import collections
import importlib.util
import os
import re
from pathlib import Path


UpperCAmelCase_           =				'''src/transformers'''


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




def    lowerCAmelCase_  (		lowercase:				int		)     ->					List[str]:







 '''simple docstring'''
 if _re_test_backend.search(lowercase		) is None:
  return None
 _UpperCamelCase:  str    						=			[b[0] for b in _re_backend.findall(lowercase		)]
 backends.sort()
 return "_and_".join(lowercase		)




def    lowerCAmelCase_  (		lowercase:				Union[str, Any]		)     ->					Union[str, Any]:







 '''simple docstring'''
 with open(lowercase	,       '''r'''	,       encoding='''utf-8'''	,       newline='''\n'''		) as f:
  _UpperCamelCase:  Optional[Any]    						=			f.readlines()

 _UpperCamelCase:  str    						=			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
 _UpperCamelCase:  List[str]    						=			[]
 while not lines[line_index].startswith('''if TYPE_CHECKING'''		) and find_backend(lines[line_index]		) is None:
  _UpperCamelCase:  Any    						=			lines[line_index]
  # If we have everything on a single line, let's deal with it.
  if _re_one_line_import_struct.search(lowercase		):
   _UpperCamelCase:  Tuple    						=			_re_one_line_import_struct.search(lowercase		).groups()[0]
   _UpperCamelCase:  Union[str, Any]    						=			re.findall('''\[([^\]]+)\]'''	,       lowercase		)
   for imp in imports:
    objects.extend([obj[1:-1] for obj in imp.split(''', '''		)]		)
   line_index += 1
   continue
  _UpperCamelCase:  Tuple    						=			_re_import_struct_key_value.search(lowercase		)
  if single_line_import_search is not None:
   _UpperCamelCase:  List[Any]    						=			[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

 _UpperCamelCase:  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.
  _UpperCamelCase:  Tuple    						=			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:
   _UpperCamelCase:  Union[str, 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

   _UpperCamelCase:  List[Any]    						=			[]
   # Until we unindent, add backend objects to the list
   while len(lines[line_index]		) <= 1 or lines[line_index].startswith(''' ''' * 4		):
    _UpperCamelCase:  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:
     _UpperCamelCase:  Any    						=			_re_import_struct_add_many.search(lowercase		).groups()[0].split(''', '''		)
     _UpperCamelCase:  Tuple    						=			[obj[1:-1] for obj in imports if len(lowercase		) > 0]
     objects.extend(lowercase		)
    elif _re_between_brackets.search(lowercase		) is not None:
     _UpperCamelCase:  int    						=			_re_between_brackets.search(lowercase		).groups()[0].split(''', '''		)
     _UpperCamelCase:  Optional[int]    						=			[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

   _UpperCamelCase:  Tuple    						=			objects
  else:
   line_index += 1

    # At this stage we are in the TYPE_CHECKING part, first grab the objects without a specific backend
 _UpperCamelCase:  List[str]    						=			[]
 while (
     line_index < len(lowercase		)
     and find_backend(lines[line_index]		) is None
     and not lines[line_index].startswith('''else'''		)
 ):
  _UpperCamelCase:  Dict    						=			lines[line_index]
  _UpperCamelCase:  Optional[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

 _UpperCamelCase:  Union[str, Any]    						=			{'''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.
  _UpperCamelCase:  str    						=			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:
   _UpperCamelCase:  int    						=			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

   _UpperCamelCase:  Dict    						=			[]
   # Until we unindent, add backend objects to the list
   while len(lines[line_index]		) <= 1 or lines[line_index].startswith(''' ''' * 8		):
    _UpperCamelCase:  List[str]    						=			lines[line_index]
    _UpperCamelCase:  Optional[int]    						=			_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

   _UpperCamelCase:  int    						=			objects
  else:
   line_index += 1

 return import_dict_objects, type_hint_objects




def    lowerCAmelCase_  (		lowercase:				Optional[int]	,       lowercase:				str		)     ->					Optional[int]:







 '''simple docstring'''

 def find_duplicates(lowercase:				int		):
  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!"]

