    = 0
  while line_index < len(lowerCamelCase__				) and not lines[line_index].startswith("_import_structure = {"				):
    line_index += 1

  # If this is a traditional init, just return.
  if line_index >= len(lowerCamelCase__				):
    return None

  # First grab the objects without a specific backend in _import_structure
  _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = []
  while not lines[line_index].startswith("if TYPE_CHECKING"				) and find_backend(lines[line_index]				) is None:
    _SCREAMING_SNAKE_CASE   :				int							    = lines[line_index]
    # If we have everything on a single line, let's deal with it.
    if _re_one_line_import_struct.search(lowerCamelCase__				):
      _SCREAMING_SNAKE_CASE   :				Optional[int]							    = _re_one_line_import_struct.search(lowerCamelCase__				).groups()[0]
      _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = re.findall(R"\[([^\]]+)\]",						lowerCamelCase__				)
      for imp in imports:
        objects.extend([obj[1:-1] for obj in imp.split(", "				)]				)
      line_index += 1
      continue
    _SCREAMING_SNAKE_CASE   :				Tuple							    = _re_import_struct_key_value.search(lowerCamelCase__				)
    if single_line_import_search is not None:
      _SCREAMING_SNAKE_CASE   :				Any							    = [obj[1:-1] for obj in single_line_import_search.groups()[0].split(", "				) if len(lowerCamelCase__				) > 0]
      objects.extend(lowerCamelCase__				)
    elif line.startswith(" " * 8 + "\""				):
      objects.append(line[9:-3]				)
    line_index += 1

  _SCREAMING_SNAKE_CASE   :				Dict							    = {"none": objects}
  # Let's continue with backend-specific objects in _import_structure
  while not lines[line_index].startswith("if TYPE_CHECKING"				):
    # If the line is an if not is_backend_available, we grab all objects associated.
    _SCREAMING_SNAKE_CASE   :				Any							    = find_backend(lines[line_index]				)
    # Check if the backend declaration is inside a try block:
    if _re_try.search(lines[line_index - 1]				) is None:
      _SCREAMING_SNAKE_CASE   :				Any							    = None

    if backend is not None:
      line_index += 1

      # Scroll until we hit the else block of try-except-else
      while _re_else.search(lines[line_index]				) is None:
        line_index += 1

      line_index += 1

      _SCREAMING_SNAKE_CASE   :				Optional[int]							    = []
      # Until we unindent, add backend objects to the list
      while len(lines[line_index]				) <= 1 or lines[line_index].startswith(" " * 4				):
        _SCREAMING_SNAKE_CASE   :				Any							    = lines[line_index]
        if _re_import_struct_add_one.search(lowerCamelCase__				) is not None:
          objects.append(_re_import_struct_add_one.search(lowerCamelCase__				).groups()[0]				)
        elif _re_import_struct_add_many.search(lowerCamelCase__				) is not None:
          _SCREAMING_SNAKE_CASE   :				List[Any]							    = _re_import_struct_add_many.search(lowerCamelCase__				).groups()[0].split(", "				)
          _SCREAMING_SNAKE_CASE   :				Any							    = [obj[1:-1] for obj in imports if len(lowerCamelCase__				) > 0]
          objects.extend(lowerCamelCase__				)
        elif _re_between_brackets.search(lowerCamelCase__				) is not None:
          _SCREAMING_SNAKE_CASE   :				List[str]							    = _re_between_brackets.search(lowerCamelCase__				).groups()[0].split(", "				)
          _SCREAMING_SNAKE_CASE   :				Any							    = [obj[1:-1] for obj in imports if len(lowerCamelCase__				) > 0]
          objects.extend(lowerCamelCase__				)
        elif _re_quote_object.search(lowerCamelCase__				) is not None:
          objects.append(_re_quote_object.search(lowerCamelCase__				).groups()[0]				)
        elif line.startswith(" " * 8 + "\""				):
          objects.append(line[9:-3]				)
        elif line.startswith(" " * 1_2 + "\""				):
          objects.append(line[1_3:-3]				)
        line_index += 1

