
"""simple docstring"""
import tempfile
import unittest

import numpy as np

import transformers
from transformers import GPTaTokenizer, GPTJConfig, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, tooslow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


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

      from transformers.modeling_flax_pytorch_utils import (
          convert_pytorch_state_dict_to_flax,
          load_flax_weights_in_pytorch_model,
      )
      from transformers.models.gptj.modeling_flax_gptj import FlaxGPTJForCausalLM, FlaxGPTJModel

if is_torch_available():
      import torch
class 			UpperCamelCase      :
     def __init__(     self  ,     snake_case__  ,     snake_case__=14  ,     snake_case__=7  ,     snake_case__=True  ,     snake_case__=True  ,     snake_case__=False  ,     snake_case__=True  ,     snake_case__=99  ,     snake_case__=32  ,     snake_case__=4  ,     snake_case__=4  ,     snake_case__=4  ,     snake_case__=37  ,     snake_case__="gelu"  ,     snake_case__=0.1  ,     snake_case__=0.1  ,     snake_case__=512  ,     snake_case__=0.02  ,     ):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				str							    = parent
       _SCREAMING_SNAKE_CASE   :				List[Any]							    = batch_size
       _SCREAMING_SNAKE_CASE   :				Tuple							    = seq_length
       _SCREAMING_SNAKE_CASE   :				Dict							    = is_training
       _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = use_input_mask
       _SCREAMING_SNAKE_CASE   :				Optional[int]							    = use_token_type_ids
       _SCREAMING_SNAKE_CASE   :				Any							    = use_labels
       _SCREAMING_SNAKE_CASE   :				List[str]							    = vocab_size
       _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = hidden_size
       _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = rotary_dim
       _SCREAMING_SNAKE_CASE   :				List[str]							    = num_hidden_layers
       _SCREAMING_SNAKE_CASE   :				Any							    = num_attention_heads
       _SCREAMING_SNAKE_CASE   :				int							    = intermediate_size
       _SCREAMING_SNAKE_CASE   :				int							    = hidden_act
       _SCREAMING_SNAKE_CASE   :				str							    = hidden_dropout_prob
       _SCREAMING_SNAKE_CASE   :				List[str]							    = attention_probs_dropout_prob
       _SCREAMING_SNAKE_CASE   :				List[Any]							    = max_position_embeddings
       _SCREAMING_SNAKE_CASE   :				List[Any]							    = initializer_range
       _SCREAMING_SNAKE_CASE   :				Tuple							    = None
       _SCREAMING_SNAKE_CASE   :				str							    = vocab_size - 1
       _SCREAMING_SNAKE_CASE   :				Dict							    = vocab_size - 1
       _SCREAMING_SNAKE_CASE   :				Dict							    = vocab_size - 1
     def 				__SCREAMING_SNAKE_CASE							(     self				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = ids_tensor([self.batch_size, self.seq_length]  ,     self.vocab_size				)

       _SCREAMING_SNAKE_CASE   :				str							    = None
       if self.use_input_mask:
         _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = random_attention_mask([self.batch_size, self.seq_length]				)

       _SCREAMING_SNAKE_CASE   :				str							    = GPTJConfig(
           vocab_size=self.vocab_size  ,     n_embd=self.hidden_size  ,     n_layer=self.num_hidden_layers  ,     n_head=self.num_attention_heads  ,     n_positions=self.max_position_embeddings  ,     use_cache=snake_case__  ,     bos_token_id=self.bos_token_id  ,     eos_token_id=self.eos_token_id  ,     pad_token_id=self.pad_token_id  ,     rotary_dim=self.rotary_dim  ,     )

       return (config, input_ids, input_mask)
     def 				__SCREAMING_SNAKE_CASE							(     self				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				List[Any]							    = self.prepare_config_and_inputs()
       _SCREAMING_SNAKE_CASE    ,		_SCREAMING_SNAKE_CASE    ,		_SCREAMING_SNAKE_CASE   :				Optional[Any]							    = config_and_inputs
       _SCREAMING_SNAKE_CASE   :				List[Any]							    = {"input_ids": input_ids, "attention_mask": attention_mask}
       return config, inputs_dict
     def 				__SCREAMING_SNAKE_CASE							(     self  ,     snake_case__  ,     snake_case__  ,     snake_case__  ,     snake_case__				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				int							    = 20
       _SCREAMING_SNAKE_CASE   :				List[str]							    = model_class_name(snake_case__				)