 _UpperCamelCase:  Any    						=			[]
 for key in import_dict_objects.keys():
  _UpperCamelCase:  Optional[int]    						=			find_duplicates(import_dict_objects[key]		)
  if duplicate_imports:
   errors.append(F"""Duplicate _import_structure definitions for: {duplicate_imports}"""		)
  _UpperCamelCase:  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]		)		):
   _UpperCamelCase:  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]:







 '''simple docstring'''
 _UpperCamelCase:  List[str]    						=			[]
 for root, _, files in os.walk(lowercase		):
  if "__init__.py" in files:
   _UpperCamelCase:  Optional[Any]    						=			os.path.join(lowercase	,       '''__init__.py'''		)
   _UpperCamelCase:  Union[str, Any]    						=			parse_init(lowercase		)
   if objects is not None:
    _UpperCamelCase:  Tuple    						=			analyze_results(*lowercase		)
    if len(lowercase		) > 0:
     _UpperCamelCase:  List[str]    						=			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    lowerCAmelCase_  (		)     ->					str:







 '''simple docstring'''
 _UpperCamelCase:  Union[str, Any]    						=			[]
 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
   _UpperCamelCase:  Optional[Any]    						=			str((Path(lowercase		) / folder).relative_to(lowercase		)		)
   _UpperCamelCase:  str    						=			short_path.replace(os.path.sep	,       '''.'''		)
   submodules.append(lowercase		)
  for fname in files:
   if fname == "__init__.py":
    continue
   _UpperCamelCase:  Optional[int]    						=			str((Path(lowercase		) / fname).relative_to(lowercase		)		)
   _UpperCamelCase:  Any    						=			short_path.replace('''.py'''	,       ''''''		).replace(os.path.sep	,       '''.'''		)
   if len(submodule.split('''.'''		)		) == 1:
    submodules.append(lowercase		)
 return submodules


UpperCAmelCase_           =				[
    '''convert_pytorch_checkpoint_to_tf2''',
    '''modeling_flax_pytorch_utils''',
]




def    lowerCAmelCase_  (		)     ->					Tuple:







 '''simple docstring'''
 # This is to make sure the transformers module imported is the one in the repo.
 _UpperCamelCase:  Optional[Any]    						=			importlib.util.spec_from_file_location(
     '''transformers'''	,       os.path.join(lowercase	,       '''__init__.py'''		)	,       submodule_search_locations=[PATH_TO_TRANSFORMERS]	,       )
 _UpperCamelCase:  Tuple    						=			spec.loader.load_module()

 _UpperCamelCase:  Optional[Any]    						=			[
     module
     for module in get_transformers_submodules()
     if module not in IGNORE_SUBMODULES and module not in transformers._import_structure.keys()
 ]
 if len(lowercase		) > 0:
  _UpperCamelCase:  List[Any]    						=			'''\n'''.join(F"""- {module}""" for module in module_not_registered		)
  raise ValueError(
      '''The following submodules are not properly registered 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()
from __future__ import annotations

import unittest

from transformers import LEDConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
  import tensorflow as tf

  from transformers import TFLEDForConditionalGeneration, TFLEDModel




@require_tf
class 			__magic_name__	:





   """simple docstring"""




   lowerCAmelCase   :     Optional[int]	      = LEDConfig
   lowerCAmelCase   :     List[str]	      = {}
   lowerCAmelCase   :     str	      = '''gelu'''



   def __init__(						self       :	int		,	_lowercase       :	Tuple		,	_lowercase       :	Dict=13		,	_lowercase       :	Union[str, Any]=7		,	_lowercase       :	Optional[int]=True		,	_lowercase       :	Any=False		,	_lowercase       :	Optional[int]=99		,	_lowercase       :	Dict=32		,	_lowercase       :	Optional[Any]=2		,	_lowercase       :	Union[str, Any]=4		,	_lowercase       :	List[str]=37		,	_lowercase       :	Optional[Any]=0.1		,	_lowercase       :	List[Any]=0.1		,	_lowercase       :	Union[str, Any]=20		,	_lowercase       :	Tuple=2		,	_lowercase       :	str=1		,	_lowercase       :	Optional[int]=0		,	_lowercase       :	List[str]=4		,	):



