


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        _lowercase      :






				'''simple docstring'''



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


				def __init__(       self			:int			,			lowerCAmelCase__			:Optional[int]			,			lowerCAmelCase__			:Optional[int]=14			,			lowerCAmelCase__			:Union[str, Any]=7			,			lowerCAmelCase__			:Tuple=True			,			lowerCAmelCase__			:str=True			,			lowerCAmelCase__			:Optional[int]=True			,			lowerCAmelCase__			:Union[str, Any]=99			,			lowerCAmelCase__			:List[Any]=32			,			lowerCAmelCase__			:Tuple=2			,			lowerCAmelCase__			:List[str]=4			,			lowerCAmelCase__			:Tuple=37			,			lowerCAmelCase__			:str="gelu"			,			lowerCAmelCase__			:Optional[int]=0.1			,			lowerCAmelCase__			:List[Any]=0.1			,			lowerCAmelCase__			:Tuple=512			,			lowerCAmelCase__			:Optional[Any]=0.02			,			)		->			Union[str, Any]:
										__SCREAMING_SNAKE_CASE						:     Optional[int]					=		parent
										__SCREAMING_SNAKE_CASE						:     Tuple					=		batch_size
										__SCREAMING_SNAKE_CASE						:     Optional[int]					=		seq_length
										__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		is_training
										__SCREAMING_SNAKE_CASE						:     List[Any]					=		use_input_mask
										__SCREAMING_SNAKE_CASE						:     Tuple					=		use_labels
										__SCREAMING_SNAKE_CASE						:     List[Any]					=		vocab_size
										__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		d_model
										__SCREAMING_SNAKE_CASE						:     Optional[int]					=		num_hidden_layers
										__SCREAMING_SNAKE_CASE						:     List[Any]					=		num_attention_heads
										__SCREAMING_SNAKE_CASE						:     List[Any]					=		ffn_dim
										__SCREAMING_SNAKE_CASE						:     int					=		activation_function
										__SCREAMING_SNAKE_CASE						:     List[str]					=		activation_dropout
										__SCREAMING_SNAKE_CASE						:     List[Any]					=		attention_dropout
										__SCREAMING_SNAKE_CASE						:     List[str]					=		max_position_embeddings
										__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		initializer_range
										__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		None
										__SCREAMING_SNAKE_CASE						:     Dict					=		0
										__SCREAMING_SNAKE_CASE						:     List[str]					=		2
										__SCREAMING_SNAKE_CASE						:     Tuple					=		1


				def 		__magic_name__(       self			:List[str]	)		->			Any:
										return XGLMConfig.from_pretrained('''facebook/xglm-564M'''	)


				def 		__magic_name__(       self			:List[Any]	)		->			int:
										__SCREAMING_SNAKE_CASE						:     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	)

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

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

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

										return (
										    config,
										    input_ids,
										    input_mask,
										    head_mask,
										)


				def 		__magic_name__(       self			:Union[str, Any]	)		->			List[str]:
										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 		__magic_name__(       self			:Optional[int]	)		->			List[Any]:
										__SCREAMING_SNAKE_CASE						:     List[Any]					=		self.prepare_config_and_inputs()

										(
										    __SCREAMING_SNAKE_CASE   
										)						:     Any					=		config_and_inputs

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

										return config, inputs_dict




@require_tf
class        _lowercase      (			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 		__magic_name__(       self			:Any	)		->			List[Any]:
										__SCREAMING_SNAKE_CASE						:     str					=		TFXGLMModelTester(self	)
										__SCREAMING_SNAKE_CASE						:     Dict					=		ConfigTester(self			,			config_class=lowercase_			,			n_embd=37	)


				def 		__magic_name__(       self			:Optional[Any]	)		->			Tuple:
										self.config_tester.run_common_tests()


				@slow
				def 		__magic_name__(       self			:str	)		->			Optional[Any]:
										for model_name in TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		TFXGLMModel.from_pretrained(lowercase_	)
																self.assertIsNotNone(lowercase_	)



				@unittest.skip(reason='''Currently, model embeddings are going to undergo a major refactor.'''	)
				def 		__magic_name__(       self			:Dict	)		->			Optional[Any]:
										super().test_resize_token_embeddings()




@require_tf
class        _lowercase      (			unittest.TestCase       ):






				'''simple docstring'''



