




import unittest

from transformers import XLMConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
      import torch

      from transformers import (
          XLMForMultipleChoice,
          XLMForQuestionAnswering,
          XLMForQuestionAnsweringSimple,
          XLMForSequenceClassification,
          XLMForTokenClassification,
          XLMModel,
          XLMWithLMHeadModel,
      )
      from transformers.models.xlm.modeling_xlm import XLM_PRETRAINED_MODEL_ARCHIVE_LIST





class     SCREAMING_SNAKE_CASE__		:
     def __init__(						self	:		Optional[int]       ,						__lowerCamelCase	:		Union[str, Any]       ,						__lowerCamelCase	:		Dict=13       ,						__lowerCamelCase	:		Dict=7       ,						__lowerCamelCase	:		List[str]=True       ,						__lowerCamelCase	:		List[str]=True       ,						__lowerCamelCase	:		Optional[Any]=True       ,						__lowerCamelCase	:		Dict=True       ,						__lowerCamelCase	:		str=True       ,						__lowerCamelCase	:		Optional[Any]=False       ,						__lowerCamelCase	:		str=False       ,						__lowerCamelCase	:		Optional[Any]=False       ,						__lowerCamelCase	:		List[Any]=2       ,						__lowerCamelCase	:		Union[str, Any]=99       ,						__lowerCamelCase	:		Optional[Any]=0       ,						__lowerCamelCase	:		Optional[int]=32       ,						__lowerCamelCase	:		List[Any]=5       ,						__lowerCamelCase	:		Optional[Any]=4       ,						__lowerCamelCase	:		int=0.1       ,						__lowerCamelCase	:		Any=0.1       ,						__lowerCamelCase	:		Any=5_12       ,						__lowerCamelCase	:		Dict=2       ,						__lowerCamelCase	:		List[Any]=0.02       ,						__lowerCamelCase	:		Dict=2       ,						__lowerCamelCase	:		int=4       ,						__lowerCamelCase	:		Any="last"       ,						__lowerCamelCase	:		List[str]=True       ,						__lowerCamelCase	:		int=None       ,						__lowerCamelCase	:		List[Any]=0       ,						):



       """simple docstring"""
       lowerCAmelCase__										=      parent
       lowerCAmelCase__										=      batch_size
       lowerCAmelCase__										=      seq_length
       lowerCAmelCase__										=      is_training
       lowerCAmelCase__										=      use_input_lengths
       lowerCAmelCase__										=      use_token_type_ids
       lowerCAmelCase__										=      use_labels
       lowerCAmelCase__										=      gelu_activation
       lowerCAmelCase__										=      sinusoidal_embeddings
       lowerCAmelCase__										=      causal
       lowerCAmelCase__										=      asm
       lowerCAmelCase__										=      n_langs
       lowerCAmelCase__										=      vocab_size
       lowerCAmelCase__										=      n_special
       lowerCAmelCase__										=      hidden_size
       lowerCAmelCase__										=      num_hidden_layers
       lowerCAmelCase__										=      num_attention_heads
       lowerCAmelCase__										=      hidden_dropout_prob
       lowerCAmelCase__										=      attention_probs_dropout_prob
       lowerCAmelCase__										=      max_position_embeddings
       lowerCAmelCase__										=      type_sequence_label_size
       lowerCAmelCase__										=      initializer_range
       lowerCAmelCase__										=      num_labels
       lowerCAmelCase__										=      num_choices
       lowerCAmelCase__										=      summary_type
       lowerCAmelCase__										=      use_proj
       lowerCAmelCase__										=      scope
       lowerCAmelCase__										=      bos_token_id
     def        A__      (						self	:		Optional[Any]						):



