
'''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   a_ :
			lowerCamelCase__	:			Optional[int]       =    XGLMConfig
			lowerCamelCase__	:			Tuple       =    {}
			lowerCamelCase__	:			str       =    '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=5_12					,   UpperCAmelCase=0.02					,   ):
				a_      				=						parent
				a_      				=						batch_size
				a_      				=						seq_length
				a_      				=						is_training
				a_      				=						use_input_mask
				a_      				=						use_labels
				a_      				=						vocab_size
				a_      				=						d_model
				a_      				=						num_hidden_layers
				a_      				=						num_attention_heads
				a_      				=						ffn_dim
				a_      				=						activation_function
				a_      				=						activation_dropout
				a_      				=						attention_dropout
				a_      				=						max_position_embeddings
				a_      				=						initializer_range
				a_      				=						None
				a_      				=						0
				a_      				=						2
				a_      				=						1




			def 					lowerCAmelCase__					(       self	):
				return XGLMConfig.from_pretrained("""facebook/xglm-564M"""	)




			def 					lowerCAmelCase__					(       self	):
				a_      				=						tf.clip_by_value(
				    ids_tensor([self.batch_size, self.seq_length]					,   self.vocab_size	)					,   clip_value_min=0					,   clip_value_max=3	)

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

				a_      				=						self.get_config()

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

				return (
				    config,
				    input_ids,
				    input_mask,
				    head_mask,
				)




			def 					lowerCAmelCase__					(       self	):
				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 					lowerCAmelCase__					(       self	):
				a_      				=						self.prepare_config_and_inputs()

				(
				    (
				    a_
				)				,  (
				    a_
				)				,  (
				    a_
				)				,  (
				    a_
				)				,  
				)      				=						config_and_inputs

				a_      				=						{
				    """input_ids""": input_ids,
				    """head_mask""": head_mask,
				}

				return config, inputs_dict







@require_tf
class   a_ (      UpperCamelCase__		,  UpperCamelCase__		,  unittest.TestCase					):
			lowerCamelCase__	:			List[str]       =    (TFXGLMModel, TFXGLMForCausalLM) if is_tf_available() else ()
			lowerCamelCase__	:			Optional[Any]       =    (TFXGLMForCausalLM,) if is_tf_available() else ()
			lowerCamelCase__	:			Optional[int]       =    (
			    {'feature-extraction': TFXGLMModel, 'text-generation': TFXGLMForCausalLM} if is_tf_available() else {}
			)
			lowerCamelCase__	:			Union[str, Any]       =    False
			lowerCamelCase__	:			Dict       =    False
			lowerCamelCase__	:			Optional[Any]       =    False




			def 					lowerCAmelCase__					(       self	):
				a_      				=						TFXGLMModelTester(self	)
				a_      				=						ConfigTester(self					,   config_class=UpperCAmelCase					,   n_embd=37	)




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




			@slow
			def 					lowerCAmelCase__					(       self	):
				for model_name in TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
					a_      				=						TFXGLMModel.from_pretrained(UpperCAmelCase	)
					self.assertIsNotNone(UpperCAmelCase	)






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







@require_tf
class   a_ (      unittest.TestCase					):




			@slow
			def 					lowerCAmelCase__					(       self					,   UpperCAmelCase=True	):
				a_      				=						TFXGLMForCausalLM.from_pretrained("""facebook/xglm-564M"""	)
				a_      				=						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
				a_      				=						[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
				a_      				=						model.generate(UpperCAmelCase					,   do_sample=UpperCAmelCase					,   num_beams=1	)
				if verify_outputs:
					self.assertListEqual(output_ids[0].numpy().tolist()					,   UpperCAmelCase	)




			@slow
			def 					lowerCAmelCase__					(       self	):
				a_      				=						XGLMTokenizer.from_pretrained("""facebook/xglm-564M"""	)
				a_      				=						TFXGLMForCausalLM.from_pretrained("""facebook/xglm-564M"""	)

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

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






			@slow
			def 					lowerCAmelCase__					(       self	):
				a_      				=						TFXGLMForCausalLM.from_pretrained("""facebook/xglm-564M"""	)
				a_      				=						XGLMTokenizer.from_pretrained("""facebook/xglm-564M"""	)

				a_      				=						"""left"""

				# use different length sentences to test batching
				a_      				=						[
				    """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""",
				]

				a_      				=						tokenizer(UpperCAmelCase					,   return_tensors="""tf"""					,   padding=UpperCAmelCase	)
				a_      				=						inputs["""input_ids"""]

				a_      				=						model.generate(input_ids=UpperCAmelCase					,   attention_mask=inputs["""attention_mask"""]					,   max_new_tokens=12	)

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

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

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

				a_      				=						[
				    """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]	)


'''simple docstring'''
from math import log

from scipy.constants import Boltzmann, physical_constants

lowercase__											=3_00  # TEMPERATURE (unit = K)






def      UpperCamelCase_				(      A__  ,      A__  ,      A__  ,      ):

	if donor_conc <= 0:
		raise ValueError("""Donor concentration should be positive"""						)
	elif acceptor_conc <= 0:
		raise ValueError("""Acceptor concentration should be positive"""						)
	elif intrinsic_conc <= 0:
		raise ValueError("""Intrinsic concentration should be positive"""						)
	elif donor_conc <= intrinsic_conc:
		raise ValueError(
		    """Donor concentration should be greater than intrinsic concentration"""						)
	elif acceptor_conc <= intrinsic_conc:
		raise ValueError(
		    """Acceptor concentration should be greater than intrinsic concentration"""						)
	else:
		return (
		    Boltzmann
		    * T
		    * log((donor_conc * acceptor_conc) / intrinsic_conc**2						)
		    / physical_constants["electron volt"][0]
		)


if __name__ == "__main__":
		import doctest

		doctest.testmod()