    """simple docstring"""



    _UpperCamelCase:  Optional[Any]    						=			parent
    _UpperCamelCase:  Union[str, Any]    						=			batch_size
    _UpperCamelCase:  Optional[Any]    						=			seq_length
    _UpperCamelCase:  str    						=			is_training
    _UpperCamelCase:  Dict    						=			use_labels
    _UpperCamelCase:  List[Any]    						=			vocab_size
    _UpperCamelCase:  Dict    						=			hidden_size
    _UpperCamelCase:  int    						=			num_hidden_layers
    _UpperCamelCase:  Dict    						=			num_attention_heads
    _UpperCamelCase:  Dict    						=			intermediate_size

    _UpperCamelCase:  Union[str, Any]    						=			hidden_dropout_prob
    _UpperCamelCase:  Any    						=			attention_probs_dropout_prob
    _UpperCamelCase:  Optional[Any]    						=			max_position_embeddings
    _UpperCamelCase:  Union[str, Any]    						=			eos_token_id
    _UpperCamelCase:  Union[str, Any]    						=			pad_token_id
    _UpperCamelCase:  Optional[Any]    						=			bos_token_id
    _UpperCamelCase:  Optional[Any]    						=			attention_window

    # `ModelTesterMixin.test_attention_outputs` is expecting attention tensors to be of size
    # [num_attention_heads, encoder_seq_length, encoder_key_length], but TFLongformerSelfAttention
    # returns attention of shape [num_attention_heads, encoder_seq_length, self.attention_window + 1]
    # because its local attention only attends to `self.attention_window` and one before and one after
    _UpperCamelCase:  Any    						=			self.attention_window + 2

    # because of padding `encoder_seq_length`, is different from `seq_length`. Relevant for
    # the `test_attention_outputs` and `test_hidden_states_output` tests
    _UpperCamelCase:  Tuple    						=			(
        self.seq_length + (self.attention_window - self.seq_length % self.attention_window) % self.attention_window
    )



   def 	lowerCAmelCase    (						self       :	int				):



    """simple docstring"""



    _UpperCamelCase:  Union[str, Any]    						=			ids_tensor([self.batch_size, self.seq_length - 1]		,	self.vocab_size				)
    _UpperCamelCase:  Union[str, Any]    						=			tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size				)		,	1				)
    _UpperCamelCase:  Union[str, Any]    						=			tf.concat([input_ids, eos_tensor]		,	axis=1				)

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

    _UpperCamelCase:  Any    						=			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		,	attention_window=self.attention_window		,	**self.config_updates		,	)
    _UpperCamelCase:  Dict    						=			prepare_led_inputs_dict(_lowercase		,	_lowercase		,	_lowercase				)
    _UpperCamelCase:  Tuple    						=			tf.concat(
        [tf.zeros_like(_lowercase				)[:, :-1], tf.ones_like(_lowercase				)[:, -1:]]		,	axis=-1		,	)
    _UpperCamelCase:  List[Any]    						=			global_attention_mask
    return config, inputs_dict



   def 	lowerCAmelCase    (						self       :	Dict		,	_lowercase       :	List[str]		,	_lowercase       :	Union[str, Any]				):



    """simple docstring"""



    _UpperCamelCase:  Optional[Any]    						=			TFLEDModel(config=_lowercase				).get_decoder()
    _UpperCamelCase:  Optional[int]    						=			inputs_dict['''input_ids''']

    _UpperCamelCase:  Dict    						=			input_ids[:1, :]
    _UpperCamelCase:  Optional[Any]    						=			inputs_dict['''attention_mask'''][:1, :]
    _UpperCamelCase:  Optional[int]    						=			1

    # first forward pass
    _UpperCamelCase:  Optional[Any]    						=			model(_lowercase		,	attention_mask=_lowercase		,	use_cache=_lowercase				)

    _UpperCamelCase						,				_UpperCamelCase:  Union[str, Any]    						=			outputs.to_tuple()