       """simple docstring"""
       lowerCAmelCase__										=      ids_tensor([self.batch_size, self.seq_length]       ,						self.vocab_size						)
       lowerCAmelCase__										=      random_attention_mask([self.batch_size, self.seq_length]						)

       lowerCAmelCase__										=      None
       if self.use_input_lengths:
         lowerCAmelCase__										=      (
             ids_tensor([self.batch_size]       ,						vocab_size=2						) + self.seq_length - 2
         )  # small variation of seq_length

       lowerCAmelCase__										=      None
       if self.use_token_type_ids:
         lowerCAmelCase__										=      ids_tensor([self.batch_size, self.seq_length]       ,						self.n_langs						)

       lowerCAmelCase__										=      None
       lowerCAmelCase__										=      None
       lowerCAmelCase__										=      None
       if self.use_labels:
         lowerCAmelCase__										=      ids_tensor([self.batch_size]       ,						self.type_sequence_label_size						)
         lowerCAmelCase__										=      ids_tensor([self.batch_size, self.seq_length]       ,						self.num_labels						)
         lowerCAmelCase__										=      ids_tensor([self.batch_size]       ,						2						).float()
         lowerCAmelCase__										=      ids_tensor([self.batch_size]       ,						self.num_choices						)

       lowerCAmelCase__										=      self.get_config()

       return (
           config,
           input_ids,
           token_type_ids,
           input_lengths,
           sequence_labels,
           token_labels,
           is_impossible_labels,
           choice_labels,
           input_mask,
       )
     def        A__      (						self	:		Optional[Any]						):



       """simple docstring"""
       return XLMConfig(
           vocab_size=self.vocab_size       ,						n_special=self.n_special       ,						emb_dim=self.hidden_size       ,						n_layers=self.num_hidden_layers       ,						n_heads=self.num_attention_heads       ,						dropout=self.hidden_dropout_prob       ,						attention_dropout=self.attention_probs_dropout_prob       ,						gelu_activation=self.gelu_activation       ,						sinusoidal_embeddings=self.sinusoidal_embeddings       ,						asm=self.asm       ,						causal=self.causal       ,						n_langs=self.n_langs       ,						max_position_embeddings=self.max_position_embeddings       ,						initializer_range=self.initializer_range       ,						summary_type=self.summary_type       ,						use_proj=self.use_proj       ,						num_labels=self.num_labels       ,						bos_token_id=self.bos_token_id       ,						)
     def        A__      (						self	:		Optional[Any]       ,						__lowerCamelCase	:		Any       ,						__lowerCamelCase	:		List[Any]       ,						__lowerCamelCase	:		Tuple       ,						__lowerCamelCase	:		Dict       ,						__lowerCamelCase	:		int       ,						__lowerCamelCase	:		Optional[int]       ,						__lowerCamelCase	:		Tuple       ,						__lowerCamelCase	:		List[str]       ,						__lowerCamelCase	:		List[Any]       ,						):



       """simple docstring"""
       lowerCAmelCase__										=      XLMModel(config=A__						)
       model.to(A__						)
       model.eval()
       lowerCAmelCase__										=      model(A__       ,						lengths=A__       ,						langs=A__						)
       lowerCAmelCase__										=      model(A__       ,						langs=A__						)
       lowerCAmelCase__										=      model(A__						)
       self.parent.assertEqual(result.last_hidden_state.shape       ,						(self.batch_size, self.seq_length, self.hidden_size)						)
     def        A__      (						self	:		List[str]       ,						__lowerCamelCase	:		Optional[int]       ,						__lowerCamelCase	:		int       ,						__lowerCamelCase	:		Optional[Any]       ,						__lowerCamelCase	:		Dict       ,						__lowerCamelCase	:		Union[str, Any]       ,						__lowerCamelCase	:		List[Any]       ,						__lowerCamelCase	:		Dict       ,						__lowerCamelCase	:		Optional[int]       ,						__lowerCamelCase	:		List[str]       ,						):



       """simple docstring"""
       lowerCAmelCase__										=      XLMWithLMHeadModel(A__						)
       model.to(A__						)
       model.eval()

       lowerCAmelCase__										=      model(A__       ,						token_type_ids=A__       ,						labels=A__						)
       self.parent.assertEqual(result.loss.shape       ,						()						)
       self.parent.assertEqual(result.logits.shape       ,						(self.batch_size, self.seq_length, self.vocab_size)						)
     def        A__      (						self	:		Optional[int]       ,						__lowerCamelCase	:		List[Any]       ,						__lowerCamelCase	:		Tuple       ,						__lowerCamelCase	:		Union[str, Any]       ,						__lowerCamelCase	:		Optional[int]       ,						__lowerCamelCase	:		Union[str, Any]       ,						__lowerCamelCase	:		Any       ,						__lowerCamelCase	:		Dict       ,						__lowerCamelCase	:		int       ,						__lowerCamelCase	:		Any       ,						):



