





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:
     __A	  =       XGLMConfig
     __A	  =       {}
     __A	  =       "gelu"

     def __init__(      self,       A,       A=14,       A=7,       A=True,       A=True,       A=True,       A=99,       A=32,       A=2,       A=4,       A=37,       A="gelu",       A=0.1,       A=0.1,       A=512,       A=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=A,       bos_token_id=self.bos_token_id,       eos_token_id=self.eos_token_id,       pad_token_id=self.pad_token_id,       return_dict=A,       )



     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  __A(						__UpperCAmelCase						,			__UpperCAmelCase						,			unittest.TestCase ):
     __A	  =       (TFXGLMModel, TFXGLMForCausalLM) if is_tf_available() else ()
     __A	  =       (TFXGLMForCausalLM,) if is_tf_available() else ()
     __A	  =       (
         {"feature-extraction": TFXGLMModel, "text-generation": TFXGLMForCausalLM} if is_tf_available() else {}
     )
     __A	  =       False
     __A	  =       False
     __A	  =       False

     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            _UpperCamelCase              =						TFXGLMModelTester(self      )
            _UpperCamelCase              =						ConfigTester(self,       config_class=A,       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(A      )
                   self.assertIsNotNone(A      )



     @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 ):

     @slow
     def 	_UpperCamelCase     (      self,       A=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(A,       do_sample=A,       num_beams=1      )
            if verify_outputs:
                   self.assertListEqual(output_ids[0].numpy().tolist(),       A      )

     @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(A,       do_sample=A,       seed=[7, 0]      )
            _UpperCamelCase              =						tokenizer.decode(output_ids[0],       skip_special_tokens=A      )

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



     @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(A,       return_tensors='''tf''',       padding=A      )
            _UpperCamelCase              =						inputs['''input_ids''']

            _UpperCamelCase              =						model.generate(input_ids=A,       attention_mask=inputs['''attention_mask'''],       max_new_tokens=12      )

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

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

            _UpperCamelCase              =						tokenizer.batch_decode(A,       skip_special_tokens=A      )
            _UpperCamelCase              =						tokenizer.decode(output_non_padded[0],       skip_special_tokens=A      )
            _UpperCamelCase              =						tokenizer.decode(output_padded[0],       skip_special_tokens=A      )

            _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(A,       A      )
            self.assertListEqual(A,       [non_padded_sentence, padded_sentence]      )













def     SCREAMING_SNAKE_CASE			(      lowerCAmelCase					):
       _UpperCamelCase              =						0
       for ch in input_str:
              _UpperCamelCase              =						ord(lowerCAmelCase					)
              _UpperCamelCase              =						pow(2    ,       lowerCAmelCase					)

              # If we already turned on bit for current character's unicode
              if bitmap >> ch_unicode & 1 == 1:
                     return False
              bitmap |= ch_bit_index_on
       return True


if __name__ == "__main__":
       import doctest

       doctest.testmod()