      _SCREAMING_SNAKE_CASE   :				str							    = objects
    else:
      line_index += 1

    # At this stage we are in the TYPE_CHECKING part, first grab the objects without a specific backend
  _SCREAMING_SNAKE_CASE   :				Any							    = []
  while (
      line_index < len(lowerCamelCase__				)
      and find_backend(lines[line_index]				) is None
      and not lines[line_index].startswith("else"				)
  ):
    _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = lines[line_index]
    _SCREAMING_SNAKE_CASE   :				int							    = _re_import.search(lowerCamelCase__				)
    if single_line_import_search is not None:
      objects.extend(single_line_import_search.groups()[0].split(", "				)				)
    elif line.startswith(" " * 8				):
      objects.append(line[8:-2]				)
    line_index += 1

  _SCREAMING_SNAKE_CASE   :				Optional[int]							    = {"none": objects}
  # Let's continue with backend-specific objects
  while line_index < len(lowerCamelCase__				):
    # If the line is an if is_backend_available, we grab all objects associated.
    _SCREAMING_SNAKE_CASE   :				int							    = find_backend(lines[line_index]				)
    # Check if the backend declaration is inside a try block:
    if _re_try.search(lines[line_index - 1]				) is None:
      _SCREAMING_SNAKE_CASE   :				Tuple							    = None

    if backend is not None:
      line_index += 1

      # Scroll until we hit the else block of try-except-else
      while _re_else.search(lines[line_index]				) is None:
        line_index += 1

      line_index += 1

      _SCREAMING_SNAKE_CASE   :				Dict							    = []
      # Until we unindent, add backend objects to the list
      while len(lines[line_index]				) <= 1 or lines[line_index].startswith(" " * 8				):
        _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = lines[line_index]
        _SCREAMING_SNAKE_CASE   :				List[Any]							    = _re_import.search(lowerCamelCase__				)
        if single_line_import_search is not None:
          objects.extend(single_line_import_search.groups()[0].split(", "				)				)
        elif line.startswith(" " * 1_2				):
          objects.append(line[1_2:-2]				)
        line_index += 1

      _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = objects
    else:
      line_index += 1

  return import_dict_objects, type_hint_objects





def    _lowerCAmelCase    (     lowerCamelCase__							:							Tuple,						lowerCamelCase__							:							Dict				)  ->    Tuple:

  def find_duplicates(lowerCamelCase__							:							List[str]				):
    return [k for k, v in collections.Counter(lowerCamelCase__				).items() if v > 1]

  if list(import_dict_objects.keys()				) != list(type_hint_objects.keys()				):
    return ["Both sides of the init do not have the same backends!"]

  _SCREAMING_SNAKE_CASE   :				Any							    = []
  for key in import_dict_objects.keys():
    _SCREAMING_SNAKE_CASE   :				Dict							    = find_duplicates(import_dict_objects[key]				)
    if duplicate_imports:
      errors.append(f'''Duplicate _import_structure definitions for: {duplicate_imports}'''				)
    _SCREAMING_SNAKE_CASE   :				List[Any]							    = find_duplicates(type_hint_objects[key]				)
    if duplicate_type_hints:
      errors.append(f'''Duplicate TYPE_CHECKING objects for: {duplicate_type_hints}'''				)

    if sorted(set(import_dict_objects[key]				)				) != sorted(set(type_hint_objects[key]				)				):
      _SCREAMING_SNAKE_CASE   :				Dict							    = "base imports" if key == "none" else f'''{key} backend'''
      errors.append(f'''Differences for {name}:'''				)
      for a in type_hint_objects[key]:
        if a not in import_dict_objects[key]:
          errors.append(f'''  {a} in TYPE_HINT but not in _import_structure.'''				)
      for a in import_dict_objects[key]:
        if a not in type_hint_objects[key]:
          errors.append(f'''  {a} in _import_structure but not in TYPE_HINT.'''				)
  return errors