       """simple docstring"""
       lowerCAmelCase__										=      XLMForQuestionAnsweringSimple(A__						)
       model.to(A__						)
       model.eval()

       lowerCAmelCase__										=      model(A__						)

       lowerCAmelCase__										=      model(A__       ,						start_positions=A__       ,						end_positions=A__						)
       lowerCAmelCase__										=      outputs
       self.parent.assertEqual(result.start_logits.shape       ,						(self.batch_size, self.seq_length)						)
       self.parent.assertEqual(result.end_logits.shape       ,						(self.batch_size, self.seq_length)						)
     def        A__      (						self	:		List[str]       ,						__lowerCamelCase	:		str       ,						__lowerCamelCase	:		Optional[Any]       ,						__lowerCamelCase	:		List[str]       ,						__lowerCamelCase	:		Union[str, Any]       ,						__lowerCamelCase	:		Optional[int]       ,						__lowerCamelCase	:		Optional[Any]       ,						__lowerCamelCase	:		Tuple       ,						__lowerCamelCase	:		List[str]       ,						__lowerCamelCase	:		Tuple       ,						):



       """simple docstring"""
       lowerCAmelCase__										=      XLMForQuestionAnswering(A__						)
       model.to(A__						)
       model.eval()

       lowerCAmelCase__										=      model(A__						)

       lowerCAmelCase__										=      model(
           A__       ,						start_positions=A__       ,						end_positions=A__       ,						cls_index=A__       ,						is_impossible=A__       ,						p_mask=A__       ,						)

       lowerCAmelCase__										=      model(
           A__       ,						start_positions=A__       ,						end_positions=A__       ,						cls_index=A__       ,						is_impossible=A__       ,						)

       (lowerCAmelCase__  )										=      result_with_labels.to_tuple()

       lowerCAmelCase__										=      model(A__       ,						start_positions=A__       ,						end_positions=A__						)

       (lowerCAmelCase__  )										=      result_with_labels.to_tuple()

       self.parent.assertEqual(result_with_labels.loss.shape       ,						()						)
       self.parent.assertEqual(result.start_top_log_probs.shape       ,						(self.batch_size, model.config.start_n_top)						)
       self.parent.assertEqual(result.start_top_index.shape       ,						(self.batch_size, model.config.start_n_top)						)
       self.parent.assertEqual(
           result.end_top_log_probs.shape       ,						(self.batch_size, model.config.start_n_top * model.config.end_n_top)						)
       self.parent.assertEqual(
           result.end_top_index.shape       ,						(self.batch_size, model.config.start_n_top * model.config.end_n_top)						)
       self.parent.assertEqual(result.cls_logits.shape       ,						(self.batch_size,)						)
     def        A__      (						self	:		Optional[int]       ,						__lowerCamelCase	:		Dict       ,						__lowerCamelCase	:		str       ,						__lowerCamelCase	:		str       ,						__lowerCamelCase	:		Optional[int]       ,						__lowerCamelCase	:		List[Any]       ,						__lowerCamelCase	:		List[Any]       ,						__lowerCamelCase	:		Tuple       ,						__lowerCamelCase	:		Dict       ,						__lowerCamelCase	:		Optional[Any]       ,						):



       """simple docstring"""
       lowerCAmelCase__										=      XLMForSequenceClassification(A__						)
       model.to(A__						)
       model.eval()

       lowerCAmelCase__										=      model(A__						)
       lowerCAmelCase__										=      model(A__       ,						labels=A__						)
       self.parent.assertEqual(result.loss.shape       ,						()						)
       self.parent.assertEqual(result.logits.shape       ,						(self.batch_size, self.type_sequence_label_size)						)
     def        A__      (						self	:		Optional[Any]       ,						__lowerCamelCase	:		Any       ,						__lowerCamelCase	:		List[str]       ,						__lowerCamelCase	:		int       ,						__lowerCamelCase	:		Dict       ,						__lowerCamelCase	:		Optional[Any]       ,						__lowerCamelCase	:		Any       ,						__lowerCamelCase	:		Optional[int]       ,						__lowerCamelCase	:		List[str]       ,						__lowerCamelCase	:		List[Any]       ,						):