       _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = model.init_cache(input_ids.shape[0]  ,     snake_case__				)
       _SCREAMING_SNAKE_CASE   :				int							    = jnp.ones((input_ids.shape[0], max_decoder_length)  ,     dtype="i4"				)

       _SCREAMING_SNAKE_CASE   :				str							    = jnp.broadcast_to(
           jnp.arange(input_ids.shape[-1] - 1				)[None, :]  ,     (input_ids.shape[0], input_ids.shape[-1] - 1)				)
       _SCREAMING_SNAKE_CASE   :				int							    = model(
           input_ids[:, :-1]  ,     attention_mask=snake_case__  ,     past_key_values=snake_case__  ,     position_ids=snake_case__  ,     )

       _SCREAMING_SNAKE_CASE   :				str							    = jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]  ,     dtype="i4"				)
       _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = model(
           input_ids[:, -1:]  ,     attention_mask=snake_case__  ,     past_key_values=outputs_cache.past_key_values  ,     position_ids=snake_case__  ,     )

       _SCREAMING_SNAKE_CASE   :				Any							    = model(snake_case__				)

       _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__  ,     snake_case__				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				List[str]							    = 20
       _SCREAMING_SNAKE_CASE   :				Any							    = model_class_name(snake_case__				)

       _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = jnp.concatenate(
           [attention_mask, jnp.zeros((attention_mask.shape[0], max_decoder_length - attention_mask.shape[1])				)]  ,     axis=-1  ,     )

       _SCREAMING_SNAKE_CASE   :				int							    = model.init_cache(input_ids.shape[0]  ,     snake_case__				)
       _SCREAMING_SNAKE_CASE   :				Any							    = jnp.broadcast_to(
           jnp.arange(input_ids.shape[-1] - 1				)[None, :]  ,     (input_ids.shape[0], input_ids.shape[-1] - 1)				)

       _SCREAMING_SNAKE_CASE   :				List[str]							    = model(
           input_ids[:, :-1]  ,     attention_mask=snake_case__  ,     past_key_values=snake_case__  ,     position_ids=snake_case__  ,     )
       _SCREAMING_SNAKE_CASE   :				Optional[int]							    = jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]  ,     dtype="i4"				)
       _SCREAMING_SNAKE_CASE   :				Tuple							    = model(
           input_ids[:, -1:]  ,     past_key_values=outputs_cache.past_key_values  ,     attention_mask=snake_case__  ,     position_ids=snake_case__  ,     )

       _SCREAMING_SNAKE_CASE   :				Tuple							    = model(snake_case__  ,     attention_mask=snake_case__				)

       _SCREAMING_SNAKE_CASE   :				List[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}'''				)
@require_flax
class 			UpperCamelCase      (							__SCREAMING_SNAKE_CASE      ,       __SCREAMING_SNAKE_CASE      ,       unittest.TestCase  ):
     A__      =			(FlaxGPTJModel, FlaxGPTJForCausalLM) if is_flax_available() else ()
     A__      =			(FlaxGPTJForCausalLM,) if is_flax_available() else ()
     def 				__SCREAMING_SNAKE_CASE							(     self				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				List[Any]							    = FlaxGPTJModelTester(self				)
     def 				__SCREAMING_SNAKE_CASE							(     self				):




       """simple docstring"""


       for model_class_name in self.all_model_classes:
         _SCREAMING_SNAKE_CASE    ,		_SCREAMING_SNAKE_CASE    ,		_SCREAMING_SNAKE_CASE   :				Optional[int]							    = self.model_tester.prepare_config_and_inputs()
         self.model_tester.check_use_cache_forward(snake_case__  ,     snake_case__  ,     snake_case__  ,     snake_case__				)
     def 				__SCREAMING_SNAKE_CASE							(     self				):




       """simple docstring"""


       for model_class_name in self.all_model_classes:
         _SCREAMING_SNAKE_CASE    ,		_SCREAMING_SNAKE_CASE    ,		_SCREAMING_SNAKE_CASE   :				Dict							    = self.model_tester.prepare_config_and_inputs()
         self.model_tester.check_use_cache_forward_with_attn_mask(
             snake_case__  ,     snake_case__  ,     snake_case__  ,     snake_case__				)
     @tooslow
     def 				__SCREAMING_SNAKE_CASE							(     self				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				Dict							    = GPTaTokenizer.from_pretrained("gpt2"  ,     pad_token="<|endoftext|>"  ,     padding_side="left"				)
       _SCREAMING_SNAKE_CASE   :				int							    = tokenizer(["Hello this is a long string", "Hey"]  ,     return_tensors="np"  ,     padding=snake_case__  ,     truncation=snake_case__				)

