






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 __lowerCamelCase :





	"""simple docstring"""






	UpperCamelCase__								=      XGLMConfig
	UpperCamelCase__								=      {}
	UpperCamelCase__								=      "gelu"

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







							_UpperCAmelCase									=			parent
							_UpperCAmelCase									=			batch_size
							_UpperCAmelCase									=			seq_length
							_UpperCAmelCase									=			is_training
							_UpperCAmelCase									=			use_input_mask
							_UpperCAmelCase									=			use_labels
							_UpperCAmelCase									=			vocab_size
							_UpperCAmelCase									=			d_model
							_UpperCAmelCase									=			num_hidden_layers
							_UpperCAmelCase									=			num_attention_heads
							_UpperCAmelCase									=			ffn_dim
							_UpperCAmelCase									=			activation_function
							_UpperCAmelCase									=			activation_dropout
							_UpperCAmelCase									=			attention_dropout
							_UpperCAmelCase									=			max_position_embeddings
							_UpperCAmelCase									=			initializer_range
							_UpperCAmelCase									=			None
							_UpperCAmelCase									=			0
							_UpperCAmelCase									=			2
							_UpperCAmelCase									=			1

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							return XGLMConfig.from_pretrained('facebook/xglm-564M'    )

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							_UpperCAmelCase									=			tf.clip_by_value(
							    ids_tensor([self.batch_size, self.seq_length]       ,    self.vocab_size    )       ,    clip_value_min=0       ,    clip_value_max=3    )

							_UpperCAmelCase									=			None
							if self.use_input_mask:
													_UpperCAmelCase									=			random_attention_mask([self.batch_size, self.seq_length]    )

							_UpperCAmelCase									=			self.get_config()

							_UpperCAmelCase									=			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=UpperCAmelCase       ,    bos_token_id=self.bos_token_id       ,    eos_token_id=self.eos_token_id       ,    pad_token_id=self.pad_token_id       ,    return_dict=UpperCAmelCase       ,    )

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							_UpperCAmelCase									=			self.prepare_config_and_inputs()

							(
							    (
							    _UpperCAmelCase
							)					, (
							    _UpperCAmelCase
							)					, (
							    _UpperCAmelCase
							)					, (
							    _UpperCAmelCase
							)					, 
							)									=			config_and_inputs

							_UpperCAmelCase									=			{
							    'input_ids': input_ids,
							    'head_mask': head_mask,
							}

							return config, inputs_dict





@require_tf
class __lowerCamelCase (      snake_case__     ,       snake_case__     ,       unittest.TestCase):





	"""simple docstring"""






	UpperCamelCase__								=      (TFXGLMModel, TFXGLMForCausalLM) if is_tf_available() else ()
	UpperCamelCase__								=      (TFXGLMForCausalLM,) if is_tf_available() else ()
	UpperCamelCase__								=      (
	    {"feature-extraction": TFXGLMModel, "text-generation": TFXGLMForCausalLM} if is_tf_available() else {}
	)
	UpperCamelCase__								=      False
	UpperCamelCase__								=      False
	UpperCamelCase__								=      False

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							_UpperCAmelCase									=			TFXGLMModelTester(self    )
							_UpperCAmelCase									=			ConfigTester(self       ,    config_class=UpperCAmelCase       ,    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									=			TFXGLMModel.from_pretrained(UpperCAmelCase    )
													self.assertIsNotNone(UpperCAmelCase    )

	@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 __lowerCamelCase (      unittest.TestCase):





	"""simple docstring"""






	@slow
	def        UpperCamelCase    (							self       ,    UpperCAmelCase=True    ):
							"""simple docstring"""







							_UpperCAmelCase									=			TFXGLMForCausalLM.from_pretrained('facebook/xglm-564M'    )
							_UpperCAmelCase									=			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									=			[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									=			model.generate(UpperCAmelCase       ,    do_sample=UpperCAmelCase       ,    num_beams=1    )
							if verify_outputs:
													self.assertListEqual(output_ids[0].numpy().tolist()       ,    UpperCAmelCase    )