       """simple docstring"""
       lowerCAmelCase__										=      self.num_labels
       lowerCAmelCase__										=      XLMForTokenClassification(A__						)
       model.to(A__						)
       model.eval()

       lowerCAmelCase__										=      model(A__       ,						attention_mask=A__       ,						labels=A__						)
       self.parent.assertEqual(result.logits.shape       ,						(self.batch_size, self.seq_length, self.num_labels)						)
     def        A__      (						self	:		str       ,						__lowerCamelCase	:		Optional[int]       ,						__lowerCamelCase	:		str       ,						__lowerCamelCase	:		Optional[int]       ,						__lowerCamelCase	:		List[Any]       ,						__lowerCamelCase	:		List[Any]       ,						__lowerCamelCase	:		Dict       ,						__lowerCamelCase	:		Optional[Any]       ,						__lowerCamelCase	:		Any       ,						__lowerCamelCase	:		Tuple       ,						):



       """simple docstring"""
       lowerCAmelCase__										=      self.num_choices
       lowerCAmelCase__										=      XLMForMultipleChoice(config=A__						)
       model.to(A__						)
       model.eval()
       lowerCAmelCase__										=      input_ids.unsqueeze(1						).expand(-1       ,						self.num_choices       ,						-1						).contiguous()
       lowerCAmelCase__										=      token_type_ids.unsqueeze(1						).expand(-1       ,						self.num_choices       ,						-1						).contiguous()
       lowerCAmelCase__										=      input_mask.unsqueeze(1						).expand(-1       ,						self.num_choices       ,						-1						).contiguous()
       lowerCAmelCase__										=      model(
           A__       ,						attention_mask=A__       ,						token_type_ids=A__       ,						labels=A__       ,						)
       self.parent.assertEqual(result.logits.shape       ,						(self.batch_size, self.num_choices)						)



     def        A__      (						self	:		Optional[Any]						):



       """simple docstring"""
       lowerCAmelCase__										=      self.prepare_config_and_inputs()
       (
           lowerCAmelCase__  
       )										=      config_and_inputs
       lowerCAmelCase__										=      {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """lengths""": input_lengths}
       return config, inputs_dict





@require_torch
class     SCREAMING_SNAKE_CASE__		(_a    ,					_a    ,					_a    ,					unittest.TestCase			):
     lowercase_ :      Tuple				= (
         (
             XLMModel,
             XLMWithLMHeadModel,
             XLMForQuestionAnswering,
             XLMForSequenceClassification,
             XLMForQuestionAnsweringSimple,
             XLMForTokenClassification,
             XLMForMultipleChoice,
         )
         if is_torch_available()
         else ()
     )
     lowercase_ :      int				= (
         (XLMWithLMHeadModel,) if is_torch_available() else ()
     )  # TODO (PVP): Check other models whether language generation is also applicable
     lowercase_ :      Optional[Any]				= (
         {
             '''feature-extraction''': XLMModel,
             '''fill-mask''': XLMWithLMHeadModel,
             '''question-answering''': XLMForQuestionAnsweringSimple,
             '''text-classification''': XLMForSequenceClassification,
             '''text-generation''': XLMWithLMHeadModel,
             '''token-classification''': XLMForTokenClassification,
             '''zero-shot''': XLMForSequenceClassification,
         }
         if is_torch_available()
         else {}
     )
     def        A__      (						self	:		Optional[int]       ,						__lowerCamelCase	:		int       ,						__lowerCamelCase	:		Tuple       ,						__lowerCamelCase	:		Dict       ,						__lowerCamelCase	:		Dict       ,						__lowerCamelCase	:		List[str]						):



