
"""simple docstring"""





from __future__ import annotations

import unittest

from transformers import XGLMConfig, XGLMTokenizer, is_tf_available
from transformers.testing_utils import require_tf, slow

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


if is_tf_available():
    import tensorflow as tf

    from transformers.models.xglm.modeling_tf_xglm import (
        TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFXGLMForCausalLM,
        TFXGLMModel,
    )


@require_tf
class A_	:




  '''simple docstring'''
  SCREAMING_SNAKE_CASE__			:						Tuple       =		XGLMConfig
  SCREAMING_SNAKE_CASE__			:						Union[str, Any]       =		{}
  SCREAMING_SNAKE_CASE__			:						Optional[Any]       =		"""gelu"""







  def __init__(		self				,   lowercase_				,   lowercase_=14				,   lowercase_=7				,   lowercase_=True				,   lowercase_=True				,   lowercase_=True				,   lowercase_=99				,   lowercase_=32				,   lowercase_=2				,   lowercase_=4				,   lowercase_=37				,   lowercase_="gelu"				,   lowercase_=0.1				,   lowercase_=0.1				,   lowercase_=512				,   lowercase_=0.02				,   ):

     """simple docstring"""
     UpperCAmelCase_					:					Optional[int]			     =    parent
     UpperCAmelCase_					:					Tuple			     =    batch_size
     UpperCAmelCase_					:					Optional[int]			     =    seq_length
     UpperCAmelCase_					:					Union[str, Any]			     =    is_training
     UpperCAmelCase_					:					List[Any]			     =    use_input_mask
     UpperCAmelCase_					:					Tuple			     =    use_labels
     UpperCAmelCase_					:					List[Any]			     =    vocab_size
     UpperCAmelCase_					:					Union[str, Any]			     =    d_model
     UpperCAmelCase_					:					Optional[int]			     =    num_hidden_layers
     UpperCAmelCase_					:					List[Any]			     =    num_attention_heads
     UpperCAmelCase_					:					List[Any]			     =    ffn_dim
     UpperCAmelCase_					:					int			     =    activation_function
     UpperCAmelCase_					:					List[str]			     =    activation_dropout
     UpperCAmelCase_					:					List[Any]			     =    attention_dropout
     UpperCAmelCase_					:					List[str]			     =    max_position_embeddings
     UpperCAmelCase_					:					Optional[Any]			     =    initializer_range
     UpperCAmelCase_					:					Optional[Any]			     =    None
     UpperCAmelCase_					:					Dict			     =    0
     UpperCAmelCase_					:					List[str]			     =    2
     UpperCAmelCase_					:					Tuple			     =    1







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     return XGLMConfig.from_pretrained("facebook/xglm-564M"  )







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     UpperCAmelCase_					:					Union[str, Any]			     =    tf.clip_by_value(
         ids_tensor([self.batch_size, self.seq_length]				,   self.vocab_size  )				,   clip_value_min=0				,   clip_value_max=3  )

     UpperCAmelCase_					:					Optional[int]			     =    None
     if self.use_input_mask:
        UpperCAmelCase_					:					str			     =    random_attention_mask([self.batch_size, self.seq_length]  )

     UpperCAmelCase_					:					Union[str, Any]			     =    self.get_config()

     UpperCAmelCase_					:					List[Any]			     =    floats_tensor([self.num_hidden_layers, self.num_attention_heads]				,   2  )

     return (
         config,
         input_ids,
         input_mask,
         head_mask,
     )







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     return XGLMConfig(
         vocab_size=self.vocab_size				,   d_model=self.hidden_size				,   num_layers=self.num_hidden_layers				,   attention_heads=self.num_attention_heads				,   ffn_dim=self.ffn_dim				,   activation_function=self.activation_function				,   activation_dropout=self.activation_dropout				,   attention_dropout=self.attention_dropout				,   max_position_embeddings=self.max_position_embeddings				,   initializer_range=self.initializer_range				,   use_cache=lowercase_				,   bos_token_id=self.bos_token_id				,   eos_token_id=self.eos_token_id				,   pad_token_id=self.pad_token_id				,   return_dict=lowercase_				,   )







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     UpperCAmelCase_					:					List[Any]			     =    self.prepare_config_and_inputs()

     (
         (
         UpperCAmelCase_
     )     ,   (
         UpperCAmelCase_
     )     ,   (
         UpperCAmelCase_
     )     ,   (
         UpperCAmelCase_
     )     ,   
     )					:					Any			     =    config_and_inputs

     UpperCAmelCase_					:					List[str]			     =    {
         "input_ids": input_ids,
         "head_mask": head_mask,
     }

     return config, inputs_dict




@require_tf
class A_	(lowercase__				,lowercase__				,unittest.TestCase      ):