    # create hypothetical next token and extent to next_input_ids
    _UpperCamelCase:  Any    						=			ids_tensor((self.batch_size, 3)		,	config.vocab_size				)
    _UpperCamelCase:  List[str]    						=			tf.cast(ids_tensor((self.batch_size, 3)		,	2				)		,	tf.inta				)

    # append to next input_ids and
    _UpperCamelCase:  Optional[int]    						=			tf.concat([input_ids, next_tokens]		,	axis=-1				)
    _UpperCamelCase:  Union[str, Any]    						=			tf.concat([attention_mask, next_attn_mask]		,	axis=-1				)

    _UpperCamelCase:  str    						=			model(_lowercase		,	attention_mask=_lowercase				)[0]
    _UpperCamelCase:  Optional[int]    						=			model(_lowercase		,	attention_mask=_lowercase		,	past_key_values=_lowercase				)[0]

    self.parent.assertEqual(next_tokens.shape[1]		,	output_from_past.shape[1]				)

    # select random slice
    _UpperCamelCase:  Dict    						=			int(ids_tensor((1,)		,	output_from_past.shape[-1]				)				)
    _UpperCamelCase:  Any    						=			output_from_no_past[:, -3:, random_slice_idx]
    _UpperCamelCase:  Optional[Any]    						=			output_from_past[:, :, random_slice_idx]

    # test that outputs are equal for slice
    tf.debugging.assert_near(_lowercase		,	_lowercase		,	rtol=1E-3				)




def    lowerCAmelCase_  (		lowercase:				Tuple	,       lowercase:				List[str]	,       lowercase:				List[str]	,       lowercase:				int=None	,       lowercase:				int=None	,       lowercase:				List[str]=None	,       lowercase:				List[str]=None	,       )     ->					List[str]:







 '''simple docstring'''
 if attention_mask is None:
  _UpperCamelCase:  Optional[Any]    						=			tf.cast(tf.math.not_equal(lowercase	,       config.pad_token_id		)	,       tf.inta		)
 if decoder_attention_mask is None:
  _UpperCamelCase:  int    						=			tf.concat(
      [
          tf.ones(decoder_input_ids[:, :1].shape	,       dtype=tf.inta		),
          tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:]	,       config.pad_token_id		)	,       tf.inta		),
      ]	,       axis=-1	,       )
 if head_mask is None:
  _UpperCamelCase:  str    						=			tf.ones((config.encoder_layers, config.encoder_attention_heads)		)
 if decoder_head_mask is None:
  _UpperCamelCase:  Optional[Any]    						=			tf.ones((config.decoder_layers, config.decoder_attention_heads)		)
 return {
     "input_ids": input_ids,
     "attention_mask": attention_mask,
     "decoder_input_ids": decoder_input_ids,
     "decoder_attention_mask": decoder_attention_mask,
     "head_mask": head_mask,
     "decoder_head_mask": decoder_head_mask,
 }




@require_tf
class 			__magic_name__	(		__a   ,						__a   ,						unittest.TestCase     ):





   """simple docstring"""




   lowerCAmelCase   :     Optional[int]	      = (TFLEDForConditionalGeneration, TFLEDModel) if is_tf_available() else ()
   lowerCAmelCase   :     List[str]	      = (TFLEDForConditionalGeneration,) if is_tf_available() else ()
   lowerCAmelCase   :     Union[str, Any]	      = (
       {
           '''conversational''': TFLEDForConditionalGeneration,
           '''feature-extraction''': TFLEDModel,
           '''summarization''': TFLEDForConditionalGeneration,
           '''text2text-generation''': TFLEDForConditionalGeneration,
           '''translation''': TFLEDForConditionalGeneration,
       }
       if is_tf_available()
       else {}
   )
   lowerCAmelCase   :     str	      = True
   lowerCAmelCase   :     Any	      = False
   lowerCAmelCase   :     Union[str, Any]	      = False
   lowerCAmelCase   :     Any	      = False



   def 	lowerCAmelCase    (						self       :	Any				):



    """simple docstring"""