       """simple docstring"""
       if (
           pipeline_test_casse_name == "QAPipelineTests"
           and tokenizer_name is not None
           and not tokenizer_name.endswith('''Fast'''						)
       ):
         # `QAPipelineTests` fails for a few models when the slower tokenizer are used.
         # (The slower tokenizers were never used for pipeline tests before the pipeline testing rework)
         # TODO: check (and possibly fix) the `QAPipelineTests` with slower tokenizer
         return True

       return False
     def        A__      (						self	:		Tuple       ,						__lowerCamelCase	:		int       ,						__lowerCamelCase	:		str       ,						__lowerCamelCase	:		Tuple=False						):



       """simple docstring"""
       lowerCAmelCase__										=      super()._prepare_for_class(A__       ,						A__       ,						return_labels=A__						)

       if return_labels:
         if model_class.__name__ == "XLMForQuestionAnswering":
           lowerCAmelCase__										=      torch.zeros(
               self.model_tester.batch_size       ,						dtype=torch.long       ,						device=A__						)
           lowerCAmelCase__										=      torch.zeros(
               self.model_tester.batch_size       ,						dtype=torch.long       ,						device=A__						)

       return inputs_dict
     def        A__      (						self	:		str						):



       """simple docstring"""
       lowerCAmelCase__										=      XLMModelTester(self						)
       lowerCAmelCase__										=      ConfigTester(self       ,						config_class=A__       ,						emb_dim=37						)
     def        A__      (						self	:		List[str]						):



       """simple docstring"""
       self.config_tester.run_common_tests()
     def        A__      (						self	:		Optional[Any]						):



       """simple docstring"""
       lowerCAmelCase__										=      self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_xlm_model(*A__						)
     def        A__      (						self	:		List[str]						):



       """simple docstring"""
       lowerCAmelCase__										=      self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_xlm_lm_head(*A__						)
     def        A__      (						self	:		List[str]						):



       """simple docstring"""
       lowerCAmelCase__										=      self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_xlm_simple_qa(*A__						)
     def        A__      (						self	:		List[str]						):



       """simple docstring"""
       lowerCAmelCase__										=      self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_xlm_qa(*A__						)
     def        A__      (						self	:		Optional[int]						):



       """simple docstring"""
       lowerCAmelCase__										=      self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_xlm_sequence_classif(*A__						)
     def        A__      (						self	:		List[str]						):



       """simple docstring"""
       lowerCAmelCase__										=      self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_xlm_token_classif(*A__						)
     def        A__      (						self	:		Dict						):



       """simple docstring"""
       lowerCAmelCase__										=      self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_xlm_for_multiple_choice(*A__						)
     def        A__      (						self	:		Optional[int]       ,						__lowerCamelCase	:		Optional[Any]       ,						__lowerCamelCase	:		List[str]       ,						__lowerCamelCase	:		Union[str, Any]       ,						__lowerCamelCase	:		Dict       ,						__lowerCamelCase	:		Any       ,						__lowerCamelCase	:		int=False       ,						__lowerCamelCase	:		Dict=1						):



       """simple docstring"""
       self.assertIsInstance(A__       ,						A__						)
       self.assertListEqual(
           [isinstance(A__       ,						A__						) for iter_attentions in attentions]       ,						[True] * len(A__						)						)
       self.assertEqual(len(A__						)       ,						(max_length - min_length) * num_beam_groups						)

       for idx, iter_attentions in enumerate(A__						):
         # adds PAD dummy token
         lowerCAmelCase__										=      min_length + idx + 1
         lowerCAmelCase__										=      min_length + idx + 1

         lowerCAmelCase__										=      (
             batch_size * num_beam_groups,
             config.num_attention_heads,
             tgt_len,
             src_len,
         )
         # check attn size
         self.assertListEqual(
             [layer_attention.shape for layer_attention in iter_attentions]       ,						[expected_shape] * len(A__						)						)
     def        A__      (						self	:		List[str]       ,						__lowerCamelCase	:		Tuple       ,						__lowerCamelCase	:		Union[str, Any]       ,						__lowerCamelCase	:		List[str]       ,						__lowerCamelCase	:		str       ,						__lowerCamelCase	:		Tuple       ,						__lowerCamelCase	:		Union[str, Any]=False       ,						__lowerCamelCase	:		Tuple=1						):



