
'''simple docstring'''

from __future__ import annotations

import unittest

from transformers import AutoTokenizer, GPTJConfig, is_tf_available
from transformers.testing_utils import require_tf, slow, tooslow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
from ...utils.test_modeling_tf_core import TFCoreModelTesterMixin


if is_tf_available():
     import tensorflow as tf

     from transformers.models.gptj.modeling_tf_gptj import (
         TFGPTJForCausalLM,
         TFGPTJForQuestionAnswering,
         TFGPTJForSequenceClassification,
         TFGPTJModel,
         shape_list,
     )


class 					a_					:




       def __init__(       self				,				snake_case_					):
         _lowerCAmelCase    :					Dict							=   parent
         _lowerCAmelCase    :					Dict							=   1_3
         _lowerCAmelCase    :					Union[str, Any]							=   7
         _lowerCAmelCase    :					Tuple							=   True
         _lowerCAmelCase    :					str							=   True
         _lowerCAmelCase    :					str							=   True
         _lowerCAmelCase    :					str							=   True
         _lowerCAmelCase    :					Union[str, Any]							=   True
         _lowerCAmelCase    :					Optional[int]							=   9_9
         _lowerCAmelCase    :					Optional[int]							=   3_2
         _lowerCAmelCase    :					List[Any]							=   4
         _lowerCAmelCase    :					List[Any]							=   2
         _lowerCAmelCase    :					Optional[Any]							=   4
         _lowerCAmelCase    :					Optional[Any]							=   3_7
         _lowerCAmelCase    :					int							=   """gelu"""
         _lowerCAmelCase    :					Optional[Any]							=   0.1
         _lowerCAmelCase    :					Tuple							=   0.1
         _lowerCAmelCase    :					Tuple							=   5_1_2
         _lowerCAmelCase    :					str							=   1_6
         _lowerCAmelCase    :					List[str]							=   2
         _lowerCAmelCase    :					Dict							=   0.02
         _lowerCAmelCase    :					Optional[int]							=   3
         _lowerCAmelCase    :					List[str]							=   4
         _lowerCAmelCase    :					Tuple							=   None
         _lowerCAmelCase    :					Dict							=   self.vocab_size - 1
         _lowerCAmelCase    :					int							=   self.vocab_size - 1
         _lowerCAmelCase    :					Union[str, Any]							=   self.vocab_size - 1




       def 							__UpperCamelCase    (       self					):
         _lowerCAmelCase    :					Dict							=   ids_tensor([self.batch_size, self.seq_length]				,				self.vocab_size					)

         _lowerCAmelCase    :					List[Any]							=   None
         if self.use_input_mask:
           _lowerCAmelCase    :					str							=   random_attention_mask([self.batch_size, self.seq_length]					)

         _lowerCAmelCase    :					str							=   None
         if self.use_token_type_ids:
           _lowerCAmelCase    :					Tuple							=   ids_tensor([self.batch_size, self.seq_length]				,				self.type_vocab_size					)

         _lowerCAmelCase    :					Any							=   None
         if self.use_mc_token_ids:
           _lowerCAmelCase    :					int							=   ids_tensor([self.batch_size, self.num_choices]				,				self.seq_length					)

         _lowerCAmelCase    :					Optional[int]							=   None
         _lowerCAmelCase    :					int							=   None
         _lowerCAmelCase    :					List[Any]							=   None
         if self.use_labels:
           _lowerCAmelCase    :					int							=   ids_tensor([self.batch_size]				,				self.type_sequence_label_size					)
           _lowerCAmelCase    :					List[str]							=   ids_tensor([self.batch_size, self.seq_length]				,				self.num_labels					)
           _lowerCAmelCase    :					Tuple							=   ids_tensor([self.batch_size]				,				self.num_choices					)

         _lowerCAmelCase    :					str							=   GPTJConfig(
             vocab_size=self.vocab_size				,				n_embd=self.hidden_size				,				n_layer=self.num_hidden_layers				,				n_head=self.num_attention_heads				,				intermediate_size=self.intermediate_size				,				hidden_act=self.hidden_act				,				hidden_dropout_prob=self.hidden_dropout_prob				,				attention_probs_dropout_prob=self.attention_probs_dropout_prob				,				n_positions=self.max_position_embeddings				,				type_vocab_size=self.type_vocab_size				,				initializer_range=self.initializer_range				,				bos_token_id=self.bos_token_id				,				eos_token_id=self.eos_token_id				,				pad_token_id=self.pad_token_id				,				rotary_dim=self.rotary_dim				,				return_dict=snake_case_				,				)