    _UpperCamelCase:  Dict    						=			TFLEDModelTester(self				)
    _UpperCamelCase:  Any    						=			ConfigTester(self		,	config_class=_lowercase				)



   def 	lowerCAmelCase    (						self       :	Optional[int]				):



    """simple docstring"""



    self.config_tester.run_common_tests()



   def 	lowerCAmelCase    (						self       :	Tuple				):



    """simple docstring"""



    _UpperCamelCase:  Union[str, Any]    						=			self.model_tester.prepare_config_and_inputs_for_common()
    self.model_tester.check_decoder_model_past_large_inputs(*_lowercase				)



   def 	lowerCAmelCase    (						self       :	Optional[Any]				):



    """simple docstring"""



    _UpperCamelCase						,				_UpperCamelCase:  str    						=			self.model_tester.prepare_config_and_inputs_for_common()
    _UpperCamelCase:  str    						=			tf.zeros_like(inputs_dict['''attention_mask''']				)
    _UpperCamelCase:  Any    						=			2
    _UpperCamelCase:  List[str]    						=			tf.where(
        tf.range(self.model_tester.seq_length				)[None, :] < num_global_attn_indices		,	1		,	inputs_dict['''global_attention_mask''']		,	)

    _UpperCamelCase:  int    						=			True
    _UpperCamelCase:  str    						=			self.model_tester.seq_length
    _UpperCamelCase:  Dict    						=			self.model_tester.encoder_seq_length

    def check_decoder_attentions_output(_lowercase       :	Union[str, Any]				):
     _UpperCamelCase:  Tuple    						=			outputs.decoder_attentions
     self.assertEqual(len(_lowercase				)		,	self.model_tester.num_hidden_layers				)
     self.assertListEqual(
         list(decoder_attentions[0].shape[-3:]				)		,	[self.model_tester.num_attention_heads, seq_length, seq_length]		,	)

    def check_encoder_attentions_output(_lowercase       :	int				):
     _UpperCamelCase:  str    						=			[t.numpy() for t in outputs.encoder_attentions]
     _UpperCamelCase:  Any    						=			[t.numpy() for t in outputs.encoder_global_attentions]
     self.assertEqual(len(_lowercase				)		,	self.model_tester.num_hidden_layers				)
     self.assertEqual(len(_lowercase				)		,	self.model_tester.num_hidden_layers				)
     self.assertListEqual(
         list(attentions[0].shape[-3:]				)		,	[self.model_tester.num_attention_heads, seq_length, seq_length]		,	)
     self.assertListEqual(
         list(global_attentions[0].shape[-3:]				)		,	[self.model_tester.num_attention_heads, encoder_seq_length, num_global_attn_indices]		,	)

    for model_class in self.all_model_classes:
     _UpperCamelCase:  Optional[int]    						=			True
     _UpperCamelCase:  List[Any]    						=			False
     _UpperCamelCase:  Optional[int]    						=			False
     _UpperCamelCase:  Union[str, Any]    						=			model_class(_lowercase				)
     _UpperCamelCase:  Optional[int]    						=			model(self._prepare_for_class(_lowercase		,	_lowercase				)				)
     _UpperCamelCase:  List[str]    						=			len(_lowercase				)
     self.assertEqual(config.output_hidden_states		,	_lowercase				)
     check_encoder_attentions_output(_lowercase				)

     if self.is_encoder_decoder:
      _UpperCamelCase:  Optional[int]    						=			model_class(_lowercase				)
      _UpperCamelCase:  Union[str, Any]    						=			model(self._prepare_for_class(_lowercase		,	_lowercase				)				)
      self.assertEqual(config.output_hidden_states		,	_lowercase				)
      check_decoder_attentions_output(_lowercase				)

     # Check that output attentions can also be changed via the config
     del inputs_dict["output_attentions"]
     _UpperCamelCase:  Union[str, Any]    						=			True
     _UpperCamelCase:  List[str]    						=			model_class(_lowercase				)
     _UpperCamelCase:  Dict    						=			model(self._prepare_for_class(_lowercase		,	_lowercase				)				)
     self.assertEqual(config.output_hidden_states		,	_lowercase				)
     check_encoder_attentions_output(_lowercase				)