       """simple docstring"""
       self.assertIsInstance(A__       ,						A__						)
       self.assertListEqual(
           [isinstance(A__       ,						A__						) for iter_hidden_states in hidden_states]       ,						[True] * len(A__						)       ,						)
       self.assertEqual(len(A__						)       ,						(max_length - min_length) * num_beam_groups						)

       for idx, iter_hidden_states in enumerate(A__						):
         # adds PAD dummy token
         lowerCAmelCase__										=      min_length + idx + 1
         lowerCAmelCase__										=      (batch_size * num_beam_groups, seq_len, config.hidden_size)
         # check hidden size
         self.assertListEqual(
             [layer_hidden_states.shape for layer_hidden_states in iter_hidden_states]       ,						[expected_shape] * len(A__						)       ,						)
       pass



     @slow
     def        A__      (						self	:		Optional[Any]						):



       """simple docstring"""
       for model_name in XLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
         lowerCAmelCase__										=      XLMModel.from_pretrained(A__						)
         self.assertIsNotNone(A__						)





@require_torch
class     SCREAMING_SNAKE_CASE__		(unittest.TestCase			):
     @slow
     def        A__      (						self	:		Tuple						):



       """simple docstring"""
       lowerCAmelCase__										=      XLMWithLMHeadModel.from_pretrained('''xlm-mlm-en-2048'''						)
       model.to(A__						)
       lowerCAmelCase__										=      torch.tensor([[14, 4_47]]       ,						dtype=torch.long       ,						device=A__						)  # the president
       lowerCAmelCase__										=      [
           14,
           4_47,
           14,
           4_47,
           14,
           4_47,
           14,
           4_47,
           14,
           4_47,
           14,
           4_47,
           14,
           4_47,
           14,
           4_47,
           14,
           4_47,
           14,
           4_47,
       ]  # the president the president the president the president the president the president the president the president the president the president
       # TODO(PVP): this and other input_ids I tried for generation give pretty bad results. Not sure why. Model might just not be made for auto-regressive inference
       lowerCAmelCase__										=      model.generate(A__       ,						do_sample=A__						)
       self.assertListEqual(output_ids[0].cpu().numpy().tolist()       ,						A__						)




import collections
import os
import re
from pathlib import Path


A_			:					List[str]    			=     'src/transformers'


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

def 		UpperCamelCase   (lowercase_:					Optional[Any]   )	-> Any:
	if _re_test_backend.search(lowercase_   ) is None:
		return None
	A__				:       Optional[int]	  =						[b[0] for b in _re_backend.findall(lowercase_   )]
	backends.sort()
	return "_and_".join(lowercase_   )

def 		UpperCamelCase   (lowercase_:					Any   )	-> Dict:
	with open(lowercase_       , """r"""       , encoding="""utf-8"""       , newline="""\n"""   ) as f:
		A__				:       Optional[Any]	  =						f.readlines()

	A__				:       Optional[Any]	  =						0
	while line_index < len(lowercase_   ) and not lines[line_index].startswith("""_import_structure = {"""   ):
		line_index += 1

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

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

	A__				:       str	  =						{"""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.
		A__				:       Optional[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:
			A__				:       List[str]	  =						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

			A__				:       Tuple	  =						[]
			# Until we unindent, add backend objects to the list
			while len(lines[line_index]   ) <= 1 or lines[line_index].startswith(""" """ * 4   ):
				A__				:       Any	  =						lines[line_index]
				if _re_import_struct_add_one.search(lowercase_   ) is not None:
					objects.append(_re_import_struct_add_one.search(lowercase_   ).groups()[0]   )
				elif _re_import_struct_add_many.search(lowercase_   ) is not None:
					A__				:       Any	  =						_re_import_struct_add_many.search(lowercase_   ).groups()[0].split(""", """   )
					A__				:       int	  =						[obj[1:-1] for obj in imports if len(lowercase_   ) > 0]
					objects.extend(lowercase_   )
				elif _re_between_brackets.search(lowercase_   ) is not None:
					A__				:       Any	  =						_re_between_brackets.search(lowercase_   ).groups()[0].split(""", """   )
					A__				:       Any	  =						[obj[1:-1] for obj in imports if len(lowercase_   ) > 0]
					objects.extend(lowercase_   )
				elif _re_quote_object.search(lowercase_   ) is not None:
					objects.append(_re_quote_object.search(lowercase_   ).groups()[0]   )
				elif line.startswith(""" """ * 8 + """\""""   ):
					objects.append(line[9:-3]   )
				elif line.startswith(""" """ * 12 + """\""""   ):
					objects.append(line[13:-3]   )
				line_index += 1

