"""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   lowerCAmelCase_    :



				"""simple docstring"""
				_lowerCAmelCase     :    str			  =						XGLMConfig
				_lowerCAmelCase     :    Optional[int]			  =						{}
				_lowerCAmelCase     :    Optional[int]			  =						"""gelu"""

				def __init__(				self		,    lowerCAmelCase		,    lowerCAmelCase=14		,    lowerCAmelCase=7		,    lowerCAmelCase=True		,    lowerCAmelCase=True		,    lowerCAmelCase=True		,    lowerCAmelCase=99		,    lowerCAmelCase=32		,    lowerCAmelCase=2		,    lowerCAmelCase=4		,    lowerCAmelCase=37		,    lowerCAmelCase="gelu"		,    lowerCAmelCase=0.1		,    lowerCAmelCase=0.1		,    lowerCAmelCase=5_12		,    lowerCAmelCase=0.02		,    ):






										"""simple docstring"""

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

				def 		snake_case (				self    ):






										"""simple docstring"""

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

				def 		snake_case (				self    ):






										"""simple docstring"""

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

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

										snake_case								=			self.get_config()

										snake_case								=			floats_tensor([self.num_hidden_layers, self.num_attention_heads]		,    2    )

										return (
										    config,
										    input_ids,
										    input_mask,
										    head_mask,
										)

				def 		snake_case (				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=lowerCAmelCase		,    bos_token_id=self.bos_token_id		,    eos_token_id=self.eos_token_id		,    pad_token_id=self.pad_token_id		,    return_dict=lowerCAmelCase		,    )

				def 		snake_case (				self    ):






										"""simple docstring"""

										snake_case								=			self.prepare_config_and_inputs()

										(
										    (
										    snake_case
										)					,(
										    snake_case
										)					,(
										    snake_case
										)					,(
										    snake_case
										)					,
										)								=			config_and_inputs

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

										return config, inputs_dict





@require_tf
class   lowerCAmelCase_    (			lowerCAmelCase     ,    lowerCAmelCase     ,    unittest.TestCase					):



				"""simple docstring"""
				_lowerCAmelCase     :    int			  =						(TFXGLMModel, TFXGLMForCausalLM) if is_tf_available() else ()
				_lowerCAmelCase     :    Optional[int]			  =						(TFXGLMForCausalLM,) if is_tf_available() else ()
				_lowerCAmelCase     :    Optional[int]			  =						(
				    {"""feature-extraction""": TFXGLMModel, """text-generation""": TFXGLMForCausalLM} if is_tf_available() else {}
				)
				_lowerCAmelCase     :    Optional[int]			  =						False
				_lowerCAmelCase     :    Dict			  =						False
				_lowerCAmelCase     :    Optional[int]			  =						False

				def 		snake_case (				self    ):






										"""simple docstring"""

										snake_case								=			TFXGLMModelTester(self    )
										snake_case								=			ConfigTester(self		,    config_class=lowerCAmelCase		,    n_embd=37    )

				def 		snake_case (				self    ):






										"""simple docstring"""

										self.config_tester.run_common_tests()

				@slow
				def 		snake_case (				self    ):






										"""simple docstring"""

										for model_name in TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																snake_case								=			TFXGLMModel.from_pretrained(lowerCAmelCase    )
																self.assertIsNotNone(lowerCAmelCase    )

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






										"""simple docstring"""

										super().test_resize_token_embeddings()





@require_tf
class   lowerCAmelCase_    (			unittest.TestCase					):



				"""simple docstring"""

				@slow
				def 		snake_case (				self		,    lowerCAmelCase=True    ):






										"""simple docstring"""

										snake_case								=			TFXGLMForCausalLM.from_pretrained('facebook/xglm-564M'    )
										snake_case								=			tf.convert_to_tensor([[2, 2_68, 98_65]]		,    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
										snake_case								=			[2, 2_68, 98_65, 67, 11, 19_88, 5_72_52, 98_65, 5, 9_84, 67, 19_88, 21_38_38, 16_58, 53, 7_04_46, 33, 66_57, 2_78, 15_81]
										# fmt: on
										snake_case								=			model.generate(lowerCAmelCase		,    do_sample=lowerCAmelCase		,    num_beams=1    )
										if verify_outputs:
																self.assertListEqual(output_ids[0].numpy().tolist()		,    lowerCAmelCase    )

				@slow
				def 		snake_case (				self    ):






										"""simple docstring"""

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

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

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

				@slow
				def 		snake_case (				self    ):






										"""simple docstring"""

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

										snake_case								=			'left'

										# use different length sentences to test batching
										snake_case								=			[
										    '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',
										]

										snake_case								=			tokenizer(lowerCAmelCase		,    return_tensors='tf'		,    padding=lowerCAmelCase    )
										snake_case								=			inputs['input_ids']

										snake_case								=			model.generate(input_ids=lowerCAmelCase		,    attention_mask=inputs['attention_mask']		,    max_new_tokens=12    )

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

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

										snake_case								=			tokenizer.batch_decode(lowerCAmelCase		,    skip_special_tokens=lowerCAmelCase    )
										snake_case								=			tokenizer.decode(output_non_padded[0]		,    skip_special_tokens=lowerCAmelCase    )
										snake_case								=			tokenizer.decode(output_padded[0]		,    skip_special_tokens=lowerCAmelCase    )

										snake_case								=			[
										    '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(lowerCAmelCase		,    lowerCAmelCase    )
										self.assertListEqual(lowerCAmelCase		,    [non_padded_sentence, padded_sentence]    )







"""simple docstring"""
from ...utils import (
    OptionalDependencyNotAvailable,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)


try:
					if not (is_transformers_available() and is_torch_available()):
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					from ...utils.dummy_torch_and_transformers_objects import ShapEPipeline
else:
					from .camera import create_pan_cameras
					from .pipeline_shap_e import ShapEPipeline
					from .pipeline_shap_e_img2img import ShapEImgaImgPipeline
					from .renderer import (
					    BoundingBoxVolume,
					    ImportanceRaySampler,
					    MLPNeRFModelOutput,
					    MLPNeRSTFModel,
					    ShapEParamsProjModel,
					    ShapERenderer,
					    StratifiedRaySampler,
					    VoidNeRFModel,
					)