     # Check attention is always last and order is fine
     _UpperCamelCase:  str    						=			True
     _UpperCamelCase:  Dict    						=			True
     _UpperCamelCase:  int    						=			model_class(_lowercase				)
     _UpperCamelCase:  Optional[int]    						=			model(self._prepare_for_class(_lowercase		,	_lowercase				)				)

     self.assertEqual(out_len + (2 if self.is_encoder_decoder else 1)		,	len(_lowercase				)				)
     self.assertEqual(model.config.output_hidden_states		,	_lowercase				)
     check_encoder_attentions_output(_lowercase				)



   @unittest.skip('''LED keeps using potentially symbolic tensors in conditionals and breaks tracing.'''				)
   def 	lowerCAmelCase    (						self       :	List[Any]				):



    """simple docstring"""



    pass



   def 	lowerCAmelCase    (						self       :	Optional[int]				):



    """simple docstring"""



    pass




def    lowerCAmelCase_  (		lowercase:				Tuple		)     ->					List[str]:







 '''simple docstring'''
 return tf.constant(lowercase	,       dtype=tf.intaa		)


UpperCAmelCase_           =				1E-4





@slow
@require_tf
class 			__magic_name__	(		unittest.TestCase     ):





   """simple docstring"""




   def 	lowerCAmelCase    (						self       :	str				):



    """simple docstring"""



    _UpperCamelCase:  List[str]    						=			TFLEDForConditionalGeneration.from_pretrained('''allenai/led-base-16384'''				).led

    # change to intended input here
    _UpperCamelCase:  List[str]    						=			_long_tensor([512 * [0, 31_414, 232, 328, 740, 1_140, 12_695, 69]]				)
    _UpperCamelCase:  int    						=			_long_tensor([128 * [0, 31_414, 232, 328, 740, 1_140, 12_695, 69]]				)
    _UpperCamelCase:  Union[str, Any]    						=			prepare_led_inputs_dict(model.config		,	_lowercase		,	_lowercase				)
    _UpperCamelCase:  Optional[Any]    						=			model(**_lowercase				)[0]
    _UpperCamelCase:  Optional[int]    						=			(1, 1_024, 768)
    self.assertEqual(output.shape		,	_lowercase				)
    # change to expected output here
    _UpperCamelCase:  Optional[Any]    						=			tf.convert_to_tensor(
        [[2.3050, 2.8279, 0.6531], [-1.8457, -0.1455, -3.5661], [-1.0186, 0.4586, -2.2043]]		,	)
    tf.debugging.assert_near(output[:, :3, :3]		,	_lowercase		,	atol=1E-3				)



   def 	lowerCAmelCase    (						self       :	Union[str, Any]				):



    """simple docstring"""



    _UpperCamelCase:  Optional[Any]    						=			TFLEDForConditionalGeneration.from_pretrained('''allenai/led-base-16384'''				)

    # change to intended input here
    _UpperCamelCase:  Dict    						=			_long_tensor([512 * [0, 31_414, 232, 328, 740, 1_140, 12_695, 69]]				)
    _UpperCamelCase:  List[str]    						=			_long_tensor([128 * [0, 31_414, 232, 328, 740, 1_140, 12_695, 69]]				)
    _UpperCamelCase:  Tuple    						=			prepare_led_inputs_dict(model.config		,	_lowercase		,	_lowercase				)
    _UpperCamelCase:  Any    						=			model(**_lowercase				)[0]
    _UpperCamelCase:  Dict    						=			(1, 1_024, model.config.vocab_size)
    self.assertEqual(output.shape		,	_lowercase				)
    # change to expected output here
    _UpperCamelCase:  Optional[Any]    						=			tf.convert_to_tensor(
        [[33.6507, 6.4572, 16.8089], [5.8739, -2.4238, 11.2902], [-3.2139, -4.3149, 4.2783]]		,	)
    tf.debugging.assert_near(output[:, :3, :3]		,	_lowercase		,	atol=1E-3		,	rtol=1E-3				)