			A__				:       List[str]	  =						objects
		else:
			line_index += 1

    # At this stage we are in the TYPE_CHECKING part, first grab the objects without a specific backend
	A__				:       Any	  =						[]
	while (
	    line_index < len(lowercase_   )
	    and find_backend(lines[line_index]   ) is None
	    and not lines[line_index].startswith("""else"""   )
	):
		A__				:       Dict	  =						lines[line_index]
		A__				:       Any	  =						_re_import.search(lowercase_   )
		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

	A__				:       List[str]	  =						{"""none""": objects}
	# Let's continue with backend-specific objects
	while line_index < len(lowercase_   ):
		# If the line is an if is_backend_available, we grab all objects associated.
		A__				:       Optional[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:
			A__				:       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

			A__				:       List[str]	  =						[]
			# Until we unindent, add backend objects to the list
			while len(lines[line_index]   ) <= 1 or lines[line_index].startswith(""" """ * 8   ):
				A__				:       Union[str, Any]	  =						lines[line_index]
				A__				:       List[str]	  =						_re_import.search(lowercase_   )
				if single_line_import_search is not None:
					objects.extend(single_line_import_search.groups()[0].split(""", """   )   )
				elif line.startswith(""" """ * 12   ):
					objects.append(line[12:-2]   )
				line_index += 1

			A__				:       Tuple	  =						objects
		else:
			line_index += 1

	return import_dict_objects, type_hint_objects

def 		UpperCamelCase   (lowercase_:					Optional[Any]       , lowercase_:					Union[str, Any]   )	-> List[Any]:

	def find_duplicates(lowercase_:					Tuple   ):
		return [k for k, v in collections.Counter(lowercase_   ).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!"]

	A__				:       str	  =						[]
	for key in import_dict_objects.keys():
		A__				:       Optional[int]	  =						find_duplicates(import_dict_objects[key]   )
		if duplicate_imports:
			errors.append(f"""Duplicate _import_structure definitions for: {duplicate_imports}"""   )
		A__				:       Tuple	  =						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]   )   ):
			A__				:       Tuple	  =						"""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 		UpperCamelCase   ()	-> str:
	A__				:       str	  =						[]
	for root, _, files in os.walk(lowercase_   ):
		if "__init__.py" in files:
			A__				:       Tuple	  =						os.path.join(lowercase_       , """__init__.py"""   )
			A__				:       Union[str, Any]	  =						parse_init(lowercase_   )
			if objects is not None:
				A__				:       List[Any]	  =						analyze_results(*lowercase_   )
				if len(lowercase_   ) > 0:
					A__				:       int	  =						f"""Problem in {fname}, both halves do not define the same objects.\n{errors[0]}"""
					failures.append("""\n""".join(lowercase_   )   )
	if len(lowercase_   ) > 0:
		raise ValueError("""\n\n""".join(lowercase_   )   )

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


A_			:					str    			=     [
    'convert_pytorch_checkpoint_to_tf2',
    'modeling_flax_pytorch_utils',
    'models.esm.openfold_utils',
]

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

	A__				:       Any	  =						direct_transformers_import(lowercase_   )

	A__				:       List[str]	  =						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(lowercase_       , """__init__.py"""   )       , """r"""   ) as f:
		A__				:       str	  =						f.read()
	import_structure_keys.update(set(re.findall(r"""import_structure\[\"([^\"]*)\"\]"""       , lowercase_   )   )   )

	A__				:       Union[str, Any]	  =						[
	    module
	    for module in get_transformers_submodules()
	    if module not in IGNORE_SUBMODULES and module not in import_structure_keys
	]

	if len(lowercase_   ) > 0:
		A__				:       Dict	  =						"""\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()