         _lowerCAmelCase    :					Dict							=   ids_tensor([self.num_hidden_layers, self.num_attention_heads]				,				2					)

         return (
             config,
             input_ids,
             input_mask,
             head_mask,
             token_type_ids,
             mc_token_ids,
             sequence_labels,
             token_labels,
             choice_labels,
         )




       def 							__UpperCamelCase    (       self				,				snake_case_				,				snake_case_				,				snake_case_				,				snake_case_				,				snake_case_				,				*snake_case_					):
         _lowerCAmelCase    :					int							=   TFGPTJModel(config=snake_case_					)
         _lowerCAmelCase    :					Any							=   {
             """input_ids""": input_ids,
             """attention_mask""": input_mask,
             """token_type_ids""": token_type_ids,
         }
         _lowerCAmelCase    :					int							=   model(snake_case_					)

         _lowerCAmelCase    :					Tuple							=   [input_ids, None, input_mask]  # None is the input for 'past'
         _lowerCAmelCase    :					Tuple							=   model(snake_case_					)

         _lowerCAmelCase    :					List[Any]							=   model(snake_case_					)

         self.parent.assertEqual(result.last_hidden_state.shape				,				(self.batch_size, self.seq_length, self.hidden_size)					)




       def 							__UpperCamelCase    (       self				,				snake_case_				,				snake_case_				,				snake_case_				,				snake_case_				,				snake_case_				,				*snake_case_					):
         _lowerCAmelCase    :					Any							=   TFGPTJModel(config=snake_case_					)

         # first forward pass
         _lowerCAmelCase    :					int							=   model(snake_case_				,				token_type_ids=snake_case_				,				use_cache=snake_case_					)
         _lowerCAmelCase    :					Optional[int]							=   model(snake_case_				,				token_type_ids=snake_case_					)
         _lowerCAmelCase    :					List[str]							=   model(snake_case_				,				token_type_ids=snake_case_				,				use_cache=snake_case_					)

         self.parent.assertTrue(len(snake_case_					) == len(snake_case_					)					)
         self.parent.assertTrue(len(snake_case_					) == len(snake_case_					) + 1					)

         _lowerCAmelCase    :					Optional[int]							=   outputs.to_tuple()

         # create hypothetical next token and extent to next_input_ids
         _lowerCAmelCase    :					Any							=   ids_tensor((self.batch_size, 1)				,				config.vocab_size					)
         _lowerCAmelCase    :					Any							=   ids_tensor([self.batch_size, 1]				,				self.type_vocab_size					)

         # append to next input_ids and token_type_ids
         _lowerCAmelCase    :					Any							=   tf.concat([input_ids, next_tokens]				,				axis=-1					)
         _lowerCAmelCase    :					Dict							=   tf.concat([token_type_ids, next_token_types]				,				axis=-1					)

         _lowerCAmelCase    :					int							=   model(snake_case_				,				token_type_ids=snake_case_					)["""last_hidden_state"""]
         _lowerCAmelCase    :					List[Any]							=   model(snake_case_				,				token_type_ids=snake_case_				,				past_key_values=snake_case_					)[
             """last_hidden_state"""
         ]

         # select random slice
         _lowerCAmelCase    :					Any							=   int(ids_tensor((1,)				,				shape_list(snake_case_					)[-1]					)					)
         _lowerCAmelCase    :					Optional[Any]							=   output_from_no_past[:, -1, random_slice_idx]
         _lowerCAmelCase    :					Optional[int]							=   output_from_past[:, 0, random_slice_idx]

         # test that outputs are equal for slice
         tf.debugging.assert_near(snake_case_				,				snake_case_				,				rtol=1E-6					)




       def 							__UpperCamelCase    (       self				,				snake_case_				,				snake_case_				,				snake_case_				,				snake_case_				,				snake_case_				,				*snake_case_					):
         _lowerCAmelCase    :					List[str]							=   TFGPTJModel(config=snake_case_					)

         # create attention mask
         _lowerCAmelCase    :					Dict							=   self.seq_length // 2
         _lowerCAmelCase    :					Optional[int]							=   tf.ones((self.batch_size, half_seq_length)				,				dtype=tf.intaa					)
         _lowerCAmelCase    :					Union[str, Any]							=   tf.zeros((self.batch_size, self.seq_length - half_seq_length)				,				dtype=tf.intaa					)
         _lowerCAmelCase    :					Dict							=   tf.concat([attn_mask_begin, attn_mask_end]				,				axis=1					)