def    _lowerCAmelCase    (     )  ->    Union[str, Any]:
  _SCREAMING_SNAKE_CASE   :				int							    = []
  for root, _, files in os.walk(lowerCamelCase__				):
    if "__init__.py" in files:
      _SCREAMING_SNAKE_CASE   :				Tuple							    = os.path.join(lowerCamelCase__,						"__init__.py"				)
      _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = parse_init(lowerCamelCase__				)
      if objects is not None:
        _SCREAMING_SNAKE_CASE   :				List[str]							    = analyze_results(*lowerCamelCase__				)
        if len(lowerCamelCase__				) > 0:
          _SCREAMING_SNAKE_CASE   :				Optional[int]							    = f'''Problem in {fname}, both halves do not define the same objects.\n{errors[0]}'''
          failures.append("\n".join(lowerCamelCase__				)				)
  if len(lowerCamelCase__				) > 0:
    raise ValueError("\n\n".join(lowerCamelCase__				)				)





def    _lowerCAmelCase    (     )  ->    Tuple:
  _SCREAMING_SNAKE_CASE   :				Dict							    = []
  for path, directories, files in os.walk(lowerCamelCase__				):
    for folder in directories:
      # Ignore private modules
      if folder.startswith("_"				):
        directories.remove(lowerCamelCase__				)
        continue
      # Ignore leftovers from branches (empty folders apart from pycache)
      if len(list((Path(lowerCamelCase__				) / folder).glob("*.py"				)				)				) == 0:
        continue
      _SCREAMING_SNAKE_CASE   :				Dict							    = str((Path(lowerCamelCase__				) / folder).relative_to(lowerCamelCase__				)				)
      _SCREAMING_SNAKE_CASE   :				Any							    = short_path.replace(os.path.sep,						"."				)
      submodules.append(lowerCamelCase__				)
    for fname in files:
      if fname == "__init__.py":
        continue
      _SCREAMING_SNAKE_CASE   :				Optional[int]							    = str((Path(lowerCamelCase__				) / fname).relative_to(lowerCamelCase__				)				)
      _SCREAMING_SNAKE_CASE   :				Dict							    = short_path.replace(".py",						""				).replace(os.path.sep,						"."				)
      if len(submodule.split("."				)				) == 1:
        submodules.append(lowerCamelCase__				)
  return submodules


lowercase_       :						Optional[int]     					=	[
    '''convert_pytorch_checkpoint_to_tf2''',
    '''modeling_flax_pytorch_utils''',
    '''models.esm.openfold_utils''',
]





def    _lowerCAmelCase    (     )  ->    List[str]:
  # This is to make sure the transformers module imported is the one in the repo.
  from transformers.utils import direct_transformers_import

  _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = direct_transformers_import(lowerCamelCase__				)

  _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = set(transformers._import_structure.keys()				)
  # This contains all the base keys of the _import_structure object defined in the init, but if the user is missing
  # some optional dependencies, they may not have all of them. Thus we read the init to read all additions and
  # (potentiall re-) add them.
  with open(os.path.join(lowerCamelCase__,						"__init__.py"				),						"r"				) as f:
    _SCREAMING_SNAKE_CASE   :				Any							    = f.read()
  import_structure_keys.update(set(re.findall(R"import_structure\[\"([^\"]*)\"\]",						lowerCamelCase__				)				)				)

  _SCREAMING_SNAKE_CASE   :				int							    = [
      module
      for module in get_transformers_submodules()
      if module not in IGNORE_SUBMODULES and module not in import_structure_keys
  ]

  if len(lowerCamelCase__				) > 0:
    _SCREAMING_SNAKE_CASE   :				List[Any]							    = "\n".join(f'''- {module}''' for module in module_not_registered				)
    raise ValueError(
        "The following submodules are not properly registed in the main init of Transformers:\n"
        f'''{list_of_modules}\n'''
        "Make sure they appear somewhere in the keys of `_import_structure` with an empty list as value."				)


if __name__ == "__main__":
      check_all_inits()
      check_submodules()