       _SCREAMING_SNAKE_CASE   :				str							    = FlaxGPTJForCausalLM.from_pretrained("EleutherAI/gpt-j-6B"				)
       _SCREAMING_SNAKE_CASE   :				Tuple							    = False
       _SCREAMING_SNAKE_CASE   :				List[str]							    = model.config.eos_token_id

       _SCREAMING_SNAKE_CASE   :				Any							    = jax.jit(model.generate				)

       _SCREAMING_SNAKE_CASE   :				Dict							    = jit_generate(
           inputs["input_ids"]  ,     attention_mask=inputs["attention_mask"]  ,     pad_token_id=tokenizer.pad_token_id				).sequences

       _SCREAMING_SNAKE_CASE   :				List[Any]							    = tokenizer.batch_decode(snake_case__  ,     skip_special_tokens=snake_case__				)

       _SCREAMING_SNAKE_CASE   :				Tuple							    = [
           "Hello this is a long string of text.\n\nI'm trying to get the text of the",
           "Hey, I'm a little late to the party. I'm going to",
       ]

       self.assertListEqual(snake_case__  ,     snake_case__				)
     @is_pt_flax_cross_test
     def 				__SCREAMING_SNAKE_CASE							(     self				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE    ,		_SCREAMING_SNAKE_CASE   :				Optional[int]							    = self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
         with self.subTest(model_class.__name__				):
           # prepare inputs
           _SCREAMING_SNAKE_CASE   :				Optional[int]							    = self._prepare_for_class(snake_case__  ,     snake_case__				)
           _SCREAMING_SNAKE_CASE   :				List[str]							    = {k: torch.tensor(v.tolist()				) for k, v in prepared_inputs_dict.items()}

           # load corresponding PyTorch class
           _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = model_class.__name__[4:]  # Skip the "Flax" at the beginning
           _SCREAMING_SNAKE_CASE   :				str							    = getattr(snake_case__  ,     snake_case__				)

           _SCREAMING_SNAKE_CASE    ,		_SCREAMING_SNAKE_CASE   :				str							    = pt_inputs["input_ids"].shape
           _SCREAMING_SNAKE_CASE   :				str							    = np.random.randint(0  ,     seq_length - 1  ,     size=(batch_size,)				)
           for batch_idx, start_index in enumerate(snake_case__				):
             _SCREAMING_SNAKE_CASE   :				Tuple							    = 0
             _SCREAMING_SNAKE_CASE   :				Any							    = 1
             _SCREAMING_SNAKE_CASE   :				Tuple							    = 0
             _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = 1
           _SCREAMING_SNAKE_CASE   :				List[Any]							    = pt_model_class(snake_case__				).eval()
           _SCREAMING_SNAKE_CASE   :				Dict							    = model_class(snake_case__  ,     dtype=jnp.floataa				)

           _SCREAMING_SNAKE_CASE   :				Tuple							    = convert_pytorch_state_dict_to_flax(pt_model.state_dict()  ,     snake_case__				)
           _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = fx_state

           with torch.no_grad():
             _SCREAMING_SNAKE_CASE   :				Any							    = pt_model(**snake_case__				).to_tuple()

           _SCREAMING_SNAKE_CASE   :				Tuple							    = fx_model(**snake_case__				).to_tuple()
           self.assertEqual(len(snake_case__				)  ,     len(snake_case__				)  ,     "Output lengths differ between Flax and PyTorch"				)
           for fx_output, pt_output in zip(snake_case__  ,     snake_case__				):
             self.assert_almost_equals(fx_output[:, -1]  ,     pt_output[:, -1].numpy()  ,     4E-2				)

           with tempfile.TemporaryDirectory() as tmpdirname:
             pt_model.save_pretrained(snake_case__				)
             _SCREAMING_SNAKE_CASE   :				List[Any]							    = model_class.from_pretrained(snake_case__  ,     from_pt=snake_case__				)