         # first forward pass
         _lowerCAmelCase    :					Tuple							=   model(snake_case_				,				attention_mask=snake_case_					).to_tuple()

         # create hypothetical next token and extent to next_input_ids
         _lowerCAmelCase    :					str							=   ids_tensor((self.batch_size, 1)				,				config.vocab_size					)

         # change a random masked slice from input_ids
         _lowerCAmelCase    :					Any							=   ids_tensor((1,)				,				snake_case_					).numpy() + 1
         _lowerCAmelCase    :					List[Any]							=   ids_tensor((self.batch_size, self.seq_length)				,				config.vocab_size					)
         _lowerCAmelCase    :					List[str]							=   tf.range(self.seq_length					) == (self.seq_length - random_seq_idx_to_change)
         _lowerCAmelCase    :					Optional[Any]							=   tf.transpose(
             tf.broadcast_to(tf.expand_dims(snake_case_				,				-1					)				,				(self.seq_length, self.batch_size)					)					)
         _lowerCAmelCase    :					List[str]							=   tf.where(snake_case_				,				snake_case_				,				snake_case_					)

         # append to next input_ids and attn_mask
         _lowerCAmelCase    :					Dict							=   tf.concat([input_ids, next_tokens]				,				axis=-1					)
         _lowerCAmelCase    :					Union[str, Any]							=   tf.concat([attn_mask, tf.ones((shape_list(snake_case_					)[0], 1)				,				dtype=tf.intaa					)]				,				axis=1					)

         # get two different outputs
         _lowerCAmelCase    :					Dict							=   model(snake_case_				,				attention_mask=snake_case_					)["""last_hidden_state"""]
         _lowerCAmelCase    :					List[Any]							=   model(snake_case_				,				past_key_values=snake_case_				,				attention_mask=snake_case_					)[
             """last_hidden_state"""
         ]

         # select random slice
         _lowerCAmelCase    :					Union[str, Any]							=   int(ids_tensor((1,)				,				shape_list(snake_case_					)[-1]					)					)
         _lowerCAmelCase    :					List[str]							=   output_from_no_past[:, -1, random_slice_idx]
         _lowerCAmelCase    :					str							=   output_from_past[:, 0, random_slice_idx]

         # test that outputs are equal for slice
         tf.debugging.assert_near(snake_case_				,				snake_case_				,				rtol=1E-12					)




       def 							__UpperCamelCase    (       self				,				snake_case_				,				snake_case_				,				snake_case_				,				snake_case_				,				snake_case_				,				*snake_case_					):
         _lowerCAmelCase    :					Optional[int]							=   TFGPTJModel(config=snake_case_					)

         _lowerCAmelCase    :					List[str]							=   input_ids[:1, :]
         _lowerCAmelCase    :					Optional[Any]							=   input_mask[:1, :]
         _lowerCAmelCase    :					Optional[int]							=   token_type_ids[:1, :]
         _lowerCAmelCase    :					Optional[int]							=   1

         # first forward pass
         _lowerCAmelCase    :					int							=   model(snake_case_				,				attention_mask=snake_case_				,				token_type_ids=snake_case_				,				use_cache=snake_case_					)

         _lowerCAmelCase    :					List[str]							=   outputs.to_tuple()

         # create hypothetical next token and extent to next_input_ids
         _lowerCAmelCase    :					Optional[Any]							=   ids_tensor((self.batch_size, 3)				,				config.vocab_size					)
         _lowerCAmelCase    :					str							=   ids_tensor((self.batch_size, 3)				,				2					)
         _lowerCAmelCase    :					List[Any]							=   ids_tensor((self.batch_size, 3)				,				self.type_vocab_size					)

         # append to next input_ids and token_type_ids
         _lowerCAmelCase    :					List[str]							=   tf.concat([input_ids, next_tokens]				,				axis=-1					)
         _lowerCAmelCase    :					Union[str, Any]							=   tf.concat([input_mask, next_attn_mask]				,				axis=-1					)
         _lowerCAmelCase    :					int							=   tf.concat([token_type_ids, next_token_types]				,				axis=-1					)