				@slow
				def 		__magic_name__(       self			:Optional[Any]			,			lowerCAmelCase__			:Dict=True	)		->			Any:
										__SCREAMING_SNAKE_CASE						:     List[Any]					=		TFXGLMForCausalLM.from_pretrained('''facebook/xglm-564M'''	)
										__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		tf.convert_to_tensor([[2, 268, 9_865]]			,			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
										__SCREAMING_SNAKE_CASE						:     Tuple					=		[2, 268, 9_865, 67, 11, 1_988, 57_252, 9_865, 5, 984, 67, 1_988, 213_838, 1_658, 53, 70_446, 33, 6_657, 278, 1_581]
										# fmt: on
										__SCREAMING_SNAKE_CASE						:     List[str]					=		model.generate(lowercase_			,			do_sample=lowercase_			,			num_beams=1	)
										if verify_outputs:
																self.assertListEqual(output_ids[0].numpy().tolist()			,			lowercase_	)


				@slow
				def 		__magic_name__(       self			:Union[str, Any]	)		->			int:
										__SCREAMING_SNAKE_CASE						:     Any					=		XGLMTokenizer.from_pretrained('''facebook/xglm-564M'''	)
										__SCREAMING_SNAKE_CASE						:     Any					=		TFXGLMForCausalLM.from_pretrained('''facebook/xglm-564M'''	)

										tf.random.set_seed(0	)
										__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		tokenizer('''Today is a nice day and'''			,			return_tensors='''tf'''	)
										__SCREAMING_SNAKE_CASE						:     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'''	):
																__SCREAMING_SNAKE_CASE						:     List[Any]					=		model.generate(lowercase_			,			do_sample=lowercase_			,			seed=[7, 0]	)
										__SCREAMING_SNAKE_CASE						:     Dict					=		tokenizer.decode(output_ids[0]			,			skip_special_tokens=lowercase_	)

										__SCREAMING_SNAKE_CASE						:     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 		__magic_name__(       self			:int	)		->			str:
										__SCREAMING_SNAKE_CASE						:     Any					=		TFXGLMForCausalLM.from_pretrained('''facebook/xglm-564M'''	)
										__SCREAMING_SNAKE_CASE						:     Tuple					=		XGLMTokenizer.from_pretrained('''facebook/xglm-564M'''	)

										__SCREAMING_SNAKE_CASE						:     Any					=		"left"

										# use different length sentences to test batching
										__SCREAMING_SNAKE_CASE						:     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",
										]

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

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

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

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

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

										__SCREAMING_SNAKE_CASE						:     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
import os
import re


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_dummies.py
_a		       =							'src/diffusers'

# Matches is_xxx_available()
_a		       =							re.compile(R'is\_([a-z_]*)_available\(\)')
# Matches from xxx import bla
_a		       =							re.compile(R'\s+from\s+\S*\s+import\s+([^\(\s].*)\n')


_a		       =							'\n{0} = None\n'

_a		       =							'\nclass {0}(metaclass=DummyObject):\n    _backends = {1}\n\n    def __init__(self, *args, **kwargs):\n        requires_backends(self, {1})\n\n    @classmethod\n    def from_config(cls, *args, **kwargs):\n        requires_backends(cls, {1})\n\n    @classmethod\n    def from_pretrained(cls, *args, **kwargs):\n        requires_backends(cls, {1})\n'


_a		       =							'\ndef {0}(*args, **kwargs):\n    requires_backends({0}, {1})\n'







def 	__a     (		__lowerCamelCase  ):
   UpperCAmelCase_					:					int			     =    _re_backend.findall(__lowerCamelCase  )
   if len(__lowerCamelCase  ) == 0:
      return None

   return "_and_".join(__lowerCamelCase  )







def 	__a     (		):
   with open(os.path.join(__lowerCamelCase,		"__init__.py"  ),		"r",		encoding="utf-8",		newline="\n"  ) as f:
      UpperCAmelCase_					:					Optional[int]			     =    f.readlines()