  '''simple docstring'''
  SCREAMING_SNAKE_CASE__			:						Union[str, Any]       =		(TFXGLMModel, TFXGLMForCausalLM) if is_tf_available() else ()
  SCREAMING_SNAKE_CASE__			:						Tuple       =		(TFXGLMForCausalLM,) if is_tf_available() else ()
  SCREAMING_SNAKE_CASE__			:						List[str]       =		(
      {"""feature-extraction""": TFXGLMModel, """text-generation""": TFXGLMForCausalLM} if is_tf_available() else {}
  )
  SCREAMING_SNAKE_CASE__			:						List[str]       =		False
  SCREAMING_SNAKE_CASE__			:						List[str]       =		False
  SCREAMING_SNAKE_CASE__			:						Tuple       =		False







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     UpperCAmelCase_					:					str			     =    TFXGLMModelTester(self  )
     UpperCAmelCase_					:					Dict			     =    ConfigTester(self				,   config_class=lowercase_				,   n_embd=37  )







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     self.config_tester.run_common_tests()







  @slow
  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     for model_name in TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
        UpperCAmelCase_					:					Union[str, Any]			     =    TFXGLMModel.from_pretrained(lowercase_  )
        self.assertIsNotNone(lowercase_  )







  @unittest.skip(reason="Currently, model embeddings are going to undergo a major refactor."  )
  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     super().test_resize_token_embeddings()




@require_tf
class A_	(unittest.TestCase      ):




  '''simple docstring'''







  @slow
  def        UpperCamelCase__					(		self				,   lowercase_=True  ):

     """simple docstring"""
     UpperCAmelCase_					:					List[Any]			     =    TFXGLMForCausalLM.from_pretrained("facebook/xglm-564M"  )
     UpperCAmelCase_					:					Union[str, Any]			     =    tf.convert_to_tensor([[2, 268, 9865]]				,   dtype=tf.intaa  )  # The dog
     # </s> The dog is a very friendly dog. He is very affectionate and loves to play with other
     # fmt: off
     UpperCAmelCase_					:					Tuple			     =    [2, 268, 9865, 67, 11, 1988, 5_7252, 9865, 5, 984, 67, 1988, 21_3838, 1658, 53, 7_0446, 33, 6657, 278, 1581]
     # fmt: on
     UpperCAmelCase_					:					List[str]			     =    model.generate(lowercase_				,   do_sample=lowercase_				,   num_beams=1  )
     if verify_outputs:
        self.assertListEqual(output_ids[0].numpy().tolist()				,   lowercase_  )







  @slow
  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     UpperCAmelCase_					:					Any			     =    XGLMTokenizer.from_pretrained("facebook/xglm-564M"  )
     UpperCAmelCase_					:					Any			     =    TFXGLMForCausalLM.from_pretrained("facebook/xglm-564M"  )

     tf.random.set_seed(0  )
     UpperCAmelCase_					:					Optional[Any]			     =    tokenizer("Today is a nice day and"				,   return_tensors="tf"  )
     UpperCAmelCase_					:					Optional[Any]			     =    tokenized.input_ids
     # forces the generation to happen on CPU, to avoid GPU-related quirks (and assure same output regardless of the available devices)
     with tf.device(":/CPU:0"  ):
        UpperCAmelCase_					:					List[Any]			     =    model.generate(lowercase_				,   do_sample=lowercase_				,   seed=[7, 0]  )
     UpperCAmelCase_					:					Dict			     =    tokenizer.decode(output_ids[0]				,   skip_special_tokens=lowercase_  )

     UpperCAmelCase_					:					Dict			     =    (
         "Today is a nice day and warm evening here over Southern Alberta!! Today when they closed schools due"
     )
     self.assertEqual(lowercase_				,   lowercase_  )







  @slow
  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     UpperCAmelCase_					:					Any			     =    TFXGLMForCausalLM.from_pretrained("facebook/xglm-564M"  )
     UpperCAmelCase_					:					Tuple			     =    XGLMTokenizer.from_pretrained("facebook/xglm-564M"  )

     UpperCAmelCase_					:					Any			     =    "left"

     # use different length sentences to test batching
     UpperCAmelCase_					:					List[str]			     =    [
         "This is an extremelly long sentence that only exists to test the ability of the model to cope with "
         "left-padding, such as in batched generation. The output for the sequence below should be the same "
         "regardless of whether left padding is applied or not. When",
         "Hello, my dog is a little",
     ]

     UpperCAmelCase_					:					List[Any]			     =    tokenizer(lowercase_				,   return_tensors="tf"				,   padding=lowercase_  )
     UpperCAmelCase_					:					Optional[int]			     =    inputs["input_ids"]

     UpperCAmelCase_					:					Union[str, Any]			     =    model.generate(input_ids=lowercase_				,   attention_mask=inputs["attention_mask"]				,   max_new_tokens=12  )

     UpperCAmelCase_					:					Optional[int]			     =    tokenizer(sentences[0]				,   return_tensors="tf"  ).input_ids
     UpperCAmelCase_					:					Optional[Any]			     =    model.generate(input_ids=lowercase_				,   max_new_tokens=12  )