         _lowerCAmelCase    :					Optional[int]							=   model(
             snake_case_				,				token_type_ids=snake_case_				,				attention_mask=snake_case_					)["""last_hidden_state"""]
         _lowerCAmelCase    :					Optional[Any]							=   model(
             snake_case_				,				token_type_ids=snake_case_				,				attention_mask=snake_case_				,				past_key_values=snake_case_				,				)["""last_hidden_state"""]
         self.parent.assertTrue(output_from_past.shape[1] == next_tokens.shape[1]					)

         # select random slice
         _lowerCAmelCase    :					Optional[Any]							=   int(ids_tensor((1,)				,				shape_list(snake_case_					)[-1]					)					)
         _lowerCAmelCase    :					str							=   output_from_no_past[:, -3:, random_slice_idx]
         _lowerCAmelCase    :					Optional[int]							=   output_from_past[:, :, random_slice_idx]

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




       def 							__UpperCamelCase    (       self				,				snake_case_				,				snake_case_				,				snake_case_				,				snake_case_				,				snake_case_				,				*snake_case_					):
         _lowerCAmelCase    :					List[str]							=   TFGPTJForCausalLM(config=snake_case_					)
         _lowerCAmelCase    :					Any							=   {
             """input_ids""": input_ids,
             """attention_mask""": input_mask,
             """token_type_ids""": token_type_ids,
         }
         _lowerCAmelCase    :					Dict							=   model(snake_case_					)
         self.parent.assertEqual(result.logits.shape				,				(self.batch_size, self.seq_length, self.vocab_size)					)







       def 							__UpperCamelCase    (       self					):
         _lowerCAmelCase    :					Union[str, Any]							=   self.prepare_config_and_inputs()

         (
             _lowerCAmelCase   
         )    :					Union[str, Any]							=   config_and_inputs

         _lowerCAmelCase    :					int							=   {
             """input_ids""": input_ids,
             """token_type_ids""": token_type_ids,
             """attention_mask""": input_mask,
         }
         return config, inputs_dict



@require_tf
class 					a_					(_a							,     _a							,     _a							,     unittest.TestCase      ):
       __lowerCAmelCase      :			List[Any]      = (
           (TFGPTJForCausalLM, TFGPTJForSequenceClassification, TFGPTJForQuestionAnswering, TFGPTJModel)
           if is_tf_available()
           else ()
       )

       __lowerCAmelCase      :			Optional[Any]      = (TFGPTJForCausalLM,) if is_tf_available() else ()
       __lowerCAmelCase      :			List[str]      = (
           {
               """feature-extraction""": TFGPTJModel,
               """question-answering""": TFGPTJForQuestionAnswering,
               """text-classification""": TFGPTJForSequenceClassification,
               """text-generation""": TFGPTJForCausalLM,
               """zero-shot""": TFGPTJForSequenceClassification,
           }
           if is_tf_available()
           else {}
       )
       __lowerCAmelCase      :			Tuple      = False
       __lowerCAmelCase      :			List[str]      = False
       __lowerCAmelCase      :			Dict      = False
       __lowerCAmelCase      :			Union[str, Any]      = False




       def 							__UpperCamelCase    (       self				,				snake_case_				,				snake_case_				,				snake_case_				,				snake_case_				,				snake_case_					):
         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 							__UpperCamelCase    (       self					):
         _lowerCAmelCase    :					Tuple							=   TFGPTJModelTester(self					)
         _lowerCAmelCase    :					Dict							=   ConfigTester(self				,				config_class=snake_case_				,				n_embd=3_7					)




       def 							__UpperCamelCase    (       self					):
         self.config_tester.run_common_tests()




       def 							__UpperCamelCase    (       self					):
         _lowerCAmelCase    :					Optional[Any]							=   self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_gptj_model(*snake_case_					)




       def 							__UpperCamelCase    (       self					):
         _lowerCAmelCase    :					Union[str, Any]							=   self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_gptj_model_past(*snake_case_					)




       def 							__UpperCamelCase    (       self					):
         _lowerCAmelCase    :					Dict							=   self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_gptj_model_attention_mask_past(*snake_case_					)




       def 							__UpperCamelCase    (       self					):
         _lowerCAmelCase    :					Dict							=   self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_gptj_model_past_large_inputs(*snake_case_					)




       def 							__UpperCamelCase    (       self					):
         _lowerCAmelCase    :					str							=   self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_gptj_lm_head_model(*snake_case_					)