   # Get to the point we do the actual imports for type checking
   UpperCAmelCase_					:					Union[str, Any]			     =    0
   UpperCAmelCase_					:					Optional[int]			     =    {}
   # Go through the end of the file
   while line_index < len(__lowerCamelCase  ):
      # If the line contains is_backend_available, we grab all objects associated with the `else` block
      UpperCAmelCase_					:					Union[str, Any]			     =    find_backend(lines[line_index]  )
      if backend is not None:
         while not lines[line_index].startswith("else:"  ):
            line_index += 1
         line_index += 1
         UpperCAmelCase_					:					List[str]			     =    []
         # Until we unindent, add backend objects to the list
         while line_index < len(__lowerCamelCase  ) and len(lines[line_index]  ) > 1:
            UpperCAmelCase_					:					Union[str, Any]			     =    lines[line_index]
            UpperCAmelCase_					:					Optional[Any]			     =    _re_single_line_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

         if len(__lowerCamelCase  ) > 0:
            UpperCAmelCase_					:					Optional[int]			     =    objects
      else:
         line_index += 1

   return backend_specific_objects







def 	__a     (		__lowerCamelCase,		__lowerCamelCase  ):
   if name.isupper():
      return DUMMY_CONSTANT.format(__lowerCamelCase  )
   elif name.islower():
      return DUMMY_FUNCTION.format(__lowerCamelCase,		__lowerCamelCase  )
   else:
      return DUMMY_CLASS.format(__lowerCamelCase,		__lowerCamelCase  )







def 	__a     (		__lowerCamelCase=None  ):
   if backend_specific_objects is None:
      UpperCAmelCase_					:					Tuple			     =    read_init()
   # For special correspondence backend to module name as used in the function requires_modulename
   UpperCAmelCase_					:					str			     =    {}

   for backend, objects in backend_specific_objects.items():
      UpperCAmelCase_					:					int			     =    "[" + ", ".join(f"""\"{b}\"""" for b in backend.split("_and_"  )  ) + "]"
      UpperCAmelCase_					:					Dict			     =    "# This file is autogenerated by the command `make fix-copies`, do not edit.\n"
      dummy_file += "from ..utils import DummyObject, requires_backends\n\n"
      dummy_file += "\n".join([create_dummy_object(__lowerCamelCase,		__lowerCamelCase  ) for o in objects]  )
      UpperCAmelCase_					:					int			     =    dummy_file

   return dummy_files







def 	__a     (		__lowerCamelCase=False  ):
   UpperCAmelCase_					:					Optional[Any]			     =    create_dummy_files()
   # For special correspondence backend to shortcut as used in utils/dummy_xxx_objects.py
   UpperCAmelCase_					:					Union[str, Any]			     =    {"torch": "pt"}

   # Locate actual dummy modules and read their content.
   UpperCAmelCase_					:					List[str]			     =    os.path.join(__lowerCamelCase,		"utils"  )
   UpperCAmelCase_					:					Optional[int]			     =    {
       backend: os.path.join(__lowerCamelCase,		f"""dummy_{short_names.get(__lowerCamelCase,		__lowerCamelCase  )}_objects.py"""  )
       for backend in dummy_files.keys()
   }

   UpperCAmelCase_					:					Any			     =    {}
   for backend, file_path in dummy_file_paths.items():
      if os.path.isfile(__lowerCamelCase  ):
         with open(__lowerCamelCase,		"r",		encoding="utf-8",		newline="\n"  ) as f:
            UpperCAmelCase_					:					Optional[int]			     =    f.read()
      else:
         UpperCAmelCase_					:					Any			     =    ""

   for backend in dummy_files.keys():
      if dummy_files[backend] != actual_dummies[backend]:
         if overwrite:
            print(
                f"""Updating diffusers.utils.dummy_{short_names.get(__lowerCamelCase,		__lowerCamelCase  )}_objects.py as the main """
                "__init__ has new objects."  )
            with open(dummy_file_paths[backend],		"w",		encoding="utf-8",		newline="\n"  ) as f:
               f.write(dummy_files[backend]  )
         else:
            raise ValueError(
                "The main __init__ has objects that are not present in "
                f"""diffusers.utils.dummy_{short_names.get(__lowerCamelCase,		__lowerCamelCase  )}_objects.py. Run `make fix-copies` """
                "to fix this."  )


if __name__ == "__main__":
    _a		       =							argparse.ArgumentParser()
    parser.add_argument('--fix_and_overwrite', action='store_true', help='Whether to fix inconsistencies.')
    _a		       =							parser.parse_args()

    check_dummies(args.fix_and_overwrite)