     UpperCAmelCase_					:					List[str]			     =    tokenizer(sentences[1]				,   return_tensors="tf"  ).input_ids
     UpperCAmelCase_					:					List[Any]			     =    model.generate(input_ids=lowercase_				,   max_new_tokens=12  )

     UpperCAmelCase_					:					Optional[int]			     =    tokenizer.batch_decode(lowercase_				,   skip_special_tokens=lowercase_  )
     UpperCAmelCase_					:					List[Any]			     =    tokenizer.decode(output_non_padded[0]				,   skip_special_tokens=lowercase_  )
     UpperCAmelCase_					:					List[Any]			     =    tokenizer.decode(output_padded[0]				,   skip_special_tokens=lowercase_  )

     UpperCAmelCase_					:					Tuple			     =    [
         "This is an extremelly long sentence that only exists to test the ability of the model to cope with "
         "left-padding, such as in batched generation. The output for the sequence below should be the same "
         "regardless of whether left padding is applied or not. When left padding is applied, the sequence will be "
         "a single",
         "Hello, my dog is a little bit of a shy one, but he is very friendly",
     ]
     self.assertListEqual(lowercase_				,   lowercase_  )
     self.assertListEqual(lowercase_				,   [non_padded_sentence, padded_sentence]  )




"""simple docstring"""





import argparse
from collections import defaultdict







def 	__a     (		__lowerCamelCase,		__lowerCamelCase,		__lowerCamelCase,		__lowerCamelCase,		__lowerCamelCase  ):
   UpperCAmelCase_					:					int			     =    f"""{file}_{class_name}_{test_name}"""
   done_test[_id] += 1

   with open(__lowerCamelCase,		"r"  ) as f:
      UpperCAmelCase_					:					List[Any]			     =    f.readlines()

   UpperCAmelCase_					:					int			     =    f"""class {class_name}("""
   UpperCAmelCase_					:					Optional[Any]			     =    f"""{4 * " "}def {test_name}("""
   UpperCAmelCase_					:					Optional[Any]			     =    f"""{8 * " "}{correct_line.split()[0]}"""
   UpperCAmelCase_					:					Tuple			     =    f"""{16 * " "}{correct_line.split()[0]}"""
   UpperCAmelCase_					:					int			     =    False
   UpperCAmelCase_					:					Union[str, Any]			     =    False
   UpperCAmelCase_					:					str			     =    False
   UpperCAmelCase_					:					Optional[Any]			     =    False
   UpperCAmelCase_					:					List[str]			     =    0
   UpperCAmelCase_					:					Optional[int]			     =    0

   UpperCAmelCase_					:					int			     =    []
   for line in lines:
      if line.startswith(__lowerCamelCase  ):
         UpperCAmelCase_					:					Tuple			     =    True
      elif in_class and line.startswith(__lowerCamelCase  ):
         UpperCAmelCase_					:					Optional[int]			     =    True
      elif in_class and in_func and (line.startswith(__lowerCamelCase  ) or line.startswith(__lowerCamelCase  )):
         UpperCAmelCase_					:					Any			     =    len(line.split(correct_line.split()[0]  )[0]  )
         count += 1

         if count == done_test[_id]:
            UpperCAmelCase_					:					Union[str, Any]			     =    True

      if in_class and in_func and in_line:
         if ")" not in line:
            continue
         else:
            UpperCAmelCase_					:					Any			     =    True

      if in_class and in_func and in_line and insert_line:
         new_lines.append(f"""{spaces * " "}{correct_line}"""  )
         UpperCAmelCase_					:					int			     =    False
      else:
         new_lines.append(__lowerCamelCase  )

   with open(__lowerCamelCase,		"w"  ) as f:
      for line in new_lines:
         f.write(__lowerCamelCase  )







def 	__a     (		__lowerCamelCase,		__lowerCamelCase=None  ):
   if fail is not None:
      with open(__lowerCamelCase,		"r"  ) as f:
         UpperCAmelCase_					:					Tuple			     =    {l.strip() for l in f.readlines()}
   else:
      UpperCAmelCase_					:					str			     =    None

   with open(__lowerCamelCase,		"r"  ) as f:
      UpperCAmelCase_					:					Optional[int]			     =    f.readlines()

   UpperCAmelCase_					:					Any			     =    defaultdict(__lowerCamelCase  )
   for line in correct_lines:
      UpperCAmelCase_     ,   UpperCAmelCase_     ,   UpperCAmelCase_     ,   UpperCAmelCase_					:					Any			     =    line.split(";"  )
      if test_failures is None or "::".join([file, class_name, test_name]  ) in test_failures:
         overwrite_file(__lowerCamelCase,		__lowerCamelCase,		__lowerCamelCase,		__lowerCamelCase,		__lowerCamelCase  )


if __name__ == "__main__":
    _a		       =							argparse.ArgumentParser()
    parser.add_argument('--correct_filename', help='filename of tests with expected result')
    parser.add_argument('--fail_filename', help='filename of test failures', type=str, default=None)
    _a		       =							parser.parse_args()

    main(args.correct_filename, args.fail_filename)