       @slow
       @unittest.skipIf(
           not is_tf_available() or len(tf.config.list_physical_devices("""GPU"""					)					) > 0				,				"""skip testing on GPU for now to avoid GPU OOM."""				,				)
       def 							__UpperCamelCase    (       self					):
         _lowerCAmelCase    :					str							=   TFGPTJModel.from_pretrained("""EleutherAI/gpt-j-6B"""				,				from_pt=snake_case_					)
         self.assertIsNotNone(snake_case_					)







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



@require_tf
@tooslow
# Marked as @tooslow due to GPU OOM -- but still useful to run locally. Requires ~39GB of RAM.
class 					a_					(unittest.TestCase      ):




       def 							__UpperCamelCase    (       self					):
         _lowerCAmelCase    :					List[Any]							=   TFGPTJForCausalLM.from_pretrained("""EleutherAI/gpt-j-6B"""				,				from_pt=snake_case_					)
         _lowerCAmelCase    :					str							=   tf.convert_to_tensor([[4_6_4, 3_2_9_0]]				,				dtype=tf.intaa					)  # The dog
         # fmt: off
         # The dog is a man's best friend. It is a loyal companion, and it is a friend
         _lowerCAmelCase    :					int							=   [4_6_4, 3_2_9_0, 3_1_8, 2_5_7, 5_8_2, 3_3_8, 1_2_6_6, 1_5_4_5, 1_3, 6_3_2, 3_1_8, 2_5_7, 9_1_1_2, 1_5_1_8_5, 1_1, 2_9_0, 3_4_0, 3_1_8, 2_5_7, 1_5_4_5]
         # fmt: on
         _lowerCAmelCase    :					str							=   model.generate(snake_case_				,				do_sample=snake_case_					)
         self.assertListEqual(output_ids[0].numpy().tolist()				,				snake_case_					)




       def 							__UpperCamelCase    (       self					):
         _lowerCAmelCase    :					str							=   AutoTokenizer.from_pretrained("""EleutherAI/gpt-j-6B"""				,				revision="""float16"""					)
         _lowerCAmelCase    :					Union[str, Any]							=   TFGPTJForCausalLM.from_pretrained("""EleutherAI/gpt-j-6B"""				,				revision="""float16"""				,				from_pt=snake_case_					)

         _lowerCAmelCase    :					Any							=   tokenizer("""Today is a nice day and"""				,				return_tensors="""tf"""					)
         # forces the generation to happen on CPU, to avoid GPU-related quirks
         with tf.device(""":/CPU:0"""					):
           _lowerCAmelCase    :					Tuple							=   model.generate(**snake_case_				,				do_sample=snake_case_				,				seed=[4_2, 0]					)
         _lowerCAmelCase    :					Tuple							=   tokenizer.decode(output_ids[0]				,				skip_special_tokens=snake_case_					)

         _lowerCAmelCase    :					List[Any]							=   """Today is a nice day and I’m going to go for a walk. I’"""
         self.assertEqual(snake_case_				,				snake_case_					)




       def 							__UpperCamelCase    (       self					):
         _lowerCAmelCase    :					Optional[Any]							=   TFGPTJForCausalLM.from_pretrained("""EleutherAI/gpt-j-6B"""				,				revision="""float16"""				,				from_pt=snake_case_					)
         _lowerCAmelCase    :					Optional[int]							=   AutoTokenizer.from_pretrained("""EleutherAI/gpt-j-6B"""				,				revision="""float16"""					)

         _lowerCAmelCase    :					Optional[int]							=   """left"""

         # Define PAD Token = EOS Token = 50256
         _lowerCAmelCase    :					Dict							=   tokenizer.eos_token
         _lowerCAmelCase    :					List[Any]							=   model.config.eos_token_id

         # use different length sentences to test batching
         _lowerCAmelCase    :					str							=   [
             """Hello, my dog is a little""",
             """Today, I""",
         ]
         _lowerCAmelCase    :					Tuple							=   [
             """Hello, my dog is a little over a year old and has been diagnosed with hip dysplasia""",
             """Today, I’m going to be talking about a topic that’""",
         ]
         return model, tokenizer, sentences, expected_output_sentences




       def 							__UpperCamelCase    (       self					):
         # Confirms that we get the expected results with left-padded beam search
         _lowerCAmelCase    :					Dict							=   self._get_beam_search_test_objects()