           _SCREAMING_SNAKE_CASE   :				Tuple							    = fx_model_loaded(**snake_case__				).to_tuple()
           self.assertEqual(
               len(snake_case__				)  ,     len(snake_case__				)  ,     "Output lengths differ between Flax and PyTorch"				)
           for fx_output_loaded, pt_output in zip(snake_case__  ,     snake_case__				):
             self.assert_almost_equals(fx_output_loaded[:, -1]  ,     pt_output[:, -1].numpy()  ,     4E-2				)
     @is_pt_flax_cross_test
     def 				__SCREAMING_SNAKE_CASE							(     self				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE    ,		_SCREAMING_SNAKE_CASE   :				int							    = self.model_tester.prepare_config_and_inputs_for_common()
       for model_class in self.all_model_classes:
         with self.subTest(model_class.__name__				):
           # prepare inputs
           _SCREAMING_SNAKE_CASE   :				Any							    = self._prepare_for_class(snake_case__  ,     snake_case__				)
           _SCREAMING_SNAKE_CASE   :				str							    = {k: torch.tensor(v.tolist()				) for k, v in prepared_inputs_dict.items()}

           # load corresponding PyTorch class
           _SCREAMING_SNAKE_CASE   :				Dict							    = model_class.__name__[4:]  # Skip the "Flax" at the beginning
           _SCREAMING_SNAKE_CASE   :				List[Any]							    = getattr(snake_case__  ,     snake_case__				)

           _SCREAMING_SNAKE_CASE   :				Optional[int]							    = pt_model_class(snake_case__				).eval()
           _SCREAMING_SNAKE_CASE   :				List[str]							    = model_class(snake_case__  ,     dtype=jnp.floataa				)

           _SCREAMING_SNAKE_CASE   :				int							    = load_flax_weights_in_pytorch_model(snake_case__  ,     fx_model.params				)
           _SCREAMING_SNAKE_CASE    ,		_SCREAMING_SNAKE_CASE   :				str							    = pt_inputs["input_ids"].shape
           _SCREAMING_SNAKE_CASE   :				List[Any]							    = np.random.randint(0  ,     seq_length - 1  ,     size=(batch_size,)				)
           for batch_idx, start_index in enumerate(snake_case__				):
             _SCREAMING_SNAKE_CASE   :				List[str]							    = 0
             _SCREAMING_SNAKE_CASE   :				List[Any]							    = 1
             _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = 0
             _SCREAMING_SNAKE_CASE   :				str							    = 1

           # make sure weights are tied in PyTorch
           pt_model.tie_weights()

           with torch.no_grad():
             _SCREAMING_SNAKE_CASE   :				Optional[int]							    = pt_model(**snake_case__				).to_tuple()

           _SCREAMING_SNAKE_CASE   :				List[str]							    = fx_model(**snake_case__				).to_tuple()
           self.assertEqual(len(snake_case__				)  ,     len(snake_case__				)  ,     "Output lengths differ between Flax and PyTorch"				)
           for fx_output, pt_output in zip(snake_case__  ,     snake_case__				):
             self.assert_almost_equals(fx_output[:, -1]  ,     pt_output[:, -1].numpy()  ,     4E-2				)

           with tempfile.TemporaryDirectory() as tmpdirname:
             fx_model.save_pretrained(snake_case__				)
             _SCREAMING_SNAKE_CASE   :				int							    = pt_model_class.from_pretrained(snake_case__  ,     from_flax=snake_case__				)

           with torch.no_grad():
             _SCREAMING_SNAKE_CASE   :				Optional[int]							    = pt_model_loaded(**snake_case__				).to_tuple()

           self.assertEqual(
               len(snake_case__				)  ,     len(snake_case__				)  ,     "Output lengths differ between Flax and PyTorch"				)
           for fx_output, pt_output in zip(snake_case__  ,     snake_case__				):
             self.assert_almost_equals(fx_output[:, -1]  ,     pt_output[:, -1].numpy()  ,     4E-2				)




     @tooslow
     def 				__SCREAMING_SNAKE_CASE							(     self				):




       """simple docstring"""


       for model_class_name in self.all_model_classes:
         _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = model_class_name.from_pretrained("EleutherAI/gpt-j-6B"				)
         _SCREAMING_SNAKE_CASE   :				List[str]							    = model(np.ones((1, 1)				)				)
         self.assertIsNotNone(snake_case__				)





"""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()