	@slow
	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							_UpperCAmelCase									=			XGLMTokenizer.from_pretrained('facebook/xglm-564M'    )
							_UpperCAmelCase									=			TFXGLMForCausalLM.from_pretrained('facebook/xglm-564M'    )

							tf.random.set_seed(0    )
							_UpperCAmelCase									=			tokenizer('Today is a nice day and'       ,    return_tensors='tf'    )
							_UpperCAmelCase									=			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									=			model.generate(UpperCAmelCase       ,    do_sample=UpperCAmelCase       ,    seed=[7, 0]    )
							_UpperCAmelCase									=			tokenizer.decode(output_ids[0]       ,    skip_special_tokens=UpperCAmelCase    )

							_UpperCAmelCase									=			(
							    'Today is a nice day and warm evening here over Southern Alberta!! Today when they closed schools due'
							)
							self.assertEqual(UpperCAmelCase       ,    UpperCAmelCase    )

	@slow
	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							_UpperCAmelCase									=			TFXGLMForCausalLM.from_pretrained('facebook/xglm-564M'    )
							_UpperCAmelCase									=			XGLMTokenizer.from_pretrained('facebook/xglm-564M'    )

							_UpperCAmelCase									=			'left'

							# use different length sentences to test batching
							_UpperCAmelCase									=			[
							    '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									=			tokenizer(UpperCAmelCase       ,    return_tensors='tf'       ,    padding=UpperCAmelCase    )
							_UpperCAmelCase									=			inputs['input_ids']

							_UpperCAmelCase									=			model.generate(input_ids=UpperCAmelCase       ,    attention_mask=inputs['attention_mask']       ,    max_new_tokens=12    )

							_UpperCAmelCase									=			tokenizer(sentences[0]       ,    return_tensors='tf'    ).input_ids
							_UpperCAmelCase									=			model.generate(input_ids=UpperCAmelCase       ,    max_new_tokens=12    )

							_UpperCAmelCase									=			tokenizer(sentences[1]       ,    return_tensors='tf'    ).input_ids
							_UpperCAmelCase									=			model.generate(input_ids=UpperCAmelCase       ,    max_new_tokens=12    )

							_UpperCAmelCase									=			tokenizer.batch_decode(UpperCAmelCase       ,    skip_special_tokens=UpperCAmelCase    )
							_UpperCAmelCase									=			tokenizer.decode(output_non_padded[0]       ,    skip_special_tokens=UpperCAmelCase    )
							_UpperCAmelCase									=			tokenizer.decode(output_padded[0]       ,    skip_special_tokens=UpperCAmelCase    )

							_UpperCAmelCase									=			[
							    '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(UpperCAmelCase       ,    UpperCAmelCase    )
							self.assertListEqual(UpperCAmelCase       ,    [non_padded_sentence, padded_sentence]    )




from collections import defaultdict

def       lowercase_		(		_A			:    int						):

      """simple docstring"""




      lowerCamelCase__		:				Union[str, Any]    =  1
      lowerCamelCase__		:				Dict    =  True
      for v in tree[start]:
            if v not in visited:
                  ret += dfs(_A						)
      if ret % 2 == 0:
            cuts.append(_A						)
      return ret

def       lowercase_		(		):

      """simple docstring"""




      dfs(1						)


if __name__ == "__main__":
    A,  A      :    Tuple		=					10, 9
    A      :    int		=					defaultdict(list)
    A      :    dict[int, bool]		=					{}
    A      :    list[int]		=					[]
    A      :    List[str]		=					0
    A      :    Tuple		=					[(2, 1), (3, 1), (4, 3), (5, 2), (6, 1), (7, 2), (8, 6), (9, 8), (10, 8)]
    for u, v in edges:
        tree[u].append(v)
        tree[v].append(u)
    even_tree()
    print(len(cuts) - 1)