         _lowerCAmelCase    :					Dict							=   tokenizer(snake_case_				,				return_tensors="""tf"""				,				padding=snake_case_					)
         _lowerCAmelCase    :					Any							=   model.generate(**snake_case_				,				do_sample=snake_case_				,				num_beams=2					)
         _lowerCAmelCase    :					Optional[Any]							=   tokenizer.batch_decode(snake_case_				,				skip_special_tokens=snake_case_					)
         self.assertListEqual(snake_case_				,				snake_case_					)




       def 							__UpperCamelCase    (       self					):
         # Confirms that left-padding is working properly
         _lowerCAmelCase    :					Tuple							=   self._get_beam_search_test_objects()

         _lowerCAmelCase    :					str							=   tokenizer(snake_case_				,				return_tensors="""tf"""				,				padding=snake_case_					)
         _lowerCAmelCase    :					List[Any]							=   tokenizer(sentences[0]				,				return_tensors="""tf"""					)
         _lowerCAmelCase    :					Optional[Any]							=   model.generate(**snake_case_				,				do_sample=snake_case_				,				num_beams=2					)
         _lowerCAmelCase    :					str							=   (
             shape_list(inputs_non_padded["""input_ids"""]					)[-1]
             - tf.reduce_sum(tf.cast(inputs["""attention_mask"""][-1]				,				tf.intaa					)					).numpy()
         )
         _lowerCAmelCase    :					Any							=   tokenizer(sentences[1]				,				return_tensors="""tf"""					)
         _lowerCAmelCase    :					Optional[Any]							=   model.generate(
             **snake_case_				,				do_sample=snake_case_				,				num_beams=2				,				max_length=model.config.max_length - num_paddings					)
         _lowerCAmelCase    :					Optional[Any]							=   tokenizer.decode(output_non_padded[0]				,				skip_special_tokens=snake_case_					)
         _lowerCAmelCase    :					Any							=   tokenizer.decode(output_padded[0]				,				skip_special_tokens=snake_case_					)
         self.assertListEqual(snake_case_				,				[non_padded_sentence, padded_sentence]					)







       def 							__UpperCamelCase    (       self					):
         # Confirms that XLA is working properly
         _lowerCAmelCase    :					Dict							=   self._get_beam_search_test_objects()

         _lowerCAmelCase    :					Optional[int]							=   tokenizer(snake_case_				,				return_tensors="""tf"""				,				padding=snake_case_					)
         _lowerCAmelCase    :					Any							=   tf.function(model.generate				,				jit_compile=snake_case_					)
         _lowerCAmelCase    :					Any							=   xla_generate(**snake_case_				,				do_sample=snake_case_				,				num_beams=2					)
         _lowerCAmelCase    :					int							=   tokenizer.batch_decode(snake_case_				,				skip_special_tokens=snake_case_					)
         self.assertListEqual(snake_case_				,				snake_case_					)



'''simple docstring'''

from __future__ import annotations


class 					a_					:




       def __init__(       self				,				snake_case_ = 0					):
         _lowerCAmelCase    :					Optional[Any]							=   value
         _lowerCAmelCase    :					Node | None							=   None
         _lowerCAmelCase    :					Node | None							=   None



def 		_UpperCAmelCase			(					_lowerCamelCase  :    Node | None					, _lowerCamelCase  :    Node | None						)      ->  Node | None:
  if treea is None:
    return treea
  if treea is None:
    return treea

  _lowerCAmelCase    :					Any							=   treea.value + treea.value
  _lowerCAmelCase    :					Tuple							=   merge_two_binary_trees(treea.left					, treea.left						)
  _lowerCAmelCase    :					List[str]							=   merge_two_binary_trees(treea.right					, treea.right						)
  return treea


def 		_UpperCAmelCase			(					_lowerCamelCase  :    Node | None						)      ->  None:
  if root:
    print(root.value						)
    print_preorder(root.left						)
    print_preorder(root.right						)


if __name__ == "__main__":
     UpperCamelCase_            =    Node(1)
     UpperCamelCase_            =    Node(2)
     UpperCamelCase_            =    Node(3)
     UpperCamelCase_            =    Node(4)

     UpperCamelCase_            =    Node(2)
     UpperCamelCase_            =    Node(4)
     UpperCamelCase_            =    Node(6)
     UpperCamelCase_            =    Node(9)
     UpperCamelCase_            =    Node(5)

     print("""Tree1 is: """)
     print_preorder(treea)
     print("""Tree2 is: """)
     print_preorder(treea)
     UpperCamelCase_            =    merge_two_binary_trees(treea, treea)
     print("""Merged Tree is: """)
     print_preorder(merged_tree)


